Podcasts about EC2

Akanksha Bilani of Intel shares how businesses can successfully adopt generative AI with significant performance gains while saving on costs.Topics Include:Akanksha runs go-to-market team for Amazon at IntelPersonal and business devices transformed how we communicateForrester predicts 500 billion connected devices by 20265,000 billion sensors will be smartly connected online40% of machines will communicate machine-to-machineWe're living in a world of data delugeAI and Gen AI help make data effectiveGoal is making businesses more profitable and effectiveVarious industries need Gen AI and data transformationIntel advises companies as partners with AWSThree factors determine which Gen AI use cases adoptFactor one: availability and ease of use casesHow unique and important are they for business?Does it have enough data for right analytics?Factor two: purchasing power for Gen AI adoption70% of companies target Gen AI but lack clarityLeaders must ensure capability and purchasing power existFactor three: necessary skill sets for implementationNeed access to right partnerships if lacking skillsIntel and AWS partnered for 18 years since inceptionIntel provides latest silicon customized for Amazon servicesEngineer-to-engineer collaboration on each processor generation92% of EC2 runs on Intel processorsIntel powers compute capability for EC2-based servicesIntel ensures access to skillsets making cloud aliveAWS services include Bedrock, SageMaker, DLAMIs, KinesisPerformance is the top three priorities for successNot every use case requires expensive GPU acceleratorsCPUs can power AI inference and training effectivelyEvery GPU has a CPU head node component Participants:Akanksha Bilani – Global Sales Director, IntelSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Episode SummaryDive into the ever-evolving world of platform engineering with Cory O'Daniel, CEO and co-founder of Massdriver. This episode explores the journey of DevOps, the challenges of building and scaling infrastructure, and the crucial role of creating effective abstractions to empower developers. Cory shares his insights on the shift towards platform engineering as a means to build more secure and efficient software by default.Show NotesIn this episode of The Secure Developer, host Danny Allan sits down with Cory O'Daniel, CEO and co-founder of Massdriver, to discuss the dynamic landscape of platform engineering. Cory, a seasoned software engineer and first-time CEO, shares his extensive experience in the Infrastructure as Code (IaC) space, tracing his journey from early encounters with EC2 to founding Massdriver. He offers candid advice for developers aspiring to become CEOs, emphasizing the importance of passion and early customer engagement.  The conversation delves into the evolution of DevOps over the past two decades, highlighting the constant changes in how software is run, from mainframes to serverless containers and now AI. Cory argues that the true spirit of DevOps lies in operations teams producing products that developers can easily use. He points out the challenge of scaling operations expertise, suggesting that IT and Cloud practices need to mature in software development to create better abstractions for developers, rather than expecting developers to become infrastructure experts.  A significant portion of the discussion focuses on the current state of abstractions in IaC. Cory contends that existing public abstractions, like open-source Terraform modules, are often too generic and don't account for specific business logic, security, or compliance requirements. He advocates for operations teams building their own prescriptive modules that embed organizational standards, effectively shifting security left by design rather than by burdening developers. The episode also touches upon the potential and limitations of AI in the operations space, with Cory expressing skepticism about AI's current ability to handle the contextual complexities of infrastructure without significant, organization-specific training data. Finally, Cory shares his optimism for the future of platform engineering, viewing it as a return to the original intentions of DevOps, where operations teams ship software with ingrained security and compliance, leading to more secure systems by default.LinksMassDriverAnsibleChefTerraformDevOps is BullshitElephant in the CloudDockerPostgresOpenTofuHelmRedisElixirSnyk - The Developer Security Company Follow UsOur WebsiteOur LinkedIn

AWS Morning Brief for the week of April 28th, with Corey Quinn. Links:Amazon CloudWatch agent now supports Red Hat OpenShift Service on AWS (ROSA) Amazon Cognito now supports refresh token rotation Amazon Q Developer releases state-of-the-art agent for feature development AWS Account Management now supports IAM-based account name updates AWS CodeBuild adds support for specifying EC2 instance type and configurable storage size AWS Console Mobile Application adds support for Amazon Lightsail AWS STS global endpoint now serves your requests locally in Regions enabled by default AWS Transfer Family introduces Terraform module for deploying SFTP server endpoints How Smartsheet reduced latency and optimized costs in their serverless architecture In the works – New Availability Zone in Maryland for US East (Northern Virginia) Region CVE-2025-3857 – Infinite loop condition in Amazon.IonDotnet I annotated Amazon CEO Andy Jassy's 2024 Letter to Shareholders 

Joining Mario Nigro for this episode is Wandrille Lefèvre of EC2, a Québec-based consulting firm that provides specialized M&A and corporate finance advice to midmarket companies. According to Wandrille, the Québec market, like other Canadian markets, is driven by factors like baby boomer retirements and a need for technology investment. The range of buyers in the province includes a similar mix of private equity and strategics as is found elsewhere. Even in the uncertain tariff environment, EC2 is seeing steady interest from potential sellers, with the caveat that those sellers are deliberating more about how to proceed, while buyers are looking more intently at the tariff exposure of potential acquisitions.

Win95, Shuckworm, Ottokit, DCs, EC2, IAB, OSS, Recall, Josh Marpet, and More, on this edition of the Security Weekly News. Visit https://www.securityweekly.com/swn for all the latest episodes! Show Notes: https://securityweekly.com/swn-467

Win95, Shuckworm, Ottokit, DCs, EC2, IAB, OSS, Recall, Josh Marpet, and More, on this edition of the Security Weekly News. Show Notes: https://securityweekly.com/swn-467

Win95, Shuckworm, Ottokit, DCs, EC2, IAB, OSS, Recall, Josh Marpet, and More, on this edition of the Security Weekly News. Visit https://www.securityweekly.com/swn for all the latest episodes! Show Notes: https://securityweekly.com/swn-467

Win95, Shuckworm, Ottokit, DCs, EC2, IAB, OSS, Recall, Josh Marpet, and More, on this edition of the Security Weekly News. Show Notes: https://securityweekly.com/swn-467

Brandon Liu is an open source developer and creator of the Protomaps basemap project. We talk about how static maps help developers build sites that last, the PMTiles file format, the role of OpenStreetMap, and his experience funding and running an open source project full time. Protomaps Protomaps PMTiles (File format used by Protomaps) Self-hosted slippy maps, for novices (like me) Why Deploy Protomaps on a CDN User examples Flickr Pinball Map Toilet Map Related projects OpenStreetMap (Dataset protomaps is based on) Mapzen (Former company that released details on what to display based on zoom levels) Mapbox GL JS (Mapbox developed source available map rendering library) MapLibre GL JS (Open source fork of Mapbox GL JS) Other links HTTP range requests (MDN) Hilbert curve Transcript You can help correct transcripts on GitHub. Intro [00:00:00] Jeremy: I'm talking to Brandon Liu. He's the creator of Protomaps, which is a way to easily create and host your own maps. Let's get into it. [00:00:09] Brandon: Hey, so thanks for having me on the podcast. So I'm Brandon. I work on an open source project called Protomaps. What it really is, is if you're a front end developer and you ever wanted to put maps on a website or on a mobile app, then Protomaps is sort of an open source solution for doing that that I hope is something that's way easier to use than, um, a lot of other open source projects. Why not just use Google Maps? [00:00:36] Jeremy: A lot of people are gonna be familiar with Google Maps. Why should they worry about whether something's open source? Why shouldn't they just go and use the Google maps API? [00:00:47] Brandon: So Google Maps is like an awesome thing it's an awesome product. Probably one of the best tech products ever right? And just to have a map that tells you what restaurants are open and something that I use like all the time especially like when you're traveling it has all that data. And the most amazing part is that it's free for consumers but it's not necessarily free for developers. Like if you wanted to embed that map onto your website or app, that usually has an API cost which still has a free tier and is affordable. But one motivation, one basic reason to use open source is if you have some project that doesn't really fit into that pricing model. You know like where you have to pay the cost of Google Maps, you have a side project, a nonprofit, that's one reason. But there's lots of other reasons related to flexibility or customization where you might want to use open source instead. Protomaps examples [00:01:49] Jeremy: Can you give some examples where people have used Protomaps and where that made sense for them? [00:01:56] Brandon: I follow a lot of the use cases and I also don't know about a lot of them because I don't have an API where I can track a hundred percent of the users. Some of them use the hosted version, but I would say most of them probably use it on their own infrastructure. One of the cool projects I've been seeing is called Toilet Map. And what toilet map is if you're in the UK and you want find a public restroom then it maps out, sort of crowdsourced all of the public restrooms. And that's important for like a lot of people if they have health issues, they need to find that information. And just a lot of different projects in the same vein. There's another one called Pinball Map which is sort of a hobby project to find all the pinball machines in the world. And they wanted to have a customized map that fit in with their theme of pinball. So these sorts of really cool indie projects are the ones I'm most excited about. Basemaps vs Overlays [00:02:57] Jeremy: And if we talk about, like the pinball map as an example, there's this concept of a basemap and then there's the things that you lay on top of it. What is a basemap and then is the pinball locations is that part of it or is that something separate? [00:03:12] Brandon: It's usually something separate. The example I usually use is if you go to a real estate site, like Zillow, you'll open up the map of Seattle and it has a bunch of pins showing all the houses, and then it has some information beneath it. That information beneath it is like labels telling, this neighborhood is Capitol Hill, or there is a park here. But all that information is common to a lot of use cases and it's not specific to real estate. So I think usually that's the distinction people use in the industry between like a base map versus your overlay. The overlay is like the data for your product or your company while the base map is something you could get from Google or from Protomaps or from Apple or from Mapbox that kind of thing. PMTiles for hosting the basemap and overlays [00:03:58] Jeremy: And so Protomaps in particular is responsible for the base map, and that information includes things like the streets and the locations of landmarks and things like that. Where is all that information coming from? [00:04:12] Brandon: So the base map information comes from a project called OpenStreetMap. And I would also, point out that for Protomaps as sort of an ecosystem. You can also put your overlay data into a format called PMTiles, which is sort of the core of what Protomaps is. So it can really do both. It can transform your data into the PMTiles format which you can host and you can also host the base map. So you kind of have both of those sides of the product in one solution. [00:04:43] Jeremy: And so when you say you have both are you saying that the PMTiles file can have, the base map in one file and then you would have the data you're laying on top in another file? Or what are you describing there? [00:04:57] Brandon: That's usually how I recommend to do it. Oftentimes there'll be sort of like, a really big basemap 'cause it has all of that data about like where the rivers are. Or while, if you want to put your map of toilets or park benches or pickleball courts on top, that's another file. But those are all just like assets you can move around like JSON or CSV files. Statically Hosted [00:05:19] Jeremy: And I think one of the things you mentioned was that your goal was to make Protomaps or the, the use of these PMTiles files easy to use. What does that look like for, for a developer? I wanna host a map. What do I actually need to, to put on my servers? [00:05:38] Brandon: So my usual pitch is that basically if you know how to use S3 or cloud storage, that you know how to deploy a map. And that, I think is the main sort of differentiation from most open source projects. Like a lot of them, they call themselves like, like some sort of self-hosted solution. But I've actually avoided using the term self-hosted because I think in most cases that implies a lot of complexity. Like you have to log into a Linux server or you have to use Kubernetes or some sort of Docker thing. What I really want to emphasize is the idea that, for Protomaps, it's self-hosted in the same way like CSS is self-hosted. So you don't really need a service from Amazon to host the JSON files or CSV files. It's really just a static file. [00:06:32] Jeremy: When you say static file that means you could use any static web host to host your HTML file, your JavaScript that actually renders the map. And then you have your PMTiles files, and you're not running a process or anything, you're just putting your files on a static file host. [00:06:50] Brandon: Right. So I think if you're a developer, you can also argue like a static file server is a server. It's you know, it's the cloud, it's just someone else's computer. It's really just nginx under the hood. But I think static storage is sort of special. If you look at things like static site generators, like Jekyll or Hugo, they're really popular because they're a commodity or like the storage is a commodity. And you can take your blog, make it a Jekyll blog, hosted on S3. One day, Amazon's like, we're charging three times as much so you can move it to a different cloud provider. And that's all vendor neutral. So I think that's really the special thing about static storage as a primitive on the web. Why running servers is a problem for resilience [00:07:36] Jeremy: Was there a prior experience you had? Like you've worked with maps for a very long time. Were there particular difficulties you had where you said I just gotta have something that can be statically hosted? [00:07:50] Brandon: That's sort of exactly why I got into this. I've been working sort of in and around the map space for over a decade, and Protomaps is really like me trying to solve the same problem I've had over and over again in the past, just like once and forever right? Because like once this problem is solved, like I don't need to deal with it again in the future. So I've worked at a couple of different companies before, mostly as a contractor, for like a humanitarian nonprofit for a design company doing things like, web applications to visualize climate change. Or for even like museums, like digital signage for museums. And oftentimes they had some sort of data visualization component, but always sort of the challenge of how to like, store and also distribute like that data was something that there wasn't really great open source solutions. So just for map data, that's really what motivated that design for Protomaps. [00:08:55] Jeremy: And in those, those projects in the past, were those things where you had to run your own server, run your own database, things like that? [00:09:04] Brandon: Yeah. And oftentimes we did, we would spin up an EC2 instance, for maybe one client and then we would have to host this server serving map data forever. Maybe the client goes away, or I guess it's good for business if you can sign some sort of like long-term support for that client saying, Hey, you know, like we're done with a project, but you can pay us to maintain the EC2 server for the next 10 years. And that's attractive. but it's also sort of a pain, because usually what happens is if people are given the choice, like a developer between like either I can manage the server on EC2 or on Rackspace or Hetzner or whatever, or I can go pay a SaaS to do it. In most cases, businesses will choose to pay the SaaS. So that's really like what creates a sort of lock-in is this preference for like, so I have this choice between like running the server or paying the SaaS. Like businesses will almost always go and pay the SaaS. [00:10:05] Jeremy: Yeah. And in this case, you either find some kind of free hosting or low-cost hosting just to host your files and you upload the files and then you're good from there. You don't need to maintain anything. [00:10:18] Brandon: Exactly, and that's really the ideal use case. so I have some users these, climate science consulting agencies, and then they might have like a one-off project where they have to generate the data once, but instead of having to maintain this server for the lifetime of that project, they just have a file on S3 and like, who cares? If that costs a couple dollars a month to run, that's fine, but it's not like S3 is gonna be deprecated, like it's gonna be on an insecure version of Ubuntu or something. So that's really the ideal, set of constraints for using Protomaps. [00:10:58] Jeremy: Yeah. Something this also makes me think about is, is like the resilience of sites like remaining online, because I, interviewed, Kyle Drake, he runs Neocities, which is like a modern version of GeoCities. And if I remember correctly, he was mentioning how a lot of old websites from that time, if they were running a server backend, like they were running PHP or something like that, if you were to try to go to those sites, now they're like pretty much all dead because there needed to be someone dedicated to running a Linux server, making sure things were patched and so on and so forth. But for static sites, like the ones that used to be hosted on GeoCities, you can go to the internet archive or other websites and they were just files, right? You can bring 'em right back up, and if anybody just puts 'em on a web server, then you're good. They're still alive. Case study of news room preferring static hosting [00:11:53] Brandon: Yeah, exactly. One place that's kind of surprising but makes sense where this comes up, is for newspapers actually. Some of the users using Protomaps are the Washington Post. And the reason they use it, is not necessarily because they don't want to pay for a SaaS like Google, but because if they make an interactive story, they have to guarantee that it still works in a couple of years. And that's like a policy decision from like the editorial board, which is like, so you can't write an article if people can't view it in five years. But if your like interactive data story is reliant on a third party, API and that third party API becomes deprecated, or it changes the pricing or it, you know, it gets acquired, then your journalism story is not gonna work anymore. So I have seen really good uptake among local news rooms and even big ones to use things like Protomaps just because it makes sense for the requirements. Working on Protomaps as an open source project for five years [00:12:49] Jeremy: How long have you been working on Protomaps and the parts that it's made up of such as PMTiles? [00:12:58] Brandon: I've been working on it for about five years, maybe a little more than that. It's sort of my pandemic era project. But the PMTiles part, which is really the heart of it only came in about halfway. Why not make a SaaS? [00:13:13] Brandon: So honestly, like when I first started it, I thought it was gonna be another SaaS and then I looked at it and looked at what the environment was around it. And I'm like, uh, so I don't really think I wanna do that. [00:13:24] Jeremy: When, when you say you looked at the environment around it what do you mean? Why did you decide not to make it a SaaS? [00:13:31] Brandon: Because there already is a lot of SaaS out there. And I think the opportunity of making something that is unique in terms of those use cases, like I mentioned like newsrooms, was clear. Like it was clear that there was some other solution, that could be built that would fit these needs better while if it was a SaaS, there are plenty of those out there. And I don't necessarily think that they're well differentiated. A lot of them all use OpenStreetMap data. And it seems like they mainly compete on price. It's like who can build the best three column pricing model. And then once you do that, you need to build like billing and metrics and authentication and like those problems don't really interest me. So I think, although I acknowledge sort of the indie hacker ethos now is to build a SaaS product with a monthly subscription, that's something I very much chose not to do, even though it is for sure like the best way to build a business. [00:14:29] Jeremy: Yeah, I mean, I think a lot of people can appreciate that perspective because it's, it's almost like we have SaaS overload, right? Where you have so many little bills for your project where you're like, another $5 a month, another $10 a month, or if you're a business, right? Those, you add a bunch of zeros and at some point it's just how many of these are we gonna stack on here? [00:14:53] Brandon: Yeah. And honestly. So I really think like as programmers, we're not really like great at choosing how to spend money like a $10 SaaS. That's like nothing. You know? So I can go to Starbucks and I can buy a pumpkin spice latte, and that's like $10 basically now, right? And it's like I'm able to make that consumer choice in like an instant just to spend money on that. But then if you're like, oh, like spend $10 on a SaaS that somebody put a lot of work into, then you're like, oh, that's too expensive. I could just do it myself. So I'm someone that also subscribes to a lot of SaaS products. and I think for a lot of things it's a great fit. Many open source SaaS projects are not easy to self host [00:15:37] Brandon: But there's always this tension between an open source project that you might be able to run yourself and a SaaS. And I think a lot of projects are at different parts of the spectrum. But for Protomaps, it's very much like I'm trying to move maps to being it is something that is so easy to run yourself that anyone can do it. [00:16:00] Jeremy: Yeah, and I think you can really see it with, there's a few SaaS projects that are successful and they're open source, but then you go to look at the self-hosting instructions and it's either really difficult to find and you find it, and then the instructions maybe don't work, or it's really complicated. So I think doing the opposite with Protomaps. As a user, I'm sure we're all appreciative, but I wonder in terms of trying to make money, if that's difficult. [00:16:30] Brandon: No, for sure. It is not like a good way to make money because I think like the ideal situation for an open source project that is open that wants to make money is the product itself is fundamentally complicated to where people are scared to run it themselves. Like a good example I can think of is like Supabase. Supabase is sort of like a platform as a service based on Postgres. And if you wanted to run it yourself, well you need to run Postgres and you need to handle backups and authentication and logging, and that stuff all needs to work and be production ready. So I think a lot of people, like they don't trust themselves to run database backups correctly. 'cause if you get it wrong once, then you're kind of screwed. So I think that fundamental aspect of the product, like a database is something that is very, very ripe for being a SaaS while still being open source because it's fundamentally hard to run. Another one I can think of is like tailscale, which is, like a VPN that works end to end. That's something where, you know, it has this networking complexity where a lot of developers don't wanna deal with that. So they'd happily pay, for tailscale as a service. There is a lot of products or open source projects that eventually end up just changing to becoming like a hosted service. Businesses going from open source to closed or restricted licenses [00:17:58] Brandon: But then in that situation why would they keep it open source, right? Like, if it's easy to run yourself well, doesn't that sort of cannibalize their business model? And I think that's really the tension overall in these open source companies. So you saw it happen to things like Elasticsearch to things like Terraform where they eventually change the license to one that makes it difficult for other companies to compete with them. [00:18:23] Jeremy: Yeah, I mean there's been a number of cases like that. I mean, specifically within the mapping community, one I can think of was Mapbox's. They have Mapbox gl. Which was a JavaScript client to visualize maps and they moved from, I forget which license they picked, but they moved to a much more restrictive license. I wonder what your thoughts are on something that releases as open source, but then becomes something maybe a little more muddy. [00:18:55] Brandon: Yeah, I think it totally makes sense because if you look at their business and their funding, it seems like for Mapbox, I haven't used it in a while, but my understanding is like a lot of their business now is car companies and doing in dash navigation. And that is probably way better of a business than trying to serve like people making maps of toilets. And I think sort of the beauty of it is that, so Mapbox, the story is they had a JavaScript renderer called Mapbox GL JS. And they changed that to a source available license a couple years ago. And there's a fork of it that I'm sort of involved in called MapLibre GL. But I think the cool part is Mapbox paid employees for years, probably millions of dollars in total to work on this thing and just gave it away for free. Right? So everyone can benefit from that work they did. It's not like that code went away, like once they changed the license. Well, the old version has been forked. It's going its own way now. It's quite different than the new version of Mapbox, but I think it's extremely generous that they're able to pay people for years, you know, like a competitive salary and just give that away. [00:20:10] Jeremy: Yeah, so we should maybe look at it as, it was a gift while it was open source, and they've given it to the community and they're on continuing on their own path, but at least the community running Map Libre, they can run with it, right? It's not like it just disappeared. [00:20:29] Brandon: Yeah, exactly. And that is something that I use for Protomaps quite extensively. Like it's the primary way of showing maps on the web and I've been trying to like work on some enhancements to it to have like better internationalization for if you are in like South Asia like not show languages correctly. So I think it is being taken in a new direction. And I think like sort of the combination of Protomaps and MapLibre, it addresses a lot of use cases, like I mentioned earlier with like these like hobby projects, indie projects that are almost certainly not interesting to someone like Mapbox or Google as a business. But I'm happy to support as a small business myself. Financially supporting open source work (GitHub sponsors, closed source, contracts) [00:21:12] Jeremy: In my previous interview with Tom, one of the main things he mentioned was that creating a mapping business is incredibly difficult, and he said he probably wouldn't do it again. So in your case, you're building Protomaps, which you've admitted is easy to self-host. So there's not a whole lot of incentive for people to pay you. How is that working out for you? How are you supporting yourself? [00:21:40] Brandon: There's a couple of strategies that I've tried and oftentimes failed at. Just to go down the list, so I do have GitHub sponsors so I do have a hosted version of Protomaps you can use if you don't want to bother copying a big file around. But the way I do the billing for that is through GitHub sponsors. If you wanted to use this thing I provide, then just be a sponsor. And that definitely pays for itself, like the cost of running it. And that's great. GitHub sponsors is so easy to set up. It just removes you having to deal with Stripe or something. 'cause a lot of people, their credit card information is already in GitHub. GitHub sponsors I think is awesome if you want to like cover costs for a project. But I think very few people are able to make that work. A thing that's like a salary job level. It's sort of like Twitch streaming, you know, there's a handful of people that are full-time streamers and then you look down the list on Twitch and it's like a lot of people that have like 10 viewers. But some of the other things I've tried, I actually started out, publishing the base map as a closed source thing, where I would sell sort of like a data package instead of being a SaaS, I'd be like, here's a one-time download, of the premium data and you can buy it. And quite a few people bought it I just priced it at like $500 for this thing. And I thought that was an interesting experiment. The main reason it's interesting is because the people that it attracts to you in terms of like, they're curious about your products, are all people willing to pay money. While if you start out everything being open source, then the people that are gonna be try to do it are only the people that want to get something for free. So what I discovered is actually like once you transition that thing from closed source to open source, a lot of the people that used to pay you money will still keep paying you money because like, it wasn't necessarily that that closed source thing was why they wanted to pay. They just valued that thought you've put into it your expertise, for example. So I think that is one thing, that I tried at the beginning was just start out, closed source proprietary, then make it open source. That's interesting to people. Like if you release something as open source, if you go the other way, like people are really mad if you start out with something open source and then later on you're like, oh, it's some other license. Then people are like that's so rotten. But I think doing it the other way, I think is quite valuable in terms of being able to find an audience. [00:24:29] Jeremy: And when you said it was closed source and paid to open source, do you still sell those map exports? [00:24:39] Brandon: I don't right now. It's something that I might do in the future, you know, like have small customizations of the data that are available, uh, for a fee. still like the core OpenStreetMap based map that's like a hundred gigs you can just download. And that'll always just be like a free download just because that's already out there. All the source code to build it is open source. So even if I said, oh, you have to pay for it, then someone else can just do it right? So there's no real reason like to make that like some sort of like paywall thing. But I think like overall if the project is gonna survive in the long term it's important that I'd ideally like to be able to like grow like a team like have a small group of people that can dedicate the time to growing the project in the long term. But I'm still like trying to figure that out right now. [00:25:34] Jeremy: And when you mentioned that when you went from closed to open and people were still paying you, you don't sell a product anymore. What were they paying for? [00:25:45] Brandon: So I have some contracts with companies basically, like if they need a feature or they need a customization in this way then I am very open to those. And I sort of set it up to make it clear from the beginning that this is not just a free thing on GitHub, this is something that you could pay for if you need help with it, if you need support, if you wanted it. I'm also a little cagey about the word support because I think like it sounds a little bit too wishy-washy. Pretty much like if you need access to the developers of an open source project, I think that's something that businesses are willing to pay for. And I think like making that clear to potential users is a challenge. But I think that is one way that you might be able to make like a living out of open source. [00:26:35] Jeremy: And I think you said you'd been working on it for about five years. Has that mostly been full time? [00:26:42] Brandon: It's been on and off. it's sort of my pandemic era project. But I've spent a lot of time, most of my time working on the open source project at this point. So I have done some things that were more just like I'm doing a customization or like a private deployment for some client. But that's been a minority of the time. Yeah. [00:27:03] Jeremy: It's still impressive to have an open source project that is easy to self-host and yet is still able to support you working on it full time. I think a lot of people might make the assumption that there's nothing to sell if something is, is easy to use. But this sort of sounds like a counterpoint to that. [00:27:25] Brandon: I think I'd like it to be. So when you come back to the point of like, it being easy to self-host. Well, so again, like I think about it as like a primitive of the web. Like for example, if you wanted to start a business today as like hosted CSS files, you know, like where you upload your CSS and then you get developers to pay you a monthly subscription for how many times they fetched a CSS file. Well, I think most developers would be like, that's stupid because it's just an open specification, you just upload a static file. And really my goal is to make Protomaps the same way where it's obvious that there's not really some sort of lock-in or some sort of secret sauce in the server that does this thing. How PMTiles works and building a primitive of the web [00:28:16] Brandon: If you look at video for example, like a lot of the tech for how Protomaps and PMTiles works is based on parts of the HTTP spec that were made for video. And 20 years ago, if you wanted to host a video on the web, you had to have like a real player license or flash. So you had to go license some server software from real media or from macromedia so you could stream video to a browser plugin. But now in HTML you can just embed a video file. And no one's like, oh well I need to go pay for my video serving license. I mean, there is such a thing, like YouTube doesn't really use that for DRM reasons, but people just have the assumption that video is like a primitive on the web. So if we're able to make maps sort of that same way like a primitive on the web then there isn't really some obvious business or licensing model behind how that works. Just because it's a thing and it helps a lot of people do their jobs and people are happy using it. So why bother? [00:29:26] Jeremy: You mentioned that it a tech that was used for streaming video. What tech specifically is it? [00:29:34] Brandon: So it is byte range serving. So when you open a video file on the web, So let's say it's like a 100 megabyte video. You don't have to download the entire video before it starts playing. It streams parts out of the file based on like what frames... I mean, it's based on the frames in the video. So it can start streaming immediately because it's organized in a way to where the first few frames are at the beginning. And what PMTiles really is, is it's just like a video but in space instead of time. So it's organized in a way where these zoomed out views are at the beginning and the most zoomed in views are at the end. So when you're like panning or zooming in the map all you're really doing is fetching byte ranges out of that file the same way as a video. But it's organized in, this tiled way on a space filling curve. IIt's a little bit complicated how it works internally and I think it's kind of cool but that's sort of an like an implementation detail. [00:30:35] Jeremy: And to the person deploying it, it just looks like a single file. [00:30:40] Brandon: Exactly in the same way like an mp3 audio file is or like a JSON file is. [00:30:47] Jeremy: So with a video, I can sort of see how as someone seeks through the video, they start at the beginning and then they go to the middle if they wanna see the middle. For a map, as somebody scrolls around the map, are you seeking all over the file or is the way it's structured have a little less chaos? [00:31:09] Brandon: It's structured. And that's kind of the main technical challenge behind building PMTiles is you have to be sort of clever so you're not spraying the reads everywhere. So it uses something called a hilbert curve, which is a mathematical concept of a space filling curve. Where it's one continuous curve that essentially lets you break 2D space into 1D space. So if you've seen some maps of IP space, it uses this crazy looking curve that hits all the points in one continuous line. And that's the same concept behind PMTiles is if you're looking at one part of the world, you're sort of guaranteed that all of those parts you're looking at are quite close to each other and the data you have to transfer is quite minimal, compared to if you just had it at random. [00:32:02] Jeremy: How big do the files get? If I have a PMTiles of the entire world, what kind of size am I looking at? [00:32:10] Brandon: Right now, the default one I distribute is 128 gigabytes, so it's quite sizable, although you can slice parts out of it remotely. So if you just wanted. if you just wanted California or just wanted LA or just wanted only a couple of zoom levels, like from zero to 10 instead of zero to 15, there is a command line tool that's also called PMTiles that lets you do that. Issues with CDNs and range queries [00:32:35] Jeremy: And when you're working with files of this size, I mean, let's say I am working with a CDN in front of my application. I'm not typically accustomed to hosting something that's that large and something that's where you're seeking all over the file. is that, ever an issue or is that something that's just taken care of by the browser and, and taken care of by, by the hosts? [00:32:58] Brandon: That is an issue actually, so a lot of CDNs don't deal with it correctly. And my recommendation is there is a kind of proxy server or like a serverless proxy thing that I wrote. That runs on like cloudflare workers or on Docker that lets you proxy those range requests into a normal URL and then that is like a hundred percent CDN compatible. So I would say like a lot of the big commercial installations of this thing, they use that because it makes more practical sense. It's also faster. But the idea is that this solution sort of scales up and scales down. If you wanted to host just your city in like a 10 megabyte file, well you can just put that into GitHub pages and you don't have to worry about it. If you want to have a global map for your website that serves a ton of traffic then you probably want a little bit more sophisticated of a solution. It still does not require you to run a Linux server, but it might require (you) to use like Lambda or Lambda in conjunction with like a CDN. [00:34:09] Jeremy: Yeah. And that sort of ties into what you were saying at the beginning where if you can host on something like CloudFlare Workers or Lambda, there's less time you have to spend keeping these things running. [00:34:26] Brandon: Yeah, exactly. and I think also the Lambda or CloudFlare workers solution is not perfect. It's not as perfect as S3 or as just static files, but in my experience, it still is better at building something that lasts on the time span of years than being like I have a server that is on this Ubuntu version and in four years there's all these like security patches that are not being applied. So it's still sort of serverless, although not totally vendor neutral like S3. Customizing the map [00:35:03] Jeremy: We've mostly been talking about how you host the map itself, but for someone who's not familiar with these kind of tools, how would they be customizing the map? [00:35:15] Brandon: For customizing the map there is front end style customization and there's also data customization. So for the front end if you wanted to change the water from the shade of blue to another shade of blue there is a TypeScript API where you can customize it almost like a text editor color scheme. So if you're able to name a bunch of colors, well you can customize the map in that way you can change the fonts. And that's all done using MapLibre GL using a TypeScript API on top of that for customizing the data. So all the pipeline to generate this data from OpenStreetMap is open source. There is a Java program using a library called PlanetTiler which is awesome, which is this super fast multi-core way of building map tiles. And right now there isn't really great hooks to customize what data goes into that. But that's something that I do wanna work on. And finally, because the data comes from OpenStreetMap if you notice data that's missing or you wanted to correct data in OSM then you can go into osm.org. You can get involved in contributing the data to OSM and the Protomaps build is daily. So if you make a change, then within 24 hours you should see the new base map. Have that change. And of course for OSM your improvements would go into every OSM based project that is ingesting that data. So it's not a protomap specific thing. It's like this big shared data source, almost like Wikipedia. OpenStreetMap is a dataset and not a map [00:37:01] Jeremy: I think you were involved with OpenStreetMap to some extent. Can you speak a little bit to that for people who aren't familiar, what OpenStreetMap is? [00:37:11] Brandon: Right. So I've been using OSM as sort of like a tools developer for over a decade now. And one of the number one questions I get from developers about what is Protomaps is why wouldn't I just use OpenStreetMap? What's the distinction between Protomaps and OpenStreetMap? And it's sort of like this funny thing because even though OSM has map in the name it's not really a map in that you can't... In that it's mostly a data set and not a map. It does have a map that you can see that you can pan around to when you go to the website but the way that thing they show you on the website is built is not really that easily reproducible. It involves a lot of c++ software you have to run. But OpenStreetMap itself, the heart of it is almost like a big XML file that has all the data in the map and global. And it has tagged features for example. So you can go in and edit that. It has a web front end to change the data. It does not directly translate into making a map actually. Protomaps decides what shows at each zoom level [00:38:24] Brandon: So a lot of the pipeline, that Java program I mentioned for building this basemap for protomaps is doing things like you have to choose what data you show when you zoom out. You can't show all the data. For example when you're zoomed out and you're looking at all of a state like Colorado you don't see all the Chipotle when you're zoomed all the way out. That'd be weird, right? So you have to make some sort of decision in logic that says this data only shows up at this zoom level. And that's really what is the challenge in optimizing the size of that for the Protomaps map project. [00:39:03] Jeremy: Oh, so those decisions of what to show at different Zoom levels those are decisions made by you when you're creating the PMTiles file with Protomaps. [00:39:14] Brandon: Exactly. It's part of the base maps build pipeline. and those are honestly very subjective decisions. Who really decides when you're zoomed out should this hospital show up or should this museum show up nowadays in Google, I think it shows you ads. Like if someone pays for their car repair shop to show up when you're zoomed out like that that gets surfaced. But because there is no advertising auction in Protomaps that doesn't happen obviously. So we have to sort of make some reasonable choice. A lot of that right now in Protomaps actually comes from another open source project called Mapzen. So Mapzen was a company that went outta business a couple years ago. They did a lot of this work in designing which data shows up at which Zoom level and open sourced it. And then when they shut down, they transferred that code into the Linux Foundation. So it's this totally open source project, that like, again, sort of like Mapbox gl has this awesome legacy in that this company funded it for years for smart people to work on it and now it's just like a free thing you can use. So the logic in Protomaps is really based on mapzen. [00:40:33] Jeremy: And so the visualization of all this... I think I understand what you mean when people say oh, why not use OpenStreetMaps because it's not really clear it's hard to tell is this the tool that's visualizing the data? Is it the data itself? So in the case of using Protomaps, it sounds like Protomaps itself has all of the data from OpenStreetMap and then it has made all the decisions for you in terms of what to show at different Zoom levels and what things to have on the map at all. And then finally, you have to have a separate, UI layer and in this case, it sounds like the one that you recommend is the Map Libre library. [00:41:18] Brandon: Yeah, that's exactly right. For Protomaps, it has a portion or a subset of OSM data. It doesn't have all of it just because there's too much, like there's data in there. people have mapped out different bushes and I don't include that in Protomaps if you wanted to go in and edit like the Java code to add that you can. But really what Protomaps is positioned at is sort of a solution for developers that want to use OSM data to make a map on their app or their website. because OpenStreetMap itself is mostly a data set, it does not really go all the way to having an end-to-end solution. Financials and the idea of a project being complete [00:41:59] Jeremy: So I think it's great that somebody who wants to make a map, they have these tools available, whether it's from what was originally built by Mapbox, what's built by Open StreetMap now, the work you're doing with Protomaps. But I wonder one of the things that I talked about with Tom was he was saying he was trying to build this mapping business and based on the financials of what was coming in he was stressed, right? He was struggling a bit. And I wonder for you, you've been working on this open source project for five years. Do you have similar stressors or do you feel like I could keep going how things are now and I feel comfortable? [00:42:46] Brandon: So I wouldn't say I'm a hundred percent in one bucket or the other. I'm still seeing it play out. One thing, that I really respect in a lot of open source projects, which I'm not saying I'm gonna do for Protomaps is the idea that a project is like finished. I think that is amazing. If a software project can just be done it's sort of like a painting or a novel once you write, finish the last page, have it seen by the editor. I send it off to the press is you're done with a book. And I think one of the pains of software is so few of us can actually do that. And I don't know obviously people will say oh the map is never finished. That's more true of OSM, but I think like for Protomaps. One thing I'm thinking about is how to limit the scope to something that's quite narrow to where we could be feature complete on the core things in the near term timeframe. That means that it does not address a lot of things that people want. Like search, like if you go to Google Maps and you search for a restaurant, you will get some hits. that's like a geocoding issue. And I've already decided that's totally outta scope for Protomaps. So, in terms of trying to think about the future of this, I'm mostly looking for ways to cut scope if possible. There are some things like better tooling around being able to work with PMTiles that are on the roadmap. but for me, I am still enjoying working on the project. It's definitely growing. So I can see on NPM downloads I can see the growth curve of people using it and that's really cool. So I like hearing about when people are using it for cool projects. So it seems to still be going okay for now. [00:44:44] Jeremy: Yeah, that's an interesting perspective about how you were talking about projects being done. Because I think when people look at GitHub projects and they go like, oh, the last commit was X months ago. They go oh well this is dead right? But maybe that's the wrong framing. Maybe you can get a project to a point where it's like, oh, it's because it doesn't need to be updated. [00:45:07] Brandon: Exactly, yeah. Like I used to do a lot of c++ programming and the best part is when you see some LAPACK matrix math library from like 1995 that still works perfectly in c++ and you're like, this is awesome. This is the one I have to use. But if you're like trying to use some like React component library and it hasn't been updated in like a year, you're like, oh, that's a problem. So again, I think there's some middle ground between those that I'm trying to find. I do like for Protomaps, it's quite dependency light in terms of the number of hard dependencies I have in software. but I do still feel like there is a lot of work to be done in terms of project scope that needs to have stuff added. You mostly only hear about problems instead of people's wins [00:45:54] Jeremy: Having run it for this long. Do you have any thoughts on running an open source project in general? On dealing with issues or managing what to work on things like that? [00:46:07] Brandon: Yeah. So I have a lot. I think one thing people point out a lot is that especially because I don't have a direct relationship with a lot of the people using it a lot of times I don't even know that they're using it. Someone sent me a message saying hey, have you seen flickr.com, like the photo site? And I'm like, no. And I went to flickr.com/map and it has Protomaps for it. And I'm like, I had no idea. But that's cool, if they're able to use Protomaps for this giant photo sharing site that's awesome. But that also means I don't really hear about when people use it successfully because you just don't know, I guess they, NPM installed it and it works perfectly and you never hear about it. You only hear about people's negative experiences. You only hear about people that come and open GitHub issues saying this is totally broken, and why doesn't this thing exist? And I'm like, well, it's because there's an infinite amount of things that I want to do, but I have a finite amount of time and I just haven't gone into that yet. And that's honestly a lot of the things and people are like when is this thing gonna be done? So that's, that's honestly part of why I don't have a public roadmap because I want to avoid that sort of bickering about it. I would say that's one of my biggest frustrations with running an open source project is how it's self-selected to only hear the negative experiences with it. Be careful what PRs you accept [00:47:32] Brandon: 'cause you don't hear about those times where it works. I'd say another thing is it's changed my perspective on contributing to open source because I think when I was younger or before I had become a maintainer I would open a pull request on a project unprompted that has a hundred lines and I'd be like, Hey, just merge this thing. But I didn't realize when I was younger well if I just merge it and I disappear, then the maintainer is stuck with what I did forever. You know if I add some feature then that person that maintains the project has to do that indefinitely. And I think that's very asymmetrical and it's changed my perspective a lot on accepting open source contributions. I wanna have it be open to anyone to contribute. But there is some amount of back and forth where it's almost like the default answer for should I accept a PR is no by default because you're the one maintaining it. And do you understand the shape of that solution completely to where you're going to support it for years because the person that's contributing it is not bound to those same obligations that you are. And I think that's also one of the things where I have a lot of trepidation around open source is I used to think of it as a lot more bazaar-like in terms of anyone can just throw their thing in. But then that creates a lot of problems for the people who are expected out of social obligation to continue this thing indefinitely. [00:49:23] Jeremy: Yeah, I can totally see why that causes burnout with a lot of open source maintainers, because you probably to some extent maybe even feel some guilt right? You're like, well, somebody took the time to make this. But then like you said you have to spend a lot of time trying to figure out is this something I wanna maintain long term? And one wrong move and it's like, well, it's in here now. [00:49:53] Brandon: Exactly. To me, I think that is a very common failure mode for open source projects is they're too liberal in the things they accept. And that's a lot of why I was talking about how that choice of what features show up on the map was inherited from the MapZen projects. If I didn't have that then somebody could come in and say hey, you know, I want to show power lines on the map. And they open a PR for power lines and now everybody who's using Protomaps when they're like zoomed out they see power lines are like I didn't want that. So I think that's part of why a lot of open source projects eventually evolve into a plugin system is because there is this demand as the project grows for more and more features. But there is a limit in the maintainers. It's like the demand for features is exponential while the maintainer amount of time and effort is linear. Plugin systems might reduce need for PRs [00:50:56] Brandon: So maybe the solution to smash that exponential down to quadratic maybe is to add a plugin system. But I think that is one of the biggest tensions that only became obvious to me after working on this for a couple of years. [00:51:14] Jeremy: Is that something you're considering doing now? [00:51:18] Brandon: Is the plugin system? Yeah. I think for the data customization, I eventually wanted to have some sort of programmatic API to where you could declare a config file that says I want ski routes. It totally makes sense. The power lines example is maybe a little bit obscure but for example like a skiing app and you want to be able to show ski slopes when you're zoomed out well you're not gonna be able to get that from Mapbox or from Google because they have a one size fits all map that's not specialized to skiing or to golfing or to outdoors. But if you like, in theory, you could do this with Protomaps if you changed the Java code to show data at different zoom levels. And that is to me what makes the most sense for a plugin system and also makes the most product sense because it enables a lot of things you cannot do with the one size fits all map. [00:52:20] Jeremy: It might also increase the complexity of the implementation though, right? [00:52:25] Brandon: Yeah, exactly. So that's like. That's really where a lot of the terrifying thoughts come in, which is like once you create this like config file surface area, well what does that look like? Is that JSON? Is that TOML, is that some weird like everything eventually evolves into some scripting language right? Where you have logic inside of your templates and I honestly do not really know what that looks like right now. That feels like something in the medium term roadmap. [00:52:58] Jeremy: Yeah and then in terms of bug reports or issues, now it's not just your code it's this exponential combination of whatever people put into these config files. [00:53:09] Brandon: Exactly. Yeah. so again, like I really respect the projects that have done this well or that have done plugins well. I'm trying to think of some, I think obsidian has plugins, for example. And that seems to be one of the few solutions to try and satisfy the infinite desire for features with the limited amount of maintainer time. Time split between code vs triage vs talking to users [00:53:36] Jeremy: How would you say your time is split between working on the code versus issue and PR triage? [00:53:43] Brandon: Oh, it varies really. I think working on the code is like a minority of it. I think something that I actually enjoy is talking to people, talking to users, getting feedback on it. I go to quite a few conferences to talk to developers or people that are interested and figure out how to refine the message, how to make it clearer to people, like what this is for. And I would say maybe a plurality of my time is spent dealing with non-technical things that are neither code or GitHub issues. One thing I've been trying to do recently is talk to people that are not really in the mapping space. For example, people that work for newspapers like a lot of them are front end developers and if you ask them to run a Linux server they're like I have no idea. But that really is like one of the best target audiences for Protomaps. So I'd say a lot of the reality of running an open source project is a lot like a business is it has all the same challenges as a business in terms of you have to figure out what is the thing you're offering. You have to deal with people using it. You have to deal with feedback, you have to deal with managing emails and stuff. I don't think the payoff is anywhere near running a business or a startup that's backed by VC money is but it's definitely not the case that if you just want to code, you should start an open source project because I think a lot of the work for an opensource project has nothing to do with just writing the code. It is in my opinion as someone having done a VC backed business before, it is a lot more similar to running, a tech company than just putting some code on GitHub. Running a startup vs open source project [00:55:43] Jeremy: Well, since you've done both at a high level what did you like about running the company versus maintaining the open source project? [00:55:52] Brandon: So I have done some venture capital accelerator programs before and I think there is an element of hype and energy that you get from that that is self perpetuating. Your co-founder is gungho on like, yeah, we're gonna do this thing. And your investors are like, you guys are geniuses. You guys are gonna make a killing doing this thing. And the way it's framed is sort of obvious to everyone that it's like there's a much more traditional set of motivations behind that, that people understand while it's definitely not the case for running an open source project. Sometimes you just wake up and you're like what the hell is this thing for, it is this thing you spend a lot of time on. You don't even know who's using it. The people that use it and make a bunch of money off of it they know nothing about it. And you know, it's just like cool. And then you only hear from people that are complaining about it. And I think like that's honestly discouraging compared to the more clear energy and clearer motivation and vision behind how most people think about a company. But what I like about the open source project is just the lack of those constraints you know? Where you have a mandate that you need to have this many customers that are paying by this amount of time. There's that sort of pressure on delivering a business result instead of just making something that you're proud of that's simple to use and has like an elegant design. I think that's really a difference in motivation as well. Having control [00:57:50] Jeremy: Do you feel like you have more control? Like you mentioned how you've decided I'm not gonna make a public roadmap. I'm the sole developer. I get to decide what goes in. What doesn't. Do you feel like you have more control in your current position than you did running the startup? [00:58:10] Brandon: Definitely for sure. Like that agency is what I value the most. It is possible to go too far. Like, so I'm very wary of the BDFL title, which I think is how a lot of open source projects succeed. But I think there is some element of for a project to succeed there has to be somebody that makes those decisions. Sometimes those decisions will be wrong and then hopefully they can be rectified. But I think going back to what I was talking about with scope, I think the overall vision and the scope of the project is something that I am very opinionated about in that it should do these things. It shouldn't do these things. It should be easy to use for this audience. Is it gonna be appealing to this other audience? I don't know. And I think that is really one of the most important parts of that leadership role, is having the power to decide we're doing this, we're not doing this. I would hope other developers would be able to get on board if they're able to make good use of the project, if they use it for their company, if they use it for their business, if they just think the project is cool. So there are other contributors at this point and I want to get more involved. But I think being able to make those decisions to what I believe is going to be the best project is something that is very special about open source, that isn't necessarily true about running like a SaaS business. [00:59:50] Jeremy: I think that's a good spot to end it on, so if people want to learn more about Protomaps or they wanna see what you're up to, where should they head? [01:00:00] Brandon: So you can go to Protomaps.com, GitHub, or you can find me or Protomaps on bluesky or Mastodon. [01:00:09] Jeremy: All right, Brandon, thank you so much for chatting today. [01:00:12] Brandon: Great. Thank you very much.

AWS Morning Brief for the week of March 31st, with Corey Quinn. Links:Amazon DynamoDB now supports percentile statistics for request latencyAmazon EKS now enforces upgrade insights checks as part of cluster upgradesAmazon GameLift Servers expands instance support with next-generation EC2 instance familiesAWS CloudFormation now supports targeted resource scans in the IaC generatorAWS adds currency selection to Payment ProfilesAWS Deadline Cloud now supports Internet Protocol Version 6 (IPv6)AWS announces expanded service support in the AWS Console Mobile AppAWS Network Manager and AWS Cloud WAN now support AWS PrivateLink and IPv6Unlocking the power of Splunk with Amazon Bedrock – Build AI assistant using agentsFrom virtual machine to Kubernetes to serverless: How dacadoo saved 78% on cloud costs and automated operationsAccelerating CI with AWS CodeBuild: Parallel test execution now availableAmazon S3 Path Deprecation Plan – The Rest of the Story | AWS News BlogDetailed geographic information for all AWS Regions and Availability Zones is now availableOptimizing network footprint in serverless applicationsSimplifying private API integrations with Amazon EventBridge and AWS Step FunctionsAnnouncing the Developer Preview of Amazon S3 Transfer Manager in RustAWS SDK for Ruby: Deprecating Ruby 2.5 & 2.6 Runtime Supports and Future CompatibilityAnnouncing the AWS CDK L2 Construct for Amazon Cognito Identity PoolsAWS re:Invent 2024 recap for government agencies

Today's episode is with Paul Klein, founder of Browserbase. We talked about building browser infrastructure for AI agents, the future of agent authentication, and their open source framework Stagehand.* [00:00:00] Introductions* [00:04:46] AI-specific challenges in browser infrastructure* [00:07:05] Multimodality in AI-Powered Browsing* [00:12:26] Running headless browsers at scale* [00:18:46] Geolocation when proxying* [00:21:25] CAPTCHAs and Agent Auth* [00:28:21] Building “User take over” functionality* [00:33:43] Stagehand: AI web browsing framework* [00:38:58] OpenAI's Operator and computer use agents* [00:44:44] Surprising use cases of Browserbase* [00:47:18] Future of browser automation and market competition* [00:53:11] Being a solo founderTranscriptAlessio [00:00:04]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, partner and CTO at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol.ai.swyx [00:00:12]: Hey, and today we are very blessed to have our friends, Paul Klein, for the fourth, the fourth, CEO of Browserbase. Welcome.Paul [00:00:21]: Thanks guys. Yeah, I'm happy to be here. I've been lucky to know both of you for like a couple of years now, I think. So it's just like we're hanging out, you know, with three ginormous microphones in front of our face. It's totally normal hangout.swyx [00:00:34]: Yeah. We've actually mentioned you on the podcast, I think, more often than any other Solaris tenant. Just because like you're one of the, you know, best performing, I think, LLM tool companies that have started up in the last couple of years.Paul [00:00:50]: Yeah, I mean, it's been a whirlwind of a year, like Browserbase is actually pretty close to our first birthday. So we are one years old. And going from, you know, starting a company as a solo founder to... To, you know, having a team of 20 people, you know, a series A, but also being able to support hundreds of AI companies that are building AI applications that go out and automate the web. It's just been like, really cool. It's been happening a little too fast. I think like collectively as an AI industry, let's just take a week off together. I took my first vacation actually two weeks ago, and Operator came out on the first day, and then a week later, DeepSeat came out. And I'm like on vacation trying to chill. I'm like, we got to build with this stuff, right? So it's been a breakneck year. But I'm super happy to be here and like talk more about all the stuff we're seeing. And I'd love to hear kind of what you guys are excited about too, and share with it, you know?swyx [00:01:39]: Where to start? So people, you've done a bunch of podcasts. I think I strongly recommend Jack Bridger's Scaling DevTools, as well as Turner Novak's The Peel. And, you know, I'm sure there's others. So you covered your Twilio story in the past, talked about StreamClub, you got acquired to Mux, and then you left to start Browserbase. So maybe we just start with what is Browserbase? Yeah.Paul [00:02:02]: Browserbase is the web browser for your AI. We're building headless browser infrastructure, which are browsers that run in a server environment that's accessible to developers via APIs and SDKs. It's really hard to run a web browser in the cloud. You guys are probably running Chrome on your computers, and that's using a lot of resources, right? So if you want to run a web browser or thousands of web browsers, you can't just spin up a bunch of lambdas. You actually need to use a secure containerized environment. You have to scale it up and down. It's a stateful system. And that infrastructure is, like, super painful. And I know that firsthand, because at my last company, StreamClub, I was CTO, and I was building our own internal headless browser infrastructure. That's actually why we sold the company, is because Mux really wanted to buy our headless browser infrastructure that we'd built. And it's just a super hard problem. And I actually told my co-founders, I would never start another company unless it was a browser infrastructure company. And it turns out that's really necessary in the age of AI, when AI can actually go out and interact with websites, click on buttons, fill in forms. You need AI to do all of that work in an actual browser running somewhere on a server. And BrowserBase powers that.swyx [00:03:08]: While you're talking about it, it occurred to me, not that you're going to be acquired or anything, but it occurred to me that it would be really funny if you became the Nikita Beer of headless browser companies. You just have one trick, and you make browser companies that get acquired.Paul [00:03:23]: I truly do only have one trick. I'm screwed if it's not for headless browsers. I'm not a Go programmer. You know, I'm in AI grant. You know, browsers is an AI grant. But we were the only company in that AI grant batch that used zero dollars on AI spend. You know, we're purely an infrastructure company. So as much as people want to ask me about reinforcement learning, I might not be the best guy to talk about that. But if you want to ask about headless browser infrastructure at scale, I can talk your ear off. So that's really my area of expertise. And it's a pretty niche thing. Like, nobody has done what we're doing at scale before. So we're happy to be the experts.swyx [00:03:59]: You do have an AI thing, stagehand. We can talk about the sort of core of browser-based first, and then maybe stagehand. Yeah, stagehand is kind of the web browsing framework. Yeah.What is Browserbase? Headless Browser Infrastructure ExplainedAlessio [00:04:10]: Yeah. Yeah. And maybe how you got to browser-based and what problems you saw. So one of the first things I worked on as a software engineer was integration testing. Sauce Labs was kind of like the main thing at the time. And then we had Selenium, we had Playbrite, we had all these different browser things. But it's always been super hard to do. So obviously you've worked on this before. When you started browser-based, what were the challenges? What were the AI-specific challenges that you saw versus, there's kind of like all the usual running browser at scale in the cloud, which has been a problem for years. What are like the AI unique things that you saw that like traditional purchase just didn't cover? Yeah.AI-specific challenges in browser infrastructurePaul [00:04:46]: First and foremost, I think back to like the first thing I did as a developer, like as a kid when I was writing code, I wanted to write code that did stuff for me. You know, I wanted to write code to automate my life. And I do that probably by using curl or beautiful soup to fetch data from a web browser. And I think I still do that now that I'm in the cloud. And the other thing that I think is a huge challenge for me is that you can't just create a web site and parse that data. And we all know that now like, you know, taking HTML and plugging that into an LLM, you can extract insights, you can summarize. So it was very clear that now like dynamic web scraping became very possible with the rise of large language models or a lot easier. And that was like a clear reason why there's been more usage of headless browsers, which are necessary because a lot of modern websites don't expose all of their page content via a simple HTTP request. You know, they actually do require you to run this type of code for a specific time. JavaScript on the page to hydrate this. Airbnb is a great example. You go to airbnb.com. A lot of that content on the page isn't there until after they run the initial hydration. So you can't just scrape it with a curl. You need to have some JavaScript run. And a browser is that JavaScript engine that's going to actually run all those requests on the page. So web data retrieval was definitely one driver of starting BrowserBase and the rise of being able to summarize that within LLM. Also, I was familiar with if I wanted to automate a website, I could write one script and that would work for one website. It was very static and deterministic. But the web is non-deterministic. The web is always changing. And until we had LLMs, there was no way to write scripts that you could write once that would run on any website. That would change with the structure of the website. Click the login button. It could mean something different on many different websites. And LLMs allow us to generate code on the fly to actually control that. So I think that rise of writing the generic automation scripts that can work on many different websites, to me, made it clear that browsers are going to be a lot more useful because now you can automate a lot more things without writing. If you wanted to write a script to book a demo call on 100 websites, previously, you had to write 100 scripts. Now you write one script that uses LLMs to generate that script. That's why we built our web browsing framework, StageHand, which does a lot of that work for you. But those two things, web data collection and then enhanced automation of many different websites, it just felt like big drivers for more browser infrastructure that would be required to power these kinds of features.Alessio [00:07:05]: And was multimodality also a big thing?Paul [00:07:08]: Now you can use the LLMs to look, even though the text in the dome might not be as friendly. Maybe my hot take is I was always kind of like, I didn't think vision would be as big of a driver. For UI automation, I felt like, you know, HTML is structured text and large language models are good with structured text. But it's clear that these computer use models are often vision driven, and they've been really pushing things forward. So definitely being multimodal, like rendering the page is required to take a screenshot to give that to a computer use model to take actions on a website. And it's just another win for browser. But I'll be honest, that wasn't what I was thinking early on. I didn't even think that we'd get here so fast with multimodality. I think we're going to have to get back to multimodal and vision models.swyx [00:07:50]: This is one of those things where I forgot to mention in my intro that I'm an investor in Browserbase. And I remember that when you pitched to me, like a lot of the stuff that we have today, we like wasn't on the original conversation. But I did have my original thesis was something that we've talked about on the podcast before, which is take the GPT store, the custom GPT store, all the every single checkbox and plugin is effectively a startup. And this was the browser one. I think the main hesitation, I think I actually took a while to get back to you. The main hesitation was that there were others. Like you're not the first hit list browser startup. It's not even your first hit list browser startup. There's always a question of like, will you be the category winner in a place where there's a bunch of incumbents, to be honest, that are bigger than you? They're just not targeted at the AI space. They don't have the backing of Nat Friedman. And there's a bunch of like, you're here in Silicon Valley. They're not. I don't know.Paul [00:08:47]: I don't know if that's, that was it, but like, there was a, yeah, I mean, like, I think I tried all the other ones and I was like, really disappointed. Like my background is from working at great developer tools, companies, and nothing had like the Vercel like experience. Um, like our biggest competitor actually is partly owned by private equity and they just jacked up their prices quite a bit. And the dashboard hasn't changed in five years. And I actually used them at my last company and tried them and I was like, oh man, like there really just needs to be something that's like the experience of these great infrastructure companies, like Stripe, like clerk, like Vercel that I use in love, but oriented towards this kind of like more specific category, which is browser infrastructure, which is really technically complex. Like a lot of stuff can go wrong on the internet when you're running a browser. The internet is very vast. There's a lot of different configurations. Like there's still websites that only work with internet explorer out there. How do you handle that when you're running your own browser infrastructure? These are the problems that we have to think about and solve at BrowserBase. And it's, it's certainly a labor of love, but I built this for me, first and foremost, I know it's super cheesy and everyone says that for like their startups, but it really, truly was for me. If you look at like the talks I've done even before BrowserBase, and I'm just like really excited to try and build a category defining infrastructure company. And it's, it's rare to have a new category of infrastructure exists. We're here in the Chroma offices and like, you know, vector databases is a new category of infrastructure. Is it, is it, I mean, we can, we're in their office, so, you know, we can, we can debate that one later. That is one.Multimodality in AI-Powered Browsingswyx [00:10:16]: That's one of the industry debates.Paul [00:10:17]: I guess we go back to the LLMOS talk that Karpathy gave way long ago. And like the browser box was very clearly there and it seemed like the people who were building in this space also agreed that browsers are a core primitive of infrastructure for the LLMOS that's going to exist in the future. And nobody was building something there that I wanted to use. So I had to go build it myself.swyx [00:10:38]: Yeah. I mean, exactly that talk that, that honestly, that diagram, every box is a startup and there's the code box and then there's the. The browser box. I think at some point they will start clashing there. There's always the question of the, are you a point solution or are you the sort of all in one? And I think the point solutions tend to win quickly, but then the only ones have a very tight cohesive experience. Yeah. Let's talk about just the hard problems of browser base you have on your website, which is beautiful. Thank you. Was there an agency that you used for that? Yeah. Herb.paris.Paul [00:11:11]: They're amazing. Herb.paris. Yeah. It's H-E-R-V-E. I highly recommend for developers. Developer tools, founders to work with consumer agencies because they end up building beautiful things and the Parisians know how to build beautiful interfaces. So I got to give prep.swyx [00:11:24]: And chat apps, apparently are, they are very fast. Oh yeah. The Mistral chat. Yeah. Mistral. Yeah.Paul [00:11:31]: Late chat.swyx [00:11:31]: Late chat. And then your videos as well, it was professionally shot, right? The series A video. Yeah.Alessio [00:11:36]: Nico did the videos. He's amazing. Not the initial video that you shot at the new one. First one was Austin.Paul [00:11:41]: Another, another video pretty surprised. But yeah, I mean, like, I think when you think about how you talk about your company. You have to think about the way you present yourself. It's, you know, as a developer, you think you evaluate a company based on like the API reliability and the P 95, but a lot of developers say, is the website good? Is the message clear? Do I like trust this founder? I'm building my whole feature on. So I've tried to nail that as well as like the reliability of the infrastructure. You're right. It's very hard. And there's a lot of kind of foot guns that you run into when running headless browsers at scale. Right.Competing with Existing Headless Browser Solutionsswyx [00:12:10]: So let's pick one. You have eight features here. Seamless integration. Scalability. Fast or speed. Secure. Observable. Stealth. That's interesting. Extensible and developer first. What comes to your mind as like the top two, three hardest ones? Yeah.Running headless browsers at scalePaul [00:12:26]: I think just running headless browsers at scale is like the hardest one. And maybe can I nerd out for a second? Is that okay? I heard this is a technical audience, so I'll talk to the other nerds. Whoa. They were listening. Yeah. They're upset. They're ready. The AGI is angry. Okay. So. So how do you run a browser in the cloud? Let's start with that, right? So let's say you're using a popular browser automation framework like Puppeteer, Playwright, and Selenium. Maybe you've written a code, some code locally on your computer that opens up Google. It finds the search bar and then types in, you know, search for Latent Space and hits the search button. That script works great locally. You can see the little browser open up. You want to take that to production. You want to run the script in a cloud environment. So when your laptop is closed, your browser is doing something. The browser is doing something. Well, I, we use Amazon. You can see the little browser open up. You know, the first thing I'd reach for is probably like some sort of serverless infrastructure. I would probably try and deploy on a Lambda. But Chrome itself is too big to run on a Lambda. It's over 250 megabytes. So you can't easily start it on a Lambda. So you maybe have to use something like Lambda layers to squeeze it in there. Maybe use a different Chromium build that's lighter. And you get it on the Lambda. Great. It works. But it runs super slowly. It's because Lambdas are very like resource limited. They only run like with one vCPU. You can run one process at a time. Remember, Chromium is super beefy. It's barely running on my MacBook Air. I'm still downloading it from a pre-run. Yeah, from the test earlier, right? I'm joking. But it's big, you know? So like Lambda, it just won't work really well. Maybe it'll work, but you need something faster. Your users want something faster. Okay. Well, let's put it on a beefier instance. Let's get an EC2 server running. Let's throw Chromium on there. Great. Okay. I can, that works well with one user. But what if I want to run like 10 Chromium instances, one for each of my users? Okay. Well, I might need two EC2 instances. Maybe 10. All of a sudden, you have multiple EC2 instances. This sounds like a problem for Kubernetes and Docker, right? Now, all of a sudden, you're using ECS or EKS, the Kubernetes or container solutions by Amazon. You're spending up and down containers, and you're spending a whole engineer's time on kind of maintaining this stateful distributed system. Those are some of the worst systems to run because when it's a stateful distributed system, it means that you are bound by the connections to that thing. You have to keep the browser open while someone is working with it, right? That's just a painful architecture to run. And there's all this other little gotchas with Chromium, like Chromium, which is the open source version of Chrome, by the way. You have to install all these fonts. You want emojis working in your browsers because your vision model is looking for the emoji. You need to make sure you have the emoji fonts. You need to make sure you have all the right extensions configured, like, oh, do you want ad blocking? How do you configure that? How do you actually record all these browser sessions? Like it's a headless browser. You can't look at it. So you need to have some sort of observability. Maybe you're recording videos and storing those somewhere. It all kind of adds up to be this just giant monster piece of your project when all you wanted to do was run a lot of browsers in production for this little script to go to google.com and search. And when I see a complex distributed system, I see an opportunity to build a great infrastructure company. And we really abstract that away with Browserbase where our customers can use these existing frameworks, Playwright, Publisher, Selenium, or our own stagehand and connect to our browsers in a serverless-like way. And control them, and then just disconnect when they're done. And they don't have to think about the complex distributed system behind all of that. They just get a browser running anywhere, anytime. Really easy to connect to.swyx [00:15:55]: I'm sure you have questions. My standard question with anything, so essentially you're a serverless browser company, and there's been other serverless things that I'm familiar with in the past, serverless GPUs, serverless website hosting. That's where I come from with Netlify. One question is just like, you promised to spin up thousands of servers. You promised to spin up thousands of browsers in milliseconds. I feel like there's no real solution that does that yet. And I'm just kind of curious how. The only solution I know, which is to kind of keep a kind of warm pool of servers around, which is expensive, but maybe not so expensive because it's just CPUs. So I'm just like, you know. Yeah.Browsers as a Core Primitive in AI InfrastructurePaul [00:16:36]: You nailed it, right? I mean, how do you offer a serverless-like experience with something that is clearly not serverless, right? And the answer is, you need to be able to run... We run many browsers on single nodes. We use Kubernetes at browser base. So we have many pods that are being scheduled. We have to predictably schedule them up or down. Yes, thousands of browsers in milliseconds is the best case scenario. If you hit us with 10,000 requests, you may hit a slower cold start, right? So we've done a lot of work on predictive scaling and being able to kind of route stuff to different regions where we have multiple regions of browser base where we have different pools available. You can also pick the region you want to go to based on like lower latency, round trip, time latency. It's very important with these types of things. There's a lot of requests going over the wire. So for us, like having a VM like Firecracker powering everything under the hood allows us to be super nimble and spin things up or down really quickly with strong multi-tenancy. But in the end, this is like the complex infrastructural challenges that we have to kind of deal with at browser base. And we have a lot more stuff on our roadmap to allow customers to have more levers to pull to exchange, do you want really fast browser startup times or do you want really low costs? And if you're willing to be more flexible on that, we may be able to kind of like work better for your use cases.swyx [00:17:44]: Since you used Firecracker, shouldn't Fargate do that for you or did you have to go lower level than that? We had to go lower level than that.Paul [00:17:51]: I find this a lot with Fargate customers, which is alarming for Fargate. We used to be a giant Fargate customer. Actually, the first version of browser base was ECS and Fargate. And unfortunately, it's a great product. I think we were actually the largest Fargate customer in our region for a little while. No, what? Yeah, seriously. And unfortunately, it's a great product, but I think if you're an infrastructure company, you actually have to have a deeper level of control over these primitives. I think it's the same thing is true with databases. We've used other database providers and I think-swyx [00:18:21]: Yeah, serverless Postgres.Paul [00:18:23]: Shocker. When you're an infrastructure company, you're on the hook if any provider has an outage. And I can't tell my customers like, hey, we went down because so-and-so went down. That's not acceptable. So for us, we've really moved to bringing things internally. It's kind of opposite of what we preach. We tell our customers, don't build this in-house, but then we're like, we build a lot of stuff in-house. But I think it just really depends on what is in the critical path. We try and have deep ownership of that.Alessio [00:18:46]: On the distributed location side, how does that work for the web where you might get sort of different content in different locations, but the customer is expecting, you know, if you're in the US, I'm expecting the US version. But if you're spinning up my browser in France, I might get the French version. Yeah.Paul [00:19:02]: Yeah. That's a good question. Well, generally, like on the localization, there is a thing called locale in the browser. You can set like what your locale is. If you're like in the ENUS browser or not, but some things do IP, IP based routing. And in that case, you may want to have a proxy. Like let's say you're running something in the, in Europe, but you want to make sure you're showing up from the US. You may want to use one of our proxy features so you can turn on proxies to say like, make sure these connections always come from the United States, which is necessary too, because when you're browsing the web, you're coming from like a, you know, data center IP, and that can make things a lot harder to browse web. So we do have kind of like this proxy super network. Yeah. We have a proxy for you based on where you're going, so you can reliably automate the web. But if you get scheduled in Europe, that doesn't happen as much. We try and schedule you as close to, you know, your origin that you're trying to go to. But generally you have control over the regions you can put your browsers in. So you can specify West one or East one or Europe. We only have one region of Europe right now, actually. Yeah.Alessio [00:19:55]: What's harder, the browser or the proxy? I feel like to me, it feels like actually proxying reliably at scale. It's much harder than spending up browsers at scale. I'm curious. It's all hard.Paul [00:20:06]: It's layers of hard, right? Yeah. I think it's different levels of hard. I think the thing with the proxy infrastructure is that we work with many different web proxy providers and some are better than others. Some have good days, some have bad days. And our customers who've built browser infrastructure on their own, they have to go and deal with sketchy actors. Like first they figure out their own browser infrastructure and then they got to go buy a proxy. And it's like you can pay in Bitcoin and it just kind of feels a little sus, right? It's like you're buying drugs when you're trying to get a proxy online. We have like deep relationships with these counterparties. We're able to audit them and say, is this proxy being sourced ethically? Like it's not running on someone's TV somewhere. Is it free range? Yeah. Free range organic proxies, right? Right. We do a level of diligence. We're SOC 2. So we have to understand what is going on here. But then we're able to make sure that like we route around proxy providers not working. There's proxy providers who will just, the proxy will stop working all of a sudden. And then if you don't have redundant proxying on your own browsers, that's hard down for you or you may get some serious impacts there. With us, like we intelligently know, hey, this proxy is not working. Let's go to this one. And you can kind of build a network of multiple providers to really guarantee the best uptime for our customers. Yeah. So you don't own any proxies? We don't own any proxies. You're right. The team has been saying who wants to like take home a little proxy server, but not yet. We're not there yet. You know?swyx [00:21:25]: It's a very mature market. I don't think you should build that yourself. Like you should just be a super customer of them. Yeah. Scraping, I think, is the main use case for that. I guess. Well, that leads us into CAPTCHAs and also off, but let's talk about CAPTCHAs. You had a little spiel that you wanted to talk about CAPTCHA stuff.Challenges of Scaling Browser InfrastructurePaul [00:21:43]: Oh, yeah. I was just, I think a lot of people ask, if you're thinking about proxies, you're thinking about CAPTCHAs too. I think it's the same thing. You can go buy CAPTCHA solvers online, but it's the same buying experience. It's some sketchy website, you have to integrate it. It's not fun to buy these things and you can't really trust that the docs are bad. What Browserbase does is we integrate a bunch of different CAPTCHAs. We do some stuff in-house, but generally we just integrate with a bunch of known vendors and continually monitor and maintain these things and say, is this working or not? Can we route around it or not? These are CAPTCHA solvers. CAPTCHA solvers, yeah. Not CAPTCHA providers, CAPTCHA solvers. Yeah, sorry. CAPTCHA solvers. We really try and make sure all of that works for you. I think as a dev, if I'm buying infrastructure, I want it all to work all the time and it's important for us to provide that experience by making sure everything does work and monitoring it on our own. Yeah. Right now, the world of CAPTCHAs is tricky. I think AI agents in particular are very much ahead of the internet infrastructure. CAPTCHAs are designed to block all types of bots, but there are now good bots and bad bots. I think in the future, CAPTCHAs will be able to identify who a good bot is, hopefully via some sort of KYC. For us, we've been very lucky. We have very little to no known abuse of Browserbase because we really look into who we work with. And for certain types of CAPTCHA solving, we only allow them on certain types of plans because we want to make sure that we can know what people are doing, what their use cases are. And that's really allowed us to try and be an arbiter of good bots, which is our long term goal. I want to build great relationships with people like Cloudflare so we can agree, hey, here are these acceptable bots. We'll identify them for you and make sure we flag when they come to your website. This is a good bot, you know?Alessio [00:23:23]: I see. And Cloudflare said they want to do more of this. So they're going to set by default, if they think you're an AI bot, they're going to reject. I'm curious if you think this is something that is going to be at the browser level or I mean, the DNS level with Cloudflare seems more where it should belong. But I'm curious how you think about it.Paul [00:23:40]: I think the web's going to change. You know, I think that the Internet as we have it right now is going to change. And we all need to just accept that the cat is out of the bag. And instead of kind of like wishing the Internet was like it was in the 2000s, we can have free content line that wouldn't be scraped. It's just it's not going to happen. And instead, we should think about like, one, how can we change? How can we change the models of, you know, information being published online so people can adequately commercialize it? But two, how do we rebuild applications that expect that AI agents are going to log in on their behalf? Those are the things that are going to allow us to kind of like identify good and bad bots. And I think the team at Clerk has been doing a really good job with this on the authentication side. I actually think that auth is the biggest thing that will prevent agents from accessing stuff, not captchas. And I think there will be agent auth in the future. I don't know if it's going to happen from an individual company, but actually authentication providers that have a, you know, hidden login as agent feature, which will then you put in your email, you'll get a push notification, say like, hey, your browser-based agent wants to log into your Airbnb. You can approve that and then the agent can proceed. That really circumvents the need for captchas or logging in as you and sharing your password. I think agent auth is going to be one way we identify good bots going forward. And I think a lot of this captcha solving stuff is really short-term problems as the internet kind of reorients itself around how it's going to work with agents browsing the web, just like people do. Yeah.Managing Distributed Browser Locations and Proxiesswyx [00:24:59]: Stitch recently was on Hacker News for talking about agent experience, AX, which is a thing that Netlify is also trying to clone and coin and talk about. And we've talked about this on our previous episodes before in a sense that I actually think that's like maybe the only part of the tech stack that needs to be kind of reinvented for agents. Everything else can stay the same, CLIs, APIs, whatever. But auth, yeah, we need agent auth. And it's mostly like short-lived, like it should not, it should be a distinct, identity from the human, but paired. I almost think like in the same way that every social network should have your main profile and then your alt accounts or your Finsta, it's almost like, you know, every, every human token should be paired with the agent token and the agent token can go and do stuff on behalf of the human token, but not be presumed to be the human. Yeah.Paul [00:25:48]: It's like, it's, it's actually very similar to OAuth is what I'm thinking. And, you know, Thread from Stitch is an investor, Colin from Clerk, Octaventures, all investors in browser-based because like, I hope they solve this because they'll make browser-based submission more possible. So we don't have to overcome all these hurdles, but I think it will be an OAuth-like flow where an agent will ask to log in as you, you'll approve the scopes. Like it can book an apartment on Airbnb, but it can't like message anybody. And then, you know, the agent will have some sort of like role-based access control within an application. Yeah. I'm excited for that.swyx [00:26:16]: The tricky part is just, there's one, one layer of delegation here, which is like, you're authoring my user's user or something like that. I don't know if that's tricky or not. Does that make sense? Yeah.Paul [00:26:25]: You know, actually at Twilio, I worked on the login identity and access. Management teams, right? So like I built Twilio's login page.swyx [00:26:31]: You were an intern on that team and then you became the lead in two years? Yeah.Paul [00:26:34]: Yeah. I started as an intern in 2016 and then I was the tech lead of that team. How? That's not normal. I didn't have a life. He's not normal. Look at this guy. I didn't have a girlfriend. I just loved my job. I don't know. I applied to 500 internships for my first job and I got rejected from every single one of them except for Twilio and then eventually Amazon. And they took a shot on me and like, I was getting paid money to write code, which was my dream. Yeah. Yeah. I'm very lucky that like this coding thing worked out because I was going to be doing it regardless. And yeah, I was able to kind of spend a lot of time on a team that was growing at a company that was growing. So it informed a lot of this stuff here. I think these are problems that have been solved with like the SAML protocol with SSO. I think it's a really interesting stuff with like WebAuthn, like these different types of authentication, like schemes that you can use to authenticate people. The tooling is all there. It just needs to be tweaked a little bit to work for agents. And I think the fact that there are companies that are already. Providing authentication as a service really sets it up. Well, the thing that's hard is like reinventing the internet for agents. We don't want to rebuild the internet. That's an impossible task. And I think people often say like, well, we'll have this second layer of APIs built for agents. I'm like, we will for the top use cases, but instead of we can just tweak the internet as is, which is on the authentication side, I think we're going to be the dumb ones going forward. Unfortunately, I think AI is going to be able to do a lot of the tasks that we do online, which means that it will be able to go to websites, click buttons on our behalf and log in on our behalf too. So with this kind of like web agent future happening, I think with some small structural changes, like you said, it feels like it could all slot in really nicely with the existing internet.Handling CAPTCHAs and Agent Authenticationswyx [00:28:08]: There's one more thing, which is the, your live view iframe, which lets you take, take control. Yeah. Obviously very key for operator now, but like, was, is there anything interesting technically there or that the people like, well, people always want this.Paul [00:28:21]: It was really hard to build, you know, like, so, okay. Headless browsers, you don't see them, right. They're running. They're running in a cloud somewhere. You can't like look at them. And I just want to really make, it's a weird name. I wish we came up with a better name for this thing, but you can't see them. Right. But customers don't trust AI agents, right. At least the first pass. So what we do with our live view is that, you know, when you use browser base, you can actually embed a live view of the browser running in the cloud for your customer to see it working. And that's what the first reason is the build trust, like, okay, so I have this script. That's going to go automate a website. I can embed it into my web application via an iframe and my customer can watch. I think. And then we added two way communication. So now not only can you watch the browser kind of being operated by AI, if you want to pause and actually click around type within this iframe that's controlling a browser, that's also possible. And this is all thanks to some of the lower level protocol, which is called the Chrome DevTools protocol. It has a API called start screencast, and you can also send mouse clicks and button clicks to a remote browser. And this is all embeddable within iframes. You have a browser within a browser, yo. And then you simulate the screen, the click on the other side. Exactly. And this is really nice often for, like, let's say, a capture that can't be solved. You saw this with Operator, you know, Operator actually uses a different approach. They use VNC. So, you know, you're able to see, like, you're seeing the whole window here. What we're doing is something a little lower level with the Chrome DevTools protocol. It's just PNGs being streamed over the wire. But the same thing is true, right? Like, hey, I'm running a window. Pause. Can you do something in this window? Human. Okay, great. Resume. Like sometimes 2FA tokens. Like if you get that text message, you might need a person to type that in. Web agents need human-in-the-loop type workflows still. You still need a person to interact with the browser. And building a UI to proxy that is kind of hard. You may as well just show them the whole browser and say, hey, can you finish this up for me? And then let the AI proceed on afterwards. Is there a future where I stream my current desktop to browser base? I don't think so. I think we're very much cloud infrastructure. Yeah. You know, but I think a lot of the stuff we're doing, we do want to, like, build tools. Like, you know, we'll talk about the stage and, you know, web agent framework in a second. But, like, there's a case where a lot of people are going desktop first for, you know, consumer use. And I think cloud is doing a lot of this, where I expect to see, you know, MCPs really oriented around the cloud desktop app for a reason, right? Like, I think a lot of these tools are going to run on your computer because it makes... I think it's breaking out. People are putting it on a server. Oh, really? Okay. Well, sweet. We'll see. We'll see that. I was surprised, though, wasn't I? I think that the browser company, too, with Dia Browser, it runs on your machine. You know, it's going to be...swyx [00:30:50]: What is it?Paul [00:30:51]: So, Dia Browser, as far as I understand... I used to use Arc. Yeah. I haven't used Arc. But I'm a big fan of the browser company. I think they're doing a lot of cool stuff in consumer. As far as I understand, it's a browser where you have a sidebar where you can, like, chat with it and it can control the local browser on your machine. So, if you imagine, like, what a consumer web agent is, which it lives alongside your browser, I think Google Chrome has Project Marina, I think. I almost call it Project Marinara for some reason. I don't know why. It's...swyx [00:31:17]: No, I think it's someone really likes the Waterworld. Oh, I see. The classic Kevin Costner. Yeah.Paul [00:31:22]: Okay. Project Marinara is a similar thing to the Dia Browser, in my mind, as far as I understand it. You have a browser that has an AI interface that will take over your mouse and keyboard and control the browser for you. Great for consumer use cases. But if you're building applications that rely on a browser and it's more part of a greater, like, AI app experience, you probably need something that's more like infrastructure, not a consumer app.swyx [00:31:44]: Just because I have explored a little bit in this area, do people want branching? So, I have the state. Of whatever my browser's in. And then I want, like, 100 clones of this state. Do people do that? Or...Paul [00:31:56]: People don't do it currently. Yeah. But it's definitely something we're thinking about. I think the idea of forking a browser is really cool. Technically, kind of hard. We're starting to see this in code execution, where people are, like, forking some, like, code execution, like, processes or forking some tool calls or branching tool calls. Haven't seen it at the browser level yet. But it makes sense. Like, if an AI agent is, like, using a website and it's not sure what path it wants to take to crawl this website. To find the information it's looking for. It would make sense for it to explore both paths in parallel. And that'd be a very, like... A road not taken. Yeah. And hopefully find the right answer. And then say, okay, this was actually the right one. And memorize that. And go there in the future. On the roadmap. For sure. Don't make my roadmap, please. You know?Alessio [00:32:37]: How do you actually do that? Yeah. How do you fork? I feel like the browser is so stateful for so many things.swyx [00:32:42]: Serialize the state. Restore the state. I don't know.Paul [00:32:44]: So, it's one of the reasons why we haven't done it yet. It's hard. You know? Like, to truly fork, it's actually quite difficult. The naive way is to open the same page in a new tab and then, like, hope that it's at the same thing. But if you have a form halfway filled, you may have to, like, take the whole, you know, container. Pause it. All the memory. Duplicate it. Restart it from there. It could be very slow. So, we haven't found a thing. Like, the easy thing to fork is just, like, copy the page object. You know? But I think there needs to be something a little bit more robust there. Yeah.swyx [00:33:12]: So, MorphLabs has this infinite branch thing. Like, wrote a custom fork of Linux or something that let them save the system state and clone it. MorphLabs, hit me up. I'll be a customer. Yeah. That's the only. I think that's the only way to do it. Yeah. Like, unless Chrome has some special API for you. Yeah.Paul [00:33:29]: There's probably something we'll reverse engineer one day. I don't know. Yeah.Alessio [00:33:32]: Let's talk about StageHand, the AI web browsing framework. You have three core components, Observe, Extract, and Act. Pretty clean landing page. What was the idea behind making a framework? Yeah.Stagehand: AI web browsing frameworkPaul [00:33:43]: So, there's three frameworks that are very popular or already exist, right? Puppeteer, Playwright, Selenium. Those are for building hard-coded scripts to control websites. And as soon as I started to play with LLMs plus browsing, I caught myself, you know, code-genning Playwright code to control a website. I would, like, take the DOM. I'd pass it to an LLM. I'd say, can you generate the Playwright code to click the appropriate button here? And it would do that. And I was like, this really should be part of the frameworks themselves. And I became really obsessed with SDKs that take natural language as part of, like, the API input. And that's what StageHand is. StageHand exposes three APIs, and it's a super set of Playwright. So, if you go to a page, you may want to take an action, click on the button, fill in the form, etc. That's what the act command is for. You may want to extract some data. This one takes a natural language, like, extract the winner of the Super Bowl from this page. You can give it a Zod schema, so it returns a structured output. And then maybe you're building an API. You can do an agent loop, and you want to kind of see what actions are possible on this page before taking one. You can do observe. So, you can observe the actions on the page, and it will generate a list of actions. You can guide it, like, give me actions on this page related to buying an item. And you can, like, buy it now, add to cart, view shipping options, and pass that to an LLM, an agent loop, to say, what's the appropriate action given this high-level goal? So, StageHand isn't a web agent. It's a framework for building web agents. And we think that agent loops are actually pretty close to the application layer because every application probably has different goals or different ways it wants to take steps. I don't think I've seen a generic. Maybe you guys are the experts here. I haven't seen, like, a really good AI agent framework here. Everyone kind of has their own special sauce, right? I see a lot of developers building their own agent loops, and they're using tools. And I view StageHand as the browser tool. So, we expose act, extract, observe. Your agent can call these tools. And from that, you don't have to worry about it. You don't have to worry about generating playwright code performantly. You don't have to worry about running it. You can kind of just integrate these three tool calls into your agent loop and reliably automate the web.swyx [00:35:48]: A special shout-out to Anirudh, who I met at your dinner, who I think listens to the pod. Yeah. Hey, Anirudh.Paul [00:35:54]: Anirudh's a man. He's a StageHand guy.swyx [00:35:56]: I mean, the interesting thing about each of these APIs is they're kind of each startup. Like, specifically extract, you know, Firecrawler is extract. There's, like, Expand AI. There's a whole bunch of, like, extract companies. They just focus on extract. I'm curious. Like, I feel like you guys are going to collide at some point. Like, right now, it's friendly. Everyone's in a blue ocean. At some point, it's going to be valuable enough that there's some turf battle here. I don't think you have a dog in a fight. I think you can mock extract to use an external service if they're better at it than you. But it's just an observation that, like, in the same way that I see each option, each checkbox in the side of custom GBTs becoming a startup or each box in the Karpathy chart being a startup. Like, this is also becoming a thing. Yeah.Paul [00:36:41]: I mean, like, so the way StageHand works is that it's MIT-licensed, completely open source. You bring your own API key to your LLM of choice. You could choose your LLM. We don't make any money off of the extract or really. We only really make money if you choose to run it with our browser. You don't have to. You can actually use your own browser, a local browser. You know, StageHand is completely open source for that reason. And, yeah, like, I think if you're building really complex web scraping workflows, I don't know if StageHand is the tool for you. I think it's really more if you're building an AI agent that needs a few general tools or if it's doing a lot of, like, web automation-intensive work. But if you're building a scraping company, StageHand is not your thing. You probably want something that's going to, like, get HTML content, you know, convert that to Markdown, query it. That's not what StageHand does. StageHand is more about reliability. I think we focus a lot on reliability and less so on cost optimization and speed at this point.swyx [00:37:33]: I actually feel like StageHand, so the way that StageHand works, it's like, you know, page.act, click on the quick start. Yeah. It's kind of the integration test for the code that you would have to write anyway, like the Puppeteer code that you have to write anyway. And when the page structure changes, because it always does, then this is still the test. This is still the test that I would have to write. Yeah. So it's kind of like a testing framework that doesn't need implementation detail.Paul [00:37:56]: Well, yeah. I mean, Puppeteer, Playwright, and Slenderman were all designed as testing frameworks, right? Yeah. And now people are, like, hacking them together to automate the web. I would say, and, like, maybe this is, like, me being too specific. But, like, when I write tests, if the page structure changes. Without me knowing, I want that test to fail. So I don't know if, like, AI, like, regenerating that. Like, people are using StageHand for testing. But it's more for, like, usability testing, not, like, testing of, like, does the front end, like, has it changed or not. Okay. But generally where we've seen people, like, really, like, take off is, like, if they're using, you know, something. If they want to build a feature in their application that's kind of like Operator or Deep Research, they're using StageHand to kind of power that tool calling in their own agent loop. Okay. Cool.swyx [00:38:37]: So let's go into Operator, the first big agent launch of the year from OpenAI. Seems like they have a whole bunch scheduled. You were on break and your phone blew up. What's your just general view of computer use agents is what they're calling it. The overall category before we go into Open Operator, just the overall promise of Operator. I will observe that I tried it once. It was okay. And I never tried it again.OpenAI's Operator and computer use agentsPaul [00:38:58]: That tracks with my experience, too. Like, I'm a huge fan of the OpenAI team. Like, I think that I do not view Operator as the company. I'm not a company killer for browser base at all. I think it actually shows people what's possible. I think, like, computer use models make a lot of sense. And I'm actually most excited about computer use models is, like, their ability to, like, really take screenshots and reasoning and output steps. I think that using mouse click or mouse coordinates, I've seen that proved to be less reliable than I would like. And I just wonder if that's the right form factor. What we've done with our framework is anchor it to the DOM itself, anchor it to the actual item. So, like, if it's clicking on something, it's clicking on that thing, you know? Like, it's more accurate. No matter where it is. Yeah, exactly. Because it really ties in nicely. And it can handle, like, the whole viewport in one go, whereas, like, Operator can only handle what it sees. Can you hover? Is hovering a thing that you can do? I don't know if we expose it as a tool directly, but I'm sure there's, like, an API for hovering. Like, move mouse to this position. Yeah, yeah, yeah. I think you can trigger hover, like, via, like, the JavaScript on the DOM itself. But, no, I think, like, when we saw computer use, everyone's eyes lit up because they realized, like, wow, like, AI is going to actually automate work for people. And I think seeing that kind of happen from both of the labs, and I'm sure we're going to see more labs launch computer use models, I'm excited to see all the stuff that people build with it. I think that I'd love to see computer use power, like, controlling a browser on browser base. And I think, like, Open Operator, which was, like, our open source version of OpenAI's Operator, was our first take on, like, how can we integrate these models into browser base? And we handle the infrastructure and let the labs do the models. I don't have a sense that Operator will be released as an API. I don't know. Maybe it will. I'm curious to see how well that works because I think it's going to be really hard for a company like OpenAI to do things like support CAPTCHA solving or, like, have proxies. Like, I think it's hard for them structurally. Imagine this New York Times headline, OpenAI CAPTCHA solving. Like, that would be a pretty bad headline, this New York Times headline. Browser base solves CAPTCHAs. No one cares. No one cares. And, like, our investors are bored. Like, we're all okay with this, you know? We're building this company knowing that the CAPTCHA solving is short-lived until we figure out how to authenticate good bots. I think it's really hard for a company like OpenAI, who has this brand that's so, so good, to balance with, like, the icky parts of web automation, which it can be kind of complex to solve. I'm sure OpenAI knows who to call whenever they need you. Yeah, right. I'm sure they'll have a great partnership.Alessio [00:41:23]: And is Open Operator just, like, a marketing thing for you? Like, how do you think about resource allocation? So, you can spin this up very quickly. And now there's all this, like, open deep research, just open all these things that people are building. We started it, you know. You're the original Open. We're the original Open operator, you know? Is it just, hey, look, this is a demo, but, like, we'll help you build out an actual product for yourself? Like, are you interested in going more of a product route? That's kind of the OpenAI way, right? They started as a model provider and then…Paul [00:41:53]: Yeah, we're not interested in going the product route yet. I view Open Operator as a model provider. It's a reference project, you know? Let's show people how to build these things using the infrastructure and models that are out there. And that's what it is. It's, like, Open Operator is very simple. It's an agent loop. It says, like, take a high-level goal, break it down into steps, use tool calling to accomplish those steps. It takes screenshots and feeds those screenshots into an LLM with the step to generate the right action. It uses stagehand under the hood to actually execute this action. It doesn't use a computer use model. And it, like, has a nice interface using the live view that we talked about, the iframe, to embed that into an application. So I felt like people on launch day wanted to figure out how to build their own version of this. And we turned that around really quickly to show them. And I hope we do that with other things like deep research. We don't have a deep research launch yet. I think David from AOMNI actually has an amazing open deep research that he launched. It has, like, 10K GitHub stars now. So he's crushing that. But I think if people want to build these features natively into their application, they need good reference projects. And I think Open Operator is a good example of that.swyx [00:42:52]: I don't know. Actually, I'm actually pretty bullish on API-driven operator. Because that's the only way that you can sort of, like, once it's reliable enough, obviously. And now we're nowhere near. But, like, give it five years. It'll happen, you know. And then you can sort of spin this up and browsers are working in the background and you don't necessarily have to know. And it just is booking restaurants for you, whatever. I can definitely see that future happening. I had this on the landing page here. This might be a slightly out of order. But, you know, you have, like, sort of three use cases for browser base. Open Operator. Or this is the operator sort of use case. It's kind of like the workflow automation use case. And it completes with UiPath in the sort of RPA category. Would you agree with that? Yeah, I would agree with that. And then there's Agents we talked about already. And web scraping, which I imagine would be the bulk of your workload right now, right?Paul [00:43:40]: No, not at all. I'd say actually, like, the majority is browser automation. We're kind of expensive for web scraping. Like, I think that if you're building a web scraping product, if you need to do occasional web scraping or you have to do web scraping that works every single time, you want to use browser automation. Yeah. You want to use browser-based. But if you're building web scraping workflows, what you should do is have a waterfall. You should have the first request is a curl to the website. See if you can get it without even using a browser. And then the second request may be, like, a scraping-specific API. There's, like, a thousand scraping APIs out there that you can use to try and get data. Scraping B. Scraping B is a great example, right? Yeah. And then, like, if those two don't work, bring out the heavy hitter. Like, browser-based will 100% work, right? It will load the page in a real browser, hydrate it. I see.swyx [00:44:21]: Because a lot of people don't render to JS.swyx [00:44:25]: Yeah, exactly.Paul [00:44:26]: So, I mean, the three big use cases, right? Like, you know, automation, web data collection, and then, you know, if you're building anything agentic that needs, like, a browser tool, you want to use browser-based.Alessio [00:44:35]: Is there any use case that, like, you were super surprised by that people might not even think about? Oh, yeah. Or is it, yeah, anything that you can share? The long tail is crazy. Yeah.Surprising use cases of BrowserbasePaul [00:44:44]: One of the case studies on our website that I think is the most interesting is this company called Benny. So, the way that it works is if you're on food stamps in the United States, you can actually get rebates if you buy certain things. Yeah. You buy some vegetables. You submit your receipt to the government. They'll give you a little rebate back. Say, hey, thanks for buying vegetables. It's good for you. That process of submitting that receipt is very painful. And the way Benny works is you use their app to take a photo of your receipt, and then Benny will go submit that receipt for you and then deposit the money into your account. That's actually using no AI at all. It's all, like, hard-coded scripts. They maintain the scripts. They've been doing a great job. And they build this amazing consumer app. But it's an example of, like, all these, like, tedious workflows that people have to do to kind of go about their business. And they're doing it for the sake of their day-to-day lives. And I had never known about, like, food stamp rebates or the complex forms you have to do to fill them. But the world is powered by millions and millions of tedious forms, visas. You know, Emirate Lighthouse is a customer, right? You know, they do the O1 visa. Millions and millions of forms are taking away humans' time. And I hope that Browserbase can help power software that automates away the web forms that we don't need anymore. Yeah.swyx [00:45:49]: I mean, I'm very supportive of that. I mean, forms. I do think, like, government itself is a big part of it. I think the government itself should embrace AI more to do more sort of human-friendly form filling. Mm-hmm. But I'm not optimistic. I'm not holding my breath. Yeah. We'll see. Okay. I think I'm about to zoom out. I have a little brief thing on computer use, and then we can talk about founder stuff, which is, I tend to think of developer tooling markets in impossible triangles, where everyone starts in a niche, and then they start to branch out. So I already hinted at a little bit of this, right? We mentioned more. We mentioned E2B. We mentioned Firecrawl. And then there's Browserbase. So there's, like, all this stuff of, like, have serverless virtual computer that you give to an agent and let them do stuff with it. And there's various ways of connecting it to the internet. You can just connect to a search API, like SERP API, whatever other, like, EXA is another one. That's what you're searching. You can also have a JSON markdown extractor, which is Firecrawl. Or you can have a virtual browser like Browserbase, or you can have a virtual machine like Morph. And then there's also maybe, like, a virtual sort of code environment, like Code Interpreter. So, like, there's just, like, a bunch of different ways to tackle the problem of give a computer to an agent. And I'm just kind of wondering if you see, like, everyone's just, like, happily coexisting in their respective niches. And as a developer, I just go and pick, like, a shopping basket of one of each. Or do you think that you eventually, people will collide?Future of browser automation and market competitionPaul [00:47:18]: I think that currently it's not a zero-sum market. Like, I think we're talking about... I think we're talking about all of knowledge work that people do that can be automated online. All of these, like, trillions of hours that happen online where people are working. And I think that there's so much software to be built that, like, I tend not to think about how these companies will collide. I just try to solve the problem as best as I can and make this specific piece of infrastructure, which I think is an important primitive, the best I possibly can. And yeah. I think there's players that are actually going to like it. I think there's players that are going to launch, like, over-the-top, you know, platforms, like agent platforms that have all these tools built in, right? Like, who's building the rippling for agent tools that has the search tool, the browser tool, the operating system tool, right? There are some. There are some. There are some, right? And I think in the end, what I have seen as my time as a developer, and I look at all the favorite tools that I have, is that, like, for tools and primitives with sufficient levels of complexity, you need to have a solution that's really bespoke to that primitive, you know? And I am sufficiently convinced that the browser is complex enough to deserve a primitive. Obviously, I have to. I'm the founder of BrowserBase, right? I'm talking my book. But, like, I think maybe I can give you one spicy take against, like, maybe just whole OS running. I think that when I look at computer use when it first came out, I saw that the majority of use cases for computer use were controlling a browser. And do we really need to run an entire operating system just to control a browser? I don't think so. I don't think that's necessary. You know, BrowserBase can run browsers for way cheaper than you can if you're running a full-fledged OS with a GUI, you know, operating system. And I think that's just an advantage of the browser. It is, like, browsers are little OSs, and you can run them very efficiently if you orchestrate it well. And I think that allows us to offer 90% of the, you know, functionality in the platform needed at 10% of the cost of running a full OS. Yeah.Open Operator: Browserbase's Open-Source Alternativeswyx [00:49:16]: I definitely see the logic in that. There's a Mark Andreessen quote. I don't know if you know this one. Where he basically observed that the browser is turning the operating system into a poorly debugged set of device drivers, because most of the apps are moved from the OS to the browser. So you can just run browsers.Paul [00:49:31]: There's a place for OSs, too. Like, I think that there are some applications that only run on Windows operating systems. And Eric from pig.dev in this upcoming YC batch, or last YC batch, like, he's building all run tons of Windows operating systems for you to control with your agent. And like, there's some legacy EHR systems that only run on Internet-controlled systems. Yeah.Paul [00:49:54]: I think that's it. I think, like, there are use cases for specific operating systems for specific legacy software. And like, I'm excited to see what he does with that. I just wanted to give a shout out to the pig.dev website.swyx [00:50:06]: The pigs jump when you click on them. Yeah. That's great.Paul [00:50:08]: Eric, he's the former co-founder of banana.dev, too.swyx [00:50:11]: Oh, that Eric. Yeah. That Eric. Okay. Well, he abandoned bananas for pigs. I hope he doesn't start going around with pigs now.Alessio [00:50:18]: Like he was going around with bananas. A little toy pig. Yeah. Yeah. I love that. What else are we missing? I think we covered a lot of, like, the browser-based product history, but. What do you wish people asked you? Yeah.Paul [00:50:29]: I wish people asked me more about, like, what will the future of software look like? Because I think that's really where I've spent a lot of time about why do browser-based. Like, for me, starting a company is like a means of last resort. Like, you shouldn't start a company unless you absolutely have to. And I remain convinced that the future of software is software that you're going to click a button and it's going to do stuff on your behalf. Right now, software. You click a button and it maybe, like, calls it back an API and, like, computes some numbers. It, like, modifies some text, whatever. But the future of software is software using software. So, I may log into my accounting website for my business, click a button, and it's going to go load up my Gmail, search my emails, find the thing, upload the receipt, and then comment it for me. Right? And it may use it using APIs, maybe a browser. I don't know. I think it's a little bit of both. But that's completely different from how we've built software so far. And that's. I think that future of software has different infrastructure requirements. It's going to require different UIs. It's going to require different pieces of infrastructure. I think the browser infrastructure is one piece that fits into that, along with all the other categories you mentioned. So, I think that it's going to require developers to think differently about how they've built software for, you know

The Rise of Expertise Inequality in AIKey PointsSimilar to income inequality growth since 1980, we may now be witnessing the emergence of expertise inequality with AIProblem: Automation Claims Lack NuanceClaims about "automating coders" or eliminating software developers oversimplify complex realitiesExample: AWS deployment decisions require expertiseMultiple compute options (EC2, Lambda, ECS Fargate, EKS, Elastic Beanstalk)Each option has significant tradeoffs and use casesSurface-level AI answers lack depth for informed decision-makingExpertise Inequality DynamicsExperts Will ThriveDeep experts can leverage AI effectively They understand fundamental tradeoffs (e.g., compiled vs scripting languages)Can make optimized choices (e.g., Rust for Lambda functions)Know exactly what questions to ask AI systemsBeginners Will StruggleLack domain knowledge to evaluate AI suggestionsDon't understand fundamental distinctions (website vs web service)Cannot properly prompt AI systems due to knowledge gapsOrganizational ImpactDysfunctional organizations at riskHIPAA-driven (High-Paid Person's Opinion)University systemsCorporate bureaucraciesExpert individuals may outperform entire teamsExperts with AI might deliver in one day what organizations take a full year to completeAI Reality CheckCurrent generative AI is fundamentally:Enhanced Stack OverflowFancy search enginePattern recognition systemNot truly "intelligent" - builds on existing information servicesWill reach perfect competition as technologies standardizeOpen source solutions rapidly approaching commercial offeringsFuture PredictionsExperts become increasingly valuableBeginners face decreased demandDysfunctional organizations accelerate toward failure Expertise inequality may become as concerning as income inequalityConclusionThe AI revolution isn't replacing expertise - it's making it more valuable than ever.

* "whoAMI" Attack Exploits AWS for Code Execution* Chinese Hackers Exploit Cisco Devices in Global Telecom Attacks* Australian National University Investigates Alleged Ransomware Attack* Phishing Season 2025: Zscaler Predicts Increased Sophistication and New Threats* Chinese Cybercriminals Revive Carding with Mobile Wallet Attacks"whoAMI" Attack Exploits AWS for Code Executionhttps://www.bleepingcomputer.com/news/security/whoami-attacks-give-hackers-code-execution-on-amazon-ec2-instances/Security researchers have discovered a critical vulnerability in Amazon Web Services (AWS) that allows attackers to gain unauthorized code execution on EC2 instances.Dubbed "whoAMI," the attack exploits a flaw in how users select Amazon Machine Images (AMIs), the pre-configured templates used to create virtual servers.Attackers can publish malicious AMIs with names that mimic those of legitimate AMIs, tricking users into selecting and launching these malicious images. This can occur when users:* Fail to specify the owner of the AMI: When retrieving AMIs, users should always specify the owner to ensure they are selecting trusted images.* Use wildcards in their AMI searches: This can inadvertently include malicious AMIs that match the search criteria.* Utilize "most_recent=true" in tools like Terraform: This setting automatically selects the latest matching AMI, which could be a malicious one.AWS has acknowledged the vulnerability and implemented a fix. However, organizations must update their code and configurations to mitigate the risk.This attack highlights the importance of secure coding practices and careful consideration of security measures when utilizing cloud services.Chinese Hackers Exploit Cisco Devices in Global Telecom Attackshttps://cyberinsider.com/chinese-hackers-breach-cisco-devices-in-global-telecom-attacks/A new report reveals that the Chinese state-sponsored hacking group Salt Typhoon has compromised Cisco devices worldwide, targeting telecommunications providers and universities across multiple countries, including the United States, the United Kingdom, and South Africa.The attacks exploit critical vulnerabilities in Cisco IOS XE software, allowing the hackers to gain unauthorized access and establish persistent backdoors within targeted networks.Salt Typhoon leverages these compromised devices to eavesdrop on sensitive communications, manipulate data traffic, and potentially disrupt critical infrastructure.This campaign highlights the growing threat of state-sponsored cyberattacks targeting critical infrastructure and underscores the need for robust cybersecurity measures to protect against these sophisticated threats.Australian National University Investigates Alleged Ransomware Attackhttps://www.cyberdaily.au/security/11716-exclusive-australian-national-university-investigating-alleged-cyber-attackThe Australian National University (ANU) is investigating a potential ransomware attack after the hacking group FSociety claimed to have breached the institution's systems and threatened to leak sensitive data.FSociety, known for its ransomware-as-a-service operations, listed ANU on its dark web leak site, threatening to release stolen data within seven days if a ransom is not paid.While the university is currently investigating the claims, the incident has raised concerns about the growing cyber threat landscape and the increasing sophistication of ransomware attacks.This is not the first time ANU has faced a data breach. In 2019, a significant data breach impacted 19 years of personal data from both students and staff, with Chinese state actors suspected to be behind the attack.The incident underscores the critical importance of robust cybersecurity measures for educational institutions and the ongoing challenge of protecting sensitive data in the face of evolving cyber threats.Phishing Season 2025: Zscaler Predicts Increased Sophistication and New Threatshttps://www.zscaler.com/blogs/security-research/phishing-season-2025-latest-predictions-unveiledZscaler's ThreatLabz has released its predictions for phishing attacks in 2025, highlighting a surge in sophistication and new attack vectors.Key predictions include:* I-powered phishing: Both attackers and defenders will leverage AI, with attackers using AI to craft more convincing and personalized phishing emails and security vendors utilizing AI to enhance threat detection.* MFA bypass: Phishing attacks will increasingly focus on bypassing multi-factor authentication (MFA) through techniques like adversary-in-the-middle attacks and localized phishing content.* Vishing attacks on the rise: Voice phishing (vishing) attacks will become more sophisticated, utilizing AI-powered voice cloning technology to mimic trusted individuals and deceive victims.* Mobile device targeting: Attackers will exploit vulnerabilities in mobile devices and platforms, leveraging social engineering tactics and exploiting trust in common communication channels like push notifications.* Politically motivated attacks: Phishing attacks will capitalize on political events and heightened emotions, targeting voters and political campaigns with deceptive communications.* Exploitation of encrypted messaging platforms: Cybercriminals will increasingly utilize encrypted messaging platforms to launch phishing attacks, leveraging bots to automate malicious activities and evade detection.* Browser-in-the-browser attacks: These attacks will become more sophisticated, with AI-driven customization to mimic browser environments more convincingly and adapt to user interactions.These predictions underscore the evolving threat landscape and the need for organizations and individuals to remain vigilant against increasingly sophisticated phishing attacks.Chinese Cybercriminals Revive Carding with Mobile Wallet Attackshttps://krebsonsecurity.com/2025/02/how-phished-data-turns-into-apple-google-wallets/Chinese cybercriminal groups have revitalized the carding industry by turning phished credit card data into mobile wallets, enabling widespread fraud.These groups utilize sophisticated phishing techniques, primarily through iMessage and RCS, to capture victims' payment card information and one-time codes used for mobile wallet provisioning. They then link the stolen card data to new mobile wallets on devices they control, often selling these pre-loaded phones in bulk.These cybercriminals are also using innovative techniques like "ghost tap" software, which relays NFC transactions from anywhere in the world, enabling them to cash out stolen funds at local payment terminals or ATMs.The phishing operations are highly organized, with vendors selling sophisticated phishing kits that include features like real-time data capture, back-end databases for storing stolen information, and automated tools for creating fake payment card images for easy mobile wallet enrollment.This resurgence of carding through mobile wallets has resulted in significant financial losses, highlighting the need for enhanced security measures in mobile wallet provisioning and payment systems.Special Thanks to Justin Butterfield for contributing some of the interesting stories for this week's cyber bites. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit edwinkwan.substack.com

News includes upcoming improvements to ex_doc for version navigation, the release of Phoenix Analytics 0.3.0 for plug-and-play application metrics, José Valim's detailed exploration of set-theoretic types for better library compatibility, German Velasco's demonstration of Elixir 1.18's enhanced type system, the beta release of the Ash Framework book on PragProg, and exciting developments in the FLAME ecosystem with AWS EC2 support, and more! Show Notes online - http://podcast.thinkingelixir.com/237 (http://podcast.thinkingelixir.com/237) Elixir Community News https://bsky.app/profile/david.bernheisel.com/post/3lffr6xdvq22r (https://bsky.app/profile/david.bernheisel.com/post/3lffr6xdvq22r?utm_source=thinkingelixir&utm_medium=shownotes) – ex_doc will soon feature a new button to navigate to the latest version's documentation when viewing older versions. https://x.com/mrpopov_com/status/1878817795049488421 (https://x.com/mrpopov_com/status/1878817795049488421?utm_source=thinkingelixir&utm_medium=shownotes) – Phoenix Analytics 0.3.0 released with improved support for Fly.io and Heroku deployments. https://github.com/lalabuy948/PhoenixAnalytics (https://github.com/lalabuy948/PhoenixAnalytics?utm_source=thinkingelixir&utm_medium=shownotes) – Plug and play analytics solution for Phoenix applications, offering embedded dashboard functionality. https://dashbit.co/blog/data-evolution-with-set-theoretic-types (https://dashbit.co/blog/data-evolution-with-set-theoretic-types?utm_source=thinkingelixir&utm_medium=shownotes) – José Valim's article explaining how set-theoretic types will improve library backwards-compatibility in Elixir. https://www.elixirstreams.com/tips/elixir-118-type-system-changes (https://www.elixirstreams.com/tips/elixir-118-type-system-changes?utm_source=thinkingelixir&utm_medium=shownotes) – German Velasco's ElixirStream video demonstrating the improved type system changes in Elixir 1.18. https://pragprog.com/titles/ldash/ash-framework/ (https://pragprog.com/titles/ldash/ash-framework/?utm_source=thinkingelixir&utm_medium=shownotes) – Ash Framework book by Rebecca Le and Zach Daniel released in beta on PragProg, covering LiveView, auth, search, APIs, and notifications. https://github.com/phoenixframework/flame (https://github.com/phoenixframework/flame?utm_source=thinkingelixir&utm_medium=shownotes) – FLAME (Fleeting Lambda Application for Modular Execution) by Chris McCord enables dynamic resource scaling on Fly.io. https://github.com/probably-not/flame-ec2 (https://github.com/probably-not/flame-ec2?utm_source=thinkingelixir&utm_medium=shownotes) – FlameEC2 library extends FLAME functionality to AWS EC2 machines. https://bsky.app/profile/codebeam.bsky.social/post/3lfp4penmik2v (https://bsky.app/profile/codebeam.bsky.social/post/3lfp4penmik2v?utm_source=thinkingelixir&utm_medium=shownotes) – Code BEAM Lite London 2025 is on January 31, featuring Michał Muskała as speaker. https://alchemyconf.com/ (https://alchemyconf.com/?utm_source=thinkingelixir&utm_medium=shownotes) – Alchemy Conf scheduled for March 31 - April 3 in Braga, Portugal. https://membrz.club/alchemyconf/events?tag=workshop (https://membrz.club/alchemyconf/events?tag=workshop?utm_source=thinkingelixir&utm_medium=shownotes) – Alchemy Conf workshops announced featuring Saša Jurić, Zach Daniel, and Andrea Leopardi. https://x.com/Alchemy_Conf/status/1879136370691862929 (https://x.com/Alchemy_Conf/status/1879136370691862929?utm_source=thinkingelixir&utm_medium=shownotes) – Additional announcement about Alchemy Conf workshop details. Do you have some Elixir news to share? Tell us at @ThinkingElixir (https://twitter.com/ThinkingElixir) or email at show@thinkingelixir.com (mailto:show@thinkingelixir.com) Find us online - Message the show - Bluesky (https://bsky.app/profile/thinkingelixir.com) - Message the show - X (https://x.com/ThinkingElixir) - Message the show on Fediverse - @ThinkingElixir@genserver.social (https://genserver.social/ThinkingElixir) - Email the show - show@thinkingelixir.com (mailto:show@thinkingelixir.com) - Mark Ericksen on X - @brainlid (https://x.com/brainlid) - Mark Ericksen on Bluesky - @brainlid.bsky.social (https://bsky.app/profile/brainlid.bsky.social) - Mark Ericksen on Fediverse - @brainlid@genserver.social (https://genserver.social/brainlid) - David Bernheisel on Bluesky - @david.bernheisel.com (https://bsky.app/profile/david.bernheisel.com) - David Bernheisel on Fediverse - @dbern@genserver.social (https://genserver.social/dbern)

There are many, many choices for cloud database services these days. I would hope everyone is aware of the various IaaS options in public clouds with EC2, Azure VMs, GCP Compute Engine, and others. These are often the easiest way to move your workload, but you've really just moved a VM from one place to another (likely more expensive) place. For managed databases, there are lots of choices, but you might not be aware of your options. I ran across an article that discusses the various flavors of managed databases in the big three public clouds for SQL Server. In the piece, there is a section that talks about when a managed database makes sense. I like that it discloses the development on a managed service is expensive. Read the rest of The Managed Cloud Database Options

"Le dernier auditeur pensait que tout avait été codé par la même personne" Le D.E.V. de la semaine est Simon Parisot, CEO et cofondateur de Blank. Simon a fait un pari, un peu fou, au début de l'aventure Blank : avoir un environnement 100% serverless ! Lambda, DynamoDB, S3, &hellip il connait tous les services AWS, mais n'utilise pas une seule EC2 !! Il vient nous raconter comment il a construit cette plateforme, et surtout pourquoi ! Il nous explique aussi les changements que cela a sur le travail des dev (le dev en local est compllqué), les impératifs de qualité du code que cela implique et aussi comment le recrutement doit s'adapter à ce choix technique.Liens évoqués pendant l'émissionIFTTD avec Olivier Dupuis - Faites entrer le hackeurFramework serverless 🎙️ Soutenez le podcast If This Then Dev ! 🎙️ Chaque contribution aide à maintenir et améliorer nos épisodes. Cliquez ici pour nous soutenir sur Tipeee 🙏Archives | Site | Boutique | TikTok | Discord | Twitter | LinkedIn | Instagram | Youtube | Twitch | Job Board |

The annual AWS re:Invent conference in Las Vegas has long been a marquee event for technologists and business leaders. But in 2024, it served as a rallying cry for a new technological epoch - one where generative AI (GenAI) is no longer a nascent tool but a transformative force shaping industries, economies, and creativity. At the heart of this year's address was Dr. Swami Sivasubramanian, AWS's Vice President of AI and Data, who positioned Amazon's cloud division not just as a vendor but as an architect of this revolution. Dr. Sivasubramanian began with a historical overture, likening the current moment to the Wright Brothers' first flight in 1903. That 12-second triumph, he noted, was not an isolated miracle but the result of centuries of cumulative innovation - from Leonardo da Vinci's aeronautical sketches to steam-powered gliders. In the same vein, GenAI represents the culmination of decades of research in neural networks, backpropagation algorithms, and the transformative power of Transformer architectures. However, technological breakthroughs alone were not enough. What set the stage for GenAI's explosive growth, Dr. Sivasubramanian argued, was the convergence of cloud computing, vast data lakes, and affordable machine-learning infrastructure - elements AWS has spent the better part of two decades perfecting. AWS SageMaker: The Vanguard of AI Democratization Central to AWS's GenAI arsenal is Amazon SageMaker, a comprehensive platform designed to simplify machine learning workflows. Over the past year, AWS has added more than 140 features to SageMaker, underscoring its ambition to stay ahead in the arms race of AI development. Among these innovations is SageMaker HyperPod, which provides robust tools for training the mammoth foundational models that underpin GenAI. HyperPod automates complex tasks like checkpointing, resource recovery, and distributed training, enabling enterprises like Salesforce and Thomson Reuters to train billion-parameter models without the logistical headaches. But SageMaker is evolving beyond its core machine-learning roots into a unified platform for data analytics, big data processing, and GenAI workflows. The platform's latest iteration consolidates disparate tools into a single, user-friendly interface, offering businesses an integrated suite for data preparation, model development, and deployment. Training Titans: HyperPod and Bedrock As GenAI models grow in size and sophistication, the cost and complexity of training them have skyrocketed. Dr. Sivasubramanian introduced two pivotal innovations aimed at alleviating these challenges. First, HyperPod Flexible Training Plans address the inefficiencies of securing and managing compute resources for training large models. By automating the reservation of EC2 capacity and distributing workloads intelligently, these plans reduce downtime and optimize costs. Second, Bedrock, AWS's managed service for deploying foundational models, makes it easier for developers to select, customize, and optimize GenAI models. Bedrock offers cutting-edge features like Prompt Caching - a cost-saving tool that reduces latency by storing frequently used queries - and Intelligent Prompt Routing, which directs tasks to the most cost-effective model without sacrificing quality. Case Studies in Innovation Throughout his keynote, Dr. Sivasubramanian showcased real-world applications of AWS's GenAI capabilities. Autodesk, the software titan renowned for its design and engineering tools, is leveraging SageMaker to develop GenAI models that combine spatial reasoning with physics-based design principles. These models allow architects to create structurally sound and manufacturable 3D designs, effectively automating tedious aspects of the creative process. Meanwhile, Rocket Companies, a leader in mortgage lending, has deployed Amazon Bedrock to create AI agents that handle 70% of customer interactions autonomously. These agents, embedded in Rocket's AI-driven platform, streamli...

What if you could scale your SaaS platforms effortlessly across diverse hosting services? Join us as we welcome Adam McCrea, the brilliant mind behind JudoScale, who takes us through his fascinating evolution from being a Rails developer to creating a cutting-edge autoscaling solution. Adam opens up about the technical challenges he faced while adapting JudoScale for platforms like Render, Fly, and Railway, and how Heroku's unique architecture initially shaped his approach. His journey is one of innovation driven by necessity, as JudoScale originated from a need to optimize costs more efficiently than existing solutions.Our conversation doesn't shy away from complexity; in fact, it embraces it. Adam shares his experiences of grappling with AWS integration, navigating the intricate maze of ECS, EC2, Fargate, and IAM, all driven by customer demand. We explore the strategic shift from metered billing to flat-tiered pricing and the hurdles faced while setting up a staging environment on Render, ultimately reaffirming Heroku's smoother experience. This episode promises valuable insights into the strategic decisions and architectural reimaginations that keep JudoScale ahead of the game.Adding a creative flair, we delve into the entertaining world of infomercial production, as Adam recounts his experience crafting a humorous Billy Mays-inspired ad for JudoScale. With the aid of AI tools like ChatGPT and Descript, Adam turned a fun concept into an engaging reality. As we wrap up, Adam shares his excitement for RailsConf in Philadelphia and the significance of fostering connections through digital networking. Whether you're a tech enthusiast or a developer seeking innovative scaling solutions, this episode is brimming with insightful takeaways and creative inspiration.Send us some love.HoneybadgerHoneybadger is an application health monitoring tool built by developers for developers.HoneybadgerHoneybadger is an application health monitoring tool built by developers for developers.Disclaimer: This post contains affiliate links. If you make a purchase, I may receive a commission at no extra cost to you.Support the showReady to start your own podcast?This show is hosted on Buzzsprout and it's awesome, not to mention a Ruby on Rails application. Let Buzzsprout know we sent you and you'll get a $20 Amazon gift card if you sign up for a paid plan, and it helps support our show.

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In the fast-paced world of technology, staying relevant means continually adapting to new tools and paradigms. One of the most transformative shifts in recent years has been the rise of cloud computing. In this episode of the Building Better Developers podcast, hosts Rob Broadhead and Michael Meloche explore how cultivating smart cloud development habits can help you stay ahead in an ever-evolving industry. Whether you're a seasoned developer or just starting your journey, embracing cloud technologies can enhance your skills, expand your capabilities, and open doors to exciting opportunities. From practical tips on leveraging free-tier cloud services to insights on earning valuable certifications, this discussion is packed with actionable advice to help you master the cloud and boost your career. Let's dive in and explore how to build the habits that will make cloud technologies a cornerstone of your development journey. Simplify and Expand Your Reach with the Cloud Rob introduces the cloud as a game-changer in the tech space, tracing its evolution since Amazon Web Services (AWS) debuted over a decade ago. Initially limited to services like EC2 and S3, AWS now boasts a staggering array of offerings, with Microsoft Azure and Google following suit. These platforms have become indispensable for developers, offering scalable solutions, robust APIs, and opportunities for experimentation. The hosts emphasize that the cloud isn't just for DevOps or system administrators. Developers stand to gain tremendously from engaging with these platforms. Whether it's spinning up a virtual machine, deploying a simple database, or experimenting with Infrastructure-as-Code, learning these skills bolsters your ability to adapt and solve problems. Build Habits Around Cloud Exploration One of the key takeaways from the episode is to treat cloud exploration as a habit rather than an overwhelming task. Start small: Sign Up and Play Around: Take advantage of free-tier options provided by AWS, Azure, or Google. For example, launch an EC2 instance, set up a database, or explore a service you've never tried before. Time-Box Your Efforts: Dedicate 10–15 minutes daily to exploring cloud services. Over time, this adds up to significant progress without feeling overwhelming. Experiment with Real Use Cases: Rob suggests transferring your local data to the cloud or using a cloud IDE like AWS Cloud9 for coding on the go. These practical applications build confidence while solving real problems. For those who prefer a structured approach, Rob mentions the Launch Your Internet Business series on Developer.com. This step-by-step guide helps you create a server, set up a WordPress site, and learn Linux basics—all while working within a cloud environment. Why Certifications Matter Michael highlights the value of certifications in the cloud domain, particularly for developers aiming to stand out in a competitive job market. Certification programs for platforms like AWS, Azure, and Google often include foundational courses that are approachable even for beginners. These certifications not only validate your skills but also deepen your understanding of specific cloud environments and tools. Michael shares his own experience of obtaining an AWS foundational certification, noting that while the preparation felt daunting at first, the actual process was manageable and rewarding. He encourages developers to take the plunge, as certifications can lead to tangible career benefits. Cloud as a Developer's Playground Beyond certifications and practical applications, the cloud is a playground for innovation. Michael suggests using tools like Docker, Kubernetes, or open-source alternatives to create your own cloud-like environment. Services such as AWS Cloud9 and Eclipse Che allow developers to experiment with coding directly in the cloud, offering unmatched flexibility for remote work and collaboration. For developers working with web technologies, tools like JSFiddle demonstrate the power of browser-based environments. These platforms remove barriers to entry, enabling you to test and deploy ideas without investing in extensive infrastructure. Challenges and Final Thoughts To solidify the lessons from this episode, Rob challenges listeners to take action: Sign up for a cloud provider and explore its offerings. Set up a simple project, such as deploying a virtual server or experimenting with APIs. If certifications interest you, research beginner-friendly options and set a goal to achieve one. As the episode concludes, Rob and Michael remind listeners that the cloud is more than a tool; it's an opportunity to build better habits, expand your knowledge, and position yourself as a forward-thinking developer. Whether you're a beginner or an experienced professional, there's always something new to discover in the ever-evolving cloud landscape. Stay Connected: Join the Develpreneur Community We invite you to join our community and share your coding journey with us. Whether you're a seasoned developer or just starting, there's always room to learn and grow together. Contact us at info@develpreneur.com with your questions, feedback, or suggestions for future episodes. Together, let's continue exploring the exciting world of software development. Additional Resources Free Editors to Help With Web Development AWS Management Tools Google Cloud Platform: Using the platform Building a Portable Development Environment That is OS-agnostic Building Better Habits Videos – With Bonus Content

AI Advances, X Exodus, China Export Bans, and OpenAI's ChatGPT Restrictions In this episode of Hashtag Trending, Jim Love covers major highlights from AWS reInvent, including the launch of Tranium 2 powered EC2 instances, updates to the Amazon Bedrock platform, and collaborations with top companies for AI advancement. Also discussed is the European Federation of Journalists' departure from X (formerly Twitter) over disinformation concerns, China's export restrictions on key materials for technology and defense, and the discovery of ChatGPT's forbidden names list. Tune in for insight into these significant tech developments and their broader implications. 00:00 Major AI Announcements at AWS reInvent 03:27 European Journalists Leave Twitter 04:58 China's Tech Trade War Escalates 06:34 ChatGPT's Forbidden Names 08:38 Conclusion and Contact Information

Hoje é dia de sobre carreira! No episódio de estreia da série especial do podcast, conversamos com Erika Nagamine, Golden Jacket da AWS, sobre a sua trajetória, sobre as suas decisões, e sobre o poder que a curiosidade teve para lhe impulsionar ao longo de toda a sua carreira. Vem ver quem participou desse papo: Paulo Silveira, o host que gosta de certificação André David, o cohost que está rolando até agora Erika Nagamine, Arquiteta de Soluções Especialista em Dados & AI - Analytics na AWS

In this episode of the mnemonic security podcast, Robby is joined by Scott Piper from Wiz and Håkon Sørum from O3 Cyber to talk cloud security. They cover the evolution of cloud security products since Amazon's release of S3 and EC2 in 2006 and how the market has matured into the CNAPP we know today.  They chime in on most of the buzzwords associated with CNAPP, including Cloud Security Posture Management (CSPM), Cloud Workload Protection Platform (CWPP), Cloud Infrastructure Entitlement Management (CIEM), and Cloud Detection and Response (CDR), as well as other key areas of CNAPP such as vulnerability scanning, "shift-left" security, cloud data security, and compliance.   They explain the definition and challenges of "cloud-native attacks" and misconfigurations and discuss whether third-party SOCs can add context and enhance detection capabilities.  

Hoje é dia de falar de nuvem! Neste episódio, exploramos a surpreendente relação entre a AWS e a Amazon Brasil, e as importantes questões ligadas a dimensionamento, escalabilidade e, é claro, segurança quando o assunto é nuvem. Vem ver quem participou desse papo: André David, o host que fica ligado em palavrinhas-chave Vinny Neves, co-host e Tech Lead na UsTwo Bruno Toffolo, Principal Software Development Engineer na Amazon Gaston Perez, Principal Solutions Architect na AWS

AWS Morning Brief for the week of October 7, with Corey Quinn. Links:AWS CloudShell extends most recent capabilities to all commercial RegionsAmazon Aurora Serverless v2 now supports up to 256 ACUsAmazon S3 adds Service Quotas support for S3 general purpose bucketsAWS announces Reserved Nodes flexibility for Amazon ElastiCacheDuckbill Guide to AWS Reserved InstancesDeprecation of Lake Formation's Governed Tables FeatureAnnouncing AWS Neuron Helm ChartLeverage IAM Roles for email sending via SES from EC2 and eliminate a common credential riskIssue with NVIDIA Container Toolkit (CVE-2024-0132, CVE-2024-0133)

In this episode, we provided an overview of GitHub Action Runners and discussed the benefits of using self-hosted runners on AWS. We covered options including EC2 and CodeBuild for running GitHub Actions, compared pricing across solutions, and shared our hands-on experience setting things up. Overall, using AWS services can provide more control, lower latency, and cost optimization compared to GitHub hosted runners.

Welcome to episode 276 of The Cloud Pod, where the forecast is always cloudy! This week, our hosts Justin, Matthew, and Jonathan do a speedrun of OpenWorld news, talk about energy needs and the totally not controversial decision to reopen 3 Mile Island, a “managed” exodus from cloud, and Kubernetes news. As well as Amazon’s RTO we are calling “Elastic Commute”. All this and more, right now on The Cloud Pod.  Titles we almost went with this week: The Cloud Pod Hosts don't own enough pants for five days a week IBM thinks it can contain the cost of K8s Microsoft loves nuclear energy The Cloudpod tries to give Oracle some love and still does not care The cloud pod goes nuclear on k8s costs Can IBM contain the costs of Kubernetes and Nuclear Power?  Google takes on take over while microsoft takes on nuclear AWS Launches ‘Managed Exodus’: Streamline Your Talent Drain Introducing Amazon WorkForce Alienation: Scale Your Employee Discontent to the Cloud Amazon SageMaker Studio Lab: Now with Real-Time Resignation Prediction A big thanks to this week's sponsor: We're sponsorless! Want to get your brand, company, or service in front of a very enthusiastic group of cloud news seekers? You've come to the right place! Send us an email or hit us up on our slack channel for more info.  General News 01:08 IBM acquires Kubernetes cost optimization startup Kubecost  IBM is quickly becoming the place where cloud cost companies go to assimilate? Or Die? Rebirthed mabe? Either way, it's not a great place to end up.  On Tuesday they announced the acquisition of Kubecost, a FinOps startup that helps teams monitor and optimize their K8 clusters, with a focus on efficiency – and ultimately cost.  This acquisition follows the acquisitions of Apptio, Turbonomic, and Instana over the years.  Kubecost is the company behind OpenCost; a vendor-neutral open source project that forms part of the core Kubecost commercial offering.   OpenCost is part of the Cloud Native Computing Foundations cohort of sandbox projects. Kubecost is expected to be integrated into IBM’s FinOps Suite, which combines Cloudability and Turbonomic.  There is also speculation that it might make its way to OpenShift, too. 02:26 Jsutin- “…so KubeCost lives inside of Kubernetes, and basically has the ability to see how much CPU, how much memory they’re using, then calculate basically the price of the EC2 broken down into the different pods and services.” AI Is Going Great –

An airhacks.fm conversation with Jonathan Schneider (@jon_k_schneider) about: Spinnaker's role in continuous delivery and multi-cloud deployments, multi-cloud architectures, Micrometer's origin and design as a vendor-neutral metrics abstraction library, comparison of micrometer to other metrics solutions like opentelemetry and MicroProfile Metrics, exploration of Micrometer's architecture including registries and meter types, debate on static vs dependency-injected registries, explanation of distribution summaries and their use cases, consideration of unit testing metrics, examination of Micrometer's support for multiple monitoring systems simultaneously, discussion of meter filters for customizing metric output, reflection on the trade-offs between language support and monitoring system support in metrics libraries, insights into the separation of application and runtime metrics, Jonathan's experience developing Micrometer at Netflix and Pivotal, current usage of Micrometer and prometheus in Modern's multi-tenant SaaS architecture, comparison of serverless and EC2-based deployments for different use cases, OpenRewrite's growing popularity in Europe Jonathan Schneider on twitter: @jon_k_schneider

AWS Morning Brief for the week of Monday, August 5th with Mike Julian. Links:Introducing AWS End User MessagingAWS Graviton-based EC2 instances now support hibernationNew Amazon CloudWatch dimensions for Amazon EC2 On Demand Capacity ReservationsAWS and Multicloud: Existing capabilities & continued enhancementsDeliver Amazon CloudWatch logs to Amazon OpenSearch ServerlessCost Optimizer for Amazon WorkSpaces 2.7 releasedJeff Barr, Chief Evangelist at AWS, confirms service deprecations via Twitter

AWS Morning Brief for the week of Monday, July 1st, with Corey Quinn. Links:Amazon DocumentDB announces IAM database authenticationAmazon Redshift Query Editor V2 now supports 100MB file uploadsAmazon Time Sync Service expands microsecond-accurate time to 27 EC2 instance typesAnnouncing Amazon WorkSpaces Pools, a new feature of Amazon WorkSpacesAWS CodeBuild supports Arm-based workloads using AWS Graviton3Optimizing Amazon Simple Queue Service (SQS) for speed and scaleTen Ways to Improve Your AWS Operations

Bret and Nirmal are joined by Michael Fischer of AWS to discuss why we should use Graviton, their arm64 compute with AWS-designed CPUs.Graviton is AWS' term for their custom ARM-based EC2 instances. We now have all major clouds offering an ARM-based option for their server instances, but AWS was first, way back in 2018. Fast forward 6 years and AWS is releasing their 4th generation Graviton instances, and they deliver all the CPU, networking, memory and storage performance that you'd expect from their x86 instances and beyond.I'm a big fan of ARM-based servers and the price points that AWS gives us. They have been my default EC2 instance type for years now, and I recommend it for all projects I'm working on with companies.We get into the history of Graviton, how easy it is to build and deploy containers and Kubernetes clusters that have Graviton and even two different platform types in the same cluster. We also cover how to build multi-platform images using Docker BuildKit.Be sure to check out the live recording of the complete show from May 9, 2024 on YouTube (Ep. 265). Includes demos. ★Topics★Graviton + GitLab + EKSPorting Advisor for GravitonGraviton Getting StartedCreators & Guests Cristi Cotovan - Editor Beth Fisher - Producer Bret Fisher - Host Nirmal Mehta - Host Michael Fischer - Guest (00:00) - Intro (06:19) - AWS and ARM64: Evolution to Graviton 4 (07:55) - AWS EC2 Nitro: Why and How? (11:53) - Nitro and Graviton's Evolution (18:35) - What Can't Run on Graviton? (23:15) - Moving Your Workloads to Graviton (27:19) - K8s Tooling and Multi-Platform Images (37:07) - Tips for Getting Started with Graviton You can also support my free material by subscribing to my YouTube channel and my weekly newsletter at bret.news!Grab the best coupons for my Docker and Kubernetes courses.Join my cloud native DevOps community on Discord.Grab some merch at Bret's Loot BoxHomepage bretfisher.com

This episode discusses solutions for securely accessing private VPC resources for debugging and troubleshooting. We cover traditional approaches like bastion hosts and VPNs and newer solutions using containers and AWS services like Fargate, ECS, and SSM. We explain how to set up a Fargate task with a container image with the necessary tools, enable ECS integration with SSM, and use SSM to start remote shells and port forwarding tunnels into the container. This provides on-demand access without exposing resources on the public internet. We share a Python script to simplify the process. We suggest ideas for improvements like auto-scaling the container down when idle. Overall, this lightweight containerized approach can provide easy access for debugging compared to managing EC2 instances.

Join us at our first in-person conference on June 25 all about AI Quality: https://www.aiqualityconference.com/ Matthew McClean is a Machine Learning Technology Leader with the leading Amazon Web Services (AWS) cloud platform. He leads the customer engineering teams at Annapurna ML helping customers adopt AWS Trainium and Inferentia for their Gen AI workloads. Kamran Khan, Sr Technical Business Development Manager for AWS Inferentina/Trianium at AWS. He has over a decade of experience helping customers deploy and optimize deep learning training and inference workloads using AWS Inferentia and AWS Trainium. AWS Tranium and Inferentia // MLOps podcast #238 with Kamran Khan, BD, Annapurna ML and Matthew McClean, Annapurna Labs Lead Solution Architecture at AWS. Huge thank you to AWS for sponsoring this episode. AWS - https://aws.amazon.com/ // Abstract Unlock unparalleled performance and cost savings with AWS Trainium and Inferentia! These powerful AI accelerators offer MLOps community members enhanced availability, compute elasticity, and energy efficiency. Seamlessly integrate with PyTorch, JAX, and Hugging Face, and enjoy robust support from industry leaders like W&B, Anyscale, and Outerbounds. Perfectly compatible with AWS services like Amazon SageMaker, getting started has never been easier. Elevate your AI game with AWS Trainium and Inferentia! // Bio Kamran Khan Helping developers and users achieve their AI performance and cost goals for almost 2 decades. Matthew McClean Leads the Annapurna Labs Solution Architecture and Prototyping teams helping customers train and deploy their Generative AI models with AWS Trainium and AWS Inferentia // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links AWS Trainium: https://aws.amazon.com/machine-learning/trainium/ AWS Inferentia: https://aws.amazon.com/machine-learning/inferentia/ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Kamran on LinkedIn: https://www.linkedin.com/in/kamranjk/ Connect with Matt on LinkedIn: https://www.linkedin.com/in/matthewmcclean/ Timestamps: [00:00] Matt's & Kamran's preferred coffee [00:53] Takeaways [01:57] Please like, share, leave a review, and subscribe to our MLOps channels! [02:22] AWS Trainium and Inferentia rundown [06:04] Inferentia vs GPUs: Comparison [11:20] Using Neuron for ML [15:54] Should Trainium and Inferentia go together? [18:15] ML Workflow Integration Overview [23:10] The Ec2 instance [24:55] Bedrock vs SageMaker [31:16] Shifting mindset toward open source in enterprise [35:50] Fine-tuning open-source models, reducing costs significantly [39:43] Model deployment cost can be reduced innovatively [43:49] Benefits of using Inferentia and Trainium [45:03] Wrap up

Danielle Lancashire is here to tell us how Fermyon cloud is built on top of nomad and EC2 and how they put it in a box with Kubernetes and WebAssembly.

Isaac and Jeffrey discuss the topic of doing full rewrites when it comes to architecture. They explore a case where a client needed to move from a single EC2 instance to a more stable system with load-balanced instances. They discuss the parallels between rewriting code and rewriting architecture, and the challenges and risks involved.

AWS Morning Brief for the week of April 29th, 2024, with Corey Quinn. Links:Amazon GameLift now includes containers support (Preview)Introducing Amazon Route 53 Profiles Amazon Simple Email Service is now available in the AWS GovCloud (US-East) Region Amazon Time Sync Service expands Microsecond-Accurate time to 87 additonal EC2 instance typesHow to Migrate Content from Amazon WorkDocs Build and deploy a 1 TB/s file system in under an hourAWS Response to March 2024 CSRB report chance to be actual leaderspeople turning down job offers

AWS Morning Brief for the week of April 1, 2024, with Corey Quinn. Links:AI recommendations for descriptions in Amazon DataZone now generally availableAmazon DynamoDB Import from S3 now supports up to 50,000 Amazon S3 objects in a single bulk importAmazon Time Sync Service now supports microsecond-accurate time in US East (N. Virginia) Region AWS Billing and Cost Management Data Exports now supports AWS CloudFormation AWS Compute Optimizer introduces memory customizability for EC2 rightsizing recommendationsAWS Cost Allocation Tags now support retroactive applicationEstimating the charges for Amazon RDS Extended SupportAmazon completes $4B Anthropic investment to advance generative AI 

Welcome to part two in the AWS Certification Exam Prep Mini-Series! Whether you're an aspiring cloud enthusiast or a seasoned developer looking to deepen your architectural acumen, you've landed in the perfect spot. In this six-part saga, we're demystifying the pivotal role of a Solutions Architect in the AWS cloud computing cosmos. In this second episode, join Caroline and Dave as they host a riveting discussion with two cloud virtuosos - Anya Derbakova, a Senior Startup Solutions Architect at AWS, known for weaving social media magic, and Ted Trentler, a Senior AWS Technical Instructor with a knack for simplifying the complex. Together, they embark on a narrative journey through the clouds and break down the AWS Certification labyrinth. Expect to uncover: Designing Secure Access to AWS Resources: • Introduction to IAM for identity management. • Role switching with AWS STS. • Multi-account strategies using AWS Control Tower and federation with IAM roles. Designing Secure Workloads and Applications: • Architecting VPCs. • Implementing layered security models with security groups, NACLs, and gateways. Determining Appropriate Data Security Controls: • Data access, governance, and responsibility. • Data recovery, retention, and classification strategies. • Encryption and key management practices. Anatomy of an Exam Question: • Exam question formats: Multiple Choice and Multiple Response. • Dissecting questions: Stem, key, and distractors. • Scenario-based questions reflecting real-life challenges. • Alignment with the Well-Architected Framework. Sample Exam Question: • Scenario-based question on enabling software patch downloads for EC2 instances in private subnets without direct internet access. Whether you're aiming to elevate your career, transition roles, or simply quench your curiosity about the cloud, tune in to transform your understanding of AWS and gear up for success in the Solutions Architect realm. Anya on LinkedIn: https://www.linkedin.com/in/annadderbakova/ Ted on Twitter: https://twitter.com/ttrentler Ted on LinkedIn: https://linkedin/in/tedtrentler Caroline on Twitter: https://twitter.com/carolinegluck Caroline on LinkedIn: https://www.linkedin.com/in/cgluck/ Dave on Twitter: https://twitter.com/thedavedev Dave on LinkedIn: https://www.linkedin.com/in/davidisbitski AWS SAA Exam Guide - https://d1.awsstatic.com/training-and-certification/docs-sa-assoc/AWS-Certified-Solutions-Architect-Associate_Exam-Guide.pdf Party Rock for Exam Study - https://partyrock.aws/u/tedtrent/KQtYIhbJb/Solutions-Architect-Study-Buddy All Things AWS Training - Links to Self-paced and Instructor Led https://aws.amazon.com/training/ AWS Skill Builder – Free CPE Course - https://explore.skillbuilder.aws/learn/course/134/aws-cloud-practitioner-essentials AWS Skill Builder – Learning Badges - https://explore.skillbuilder.aws/learn/public/learning_plan/view/1044/solutions-architect-knowledge-badge-readiness-path AWS Well-Architected Framework - https://docs.aws.amazon.com/wellarchitected/latest/framework/welcome.html Controlling the Network Free Lab - https://explore.skillbuilder.aws/learn/course/internal/view/elearning/11237/controlling-the-network-amazon Exam Prep Official Practice Question Set: AWS Certified Solutions Architect - Associate (SAA-C03 - English) - https://explore.skillbuilder.aws/learn/course/external/view/elearning/13266/aws-certified-solutions-architect-associate-official-practice-question-set-saa-c03-english?saa=sec&sec=prep AWS Skill Builder - Networking Core - Knowledge Badge Readiness Path - https://explore.skillbuilder.aws/learn/public/learning_plan/view/1944/networking-core-knowledge-badge-readiness-path Subscribe: Spotify: https://open.spotify.com/show/7rQjgnBvuyr18K03tnEHBI Apple Podcasts: https://podcasts.apple.com/us/podcast/aws-developers-podcast/id1574162669 RSS Feed: https://feeds.soundcloud.com/users/soundcloud:users:994363549/sounds.rss

Welcome to part three in the AWS Certification Exam Prep Mini-Series! Whether you're an aspiring cloud enthusiast or a seasoned developer looking to deepen your architectural acumen, you've landed in the perfect spot. In this six-part saga, we're demystifying the pivotal role of a Solutions Architect in the AWS cloud computing cosmos. In this third episode, join Caroline and Dave as they host a riveting discussion with two cloud virtuosos - Anya Derbakova, a Senior Startup Solutions Architect at AWS, known for weaving social media magic, and Ted Trentler, a Senior AWS Technical Instructor with a knack for simplifying the complex. Together, they embark on a narrative journey through the clouds, sharing their personal paths to mastering AWS, elucidating the essence of the Solutions Architect role, and breaking down the AWS Certification labyrinth. Expect to uncover: Overview of Exam Domain 2: Design Resilient Architectures (26% of scored test content): • Emphasis on designing scalable and loosely coupled architectures. • Introduction to API creation and management. • Utilization of AWS managed services for efficiency. • Principles of microservices design for robust applications. • Strategies for horizontal and vertical scaling. • Understanding of load balancing techniques. • Comparison of storage types: Object, Block, File. Designing Highly Available and/or Fault-Tolerant Architectures: • Exploration of AWS global infrastructure: Availability Zones, AWS Regions, Amazon Route 53. • Basics of networking concepts, including route tables. • Disaster recovery (DR) strategies: backup and restore, pilot light, warm standby, active-active failover, RPO, RTO. • Immutable infrastructure for consistent deployment. • Storage options and their characteristics: durability, replication. • Enhancing workload visibility with tools like AWS X-Ray. Sample Exam Question: • Scenario involving a two-tier application architecture with resilience considerations, focusing on EC2 instances across public and private subnets within a single Availability Zone. This series is your beacon through the fog of cloud computing, illuminating the path to AWS mastery. Whether you're aiming to elevate your career, transition roles, or simply quench your curiosity about the cloud, tune in to transform your understanding of AWS and gear up for success in the Solutions Architect realm. Anya on LinkedIn: https://www.linkedin.com/in/annadderbakova/ Ted on Twitter: https://twitter.com/ttrentler Ted on LinkedIn: https://linkedin/in/tedtrentler Caroline on Twitter: https://twitter.com/carolinegluck Caroline on LinkedIn: https://www.linkedin.com/in/cgluck/ Dave on Twitter: https://twitter.com/thedavedev Dave on LinkedIn: https://www.linkedin.com/in/davidisbitski AWS SAA Exam Guide - https://d1.awsstatic.com/training-and-certification/docs-sa-assoc/AWS-Certified-Solutions-Architect-Associate_Exam-Guide.pdf Party Rock for Exam Study - https://partyrock.aws/u/tedtrent/KQtYIhbJb/Solutions-Architect-Study-Buddy All Things AWS Training - Links to Self-paced and Instructor Led https://aws.amazon.com/training/ AWS Skill Builder – Free CPE Course - https://explore.skillbuilder.aws/learn/course/134/aws-cloud-practitioner-essentials AWS Skill Builder – Learning Badges - https://explore.skillbuilder.aws/learn/public/learning_plan/view/1044/solutions-architect-knowledge-badge-readiness-path AWS Well-Architected Framework - https://docs.aws.amazon.com/wellarchitected/latest/framework/welcome.html Subscribe: Spotify: https://open.spotify.com/show/7rQjgnBvuyr18K03tnEHBI Apple Podcasts: https://podcasts.apple.com/us/podcast/aws-developers-podcast/id1574162669

This Friday we're doing a special crossover event in SF with of SemiAnalysis (previous guest!), and we will do a live podcast on site. RSVP here. Also join us on June 25-27 for the biggest AI Engineer conference of the year!Replicate is one of the most popular AI inference providers, reporting over 2 million users as of their $40m Series B with a16z. But how did they get there? The Definitive Replicate Story (warts and all)Their overnight success took 5 years of building, and it all started with arXiv Vanity, which was a 2017 vacation project that scrapes arXiv PDFs and re-renders them into semantic web pages that reflow nicely with better typography and whitespace. From there, Ben and Andreas' idea was to build tools to make ML research more robust and reproducible by making it easy to share code artefacts alongside papers. They had previously created Fig, which made it easy to spin up dev environments; it was eventually acquired by Docker and turned into `docker-compose`, the industry standard way to define services from containerized applications. 2019: CogThe first iteration of Replicate was a Fig-equivalent for ML workloads which they called Cog; it made it easy for researchers to package all their work and share it with peers for review and reproducibility. But they found that researchers were terrible users: they'd do all this work for a paper, publish it, and then never return to it again. “We talked to a bunch of researchers and they really wanted that.... But how the hell is this a business, you know, like how are we even going to make any money out of this? …So we went and talked to a bunch of companies trying to sell them something which didn't exist. So we're like, hey, do you want a way to share research inside your company so that other researchers or say like the product manager can test out the machine learning model? They're like, maybe. Do you want like a deployment platform for deploying models? Do you want a central place for versioning models? We were trying to think of lots of different products we could sell that were related to this thing…So we then got halfway through our YC batch. We hadn't built a product. We had no users. We had no idea what our business was going to be because we couldn't get anybody to like buy something which didn't exist. And actually there was quite a way through our, I think it was like two thirds the way through our YC batch or something. And we're like, okay, well we're kind of screwed now because we don't have anything to show at demo day.”The team graduated YCombinator with no customers, no product and nothing to demo - which was fine because demo day got canceled as the YC W'20 class graduated right into the pandemic. The team spent the next year exploring and building Covid tools.2021: CLIP + GAN = PixRayBy 2021, OpenAI released CLIP. Overnight dozens of Discord servers got spun up to hack on CLIP + GANs. Unlike academic researchers, this community was constantly releasing new checkpoints and builds of models. PixRay was one of the first models being built on Replicate, and it quickly started taking over the community. Chris Dixon has a famous 2010 post titled “The next big thing will start out looking like a toy”; image generation would have definitely felt like a toy in 2021, but it gave Replicate its initial boost.2022: Stable DiffusionIn August 2022 Stable Diffusion came out, and all the work they had been doing to build this infrastructure for CLIP / GANs models became the best way for people to share their StableDiffusion fine-tunes:And like the first week we saw people making animation models out of it. We saw people make game texture models that use circular convolutions to make repeatable textures. We saw a few weeks later, people were fine tuning it so you could put your face in these models and all of these other ways. […] So tons of product builders wanted to build stuff with it. And we were just sitting in there in the middle, as the interface layer between all these people who wanted to build, and all these machine learning experts who were building cool models. And that's really where it took off. Incredible supply, incredible demand, and we were just in the middle.(Stable Diffusion also spawned Latent Space as a newsletter)The landing page paved the cowpath for the intense interest in diffusion model APIs.2023: Llama & other multimodal LLMsBy 2023, Replicate's growing visibility in the Stable Diffusion indie hacker community came from top AI hackers like Pieter Levels and Danny Postmaa, each making millions off their AI apps:Meta then released LLaMA 1 and 2 (our coverage of it), greatly pushing forward the SOTA open source model landscape. Demand for text LLMs and other modalities rose, and Replicate broadened its focus accordingly, culminating in a $18m Series A and $40m Series B from a16z (at a $350m valuation).Building standards for the AI worldNow that the industry is evolving from toys to enterprise use cases, all these companies are working to set standards for their own space. We cover this at ~45 mins in the podcast. Some examples:* LangChain has been trying to establish "chain” as the standard mental models when putting multiple prompts and models together, and the “LangChain Expression Language” to go with it. (Our episode with Harrison)* LLamaHub for packaging RAG utilities. (Our episode with Jerry)* Ollama's Modelfile to define runtimes for different model architectures. These are usually targeted at local inference. * Cog (by Replicate) to create environments to which you can easily attach CUDA devices and make it easy to spin up inference on remote servers. * GGUF as the filetype ggml-based executors. None of them have really broken out yet, but this is going to become a fiercer competition as the market matures. Full Video PodcastAs a reminder, all Latent Space pods now come in full video on our YouTube, with bonus content that we cut for time!Show Notes* Ben Firshman* Replicate* Free $10 credit for Latent Space readers* Andreas Jansson (Ben's co-founder)* Charlie Holtz (Replicate's Hacker in Residence)* Fig (now Docker Compose)* Command Line Interface Guidelines (clig)* Apple Human Interface Guidelines* arXiv Vanity* Open Interpreter* PixRay* SF Compute* Big Sleep by Advadnoun* VQGAN-CLIP by Rivers Have WingsTimestamps* [00:00:00] Introductions* [00:01:17] Low latency is all you need* [00:04:08] Evolution of CLIs* [00:05:59] How building ArxivVanity led to Replicate* [00:11:37] Making ML research replicable with containers* [00:17:22] Doing YC in 2020 and pivoting to tools for COVID* [00:20:22] Launching the first version of Replicate* [00:25:51] Embracing the generative image community* [00:28:04] Getting reverse engineered into an API product* [00:31:25] Growing to 2 million users* [00:34:29] Indie vs Enterprise customers* [00:37:09] How Unsplash uses Replicate* [00:38:29] Learnings from Docker that went into Cog* [00:45:25] Creating AI standards* [00:50:05] Replicate's compute availability* [00:53:55] Fixing GPU waste* [01:00:39] What's open source AI?* [01:04:46] Building for AI engineers* [01:06:41] Hiring at ReplicateThis summary covers the full range of topics discussed throughout the episode, providing a comprehensive overview of the content and insights shared.TranscriptAlessio [00:00:00]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, partner and CTO in Residence at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol AI.Swyx [00:00:14]: Hey, and today we have Ben Firshman in the studio. Welcome Ben.Ben [00:00:18]: Hey, good to be here.Swyx [00:00:19]: Ben, you're a co-founder and CEO of Replicate. Before that, you were most notably founder of Fig, which became Docker Compose. You also did a couple of other things before that, but that's what a lot of people know you for. What should people know about you that, you know, outside of your, your sort of LinkedIn profile?Ben [00:00:35]: Yeah. Good question. I think I'm a builder and tinkerer, like in a very broad sense. And I love using my hands to make things. So like I work on, you know, things may be a bit closer to tech, like electronics. I also like build things out of wood and I like fix cars and I fix my bike and build bicycles and all this kind of stuff. And there's so much, I think I've learned from transferable skills, from just like working in the real world to building things, building things in software. And you know, it's so much about being a builder, both in real life and, and in software that crosses over.Swyx [00:01:11]: Is there a real world analogy that you use often when you're thinking about like a code architecture or problem?Ben [00:01:17]: I like to build software tools as if they were something real. So I wrote this thing called the command line interface guidelines, which was a bit like sort of the Mac human interface guidelines, but for command line interfaces, I did it with the guy I created Docker Compose with and a few other people. And I think something in there, I think I described that your command line interface should feel like a big iron machine where you pull a lever and it goes clunk and like things should respond within like 50 milliseconds as if it was like a real life thing. And like another analogy here is like in the real life, you know, when you press a button on an electronic device and it's like a soft switch and you press it and nothing happens and there's no physical feedback of anything happening, then like half a second later, something happens. Like that's how a lot of software feels, but instead like software should feel more like something that's real where you touch, you pull a physical lever and the physical lever moves, you know, and I've taken that lesson of kind of human interface to, to software a ton. You know, it's all about kind of low latency of feeling, things feeling really solid and robust, both the command lines and, and user interfaces as well.Swyx [00:02:22]: And how did you operationalize that for Fig or Docker?Ben [00:02:27]: A lot of it's just low latency. Actually, we didn't do it very well for Fig in the first place. We used Python, which was a big mistake where Python's really hard to get booting up fast because you have to load up the whole Python runtime before it can run anything. Okay. Go is much better at this where like Go just instantly starts.Swyx [00:02:45]: You have to be under 500 milliseconds to start up?Ben [00:02:48]: Yeah, effectively. I mean, I mean, you know, perception of human things being immediate is, you know, something like a hundred milliseconds. So anything like that is, is yeah, good enough.Swyx [00:02:57]: Yeah. Also, I should mention, since we're talking about your side projects, well, one thing is I am maybe one of a few fellow people who have actually written something about CLI design principles because I was in charge of the Netlify CLI back in the day and had many thoughts. One of my fun thoughts, I'll just share it in case you have thoughts, is I think CLIs are effectively starting points for scripts that are then run. And the moment one of the script's preconditions are not fulfilled, typically they end. So the CLI developer will just exit the program. And the way that I designed, I really wanted to create the Netlify dev workflow was for it to be kind of a state machine that would resolve itself. If it detected a precondition wasn't fulfilled, it would actually delegate to a subprogram that would then fulfill that precondition, asking for more info or waiting until a condition is fulfilled. Then it would go back to the original flow and continue that. I don't know if that was ever tried or is there a more formal definition of it? Because I just came up with it randomly. But it felt like the beginnings of AI in the sense that when you run a CLI command, you have an intent to do something and you may not have given the CLI all the things that it needs to do, to execute that intent. So that was my two cents.Ben [00:04:08]: Yeah, that reminds me of a thing we sort of thought about when writing the CLI guidelines, where CLIs were designed in a world where the CLI was really a programming environment and it's primarily designed for machines to use all of these commands and scripts. Whereas over time, the CLI has evolved to humans. It was back in a world where the primary way of using computers was writing shell scripts effectively. We've transitioned to a world where actually humans are using CLI programs much more than they used to. And the current sort of best practices about how Unix was designed, there's lots of design documents about Unix from the 70s and 80s, where they say things like, command line commands should not output anything on success. It should be completely silent, which makes sense if you're using it in a shell script. But if a user is using that, it just looks like it's broken. If you type copy and it just doesn't say anything, you assume that it didn't work as a new user. I think what's really interesting about the CLI is that it's actually a really good, to your point, it's a really good user interface where it can be like a conversation, where it feels like you're, instead of just like you telling the computer to do this thing and either silently succeeding or saying, no, you did, failed, it can guide you in the right direction and tell you what your intent might be, and that kind of thing in a way that's actually, it's almost more natural to a CLI than it is in a graphical user interface because it feels like this back and forth with the computer, almost funnily like a language model. So I think there's some interesting intersection of CLIs and language models actually being very sort of closely related and a good fit for each other.Swyx [00:05:59]: Yeah, I'll say one of the surprises from last year, I worked on a coding agent, but I think the most successful coding agent of my cohort was Open Interpreter, which was a CLI implementation. And I have chronically, even as a CLI person, I have chronically underestimated the CLI as a useful interface. You also developed ArchiveVanity, which you recently retired after a glorious seven years.Ben [00:06:22]: Something like that.Swyx [00:06:23]: Which is nice, I guess, HTML PDFs.Ben [00:06:27]: Yeah, that was actually the start of where Replicate came from. Okay, we can tell that story. So when I quit Docker, I got really interested in science infrastructure, just as like a problem area, because it is like science has created so much progress in the world. The fact that we're, you know, can talk to each other on a podcast and we use computers and the fact that we're alive is probably thanks to medical research, you know. But science is just like completely archaic and broken and it's like 19th century processes that just happen to be copied to the internet rather than take into account that, you know, we can transfer information at the speed of light now. And the whole way science is funded and all this kind of thing is all kind of very broken. And there's just so much potential for making science work better. And I realized that I wasn't a scientist and I didn't really have any time to go and get a PhD and become a researcher, but I'm a tool builder and I could make existing scientists better at their job. And if I could make like a bunch of scientists a little bit better at their job, maybe that's the kind of equivalent of being a researcher. So one particular thing I dialed in on is just how science is disseminated in that all of these PDFs, quite often behind paywalls, you know, on the internet.Swyx [00:07:34]: And that's a whole thing because it's funded by national grants, government grants, then they're put behind paywalls. Yeah, exactly.Ben [00:07:40]: That's like a whole, yeah, I could talk for hours about that. But the particular thing we got dialed in on was, interestingly, these PDFs are also, there's a bunch of open science that happens as well. So math, physics, computer science, machine learning, notably, is all published on the archive, which is actually a surprisingly old institution.Swyx [00:08:00]: Some random Cornell.Ben [00:08:01]: Yeah, it was just like somebody in Cornell who started a mailing list in the 80s. And then when the web was invented, they built a web interface around it. Like it's super old.Swyx [00:08:11]: And it's like kind of like a user group thing, right? That's why they're all these like numbers and stuff.Ben [00:08:15]: Yeah, exactly. Like it's a bit like something, yeah. That's where all basically all of math, physics and computer science happens. But it's still PDFs published to this thing. Yeah, which is just so infuriating. The web was invented at CERN, a physics institution, to share academic writing. Like there are figure tags, there are like author tags, there are heading tags, there are site tags. You know, hyperlinks are effectively citations because you want to link to another academic paper. But instead, you have to like copy and paste these things and try and get around paywalls. Like it's absurd, you know. And now we have like social media and things, but still like academic papers as PDFs, you know. This is not what the web was for. So anyway, I got really frustrated with that. And I went on vacation with my old friend Andreas. So we were, we used to work together in London on a startup, at somebody else's startup. And we were just on vacation in Greece for fun. And he was like trying to read a machine learning paper on his phone, you know, like we had to like zoom in and like scroll line by line on the PDF. And he was like, this is f*****g stupid. So I was like, I know, like this is something we discovered our mutual hatred for this, you know. And we spent our vacation sitting by the pool, like making latex to HTML, like converters, making the first version of Archive Vanity. Anyway, that was up then a whole thing. And the story, we shut it down recently because they caught the eye of Archive. They were like, oh, this is great. We just haven't had the time to work on this. And what's tragic about the Archive, it's like this project of Cornell that's like, they can barely scrounge together enough money to survive. I think it might be better funded now than it was when we were, we were collaborating with them. And compared to these like scientific journals, it's just that this is actually where the work happens. But they just have a fraction of the money that like these big scientific journals have, which is just so tragic. But anyway, they were like, yeah, this is great. We can't afford to like do it, but do you want to like as a volunteer integrate arXiv Vanity into arXiv?Swyx [00:10:05]: Oh, you did the work.Ben [00:10:06]: We didn't do the work. We started doing the work. We did some. I think we worked on this for like a few months to actually get it integrated into arXiv. And then we got like distracted by Replicate. So a guy called Dan picked up the work and made it happen. Like somebody who works on one of the, the piece of the libraries that powers arXiv Vanity. Okay.Swyx [00:10:26]: And the relationship with arXiv Sanity?Ben [00:10:28]: None.Swyx [00:10:30]: Did you predate them? I actually don't know the lineage.Ben [00:10:32]: We were after, we both were both users of arXiv Sanity, which is like a sort of arXiv...Ben [00:10:37]: Which is Andre's RecSys on top of arXiv.Ben [00:10:40]: Yeah. Yeah. And we were both users of that. And I think we were trying to come up with a working name for arXiv and Andreas just like cracked a joke of like, oh, let's call it arXiv Vanity. Let's make the papers look nice. Yeah. Yeah. And that was the working name and it just stuck.Swyx [00:10:52]: Got it.Ben [00:10:53]: Got it.Alessio [00:10:54]: Yeah. And then from there, tell us more about why you got distracted, right? So Replicate, maybe it feels like an overnight success to a lot of people, but you've been building this since 2019. Yeah.Ben [00:11:04]: So what prompted the start?Alessio [00:11:05]: And we've been collaborating for even longer.Ben [00:11:07]: So we created arXiv Vanity in 2017. So in some sense, we've been doing this almost like six, seven years now, a classic seven year.Swyx [00:11:16]: Overnight success.Ben [00:11:17]: Yeah. Yes. We did arXiv Vanity and then worked on a bunch of like surrounding projects. I was still like really interested in science publishing at that point. And I'm trying to remember, because I tell a lot of like the condensed story to people because I can't really tell like a seven year history. So I'm trying to figure out like the right. Oh, we got room. The right length.Swyx [00:11:35]: We want to nail the definitive Replicate story here.Ben [00:11:37]: One thing that's really interesting about these machine learning papers is that these machine learning papers are published on arXiv and a lot of them are actual fundamental research. So like should be like prose describing a theory. But a lot of them are just running pieces of software that like a machine learning researcher made that did something, you know, it was like an image classification model or something. And they managed to make an image classification model that was better than the existing state of the art. And they've made an actual running piece of software that does image segmentation. And then what they had to do is they then had to take that piece of software and write it up as prose and math in a PDF. And what's frustrating about that is like if you want to. So this was like Andreas is, Andreas was a machine learning engineer at Spotify. And some of his job was like he did pure research as well. Like he did a PhD and he was doing a lot of stuff internally. But part of his job was also being an engineer and taking some of these existing things that people have made and published and trying to apply them to actual problems at Spotify. And he was like, you know, you get given a paper which like describes roughly how the model works. It's probably listing lots of crucial information. There's sometimes code on GitHub. More and more there's code on GitHub. But back then it was kind of relatively rare. But it's quite often just like scrappy research code and didn't actually run. And, you know, there was maybe the weights that were on Google Drive, but they accidentally deleted the weights of Google Drive, you know, and it was like really hard to like take this stuff and actually use it for real things. We just started talking together about like his problems at Spotify and I connected this back to my work at Docker as well. I was like, oh, this is what we created containers for. You know, we solved this problem for normal software by putting the thing inside a container so you could ship it around and it kept on running. So we were sort of hypothesizing about like, hmm, what if we put machine learning models inside containers so they could actually be shipped around and they could be defined in like some production ready formats and other researchers could run them to generate baselines and you could people who wanted to actually apply them to real problems in the world could just pick up the container and run it, you know. And we then thought this is quite whether it gets normally in this part of the story I skip forward to be like and then we created cog this container stuff for machine learning models and we created Replicate, the place for people to publish these machine learning models. But there's actually like two or three years between that. The thing we then got dialed into was Andreas was like, what if there was a CI system for machine learning? It's like one of the things he really struggled with as a researcher is generating baselines. So when like he's writing a paper, he needs to like get like five other models that are existing work and get them running.Swyx [00:14:21]: On the same evals.Ben [00:14:22]: Exactly, on the same evals so you can compare apples to apples because you can't trust the numbers in the paper.Swyx [00:14:26]: So you can be Google and just publish them anyway.Ben [00:14:31]: So I think this was coming from the thinking of like there should be containers for machine learning, but why are people going to use that? Okay, maybe we can create a supply of containers by like creating this useful tool for researchers. And the useful tool was like, let's get researchers to package up their models and push them to the central place where we run a standard set of benchmarks across the models so that you can trust those results and you can compare these models apples to apples and for like a researcher for Andreas, like doing a new piece of research, he could trust those numbers and he could like pull down those models, confirm it on his machine, use the standard benchmark to then measure his model and you know, all this kind of stuff. And so we started building that. That's what we applied to YC with, got into YC and we started sort of building a prototype of this. And then this is like where it all starts to fall apart. We were like, okay, that sounds great. And we talked to a bunch of researchers and they really wanted that and that sounds brilliant. That's a great way to create a supply of like models on this research platform. But how the hell is this a business, you know, like how are we even going to make any money out of this? And we're like, oh s**t, that's like the, that's the real unknown here of like what the business is. So we thought it would be a really good idea to like, okay, before we get too deep into this, let's try and like reduce the risk of this turning into a business. So let's try and like research what the business could be for this research tool effectively. So we went and talked to a bunch of companies trying to sell them something which didn't exist. So we're like, hey, do you want a way to share research inside your company so that other researchers or say like the product manager can test out the machine learning model? They're like, maybe. And we were like, do you want like a deployment platform for deploying models? Like, do you want like a central place for versioning models? Like we're trying to think of like lots of different like products we could sell that were like related to this thing. And terrible idea. Like we're not sales people and like people don't want to buy something that doesn't exist. I think some people can pull this off, but we were just like, you know, a bunch of product people, products and engineer people, and we just like couldn't pull this off. So we then got halfway through our YC batch. We hadn't built a product. We had no users. We had no idea what our business was going to be because we couldn't get anybody to like buy something which didn't exist. And actually there was quite a way through our, I think it was like two thirds the way through our YC batch or something. And we're like, okay, well we're kind of screwed now because we don't have anything to show at demo day. And then we then like tried to figure out, okay, what can we build in like two weeks that'll be something. So we like desperately tried to, I can't remember what we've tried to build at that point. And then two weeks before demo day, I just remember it was all, we were going down to Mountain View every week for dinners and we got called on to like an all hands Zoom call, which was super weird. We're like, what's going on? And they were like, don't come to dinner tomorrow. And we realized, we kind of looked at the news and we were like, oh, there's a pandemic going on. We were like so deep in our startup. We were just like completely oblivious to what was going on around us.Swyx [00:17:20]: Was this Jan or Feb 2020?Ben [00:17:22]: This was March 2020. March 2020. 2020.Swyx [00:17:25]: Yeah. Because I remember Silicon Valley at the time was early to COVID. Like they started locking down a lot faster than the rest of the US.Ben [00:17:32]: Yeah, exactly. And I remember, yeah, soon after that, like there was the San Francisco lockdowns and then like the YC batch just like stopped. There wasn't demo day and it was in a sense a blessing for us because we just kind ofSwyx [00:17:43]: In the normal course of events, you're actually allowed to defer to a future demo day. Yeah.Ben [00:17:51]: So we didn't even take any defer because it just kind of didn't happen.Swyx [00:17:55]: So was YC helpful?Ben [00:17:57]: Yes. We completely screwed up the batch and that was our fault. I think the thing that YC has become incredibly valuable for us has been after YC. I think there was a reason why we couldn't, didn't need to do YC to start with because we were quite experienced. We had done some startups before. We were kind of well connected with VCs, you know, it was relatively easy to raise money because we were like a known quantity. You know, if you go to a VC and be like, Hey, I made this piece of-Swyx [00:18:24]: It's Docker Compose for AI.Ben [00:18:26]: Exactly. Yeah. And like, you know, people can pattern match like that and they can have some trust, you know what you're doing. Whereas it's much harder for people straight out of college and that's where like YC sweet spot is like helping people straight out of college who are super promising, like figure out how to do that.Swyx [00:18:40]: No credentials.Ben [00:18:41]: Yeah, exactly. We don't need that. But the thing that's been incredibly useful for us since YC has been, this was actually, I think, so Docker was a YC company and Solomon, the founder of Docker, I think told me this. He was like, a lot of people underestimate the value of YC after you finish the batch. And his biggest regret was like not staying in touch with YC. I might be misattributing this, but I think it was him. And so we made a point of that. And we just stayed in touch with our batch partner, who Jared at YC has been fantastic.Ben [00:19:10]: Jared Friedman. All of like the team at YC, there was the growth team at YC when they were still there and they've been super helpful. And two things have been super helpful about that is like raising money, like they just know exactly how to raise money. And they've been super helpful during that process in all of our rounds, like we've done three rounds since we did YC and they've been super helpful during the whole process. And also just like reaching a ton of customers. So like the magic of YC is that you have all of, like there's thousands of YC companies, I think, on the order of thousands, I think. And they're all of your first customers. And they're like super helpful, super receptive, really want to like try out new things. You have like a warm intro to every one of them basically. And there's this mailing list where you can post about updates to your products, which is like really receptive. And that's just been fantastic for us. Like we've just like got so many of our users and customers through YC. Yeah.Swyx [00:20:00]: Well, so the classic criticism or the sort of, you know, pushback is people don't buy you because you are both from YC. But at least they'll open the email. Right. Like that's the... Okay.Ben [00:20:13]: Yeah. Yeah. Yeah.Swyx [00:20:16]: So that's been a really, really positive experience for us. And sorry, I interrupted with the YC question. Like you were, you make it, you just made it out of the YC, survived the pandemic.Ben [00:20:22]: I'll try and condense this a little bit. Then we started building tools for COVID weirdly. We were like, okay, we don't have a startup. We haven't figured out anything. What's the most useful thing we could be doing right now?Swyx [00:20:32]: Save lives.Ben [00:20:33]: So yeah. Let's try and save lives. I think we failed at that as well. We had a bunch of products that didn't really go anywhere. We kind of worked on, yeah, a bunch of stuff like contact tracing, which turned out didn't really be a useful thing. Sort of Andreas worked on like a door dash for like people delivering food to people who are vulnerable. What else did we do? The meta problem of like helping people direct their efforts to what was most useful and a few other things like that. It didn't really go anywhere. So we're like, okay, this is not really working either. We were considering actually just like doing like work for COVID. We have this decision document early on in our company, which is like, should we become a like government app contracting shop? We decided no.Swyx [00:21:11]: Because you also did work for the gov.uk. Yeah, exactly.Ben [00:21:14]: We had experience like doing some like-Swyx [00:21:17]: And the Guardian and all that.Ben [00:21:18]: Yeah. For like government stuff. And we were just like really good at building stuff. Like we were just like product people. Like I was like the front end product side and Andreas was the back end side. So we were just like a product. And we were working with a designer at the time, a guy called Mark, who did our early designs for Replicate. And we were like, hey, what if we just team up and like become and build stuff? And yeah, we gave up on that in the end for, I can't remember the details. So we went back to machine learning. And then we were like, well, we're not really sure if this is going to work. And one of my most painful experiences from previous startups is shutting them down. Like when you realize it's not really working and having to shut it down, it's like a ton of work and it's people hate you and it's just sort of, you know. So we were like, how can we make something we don't have to shut down? And even better, how can we make something that won't page us in the middle of the night? So we made an open source project. We made a thing which was an open source Weights and Biases, because we had this theory that like people want open source tools. There should be like an open source, like version control, experiment tracking like thing. And it was intuitive to us and we're like, oh, we're software developers and we like command line tools. Like everyone loves command line tools and open source stuff, but machine learning researchers just really didn't care. Like they just wanted to click on buttons. They didn't mind that it was a cloud service. It was all very visual as well, that you need lots of graphs and charts and stuff like this. So it wasn't right. Like it was right. We actually were building something that Andreas made at Spotify for just like saving experiments to cloud storage automatically, but other people didn't really want this. So we kind of gave up on that. And then that was actually originally called Replicate and we renamed that out of the way. So it's now called Keepsake and I think some people still use it. Then we sort of came back, we looped back to our original idea. So we were like, oh, maybe there was a thing in that thing we were originally sort of thinking about of like researchers sharing their work and containers for machine learning models. So we just built that. And at that point we were kind of running out of the YC money. So we were like, okay, this like feels good though. Let's like give this a shot. So that was the point we raised a seed round. We raised seed round. Pre-launch. We raised pre-launch and pre-team. It was an idea basically. We had a little prototype. It was just an idea and a team. But we were like, okay, like, you know, bootstrapping this thing is getting hard. So let's actually raise some money. Then we made Cog and Replicate. It initially didn't have APIs, interestingly. It was just the bit that I was talking about before of helping researchers share their work. So it was a way for researchers to put their work on a webpage such that other people could try it out and so that you could download the Docker container. We cut the benchmarks thing of it because we thought that was just like too complicated. But it had a Docker container that like, you know, Andreas in a past life could download and run with his benchmark and you could compare all these models apples to apples. So that was like the theory behind it. That kind of started to work. It was like still when like, you know, it was long time pre-AI hype and there was lots of interesting stuff going on, but it was very much in like the classic deep learning era. So sort of image segmentation models and sentiment analysis and all these kinds of things, you know, that people were using, that we're using deep learning models for. And we were very much building for research because all of this stuff was happening in research institutions, you know, the sort of people who'd be publishing to archive. So we were creating an accompanying material for their models, basically, you know, they wanted a demo for their models and we were creating a company material for it. What was funny about that is they were like not very good users. Like they were, they were doing great work obviously, but, but the way that research worked is that they, they just made like one thing every six months and they just fired and forget it, forgot it. Like they, they published this piece of paper and like, done, I've, I've published it. So they like output it to Replicate and then they just stopped using Replicate. You know, they were like once every six monthly users and that wasn't great for us, but we stumbled across this early community. This was early 2021 when OpenAI created this, created CLIP and people started smushing CLIP and GANs together to produce image generation models. And this started with, you know, it was just a bunch of like tinkerers on Discord, basically. There was an early model called Big Sleep by Advadnoun. And then there was VQGAN Clip, which was like a bit more popular by Rivers Have Wings. And it was all just people like tinkering on stuff in Colabs and it was very dynamic and it was people just making copies of co-labs and playing around with things and forking in. And to me this, I saw this and I was like, oh, this feels like open source software, like so much more than the research world where like people are publishing these papers.Swyx [00:25:48]: You don't know their real names and it's just like a Discord.Ben [00:25:51]: Yeah, exactly. But crucially, it was like people were tinkering and forking and things were moving really fast and it just felt like this creative, dynamic, collaborative community in a way that research wasn't really, like it was still stuck in this kind of six month publication cycle. So we just kind of latched onto that and started building for this community. And you know, a lot of those early models were published on Replicate. I think the first one that was really primarily on Replicate was one called Pixray, which was sort of mid 2021 and it had a really cool like pixel art output, but it also just like produced general, you know, the sort of, they weren't like crisp in images, but they were quite aesthetically pleasing, like some of these early image generation models. And you know, that was like published primarily on Replicate and then a few other models around that were like published on Replicate. And that's where we really started to find our early community and like where we really found like, oh, we've actually built a thing that people want and they were great users as well. And people really want to try out these models. Lots of people were like running the models on Replicate. We still didn't have APIs though, interestingly, and this is like another like really complicated part of the story. We had no idea what a business model was still at this point. I don't think people could even pay for it. You know, it was just like these web forms where people could run the model.Swyx [00:27:06]: Just for historical interest, which discords were they and how did you find them? Was this the Lion Discord? Yeah, Lion. This is Eleuther.Ben [00:27:12]: Eleuther, yeah. It was the Eleuther one. These two, right? There was a channel where Viki Gangklep, this was early 2021, where Viki Gangklep was set up as a Discord bot. I just remember being completely just like captivated by this thing. I was just like playing around with it all afternoon and like the sort of thing. In Discord. Oh s**t, it's 2am. You know, yeah.Swyx [00:27:33]: This is the beginnings of Midjourney.Ben [00:27:34]: Yeah, exactly. And Stability. It was the start of Midjourney. And you know, it's where that kind of user interface came from. Like what's beautiful about the user interface is like you could see what other people are doing. And you could riff off other people's ideas. And it was just so much fun to just like play around with this in like a channel full of a hundred people. And yeah, that just like completely captivated me and I'm like, okay, this is something, you know. So like we should get these things on Replicate. Yeah, that's where that all came from.Swyx [00:28:00]: And then you moved on to, so was it APIs next or was it Stable Diffusion next?Ben [00:28:04]: It was APIs next. And the APIs happened because one of our users, our web form had like an internal API for making the web form work, like with an API that was called from JavaScript. And somebody like reverse engineered that to start generating images with a script. You know, they did like, you know, Web Inspector Coffee is Carl, like figured out what the API request was. And it wasn't secured or anything.Swyx [00:28:28]: Of course not.Ben [00:28:29]: They started generating a bunch of images and like we got tons of traffic and like what's going on? And I think like a sort of usual reaction to that would be like, hey, you're abusing our API and to shut them down. And instead we're like, oh, this is interesting. Like people want to run these models. So we documented the API in a Notion document, like our internal API in a Notion document and like message this person being like, hey, you seem to have found our API. Here's the documentation. That'll be like a thousand bucks a month, please, with a straight form, like we just click some buttons to make. And they were like, sure, that sounds great. So that was our first customer.Swyx [00:29:05]: A thousand bucks a month.Ben [00:29:07]: It was a surprising amount of money. That's not casual. It was on the order of a thousand bucks a month.Swyx [00:29:11]: So was it a business?Ben [00:29:13]: It was the creator of PixRay. Like it was, he generated NFT art. And so he like made a bunch of art with these models and was, you know, selling these NFTs effectively. And I think lots of people in his community were doing similar things. And like he then referred us to other people who were also generating NFTs and he joined us with models. We started our API business. Yeah. Then we like made an official API and actually like added some billing to it. So it wasn't just like a fixed fee.Swyx [00:29:40]: And now people think of you as the host and models API business. Yeah, exactly.Ben [00:29:44]: But that just turned out to be our business, you know, but what ended up being beautiful about this is it was really fulfilling. Like the original goal of what we wanted to do is that we wanted to make this research that people were making accessible to like other people and for it to be used in the real world. And this was like the just like ultimately the right way to do it because all of these people making these generative models could publish them to replicate and they wanted a place to publish it. And software engineers, you know, like myself, like I'm not a machine learning expert, but I want to use this stuff, could just run these models with a single line of code. And we thought, oh, maybe the Docker image is enough, but it's actually super hard to get the Docker image running on a GPU and stuff. So it really needed to be the hosted API for this to work and to make it accessible to software engineers. And we just like wound our way to this. Yeah.Swyx [00:30:30]: Two years to the first paying customer. Yeah, exactly.Alessio [00:30:33]: Did you ever think about becoming Midjourney during that time? You have like so much interest in image generation.Swyx [00:30:38]: I mean, you're doing fine for the record, but, you know, it was right there, you were playing with it.Ben [00:30:46]: I don't think it was our expertise. Like I think our expertise was DevTools rather than like Midjourney is almost like a consumer products, you know? Yeah. So I don't think it was our expertise. It certainly occurred to us. I think at the time we were thinking about like, oh, maybe we could hire some of these people in this community and make great models and stuff like this. But we ended up more being at the tooling. Like I think like before I was saying, like I'm not really a researcher, but I'm more like the tool builder, the behind the scenes. And I think both me and Andreas are like that.Swyx [00:31:09]: I think this is an illustration of the tool builder philosophy. Something where you latch on to in DevTools, which is when you see people behaving weird, it's not their fault, it's yours. And you want to pave the cow paths is what they say, right? Like the unofficial paths that people are making, like make it official and make it easy for them and then maybe charge a bit of money.Alessio [00:31:25]: And now fast forward a couple of years, you have 2 million developers using Replicate. Maybe more. That was the last public number that I found.Ben [00:31:33]: It's 2 million users. Not all those people are developers, but a lot of them are developers, yeah.Alessio [00:31:38]: And then 30,000 paying customers was the number late in space runs on Replicate. So we had a small podcaster and we host a whisper diarization on Replicate. And we're paying. So we're late in space in the 30,000. You raised a $40 million dollars, Series B. I would say that maybe the stable diffusion time, August 22, was like really when the company started to break out. Tell us a bit about that and the community that came out and I know now you're expanding beyond just image generation.Ben [00:32:06]: Yeah, like I think we kind of set ourselves, like we saw there was this really interesting image, generative image world going on. So we kind of, you know, like we're building the tools for that community already, really. And we knew stable diffusion was coming out. We knew it was a really exciting thing, you know, it was the best generative image model so far. I think the thing we underestimated was just like what an inflection point it would be, where it was, I think Simon Willison put it this way, where he said something along the lines of it was a model that was open source and tinkerable and like, you know, it was just good enough and open source and tinkerable such that it just kind of took off in a way that none of the models had before. And like what was really neat about stable diffusion is it was open source so you could like, compared to like Dali, for example, which was like sort of equivalent quality. And like the first week we saw like people making animation models out of it. We saw people make like game texture models that like use circular convolutions to make repeatable textures. We saw, you know, a few weeks later, like people were fine tuning it so you could make, put your face in these models and all of these other-Swyx [00:33:10]: Textual inversion.Ben [00:33:11]: Yep. Yeah, exactly. That happened a bit before that. And all of this sort of innovation was happening all of a sudden. And people were publishing on Replicate because you could just like publish arbitrary models on Replicate. So we had this sort of supply of like interesting stuff being built. But because it was a sufficiently good model, there was also just like a ton of people building with it. They were like, oh, we can build products with this thing. And this was like about the time where people were starting to get really interested in AI. So like tons of product builders wanted to build stuff with it. And we were just like sitting in there in the middle, it's like the interface layer between like all these people who wanted to build and all these like machine learning experts who were building cool models. And that's like really where it took off. We were just sort of incredible supply, incredible demand, and we were just like in the middle. And then, yeah, since then, we've just kind of grown and grown really. And we've been building a lot for like the indie hacker community, these like individual tinkerers, but also startups and a lot of large companies as well who are sort of exploring and building AI things. Then kind of the same thing happened like middle of last year with language models and Lama 2, where the same kind of stable diffusion effect happened with Lama. And Lama 2 was like our biggest week of growth ever because like tons of people wanted to tinker with it and run it. And you know, since then we've just been seeing a ton of growth in language models as well as image models. Yeah. We're just kind of riding a lot of the interest that's going on in AI and all the people building in AI, you know. Yeah.Swyx [00:34:29]: Kudos. Right place, right time. But also, you know, took a while to position for the right place before the wave came. I'm curious if like you have any insights on these different markets. So Peter Levels, notably very loud person, very picky about his tools. I wasn't sure actually if he used you. He does. So you've met him on your Series B blog posts and Danny Post might as well, his competitor all in that wave. What are their needs versus, you know, the more enterprise or B2B type needs? Did you come to a decision point where you're like, okay, you know, how serious are these indie hackers versus like the actual businesses that are bigger and perhaps better customers because they're less churny?Ben [00:35:04]: They're surprisingly similar because I think a lot of people right now want to use and build with AI, but they're not AI experts and they're not infrastructure experts either. So they want to be able to use this stuff without having to like figure out all the internals of the models and, you know, like touch PyTorch and whatever. And they also don't want to be like setting up and booting up servers. And that's the same all the way from like indie hackers just getting started because like obviously you just want to get started as quickly as possible, all the way through to like large companies who want to be able to use this stuff, but don't have like all of the experts on stuff, you know, you know, big companies like Google and so on that do actually have a lot of experts on stuff, but the vast majority of companies don't. And they're all software engineers who want to be able to use this AI stuff, but they just don't know how to use it. And it's like, you really need to be an expert and it takes a long time to like learn the skills to be able to use that. So they're surprisingly similar in that sense. I think it's kind of also unfair of like the indie community, like they're not churning surprisingly, or churny or spiky surprisingly, like they're building real established businesses, which is like, kudos to them, like building these really like large, sustainable businesses, often just as solo developers. And it's kind of remarkable how they can do that actually, and it's in credit to a lot of their like product skills. And you know, we're just like there to help them being like their machine learning team effectively to help them use all of this stuff. A lot of these indie hackers are some of our largest customers, like alongside some of our biggest customers that you would think would be spending a lot more money than them, but yeah.Swyx [00:36:35]: And we should name some of these. So you have them on your landing page, your Buzzfeed, you have Unsplash, Character AI. What do they power? What can you say about their usage?Ben [00:36:43]: Yeah, totally. It's kind of a various things.Swyx [00:36:46]: Well, I mean, I'm naming them because they're on your landing page. So you have logo rights. It's useful for people to, like, I'm not imaginative. I see monkey see monkey do, right? Like if I see someone doing something that I want to do, then I'm like, okay, Replicate's great for that.Ben [00:37:00]: Yeah, yeah, yeah.Swyx [00:37:01]: So that's what I think about case studies on company landing pages is that it's just a way of explaining like, yep, this is something that we are good for. Yeah, totally.Ben [00:37:09]: I mean, it's, these companies are doing things all the way up and down the stack at different levels of sophistication. So like Unsplash, for example, they actually publicly posted this story on Twitter where they're using BLIP to annotate all of the images in their catalog. So you know, they have lots of images in the catalog and they want to create a text description of it so you can search for it. And they're annotating images with, you know, off the shelf, open source model, you know, we have this big library of open source models that you can run. And you know, we've got lots of people are running these open source models off the shelf. And then most of our larger customers are doing more sophisticated stuff. So they're like fine tuning the models, they're running completely custom models on us. A lot of these larger companies are like, using us for a lot of their, you know, inference, but it's like a lot of custom models and them like writing the Python themselves because they've got machine learning experts on the team. And they're using us for like, you know, their inference infrastructure effectively. And so it's like lots of different levels of sophistication where like some people using these off the shelf models. Some people are fine tuning models. So like level, Peter Levels is a great example where a lot of his products are based off like fine tuning, fine tuning image models, for example. And then we've also got like larger customers who are just like using us as infrastructure effectively. So yeah, it's like all things up and down, up and down the stack.Alessio [00:38:29]: Let's talk a bit about COG and the technical layer. So there are a lot of GPU clouds. I think people have different pricing points. And I think everybody tries to offer a different developer experience on top of it, which then lets you charge a premium. Why did you want to create COG?Ben [00:38:46]: You worked at Docker.Alessio [00:38:47]: What were some of the issues with traditional container runtimes? And maybe yeah, what were you surprised with as you built it?Ben [00:38:54]: COG came right from the start, actually, when we were thinking about this, you know, evaluation, the sort of benchmarking system for machine learning researchers, where we wanted researchers to publish their models in a standard format that was guaranteed to keep on running, that you could replicate the results of, like that's where the name came from. And we realized that we needed something like Docker to make that work, you know. And I think it was just like natural from my point of view of like, obviously that should be open source, that we should try and create some kind of open standard here that people can share. Because if more people use this format, then that's great for everyone involved. I think the magic of Docker is not really in the software. It's just like the standard that people have agreed on, like, here are a bunch of keys for a JSON document, basically. And you know, that was the magic of like the metaphor of real containerization as well. It's not the containers that are interesting. It's just like the size and shape of the damn box, you know. And it's a similar thing here, where really we just wanted to get people to agree on like, this is what a machine learning model is. This is how a prediction works. This is what the inputs are, this is what the outputs are. So cog is really just a Docker container that attaches to a CUDA device, if it needs a GPU, that has a open API specification as a label on the Docker image. And the open API specification defines the interface for the machine learning model, like the inputs and outputs effectively, or the params in machine learning terminology. And you know, we just wanted to get people to kind of agree on this thing. And it's like general purpose enough, like we weren't saying like, some of the existing things were like at the graph level, but we really wanted something general purpose enough that you could just put anything inside this and it was like future compatible and it was just like arbitrary software. And you know, it'd be future compatible with like future inference servers and future machine learning model formats and all this kind of stuff. So that was the intent behind it. It just came naturally that we wanted to define this format. And that's been really working for us. Like a bunch of people have been using cog outside of replicates, which is kind of our original intention, like this should be how machine learning is packaged and how people should use it. Like it's common to use cog in situations where like maybe they can't use the SAS service because I don't know, they're in a big company and they're not allowed to use a SAS service, but they can use cog internally still. And like they can download the models from replicates and run them internally in their org, which we've been seeing happen. And that works really well. People who want to build like custom inference pipelines, but don't want to like reinvent the world, they can use cog off the shelf and use it as like a component in their inference pipelines. We've been seeing tons of usage like that and it's just been kind of happening organically. We haven't really been trying, you know, but it's like there if people want it and we've been seeing people use it. So that's great. Yeah. So a lot of it is just sort of philosophical of just like, this is how it should work from my experience at Docker, you know, and there's just a lot of value from like the core being open, I think, and that other people can share it and it's like an integration point. So, you know, if replicate, for example, wanted to work with a testing system, like a CI system or whatever, we can just like interface at the cog level, like that system just needs to put cog models and then you can like test your models on that CI system before they get deployed to replicate. And it's just like a format that everyone, we can get everyone to agree on, you know.Alessio [00:41:55]: What do you think, I guess, Docker got wrong? Because if I look at a Docker Compose and a cog definition, first of all, the cog is kind of like the Dockerfile plus the Compose versus in Docker Compose, you're just exposing the services. And also Docker Compose is very like ports driven versus you have like the actual, you know, predict this is what you have to run.Ben [00:42:16]: Yeah.Alessio [00:42:17]: Any learnings and maybe tips for other people building container based runtimes, like how much should you separate the API services versus the image building or how much you want to build them together?Ben [00:42:29]: I think it was coming from two sides. We were thinking about the design from the point of view of user needs, what are their problems and what problems can we solve for them, but also what the interface should be for a machine learning model. And it was sort of the combination of two things that led us to this design. So the thing I talked about before was a little bit of like the interface around the machine learning model. So we realized that we wanted to be general purpose. We wanted to be at the like JSON, like human readable things rather than the tensor level. So it was like an open API specification that wrapped a Docker container. And that's where that design came from. And it's really just a wrapper around Docker. So we were kind of building on, standing on shoulders there, but Docker is too low level. So it's just like arbitrary software. So we wanted to be able to like have a open API specification that defined the function effectively that is the machine learning model. But also like how that function is written, how that function is run, which is all defined in code and stuff like that. So it's like a bunch of abstraction on top of Docker to make that work. And that's where that design came from. But the core problems we were solving for users was that Docker is really hard to use and productionizing machine learning models is really hard. So on the first part of that, we knew we couldn't use Dockerfiles. Like Dockerfiles are hard enough for software developers to write. I'm saying this with love as somebody who works on Docker and like works on Dockerfiles, but it's really hard to use. And you need to know a bunch about Linux, basically, because you're running a bunch of CLI commands. You need to know a bunch about Linux and best practices and like how apt works and all this kind of stuff. So we're like, OK, we can't get to that level. We need something that machine learning researchers will be able to understand, like people who are used to like Colab notebooks. And what they understand is they're like, I need this version of Python. I need these Python packages. And somebody told me to apt-get install something. You know? If there was sudo in there, I don't really know what that means. So we tried to create a format that was at that level, and that's what cog.yaml is. And we were really kind of trying to imagine like, what is that machine learning researcher going to understand, you know, and trying to build for them. Then the productionizing machine learning models thing is like, OK, how can we package up all of the complexity of like productionizing machine learning models, like picking CUDA versions, like hooking it up to GPUs, writing an inference server, defining a schema, doing batching, all of these just like really gnarly things that everyone does again and again. And just like, you know, provide that as a tool. And that's where that side of it came from. So it's like combining those user needs with, you know, the sort of world need of needing like a common standard for like what a machine learning model is. And that's how we thought about the design. I don't know whether that answers the question.Alessio [00:45:12]: Yeah. So your idea was like, hey, you really want what Docker stands for in terms of standard, but you actually don't want people to do all the work that goes into Docker.Ben [00:45:22]: It needs to be higher level, you know?Swyx [00:45:25]: So I want to, for the listener, you're not the only standard that is out there. As with any standard, there must be 14 of them. You are surprisingly friendly with Olama, who is your former colleagues from Docker, who came out with the model file. Mozilla came out with the Lama file. And then I don't know if this is in the same category even, but I'm just going to throw it in there. Like Hugging Face has the transformers and diffusers library, which is a way of disseminating models that obviously people use. How would you compare your contrast, your approach of Cog versus all these?Ben [00:45:53]: It's kind of complementary, actually, which is kind of neat in that a lot of transformers, for example, is lower level than Cog. So it's a Python library effectively, but you still need to like...Swyx [00:46:04]: Expose them.Ben [00:46:05]: Yeah. You still need to turn that into an inference server. You still need to like install the Python packages and that kind of thing. So lots of replicate models are transformers models and diffusers models inside Cog, you know? So that's like the level that that sits. So it's very complementary in some sense. We're kind of working on integration with Hugging Face such that you can deploy models from Hugging Face into Cog models and stuff like that to replicate. And some of these things like Llamafile and what Llama are working on are also very complementary in that they're doing a lot of the sort of running these things locally on laptops, which is not a thing that works very well with Cog. Like Cog is really designed around servers and attaching to CUDA devices and NVIDIA GPUs and this kind of thing. So we're actually like, you know, figuring out ways that like we can, those things can be interoperable because, you know, they should be and they are quite complementary and that you should be able to like take a model and replicate and run it on your local machine. You should be able to take a model, you know, the machine and run it in the cloud.Swyx [00:47:02]: Is the base layer something like, is it at the like the GGUF level, which by the way, I need to get a primer on like the different formats that have emerged, or is it at the star dot file level, which is model file, Llamafile, whatever, whatever, or is it at the Cog level? I don't know, to be honest.Ben [00:47:16]: And I think this is something we still have to figure out. There's a lot yet, like exactly where those lines are drawn. Don't know exactly. I think this is something we're trying to figure out ourselves, but I think there's certainly a lot of promise about these systems interoperating. We just want things to work together. You know, we want to try and reduce the number of standards. So the more, the more these things can interoperate and, you know

We're writing this one day after the monster release of OpenAI's Sora and Gemini 1.5. We covered this on ‘s ThursdAI space, so head over there for our takes.IRL: We're ONE WEEK away from Latent Space: Final Frontiers, the second edition and anniversary of our first ever Latent Space event! Also: join us on June 25-27 for the biggest AI Engineer conference of the year!Online: All three Discord clubs are thriving. Join us every Wednesday/Friday!Almost 12 years ago, while working at Spotify, Erik Bernhardsson built one of the first open source vector databases, Annoy, based on ANN search. He also built Luigi, one of the predecessors to Airflow, which helps data teams orchestrate and execute data-intensive and long-running jobs. Surprisingly, he didn't start yet another vector database company, but instead in 2021 founded Modal, the “high-performance cloud for developers”. In 2022 they opened doors to developers after their seed round, and in 2023 announced their GA with a $16m Series A.More importantly, they have won fans among both household names like Ramp, Scale AI, Substack, and Cohere, and newer startups like (upcoming guest!) Suno.ai and individual hackers (Modal was the top tool of choice in the Vercel AI Accelerator):We've covered the nuances of GPU workloads, and how we need new developer tooling and runtimes for them (see our episodes with Chris Lattner of Modular and George Hotz of tiny to start). In this episode, we run through the major limitations of the actual infrastructure behind the clouds that run these models, and how Erik envisions the “postmodern data stack”. In his 2021 blog post “Software infrastructure 2.0: a wishlist”, Erik had “Truly serverless” as one of his points:* The word cluster is an anachronism to an end-user in the cloud! I'm already running things in the cloud where there's elastic resources available at any time. Why do I have to think about the underlying pool of resources? Just maintain it for me.* I don't ever want to provision anything in advance of load.* I don't want to pay for idle resources. Just let me pay for whatever resources I'm actually using.* Serverless doesn't mean it's a burstable VM that saves its instance state to disk during periods of idle.Swyx called this Self Provisioning Runtimes back in the day. Modal doesn't put you in YAML hell, preferring to colocate infra provisioning right next to the code that utilizes it, so you can just add GPU (and disk, and retries…):After 3 years, we finally have a big market push for this: running inference on generative models is going to be the killer app for serverless, for a few reasons:* AI models are stateless: even in conversational interfaces, each message generation is a fully-contained request to the LLM. There's no knowledge that is stored in the model itself between messages, which means that tear down / spin up of resources doesn't create any headaches with maintaining state.* Token-based pricing is better aligned with serverless infrastructure than fixed monthly costs of traditional software.* GPU scarcity makes it really expensive to have reserved instances that are available to you 24/7. It's much more convenient to build with a serverless-like infrastructure.In the episode we covered a lot more topics like maximizing GPU utilization, why Oracle Cloud rocks, and how Erik has never owned a TV in his life. Enjoy!Show Notes* Modal* ErikBot* Erik's Blog* Software Infra 2.0 Wishlist* Luigi* Annoy* Hetzner* CoreWeave* Cloudflare FaaS* Poolside AI* Modular Inference EngineChapters* [00:00:00] Introductions* [00:02:00] Erik's OSS work at Spotify: Annoy and Luigi* [00:06:22] Starting Modal* [00:07:54] Vision for a "postmodern data stack"* [00:10:43] Solving container cold start problems* [00:12:57] Designing Modal's Python SDK* [00:15:18] Self-Revisioning Runtime* [00:19:14] Truly Serverless Infrastructure* [00:20:52] Beyond model inference* [00:22:09] Tricks to maximize GPU utilization* [00:26:27] Differences in AI and data science workloads* [00:28:08] Modal vs Replicate vs Modular and lessons from Heroku's "graduation problem"* [00:34:12] Creating Erik's clone "ErikBot"* [00:37:43] Enabling massive parallelism across thousands of GPUs* [00:39:45] The Modal Sandbox for agents* [00:43:51] Thoughts on the AI Inference War* [00:49:18] Erik's best tweets* [00:51:57] Why buying hardware is a waste of money* [00:54:18] Erik's competitive programming backgrounds* [00:59:02] Why does Sweden have the best Counter Strike players?* [00:59:53] Never owning a car or TV* [01:00:21] Advice for infrastructure startupsTranscriptAlessio [00:00:00]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, partner and CTO-in-Residence at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol AI.Swyx [00:00:14]: Hey, and today we have in the studio Erik Bernhardsson from Modal. Welcome.Erik [00:00:19]: Hi. It's awesome being here.Swyx [00:00:20]: Yeah. Awesome seeing you in person. I've seen you online for a number of years as you were building on Modal and I think you're just making a San Francisco trip just to see people here, right? I've been to like two Modal events in San Francisco here.Erik [00:00:34]: Yeah, that's right. We're based in New York, so I figured sometimes I have to come out to capital of AI and make a presence.Swyx [00:00:40]: What do you think is the pros and cons of building in New York?Erik [00:00:45]: I mean, I never built anything elsewhere. I lived in New York the last 12 years. I love the city. Obviously, there's a lot more stuff going on here and there's a lot more customers and that's why I'm out here. I do feel like for me, where I am in life, I'm a very boring person. I kind of work hard and then I go home and hang out with my kids. I don't have time to go to events and meetups and stuff anyway. In that sense, New York is kind of nice. I walk to work every morning. It's like five minutes away from my apartment. It's very time efficient in that sense. Yeah.Swyx [00:01:10]: Yeah. It's also a good life. So we'll do a brief bio and then we'll talk about anything else that people should know about you. Actually, I was surprised to find out you're from Sweden. You went to college in KTH and your master's was in implementing a scalable music recommender system. Yeah.Erik [00:01:27]: I had no idea. Yeah. So I actually studied physics, but I grew up coding and I did a lot of programming competition and then as I was thinking about graduating, I got in touch with an obscure music streaming startup called Spotify, which was then like 30 people. And for some reason, I convinced them, why don't I just come and write a master's thesis with you and I'll do some cool collaborative filtering, despite not knowing anything about collaborative filtering really. But no one knew anything back then. So I spent six months at Spotify basically building a prototype of a music recommendation system and then turned that into a master's thesis. And then later when I graduated, I joined Spotify full time.Swyx [00:02:00]: So that was the start of your data career. You also wrote a couple of popular open source tooling while you were there. Is that correct?Erik [00:02:09]: No, that's right. I mean, I was at Spotify for seven years, so this is a long stint. And Spotify was a wild place early on and I mean, data space is also a wild place. I mean, it was like Hadoop cluster in the like foosball room on the floor. It was a lot of crude, like very basic infrastructure and I didn't know anything about it. And like I was hired to kind of figure out data stuff. And I started hacking on a recommendation system and then, you know, got sidetracked in a bunch of other stuff. I fixed a bunch of reporting things and set up A-B testing and started doing like business analytics and later got back to music recommendation system. And a lot of the infrastructure didn't really exist. Like there was like Hadoop back then, which is kind of bad and I don't miss it. But I spent a lot of time with that. As a part of that, I ended up building a workflow engine called Luigi, which is like briefly like somewhat like widely ended up being used by a bunch of companies. Sort of like, you know, kind of like Airflow, but like before Airflow. I think it did some things better, some things worse. I also built a vector database called Annoy, which is like for a while, it was actually quite widely used. In 2012, so it was like way before like all this like vector database stuff ended up happening. And funny enough, I was actually obsessed with like vectors back then. Like I was like, this is going to be huge. Like just give it like a few years. I didn't know it was going to take like nine years and then there's going to suddenly be like 20 startups doing vector databases in one year. So it did happen. In that sense, I was right. I'm glad I didn't start a startup in the vector database space. I would have started way too early. But yeah, that was, yeah, it was a fun seven years as part of it. It was a great culture, a great company.Swyx [00:03:32]: Yeah. Just to take a quick tangent on this vector database thing, because we probably won't revisit it but like, has anything architecturally changed in the last nine years?Erik [00:03:41]: I'm actually not following it like super closely. I think, you know, some of the best algorithms are still the same as like hierarchical navigable small world.Swyx [00:03:51]: Yeah. HNSW.Erik [00:03:52]: Exactly. I think now there's like product quantization, there's like some other stuff that I haven't really followed super closely. I mean, obviously, like back then it was like, you know, it's always like very simple. It's like a C++ library with Python bindings and you could mmap big files and into memory and like they had some lookups. I used like this kind of recursive, like hyperspace splitting strategy, which is not that good, but it sort of was good enough at that time. But I think a lot of like HNSW is still like what people generally use. Now of course, like databases are much better in the sense like to support like inserts and updates and stuff like that. I know I never supported that. Yeah, it's sort of exciting to finally see like vector databases becoming a thing.Swyx [00:04:30]: Yeah. Yeah. And then maybe one takeaway on most interesting lesson from Daniel Ek?Erik [00:04:36]: I mean, I think Daniel Ek, you know, he started Spotify very young. Like he was like 25, something like that. And that was like a good lesson. But like he, in a way, like I think he was a very good leader. Like there was never anything like, no scandals or like no, he wasn't very eccentric at all. It was just kind of like very like level headed, like just like ran the company very well, like never made any like obvious mistakes or I think it was like a few bets that maybe like in hindsight were like a little, you know, like took us, you know, too far in one direction or another. But overall, I mean, I think he was a great CEO, like definitely, you know, up there, like generational CEO, at least for like Swedish startups.Swyx [00:05:09]: Yeah, yeah, for sure. Okay, we should probably move to make our way towards Modal. So then you spent six years as CTO of Better. You were an early engineer and then you scaled up to like 300 engineers.Erik [00:05:21]: I joined as a CTO when there was like no tech team. And yeah, that was a wild chapter in my life. Like the company did very well for a while. And then like during the pandemic, yeah, it was kind of a weird story, but yeah, it kind of collapsed.Swyx [00:05:32]: Yeah, laid off people poorly.Erik [00:05:34]: Yeah, yeah. It was like a bunch of stories. Yeah. I mean, the company like grew from like 10 people when I joined at 10,000, now it's back to a thousand. But yeah, they actually went public a few months ago, kind of crazy. They're still around, like, you know, they're still, you know, doing stuff. So yeah, very kind of interesting six years of my life for non-technical reasons, like I managed like three, four hundred, but yeah, like learning a lot of that, like recruiting. I spent all my time recruiting and stuff like that. And so managing at scale, it's like nice, like now in a way, like when I'm building my own startup. It's actually something I like, don't feel nervous about at all. Like I've managed a scale, like I feel like I can do it again. It's like very different things that I'm nervous about as a startup founder. But yeah, I started Modal three years ago after sort of, after leaving Better, I took a little bit of time off during the pandemic and, but yeah, pretty quickly I was like, I got to build something. I just want to, you know. Yeah. And then yeah, Modal took form in my head, took shape.Swyx [00:06:22]: And as far as I understand, and maybe we can sort of trade off questions. So the quick history is started Modal in 2021, got your seed with Sarah from Amplify in 2022. You just announced your Series A with Redpoint. That's right. And that brings us up to mostly today. Yeah. Most people, I think, were expecting you to build for the data space.Erik: But it is the data space.Swyx:: When I think of data space, I come from like, you know, Snowflake, BigQuery, you know, Fivetran, Nearby, that kind of stuff. And what Modal became is more general purpose than that. Yeah.Erik [00:06:53]: Yeah. I don't know. It was like fun. I actually ran into like Edo Liberty, the CEO of Pinecone, like a few weeks ago. And he was like, I was so afraid you were building a vector database. No, I started Modal because, you know, like in a way, like I work with data, like throughout my most of my career, like every different part of the stack, right? Like I thought everything like business analytics to like deep learning, you know, like building, you know, training neural networks, the scale, like everything in between. And so one of the thoughts, like, and one of the observations I had when I started Modal or like why I started was like, I just wanted to make, build better tools for data teams. And like very, like sort of abstract thing, but like, I find that the data stack is, you know, full of like point solutions that don't integrate well. And still, when you look at like data teams today, you know, like every startup ends up building their own internal Kubernetes wrapper or whatever. And you know, all the different data engineers and machine learning engineers end up kind of struggling with the same things. So I started thinking about like, how do I build a new data stack, which is kind of a megalomaniac project, like, because you kind of want to like throw out everything and start over.Swyx [00:07:54]: It's almost a modern data stack.Erik [00:07:55]: Yeah, like a postmodern data stack. And so I started thinking about that. And a lot of it came from like, like more focused on like the human side of like, how do I make data teams more productive? And like, what is the technology tools that they need? And like, you know, drew out a lot of charts of like, how the data stack looks, you know, what are different components. And it shows actually very interesting, like workflow scheduling, because it kind of sits in like a nice sort of, you know, it's like a hub in the graph of like data products. But it was kind of hard to like, kind of do that in a vacuum, and also to monetize it to some extent. I got very interested in like the layers below at some point. And like, at the end of the day, like most people have code to have to run somewhere. So I think about like, okay, well, how do you make that nice? Like how do you make that? And in particular, like the thing I always like thought about, like developer productivity is like, I think the best way to measure developer productivity is like in terms of the feedback loops, like how quickly when you iterate, like when you write code, like how quickly can you get feedback. And at the innermost loop, it's like writing code and then running it. And like, as soon as you start working with the cloud, like it's like takes minutes suddenly, because you have to build a Docker container and push it to the cloud and like run it, you know. So that was like the initial focus for me was like, I just want to solve that problem. Like I want to, you know, build something less, you run things in the cloud and like retain the sort of, you know, the joy of productivity as when you're running things locally. And in particular, I was quite focused on data teams, because I think they had a couple unique needs that wasn't well served by the infrastructure at that time, or like still is in like, in particular, like Kubernetes, I feel like it's like kind of worked okay for back end teams, but not so well for data teams. And very quickly, I got sucked into like a very deep like rabbit hole of like...Swyx [00:09:24]: Not well for data teams because of burstiness. Yeah, for sure.Erik [00:09:26]: So like burstiness is like one thing, right? Like, you know, like you often have this like fan out, you want to like apply some function over very large data sets. Another thing tends to be like hardware requirements, like you need like GPUs and like, I've seen this in many companies, like you go, you know, data scientists go to a platform team and they're like, can we add GPUs to the Kubernetes? And they're like, no, like, that's, you know, complex, and we're not gonna, so like just getting GPU access. And then like, I mean, I also like data code, like frankly, or like machine learning code like tends to be like, super annoying in terms of like environments, like you end up having like a lot of like custom, like containers and like environment conflicts. And like, it's very hard to set up like a unified container that like can serve like a data scientist, because like, there's always like packages that break. And so I think there's a lot of different reasons why the technology wasn't well suited for back end. And I think the attitude at that time is often like, you know, like you had friction between the data team and the platform team, like, well, it works for the back end stuff, you know, why don't you just like, you know, make it work. But like, I actually felt like data teams, you know, or at this point now, like there's so much, so many people working with data, and like they, to some extent, like deserve their own tools and their own tool chains, and like optimizing for that is not something people have done. So that's, that's sort of like very abstract philosophical reason why I started Model. And then, and then I got sucked into this like rabbit hole of like container cold start and, you know, like whatever, Linux, page cache, you know, file system optimizations.Swyx [00:10:43]: Yeah, tell people, I think the first time I met you, I think you told me some numbers, but I don't remember, like, what are the main achievements that you were unhappy with the status quo? And then you built your own container stack?Erik [00:10:52]: Yeah, I mean, like, in particular, it was like, in order to have that loop, right? You want to be able to start, like take code on your laptop, whatever, and like run in the cloud very quickly, and like running in custom containers, and maybe like spin up like 100 containers, 1000, you know, things like that. And so container cold start was the initial like, from like a developer productivity point of view, it was like, really, what I was focusing on is, I want to take code, I want to stick it in container, I want to execute in the cloud, and like, you know, make it feel like fast. And when you look at like, how Docker works, for instance, like Docker, you have this like, fairly convoluted, like very resource inefficient way, they, you know, you build a container, you upload the whole container, and then you download it, and you run it. And Kubernetes is also like, not very fast at like starting containers. So like, I started kind of like, you know, going a layer deeper, like Docker is actually like, you know, there's like a couple of different primitives, but like a lower level primitive is run C, which is like a container runner. And I was like, what if I just take the container runner, like run C, and I point it to like my own root file system, and then I built like my own virtual file system that exposes files over a network instead. And that was like the sort of very crude version of model, it's like now I can actually start containers very quickly, because it turns out like when you start a Docker container, like, first of all, like most Docker images are like several gigabytes, and like 99% of that is never going to be consumed, like there's a bunch of like, you know, like timezone information for like Uzbekistan, like no one's going to read it. And then there's a very high overlap between the files are going to be read, there's going to be like lib torch or whatever, like it's going to be read. So you can also cache it very well. So that was like the first sort of stuff we started working on was like, let's build this like container file system. And you know, coupled with like, you know, just using run C directly. And that actually enabled us to like, get to this point of like, you write code, and then you can launch it in the cloud within like a second or two, like something like that. And you know, there's been many optimizations since then, but that was sort of starting point.Alessio [00:12:33]: Can we talk about the developer experience as well, I think one of the magic things about Modal is at the very basic layers, like a Python function decorator, it's just like stub and whatnot. But then you also have a way to define a full container, what were kind of the design decisions that went into it? Where did you start? How easy did you want it to be? And then maybe how much complexity did you then add on to make sure that every use case fit?Erik [00:12:57]: I mean, Modal, I almost feel like it's like almost like two products kind of glued together. Like there's like the low level like container runtime, like file system, all that stuff like in Rust. And then there's like the Python SDK, right? Like how do you express applications? And I think, I mean, Swix, like I think your blog was like the self-provisioning runtime was like, to me, always like to sort of, for me, like an eye-opening thing. It's like, so I didn't think about like...Swyx [00:13:15]: You wrote your post four months before me. Yeah? The software 2.0, Infra 2.0. Yeah.Erik [00:13:19]: Well, I don't know, like convergence of minds. I guess we were like both thinking. Maybe you put, I think, better words than like, you know, maybe something I was like thinking about for a long time. Yeah.Swyx [00:13:29]: And I can tell you how I was thinking about it on my end, but I want to hear you say it.Erik [00:13:32]: Yeah, yeah, I would love to. So to me, like what I always wanted to build was like, I don't know, like, I don't know if you use like Pulumi. Like Pulumi is like nice, like in the sense, like it's like Pulumi is like you describe infrastructure in code, right? And to me, that was like so nice. Like finally I can like, you know, put a for loop that creates S3 buckets or whatever. And I think like Modal sort of goes one step further in the sense that like, what if you also put the app code inside the infrastructure code and like glue it all together and then like you only have one single place that defines everything and it's all programmable. You don't have any config files. Like Modal has like zero config. There's no config. It's all code. And so that was like the goal that I wanted, like part of that. And then the other part was like, I often find that so much of like my time was spent on like the plumbing between containers. And so my thing was like, well, if I just build this like Python SDK and make it possible to like bridge like different containers, just like a function call, like, and I can say, oh, this function runs in this container and this other function runs in this container and I can just call it just like a normal function, then, you know, I can build these applications that may span a lot of different environments. Maybe they fan out, start other containers, but it's all just like inside Python. You just like have this beautiful kind of nice like DSL almost for like, you know, how to control infrastructure in the cloud. So that was sort of like how we ended up with the Python SDK as it is, which is still evolving all the time, by the way. We keep changing syntax quite a lot because I think it's still somewhat exploratory, but we're starting to converge on something that feels like reasonably good now.Swyx [00:14:54]: Yeah. And along the way you, with this expressiveness, you enabled the ability to, for example, attach a GPU to a function. Totally.Erik [00:15:02]: Yeah. It's like you just like say, you know, on the function decorator, you're like GPU equals, you know, A100 and then or like GPU equals, you know, A10 or T4 or something like that. And then you get that GPU and like, you know, you just run the code and it runs like you don't have to, you know, go through hoops to, you know, start an EC2 instance or whatever.Swyx [00:15:18]: Yeah. So it's all code. Yeah. So one of the reasons I wrote Self-Revisioning Runtimes was I was working at AWS and we had AWS CDK, which is kind of like, you know, the Amazon basics blew me. Yeah, totally. And then, and then like it creates, it compiles the cloud formation. Yeah. And then on the other side, you have to like get all the config stuff and then put it into your application code and make sure that they line up. So then you're writing code to define your infrastructure, then you're writing code to define your application. And I was just like, this is like obvious that it's going to converge, right? Yeah, totally.Erik [00:15:48]: But isn't there like, it might be wrong, but like, was it like SAM or Chalice or one of those? Like, isn't that like an AWS thing that where actually they kind of did that? I feel like there's like one.Swyx [00:15:57]: SAM. Yeah. Still very clunky. It's not, not as elegant as modal.Erik [00:16:03]: I love AWS for like the stuff it's built, you know, like historically in order for me to like, you know, what it enables me to build, but like AWS is always like struggle with developer experience.Swyx [00:16:11]: I mean, they have to not break things.Erik [00:16:15]: Yeah. Yeah. And totally. And they have to build products for a very wide range of use cases. And I think that's hard.Swyx [00:16:21]: Yeah. Yeah. So it's, it's easier to design for. Yeah. So anyway, I was, I was pretty convinced that this, this would happen. I wrote, wrote that thing. And then, you know, I imagine my surprise that you guys had it on your landing page at some point. I think, I think Akshad was just like, just throw that in there.Erik [00:16:34]: Did you trademark it?Swyx [00:16:35]: No, I didn't. But I definitely got sent a few pitch decks with my post on there and it was like really interesting. This is my first time like kind of putting a name to a phenomenon. And I think this is a useful skill for people to just communicate what they're trying to do.Erik [00:16:48]: Yeah. No, I think it's a beautiful concept.Swyx [00:16:50]: Yeah. Yeah. Yeah. But I mean, obviously you implemented it. What became more clear in your explanation today is that actually you're not that tied to Python.Erik [00:16:57]: No. I mean, I, I think that all the like lower level stuff is, you know, just running containers and like scheduling things and, you know, serving container data and stuff. So like one of the benefits of data teams is obviously like they're all like using Python, right? And so that made it a lot easier. I think, you know, if we had focused on other workloads, like, you know, for various reasons, we've like been kind of like half thinking about like CI or like things like that. But like, in a way that's like harder because like you also, then you have to be like, you know, multiple SDKs, whereas, you know, focusing on data teams, you can only, you know, Python like covers like 95% of all teams. That made it a lot easier. But like, I mean, like definitely like in the future, we're going to have others support, like supporting other languages. JavaScript for sure is the obvious next language. But you know, who knows, like, you know, Rust, Go, R, whatever, PHP, Haskell, I don't know.Swyx [00:17:42]: You know, I think for me, I actually am a person who like kind of liked the idea of programming language advancements being improvements in developer experience. But all I saw out of the academic sort of PLT type people is just type level improvements. And I always think like, for me, like one of the core reasons for self-provisioning runtimes and then why I like Modal is like, this is actually a productivity increase, right? Like, it's a language level thing, you know, you managed to stick it on top of an existing language, but it is your own language, a DSL on top of Python. And so language level increase on the order of like automatic memory management. You know, you could sort of make that analogy that like, maybe you lose some level of control, but most of the time you're okay with whatever Modal gives you. And like, that's fine. Yeah.Erik [00:18:26]: Yeah. Yeah. I mean, that's how I look at about it too. Like, you know, you look at developer productivity over the last number of decades, like, you know, it's come in like small increments of like, you know, dynamic typing or like is like one thing because not suddenly like for a lot of use cases, you don't need to care about type systems or better compiler technology or like, you know, the cloud or like, you know, relational databases. And, you know, I think, you know, you look at like that, you know, history, it's a steadily, you know, it's like, you know, you look at the developers have been getting like probably 10X more productive every decade for the last four decades or something that was kind of crazy. Like on an exponential scale, we're talking about 10X or is there a 10,000X like, you know, improvement in developer productivity. What we can build today, you know, is arguably like, you know, a fraction of the cost of what it took to build it in the eighties. Maybe it wasn't even possible in the eighties. So that to me, like, that's like so fascinating. I think it's going to keep going for the next few decades. Yeah.Alessio [00:19:14]: Yeah. Another big thing in the infra 2.0 wishlist was truly serverless infrastructure. The other on your landing page, you called them native cloud functions, something like that. I think the issue I've seen with serverless has always been people really wanted it to be stateful, even though stateless was much easier to do. And I think now with AI, most model inference is like stateless, you know, outside of the context. So that's kind of made it a lot easier to just put a model, like an AI model on model to run. How do you think about how that changes how people think about infrastructure too? Yeah.Erik [00:19:48]: I mean, I think model is definitely going in the direction of like doing more stateful things and working with data and like high IO use cases. I do think one like massive serendipitous thing that happened like halfway, you know, a year and a half into like the, you know, building model was like Gen AI started exploding and the IO pattern of Gen AI is like fits the serverless model like so well, because it's like, you know, you send this tiny piece of information, like a prompt, right, or something like that. And then like you have this GPU that does like trillions of flops, and then it sends back like a tiny piece of information, right. And that turns out to be something like, you know, if you can get serverless working with GPU, that just like works really well, right. So I think from that point of view, like serverless always to me felt like a little bit of like a solution looking for a problem. I don't actually like don't think like backend is like the problem that needs to serve it or like not as much. But I look at data and in particular, like things like Gen AI, like model inference, like it's like clearly a good fit. So I think that is, you know, to a large extent explains like why we saw, you know, the initial sort of like killer app for model being model inference, which actually wasn't like necessarily what we're focused on. But that's where we've seen like by far the most usage. Yeah.Swyx [00:20:52]: And this was before you started offering like fine tuning of language models, it was mostly stable diffusion. Yeah.Erik [00:20:59]: Yeah. I mean, like model, like I always built it to be a very general purpose compute platform, like something where you can run everything. And I used to call model like a better Kubernetes for data team for a long time. What we realized was like, yeah, that's like, you know, a year and a half in, like we barely had any users or any revenue. And like we were like, well, maybe we should look at like some use case, trying to think of use case. And that was around the same time stable diffusion came out. And the beauty of model is like you can run almost anything on model, right? Like model inference turned out to be like the place where we found initially, well, like clearly this has like 10x like better agronomics than anything else. But we're also like, you know, going back to my original vision, like we're thinking a lot about, you know, now, okay, now we do inference really well. Like what about training? What about fine tuning? What about, you know, end-to-end lifecycle deployment? What about data pre-processing? What about, you know, I don't know, real-time streaming? What about, you know, large data munging, like there's just data observability. I think there's so many things, like kind of going back to what I said about like redefining the data stack, like starting with the foundation of compute. Like one of the exciting things about model is like we've sort of, you know, we've been working on that for three years and it's maturing, but like this is so many things you can do like with just like a better compute primitive and also go up to stack and like do all this other stuff on top of it.Alessio [00:22:09]: How do you think about or rather like I would love to learn more about the underlying infrastructure and like how you make that happen because with fine tuning and training, it's a static memory. Like you exactly know what you're going to load in memory one and it's kind of like a set amount of compute versus inference, just like data is like very bursty. How do you make batches work with a serverless developer experience? You know, like what are like some fun technical challenge you solve to make sure you get max utilization on these GPUs? What we hear from people is like, we have GPUs, but we can really only get like, you know, 30, 40, 50% maybe utilization. What's some of the fun stuff you're working on to get a higher number there?Erik [00:22:48]: Yeah, I think on the inference side, like that's where we like, you know, like from a cost perspective, like utilization perspective, we've seen, you know, like very good numbers and in particular, like it's our ability to start containers and stop containers very quickly. And that means that we can auto scale extremely fast and scale down very quickly, which means like we can always adjust the sort of capacity, the number of GPUs running to the exact traffic volume. And so in many cases, like that actually leads to a sort of interesting thing where like we obviously run our things on like the public cloud, like AWS GCP, we run on Oracle, but in many cases, like users who do inference on those platforms or those clouds, even though we charge a slightly higher price per GPU hour, a lot of users like moving their large scale inference use cases to model, they end up saving a lot of money because we only charge for like with the time the GPU is actually running. And that's a hard problem, right? Like, you know, if you have to constantly adjust the number of machines, if you have to start containers, stop containers, like that's a very hard problem. Starting containers quickly is a very difficult thing. I mentioned we had to build our own file system for this. We also, you know, built our own container scheduler for that. We've implemented recently CPU memory checkpointing so we can take running containers and snapshot the entire CPU, like including registers and everything, and restore it from that point, which means we can restore it from an initialized state. We're looking at GPU checkpointing next, it's like a very interesting thing. So I think with inference stuff, that's where serverless really shines because you can drive, you know, you can push the frontier of latency versus utilization quite substantially, you know, which either ends up being a latency advantage or a cost advantage or both, right? On training, it's probably arguably like less of an advantage doing serverless, frankly, because you know, you can just like spin up a bunch of machines and try to satisfy, like, you know, train as much as you can on each machine. For that area, like we've seen, like, you know, arguably like less usage, like for modal, but there are always like some interesting use case. Like we do have a couple of customers, like RAM, for instance, like they do fine tuning with modal and they basically like one of the patterns they have is like very bursty type fine tuning where they fine tune 100 models in parallel. And that's like a separate thing that modal does really well, right? Like you can, we can start up 100 containers very quickly, run a fine tuning training job on each one of them for that only runs for, I don't know, 10, 20 minutes. And then, you know, you can do hyper parameter tuning in that sense, like just pick the best model and things like that. So there are like interesting training. I think when you get to like training, like very large foundational models, that's a use case we don't support super well, because that's very high IO, you know, you need to have like infinite band and all these things. And those are things we haven't supported yet and might take a while to get to that. So that's like probably like an area where like we're relatively weak in. Yeah.Alessio [00:25:12]: Have you cared at all about lower level model optimization? There's other cloud providers that do custom kernels to get better performance or are you just given that you're not just an AI compute company? Yeah.Erik [00:25:24]: I mean, I think like we want to support like a generic, like general workloads in a sense that like we want users to give us a container essentially or a code or code. And then we want to run that. So I think, you know, we benefit from those things in the sense that like we can tell our users, you know, to use those things. But I don't know if we want to like poke into users containers and like do those things automatically. That's sort of, I think a little bit tricky from the outside to do, because we want to be able to take like arbitrary code and execute it. But certainly like, you know, we can tell our users to like use those things. Yeah.Swyx [00:25:53]: I may have betrayed my own biases because I don't really think about modal as for data teams anymore. I think you started, I think you're much more for AI engineers. My favorite anecdotes, which I think, you know, but I don't know if you directly experienced it. I went to the Vercel AI Accelerator, which you supported. And in the Vercel AI Accelerator, a bunch of startups gave like free credits and like signups and talks and all that stuff. The only ones that stuck are the ones that actually appealed to engineers. And the top usage, the top tool used by far was modal.Erik [00:26:24]: That's awesome.Swyx [00:26:25]: For people building with AI apps. Yeah.Erik [00:26:27]: I mean, it might be also like a terminology question, like the AI versus data, right? Like I've, you know, maybe I'm just like old and jaded, but like, I've seen so many like different titles, like for a while it was like, you know, I was a data scientist and a machine learning engineer and then, you know, there was like analytics engineers and there was like an AI engineer, you know? So like, to me, it's like, I just like in my head, that's to me just like, just data, like, or like engineer, you know, like I don't really, so that's why I've been like, you know, just calling it data teams. But like, of course, like, you know, AI is like, you know, like such a massive fraction of our like workloads.Swyx [00:26:59]: It's a different Venn diagram of things you do, right? So the stuff that you're talking about where you need like infinite bands for like highly parallel training, that's not, that's more of the ML engineer, that's more of the research scientist and less of the AI engineer, which is more sort of trying to put, work at the application.Erik [00:27:16]: Yeah. I mean, to be fair to it, like we have a lot of users that are like doing stuff that I don't think fits neatly into like AI. Like we have a lot of people using like modal for web scraping, like it's kind of nice. You can just like, you know, fire up like a hundred or a thousand containers running Chromium and just like render a bunch of webpages and it takes, you know, whatever. Or like, you know, protein folding is that, I mean, maybe that's, I don't know, like, but like, you know, we have a bunch of users doing that or, or like, you know, in terms of, in the realm of biotech, like sequence alignment, like people using, or like a couple of people using like modal to run like large, like mixed integer programming problems, like, you know, using Gurobi or like things like that. So video processing is another thing that keeps coming up, like, you know, let's say you have like petabytes of video and you want to just like transcode it, like, or you can fire up a lot of containers and just run FFmpeg or like, so there are those things too. Like, I mean, like that being said, like AI is by far our biggest use case, but you know, like, again, like modal is kind of general purpose in that sense.Swyx [00:28:08]: Yeah. Well, maybe I'll stick to the stable diffusion thing and then we'll move on to the other use cases for AI that you want to highlight. The other big player in my mind is replicate. Yeah. In this, in this era, they're much more, I guess, custom built for that purpose, whereas you're more general purpose. How do you position yourself with them? Are they just for like different audiences or are you just heads on competing?Erik [00:28:29]: I think there's like a tiny sliver of the Venn diagram where we're competitive. And then like 99% of the area we're not competitive. I mean, I think for people who, if you look at like front-end engineers, I think that's where like really they found good fit is like, you know, people who built some cool web app and they want some sort of AI capability and they just, you know, an off the shelf model is like perfect for them. That's like, I like use replicate. That's great. I think where we shine is like custom models or custom workflows, you know, running things at very large scale. We need to care about utilization, care about costs. You know, we have much lower prices because we spend a lot more time optimizing our infrastructure, you know, and that's where we're competitive, right? Like, you know, and you look at some of the use cases, like Suno is a big user, like they're running like large scale, like AI. Oh, we're talking with Mikey.Swyx [00:29:12]: Oh, that's great. Cool.Erik [00:29:14]: In a month. Yeah. So, I mean, they're, they're using model for like production infrastructure. Like they have their own like custom model, like custom code and custom weights, you know, for AI generated music, Suno.AI, you know, that, that, those are the types of use cases that we like, you know, things that are like very custom or like, it's like, you know, and those are the things like it's very hard to run and replicate, right? And that's fine. Like I think they, they focus on a very different part of the stack in that sense.Swyx [00:29:35]: And then the other company pattern that I pattern match you to is Modular. I don't know.Erik [00:29:40]: Because of the names?Swyx [00:29:41]: No, no. Wow. No, but yeah, yes, the name is very similar. I think there's something that might be insightful there from a linguistics point of view. Oh no, they have Mojo, the sort of Python SDK. And they have the Modular Inference Engine, which is their sort of their cloud stack, their sort of compute inference stack. I don't know if anyone's made that comparison to you before, but like I see you evolving a little bit in parallel there.Erik [00:30:01]: No, I mean, maybe. Yeah. Like it's not a company I'm like super like familiar, like, I mean, I know the basics, but like, I guess they're similar in the sense like they want to like do a lot of, you know, they have sort of big picture vision.Swyx [00:30:12]: Yes. They also want to build very general purpose. Yeah. So they're marketing themselves as like, if you want to do off the shelf stuff, go out, go somewhere else. If you want to do custom stuff, we're the best place to do it. Yeah. Yeah. There is some overlap there. There's not overlap in the sense that you are a closed source platform. People have to host their code on you. That's true. Whereas for them, they're very insistent on not running their own cloud service. They're a box software. Yeah. They're licensed software.Erik [00:30:37]: I'm sure their VCs at some point going to force them to reconsider. No, no.Swyx [00:30:40]: Chris is very, very insistent and very convincing. So anyway, I would just make that comparison, let people make the links if they want to. But it's an interesting way to see the cloud market develop from my point of view, because I came up in this field thinking cloud is one thing, and I think your vision is like something slightly different, and I see the different takes on it.Erik [00:31:00]: Yeah. And like one thing I've, you know, like I've written a bit about it in my blog too, it's like I think of us as like a second layer of cloud provider in the sense that like I think Snowflake is like kind of a good analogy. Like Snowflake, you know, is infrastructure as a service, right? But they actually run on the like major clouds, right? And I mean, like you can like analyze this very deeply, but like one of the things I always thought about is like, why does Snowflake arbitrarily like win over Redshift? And I think Snowflake, you know, to me, one, because like, I mean, in the end, like AWS makes all the money anyway, like and like Snowflake just had the ability to like focus on like developer experience or like, you know, user experience. And to me, like really proved that you can build a cloud provider, a layer up from, you know, the traditional like public clouds. And in that layer, that's also where I would put Modal, it's like, you know, we're building a cloud provider, like we're, you know, we're like a multi-tenant environment that runs the user code. But we're also building on top of the public cloud. So I think there's a lot of room in that space, I think is very sort of interesting direction.Alessio [00:31:55]: How do you think of that compared to the traditional past history, like, you know, you had AWS, then you had Heroku, then you had Render, Railway.Erik [00:32:04]: Yeah, I mean, I think those are all like great. I think the problem that they all faced was like the graduation problem, right? Like, you know, Heroku or like, I mean, like also like Heroku, there's like a counterfactual future of like, what would have happened if Salesforce didn't buy them, right? Like, that's a sort of separate thing. But like, I think what Heroku, I think always struggled with was like, eventually companies would get big enough that you couldn't really justify running in Heroku. So they would just go and like move it to, you know, whatever AWS or, you know, in particular. And you know, that's something that keeps me up at night too, like, what does that graduation risk like look like for modal? I always think like the only way to build a successful infrastructure company in the long run in the cloud today is you have to appeal to the entire spectrum, right? Or at least like the enterprise, like you have to capture the enterprise market. But the truly good companies capture the whole spectrum, right? Like I think of companies like, I don't like Datadog or Mongo or something that were like, they both captured like the hobbyists and acquire them, but also like, you know, have very large enterprise customers. I think that arguably was like where I, in my opinion, like Heroku struggle was like, how do you maintain the customers as they get more and more advanced? I don't know what the solution is, but I think there's, you know, that's something I would have thought deeply if I was at Heroku at that time.Alessio [00:33:14]: What's the AI graduation problem? Is it, I need to fine tune the model, I need better economics, any insights from customer discussions?Erik [00:33:22]: Yeah, I mean, better economics, certainly. But although like, I would say like, even for people who like, you know, needs like thousands of GPUs, just because we can drive utilization so much better, like we, there's actually like a cost advantage of staying on modal. But yeah, I mean, certainly like, you know, and like the fact that VCs like love, you know, throwing money at least used to, you know, add companies who need it to buy GPUs. I think that didn't help the problem. And in training, I think, you know, there's less software differentiation. So in training, I think there's certainly like better economics of like buying big clusters. But I mean, my hope it's going to change, right? Like I think, you know, we're still pretty early in the cycle of like building AI infrastructure. And I think a lot of these companies over in the long run, like, you know, they're, except it may be super big ones, like, you know, on Facebook and Google, they're always going to build their own ones. But like everyone else, like some extent, you know, I think they're better off like buying platforms. And, you know, someone's going to have to build those platforms.Swyx [00:34:12]: Yeah. Cool. Let's move on to language models and just specifically that workload just to flesh it out a little bit. You already said that RAMP is like fine tuning 100 models at once simultaneously on modal. Closer to home, my favorite example is ErikBot. Maybe you want to tell that story.Erik [00:34:30]: Yeah. I mean, it was a prototype thing we built for fun, but it's pretty cool. Like we basically built this thing that hooks up to Slack. It like downloads all the Slack history and, you know, fine-tunes a model based on a person. And then you can chat with that. And so you can like, you know, clone yourself and like talk to yourself on Slack. I mean, it's like nice like demo and it's just like, I think like it's like fully contained modal. Like there's a modal app that does everything, right? Like it downloads Slack, you know, integrates with the Slack API, like downloads the stuff, the data, like just runs the fine-tuning and then like creates like dynamically an inference endpoint. And it's all like self-contained and like, you know, a few hundred lines of code. So I think it's sort of a good kind of use case for, or like it kind of demonstrates a lot of the capabilities of modal.Alessio [00:35:08]: Yeah. On a more personal side, how close did you feel ErikBot was to you?Erik [00:35:13]: It definitely captured the like the language. Yeah. I mean, I don't know, like the content, I always feel this way about like AI and it's gotten better. Like when you look at like AI output of text, like, and it's like, when you glance at it, it's like, yeah, this seems really smart, you know, but then you actually like look a little bit deeper. It's like, what does this mean?Swyx [00:35:32]: What does this person say?Erik [00:35:33]: It's like kind of vacuous, right? And that's like kind of what I felt like, you know, talking to like my clone version, like it's like says like things like the grammar is correct. Like some of the sentences make a lot of sense, but like, what are you trying to say? Like there's no content here. I don't know. I mean, it's like, I got that feeling also with chat TBT in the like early versions right now it's like better, but.Alessio [00:35:51]: That's funny. So I built this thing called small podcaster to automate a lot of our back office work, so to speak. And it's great at transcript. It's great at doing chapters. And then I was like, okay, how about you come up with a short summary? And it's like, it sounds good, but it's like, it's not even the same ballpark as like, yeah, end up writing. Right. And it's hard to see how it's going to get there.Swyx [00:36:11]: Oh, I have ideas.Erik [00:36:13]: I'm certain it's going to get there, but like, I agree with you. Right. And like, I have the same thing. I don't know if you've read like AI generated books. Like they just like kind of seem funny, right? Like there's off, right? But like you glance at it and it's like, oh, it's kind of cool. Like looks correct, but then it's like very weird when you actually read them.Swyx [00:36:30]: Yeah. Well, so for what it's worth, I think anyone can join the modal slack. Is it open to the public? Yeah, totally.Erik [00:36:35]: If you go to modal.com, there's a button in the footer.Swyx [00:36:38]: Yeah. And then you can talk to Erik Bot. And then sometimes I really like picking Erik Bot and then you answer afterwards, but then you're like, yeah, mostly correct or whatever. Any other broader lessons, you know, just broadening out from like the single use case of fine tuning, like what are you seeing people do with fine tuning or just language models on modal in general? Yeah.Erik [00:36:59]: I mean, I think language models is interesting because so many people get started with APIs and that's just, you know, they're just dominating a space in particular opening AI, right? And that's not necessarily like a place where we aim to compete. I mean, maybe at some point, but like, it's just not like a core focus for us. And I think sort of separately, it's sort of a question of like, there's economics in that long term. But like, so we tend to focus on more like the areas like around it, right? Like fine tuning, like another use case we have is a bunch of people, Ramp included, is doing batch embeddings on modal. So let's say, you know, you have like a, actually we're like writing a blog post, like we take all of Wikipedia and like parallelize embeddings in 15 minutes and produce vectors for each article. So those types of use cases, I think modal suits really well for. I think also a lot of like custom inference, like yeah, I love that.Swyx [00:37:43]: Yeah. I think you should give people an idea of the order of magnitude of parallelism, because I think people don't understand how parallel. So like, I think your classic hello world with modal is like some kind of Fibonacci function, right? Yeah, we have a bunch of different ones. Some recursive function. Yeah.Erik [00:37:59]: Yeah. I mean, like, yeah, I mean, it's like pretty easy in modal, like fan out to like, you know, at least like 100 GPUs, like in a few seconds. And you know, if you give it like a couple of minutes, like we can, you know, you can fan out to like thousands of GPUs. Like we run it relatively large scale. And yeah, we've run, you know, many thousands of GPUs at certain points when we needed, you know, big backfills or some customers had very large compute needs.Swyx [00:38:21]: Yeah. Yeah. And I mean, that's super useful for a number of things. So one of my early interactions with modal as well was with a small developer, which is my sort of coding agent. The reason I chose modal was a number of things. One, I just wanted to try it out. I just had an excuse to try it. Akshay offered to onboard me personally. But the most interesting thing was that you could have that sort of local development experience as it was running on my laptop, but then it would seamlessly translate to a cloud service or like a cloud hosted environment. And then it could fan out with concurrency controls. So I could say like, because like, you know, the number of times I hit the GPT-3 API at the time was going to be subject to the rate limit. But I wanted to fan out without worrying about that kind of stuff. With modal, I can just kind of declare that in my config and that's it. Oh, like a concurrency limit?Erik [00:39:07]: Yeah. Yeah.Swyx [00:39:09]: Yeah. There's a lot of control. And that's why it's like, yeah, this is a pretty good use case for like writing this kind of LLM application code inside of this environment that just understands fan out and rate limiting natively. You don't actually have an exposed queue system, but you have it under the hood, you know, that kind of stuff. Totally.Erik [00:39:28]: It's a self-provisioning cloud.Swyx [00:39:30]: So the last part of modal I wanted to touch on, and obviously feel free, I know you're working on new features, was the sandbox that was introduced last year. And this is something that I think was inspired by Code Interpreter. You can tell me the longer history behind that.Erik [00:39:45]: Yeah. Like we originally built it for the use case, like there was a bunch of customers who looked into code generation applications and then they came to us and asked us, is there a safe way to execute code? And yeah, we spent a lot of time on like container security. We used GeoVisor, for instance, which is a Google product that provides pretty strong isolation of code. So we built a product where you can basically like run arbitrary code inside a container and monitor its output or like get it back in a safe way. I mean, over time it's like evolved into more of like, I think the long-term direction is actually I think more interesting, which is that I think modal as a platform where like I think the core like container infrastructure we offer could actually be like, you know, unbundled from like the client SDK and offer to like other, you know, like we're talking to a couple of like other companies that want to run, you know, through their packages, like run, execute jobs on modal, like kind of programmatically. So that's actually the direction like Sandbox is going. It's like turning into more like a platform for platforms is kind of what I've been thinking about it as.Swyx [00:40:45]: Oh boy. Platform. That's the old Kubernetes line.Erik [00:40:48]: Yeah. Yeah. Yeah. But it's like, you know, like having that ability to like programmatically, you know, create containers and execute them, I think, I think is really cool. And I think it opens up a lot of interesting capabilities that are sort of separate from the like core Python SDK in modal. So I'm really excited about C. It's like one of those features that we kind of released and like, you know, then we kind of look at like what users actually build with it and people are starting to build like kind of crazy things. And then, you know, we double down on some of those things because when we see like, you know, potential new product features and so Sandbox, I think in that sense, it's like kind of in that direction. We found a lot of like interesting use cases in the direction of like platformized container runner.Swyx [00:41:27]: Can you be more specific about what you're double down on after seeing users in action?Erik [00:41:32]: I mean, we're working with like some companies that, I mean, without getting into specifics like that, need the ability to take their users code and then launch containers on modal. And it's not about security necessarily, like they just want to use modal as a back end, right? Like they may already provide like Kubernetes as a back end, Lambda as a back end, and now they want to add modal as a back end, right? And so, you know, they need a way to programmatically define jobs on behalf of their users and execute them. And so, I don't know, that's kind of abstract, but does that make sense? I totally get it.Swyx [00:42:03]: It's sort of one level of recursion to sort of be the Modal for their customers.Erik [00:42:09]: Exactly.Swyx [00:42:10]: Yeah, exactly. And Cloudflare has done this, you know, Kenton Vardar from Cloudflare, who's like the tech lead on this thing, called it sort of functions as a service as a service.Erik [00:42:17]: Yeah, that's exactly right. FaSasS.Swyx [00:42:21]: FaSasS. Yeah, like, I mean, like that, I think any base layer, second layer cloud provider like yourself, compute provider like yourself should provide, you know, it's a mark of maturity and success that people just trust you to do that. They'd rather build on top of you than compete with you. The more interesting thing for me is like, what does it mean to serve a computer like an LLM developer, rather than a human developer, right? Like, that's what a sandbox is to me, that you have to redefine modal to serve a different non-human audience.Erik [00:42:51]: Yeah. Yeah, and I think there's some really interesting people, you know, building very cool things.Swyx [00:42:55]: Yeah. So I don't have an answer, but, you know, I imagine things like, hey, the way you give feedback is different. Maybe you have to like stream errors, log errors differently. I don't really know. Yeah. Obviously, there's like safety considerations. Maybe you have an API to like restrict access to the web. Yeah. I don't think anyone would use it, but it's there if you want it.Erik [00:43:17]: Yeah.Swyx [00:43:18]: Yeah. Any other sort of design considerations? I have no idea.Erik [00:43:21]: With sandboxes?Swyx [00:43:22]: Yeah. Yeah.Erik [00:43:24]: Open-ended question here. Yeah. I mean, no, I think, yeah, the network restrictions, I think, make a lot of sense. Yeah. I mean, I think, you know, long-term, like, I think there's a lot of interesting use cases where like the LLM, in itself, can like decide, I want to install these packages and like run this thing. And like, obviously, for a lot of those use cases, like you want to have some sort of control that it doesn't like install malicious stuff and steal your secrets and things like that. But I think that's what's exciting about the sandbox primitive, is like it lets you do that in a relatively safe way.Alessio [00:43:51]: Do you have any thoughts on the inference wars? A lot of providers are just rushing to the bottom to get the lowest price per million tokens. Some of them, you know, the Sean Randomat, they're just losing money and there's like the physics of it just don't work out for them to make any money on it. How do you think about your pricing and like how much premium you can get and you can kind of command versus using lower prices as kind of like a wedge into getting there, especially once you have model instrumented? What are the tradeoffs and any thoughts on strategies that work?Erik [00:44:23]: I mean, we focus more on like custom models and custom code. And I think in that space, there's like less competition and I think we can have a pricing markup, right? Like, you know, people will always compare our prices to like, you know, the GPU power they can get elsewhere. And so how big can that markup be? Like it never can be, you know, we can never charge like 10x more, but we can certainly charge a premium. And like, you know, for that reason, like we can have pretty good margins. The LLM space is like the opposite, like the switching cost of LLMs is zero. If all you're doing is like straight up, like at least like open source, right? Like if all you're doing is like, you know, using some, you know, inference endpoint that serves an open source model and, you know, some other provider comes along and like offers a lower price, you're just going to switch, right? So I don't know, to me that reminds me a lot of like all this like 15 minute delivery wars or like, you know, like Uber versus Lyft, you know, and like maybe going back even further, like I think a lot about like sort of, you know, flip side of this is like, it's actually a positive side, which is like, I thought a lot about like fiber optics boom of like 98, 99, like the other day, or like, you know, and also like the overinvestment in GPU today. Like, like, yeah, like, you know, I don't know, like in the end, like, I don't think VCs will have the return they expected, like, you know, in these things, but guess who's going to benefit, like, you know, is the consumers, like someone's like reaping the value of this. And that's, I think an amazing flip side is that, you know, we should be very grateful, the fact that like VCs want to subsidize these things, which is, you know, like you go back to fiber optics, like there was an extreme, like overinvestment in fiber optics network in like 98. And no one made money who did that. But consumers, you know, got tremendous benefits of all the fiber optics cables that were led, you know, throughout the country in the decades after. I feel something similar abou

In this episode, Lane talks to Alex DeBrie, author of the DynamoDB book. Today's talk covers various aspects such as DynamoDB's comparison with Amazon S3, its benefits, use cases, constraints, and cost considerations, while also covering other AWS and Google Cloud services. Alex also shares his insights into his journey of writing the book on DynamoDB and touches on topics like access patterns, secondary indexes, and billing modes. Alex also shares his professional experiences, including consulting vs freelancing, thoughts of entrepreneurial aspirations, and gives helpful advice for those that are considering pursuing a similar career.Learn back-end development - https://boot.devListen on your favorite podcast player: https://www.backendbanter.fmAlex's Twitter: https://twitter.com/alexbdebrieAlex's Website: https://www.alexdebrie.com(00:00) - Introduction (01:27) - Who is Alex DeBrie? (02:39) - What is DynamoDB? (04:15) - EC2 instance (05:50) - Amazon S3 (06:25) - DynamoDB is more like S3 (07:40) - Difference between DynamoDB and S3 (08:20) - What do we mean when we say NoSQL (10:08) - BigQuery and BigTable (12:31) - Some of DynamoDB's benefits (13:15) - When to use DynamoDB (15:58) - Constraint of number of connections (18:06) - DynamoDB is a multi-tenant service (19:21) - How does DynamoDB shake up against something like MongoDB (22:22) - DynamoDB is opinionated, but it provides good results consistently (25:54) - You can only do certain things in DynamoDB, but they are guaranteed to be fast (26:42) - Relational Databases - Theory vs Practicality (31:08) - How Alex came to write a book about DynamoDB (32:15) - What happens when SQL runs, depends heavily on the system underneath (33:57) - DynamoDB doesn't have a query planner (36:08) - Access patterns (38:04) - Use case for Secondary Indexes (39:43) - Costs of DynamoDB (40:45) - Billing modes for DynamoDB (45:26) - Provisioning and planning for expenses (48:40) - Super Mario 64 Hack (49:34) - What Was Alex's Last Full Time Job (51:02) - Consulting vs Freelancing (52:23) - Does Alex see himself going back to a Full Time Job? (53:07) - Does Alex have any entrepreneurial urges? (54:01) - What you should think about before jumping into freelance/consulting (56:01) - Authority in the consulting world (57:11) - Where to find Alex

Evelyn Osman, Principal Platform Engineer at AutoScout24, joins Corey on Screaming in the Cloud to discuss the dire need for developers to agree on a standardized tool set in order to scale their projects and innovate quickly. Corey and Evelyn pick apart the new products being launched in cloud computing and discover a large disconnect between what the industry needs and what is actually being created. Evelyn shares her thoughts on why viewing platforms as products themselves forces developers to get into the minds of their users and produces a better end result.About EvelynEvelyn is a recovering improviser currently role playing as a Lead Platform Engineer at Autoscout24 in Munich, Germany. While she says she specializes in AWS architecture and integration after spending 11 years with it, in truth she spends her days convincing engineers that a product mindset will make them hate their product managers less.Links Referenced:LinkedIn: https://www.linkedin.com/in/evelyn-osman/TranscriptAnnouncer: Hello, and welcome to Screaming in the Cloud with your host, Chief Cloud Economist at The Duckbill Group, Corey Quinn. This weekly show features conversations with people doing interesting work in the world of cloud, thoughtful commentary on the state of the technical world, and ridiculous titles for which Corey refuses to apologize. This is Screaming in the Cloud.Corey: Welcome to Screaming in the Cloud. I'm Corey Quinn. My guest today is Evelyn Osman, engineering manager at AutoScout24. Evelyn, thank you for joining me.Evelyn: Thank you very much, Corey. It's actually really fun to be on here.Corey: I have to say one of the big reasons that I was enthused to talk to you is that you have been using AWS—to be direct—longer than I have, and that puts you in a somewhat rarefied position where AWS's customer base has absolutely exploded over the past 15 years that it's been around, but at the beginning, it was a very different type of thing. Nowadays, it seems like we've lost some of that magic from the beginning. Where do you land on that whole topic?Evelyn: That's actually a really good point because I always like to say, you know, when I come into a room, you know, I really started doing introductions like, “Oh, you know, hey,” I'm like, you know, “I'm this director, I've done this XYZ,” and I always say, like, “I'm Evelyn, engineering manager, or architect, or however,” and then I say, you know, “I've been working with AWS, you know, 11, 12 years,” or now I can't quite remember.Corey: Time becomes a flat circle. The pandemic didn't help.Evelyn: [laugh] Yeah, I just, like, a look at that the year, and I'm like, “Jesus. It's been that long.” Yeah. And usually, like you know, you get some odd looks like, “Oh, my God, you must be a sage.” And for me, I'm… you see how different services kind of, like, have just been reinventions of another one, or they just take a managed service and make another managed service around it. So, I feel that there's a lot of where it's just, you know, wrapping up a pretty bow, and calling it something different, it feels like.Corey: That's what I've been low-key asking people for a while now over the past year, namely, “What is the most foundational, interesting thing that AWS has done lately, that winds up solving for this problem of whatever it is you do as a company? What is it that has foundationally made things better that AWS has put out in the last service? What was it?” And the answers I get are all depressingly far in the past, I have to say. What's yours?Evelyn: Honestly, I think the biggest game-changer I remember experiencing was at an analyst summit in Stockholm when they announced Lambda.Corey: That was announced before I even got into this space, as an example of how far back things were. And you're right. That was transformative. That was awesome.Evelyn: Yeah, precisely. Because before, you know, we were always, like, trying to figure, okay, how do we, like, launch an instance, run some short code, and then clean it up. AWS is going to charge for an hour, so we need to figure out, you know, how to pack everything into one instance, run for one hour. And then they announced Lambda, and suddenly, like, holy shit, this is actually a game changer. We can actually write small functions that do specific things.And, you know, you go from, like, microservices, like, to like, tiny, serverless functions. So, that was huge. And then DynamoDB along with that, really kind of like, transformed the entire space for us in many ways. So, back when I was at TIBCO, there was a few innovations around that, even, like, one startup inside TIBCO that quite literally, their entire product was just Lambda functions. And one of their problems was, they wanted to sell in the Marketplace, and they couldn't figure out how to sell Lambda on the marketplace.Corey: It's kind of wild when we see just how far it's come, but also how much they've announced that doesn't change that much, to be direct. For me, one of the big changes that I remember that really made things better for customers—thought it took a couple of years—was EFS. And even that's a little bit embarrassing because all that is, “All right, we finally found a way to stuff a NetApp into us-east-1,” so now NFS, just like you used to use it in the 90s and the naughts, can be done responsibly in the cloud. And that, on some level, wasn't a feature launch so much as it was a concession to the ways that companies had built things and weren't likely to change.Evelyn: Honestly, I found the EFS launch to be a bit embarrassing because, like, you know, when you look closer at it, you realize, like, the performance isn't actually that great.Corey: Oh, it was horrible when it launched. It would just slam to a halt because you got the IOPS scaled with how much data you stored on it. The documentation explicitly said to use dd to start loading a bunch of data onto it to increase the performance. It's like, “Look, just sandbag the thing so it does what you'd want.” And all that stuff got fixed, but at the time it looked like it was clown shoes.Evelyn: Yeah, and that reminds me of, like, EBS's, like, gp2 when we're, like you know, we're talking, like, okay, provision IOPS with gp2. We just kept saying, like, just give yourself really big volume for performance. And it feel like they just kind of kept that with EFS. And it took years for them to really iterate off of that. Yeah, so, like, EFS was a huge thing, and I see us, we're still using it now today, and like, we're trying to integrate, especially for, like, data center migrations, but yeah, you always see that a lot of these were first more for, like, you know, data centers to the cloud, you know. So, first I had, like, EC2 classic. That's where I started. And I always like to tell a story that in my team, we're talking about using AWS, I was the only person fiercely against it because we did basically large data processing—sorry, I forget the right words—data analytics. There we go [laugh].Corey: I remember that, too. When it first came out, it was, “This sounds dangerous and scary, and it's going to be a flash in the pan because who would ever trust their core compute infrastructure to some random third-party company, especially a bookstore?” And yeah, I think I got that one very wrong.Evelyn: Yeah, exactly. I was just like, no way. You know, I see all these articles talking about, like, terrible disk performance, and here I am, where it's like, it's my bread and butter. I'm specialized in it, you know? I write code in my sleep and such.[Yeah, the interesting thing is, I was like, first, it was like, I can 00:06:03] launch services, you know, to kind of replicate when you get in a data center to make it feature comparable, and then it was taking all this complex services and wrapping it up in a pretty bow for—as a managed service. Like, EKS, I think, was the biggest one, if we're looking at managed services. Technically Elasticsearch, but I feel like that was the redheaded stepchild for quite some time.Corey: Yeah, there was—Elasticsearch was a weird one, and still is. It's not a pleasant service to run in any meaningful sense. Like, what people actually want as the next enhancement that would excite everyone is, I want a serverless version of this thing where I can just point it at a bunch of data, I hit an API that I don't have to manage, and get Elasticsearch results back from. They finally launched a serverless offering that's anything but. You have to still provision compute units for it, so apparently, the word serverless just means managed service over at AWS-land now. And it just, it ties into the increasing sense of disappointment I've had with almost all of their recent launches versus what I felt they could have been.Evelyn: Yeah, the interesting thing about Elasticsearch is, a couple of years ago, they came out with OpenSearch, a competing Elasticsearch after [unintelligible 00:07:08] kind of gave us the finger and change the licensing. I mean, OpenSearch actually become a really great offering if you run it yourself, but if you use their managed service, it can kind—you lose all the benefits, in a way.Corey: I'm curious, as well, to get your take on what I've been seeing that I think could only be described as an internal shift, where it's almost as if there's been a decree passed down that every service has to run its own P&L or whatnot, and as a result, everything that gets put out seems to be monetized in weird ways, even when I'd argue it shouldn't be. The classic example I like to use for this is AWS Config, where it charges you per evaluation, and that happens whenever a cloud resource changes. What that means is that by using the cloud dynamically—the way that they supposedly want us to do—we wind up paying a fee for that as a result. And it's not like anyone is using that service in isolation; it is definitionally being used as people are using other cloud resources, so why does it cost money? And the answer is because literally everything they put out costs money.Evelyn: Yep, pretty simple. Oftentimes, there's, like, R&D that goes into it, but the charges seem a bit… odd. Like from an S3 lens, was, I mean, that's, like, you know, if you're talking about services, that was actually a really nice one, very nice holistic overview, you know, like, I could drill into a data lake and, like, look into things. But if you actually want to get anything useful, you have to pay for it.Corey: Yeah. Everything seems to, for one reason or another, be stuck in this place where, “Well, if you want to use it, it's going to cost.” And what that means is that it gets harder and harder to do anything that even remotely resembles being able to wind up figuring out where's the spend going, or what's it going to cost me as time goes on? Because it's not just what are the resources I'm spinning up going to cost, what are the second, third, and fourth-order effects of that? And the honest answer is, well, nobody knows. You're going to have to basically run an experiment and find out.Evelyn: Yeah. No, true. So, what I… at AutoScout, we actually ended up doing is—because we're trying to figure out how to tackle these costs—is they—we built an in-house cost allocation solution so we could track all of that. Now, AWS has actually improved Cost Explorer quite a bit, and even, I think, Billing Conductor was one that came out [unintelligible 00:09:21], kind of like, do a custom tiered and account pricing model where you can kind of do the same thing. But even that also, there is a cost with it.I think that was trying to compete with other, you know, vendors doing similar solutions. But it still isn't something where we see that either there's, like, arbitrarily low pricing there, or the costs itself doesn't really quite make sense. Like, AWS [unintelligible 00:09:45], as you mentioned, it's a terrific service. You know, we try to use it for compliance enforcement and other things, catching bad behavior, but then as soon as people see the price tag, we just run away from it. So, a lot of the security services themselves, actually, the costs, kind of like, goes—skyrockets tremendously when you start trying to use it across a large organization. And oftentimes, the organization isn't actually that large.Corey: Yeah, it gets to this point where, especially in small environments, you have to spend more energy and money chasing down what the cost is than you're actually spending on the thing. There were blog posts early on that, “Oh, here's how you analyze your bill with Redshift,” and that was a minimum 750 bucks a month. It's, well, I'm guessing that that's not really for my $50 a month account.Evelyn: Yeah. No, precisely. I remember seeing that, like, entire ETL process is just, you know, analyze your invoice. Cost [unintelligible 00:10:33], you know, is fantastic, but at the end of the day, like, what you're actually looking at [laugh], is infinitesimally small compared to all the data in that report. Like, I think oftentimes, it's simply, you know, like, I just want to look at my resources and allocate them in a multidimensional way. Which actually isn't really that multidimensional, when you think about it [laugh].Corey: Increasingly, Cost Explorer has gotten better. It's not a new service, but every iteration seems to improve it to a point now where I'm talking to folks, and they're having a hard time justifying most of the tools in the cost optimization space, just because, okay, they want a percentage of my spend on AWS to basically be a slightly better version of a thing that's already improving and works for free. That doesn't necessarily make sense. And I feel like that's what you get trapped into when you start going down the VC path in the cost optimization space. You've got to wind up having a revenue model and an offering that scales through software… and I thought, originally, I was going to be doing something like that. At this point, I'm unconvinced that anything like that is really tenable.Evelyn: Yeah. When you're a small organization you're trying to optimize, you might not have the expertise and the knowledge to do so, so when one of these small consultancies comes along, saying, “Hey, we're going to charge you a really small percentage of your invoice,” like, okay, great. That's, like, you know, like, a few $100 a month to make sure I'm fully optimized, and I'm saving, you know, far more than that. But as soon as your invoice turns into, you know, it's like $100,000, or $300,000 or more, that percentage becomes rather significant. And I've had vendors come to me and, like, talk to me and is like, “Hey, we can, you know, for a small percentage, you know, we're going to do this machine learning, you know, AI optimization for you. You know, you don't have to do anything. We guaranteed buybacks your RIs.” And as soon as you look at the price tag with it, we just have to walk away. Or oftentimes we look at it, and there are truly very simple ways to do it on your own, if you just kind of put some thought into it.Corey: While we want to talking a bit before this show, you taught me something new about GameLift, which I think is a different problem that AWS has been dealing with lately. I've never paid much attention to it because it is the—as I assume from what it says on the tin, oh, it's a service for just running a whole bunch of games at scale, and I'm not generally doing that. My favorite computer game remains to be Twitter at this point, but that's okay. What is GameLift, though, because you want to shining a different light on it, which makes me annoyed that Amazon Marketing has not pointed this out.Evelyn: Yeah, so I'll preface this by saying, like, I'm not an expert on GameLift. I haven't even spun it up myself because there's quite a bit of price. I learned this fall while chatting with an SA who works in the gaming space, and it kind of like, I went, like, “Back up a second.” If you think about, like, I'm, you know, like, World of Warcraft, all you have are thousands of game clients all over the world, playing the same game, you know, on the same server, in the same instance, and you need to make sure, you know, that when I'm running, and you're running, that we know that we're going to reach the same point the same time, or if there's one object in that room, that only one of us can get it. So, all these servers are doing is tracking state across thousands of clients.And GameLift, when you think about your dedicated game service, it really is just multi-region distributed state management. Like, at the basic, that's really what it is. Now, there's, you know, quite a bit more happening within GameLift, but that's what I was going to explain is, like, it's just state management. And there are far more use cases for it than just for video games.Corey: That's maddening to me because having a global session state store, for lack of a better term, is something that so many customers have built themselves repeatedly. They can build it on top of primitives like DynamoDB global tables, or alternately, you have a dedicated region where that thing has to live and everything far away takes forever to round-trip. If they've solved some of those things, why on earth would they bury it under a gaming-branded service? Like, offer that primitive to the rest of us because that's useful.Evelyn: No, absolutely. And honestly, I wouldn't be surprised if you peeled back the curtain with GameLift, you'll find a lot of—like, several other you know, AWS services that it's just built on top of. I kind of mentioned earlier is, like, what I see now with innovation, it's like we just see other services packaged together and releases a new product.Corey: Yeah, IoT had the same problem going on for years where there was a lot of really good stuff buried in there, like IOT events. People were talking about using that for things like browser extensions and whatnot, but you need to be explicitly told that that's a thing that exists and is handy, but otherwise you'd never know it was there because, “Well, I'm not building anything that's IoT-related. Why would I bother?” It feels like that was one direction that they tended to go in.And now they take existing services that are, mmm, kind of milquetoast, if I'm being honest, and then saying, “Oh, like, we have Comprehend that does, effectively detection of themes, keywords, and whatnot, from text. We're going to wind up re-releasing that as Comprehend Medical.” Same type of thing, but now focused on a particular vertical. Seems to me that instead of being a specific service for that vertical, just improve the baseline the service and offer HIPAA compliance if it didn't exist already, and you're mostly there. But what do I know? I'm not a product manager trying to get promoted.Evelyn: Yeah, that's true. Well, I was going to mention that maybe it's the HIPAA compliance, but actually, a lot of their services already have HIPAA compliance. And I've stared far too long at that compliance section on AWS's site to know this, but you know, a lot of them actually are HIPAA-compliant, they're PCI-compliant, and ISO-compliant, and you know, and everything. So, I'm actually pretty intrigued to know why they [wouldn't 00:16:04] take that advantage.Corey: I just checked. Amazon Comprehend is itself HIPAA-compliant and is qualified and certified to hold Personal Health Information—PHI—Private Health Information, whatever the acronym stands for. Now, what's the difference, then, between that and Medical? In fact, the HIPAA section says for Comprehend Medical, “For guidance, see the previous section on Amazon Comprehend.” So, there's no difference from a regulatory point of view.Evelyn: That's fascinating. I am intrigued because I do know that, like, within AWS, you know, they have different segments, you know? There's, like, Digital Native Business, there's Enterprise, there's Startup. So, I am curious how things look over the engineering side. I'm going to talk to somebody about this now [laugh].Corey: Yeah, it's the—like, I almost wonder, on some level, it feels like, “Well, we wound to building this thing in the hopes that someone would use it for something. And well, if we just use different words, it checks a box in some analyst's chart somewhere.” I don't know. I mean, I hate to sound that negative about it, but it's… increasingly when I talk to customers who are active in these spaces around the industry vertical targeted stuff aimed at their industry, they're like, “Yeah, we took a look at it. It was adorable, but we're not using it that way. We're going to use either the baseline version or we're going to work with someone who actively gets our industry.” And I've heard that repeated about three or four different releases that they've put out across the board of what they've been doing. It feels like it is a misunderstanding between what the world needs and what they're able to or willing to build for us.Evelyn: Not sure. I wouldn't be surprised, if we go far enough, it could probably be that it's just a product manager saying, like, “We have to advertise directly to the industry.” And if you look at it, you know, in the backend, you know, it's an engineer, you know, kicking off a build and just changing the name from Comprehend to Comprehend Medical.Corey: And, on some level, too, they're moving a lot more slowly than they used to. There was a time where they were, in many cases, if not the first mover, the first one to do it well. Take Code Whisperer, their AI powered coding assistant. That would have been a transformative thing if GitHub Copilot hadn't beaten them every punch, come out with new features, and frankly, in head-to-head experiments that I've run, came out way better as a product than what Code Whisperer is. And while I'd like to say that this is great, but it's too little too late. And when I talk to engineers, they're very excited about what Copilot can do, and the only people I see who are even talking about Code Whisperer work at AWS.Evelyn: No, that's true. And so, I think what's happening—and this is my opinion—is that first you had AWS, like, launching a really innovative new services, you know, that kind of like, it's like, “Ah, it's a whole new way of running your workloads in the cloud.” Instead of you know, basically, hiring a whole team, I just click a button, you have your instance, you use it, sell software, blah, blah, blah, blah. And then they went towards serverless, and then IoT, and then it started targeting large data lakes, and then eventually that kind of run backwards towards security, after the umpteenth S3 data leak.Corey: Oh, yeah. And especially now, like, so they had a hit in some corners with SageMaker, so now there are 40 services all starting with the word SageMaker. That's always pleasant.Evelyn: Yeah, precisely. And what I kind of notice is… now they're actually having to run it even further back because they caught all the corporations that could pivot to the cloud, they caught all the startups who started in the cloud, and now they're going for the larger behemoths who have massive data centers, and they don't want to innovate. They just want to reduce this massive sysadmin team. And I always like to use the example of a Bare Metal. When that came out in 2019, everybody—we've all kind of scratched your head. I'm like, really [laugh]?Corey: Yeah, I could see where it makes some sense just for very specific workloads that involve things like specific capabilities of processors that don't work under emulation in some weird way, but it's also such a weird niche that I'm sure it's there for someone. My default assumption, just given the breadth of AWS's customer base, is that whenever I see something that they just announced, well, okay, it's clearly not for me; that doesn't mean it's not meeting the needs of someone who looks nothing like me. But increasingly as I start exploring the industry in these services have time to percolate in the popular imagination and I still don't see anything interesting coming out with it, it really makes you start to wonder.Evelyn: Yeah. But then, like, I think, like, roughly a year or something, right after Bare Metal came out, they announced Outposts. So, then it was like, another way to just stay within your data center and be in the cloud.Corey: Yeah. There's a bunch of different ways they have that, okay, here's ways you can run AWS services on-prem, but still pay us by the hour for the privilege of running things that you have living in your facility. And that doesn't seem like it's quite fair.Evelyn: That's exactly it. So, I feel like now it's sort of in diminishing returns and sort of doing more cloud-native work compared to, you know, these huge opportunities, which is everybody who still has a data center for various reasons, or they're cloud-native, and they grow so big, that they actually start running their own data centers.Corey: I want to call out as well before we wind up being accused of being oblivious, that we're recording this before re:Invent. So, it's entirely possible—I hope this happens—that they announce something or several some things that make this look ridiculous, and we're embarrassed to have had this conversation. And yeah, they're totally getting it now, and they have completely surprised us with stuff that's going to be transformative for almost every customer. I've been expecting and hoping for that for the last three or four re:Invents now, and I haven't gotten it.Evelyn: Yeah, that's right. And I think there's even a new service launches that actually are missing fairly obvious things in a way. Like, mine is the Managed Workflow for Amazon—it's Managed Airflow, sorry. So, we were using Data Pipeline for, you know, big ETL processing, so it was an in-house tool we kind of built at Autoscout, we do platform engineering.And it was deprecated, so we looked at a new—what to replace it with. And so, we looked at Airflow, and we decided this is the way to go, we want to use managed because we don't want to maintain our own infrastructure. And the problem we ran into is that it doesn't have support for shared VPCs. And we actually talked to our account team, and they were confused. Because they said, like, “Well, every new service should support it natively.” But it just didn't have it. And that's, kind of, what, I kind of found is, like, there's—it feels—sometimes it's—there's a—it's getting rushed out the door, and it'll actually have a new managed service or new service launched out, but they're also sort of cutting some corners just to actually make sure it's packaged up and ready to go.Corey: When I'm looking at this, and seeing how this stuff gets packaged, and how it's built out, I start to understand a pattern that I've been relatively down on across the board. I'm curious to get your take because you work at a fairly sizable company as an engineering manager, running teams of people who do this sort of thing. Where do you land on the idea of companies building internal platforms to wrap around the offerings that the cloud service providers that they use make available to them?Evelyn: So, my opinion is that you need to build out some form of standardized tool set in order to actually be able to innovate quickly. Now, this sounds counterintuitive because everyone is like, “Oh, you know, if I want to innovate, I should be able to do this experiment, and try out everything, and use what works, and just release it.” And that greatness [unintelligible 00:23:14] mentality, you know, it's like five talented engineers working to build something. But when you have, instead of five engineers, you have five teams of five engineers each, and every single team does something totally different. You know, one uses Scala, and other on TypeScript, another one, you know .NET, and then there could have been a [last 00:23:30] one, you know, comes in, you know, saying they're still using Ruby.And then next thing you know, you know, you have, like, incredibly diverse platforms for services. And if you want to do any sort of like hiring or cross-training, it becomes incredibly difficult. And actually, as the organization grows, you want to hire talent, and so you're going to have to hire, you know, a developer for this team, you going to have to hire, you know, Ruby developer for this one, a Scala guy here, a Node.js guy over there.And so, this is where we say, “Okay, let's agree. We're going to be a Scala shop. Great. All right, are we running serverless? Are we running containerized?” And you agree on those things. So, that's already, like, the formation of it. And oftentimes, you start with DevOps. You'll say, like, “I'm a DevOps team,” you know, or doing a DevOps culture, if you do it properly, but you always hit this scaling issue where you start growing, and then how do you maintain that common tool set? And that's where we start looking at, you know, having a platform… approach, but I'm going to say it's Platform-as-a-Product. That's the key.Corey: Yeah, that's a good way of framing it because originally, the entire world needed that. That's what RightScale was when EC2 first came out. It was a reimagining of the EC2 console that was actually usable. And in time, AWS improved that to the point where RightScale didn't really have a place anymore in a way that it had previously, and that became a business challenge for them. But you have, what is it now, 2, 300 services that AWS has put out, and out, and okay, great. Most companies are really only actively working with a handful of those. How do you make those available in a reasonable way to your teams, in ways that aren't distracting, dangerous, et cetera? I don't know the answer on that one.Evelyn: Yeah. No, that's true. So, full disclosure. At AutoScout, we do platform engineering. So, I'm part of, like, the platform engineering group, and we built a platform for our product teams. It's kind of like, you need to decide to [follow 00:25:24] those answers, you know? Like, are we going to be fully containerized? Okay, then, great, we're going to use Fargate. All right, how do we do it so that developers don't actually—don't need to think that they're running Fargate workloads?And that's, like, you know, where it's really important to have those standardized abstractions that developers actually enjoy using. And I'd even say that, before you start saying, “Ah, we're going to do platform,” you say, “We should probably think about developer experience.” Because you can do a developer experience without a platform. You can do that, you know, in a DevOps approach, you know? It's basically build tools that makes it easy for developers to write code. That's the first step for anything. It's just, like, you have people writing the code; make sure that they can do the things easily, and then look at how to operate it.Corey: That sure would be nice. There's a lack of focus on usability, especially when it comes to a number of developer tools that we see out there in the wild, in that, they're clearly built by people who understand the problem space super well, but they're designing these things to be used by people who just want to make the website work. They don't have the insight, the knowledge, the approach, any of it, nor should they necessarily be expected to.Evelyn: No, that's true. And what I see is, a lot of the times, it's a couple really talented engineers who are just getting shit done, and they get shit done however they can. So, it's basically like, if they're just trying to run the website, they're just going to write the code to get things out there and call it a day. And then somebody else comes along, has a heart attack when see what's been done, and they're kind of stuck with it because there is no guardrails or paved path or however you want to call it.Corey: I really hope—truly—that this is going to be something that we look back and laugh when this episode airs, that, “Oh, yeah, we just got it so wrong. Look at all the amazing stuff that came out of re:Invent.” Are you going to be there this year?Evelyn: I am going to be there this year.Corey: My condolences. I keep hoping people get to escape.Evelyn: This is actually my first one in, I think, five years. So, I mean, the last time I was there was when everybody's going crazy over pins. And I still have a bag of them [laugh].Corey: Yeah, that did seem like a hot-second collectable moment, didn't it?Evelyn: Yeah. And then at the—I think, what, the very last day, as everybody's heading to re:Play, you could just go into the registration area, and they just had, like, bags of them lying around to take. So, all the competing, you know, to get the requirements for a pin was kind of moot [laugh].Corey: Don't you hate it at some point where it's like, you feel like I'm going to finally get this crowning achievement, it's like or just show up at the buffet at the end and grab one of everything, and wow, that would have saved me a lot of pain and trouble.Evelyn: Yeah.Corey: Ugh, scavenger hunts are hard, as I'm about to learn to my own detriment.Evelyn: Yeah. No, true. Yeah. But I am really hoping that re:Invent proves me wrong. Embarrassingly wrong, and then all my colleagues can proceed to mock me for this ridiculous podcast that I made with you. But I am a fierce skeptic. Optimistic nihilist, but still a nihilist, so we'll see how re:Invent turns out.Corey: So, I am curious, given your experience at more large companies than I tend to be embedded with for any period of time, how have you found that these large organizations tend to pick up new technologies? What does the adoption process look like? And honestly, if you feel like throwing some shade, how do they tend to get it wrong?Evelyn: In most cases, I've seen it go… terrible. Like, it just blows up in their face. And I say that is because a lot of the time, an organization will say, “Hey, we're going to adopt this new way of organizing teams or developing products,” and they look at all the practices. They say, “Okay, great. Product management is going to bring it in, they're going to structure things, how we do the planning, here's some great charts and diagrams,” but they don't really look at the culture aspect.And that's always where I've seen things fall apart. I've been in a room where, you know, our VP was really excited about team topologies and say, “Hey, we're going to adopt it.” And then an engineering manager proceeded to say, “Okay, you're responsible for this team, you're responsible for that team, you're responsible for this team talking to, like, a team of, like, five engineers,” which doesn't really work at all. Or, like, I think the best example is DevOps, you know, where you say, “Ah, we're going to adopt DevOps, we're going to have a DevOps team, or have a DevOps engineer.”Corey: Step one: we're going to rebadge everyone with existing job titles to have the new fancy job titles that reflect it. It turns out that's not necessarily sufficient in and of itself.Evelyn: Not really. The Spotify model. People say, like, “Oh, we're going to do the Spotify model. We're going to do skills, tribes, you know, and everything. It's going to be awesome, it's going to be great, you know, and nice, cross-functional.”The reason I say it bails on us every single time is because somebody wants to be in control of the process, and if the process is meant to encourage collaboration and innovation, that person actually becomes a chokehold for it. And it could be somebody that says, like, “Ah, I need to be involved in every single team, and listen to know what's happening, just so I'm aware of it.” What ends up happening is that everybody differs to them. So, there is no collaboration, there is no innovation. DevOps, you say, like, “Hey, we're going to have a team to do everything, so your developers don't need to worry about it.” What ends up happening is you're still an ops team, you still have your silos.And that's always a challenge is you actually have to say, “Okay, what are the cultural values around this process?” You know, what is SRE? What is DevOps, you know? Is it seen as processes, is it a series of principles, platform, maybe, you know? We have to say, like—that's why I say, Platform-as-a-Product because you need to have that product mindset, that culture of product thinking, to really build a platform that works because it's all about the user journey.It's not about building a common set of tools. It's the user journey of how a person interacts with their code to get it into a production environment. And so, you need to understand how that person sits down at their desk, starts the laptop up, logs in, opens the IDE, what they're actually trying to get done. And once you understand that, then you know your requirements, and you build something to fill those things so that they are happy to use it, as opposed to saying, “This is our platform, and you're going to use it.” And they're probably going to say, “No.” And the next thing, you know, they're just doing their own thing on the side.Corey: Yeah, the rise of Shadow IT has never gone away. It's just, on some level, it's the natural expression, I think it's an immune reaction that companies tend to have when process gets in the way. Great, we have an outcome that we need to drive towards; we don't have a choice. Cloud empowered a lot of that and also has given tools to help rein it in, and as with everything, the arms race continues.Evelyn: Yeah. And so, what I'm going to continue now, kind of like, toot the platform horn. So, Gregor Hohpe, he's a [solutions architect 00:31:56]—I always f- up his name. I'm so sorry, Gregor. He has a great book, and even a talk, called The Magic of Platforms, that if somebody is actually curious about understanding of why platforms are nice, they should really watch that talk.If you see him at re:Invent, or a summit or somewhere giving a talk, go listen to that, and just pick his brain. Because that's—for me, I really kind of strongly agree with his approach because that's really how, like, you know, as he says, like, boost innovation is, you know, where you're actually building a platform that really works.Corey: Yeah, it's a hard problem, but it's also one of those things where you're trying to focus on—at least ideally—an outcome or a better situation than you currently find yourselves in. It's hard to turn down things that might very well get you there sooner, faster, but it's like trying to effectively cargo-cult the leadership principles from your last employer into your new one. It just doesn't work. I mean, you see more startups from Amazonians who try that, and it just goes horribly because without the cultural understanding and the supporting structures, it doesn't work.Evelyn: Exactly. So, I've worked with, like, organizations, like, 4000-plus people, I've worked for, like, small startups, consulted, and this is why I say, almost every single transformation, it fails the first time because somebody needs to be in control and track things and basically be really, really certain that people are doing it right. And as soon as it blows up in their face, that's when they realize they should actually take a step back. And so, even for building out a platform, you know, doing Platform-as-a-Product, I always reiterate that you have to really be willing to just invest upfront, and not get very much back. Because you have to figure out the whole user journey, and what you're actually building, before you actually build it.Corey: I really want to thank you for taking the time to speak with me today. If people want to learn more, where's the best place for them to find you?Evelyn: So, I used to be on Twitter, but I've actually got off there after it kind of turned a bit toxic and crazy.Corey: Feels like that was years ago, but that's beside the point.Evelyn: Yeah, precisely. So, I would even just say because this feels like a corporate show, but find me on LinkedIn of all places because I will be sharing whatever I find on there, you know? So, just look me up on my name, Evelyn Osman, and give me a follow, and I'll probably be screaming into the cloud like you are.Corey: And we will, of course, put links to that in the show notes. Thank you so much for taking the time to speak with me. I appreciate it.Evelyn: Thank you, Corey.Corey: Evelyn Osman, engineering manager at AutoScout24. I'm Cloud Economist Corey Quinn, and this is Screaming in the Cloud. If you've enjoyed this podcast, please leave a five-star review on your podcast platform of choice, whereas if you've hated this podcast, please leave a five-star review on your podcast platform of choice, and I will read it once I finish building an internal platform to normalize all of those platforms together into one.Corey: If your AWS bill keeps rising and your blood pressure is doing the same, then you need The Duckbill Group. We help companies fix their AWS bill by making it smaller and less horrifying. The Duckbill Group works for you, not AWS. We tailor recommendations to your business, and we get to the point. Visit duckbillgroup.com to get started.

Anna Belak, Director of the Office of Cybersecurity Strategy at Sysdig, joins Corey on Screaming in the Cloud to discuss the newest benchmark for responding to security threats, 5/5/5. Anna describes why it was necessary to set a new benchmark for responding to security threats in a timely manner, and how the Sysdig team did research to determine the best practices for detecting, correlating, and responding to potential attacks. Corey and Anna discuss the importance of focusing on improving your own benchmarks towards a goal, as well as how prevention and threat detection are both essential parts of a solid security program. About AnnaAnna has nearly ten years of experience researching and advising organizations on cloud adoption with a focus on security best practices. As a Gartner Analyst, Anna spent six years helping more than 500 enterprises with vulnerability management, security monitoring, and DevSecOps initiatives. Anna's research and talks have been used to transform organizations' IT strategies and her research agenda helped to shape markets. Anna is the Director of Thought Leadership at Sysdig, using her deep understanding of the security industry to help IT professionals succeed in their cloud-native journey. Anna holds a PhD in Materials Engineering from the University of Michigan, where she developed computational methods to study solar cells and rechargeable batteries.Links Referenced: Sysdig: https://sysdig.com/ Sysdig 5/5/5 Benchmark: https://sysdig.com/555 TranscriptAnnouncer: Hello, and welcome to Screaming in the Cloud with your host, Chief Cloud Economist at The Duckbill Group, Corey Quinn. This weekly show features conversations with people doing interesting work in the world of cloud, thoughtful commentary on the state of the technical world, and ridiculous titles for which Corey refuses to apologize. This is Screaming in the Cloud.Corey: Welcome to Screaming in the Cloud. I'm Corey Quinn. I am joined again—for another time this year—on this promoted guest episode brought to us by our friends at Sysdig, returning is Anna Belak, who is their director of the Office of Cybersecurity Strategy at Sysdig. Anna, welcome back. It's been a hot second.Anna: Thank you, Corey. It's always fun to join you here.Corey: Last time we were here, we were talking about your report that you folks had come out with, the, “Cybersecurity Threat Landscape for 2022.” And when I saw you were doing another one of these to talk about something, I was briefly terrified. “Oh, wow, please tell me we haven't gone another year and the cybersecurity threat landscape is moving that quickly.” And it sort of is, sort of isn't. You're here today to talk about something different, but it also—to my understanding—distills down to just how quickly that landscape is moving. What have you got for us today?Anna: Exactly. For those of you who remember that episode, one of the key findings in the Threat Report for 2023 was that the average length of an attack in the cloud is ten minutes. To be clear, that is from when you are found by an adversary to when they have caused damage to your system. And that is really fast. Like, we talked about how that relates to on-prem attacks or other sort of averages from other organizations reporting how long it takes to attack people.And so, we went from weeks or days to minutes, potentially seconds. And so, what we've done is we looked at all that data, and then we went and talked to our amazing customers and our many friends at analyst firms and so on, to kind of get a sense for if this is real, like, if everyone is seeing this or if we're just seeing this. Because I'm always like, “Oh, God. Like, is this real? Is it just me?”And as it turns out, everyone's not only—I mean, not necessarily everyone's seeing it, right? Like, there's not really been proof until this year, I would say because there's a few reports that came out this year, but lots of people sort of anticipated this. And so, when we went to our customers, and we asked for their SLAs, for example, they were like, “Oh, yeah, my SLA for a [PCRE 00:02:27] cloud is like 10, 15 minutes.” And I was like, “Oh, okay.” So, what we set out to do is actually set a benchmark, essentially, to see how well are you doing. Like, are you equipped with your cloud security program to respond to the kind of attack that a cloud security attacker is going to—sorry, an anti-cloud security—I guess—attacker is going to perpetrate against you.And so, the benchmark is—drumroll—5/5/5. You have five seconds to detect a signal that is relevant to potentially some attack in the cloud—hopefully, more than one such signal—you have five minutes to correlate all such relevant signals to each other so that you have a high fidelity detection of this activity, and then you have five more minutes to initiate an incident response process to hopefully shut this down, or at least interrupt the kill chain before your environments experience any substantial damage.Corey: To be clear, that is from a T0, a starting point, the stopwatch begins, the clock starts when the event happens, not when an event shows up in your logs, not once someone declares an incident. From J. Random Hackerman, effectively, we're pressing the button and getting the response from your API.Anna: That's right because the attackers don't really care how long it takes you to ship logs to wherever you're mailing them to. And that's why it is such a short timeframe because we're talking about, they got in, you saw something hopefully—and it may take time, right? Like, some of the—which we'll describe a little later, some of the activities that they perform in the early stages of the attack are not necessarily detectable as malicious right away, which is why your correlation has to occur, kind of, in real time. Like, things happen, and you're immediately adding them, sort of like, to increase the risk of this detection, right, to say, “Hey, this is actually something,” as opposed to, you know, three weeks later, I'm parsing some logs and being like, “Oh, wow. Well, that's not good.” [laugh].Corey: The number five seemed familiar to me in this context, so I did a quick check, and sure enough, allow me to quote from chapter and verse from the CloudTrail documentation over an AWS-land. “CloudTrail typically delivers logs within an average of about five minutes of an API call. This time is not guaranteed.” So effectively, if you're waiting for anything that's CloudTrail-driven to tell you that you have a problem, it is almost certainly too late by the time that pops up, no matter what that notification vector is.Anna: That is, unfortunately or fortunately, true. I mean, it's kind of a fact of life. I guess there is a little bit of a veiled [unintelligible 00:04:43] at our cloud provider friends because, really, they have to do better ultimately. But the flip side to that argument is CloudTrail—or your cloud log source of choice—cannot be your only source of data for detecting security events, right? So, if you are operating purely on the basis of, “Hey, I have information in CloudTrail; that is my security information,” you are going to have a bad time, not just because it's not fast enough, but also because there's not enough data in there, right? Which is why part of the first, kind of, benchmark component is that you must have multiple data sources for the signals, and they—ideally—all will be delivered to you within five seconds of an event occurring or a signal being generated.Corey: And give me some more information on that because I have my own alerter, specifically, it's a ClickOps detector. Whenever someone in one of my accounts does something in the console, that has a write aspect to it rather than just a read component—which again, look at what you want in the console, that's fine—if you're changing things that is not being managed by code, I want to know that it's happening. It's not necessarily bad, but I want to at least have visibility into it. And that spits out the principal, the IP address it emits from, and the rest. I haven't had a whole lot where I need to correlate those between different areas. Talk to me more about the triage step.Anna: Yeah, so I believe that the correlation step is the hardest, actually.Corey: Correlation step. My apologies.Anna: Triage is fine. It's [crosstalk 00:06:06]—Corey: Triage, correlations, the words we use matter on these things.Anna: Dude, we argued about the words on this for so long, you could even imagine. Yeah, triage, correlation, detection, you name it, we are looking at multiple pieces of data, we're going to connect them to each other meaningfully, and that is going to provide us with some insight about the fact that a bad thing is happening, and we should respond to it. Perhaps automatically respond to it, but we'll get to that. So, a correlation, okay. The first thing is, like I said, you must have more than one data source because otherwise, I mean, you could correlate information from one data source; you actually should do that, but you are going to get richer information if you can correlate multiple data sources, and if you can access, for example, like through an API, some sort of enrichment for that information.Like, I'll give you an example. For SCARLETEEL, which is an attack we describe in the thread report, and we actually described before, this is—we're, like—on SCARLETEEL, I think, version three now because there's so much—this particular certain actor is very active [laugh].Corey: And they have a better versioning scheme than most companies I've spoken to, but that's neither here nor there.Anna: [laugh]. Right? So, one of the interesting things about SCARLETEEL is you could eventually detect that it had happened if you only had access to CloudTrail, but you wouldn't have the full picture ever. In our case, because we are a company that relies heavily on system calls and machine learning detections, we [are able to 00:07:19] connect the system call events to the CloudTrail events, and between those two data sources, we're able to figure out that there's something more profound going on than just what you see in the logs. And I'll actually tell you, which, for example, things are being detected.So, in SCARLETEEL, one thing that happens is there's a crypto miner. And a crypto miner is one of these events where you're, like, “Oh, this is obviously malicious,” because as we wrote, I think, two years ago, it costs $53 to mine $1 of Bitcoin in AWS, so it is very stupid for you to be mining Bitcoin in AWS, unless somebody else is—Corey: In your own accounts.Anna: —paying the cloud bill. Yeah, yeah [laugh] in someone else's account, absolutely. Yeah. So, if you are a sysadmin or a security engineer, and you find a crypto miner, you're like, “Obviously, just shut that down.” Great. What often happens is people see them, and they think, “Oh, this is a commodity attack,” like, people are just throwing crypto miners whatever, I shut it down, and I'm done.But in the case of this attack, it was actually a red herring. So, they deployed the miner to see if they could. They could, then they determined—presumably; this is me speculating—that, oh, these people don't have very good security because they let random idiots run crypto miners in their account in AWS, so they probed further. And when they probed further, what they did was some reconnaissance. So, they type in commands, listing, you know, like, list accounts or whatever. They try to list all the things they can list that are available in this account, and then they reach out to an EC2 metadata service to kind of like, see what they can do, right?And so, each of these events, like, each of the things that they do, like, reaching out to a EC2 metadata service, assuming a role, doing a recon, even lateral movement is, like, by itself, not necessarily a scary, big red flag malicious thing because there are lots of, sort of, legitimate reasons for someone to perform those actions, right? Like, reconnaissance, for one example, is you're, like, looking around the environment to see what's up, right? So, you're doing things, like, listing things, [unintelligible 00:09:03] things, whatever. But a lot of the graphical interfaces of security tools also perform those actions to show you what's, you know, there, so it looks like reconnaissance when your tool is just, like, listing all the stuff that's available to you to show it to you in the interface, right? So anyway, the point is, when you see them independently, these events are not scary. They're like, “Oh, this is useful information.”When you see them in rapid succession, right, or when you see them alongside a crypto miner, then your tooling and/or your process and/or your human being who's looking at this should be like, “Oh, wait a minute. Like, just the enumeration of things is not a big deal. The enumeration of things after I saw a miner, and you try and talk to the metadata service, suddenly I'm concerned.” And so, the point is, how can you connect those dots as quickly as possible and as automatically as possible, so a human being doesn't have to look at, like, every single event because there's an infinite number of them.Corey: I guess the challenge I've got is that in some cases, you're never going to be able to catch up with this. Because if it's an AWS call to one of the APIs that they manage for you, they explicitly state there's no guarantee of getting information on this until the show's all over, more or less. So, how is there… like, how is there hope?Anna: [laugh]. I mean, there's always a forensic analysis, I guess [laugh] for all the things that you've failed to respond to.Corey: Basically we're doing an after-action thing because humans aren't going to react that fast. We're just assuming it happened; we should know about it as soon as possible. On some level, just because something is too late doesn't necessarily mean there's not value added to it. But just trying to turn this into something other than a, “Yeah, they can move faster than you, and you will always lose. The end. Have a nice night.” Like, that tends not to be the best narrative vehicle for these things. You know, if you're trying to inspire people to change.Anna: Yeah, yeah, yeah, I mean, I think one clear point of hope here is that sometimes you can be fast enough, right? And a lot of this—I mean, first of all, you're probably not going to—sorry, cloud providers—you don't go into just the cloud provider defaults for that level of performance, you are going with some sort of third-party tool. On the, I guess, bright side, that tool can be open-source, like, there's a lot of open-source tooling available now that is fast and free. For example, is our favorite, of course, Falco, which is looking at system calls on endpoints, and containers, and can detect things within seconds of them occurring and let you know immediately. There is other EBPF-based instrumentation that you can use out there from various vendors and/or open-source providers, and there's of course, network telemetry.So, if you're into the world of service mesh, there is data you can get off the network, also very fast. So, the bad news or the flip side to that is you have to be able to manage all that information, right? So, that means—again, like I said, you're not expecting a SOC analyst to look at thousands of system calls and thousands of, you know, network packets or flow logs or whatever you're looking at, and just magically know that these things go together. You are expecting to build, or have built for you by a vendor or the open-source community, some sort of dissection content that is taking this into account and then is able to deliver that alert at the speed of 5/5/5.Corey: When you see the larger picture stories playing out, as far as what customers are seeing, what the actual impact is, what gave rise to the five-minute number around this? Just because that tends to feel like it's a… it is both too long and also too short on some level. I'm just wondering how you wound up at—what is this based on?Anna: Man, we went through so many numbers. So, we [laugh] started with larger numbers, and then we went to smaller numbers, then we went back to medium numbers. We align ourselves with the timeframes we're seeing for people. Like I said, a lot of folks have an SLA of responding to a P0 within 10 or 15 minutes because their point basically—and there's a little bit of bias here into our customer base because our customer base is, A, fairly advanced in terms of cloud adoption and in terms of security maturity, and also, they're heavily in let's say, financial industries and other industries that tend to be early adopters of new technology. So, if you are kind of a laggard, like, you probably aren't that close to meeting this benchmark as you are if you're saying financial, right? So, we asked them how they operate, and they basically pointed out to us that, like, knowing 15 minutes later is too late because I've already lost, like, some number of millions of dollars if my environment is compromised for 15 minutes, right? So, that's kind of where the ten minutes comes from. Like, we took our real threat research data, and then we went around and talked to folks to see kind of what they're experiencing and what their own expectations are for their incident response in SOC teams, and ten minutes is sort of where we landed.Corey: Got it. When you see this happening, I guess, in various customer environments, assuming someone has missed that five-minute window, is a game over effectively? How should people be thinking about this?Anna: No. So, I mean, it's never really game over, right? Like until your company is ransomed to bits, and you have to close your business, you still have many things that you can do, hopefully, to save yourself. And also, I want to be very clear that 5/5/5 as a benchmark is meant to be something aspirational, right? So, you should be able to meet this benchmark for, let's say, your top use cases if you are a fairly high maturity organization, in threat detection specifically, right?So, if you're just beginning your threat detection journey, like, tomorrow, you're not going to be close. Like, you're going to be not at all close. The point here, though, is that you should aspire to this level of greatness, and you're going to have to create new processes and adopt new tools to get there. Now, before you get there, I would argue that if you can do, like, 10-10-10 or, like, whatever number you start with, you're on a mission to make that number smaller, right? So, if today, you can detect a crypto miner in 30 minutes, that's not great because crypto miners are pretty detectable these days, but give yourself a goal of, like, getting that 30 minutes down to 20, or getting that 30 minutes down to 10, right?Because we are so obsessed with, like, measuring ourselves against our peers and all this other stuff that we sometimes lose track of what actually is improving our security program. So yes, compare it to yourself first. But ultimately, if you can meet the 5/5/5 benchmark, then you are doing great. Like, you are faster than the attackers in theory, so that's the dream.Corey: So, I have to ask, and I suspect I might know the answer to this, but given that it seems very hard to move this quickly, especially at scale, is there an argument to be made that effectively prevention obviates the need for any of this, where if you don't misconfigure things in ways that should be obvious, if you practice defense-in-depth to a point where you can effectively catch things that the first layer meets with successive layers, as opposed to, “Well, we have a firewall. Once we're inside of there, well [laugh], it's game over for us.” Is prevention sufficient in some ways to obviate this?Anna: I think there are a lot of people that would love to believe that that's true.Corey: Oh, I sure would. It's such a comforting story.Anna: And we've done, like, I think one of my opening sentences in the benchmark, kind of, description, actually, is that we've done a pretty good job of advertising prevention in Cloud as an important thing and getting people to actually, like, start configuring things more carefully, or like, checking how those things have been configured, and then changing that configuration should they discover that it is not compliant with some mundane standard that everyone should know, right? So, we've made great progress, I think, in cloud prevention, but as usual, like, prevention fails, right? Like I still have smoke detectors in my house, even though I have done everything possible to prevent it from catching fire and I don't plan to set it on fire, right? But like, threat detection is one of these things that you're always going to need because no matter what you do, A, you will make a mistake because you're a human being, and there are too many things, and you'll make a mistake, and B, the bad guys are literally in the business of figuring ways around your prevention and your protective systems.So, I am full on on defense-in-depth. I think it's a beautiful thing. We should only obviously do that. And I do think that prevention is your first step to a holistic security program—otherwise, what even is the point—but threat detection is always going to be necessary. And like I said, even if you can't go 5/5/5, you don't have threat detection at that speed, you need to at least be able to know what happened later so you can update your prevention system.Corey: This might be a dangerous question to get into, but why not, that's what I do here. This [could 00:17:27] potentially an argument against Cloud, by which I mean that if I compromise someone's Cloud account on any of the major cloud providers, once I have access of some level, I know where everything else in the environment is as a general rule. I know that you're using S3 or its equivalent, and what those APIs look like and the rest, whereas as an attacker, if I am breaking into someone's crappy data center-hosted environment, everything is going to be different. Maybe they don't have a SAN at all, for example. Maybe they have one that hasn't been patched in five years. Maybe they're just doing local disk for some reason.There's a lot of discovery that has to happen that is almost always removed from Cloud. I mean, take the open S3 bucket problem that we've seen as a scourge for 5, 6, 7 years now, where it's not that S3 itself is insecure, but once you make a configuration mistake, you are now in line with a whole bunch of other folks who may have much more valuable data living in that environment. Where do you land on that one?Anna: This is the ‘leave cloud to rely on security through obscurity' argument?Corey: Exactly. Which I'm not a fan of, but it's also hard to argue against from time-to-time.Anna: My other way of phrasing it is ‘the attackers are ripping up the stack' argument. Yeah, so—and there is some sort of truth in that, right? Part of the reason that attackers can move that fast—and I think we say this a lot when we talk about the threat report data, too, because we literally see them execute this behavior, right—is they know what the cloud looks like, right? They have access to all the API documentation, they kind of know what all the constructs are that you're all using, and so they literally can practice their attack and create all these scripts ahead of time to perform their reconnaissance because they know exactly what they're looking at, right? On-premise, you're right, like, they're going to get into—even to get through my firewall, whatever, they're getting into my data center, they don't do not know what disaster I have configured, what kinds of servers I have where, and, like, what the network looks like, they have no idea, right?In Cloud, this is kind of all gifted to them because it's so standard, which is a blessing and a curse. It's a blessing because—well for them, I mean, because they can just programmatically go through this stuff, right? It's a curse for them because it's a blessing for us in the same way, right? Like, the defenders… A, have a much easier time knowing what they even have available to them, right? Like, the days of there's a server in a closet I've never heard of are kind of gone, right? Like, you know what's in your Cloud account because, frankly, AWS tells you. So, I think there is a trade-off there.The other thing is—about the moving up the stack thing, right—like no matter what you do, they will come after you if you have something worth exploiting you for, right? So, by moving up the stack, I mean, listen, we have abstracted all the physical servers, all of the, like, stuff we used to have to manage the security of because the cloud just does that for us, right? Now, we can argue about whether or not they do a good job, but I'm going to be generous to them and say they do a better job than most companies [laugh] did before. So, in that regard, like, we say, thank you, and we move on to, like, fighting this battle at a higher level in the stack, which is now the workloads and the cloud control plane, and the you name it, whatever is going on after that. So, I don't actually think you can sort of trade apples for oranges here. It's just… bad in a different way.Corey: Do you think that this benchmark is going to be used by various companies who will learn about it? And if so, how do you see that playing out?Anna: I hope so. My hope when we created it was that it would sort of serve as a goalpost or a way to measure—Corey: Yeah, it would just be marketing words on a page and never mentioned anywhere, that's our dream here.Anna: Yeah, right. Yeah, I was bored. So, I wrote some—[laugh].Corey: I had a word minimum to get out the door, so there we are. It's how we work.Anna: Right. As you know, I used to be a Gartner analyst, and my desire is always to, like, create things that are useful for people to figure out how to do better in security. And my, kind of, tenure at the vendor is just a way to fund that [laugh] more effectively [unintelligible 00:21:08].Corey: Yeah, I keep forgetting you're ex-Gartner. Yeah, it's one of those fun areas of, “Oh, yeah, we just want to basically talk about all kinds of things because there's a—we have a chart to fill out here. Let's get after it.”Anna: I did not invent an acronym, at least. Yeah, so my goal was the following. People are always looking for a benchmark or a goal or standard to be like, “Hey, am I doing a good job?” Whether I'm, like a SOC analyst or director, and I'm just looking at my little SOC empire, or I'm a full on CSO, and I'm looking at my entire security program to kind of figure out risk, I need some way to know whether what is happening in my organization is, like, sufficient, or on par, or anything. Is it good or is it bad? Happy face? Sad face? Like, I need some benchmark, right?So normally, the Gartner answer to this, typically, is like, “You can only come up with benchmarks that are—” they're, like, “Only you know what is right for your company,” right? It's like, you know, the standard, ‘it depends' answer. Which is true, right, because I can't say that, like, oh, a huge multinational bank should follow the same benchmark as, like, a donut shop, right? Like, that's unreasonable. So, this is also why I say that our benchmark is probably more tailored to the more advanced organizations that are dealing with kind of high maturity phenomena and are more cloud-native, but the donut shops should kind of strive in this direction, right?So, I hope that people will think of it this way: that they will, kind of, look at their process and say, “Hey, like, what are the things that would be really bad if they happened to me, in terms of sort detection?” Like, “What are the threats I'm afraid of where if I saw this in my cloud environment, I would have a really bad day?” And, “Can I detect those threats in 5/5/5?” Because if I can, then I'm actually doing quite well. And if I can't, then I need to set, like, some sort of roadmap for myself on how I get from where I am now to 5/5/5 because that implies you would be doing a good job.So, that's sort of my hope for the benchmark is that people think of it as something to aspire to, and if they're already able to meet it, then that they'll tell us how exactly they're achieving it because I really want to be friends with them.Corey: Yeah, there's a definite lack of reasonable ways to think about these things, at least in ways that can be communicated to folks outside of the bounds of the security team. I think that's one of the big challenges currently facing the security industry is that it is easy to get so locked into the domain-specific acronyms, philosophies, approaches, and the rest, that even coming from, “Well, I'm a cloud engineer who ostensibly needs to know about these things.” Yeah, wander around the RSA floor with that as your background, and you get lost very quickly.Anna: Yeah, I think that's fair. I mean, it is a very, let's say, dynamic and rapidly evolving space. And by the way, like, it was really hard for me to pick these numbers, right, because I… very much am on that whole, ‘it depends' bandwagon of I don't know what the right answer is. Who knows what the right answer is [laugh]? So, I say 5/5/5 today. Like, tomorrow, the attack takes five minutes, and now it's two-and-a-half/two-and-a-half, right? Like it's whatever.You have to pick a number and go for it. So, I think, to some extent, we have to try to, like, make sense of the insanity and choose some best practices to anchor ourselves in or some, kind of like, sound logic to start with, and then go from there. So, that's sort of what I go for.Corey: So, as I think about the actual reaction times needed for 5/5/5 to actually be realistic, people can't reliably get a hold of me on the phone within five minutes, so it seems like this is not something you're going to have humans in the loop for. How does that interface with the idea of automating things versus giving automated systems too much power to take your site down as a potential failure mode?Anna: Yeah. I don't even answer the phone anymore, so that wouldn't work at all. That's a really, really good question, and probably the question that gives me the most… I don't know, I don't want to say lost sleep at night because it's actually, it's very interesting to think about, right? I don't think you can remove humans from the loop in the SOC. Like, certainly there will be things you can auto-respond to some extent, but there'd better be a human being in there because there are too many things at stake, right?Some of these actions could take your entire business down for far more hours or days than whatever the attacker was doing before. And that trade-off of, like, is my response to this attack actually hurting the business more than the attack itself is a question that's really hard to answer, especially for most of us technical folks who, like, don't necessarily know the business impact of any given thing. So, first of all, I think we have to embrace other response actions. Back to our favorite crypto miners, right? Like there is no reason to not automatically shut them down. There is no reason, right? Just build in a detection and an auto-response: every time you see a crypto miner, kill that process, kill that container, kill that node. I don't care. Kill it. Like, why is it running? This is crazy, right?I do think it gets nuanced very quickly, right? So again, in SCARLETEEL, there are essentially, like, five or six detections that occur, right? And each of them theoretically has a potential auto-response that you could have executed depending on your, sort of, appetite for that level of intervention, right? Like, when you see somebody assuming a role, that's perfectly normal activity most of the time. In this case, I believe they actually assumed a machine role, which is less normal. Like, that's kind of weird.And then what do you do? Well, you can just, like, remove the role. You can remove that person's ability to do anything, or remove that role's ability to do anything. But that could be very dangerous because we don't necessarily know what the full scope of that role is as this is happening, right? So, you could take, like, a more mitigated auto-response action and add a restrictive policy to that rule, for example, to just prevent activity from that IP address that you just saw, right, because we're not sure about this IP address, but we're sure about this role, right?So, you have to get into these, sort of, risk-tiered response actions where you say, “Okay, this is always okay to do automatically. And this is, like, sometimes, okay, and this is never okay.” And as you develop that muscle, it becomes much easier to do something rather than doing nothing and just, kind of like, analyzing it in forensics and being, like, “Oh, what an interesting attack story,” right? So, that's step one, is just start taking these different response actions.And then step two is more long-term, and it's that you have to embrace the cloud-native way of life, right? Like this immutable, ephemeral, distributed religion that we've been selling, it actually works really well if you, like, go all-in on the religion. I sound like a real cult leader [laugh]. Like, “If you just go all in, it's going to be great.” But it's true, right?So, if your workflows are immutable—that means they cannot change as they're running—then when you see them drifting from their original configuration, like, you know, that is bad. So, you can immediately know that it's safe to take an auto-respon—well, it's safe, relatively safe, take an auto-response action to kill that workload because you are, like, a hundred percent certain it is not doing the right things, right? And then furthermore, if all of your deployments are defined as code, which they should be, then it is approximately—[though not entirely 00:27:31]—trivial to get that workload back, right? Because you just push a button, and it just generates that same Kubernetes cluster with those same nodes doing all those same things, right? So, in the on-premise world where shooting a server was potentially the, you know, fireable offense because if that server was running something critical, and you couldn't get it back, you were done.In the cloud, this is much less dangerous because there's, like, an infinite quantity of servers that you could bring back and hopefully Infrastructure-as-Code and, kind of, Configuration-as-Code in some wonderful registry, version-controlled for you to rely on to rehydrate all that stuff, right? So again, to sort of TL;DR, get used to doing auto-response actions, but do this carefully. Like, define a scope for those actions that make sense and not just, like, “Something bad happened; burn it all down,” obviously. And then as you become more cloud-native—which sometimes requires refactoring of entire applications—by the way, this could take years—just embrace the joy of Everything-as-Code.Corey: That's a good way of thinking about it. I just, I wish there were an easier path to get there, for an awful lot of folks who otherwise don't find a clear way to unlock that.Anna: There is not, unfortunately [laugh]. I mean, again, the upside on that is, like, there are a lot of people that have done it successfully, I have to say. I couldn't have said that to you, like, six, seven years ago when we were just getting started on this journey, but especially for those of you who were just at KubeCon—however, long ago… before this airs—you see a pretty robust ecosystem around Kubernetes, around containers, around cloud in general, and so even if you feel like your organization's behind, there are a lot of folks you can reach out to to learn from, to get some help, to just sort of start joining the masses of cloud-native types. So, it's not nearly as hopeless as before. And also, one thing I like to say always is, almost every organization is going to have some technical debt and some legacy workload that they can't convert to the religion of cloud.And so, you're not going to have a 5/5/5 threat detection SLA on those workloads. Probably. I mean, maybe you can, but probably you're not, and you may not be able to take auto-response actions, and you may not have all the same benefits available to you, but like, that's okay. That's okay. Hopefully, whatever that thing is running is, you know, worth keeping alive, but set this new standard for your new workloads. So, when your team is building a new application, or if they're refactoring an application, can't afford the new world, set the standard on them and don't, kind of like, torment the legacy folks because it doesn't necessarily make sense. Like, they're going to have different SLAs for different workloads.Corey: I really want to thank you for taking the time to speak with me yet again about the stuff you folks are coming out with. If people want to learn more, where's the best place for them to go?Anna: Thanks, Corey. It's always a pleasure to be on your show. If you want to learn more about the 5/5/5 benchmark, you should go to sysdig.com/555.Corey: And we will, of course, put links to that in the show notes. Thank you so much for taking the time to speak with me today. As always, it's appreciated. Anna Belak, Director at the Office of Cybersecurity Strategy at Sysdig. I'm Cloud Economist Corey Quinn, and this has been a promoted guest episode brought to us by our friends at Sysdig. If you've enjoyed this podcast, please leave a five-star review on your podcast platform of choice, whereas if you've hated this podcast, please leave a five-star review on your podcast platform of choice, along with an angry, insulting comment that I will read nowhere even approaching within five minutes.Corey: If your AWS bill keeps rising and your blood pressure is doing the same, then you need The Duckbill Group. We help companies fix their AWS bill by making it smaller and less horrifying. The Duckbill Group works for you, not AWS. We tailor recommendations to your business, and we get to the point. Visit duckbillgroup.com to get started.

Maya Kaczorowski, Chief Product Officer at Tailscale, joins Corey on Screaming in the Cloud to discuss what sets the Tailscale product approach apart, for users of their free tier all the way to enterprise. Maya shares insight on how she evaluates feature requests, and how Tailscale's unique architecture sets them apart from competitors. Maya and Corey discuss the importance of transparency when building trust in security, as well as Tailscale's approach to new feature roll-outs and change management.About MayaMaya is the Chief Product Officer at Tailscale, providing secure networking for the long tail. She was mostly recently at GitHub in software supply chain security, and previously at Google working on container security, encryption at rest and encryption key management. Prior to Google, she was an Engagement Manager at McKinsey & Company, working in IT security for large enterprises.Maya completed her Master's in mathematics focusing on cryptography and game theory. She is bilingual in English and French.Outside of work, Maya is passionate about ice cream, puzzling, running, and reading nonfiction.Links Referenced: Tailscale: https://tailscale.com/ Tailscale features: VS Code extension: https://marketplace.visualstudio.com/items?itemName=tailscale.vscode-tailscale  Tailscale SSH: https://tailscale.com/kb/1193/tailscale-ssh  Tailnet lock: https://tailscale.com/kb/1226/tailnet-lock  Auto updates: https://tailscale.com/kb/1067/update#auto-updates  ACL tests: https://tailscale.com/kb/1018/acls#tests  Kubernetes operator: https://tailscale.com/kb/1236/kubernetes-operator  Log streaming: https://tailscale.com/kb/1255/log-streaming  Tailscale Security Bulletins: https://tailscale.com/security-bulletins  Blog post “How Our Free Plan Stays Free:” https://tailscale.com/blog/free-plan  Tailscale on AWS Marketplace: https://aws.amazon.com/marketplace/pp/prodview-nd5zazsgvu6e6  TranscriptAnnouncer: Hello, and welcome to Screaming in the Cloud with your host, Chief Cloud Economist at The Duckbill Group, Corey Quinn. This weekly show features conversations with people doing interesting work in the world of cloud, thoughtful commentary on the state of the technical world, and ridiculous titles for which Corey refuses to apologize. This is Screaming in the Cloud.Corey: Welcome to Screaming in the Cloud. I'm Corey Quinn, and I am joined today on this promoted guest episode by my friends over at Tailscale. They have long been one of my favorite products just because it has dramatically changed the way that I interact with computers, which really should be enough to terrify anyone. My guest today is Maya Kaczorowski, Chief Product Officer at Tailscale. Maya, thanks for joining me.Maya: Thank you so much for having me.Corey: I have to say originally, I was a little surprised to—“Really? You're the CPO? I really thought I would have remembered that from the last time we hung out in person.” So, congratulations on the promotion.Maya: Thank you so much. Yeah, it's exciting.Corey: Being a product person is probably a great place to start with this because we've had a number of conversations, here and otherwise, around what Tailscale is and why it's awesome. I don't necessarily know that beating the drum of why it's so awesome is going to be covering new ground, but I'm sure we're going to come up for that during the conversation. Instead, I'd like to start by talking to you about just what a product person does in the context of building something that is incredibly central not just to critical path, but also has massive security ramifications as well, when positioning something that you're building for the enterprise. It's a very hard confluence of problems, and there are days I am astonished that enterprises can get things done based purely upon so much of the mitigation of what has to happen. Tell me about that. How do you even function given the tremendous vulnerability of the attack surface you're protecting?Maya: Yeah, I don't know if you—I feel like you're talking about the product, but also the sales cycle of talking [laugh] and working with enterprise customers.Corey: The product, the sales cycle, the marketing aspects of it, and—Maya: All of it.Corey: —it all ties together. It's different facets of frankly, the same problem.Maya: Yeah. I think that ultimately, this is about really understanding who the customer that is buying the product is. And I really mean that, like, buying the product, right? Because, like, look at something like Tailscale. We're typically used by engineers, or infrastructure teams in an organization, but the buyer might be the VP of Engineering, but it might be the CISO, or the CTO, or whatever, and they're going to have a set of requirements that's going to be very different from what the end-user has as a set of requirements, so even if you have something like bottom-up adoption, in our case, like, understanding and making sure we're checking all the boxes that somebody needs to actually bring us to work.Enterprises are incredibly demanding, and to your point, have long checklists of what they need as part of an RFP or that kind of thing. I find that some of the strictest requirements tend to be in security. So like, how—to your point—if we're such a critical part of your network, how are you sure that we're always available, or how are you sure that if we're compromised, you're not compromised, and providing a lot of, like, assurances and controls around making sure that that's not the case.Corey: I think that there's a challenge in that what enterprise means to different people can be wildly divergent. I originally came from the school of obnoxious engineering where oh, as an engineer, whenever I say something is enterprise grade, that's not a compliment. That means it's going to be slow and moribund. But that is a natural consequence of a company's growth after achieving success, where okay, now we have actual obligations to customers and risk mitigation that needs to be addressed. And how do you wind up doing that without completely hobbling yourself when it comes to accelerating feature velocity? It's a very delicate balancing act.Maya: Yeah, for sure. And I think you need to balance, to your point, kind of creating demand for the product—like, it's actually solving the problem that the customer has—versus checking boxes. Like, I think about them as features, or you know, feature requests versus feature blockers or deal blockers or adoption blockers. So, somebody wants to, say, connect to an AWS VPC, but then the person who has to make sure that that's actually rolled out properly also wants audit logs and SSH session recording and RBAC-based controls and lots of other things before they're comfortable deploying that in their environment. And I'm not even talking about the list of, you know, legal, kind of, TOS requirements that they would have for that kind of situation.I think there's a couple of things that you need to do to even signal that you're in that space. One of the things that I was—I was talking to a friend of mine the other day how it feels like five years ago, like, nobody had SOC 2 reports, or very few startups had SOC 2 reports. And it's probably because of the advent of some of these other companies in this space, but like, now you can kind of throw a dart, and you'll hit five startups that have SOC 2 reports, and the amount that you need to show that you're ready to sell to these companies has changed.Corey: I think that there's a definite broadening of the use case. And I've been trying to avoid it, but let's go diving right into it. I used to view Tailscale as, oh it's a VPN. The end. Then it became something more where it effectively became the mesh overlay where all of the various things that I have that speak Tailscale—which is frankly, a disturbing number of things that I'd previously considered to be appliances—all talk to one another over a dedicated network, and as a result, can do really neat things where I don't have to spend hours on end configuring weird firewall rules.It's more secure, it's a lot simpler, and it seems like every time I get that understanding down, you folks do something that causes me to yet again reevaluate where you stand. Most recently, I was doing something horrifying in front-end work, and in VS Code the Tailscale extension popped up. “Oh, it looks like you're running a local development server. Would you like to use Tailscale Funnel to make it available to the internet?” And my response to that is, “Good lord, no, I'm ashamed of it, but thanks for asking.” Every time I think I get it, I have to reevaluate where it stands in the ecosystem. What is Tailscale now? I feel like I should get the official description of what you are.Maya: Well, I sure hope I'm not the official description. I think the closest is a little bit of what you're saying: a mesh overlay network for your infrastructure, or a programmable network that lets you mesh together your users and services and services and services, no matter where they are, including across different infrastructure providers and, to your point, on a long list of devices you might have running. People are running Tailscale on self-driving cars, on robots, on satellites, on elevators, but they're also running Tailscale on Linux running in AWS or a MacBook they have sitting under their desk or whatever it happens to be. The phrase that I like to use for that is, like, infrastructure agnostic. We're just a building block.Your infrastructure can be whatever infrastructure you want. You can have the cheapest GPUs from this cloud, or you can use the Android phone to train the model that you have sitting on your desk. We just help you connect all that stuff together so you can build your own cloud whatever way you want. To your point, that's not really a VPN [laugh]. The word VPN doesn't quite do it justice. For the remote access to prod use case, so like a user, specifically, like, a developer infra team to a production network, that probably looks the most like a zero-trust solution, but we kind of blur a lot of the lines there for what we can do.Corey: Yeah, just looking at it, at the moment, I have a bunch of Raspberries Pi, perhaps, hanging out on my tailnet. I have currently 14 machines on there, I have my NAS downstairs, I have a couple of EC2 instances, a Google Cloud instance, somewhere, I finally shut down my old Oracle Cloud instance, my pfSense box speaks it natively. I have a Thinkst Canary hanging out on there to detect if anything starts going ridiculously weird, my phone, my iPad, and a few other things here and there. And they all just talk seamlessly over the same network. I can identify them via either IP address, if I'm old, or via DNS if I want to introduce problems that will surprise me at one point or another down the road.I mean, I even have an exit node I share with my brother's Tailscale account for reasons that most people would not expect, namely that he is an American who lives abroad. So, many weird services like banks or whatnot, “Oh, you can't log in to check your bank unless you're coming from US IP space.” He clicks a button, boom, now he doesn't get yelled at to check his own accounts. Which is probably not the primary use case you'd slap on your website, but it's one of those solving everyday things in somewhat weird ways.Maya: Oh, yeah. I worked at a bank maybe ten years ago, and they would block—this little bank on the east coast of the US—they would block connections from Hawaii because why would any of your customers ever be in Hawaii? And it was like, people travel and maybe you're—Corey: How can you be in Hawaii? You don't have a passport.Maya: [laugh]. People travel. They still need to do banking. Like, it doesn't change, yeah. The internet, we've built a lot of weird controls that are IP-based, that don't really make any sense, that aren't reflective. And like, that's true for individuals—like you're describing, people who travel and need to bank or whatever they need to do when they travel—and for corporations, right? Like the old concept—this is all back to the zero trust stuff—but like, the old concept that you were trusted just because you had an IP address that was in the corp IP range is just not true anymore, right? Somebody can walk into your office and connect to the Wi-Fi and a legitimate employee can be doing their job from home or from Starbucks, right? Those are acceptable ways to work nowadays.Corey: One other thing that I wanted to talk about is, I know that in previous discussions with you folks—sometimes on the podcast sometimes when I more or less corner someone a Tailscale at your developer conference—one of the things that you folks talk about is Tailscale SSH, which is effectively a drop-in replacement for the SSH binary on systems. Full disclosure, I don't use it, mostly because I'm grumpy and I'm old. I also like having some form of separation of duties where you're the network that ties it all together, but something else winds up acting as that authentication step. That said, if I were that interesting that someone wanted to come after me, there are easier ways to get in, so I'm mostly just doing this because I'm persnickety. Are you seeing significant adoption of Tailscale SSH?Maya: I think there's a couple of features that are missing in Tailscale SSH for it to be as adopted by people like you. The main one that I would say is—so right now if you use Tailscale SSH, it runs a binary on the host, you can use your Tailscale credentials, and your Tailscale private key, effectively, to SSH something else. So, you don't have to manage a separate set of SSH keys or certs or whatever it is you want to do to manage that in your network. Your identity provider identity is tied to Tailscale, and then when you connect to that device, we still need to have an identity on the host itself, like in Unix. Right now, that's not tied to Tailscale. You can adopt an identity of something else that's already on the host, but it's not, like, corey@machine.And I think that's the number one request that we're getting for Tailscale SSH, to be able to actually generate or tie to the individual users on the host for an identity that comes from, like, Google, or GitHub, or Okta, or something like that. I'm not hearing a lot of feedback on the security concerns that you're expressing. I think part of that is that we've done a lot of work around security in general so that you feel like if Tailscale were to be compromised, your network wouldn't need to be compromised. So, Tailscale itself is end-to-end encrypted using WireGuard. We only see your public keys; the private keys remain on the device.So, in some sense the, like, quote-unquote, “Worst” that we could do would be to add a node to your network and then start to generate traffic from that or, like, mess with the configuration of your network. These are questions that have come up. In terms of adding nodes to your network, we have a feature called tailnet lock that effectively lets you sign and verify that all the nodes on your network are supposed to be there. One of the other concerns that I've heard come up is, like, what if the binary was compromised. We develop in open-source so you can see that that's the case, but like, you know, there's certainly more stuff we could be doing there to prevent, for example, like a software supply chain security attack. Yeah.Corey: Yeah, but you also have taken significant architectural steps to ensure that you are not placed in a position of undue trust around a lot of these things. Most recently, you raised a Series B, that was $100 million, and the fact that you have not gone bankrupt in the year since that happened tells me that you are very clearly not routing all customer traffic through you folks, at least on one of the major cloud providers. And in fact, a little bit of playing a-slap-and-tickle with Wireshark affirm this, that the nodes talk to each other; they do not route their traffic through you folks, by design. So one, great for the budget, I have respect for that data transfer pattern, but also it means that you are in the position of being a global observer in a way that can be, in many cases, exploited.Maya: I think that's absolutely correct. So, it was 18 months ago or so that we raised our Series B. When you use Tailscale, your traffic connects peer-to-peer directly between nodes on your network. And that has a couple of nice properties, some of what you just described, which is that we don't see your traffic. I mean, one, because it's end-to-end encrypted, but even if we could capture it, and then—we're not in the way of capturing it, let alone decrypting it.Another nice property it has is just, like, latency, right? If your user is in the UK, and they're trying to access something in Scotland, it's not, you know, hair-pinning, bouncing all the way to the West Coast or something like that. It doesn't have to go through one of our servers to get there. Another nice property that comes with that is availability. So, if our network goes down, if our control plane goes down, you're temporarily not able to add nodes or change your configuration, but everything in your network can still connect to each other, so you're not dependent on us being online in order for your network to work.And this is actually coming up more and more in customer conversations where that's a differentiator for us versus a competitor. Different competitors, also. There's a customer case study on our website about somebody who was POC'ing us with a different option, and literally during the POC, the competitor had an outage, unfortunately for them, and we didn't, and they sort of looked at our model, our deployment model and went, “Huh, this really matters to us.” And not having an outage on our network with this solution seems like a better option.Corey: Yeah, when the network is down, the computers all turn into basically space heaters.Maya: [laugh]. Yeah, as long as they're not down because, I guess, unplugged or something. But yeah, [laugh] I completely agree. Yeah. But I think there's a couple of these kinds of, like, enterprise things that people are—we're starting to do a better job of explaining and meeting customers where they are, but it's also people are realizing actually does matter when you're deploying something at this scale that's such a key part of your network.So, we talked a bit about availability, we talked a bit about things like latency. On the security side, there's a lot that we've done around, like I said, tailnet lock or that type of thing, but it's like some of the basic security features. Like, when I joined Tailscale, probably the first thing I shipped in some sense as a PM was a change log. Here's the change log of everything that we're shipping as part of these releases so that you can have confidence that we're telling you what's going on in your network, when new features are coming out, and you can trust us to be part of your network, to be part of your infrastructure.Corey: I do want to further call out that you have a—how should I frame this—a typically active security notification page.Maya: [laugh].Corey: And I think it is easy to misconstrue that as look at how terrifyingly insecure this is? Having read through it, I would argue that it is not that you are surprisingly insecure, but rather that you are extraordinarily transparent about things that are relatively minor issues. And yes, they should get fixed, but, “Oh, that could be a problem if six other things happen to fall into place just the right way.” These are not security issues of the type, “Yeah, so it turns out that what we thought was encrypting actually wasn't and we're just expensive telnet.” No, there's none of that going on.It's all been relatively esoteric stuff, but you also address it very quickly. And that is odd, as someone who has watched too many enterprise-facing companies respond to third-party vulnerability reports with rather than fixing the problem, more or less trying to get them not to talk about it, or if they do, to talk about it only using approved language. I don't see any signs of that with what you've done there. Was that a challenging internal struggle for you to pull off?Maya: I think internally, it was recognizing that security was such an important part of our value proposition that we had to be transparent. But once we kind of got past that initial hump, we've been extremely transparent, as you say. We think we can build trust through transparency, and that's the most important thing in how we respond to security incidents. But code is going to have bugs. It's going to have security bugs. There's nothing you can do to prevent that from happening.What matters is how you—and like, you should. Like, you should try to catch them early in the development process and, you know, shift left and all that kind of stuff, but some things are always going to happen [laugh] and what matters in that case is how you respond to them. And having another, you know, an app update that just says “Bug fixes” doesn't help you figure out whether or not you should actually update, it doesn't actually help you trust us. And so, being as public and as transparent as possible about what's actually happening, and when we respond to security issues and how we respond to security issues is really, really important to us. We have a policy that talks about when we will publish a bulletin.You can subscribe to our bulletins. We'll proactively email anyone who has a security contact on file, or alternatively, another contact that we have if you haven't provided us a security contact when you're subject to an issue. I think by far and large, like, Tailscale has more security bulletins just because we're transparent about them. It's like, we probably have as many bugs as anybody else does. We're just lucky that people report them to us because they see us react to them so quickly, and then we're able to fix them, right? It's a net positive for everyone involved.Corey: It's one of those hard problems to solve for across the board, just because I've seen companies in the past get more or less brutalized by the tech press when they have been overly transparent. I remember that there was a Reuters article years ago about Slack, for example, because they would pull up their status history and say, “Oh, look at all of these issues here. You folks can't keep your website up.” But no, a lot of it was like, “Oh, file uploads for a small subset of our users is causing a problem,” and so on and so forth. These relatively minor issues that, in aggregate, are very hard to represent when you're using traffic light signaling.So, then you see people effectively going full-on AWS status page where there's a significant outage lasting over a day, last month, and what you see on this is if you go really looking for it is this yellow thing buried in his absolute sea of green lights, even though that was one of the more disruptive things to have happened this year. So, it's a consistent and constant balance, and I really have a lot of empathy no matter where you wind up landing on that?Maya: Yeah, I think that's—you're saying it's sort of about transparency or being able to find the right information. I completely agree. And it's also about building trust, right? If we set expectations as to how we will respond to these things then we consistently respond to them, people believe that we're going to keep doing that. And that is almost more important than, like, committing to doing that, if that makes any sense.I remember having a conversation many years ago with an eng manager I worked with, and we were debating what the SLO for a particular service should be. And he sort of made an interesting point. He's like, “It doesn't really matter what the SLO is. It matters what you actually do because then people are going to start expecting [laugh] what you actually do.” So, being able to point at this and say, “Yes, here's what we say and here's what we actually do in practice,” I think builds so much more trust in how we respond to these kinds of things and how seriously we take security.I think one of the other things that came out of the security work is we realized—and I think you talked to Avery, the CEO of Tailscale on a prior podcast about some of this stuff—but we realized that platforms are broken, and we don't have a great way of pushing automatic updates on a lot of platforms, right? You know, if you're using the macOS store, or the Android Play Store, or iOS or whatever, you can automatically update your client when there is a security issue. On other platforms, you're kind of stuck. And so, as a result of us wanting to make sure that the fleet is as updated as possible, we've actually built an auto-update feature that's available on all of our major clients now, so people can opt in to getting those updates as quickly as needed when there is a security issue. We want to expose people to as little risk as possible.Corey: I am not a Tailscale customer. And that bugs me because until I cross that chasm into transferring $1 every month from my bank account to yours, I'm just a whiny freeloader in many respects, which is not at all how you folks who never made me feel I want to be very clear on that. But I believe in paying for the services that empower me to do my job more effectively, and Tailscale absolutely qualifies.Maya: Yeah, understood, I think that you still provide value to us in ways that aren't your data, but then in ways that help our business. One of them is that people like you tend to bring Tailscale to work. They tend to have a good experience at home connecting to their Synology, helping their brother connect to his bank account, whatever it happens to be, and they go, “Oh.” Something kind of clicks, and then they see a problem at work that looks very similar, and then they bring it to work. That is our primary path of adoption.We are a bottom-up adoption, you know, product-led growth product [laugh]. So, we have a blog post called “How Our Free Plan Stays Free” that covers some of that. I think the second thing that I don't want to undersell that a user like you also does is, you have a problem, you hit an issue, and you write into support, and you find something that nobody else has found yet [laugh].Corey: I am very good at doing that entirely by accident.Maya: [laugh]. But that helps us because that means that we see a problem that needs to get fixed, and we can catch it way sooner than before it's deployed, you know, at scale, at a large bank, and you know, it's a critical, kind of, somebody's getting paged kind of issue, right? We have a couple of bugs like that where we need, you know, we need a couple of repros from a couple different people in a couple different situations before we can really figure out what's going on. And having a wide user base who is happy to talk to us really helps us.Corey: I would say it goes beyond that, too. I have—I see things in the world of Tailscale that started off as features that I requested. One of the more recent ones is, it is annoying to me to see on the Tailscale machines list everything I have joined to the tailnet with that silly little up arrow next to it of, “Oh, time to go back and update Tailscale to the latest,” because that usually comes with decent benefits. Great, I have to go through iteratively, or use Ansible, or something like that. Well, now there's a Tailscale update option where it will keep itself current on supported operating systems.For some unknown reason, you apparently can't self-update the application on iOS or macOS. Can't imagine why. But those things tend to self-update based upon how the OS works due to all the sandboxing challenges. The only challenge I've got now is a few things that are, more or less, embedded devices that are packaged by the maintainer of that embedded system, where I'm beholden to them. Only until I get annoyed enough to start building a CI/CD system to replace their package.Maya: I can't wait till you build that CI/CD system. That'll be fun.Corey: “We wrote this code last night. Straight to the bank with it.” Yeah, that sounds awesome.Maya: [laugh] You'd get a couple of term sheets for that, I'm sure.Corey: There are. I am curious, looping back to the start of our conversation, we talked about enterprise security requirements, but how do you address enterprise change management? I find that that's something an awful lot of companies get dreadfully wrong. Most recently and most noisily on my part is Slack, a service for which I paid thousands of dollars a year, decided to roll out a UI redesign that, more or less, got in the way of a tremendous number of customers and there was no way to stop it or revert it. And that made me a lot less likely to build critical-flow business processes that depended upon Slack behaving a certain way.Just, “Oh, we decided to change everything in the user interface today just for funsies.” If Microsoft pulled that with Excel, by lunchtime they'd have reverted it because an entire universe of business users would have marched on Redmond to burn them out otherwise. That carries significant cost for businesses. Yet I still see Tailscale shipping features just as fast as you ever have. How do you square that circle?Maya: Yeah. I think there's two different kinds of change management really, which is, like—because if you think about it, it's like, an enterprise needs a way to roll out a product or a feature internally and then separately, we need a way to roll out new things to customers, right? And so, I think on the Tailscale side, we have a change log that tells you about everything that's changing, including new features, and including changes to the client. We update that religiously. Like, it's a big deal, if something doesn't make it the day that it's supposed to make it. We get very kind of concerned internally about that.A couple of things that were—that are in that space, right, we just talked about auto-updates to make it really easy for you to maintain what's actually rolled out in your infrastructure, but more importantly, for us to push changes with a new client release. Like, for example, in the case of a security incident, we want to be able to publish a version and get it rolled out to the fleet as quickly as possible. Some of the things that we don't have here, but although I hear requests for is the ability to, like, gradually roll out features to a customer. So like, “Can we change the configuration for 10% of our network and see if anything breaks before rolling back, right before rolling forward.” That's a very traditional kind of infra change management thing, but not something I've ever seen in, sort of, the networking security space to this degree, and something that I'm hearing a lot of customers ask for.In terms of other, like, internal controls that a customer might have, we have a feature called ACL Tests. So, if you're going to change the configuration of who can access what in your network, you can actually write tests. Like, your permission file is written in HuJSON and you can write a set of things like, Corey should be able to access prod. Corey should not be able to access test, or whatever it happens to be—actually, let's flip those around—and when you have a policy change that doesn't pass those tests, you actually get told right away so you're not rolling that out and accidentally breaking a large part of your network. So, we built several things into the product to do it. In terms of how we notify customers, like I said, that the primary method that we have right now is something like a change log, as well as, like, security bulletins for security updates.Corey: Yeah, it's one of the challenges, on some level, of the problem of oh, I'm going to set up a service, and then I'm going to go sail around the world, and when I come back in a year or two—depending on how long I spent stranded on an island somewhere—now I get to figure out what has changed. And to your credit, you have to affirmatively enable all of the features that you have shipped, but you've gone from, “Oh, it's a mesh network where everything can talk to each other,” to, “I can use an exit node from that thing. Oh, now I can seamlessly transfer files from one node to another with tail drop,” to, “Oh, Tailscale Funnel. Now, I can expose my horrifying developer environment to the internet.” I used that one year to give a talk at a conference, just because why not?Maya: [crosstalk 00:27:35].Corey: Everything evolves to become [unintelligible 00:27:37] email on Microsoft Outlook, or tries to be Microsoft Excel? Oh, no, no. I want you to be building Microsoft PowerPoint for me. And we eventually get there, but that is incredibly powerful functionality, but also terrifying when you think you have a handle on what's going on in a large-scale environment, and suddenly, oh, there's a whole new vector we need to think about. Which is why your—the thought and consideration you put into that is so apparent and so, frankly, welcome.Maya: Yeah, you actually kind of made a statement there that I completely missed, which is correct, which is, we don't turn features on by default. They are opt-in features. We will roll out features by default after they've kind of baked for an incredibly long period of time and with, like, a lot of fanfare and warning. So, the example that I'll give is, we have a DNS feature that was probably available for maybe 18 months before we turned it on by default for new tailnets. So didn't even turn it on for existing folks. It's called Magic DNS.We don't want to touch your configuration or your network. We know people will freak out when that happens. Knowing, to your point, that you can leave something for a year and come back, and it's going to be the same is really important. For everyone, but for an enterprise customer as well. Actually, one other thing to mention there. We have a bunch of really old versions of clients that are running in production, and we want them to keep working, so we try to be as backward compatible as possible.I think the… I think we still have clients from 2019 that are running and connecting to corp that nobody's updated. And like, it'd be great if they would update them, but like, who knows what situation they're in and if they can connect to them, and all that kind of stuff, but they still work. And the point is that you can have set it up four years ago, and it should still work, and you should still be able to connect to it, and leave it alone and come back to it in a year from now, and it should still work and [laugh] still connect without anything changing. That's a very hard guarantee to be able to make.Corey: And yet, somehow you've been able to do that, just from the perspective of not—I've never yet seen you folks make a security-oriented decision that I'm looking at and rolling my eyes and amazed that you didn't make the decision the other way. There are a lot of companies that while intending very well have done, frankly, very dumb things. I've been keeping an eye on you folks for a long time, and I would have caught that in public. I just haven't seen anything like that. It's kind of amazing.Last year, I finally took the extraordinary step of disabling SSH access anywhere except the tailnet to a number of my things. It lets my logs fill up a lot less, and you've built to that level of utility-like reliability over the series of longtime experimentation. I have yet to regret having Tailscale in the mix, which is, frankly, not something I can say about almost any product.Maya: Yeah. I'm very proud to hear that. And like, maintaining that trust—back to a lot of the conversation about security and reliability and stuff—is incredibly important to us, and we put a lot of effort into it.Corey: I really appreciate your taking the time to talk to me about how things continue to evolve over there. Anything that's new and exciting that might have gotten missed? Like, what has come out in, I guess, the last six months or so that are relevant to the business and might be useful for people looking to use it themselves?Maya: I was hoping you're going to ask me what came out in the last, you know, 20 minutes while we were talking, and the answer is probably nothing, but you never know. But [laugh]—Corey: With you folks, I wouldn't doubt it. Like, “Oh, yeah, by the way, we had to do a brand treatment redo refresh,” or something on the website? Why not? It now uses telepathy just because.Maya: It could, that'd be pretty cool. No, I mean, lots has gone on in the last six months. I think some of the things that might be more interesting to your listeners, we're now in the AWS Marketplace, so if you want to purchase Tailscale through AWS Marketplace, you can. We have a Kubernetes operator that we've released, which lets you both ingress and egress from a Kubernetes cluster to things that are elsewhere in the world on other infrastructure, and also access the Kubernetes control plane and the API server via Tailscale. I mentioned auto-updates. You mentioned the VS Code extension. That's amazing, the fact that you can kind of connect directly from within VS Code to things on your tailnet. That's a lot of the exciting stuff that we've been doing. And there's boring stuff, you know, like audit log streaming, and that kind of stuff. But it's good.Corey: Yeah, that stuff is super boring until suddenly, it's very, very exciting. And those are not generally good days.Maya: [laugh]. Yeah, agreed. It's important, but boring. But important.Corey: [laugh]. Well, thank you so much for taking the time to talk through all the stuff that you folks are up to. If people want to learn more, where's the best place for them to go to get started?Maya: tailscale.com is the best place to go. You can download Tailscale from there, get access to our documentation, all that kind of stuff.Corey: Yeah, I also just want to highlight that you can buy my attention but never my opinion on things and my opinion on Tailscale remains stratospherically high, so thank you for not making me look like a fool, by like, “Yes. And now we're pivoting to something horrifying is a business model and your data.” Thank you for not doing exactly that.Maya: Yeah, we'll keep doing that. No, no, blockchains in our future.Corey: [laugh]. Maya Kaczorowski, Chief Product Officer at Tailscale. I'm Cloud Economist Corey Quinn, and this is Screaming in the Cloud. This episode has been brought to us by our friends at Tailscale. If you enjoyed this episode, please leave a five-star review on your podcast platform of choice, whereas if you've hated this podcast, please leave a five-star review on your podcast platform of choice along with an angry, insulting comment that will never actually make it back to us because someone screwed up a firewall rule somewhere on their legacy connection.Corey: If your AWS bill keeps rising and your blood pressure is doing the same, then you need The Duckbill Group. We help companies fix their AWS bill by making it smaller and less horrifying. The Duckbill Group works for you, not AWS. We tailor recommendations to your business and we get to the point. Visit duckbillgroup.com to get started.

Dave Mangot, CEO and founder of Mangoteque, joins Coreyon Screaming in the Cloud to explain how leveraging DevOps improves the lives of engineers and results in stronger businesses. Dave talks about the importance of exclusively working for private equity firms that act ethically, the key difference between venture capital and private equity, and how conveying issues and ideas to your CEO using language he understands leads to faster results. Corey and Dave discuss why successful business are built on two things: infrastructure as code and monitoring.About DaveDave Mangot, author of DevOps Patterns for Private Equity, helps portfolio companies get good at delivering software.  He is a leading consultant, author, and speaker as the principal at Mangoteque.  A DevOps veteran, Dave has successfully led digital, SRE, and DevOps transformations at companies such as Salesforce, SolarWinds, and Cable & Wireless. He has a proven track record of working with companies to quickly mature their existing culture to improve the speed, frequency, and resilience of their software service delivery.Links Referenced: Mangoteque: https://www.mangoteque.com DevOps Patterns for Private Equity: https://www.amazon.com/DevOps-Patterns-Private-Equity-organization/dp/B0CHXVDX1K “How to Talk Business: A Short Guide for Tech Leaders”: https://itrevolution.com/articles/how-to-talk-business-a-short-guide-for-tech-leaders/ TranscriptAnnouncer: Hello, and welcome to Screaming in the Cloud with your host, Chief Cloud Economist at The Duckbill Group, Corey Quinn. This weekly show features conversations with people doing interesting work in the world of cloud, thoughtful commentary on the state of the technical world, and ridiculous titles for which Corey refuses to apologize. This is Screaming in the Cloud.Corey: Welcome to Screaming in the Cloud. I'm Corey Quinn. My guest today is someone that I have known for, well, longer than I've been doing this show. Dave Mangot is the founder and CEO at Mangoteque. Dave, thank you for joining me.Dave: Hey, Corey, it's great to be here. Nice to see you again.Corey: I have to say, your last name is Mangot and the name of your company is Mangoteque, spelled M-A-N-G-O-T-E-Q-U-E, if I got that correctly, which apparently I did. What an amazing name for a company. How on earth did you name a company so well?Dave: Yeah, I don't know. I have to think back, a few years ago, I was just getting started in consulting, and I was talking to some friends of mine who were giving me a bunch of advice—because they had been doing consulting for quite some time—about what my rates should be, about all kinds of—you know, which vendors I should work with for my legal advice. And I said, “I'm having a lot of trouble coming up with a name for the company.” And this guy, Corey Quinn, was like, “Hey, I got a name for you.” [laugh].Corey: I like that story, just because it really goes to show the fine friends of mine over at all of the large cloud services companies—but mostly AWS—that it's not that hard to name something well. The trick, I think, is just not to do it in committee.Dave: Yeah. And you know, it was a very small committee obviously of, like, three. But yeah, it's been great. I have a lot of compliments on the name of my company. And I was like, oh, “You know that guy, the QuinnyPig dude?” And they're like, “Yeah?” “Oh, yeah, it was—that was his idea.” And I liked it. And it works really well for the things that I do.Corey: It seems to. So, talk to you about what it is that you do because back when we first met and many, many years ago, you were an SRE manager at a now defunct observability company. This was so long ago, I don't think that they used the term observability. It was Librato, which, “What do you do?” “We do monitoring,” back when that didn't sound like some old-timey thing. Like, “Oh, yeah. Right, between the blacksmith and the cobbler.” But you've evolved significantly since you were doing the mundane, pedestrian tasks of keeping the service up and running. What do you do these days?Dave: Yeah, that was before the observability wars [laugh] [whatever you like 00:02:55] to call it. But over time, that company was owned by SolarWinds and I wound up being responsible for all the SolarWinds cloud company SRE organizations. So, started—ran a global organization there. And they were owned by a couple of private equity firms. And I got to know one of the firms rather well, and then when I left SolarWinds, I started working with private equity firm portfolio companies, especially software investments. And what I like to say is I teach people how to get good at delivering software.Corey: So, you recently wrote a book, and I know this because I make it a point to get a copy of the book—usually by buying it, but you beat me to it by gifting me one—of every guest I have on the show who's written a book. Sometimes that means I wind up with the eclectic collections of poetry, other times, I wind up with a number of different books around the DevOps and cloud space. And one of these days, I'm going to wind up talking to someone who wound up writing an encyclopedia or something, to where I have to back the truck around. But what I wanted to ask is about your title, of all things. It's called DevOps Patterns for Private Equity. And I have to ask, what makes private equity special?Dave: I think as a cloud economist, what you also just told me, is you owe me $17.99 for the book because it was gifted.Corey: Is that how expensive books are these days? My God, I was under the impression once you put the word ‘DevOps' in the title, that meant you're above 40 bucks, just as, you know, entrance starting fees here.Dave: I think I need to talk to my local cloud economist on how to price things. Yeah, the book is about things that I've basically seen at portfolio companies over the years. The thing about, you know, why private equity, I think it would be one question, just because I've been involved in the DevOps movement since pretty much the start, when John Willis calls me a DevOps OG, which I think is a compliment. But the thing that I like about working with private equity, and more specifically, private equity portfolio companies is, like I wrote in the book, they're serious. And serious means that they're not afraid to make a big investment, they're not afraid to change things quickly, they're not afraid to reorganize, or rethink, or whatever because a lot of these private equity firms have, how they describe it as a three to five year investment thesis. So, in three to five years, they want to have some kind of an exit event, which means that they can't just sit around and talk about things and try it and see what happens—Corey: In the fullness of time, 20 years from now. Yeah, it doesn't work that well. But let's back up a little bit here because something that I have noticed over the years is that, especially when it comes to financial institutions, the general level of knowledge is not terrific. For a time, a lot of people were very angry at Goldman Sachs, for example. But okay, fair enough. What does Goldman Sachs do? And the answer was generally incoherent.And again, I am in no way, shape or form, different from people who form angry opinions without having all of the facts. I do that myself three times before breakfast. My last startup was acquired by BlackRock, and I was the one that raised our hand internally, at the 40-person company when that was announced, as everyone was sort of sitting there stunned: “What's a BlackRock?” Because I had no idea. Well, for the next nine months, I assure you, I found out what a BlackRock is. But what is private equity? Because I see a lot of them getting beaten up for destroying companies. Everyone wants to bring up the Toys-R-Us story as a for instance. But I don't get the sense that that is the full picture. Tell me more.Dave: Yes. So, I'm probably not the best spokesperson for private equity. But—Corey: Because you don't work for a private equity firm, you only work with them, that makes you a terrific spokesperson because you're not [in 00:06:53] this position of, “Well, justify what your company does here,” situation, there's something to be said for objectivity.Dave: So, you know, like I wrote in the book, there are approximately 10,000 private equity firms in the United States. They are not all going to be ethical. That is just not a thing. I choose to work with a specific segment of private equity companies, and these private equity companies want to make a good business. That's what they're going for.And you and I, having had worked at many companies in our careers, know that there's a lot of companies out there that aren't a good business. You're like, “Why are we doing this? This doesn't make any sense. This isn't a good investment. This”—there's a lot of things and what I would call the professional level private equity firms, the ones at the top—and not all of them at the top are ethical, don't get me wrong; I have a blacklist here of companies I won't work for. I will not say who those companies are.Corey: I am in the same boat. I think that anyone who works in an industry at all and doesn't have a list of companies that they would not do business with, is, on some level, either haven't thought it through, hasn't been in business long enough, or frankly, as long as you're paying them, everything you can do is a-okay. And you know, I'm not going to sit here and say that those are terrible people, but I never wanted to do that soul-searching. I always thought the only way to really figure out where you stand is to figure it out in advance before there's money on the table. Like, do you want to go do contracting for a defense company? Well no, objectively, I don't, but that's a lot harder to say when they're sitting on the table with $20 million in front of you of, “Do you want to work with a defense company?” Because you can rationalize your way into anything when the stakes are high enough. That's where I've always stood on it. But please, continue.Dave: I'd love to be in that situation to turn down $20 million [laugh].Corey: Yeah, that's a hard situation to find yourself in, right?Dave: But regardless, there's a lot of different kinds of private equity firms. Generally the firms that I work with, they all want—not generally; the ones I work with want to make better companies. I have had operating partners at these companies tell me—because this always comes up with private equity—there's no way to cut your way to a good company. So, the private equity firms that I work with invest in these companies. Do they sell off unprofitable things? Of course they do. Do they try to streamline some things sometimes so that the company is only focused on X or Y, and then they tuck other companies into it—that's called a buy and build strategy or a platform strategy—yes. But the purpose of that is to make a better company.The thing that I see a lot of people in our industry—meaning, like, us tech kind of folks—get confused about is what the difference is between venture capital and private equity. And private equity, in general, is the thing that is the kind of financing that follows on after venture capital. So, in venture capital, you are trying to find product-market fit. The venture capitalists are putting all their bets down like they're in Vegas at re:Invent, and trying to figure out which bet is going to pay off, but they have no expectation that all of the bets are going to pay off. With private equity, the companies have product-market fit, they're profitable. If they're not profitable, they have a very clear line to profitability.And so, what these private equity firms are trying to do, no matter what the size of the company is, whether it's a 50-person company or a 5000-person company, they're trying to get these companies up to another level so that they're more profitable and more valuable, so that either a larger fish will gobble them up or they'll go out on the public markets, like onto the stock market, those kinds of things, but they're trying to make a company that's more valuable. And so, not everything looks so good [laugh] when you're looking at it from the outside, not understanding what these people are trying to do. That's not to say they're not complete jerks who are in private equity because there are.Corey: Because some parts are missing. Kidding. Kidding. Kidding.Dave: [laugh].Corey: It's a nuanced area, and it's complicated, just from the perspective of… finance is deceptively complicated. It looks simple, on some level, because on some level, you can always participate in finance. I have $10. I want to buy a thing that costs $7. How does that work? But it gets geometrically more complex the further you go. Financial engineering is very much a thing.And it is not at all obvious how those things interplay with different dynamics. One of the private equity outcomes, as you alluded to a few minutes ago, is the idea that they need to be able to rapidly effect change. It becomes a fast turnaround situation, and then have an exit event of some kind. So, the DevOps patterns that you write about are aligned with an idea of being effective, presumably, rather than, well, here's how you slowly introduce a sweeping cultural mindset shift across the organization. Like, that's great, but some of us don't have that kind of runway for what we're trying to achieve to be able to pull that off. So, I'm assuming that a lot of the patterns you talk about are emphasizing rapid results.Dave: Well, I think the best way to describe this, right, is what we've talked about is they want to make a better company. And for those of us who have worked in the DevOps movement for all these years, what's one great way of making a better company? Adopting DevOps principles, right? And so, for me, one of the things I love about my job is I get to go in and make engineers' lives better. No more working on weekends, no more we're only going to do deployments at 11 o'clock at night, no more we're going to batch things up and ship them three or four times a year, which all of us who've done DevOps stuff for years know, like, fastest way to have a catastrophe is batch up as many things as possible and release them all at once.So like, for me, I'm going in making engineers' lives better. When their lives are better, they produce better results because they're not stressed out, they're not burned out, they get to spend time with their families, all those kinds of things. When they start producing better results, the executives are happier. The executives can go to the investors and show all the great results they're getting, so the investors are happier. So, for me, I always say, like, I'm super lucky because I have a job that's win, win, win.And like, I'm helping them to make a better company, I'm helping them to ship faster, I'm helping them do things in the cloud, I'm helping them get more reliability, which helps them retain customers, all these things. Because we know from the—you know, remember the 2019 State of DevOps Report: highest performers are twice as likely to meet or exceed their organization's performance goals, and those can be customer retention, revenue, whatever those goals are. And so, I get to go in and help make a better company because I'm making people's lives better and, kind of, everybody wins. And so, for me, it's super rewarding.Corey: That's a good way of framing it. I have to ask, since the goal for private equity, as you said, is to create better companies, to effectively fix a bunch of things that, for better or worse, had not been working optimally. Let me ask the big, dumb, naive question here. Isn't that ostensibly the goal of every company? Now, everyone says it's their goal, but whether that is their goal or not, I think, is a somewhat separate question.Dave: Yeah. I—that should be the goal of every company, I agree. There are people who read my book and said, “Hey, this stuff applies far beyond private equity.” And I say, “Yeah, it absolutely does.” But there are constraints—[gold rat 00:15:10]—within private equity, about the timing, about the funding, about whatever, to get the thing to another level. And that's an interesting thing that I've seen is I've seen private equity companies take a company up to another level, have some kind of exit event, and then buy that company again years later. Which, like, what? Like, how could that be?Corey: I've seen that myself. It feels, on some level, like that company goes public, and then goes private, then goes public, then goes private to the same PE firm, and it's like, are you really a PE company or are you just secretly a giant cat, perpetually on the wrong side of a door somewhere?Dave: But that's because they will take it to a level, the company does things, things happen out in the market, and then they see another opportunity to grow them again. Where in a regular company—in theory—you're going to want to just get better all the time, forever. This is the Toyota thesis about continual improvement.Corey: I am curious as far as what you are seeing changing in the market with the current macroeconomic conditions, which is a polite way to say the industry going wonky after ten years of being relatively up and to the right.Dave: Yeah, well, I guess the fun thing is, we have interest rates, we had a pandemic, we had [laugh], like, all this exciting stuff. There's, you know, massive layoffs, [unintelligible 00:16:34] and then all this, kind of like, super churn-y things. I think the fun thing for me is, I went to a private equity conference in San Francisco, I don't know, a month ago or something like that, and they had all these panelists on stage pontificating about this and that and the other thing, and one of the women said something that I thought was really great, especially for someone like me. She said, “The next five to ten years in private equity are going to be about growth and operational efficiency.” And I was like, “That's DevOps. That's awesome.” [laugh].That really works well for me because, like, we want to have people twice as likely to meet or exceed their organization's performance goals. That's growth. And we want operational efficiency, right? Like, stop manually copying files around, start putting stuff in containers, do all these things that enable us to go fast speed and also do that with high quality. So, if the next five to ten years are going to be about growth and operational efficiency, I think it's a great opportunity for people to take in a lot of these DevOps principles.And so, the being on the Screaming in the Cloud podcast, like, I think cloud is a huge part of that. I think that's a big way to get growth and operational efficiency. Like, how better to be able to scale? How better to be able to Deming's PDSA cycle, right—Plan, Do, Study, Act—how better to run all these experiments to find out, like, how to get better, how to be more efficient, how to meet our customers' demands. I think that's a huge part of it.Corey: That is, I think, a very common sentiment as far as how folks are looking at things from a bigger picture these days. I want to go back as well to something you said earlier that I was joking around at the start of the episode about, “Wow, what an amazing name for the company. How did you come up with it?” And you mentioned that you had been asking a bunch of people for advice—or rather, you mentioned you had gotten advice from people. I want to clarify, you were in fact asking. I wasn't basically the human form of Clippy popping up, “It looks like you're starting a business. Let me give you unsolicited advice on what you should be doing.”What you've done, I think, is a terrific example of the do what I say not what I do type of problem, where you have focused on your positioning on a specific segment of the market: private equity firms and their portfolio companies. If I had been a little bit smarter, I would have done something similar in my own business. I would fix AWS bills for insurance companies in the Pacific Northwest or something like that, where people can hear the type of company they are reflected in the name of what it is that you do. I was just fortunate enough or foolish enough to be noisy enough in order to talk about what I do in a way that I was able to overcome that. But targeting the way that you have, I think is just so spot on. And it's clearly working out for you.Dave: I think a Corey Quinn Clippy would be very distracting in [laugh] my Microsoft Word, first of all [laugh]. Second of all—Corey: They're calling it Copilot now.Dave: [laugh]—there's this guy Corey and his partner Mike who turned me on to this guy, Jonathan Stark, who has his theory about your business. He calls it, like, elucidating, like, a Rolodex moment. So, if somebody's talking about X or Y, and they say, “Oh, yeah. You want to talk to Corey about that.” Or, “You want to talk to Mike about that.”And so, for me, working with private equity portfolio companies, that's a Rolodex moment. When people are like, “I'm at a portfolio company. We just got bought. They're coming in, and they want to understand what our spend is on the cloud, and this and that. Like, I don't know what I'm supposed to do here.” A lot of times people think of me because I tend to work on those kinds of problems. And so, it doesn't mean I can't work on other things, and I definitely do work on other things, I've definitely worked with companies that are not owned by private equity, but for me, that's really a place that I enjoy working, and thankfully, I get Rolodex moments from those things.Corey: That's the real value that I've found. The line I've heard is always it's not just someone at a party popping up and saying, “Oh, yeah, I have that problem.” But, “Oh, my God, you need to talk to this person I know who has that problem.” It's the introduction moment. In my case at least, it became very hard for me to find people self-identifying as having large AWS bills, just because, yeah, individual learners or small startup founders, for example, might talk about it here and there, but large companies do not tend to complain about that in Twitter because that tends to, you know, get them removed from their roles when they start going down that path. Do you find that it is easier for you to target what you do to people because it's easier to identify them in public? Because I assure you, someone with a big AWS bill is hard to spot out of a crowd.Dave: Well, I think you need to meet people where they are, I think is probably the best way of saying that. So, if you are—and this isn't something I need to explain to you, obviously, so this is more for your listeners, but like, if you're going to talk about, “Hey, I'm looking for companies with large AWS bills,” [pthhh] like that's, maybe kind of whatever. But if you say, “Hey, I want to improve your margins and your operational efficiencies,” all of a sudden, you're starting to speak their language, right? And that language is where people start to understand that, “Hey, Corey's talking about me.”Corey: A large part of how I talk about this was shaped by some of the early conversations I had. The way that I think about this stuff and the way that I talk is not necessarily what terms my customers use. Something that I found that absolutely changed my approach was having an investigative journalist—or a former investigative journalist, in this case—interview people I'd worked with to get case studies and testimonials from them. But what she would also do was get the exact phrasing that they use to describe the value that I did, and how they talked about what we'd done. Because that became something that was oh, you're effectively writing the rough draft of my marketing copy when you do that. Speaking in the language of your customer is so important, and I meet a lot of early-stage startups that haven't quite unlocked that bit of insight yet.Dave: And I think looking at that from a slightly different perspective is also super important. So, not only speaking the language of your customer, but let's say you're not a consultant like me or you. Let's say you work inside of a company. You need to learn to speak the language of business, right? And this is, like, something I wrote about in the beginning of the book about the guy in San Francisco who got locked up for not giving away the Cisco passwords, and Gavin Newsom had to go to his jail cell and all this other crazy stuff that happened is, technologists often think that the reason that they go to work is to play with technology. The reason we go to work is to enable the business.And—so shameless plug here I—wrote a paper that came out, like, two months ago with IT Revolution—so the people who do The Phoenix Project, and Accelerate, and The DevOps Handbook, and all that other stuff, I wrote this paper with, like, Courtney Kissler, and Paul Gaffney, and Scott Nasello, and a whole bunch of amazing technologists, but it's about speaking the language of business. And as technologists, if we want to really contribute and feel like the work that we're doing is contributing and valuable, you need to start understanding how those other people are talking. So, you and I were just talking about, like, operational efficiencies, and margins, and whatever. What is all that stuff? And figuring that out and being able to have that conversation with your CEO or whoever, those are the things that get people to understand exactly what you're trying to do, and what you're doing, and why this thing is so important.I talk to so many engineers that are like, “Ah, I talked to management and they just don't understand, and [da-dah].” Yeah, they don't understand because you're speaking technology language. They don't want to hear about, like, CNCF compliant this, that, and the—that doesn't mean anything to them. You need to understand in their lang—talk to them and their language and say like, “Hey, this is why this is good for the business.” And I think that's a really important thing for people to start to learn.Corey: So, a question that I have, given that you have been doing this stuff, I think, longer than I have, back when cloud wasn't really a thing, and then it was a thing, but it seemed really irresponsible to do. And then it went through several more iterations to the point where now it's everywhere. What's your philosophy of cloud?Dave: So, I'll go back to something that just came out, the 2023 State of DevOps Report just came out. I follow those things pretty closely. One of the things they talked about in the paper is one of the key differentiators to get your business to have what they call high organizational performance—again, this [laugh] is going back to business talk again—is what they call infrastructure flexibility. And I just don't think you can get infrastructure flexibility if you're not in the cloud. Can you do it? Absolutely.You know, back over a decade ago, I built out a bunch of stuff in a data center on what I called cloud principles. We could shoot things in the head, get new ones back, we did all kinds of things, we identified SKUs of, like, what kind of classes of machines we had. All that looks like a lot of stuff that you would just do in AWS, right? Like, I know, my C instances are compute. I know my M instances are memory. Like, they're all just SKUs, right?Corey: Yeah, that changed a little bit now to the point where they have so many different instance families that some of their names look like dumps of their firmware.Dave: [laugh]. That is probably true. But like, this idea that, like, I want to have this infrastructure flexibility isn't just my idea that it's going to turn out well. Like, the State of DevOps Report kind of proves it. And so, for me, like, I go back to some of the principles of the DevOps movement, and like, if you look at the DORA metrics, let's say you've got deployment frequency and lead time for changes. That's speed: how fast can I do something? And you've got time-to-recover, and you've got change failure rate. That's quality: how much can I ship without having problems, and how fast can I recover when I do?And I think this is one of the things I teach to a lot of my clients about moving into the cloud. If you want to be successful, you have to deliver with speed and quality. Speed: Infrastructure as Code, full stop. If I want to be able to go fast, I need to be able to destroy an environment, bring a new environment up, I need to be able to do that in minutes. That's speed.And then the second requirement, and the only other requirement, is build monitoring in from the start. Everything gets monitored. And that's quality. Like, if I monitor stuff, I know when I've deployed something that's spiking CPU. If that's monitored, I know that this thing is costing me a hell of a lot more than other things. I know all this stuff. And I can do capacity planning, I can do whatever the heck I want. But those are the two fundamental things: Infrastructure as Code and monitoring.And yes, like you said, I worked at a monitoring or observability company, so perhaps I'm slightly biased, but what I've seen is, like, companies that adopt those two principles, and everything else comes from that—so all my Kubernetes stuff and all those other things are not at odds with those principles—those are the people who actually wind up doing really well. And I think those are the people that have—State of DevOps Report—infrastructure flexibility, and that enables them to have higher organizational performance.Corey: I think you're onto something. Like, I still remember the days of having to figure out the number of people who you had in your ops team versus how many servers they could safely and reasonably run. And now that question has little, if any, meaning. If someone asked me, “Okay, so we're running right now 10,000 instances in our cloud environment. How many admins should it take us to run those?” The correct response is, “How the heck are you running those things?” Like, tell me more because the answer is probably terrifying. Because right now, if you do that correctly, it's you want to make a change to all of them or some subset of them? You change a parameter somewhere and computers do the heavy lifting.Dave: Yeah, I ran a content delivery network for cable and wireless. We had three types of machines. You know, it was like Windows Media Server and some squid-cache thing or whatever. And it didn't matter how many we had. It's all the same. Like, if I had 10,000 and I had 50,000, it's irrelevant. Like, they're all the same kind of crap. It's not that hard to manage a bunch of stuff that's all the same.If I have 10,000 servers and each one is a unique, special snowflake because I'm running in what I call a hosted configuration, I have 10,000 customers, therefore I have 10,000 servers, and each of them is completely different than the other, then that's going to be a hell of a lot harder to manage than 10,000 things that the load balancer is like [bbbrrrp bbbrrrp] [laugh] like, just lay it out. So, it's sort of a… kind of a nonsense question at this point. Like you're saying, like, it doesn't really matter how many. It's complexity. How much complexity do I have? And as we all say, in the DevOps movement, complexity isn't free. Which I'll bet is a large component of how you save companies money with The Duckbill Group.Corey: It goes even beyond that because cloud infrastructure is always less expensive than the people working on it, unless you do something terrifying. Otherwise, everything should be running an EC2 instances. Nothing higher-level built on top of it because if people's time is free, the cheapest thing you're going to get is a bunch of instances. The end. That is not really how you should be thinking about this.Dave: [laugh]. I know a lot of private equity firms that would love to find a place where time was free [laugh]. They could make a lot of money.Corey: Yeah. Pretty sure that the biggest—like, “What's your biggest competitive headwind?” You know [laugh], “Wage laws.” Like it doesn't work that way. I'm sorry, but it doesn't [laugh].I really want to thank you for taking the time to talk to me about what you're up to, how things are going over in your part of the universe. If people want to learn more, where's the best place for them to go to find you?Dave: They can go to mangoteque.com. I've got all the links to my blog, my mailing list. Definitely, if you're interested in this intersection of DevOps and private equity, sign up for the mailing list. For people who didn't get Corey's funky spelling of my last name, it is a play on the fact that it is French and I also work with technology companies. So, it's M-A-N-G-O-T-E-Q-U-E dot com.If you type that in—Mangoteque—to any search engine, obviously, you will find me. I am not difficult to find on the internet because I've been doing this for quite some time. But thank you for having me on the show. It's always great to catch up with you. I love hearing about what you're doing. I super appreciate you're asking me about the things that I'm working on, and you know, been a big help.Corey: No, it's deeply fascinating. It's neat to watch you continue to meet your market in a variety of different ways. Dave Mangot, CEO and founder of Mangoteque, which is excellently named. I'm Cloud Economist Corey Quinn, and this is Screaming in the Cloud. If you've enjoyed this podcast, please leave a five-star review on your podcast platform of choice, whereas if you've hated this episode, please leave a five-star review on your podcast platform of choice, along with an angry comment almost certainly filled with incoherent screaming because you tuned out just as soon as you heard the words ‘private equity.'Corey: If your AWS bill keeps rising and your blood pressure is doing the same, then you need The Duckbill Group. We help companies fix their AWS bill by making it smaller and less horrifying. The Duckbill Group works for you, not AWS. We tailor recommendations to your business, and we get to the point. Visit duckbillgroup.com to get started.

Amir Szekely, Owner at CloudSnorkel, joins Corey on Screaming in the Cloud to discuss how he got his start in the early days of cloud and his solo project, CloudSnorkel. Throughout this conversation, Corey and Amir discuss the importance of being pragmatic when moving to the cloud, and the different approaches they see in developers from the early days of cloud to now. Amir shares what motivates him to develop open-source projects, and why he finds fulfillment in fixing bugs and operating CloudSnorkel as a one-man show. About AmirAmir Szekely is a cloud consultant specializing in deployment automation, AWS CDK, CloudFormation, and CI/CD. His background includes security, virtualization, and Windows development. Amir enjoys creating open-source projects like cdk-github-runners, cdk-turbo-layers, and NSIS.Links Referenced: CloudSnorkel: https://cloudsnorkel.com/ lasttootinaws.com: https://lasttootinaws.com camelcamelcamel.com: https://camelcamelcamel.com github.com/cloudsnorkel: https://github.com/cloudsnorkel Personal website: https://kichik.com TranscriptAnnouncer: Hello, and welcome to Screaming in the Cloud with your host, Chief Cloud Economist at The Duckbill Group, Corey Quinn. This weekly show features conversations with people doing interesting work in the world of cloud, thoughtful commentary on the state of the technical world, and ridiculous titles for which Corey refuses to apologize. This is Screaming in the Cloud.Corey: Welcome to Screaming in the Cloud. I'm Corey Quinn, and this is an episode that I have been angling for for longer than you might imagine. My guest today is Amir Szekely, who's the owner at CloudSnorkel. Amir, thank you for joining me.Amir: Thanks for having me, Corey. I love being here.Corey: So, I've been using one of your open-source projects for an embarrassingly long amount of time, and for the longest time, I make the critical mistake of referring to the project itself as CloudSnorkel because that's the word that shows up in the GitHub project that I can actually see that jumps out at me. The actual name of the project within your org is cdk-github-runners if I'm not mistaken.Amir: That's real original, right?Corey: Exactly. It's like, “Oh, good, I'll just mention that, and suddenly everyone will know what I'm talking about.” But ignoring the problems of naming things well, which is a pain that everyone at AWS or who uses it knows far too well, the product is basically magic. Before I wind up basically embarrassing myself by doing a poor job of explaining what it is, how do you think about it?Amir: Well, I mean, it's a pretty simple project, which I think what makes it great as well. It creates GitHub runners with CDK. That's about it. It's in the name, and it just does that. And I really tried to make it as simple as possible and kind of learn from other projects that I've seen that are similar, and basically learn from my pain points in them.I think the reason I started is because I actually deployed CDK runners—sorry, GitHub runners—for one company, and I ended up using the Kubernetes one, right? So, GitHub in themselves, they have two projects they recommend—and not to nudge GitHub, please recommend my project one day as well—they have the Kubernetes controller and they have the Terraform deployer. And the specific client that I worked for, they wanted to use Kubernetes. And I tried to deploy it, and, Corey, I swear, I worked three days; three days to deploy the thing, which was crazy to me. And every single step of the way, I had to go and read some documentation, figure out what I did wrong, and apparently the order the documentation was was incorrect.And I had to—I even opened tickets, and they—you know, they were rightfully like, “It's open-source project. Please contribute and fix the documentation for us.” At that point, I said, “Nah.” [laugh]. Let me create something better with CDK and I decided just to have the simplest setup possible.So usually, right, what you end up doing in these projects, you have to set up either secrets or SSM parameters, and you have to prepare the ground and you have to get your GitHub token and all those things. And that's just annoying. So, I decided to create a—Corey: So much busy work.Amir: Yes, yeah, so much busy work and so much boilerplate and so much figuring out the right way and the right order, and just annoying. So, I decided to create a setup page. I thought, “What if you can actually install it just like you install any app on GitHub,” which is the way it's supposed to be right? So, when you install cdk-github-runners—CloudSnorkel—you get an HTML page and you just click a few buttons and you tell it where to install it and it just installs it for you. And it sets the secrets and everything. And if you want to change the secret, you don't have to redeploy. You can just change the secret, right? You have to roll the token over or whatever. So, it's much, much easier to install.Corey: And I feel like I discovered this project through one of the more surreal approaches—and I had cause to revisit it a few weeks ago when I was redoing my talk for the CDK Community Day, which has since happened and people liked the talk—and I mentioned what CloudSnorkel had been doing and how I was using the runners accordingly. So, that was what I accidentally caused me to pop back up with, “Hey, I've got some issues here.” But we'll get to that. Because once upon a time, I built a Twitter client for creating threads because shitposting is my love language, I would sit and create Twitter threads in the middle of live keynote talks. Threading in the native client was always terrible, and I wanted to build something that would help me do that. So, I did.And it was up for a while. It's not anymore because I'm not paying $42,000 a month in API costs to some jackass, but it still exists in the form of lasttootinaws.com if you want to create threads on Mastodon. But after I put this out, some people complained that it was slow.To which my response was, “What do you mean? It's super fast for me in San Francisco talking to it hosted in Oregon.” But on every round trip from halfway around the world, it became a problem. So, I got it into my head that since this thing was fully stateless, other than a Lambda function being fronted via an API Gateway, that I should deploy it to every region. It didn't quite fit into a Cloudflare Worker or into one of the Edge Lambda functions that AWS has given up on, but okay, how do I deploy something to every region?And the answer is, with great difficulty because it's clear that no one was ever imagining with all those regions that anyone would use all of them. It's imagined that most customers use two or three, but customers are different, so which two or three is going to be widely varied. So, anything halfway sensible about doing deployments like this didn't work out. Again, because this thing was also a Lambda function and an API Gateway, it was dirt cheap, so I didn't really want to start spending stupid amounts of money doing deployment infrastructure and the rest.So okay, how do I do this? Well, GitHub Actions is awesome. It is basically what all of AWS's code offerings wish that they were. CodeBuild is sad and this was kind of great. The problem is, once you're out of the free tier, and if you're a bad developer where you do a deploy on every iteration, suddenly it starts costing for what I was doing in every region, something like a quarter of per deploy, which adds up when you're really, really bad at programming.Amir: [laugh].Corey: So, their matrix jobs are awesome, but I wanted to do some self-hosted runners. How do I do that? And I want to keep it cheap, so how do I do a self-hosted runner inside of a Lambda function? Which led me directly to you. And it was nothing short of astonishing. This was a few years ago. I seem to recall that it used to be a bit less well-architected in terms of its elegance. Did it always use step functions, for example, to wind up orchestrating these things?Amir: Yeah, so I do remember that day. We met pretty much… basically as a joke because the Lambda Runner was a joke that I did, and I posted on Twitter, and I was half-proud of my joke that starts in ten seconds, right? But yeah, no, the—I think it always used functions. I've been kind of in love with the functions for the past two years. They just—they're nice.Corey: Oh, they're magic, and AWS is so bad at telling their story. Both of those things are true.Amir: Yeah. And the API is not amazing. But like, when you get it working—and you know, you have to spend some time to get it working—it's really nice because then you have nothing to manage, ever. And they can call APIs directly now, so you don't have to even create Lambdas. It's pretty cool.Corey: And what I loved is you wind up deploying this thing to whatever account you want it to live within. What is it, the OIDC? I always get those letters in the wrong direction. OIDC, I think, is correct.Amir: I think it's OIDC, yeah.Corey: Yeah, and it winds up doing this through a secure method as opposed to just okay, now anyone with access to the project can deploy into your account, which is not ideal. And it just works. It spins up a whole bunch of these Lambda functions that are using a Docker image as the deployment environment. And yeah, all right, if effectively my CDK deploy—which is what it's doing inside of this thing—doesn't complete within 15 minutes, then it's not going to and the thing is going to break out. We've solved the halting problem. After 15 minutes, the loop will terminate. The end.But that's never been a problem, even with getting ACM certificates spun up. It completes well within that time limit. And its cost to me is effectively nothing. With one key exception: that you made the choice to use Secrets Manager to wind up storing a lot of the things it cares about instead of Parameter Store, so I think you wind up costing me—I think there's two of those different secrets, so that's 80 cents a month. Which I will be demanding in blood one of these days if I ever catch you at re:Invent.Amir: I'll buy you beer [laugh].Corey: There we go. That'll count. That'll buy, like, several months of that. That works—at re:Invent, no. The beers there are, like, $18, so that'll cover me for years. We're set.Amir: We'll split it [laugh].Corey: Exactly. Problem solved. But I like the elegance of it, I like how clever it is, and I want to be very clear, though, it's not just for shitposting. Because it's very configurable where, yes, you can use Lambda functions, you can use Spot Instances, you can use CodeBuild containers, you can use Fargate containers, you can use EC2 instances, and it just automatically orchestrates and adds these self-hosted runners to your account, and every build gets a pristine environment as a result. That is no small thing.Amir: Oh, and I love making things configurable. People really appreciate it I feel, you know, and gives people kind of a sense of power. But as long as you make that configuration simple enough, right, or at least the defaults good defaults, right, then, even with that power, people still don't shoot themselves in the foot and it still works really well. By the way, we just added ECS recently, which people really were asking for because it gives you the, kind of, easy option to have the runner—well, not the runner but at least the runner infrastructure staying up, right? So, you can have auto-scaling group backing ECS and then the runner can start up a lot faster. It was actually very important to other people because Lambda, as fast that it is, it's limited, and Fargate, for whatever reason, still to this day, takes a minute to start up.Corey: Yeah. What's wild to me about this is, start to finish, I hit a deploy to the main branch and it sparks the thing up, runs the deploy. Deploy itself takes a little over two minutes. And every time I do this, within three minutes of me pushing to commit, the deploy is done globally. It is lightning fast.And I know it's easy to lose yourself in the idea of this being a giant shitpost, where, oh, who's going to do deployment jobs in Lambda functions? Well, kind of a lot of us for a variety of reasons, some of which might be better than others. In my case, it was just because I was cheap, but the massive parallelization ability to do 20 simultaneous deploys in a matrix configuration that doesn't wind up smacking into rate limits everywhere, that was kind of great.Amir: Yeah, we have seen people use Lambda a lot. It's mostly for, yeah, like you said, small jobs. And the environment that they give you, it's kind of limited, so you can't actually install packages, right? There is no sudo, and you can't actually install anything unless it's in your temp directory. But still, like, just being able to run a lot of little jobs, it's really great. Yeah.Corey: And you can also make sure that there's a Docker image ready to go with the stuff that you need, just by configuring how the build works in the CDK. I will admit, I did have a couple of bug reports for you. One was kind of useful, where it was not at all clear how to do this on top of a Graviton-based Lambda function—because yeah, that was back when not everything really supported ARM architectures super well—and a couple of other times when the documentation was fairly ambiguous from my perspective, where it wasn't at all clear, what was I doing? I spent four hours trying to beat my way through it, I give up, filed an issue, went to get a cup of coffee, came back, and the answer was sitting there waiting for me because I'm not convinced you sleep.Amir: Well, I am a vampire. My last name is from the Transylvania area [laugh]. So—Corey: Excellent. Excellent.Amir: By the way, not the first time people tell me that. But anyway [laugh].Corey: There's something to be said for getting immediate responsiveness because one of the reasons I'm always so loath to go and do a support ticket anywhere is this is going to take weeks. And then someone's going to come back with a, “I don't get it.” And try and, like, read the support portfolio to you. No, you went right into yeah, it's this. Fix it and your problem goes away. And sure enough, it did.Amir: The escalation process that some companies put you through is very frustrating. I mean, lucky for you, CloudSnorkel is a one-man show and this man loves solving bugs. So [laugh].Corey: Yeah. Do you know of anyone using it for anything that isn't ridiculous and trivial like what I'm using it for?Amir: Yeah, I have to think whether or not I can… I mean, so—okay. We have a bunch of dedicated users, right, the GitHub repo, that keep posting bugs and keep posting even patches, right, so you can tell that they're using it. I even have one sponsor, one recurring sponsor on GitHub that uses it.Corey: It's always nice when people thank you via money.Amir: Yeah. Yeah, it is very validating. I think [BLEEP] is using it, but I also don't think I can actually say it because I got it from the GitHub.Corey: It's always fun. That's the beautiful part about open-source. You don't know who's using this. You see what other things people are working on, and you never know, is one of their—is this someone's side project, is it a skunkworks thing, or God forbid, is this inside of every car going forward and no one bothered to tell me about that. That is the magic and mystery of open-source. And you've been doing open-source for longer than I have and I thought I was old. You were originally named in some of the WinAMP credits, for God's sake, that media player that really whipped the llama's ass.Amir: Oh, yeah, I started real early. I started about when I was 15, I think. I started off with Pascal or something or even Perl, and then I decided I have to learn C and I have to learn Windows API. I don't know what possessed me to do that. Win32 API is… unique [laugh].But once I created those applications for myself, right, I think there was—oh my God, do you know the—what is it called, Sherlock in macOS, right? And these days, for PowerToys, there is the equivalent of it called, I don't know, whatever that—PowerBar? That's exactly—that was that. That's a project I created as a kid. I wanted something where I can go to the Run menu of Windows when you hit Winkey R, and you can just type something and it will start it up, right?I didn't want to go to the Start menu and browse and click things. I wanted to do everything with the keyboard. So, I created something called Blazerun [laugh], which [laugh] helped you really easily create shortcuts that went into your path, right, the Windows path, so you can really easily start them from Winkey R. I don't think that anyone besides me used it, but anyway, that thing needed an installer, right? Because Windows, you got to install things. So, I ended up—Corey: Yeah, these days on Mac OS, I use Alfred for that which is kind of long in the tooth, but there's a launch bar and a bunch of other stuff for it. What I love is that if I—I can double-tap the command key and that just pops up whatever I need it to and tell the computer what to do. It feels like there's an AI play in there somewhere if people can figure out how to spend ten minutes on building AI that does something other than lets them fire their customer service staff.Amir: Oh, my God. Please don't fire customer service staff. AI is so bad.Corey: Yeah, when I reach out to talk to a human, I really needed a human.Amir: Yes. Like, I'm not calling you because I want to talk to a robot. I know there's a website. Leave me alone, just give me a person.Corey: Yeah. Like, you already failed to solve my problem on your website. It's person time.Amir: Exactly. Oh, my God. Anyway [laugh]. So, I had to create an installer, right, and I found it was called NSIS. So, it was a Nullsoft “SuperPiMP” installation system. Or in the future, when Justin, the guy who created Winamp and NSIS, tried to tone down a little bit, Nullsoft Scriptable Installation System. And SuperPiMP is—this is such useless history for you, right, but SuperPiMP is the next generation of PiMP which is Plug-in Mini Packager [laugh].Corey: I remember so many of the—like, these days, no one would ever name any project like that, just because it's so off-putting to people with sensibilities, but back then that was half the stuff that came out. “Oh, you don't like how this thing I built for free in the wee hours when I wasn't working at my fast food job wound up—you know, like, how I chose to name it, well, that's okay. Don't use it. Go build your own. Oh, what you're using it anyway. That's what I thought.”Amir: Yeah. The source code was filled with profanity, too. And like, I didn't care, I really did not care, but some people would complain and open bug reports and patches. And my policy was kind of like, okay if you're complaining, I'm just going to ignore you. If you're opening a patch, fine, I'm going to accept that you're—you guys want to create something that's sensible for everybody, sure.I mean, it's just source code, you know? Whatever. So yeah, I started working on that NSIS. I used it for myself and I joined the forums—and this kind of answers to your question of why I respond to things so fast, just because of the fun—I did the same when I was 15, right? I started going on the forums, you remember forums? You remember that [laugh]?Corey: Oh, yeah, back before they all became terrible and monetized.Amir: Oh, yeah. So, you know, people were using NSIS, too, and they had requests, right? They wanted. Back in the day—what was it—there was only support for 16-bit colors for the icon, so they want 32-bit colors and big colors—32—big icon, sorry, 32 pixels by 32 pixels. Remember, 32 pixels?Corey: Oh, yes. Not well, and not happily, but I remember it.Amir: Yeah. So, I started just, you know, giving people—working on that open-source and creating up a fork. It wasn't even called ‘fork' back then, but yeah, I created, like, a little fork of myself and I started adding all these features. And people were really happy, and kind of created, like, this happy cycle for myself: when people were happy, I was happy coding. And then people were happy by what I was coding. And then they were asking for more and they were getting happier, the more I responded.So, it was kind of like a serotonin cycle that made me happy and made everybody happy. So, it's like a win, win, win, win, win. And that's how I started with open-source. And eventually… NSIS—again, that installation system—got so big, like, my fork got so big, and Justin, the guy who works on WinAMP and NSIS, he had other things to deal with. You know, there's a whole history there with AOL. I'm sure you've heard all the funny stories.Corey: Oh, yes. In fact, one thing that—you want to talk about weird collisions of things crossing, one of the things I picked up from your bio when you finally got tired of telling me no and agreed to be on the show was that you're also one of the team who works on camelcamelcamel.com. And I keep forgetting that's one of those things that most people have no idea exists. But it's very simple: all it does is it tracks Amazon products that you tell it to and alerts you when there's a price drop on the thing that you're looking at.It's something that is useful. I try and use it for things of substance or hobbies because I feel really pathetic when I'm like, get excited emails about a price drop in toilet paper. But you know, it's very handy just to keep an idea for price history, where okay, am I actually being ripped off? Oh, they claim it's their big Amazon Deals day and this is 40% off. Let's see what camelcamelcamel has to say.Oh, surprise. They just jacked the price right beforehand and now knocked 40% off. Genius. I love that. It always felt like something that was going to be blown off the radar by Amazon being displeased, but I discovered you folks in 2010 and here you are now, 13 years later, still here. I will say the website looks a lot better now.Amir: [laugh]. That's a recent change. I actually joined camel, maybe two or three years ago. I wasn't there from the beginning. But I knew the guy who created it—again, as you were saying—from the Winamp days, right? So, we were both working in the free—well, it wasn't freenode. It was not freenode. It was a separate IRC server that, again, Justin created for himself. It was called landoleet.Corey: Mmm. I never encountered that one.Amir: Yeah, no, it was pretty private. The only people that cared about WinAMP and NSIS ended up joining there. But it was a lot of fun. I met a lot of friends there. And yeah, I met Daniel Green there as well, and he's the guy that created, along with some other people in there that I think want to remain anonymous so I'm not going to mention, but they also were on the camel project.And yeah, I was kind of doing my poor version of shitposting on Twitter about AWS, kind of starting to get some traction and maybe some clients and talk about AWS so people can approach me, and Daniel approached me out of the blue and he was like, “Do you just post about AWS on Twitter or do you also do some AWS work?” I was like, “I do some AWS work.”Corey: Yes, as do all of us. It's one of those, well crap, we're getting called out now. “Do you actually know how any of this stuff works?” Like, “Much to my everlasting shame, yes. Why are you asking?”Amir: Oh, my God, no, I cannot fix your printer. Leave me alone.Corey: Mm-hm.Amir: I don't want to fix your Lambdas. No, but I do actually want to fix your Lambdas. And so, [laugh] he approached me and he asked if I can help them move camelcamelcamel from their data center to AWS. So, that was a nice big project. So, we moved, actually, all of camelcamelcamel into AWS. And this is how I found myself not only in the Winamp credits, but also in the camelcamelcamel credits page, which has a great picture of me riding a camel.Corey: Excellent. But one of the things I've always found has been that when you take an application that has been pre-existing for a while in a data center and then move it into the cloud, you suddenly have to care about things that no one sensible pays any attention to in the land of the data center. Because it's like, “What do I care about how much data passes between my application server and the database? Wait, what do you mean that in this configuration, that's a chargeable data transfer? Oh, dear Lord.” And things that you've never had to think about optimizing are suddenly things are very much optimizing.Because let's face it, when it comes to putting things in racks and then running servers, you aren't auto-scaling those things, so everything tends to be running over-provisioned, for very good reasons. It's an interesting education. Anything you picked out from that process that you think it'd be useful for folks to bear in mind if they're staring down the barrel of the same thing?Amir: Yeah, for sure. I think… in general, right, not just here. But in general, you always want to be pragmatic, right? You don't want to take steps are huge, right? So, the thing we did was not necessarily rewrite everything and change everything to AWS and move everything to Lambda and move everything to Docker.Basically, we did a mini lift-and-shift, but not exactly lift-and-shift, right? We didn't take it as is. We moved to RDS, we moved to ElastiCache, right, we obviously made use of security groups and session connect and we dropped SSH Sage and we improved the security a lot and we locked everything down, all the permissions and all that kind of stuff, right? But like you said, there's stuff that you start having to pay attention to. In our case, it was less the data transfer because we have a pretty good CDN. There was more of IOPS. So—and IOPS, specifically for a database.We had a huge database with about one terabyte of data and a lot of it is that price history that you see, right? So, all those nice little graphs that we create in—what do you call them, charts—that we create in camelcamelcamel off the price history. There's a lot of data behind that. And what we always want to do is actually remove that from MySQL, which has been kind of struggling with it even before the move to AWS, but after the move to AWS, where everything was no longer over-provisioned and we couldn't just buy a few more NVMes on Amazon for 100 bucks when they were on sale—back when we had to pay Amazon—Corey: And you know, when they're on sale. That's the best part.Amir: And we know [laugh]. We get good prices on NVMe. But yeah, on Amazon—on AWS, sorry—you have to pay for io1 or something, and that adds up real quick, as you were saying. So, part of that move was also to move to something that was a little better for that data structure. And we actually removed just that data, the price history, the price points from MySQL to DynamoDB, which was a pretty nice little project.Actually, I wrote about it in my blog. There is, kind of, lessons learned from moving one terabyte from MySQL to DynamoDB, and I think the biggest lesson was about hidden price of storage in DynamoDB. But before that, I want to talk about what you asked, which was the way that other people should make that move, right? So again, be pragmatic, right? If you Google, “How do I move stuff from DynamoDB to MySQL,” everybody's always talking about their cool project using Lambda and how you throttle Lambda and how you get throttled from DynamoDB and how you set it up with an SQS, and this and that. You don't need all that.Just fire up an EC2 instance, write some quick code to do it. I used, I think it was Go with some limiter code from Uber, and that was it. And you don't need all those Lambdas and SQS and the complication. That thing was a one-time thing anyway, so it doesn't need to be super… super-duper serverless, you know?Corey: That is almost always the way that it tends to play out. You encounter these weird little things along the way. And you see so many things that are tied to this is how architecture absolutely must be done. And oh you're not a real serverless person if you don't have everything running in Lambda and the rest. There are times where yeah, spin up an EC2 box, write some relatively inefficient code in ten minutes and just do the thing, and then turn it off when you're done. Problem solved. But there's such an aversion to that. It's nice to encounter people who are pragmatists more than they are zealots.Amir: I mostly learned that lesson. And both Daniel Green and me learned that lesson from the Winamp days. Because we both have written plugins for Winamp and we've been around that area and you can… if you took one of those non-pragmatist people, right, and you had them review the Winamp code right now—or even before—they would have a million things to say. That code was—and NSIS, too, by the way—and it was so optimized. It was so not necessarily readable, right? But it worked and it worked amazing. And Justin would—if you think I respond quickly, right, Justin Frankel, the guy who wrote Winamp, he would release versions of NSIS and of Winamp, like, four versions a day, right? That was before [laugh] you had CI/CD systems and GitHub and stuff. That was just CVS. You remember CVS [laugh]?Corey: Oh, I've done multiple CVS migrations. One to Git and a couple to Subversion.Amir: Oh yeah, Subversion. Yep. Done ‘em all. CVS to Subversion to Git. Yep. Yep. That was fun.Corey: And these days, everyone's using Git because it—we're beginning to have a monoculture.Amir: Yeah, yeah. I mean, but Git is nicer than Subversion, for me, at least. I've had more fun with it.Corey: Talk about damning with faint praise.Amir: Faint?Corey: Yeah, anything's better than Subversion, let's be honest here.Amir: Oh [laugh].Corey: I mean, realistically, copying a bunch of files and directories to a.bak folder is better than Subversion.Amir: Well—Corey: At least these days. But back then it was great.Amir: Yeah, I mean, the only thing you had, right [laugh]?Corey: [laugh].Amir: Anyway, achieving great things with not necessarily the right tools, but just sheer power of will, that's what I took from the Winamp days. Just the entire world used Winamp. And by the way, the NSIS project that I was working on, right, I always used to joke that every computer in the world ran my code, every Windows computer in the world when my code, just because—Corey: Yes.Amir: So, many different companies use NSIS. And none of them cared that the code was not very readable, to put it mildly.Corey: So, many companies founder on those shores where they lose sight of the fact that I can point to basically no companies that died because their code was terrible, yeah, had an awful lot that died with great-looking code, but they didn't nail the business problem.Amir: Yeah. I would be lying if I said that I nailed exactly the business problem at NSIS because the most of the time I would spend there and actually shrinking the stub, right, there was appended to your installer data, right? So, there's a little stub that came—the executable, basically, that came before your data that was extracted. I spent, I want to say, years of my life [laugh] just shrinking it down by bytes—by literal bytes—just so it stays under 34, 35 kilobytes. It was kind of a—it was a challenge and something that people appreciated, but not necessarily the thing that people appreciate the most. I think the features—Corey: Well, no I have to do the same thing to make sure something fits into a Lambda deployment package. The scale changes, the problem changes, but somehow everything sort of rhymes with history.Amir: Oh, yeah. I hope you don't have to disassemble code to do that, though because that's uh… I mean, it was fun. It was just a lot.Corey: I have to ask, how much work went into building your cdk-github-runners as far as getting it to a point of just working out the door? Because I look at that and it feels like there's—like, the early versions, yeah, there wasn't a whole bunch of code tied to it, but geez, the iterative, “How exactly does this ridiculous step functions API work or whatnot,” feels like I'm looking at weeks of frustration. At least it would have been for me.Amir: Yeah, yeah. I mean, it wasn't, like, a day or two. It was definitely not—but it was not years, either. I've been working on it I think about a year now. Don't quote me on that. But I've put a lot of time into it. So, you know, like you said, the skeleton code is pretty simple: it's a step function, which as we said, takes a long time to get right. The functions, they are really nice, but their definition language is not very straightforward. But beyond that, right, once that part worked, it worked. Then came all the bug reports and all the little corner cases, right? We—Corey: Hell is other people's use cases. Always is. But that's honestly better than a lot of folks wind up experiencing where they'll put an open-source project up and no one ever knows. So, getting users is often one of the biggest barriers to a lot of this stuff. I've found countless hidden gems lurking around on GitHub with a very particular search for something that no one had ever looked at before, as best I can tell.Amir: Yeah.Corey: Open-source is a tricky thing. There needs to be marketing brought into it, there needs to be storytelling around it, and has to actually—dare I say—solve a problem someone has.Amir: I mean, I have many open-source projects like that, that I find super useful, I created for myself, but no one knows. I think cdk-github-runners, I'm pretty sure people know about it only because you talked about it on Screaming in the Cloud or your newsletter. And by the way, thank you for telling me that you talked about it last week in the conference because now we know why there was a spike [laugh] all of a sudden. People Googled it.Corey: Yeah. I put links to it as well, but it's the, yeah, I use this a lot and it's great. I gave a crappy explanation on how it works, but that's the trick I've found between conference talks and, dare I say, podcast episodes, you gives people a glimpse and a hook and tell them where to go to learn more. Otherwise, you're trying to explain every nuance and every intricacy in 45 minutes. And you can't do that effectively in almost every case. All you're going to do is drive people away. Make it sound exciting, get them to see the value in it, and then let them go.Amir: You have to explain the market for it, right? That's it.Corey: Precisely.Amir: And I got to say, I somewhat disagree with your—or I have a different view when you say that, you know, open-source projects needs marketing and all those things. It depends on what open-source is for you, right? I don't create open-source projects so they are successful, right? It's obviously always nicer when they're successful, but—and I do get that cycle of happiness that, like I was saying, people create bugs and I have to fix them and stuff, right? But not every open-source project needs to be a success. Sometimes it's just fun.Corey: No. When I talk about marketing, I'm talking about exactly what we're doing here. I'm not talking take out an AdWords campaign or something horrifying like that. It's you build something that solved the problem for someone. The big problem that worries me about these things is how do you not lose sleep at night about the fact that solve someone's problem and they don't know that it exists?Because that drives me nuts. I've lost count of the number of times I've been beating my head against a wall and asked someone like, “How would you handle this?” Like, “Oh, well, what's wrong with this project?” “What do you mean?” “Well, this project seems to do exactly what you want it to do.” And no one has it all stuffed in their head. But yeah, then it seems like open-source becomes a little more corporatized and it becomes a lead gen tool for people to wind up selling their SaaS services or managed offerings or the rest.Amir: Yeah.Corey: And that feels like the increasing corporatization of open-source that I'm not a huge fan of.Amir: Yeah. I mean, I'm not going to lie, right? Like, part of why I created this—or I don't know if it was part of it, but like, I had a dream that, you know, I'm going to get, oh, tons of GitHub sponsors, and everybody's going to use it and I can retire on an island and just make money out of this, right? Like, that's always a dream, right? But it's a dream, you know?And I think bottom line open-source is… just a tool, and some people use it for, like you were saying, driving sales into their SaaS, some people, like, may use it just for fun, and some people use it for other things. Or some people use it for politics, even, right? There's a lot of politics around open-source.I got to tell you a story. Back in the NSIS days, right—talking about politics—so this is not even about politics of open-source. People made NSIS a battleground for their politics. We would have translations, right? People could upload their translations. And I, you know, or other people that worked on NSIS, right, we don't speak every language of the world, so there's only so much we can do about figuring out if it's a real translation, if it's good or not.Back in the day, Google Translate didn't exist. Like, these days, we check Google Translate, we kind of ask a few questions to make sure they make sense. But back in the day, we did the best that we could. At some point, we got a patch for Catalan language, I'm probably mispronouncing it—but the separatist people in Spain, I think, and I didn't know anything about that. I was a young kid and… I just didn't know.And I just included it, you know? Someone submitted a patch, they worked hard, they wanted to be part of the open-source project. Why not? Sure I included it. And then a few weeks later, someone from Spain wanted to change Catalan into Spanish to make sure that doesn't exist for whatever reason.And then they just started fighting with each other and started making demands of me. Like, you have to do this, you have to do that, you have to delete that, you have to change the name. And I was just so baffled by why would someone fight so much over a translation of an open-source project. Like, these days, I kind of get what they were getting at, right?Corey: But they were so bad at telling that story that it was just like, so basically, screw, “You for helping,” is how it comes across.Amir: Yeah, screw you for helping. You're a pawn now. Just—you're a pawn unwittingly. Just do what I say and help me in my political cause. I ended up just telling both of them if you guys can agree on anything, I'm just going to remove both translations. And that's what I ended up doing. I just removed both translations. And then a few months later—because we had a release every month basically, I just added both of them back and I've never heard from them again. So sort of problem solved. Peace the Middle East? I don't know.Corey: It's kind of wild just to see how often that sort of thing tends to happen. It's a, I don't necessarily understand why folks are so opposed to other people trying to help. I think they feel like there's this loss of control as things are slipping through their fingers, but it's a really unwelcoming approach. One of the things that got me deep into the open-source ecosystem surprisingly late in my development was when I started pitching in on the SaltStack project right after it was founded, where suddenly everything I threw their way was merged, and then Tom Hatch, the guy who founded the project, would immediately fix all the bugs and stuff I put in and then push something else immediately thereafter. But it was such a welcoming thing.Instead of nitpicking me to death in the pull request, it just got merged in and then silently fixed. And I thought that was a classy way to do it. Of course, it doesn't scale and of course, it causes other problems, but I envy the simplicity of those days and just the ethos behind that.Amir: That's something I've learned the last few years, I would say. Back in the NSIS day, I was not like that. I nitpicked. I nitpicked a lot. And I can guess why, but it just—you create a patch—in my mind, right, like you create a patch, you fix it, right?But these days I get, I've been on the other side as well, right? Like I created patches for open-source projects and I've seen them just wither away and die, and then five years later, someone's like, “Oh, can you fix this line to have one instead of two, and then I'll merge it.” I'm like, “I don't care anymore. It was five years ago. I don't work there anymore. I don't need it. If you want it, do it.”So, I get it these days. And these days, if someone creates a patch—just yesterday, someone created a patch to format cdk-github-runners in VS Code. And they did it just, like, a little bit wrong. So, I just fixed it for them and I approved it and pushed it. You know, it's much better. You don't need to bug people for most of it.Corey: You didn't yell at them for having the temerity to contribute?Amir: My voice is so raw because I've been yelling for five days at them, yeah.Corey: Exactly, exactly. I really want to thank you for taking the time to chat with me about how all this stuff came to be and your own path. If people want to learn more, where's the best place for them to find you?Amir: So, I really appreciate you having me and driving all this traffic to my projects. If people want to learn more, they can always go to cloudsnorkel.com; it has all the projects. github.com/cloudsnorkel has a few more. And then my private blog is kichik.com. So, K-I-C-H-I-K dot com. I don't post there as much as I should, but it has some interesting AWS projects from the past few years that I've done.Corey: And we will, of course, put links to all of that in the show notes. Thank you so much for taking the time. I really appreciate it.Amir: Thank you, Corey. It was really nice meeting you.Corey: Amir Szekely, owner of CloudSnorkel. I'm Cloud Economist Corey Quinn, and this is Screaming in the Cloud. If you've enjoyed this podcast, please leave a five-star review on your podcast platform of choice, whereas if you've hated this podcast, please leave a five-star review on your podcast platform of choice, along with an insulting comment. Heck, put it on all of the podcast platforms with a step function state machine that you somehow can't quite figure out how the API works.Corey: If your AWS bill keeps rising and your blood pressure is doing the same, then you need The Duckbill Group. We help companies fix their AWS bill by making it smaller and less horrifying. The Duckbill Group works for you, not AWS. We tailor recommendations to your business and we get to the point. Visit duckbillgroup.com to get started.

Welcome to The Cloud Pod - where the forecast is always cloudy! This week your hosts, Jonathan and Ryan, are talking all about EC2 instances, including changes to AWS Systems Manager and Elastic Disaster Recovery. And speaking of disasters, we're also taking a dive into the ongoing Google DDOS attacks. Plus, we've even thrown a little earthquake warning into the podcast, just for effect.  Titles we almost went with this week: A big thanks to this week's sponsor: Foghorn Consulting provides top-notch cloud and DevOps engineers to the world's most innovative companies. Initiatives stalled because you have trouble hiring?  Foghorn can be burning down your DevOps and Cloud backlogs as soon as next week.

John Wynkoop, Cloud Economist & Platypus Herder at The Duckbill Group, joins Corey on Screaming in the Cloud to discuss why he decided to make a career move and become an AWS billing consultant. Corey and John discuss how once you're deeply familiar with one cloud provider, those skills become transferable to other cloud providers as well. John also shares the trends he has seen post-pandemic in the world of cloud, including the increased adoption of a multi-cloud strategy and the need for costs control even for VC-funded start-ups. About JohnWith over 25 years in IT, John's done almost every job in the industry, from running cable and answering helpdesk calls to leading engineering teams and advising the C-suite. Before joining The Duckbill Group, he worked across multiple industries including private sector, higher education, and national defense. Most recently he helped IGNW, an industry leading systems integration partner, get acquired by industry powerhouse CDW. When he's not helping customers spend smarter on their cloud bill, you can find him enjoying time with his family in the beautiful Smoky Mountains near his home in Knoxville, TN.Links Referenced: The Duckbill Group: https://duckbillgroup.com LinkedIn: https://www.linkedin.com/in/jlwynkoop/ TranscriptAnnouncer: Hello, and welcome to Screaming in the Cloud with your host, Chief Cloud Economist at The Duckbill Group, Corey Quinn. This weekly show features conversations with people doing interesting work in the world of cloud, thoughtful commentary on the state of the technical world, and ridiculous titles for which Corey refuses to apologize. This is Screaming in the Cloud.Corey: Welcome to Screaming in the Cloud. I'm Corey Quinn. And the times, they are changing. My guest today is John Wynkoop. John, how are you?John: Hey, Corey, I'm doing great. Thanks for having me.Corey: So, big changes are afoot for you. You've taken a new job recently. What are you doing now?John: Well [laugh], so I'm happy to say I have joined The Duckbill Group as a cloud economist. So, came out of the big company world, and have dived back in—or dove back into the startup world.Corey: It's interesting because when we talk to those big companies, they always identify us as oh, you're a startup, which is hilarious on some level because our AWS account hangs out in AWS's startup group, but if you look at the spend being remarkably level from month to month to month to year to year to year, they almost certainly view us as they're a startup, but they suck at it. They completely failed. And so, many of the email stuff that you get from them presupposes that you're venture-backed, that you're trying to conquer the entire world. We don't do that here. We have this old-timey business model that our forebears would have understood of, we make more money than we spend every month and we continue that trend for a long time. So first, thanks for joining us, both on the show and at the company. We like having you around.John: Well, thanks. And yeah, I guess that's—maybe a startup isn't the right word to describe what we do here at The Duckbill Group, but as you said, it seems to fit into the industry classification. But that was one of the things I actually really liked about the—that was appealing about joining the team was, we do spend less than we make and we're not after hyper-growth and we're not trying to consume everything.Corey: So, it's interesting when you put a job description out into the world and you see who applies—and let's be clear, for those who are unaware, job descriptions are inherently aspirational shopping lists. If you look at a job description and you check every box on the thing and you've done all the things they want, the odds are terrific you're going to be bored out of your mind when you wind up showing up to do these… whatever that job is. You should be learning stuff and growing. At least that's always been my philosophy to it. One of the interesting things about you is that you checked an awful lot of boxes, but there is one that I think would cause people to raise an eyebrow, which is, you're relatively new to the fun world of AWS.John: Yeah. So, obviously I, you know, have been around the block a few times when it comes to cloud. I've used AWS, built some things in AWS, but I wouldn't have classified myself as an AWS guru by any stretch of the imagination. I spent the last probably three years working in Google Cloud, helping customers build and deploy solutions there, but I do at least understand the fundamentals of cloud, and more importantly—at least for our customers—cloud costs because at the end of the day, they're not all that different.Corey: I do want to call out that you have a certain humility to you which I find endearing. But you're not allowed to do that here; I will sing your praises for you. Before they deprecated it like they do almost everything else, you were one of the relatively few Google Cloud Certified Fellows, which was sort of like their Heroes program only, you know, they killed it in favor of something else like there's a Champion program or whatnot. You are very deep in the world of both Kubernetes and Google Cloud.John: Yeah. So, there was a few of us that were invited to come out and help Google pilot that program in, I believe it was 2019, and give feedback to help them build the Cloud Fellows Program. And thankfully, I was selected based on some of our early experience with Anthos, and specifically, it was around Certified Fellow in what they call hybrid multi-cloud, so it was experience around Anthos. Or at the time, they hadn't called it Anthos; they were calling it CSP or Cloud Services Platform because that's not an overloaded acronym. So yeah, definitely, was very humbled to be part of that early on.I think the program, as you said, grew to about 70 or so maybe 100 certified individuals before they transitioned—not killed—transitioned to that program into the Cloud Champions program. So, those folks are all still around, myself included. They've just now changed the moniker. But we all get to use the old title still as well, so that's kind of cool.Corey: I have to ask, what would possess you to go from being one of the best in the world at using Google Cloud over here to our corner of the AWS universe? Because the inverse, if I were to somehow get ejected from here—which would be a neat trick, but I'm sure it's theoretically possible—like, “What am I going to do now?” I would almost certainly wind up doing something in the AWS ecosystem, just due to inertia, if nothing else. You clearly didn't see things quite that way. Why make the switch?John: Well, a couple of different reasons. So, being at a Google partner presents a lot of challenges and one of the things that was supremely interesting about coming to Duckbill is that we're independent. So, we're not an AWS partner. We are an independent company that is beholden only to our customers. And there isn't anything like that in the Google ecosystem today.There's, you know, there's Google partners and then there's Google customers and then there's Google. So, that was part of the appeal. And the other thing was, I enjoy learning new things, and honestly, learning, you know, into the depths of AWS cost hell is interesting. There's a lot to learn there and there's a lot of things that we can extract and use to help customers spend less. So, that to me was super interesting.And then also, I want to help build an organization. So, you know, I think what we're doing here at The Duckbill Group is cool and I think that there's an opportunity to grow our services portfolio, and so I'm excited to work with the leadership team to see what else we can bring to market that's going to help our customers, you know, not just with cost optimization, not just with contract negotiation, but you know, through the lifecycle of their AWS… journey, I guess we'll call it.Corey: It's one of those things where I always have believed, on some level, that once you're deep in a particular cloud provider, if there's reason for it, you can rescale relatively quickly to a different provider. There are nuances—deep nuances—that differ from provider to provider, but the underlying concepts generally all work the same way. There's only so many ways you can have data go from point A to point B. There's only so many ways to spin up a bunch of VMs and whatnot. And you're proof-positive that theory was correct.You'd been here less than a week before I started learning nuances about AWS billing from you. I think it was something to do with the way that late fees are assessed when companies don't pay Amazon as quickly as Amazon desires. So, we're all learning new things constantly and no one stuffs this stuff all into their head. But that, if nothing else, definitely cemented that yeah, we've got the right person in the seat.John: Yeah, well, thanks. And certainly, the deeper you go on a specific cloud provider, things become fresh in your memory, you know, other cached so to speak. So, coming up to speed on AWS has been a little bit more documentation reading than it would have been, if I were, say, jumping right into a GCP engagement. But as he said, at the end of the day, there's a lot of similarities. Obviously understanding the nuances of, for example, account organization versus, you know, GCP's Project and Folders. Well, that's a substantial difference and so there's a lot of learning that has to happen.Thankfully, you know, all these companies, maybe with the exception of Oracle, have done a really good job of documenting all of the concepts in their publicly available documentation. And then obviously, having a team of experts here at The Duckbill Group to ask stupid questions of doesn't hurt. But definitely, it's not as hard to come up to speed as one may think, once you've got it understood in one provider.Corey: I took a look recently and was kind of surprised to discover that I've been doing this—as an independent consultant prior to the formation of The Duckbill Group—for seven years now. And it's weird, but I've gone through multiple industry cycles and changes as a part of this. And it feels like I haven't been doing it all that long, but I guess I have. One thing that's definitely changed is that it used to be that companies would basically pick one provider and almost everything would live there. At any reasonable point of scale, everyone is using multiple things.I see Google in effectively every client that we have. It used to be that going to Google Cloud Next was a great place to hang out with AWS customers. But these days, it's just as true to say that a great reason to go to re:Invent is to hang out with Google Cloud customers. Everyone uses everything, and that has become much more clear over the last few years. What have you seen change over the… I guess, since the start of the pandemic, just in terms of broad cycles?John: Yeah. So, I think there's a couple of different trends that we're seeing. Obviously, one is that as you said, especially as large enterprises make moves to the cloud, you see independent teams or divisions within a given organization leveraging… maybe not the right tool for the job because I think that there's a case to be made for swapping out a specific set of tools and having your team learn it, but we do see what I like to refer to as tool fetishism where you get a team that's super, super deep into BigQuery and they're not interested in moving to Redshift, or Snowflake, or a competitor. So, you see, those start to crop up within large organizations where the distributed—the purchasing power, rather—is distributed. So, that's one of the trends is the multi-cloud adoption.And I think the big trend that I like to emphasize around multi-cloud is, just because you can run it anywhere doesn't mean you should run it everywhere. So Kubernetes, as you know, right, as it took off 2019 timeframe, 2020, we started to see a lot of people using that as an excuse to try to run their production application in two, three public cloud providers and on-prem. And unless you're a SaaS customer—or SaaS company with customers in every cloud, there's very little reason to do that. But having that flexibility—that's the other one, is we've seen that AWS has gotten a little difficult to negotiate with, or maybe Google and Microsoft have gotten a little bit more aggressive. So obviously, having that flexibility and being able to move your workloads, that was another big trend.Corey: I'm seeing a change in things that I had taken as givens, back when I started. And that's part of the reason, incidentally, I write the Last Week in AWS newsletter because once you learn a thing, it is very easy not to keep current with that thing, and things that are not possible today will be possible tomorrow. How do you keep abreast of all of those changes? And the answer is to write a deeply sarcastic newsletter that gathers in everything from the world of AWS. But I don't recommend that for most people. One thing that I've seen in more prosaic terms that you have a bit of background in is that HPC on cloud was, five, six years ago, met with, “Oh, that's a good one; now pull the other one, it has bells on it,” into something that, these days, is extremely viable. How'd that happen?John: So, [sigh] I think that's just a—again, back to trends—I think that's just a trend that we're seeing from cloud providers and listening to their customers and continuing to improve the service. So, one of the reasons that HPC was—especially we'll call it capacity-level HPC or large HPC, right—you've always been able to run high throughput; the cloud is a high throughput machine, right? You can run a thousand disconnected VMs no problem, auto-scaling, anybody who runs a massive web front-end can attest to that. But what we saw with HPC—and we used to call those [grid 00:12:45] jobs, right, the small, decoupled computing jobs—but what we've seen is a huge increase in the quality of the underlying fabric—things like RDMA being made available, things like improved network locality, where you now have predictive latency between your nodes or between your VMs—and I think those, combined with the huge investment that companies like AWS have made in their file systems, the huge investment companies like Google have made in their data storage systems have made HPC viable, especially at a small-scale—for cloud-based HPC specifically—viable for organizations.And for a small engineering team, who's looking to run say, computer-aided engineering simulation or who's looking to prototype some new way of testing or doing some kind of simulation, it's a huge, huge improvement in speed because now they don't have to order a dozen or two dozen or five dozen nodes, have them shipped, rack them, stack them, cool them, power them, right? They can just spin up the resource in the cloud, test it out, try their simulation, try out the new—the software that they want, and then spin it all down if it doesn't work. So, that elasticity has also been huge. And again, I think the big—to kind of summarize, I think the big driver there is the improvement in this the service itself, right? We're seeing cloud providers taking that discipline a little bit more seriously.Corey: I still see that there are cases where the raw math doesn't necessarily add up for sustained, long-term use cases. But I also see increasingly that with HPC, that's usually not what the workload looks like. With, you know, the exception of we're going to spend the next 18 months training some new LLM thing, but even then the pricing is ridiculous. What is it their new P6 or whatever it is—P5—the instances that have those giant half-rack Nvidia cards that are $800,000 and so a year each if you were to just rent them straight out, and then people running fleets of these things, it's… wow that's more commas in that training job than I would have expected. But I can see just now the availability for driving some of that, but the economics of that once you can get them in your data center doesn't strike me as being particularly favoring the cloud.John: Yeah, there's a couple of different reasons. So, it's almost like an inverse curve, right? There's a crossover point or a breakeven point at which—you know, and you can make this argument with almost any level of infrastructure—if you can keep it sufficiently full, whether it's AI training, AI inference, or even traditional HPC if you can keep the machine or the group of machines sufficiently full, it's probably cheaper to buy it and put it in your facility. But if you don't have a facility or if you don't need to use it a hundred percent of the time, the dividends aren't always there, right? It's not always worth, you know, buying a $250,000 compute system, you know, like say, an Nvidia, as you—you know, like, a DGX, right, is a good example.The DGX H100, I think those are a couple $100,000. If you can't keep that thing full and you just need it for training jobs or for development and you have a small team of developers that are only going to use it six hours a day, it may make sense to spin that up in the cloud and pay for a fractional use, right? It's no different than what HPC has been doing for probably the past 50 years with national supercomputing centers, which is where my background came from before cloud, right? It's just a different model, right? One is public economies of, you know, insert your credit card and spend as much as you want and the other is grant-funded and supporting academic research, but the economy of scales is kind of the same on both fronts.Corey: I'm also seeing a trend that this is something that is sort of disturbing when you realize what I've been doing and how I've been going about things, that for the last couple of years, people actually started to care about the AWS bill. And I have to say, I felt like I was severely out of sync with a lot of the world the first few years because there's giant savings lurking in your AWS bill, and the company answer in many cases was, “We don't care. We'd rather focus our energies on shipping faster, building something new, expanding, capturing market.” And that is logical. But suddenly those chickens are coming home to roost in a big way. Our phone is ringing off the hook, as I'm sure you've noticed and your time here, and suddenly money means something again. What do you think drove it?John: So, I think there's a couple of driving factors. The first is obviously the broader economic conditions, you know, with the economic growth in the US, especially slowing down post-pandemic, we're seeing organizations looking for opportunities to spend less to be able to deliver—you know, recoup that money and deliver additional value. But beyond that, right—because, okay, but startups are probably still lighting giant piles of VC money on fire, and that's okay, but what's happening, I think, is that the first wave of CIOs that said cloud-first, cloud-only basically got their comeuppance. And, you know, these enterprises saw their explosive cloud bills and they saw that, oh, you know, we moved 5000 servers to AWS or GCP or Azure and we got the bill, and that's not sustainable. And so, we see a lot of cloud repatriation, cloud optimization, right, a lot of second-gen… cloud, I'll call them second-gen cloud-native CIOs coming into these large organizations where their predecessor made some bad financial decisions and either left or got asked to leave, and now they're trying to stop from lighting their giant piles of cash on fire, they're trying to stop spending 3X what they were spending on-prem.Corey: I think an easy mistake for folks to make is to get lost in the raw infrastructure cost. I'm not saying it's not important. Obviously not, but you could save a giant pile of money on your RDS instances by running your own database software on top of EC2, but I don't generally recommend folks do it because you also need engineering time to be focusing on getting those things up, care and feeding, et cetera. And what people lose sight of is the fact that the payroll expense is almost universally more than the cloud bill at every company I've ever talked to.So, there's a consistent series of, “Well, we're just trying to get to be the absolute lowest dollar figure total.” It's the wrong thing to emphasize on, otherwise, “Cool, turn everything off and your bill drops to zero.” Or, “Migrate to another cloud provider. AWS bill becomes zero. Our job is done.” It doesn't actually solve the problem at all. It's about what's right for the business, not about getting the absolute lowest possible score like it's some kind of code golf tournament.John: Right. So, I think that there's a couple of different ways to look at that. One is obviously looking at making your workloads more cloud-native. I know that's a stupid buzzword to some people, but—Corey: The problem I have with the term is that it means so many different things to different people.John: Right. But I think the gist of that is taking advantage of what the cloud is good at. And so, what we saw was that excess capacity on-prem was effectively free once you bought it, right? There were there was no accountability for burning through extra V CPUs or extra RAM. And then you had—Corey: Right. You spin something up in your data center and the question is, “Is the physical capacity there?” And very few companies had a reaping process until they were suddenly seeing capacity issues and suddenly everyone starts asking you a whole bunch of questions about it. But that was a natural forcing function that existed. Now, S3 has infinite storage, or it might as well. They can add capacity faster than you can fill it—I know this; I've tried—and the problem that you have then is that it's always just a couple more cents per gigabyte and it keeps on going forever. There's no, we need to make an investment decision because the SAN is at 80% capacity. Do you need all those 16 copies of the production data that you haven't touched since 2012? No, I probably don't.John: Yeah, there's definitely a forcing function when you're doing your own capacity planning. And the cloud, for the most part, as you've alluded to, for most organizations is infinite capacity. So, when they're looking at AWS or they're looking at any of the public cloud providers, it's a potentially infinite bill. Now, that scares a lot of organizations, and so because they didn't have the forcing function of, hey, we're out of CPUs, or we're out of hard disk space, or we're out of network ports, I think that because the cloud was a buzzword that a lot of shareholders and boards wanted to see in IT status reports and IT strategic plans, I think we grew a little bit further than we should have, from an enterprise perspective. And I think a lot of that's now being clawed back as organizations are maturing and looking to manage cost. Obviously, the huge growth of just the term FinOps from a search perspective over the last three years has cemented that, right? We're seeing a much more cost-conscious consumer—cloud consumer—than we saw three years ago.Corey: I think that the baseline level of understanding has also risen. It used to be that I would go into a client environment, prepared to deploy all kinds of radical stuff that these days look like context-aware architecture and things that would automatically turn down developer environments when developers were done for the day or whatnot. And I would discover that, oh, you haven't bought Reserved Instances in three years. Maybe start there with the easy thing. And now you don't see those, the big misconfigurations or the big oversights the way that you once did.People are getting better at this, which is a good thing. I'm certainly not having a problem with this. It means that we get to focus on things that are more architecturally nuanced, which I love. And I think that it forces us to continue innovating rather than just doing something that basically any random software stack could provide.John: Yeah, I think to your point, the easy wins are being exhausted or have been exhausted already, right? Very rarely do we walk into a customer and see that they haven't bought a, you know, Reserved Instance, or a Savings Plan. That's just not a thing. And the proliferation of software tools to help with those things, of course, in some cases, dubious proposition of, “We'll fix your cloud bill automatically for a small percentage of the savings,” that some of those software tools have, I think those have kind of run their course. And now you've got a smarter populace or smarter consumer and it does come into the more nuanced stuff, right.All right, do you really need to replicate data across AZs? Well, not if your workloads aren't stateful. Well, so some of the old things—and Kubernetes is a great example of this, right—the age old adage of, if I'm going to spin up an EKS cluster, I need to put it in three AZs, okay, why? That's going to cost you money [laugh], the cross-AZ traffic. And I know cross-AZ traffic is a simple one, but we still see that. We still see, “Well, I don't know why I put it across all three AZs.”And so, the service-to-service communication inside that cluster, the control plane traffic inside that cluster, is costing you money. Now, it might be minimal, but as you grow and as you scale your product or the services that you're providing internally, that may grow to a non-trivial sum of money.Corey: I think that there's a tipping point where an unbounded growth problem is always going to emerge as something that needs attention and needs to be focused on. But I should ask you this because you have a skill set that is, as you know, extremely in demand. You also have that rare gift that I wish wasn't as rare as it is where you can be thrown into the deep end knowing next to nothing about a particular technology stack, and in a remarkably short period of time, develop what can only be called subject matter expertise around it. I've seen you do this years past with Kubernetes, which is something I'm still trying to wrap my head around. You have a natural gift for it which meant that, from many respects, the world was your oyster. Why this? Why now?John: So, I think there's a couple of things that are unique at this thing, at this time point, right? So obviously, helping customers has always been something that's fun and exciting for me, right? Going to an organization and solving the same problem I've solved 20 different times, for example, spinning up a Kubernetes cluster, I guess I have a little bit of a little bit of squirrel syndrome, so to speak, and that gets—it gets boring. I'd rather just automate that or build some tooling and disseminate that to the customers and let them do that. So, the thing with cost management is, it's always a different problem.Yeah, we're solving fundamentally the same problem, which is, I'm spending too much, but it's always a different root cause, you know? In one customer, it could be data transfer fees. In another customer, it could be errant development growth where they're not controlling the spend on their development environments. In yet another customer, it could be excessive object storage growth. So, being able to hunt and look for those and play detective is really fun, and I think that's one of the things that drew me to this particular area.The other is just from a timing perspective, this is a problem a lot of organizations have, and I think it's underserved. I think that there are not enough companies—service providers, whatever—focusing on the hard problem of cost optimization. There's too many people who think it's a finance problem and not enough people who think it's an engineering problem. And so, I wanted to do work on a place where we think it's an engineering problem.Corey: It's been a very… long road. And I think that engineering problems and people problems are both fascinating to me, and the AWS bill is both. It's often misunderstood as a finance problem, and finance needs to be consulted absolutely, but they can't drive an optimization project, and they don't know what the context is behind an awful lot of decisions that get made. It really is breaking down bridges. But also, there's a lot of engineering in here, too. It scratches my itch in that direction, anyway.John: Yeah, it's one of the few business problems that I think touches multiple areas. As you said, it's obviously a people problem because we want to make sure that we are supporting and educating our staff. It's a process problem. Are we making costs visible to the organization? Are we making sure that there's proper chargeback and showback methodologies, et cetera? But it's also a technology problem. Did we build this thing to take advantage of the architecture or did we shoehorn it in a way that's going to cost us a small fortune? And I think it touches all three, which I think is unique.Corey: John, I really want to thank you for taking the time to speak with me. If people want to learn more about what you're up to in a given day, where's the best place for them to find you?John: Well, thanks, Corey, and thanks for having me. And, of course obviously, our website duckbillgroup.com is a great place to find out what we're working on, what we have coming. I also, I'm pretty active on LinkedIn. I know that's [laugh]—I'm not a huge Twitter guy, but I am pretty active on LinkedIn, so you can always drop me a follow on LinkedIn. And I'll try to post interesting and useful content there for our listeners.Corey: And we will, of course, put links to that in the [show notes 00:28:37], which in my case, is of course extremely self-aggrandizing. But that's all right. We're here to do self-promotion. Thank you so much for taking the time to chat with me, John. I appreciate it. Now, get back to work.John: [laugh]. All right, thanks, Corey. Have a good one.Corey: John Wynkoop, cloud economist at The Duckbill Group. I'm Cloud Economist Corey Quinn, and this is Screaming in the Cloud. If you've enjoyed this podcast, please leave a five-star review on your podcast platform of choice, whereas if you've hated this podcast, please leave a five-star review on your podcast platform of choice while also taking pains to note how you're using multiple podcast platforms these days because that just seems to be the way the world went.Corey: If your AWS bill keeps rising and your blood pressure is doing the same, then you need The Duckbill Group. We help companies fix their AWS bill by making it smaller and less horrifying. The Duckbill Group works for you, not AWS. We tailor recommendations to your business and we get to the point. Visit duckbillgroup.com to get started.

Sysdig's Alessandro Brucato and Michael Clark join Dave to discuss their work on "AWS's Hidden Threat: AMBERSQUID Cloud-Native Cryptojacking Operation." Attackers are targeting what are typically considered secure AWS services, like AWS Fargate and Amazon SageMaker. This means that defenders generally aren't as concerned with their security from end-to-end. The research states "The AMBERSQUID operation was able to exploit cloud services without triggering the AWS requirement for approval of more resources, as would be the case if they only spammed EC2 instances." This poses additional challenges targeting multiple services since it requires finding and killing all miners in each exploited service. The research can be found here: AWS's Hidden Threat: AMBERSQUID Cloud-Native Cryptojacking Operation Learn more about your ad choices. Visit megaphone.fm/adchoices