Podcasts about Elasticsearch

Join hosts Daniel Garcia and Grant Copley as they dive into the latest news and updates in the BoxLang and CFML world. Don't miss out on insights, discussions, and what's coming next for modern software development!

In a new season of the Oracle University Podcast, Lois Houston and Nikita Abraham dive into the world of Oracle GoldenGate 23ai, a cutting-edge software solution for data management. They are joined by Nick Wagner, a seasoned expert in database replication, who provides a comprehensive overview of this powerful tool.   Nick highlights GoldenGate's ability to ensure continuous operations by efficiently moving data between databases and platforms with minimal overhead. He emphasizes its role in enabling real-time analytics, enhancing data security, and reducing costs by offloading data to low-cost hardware. The discussion also covers GoldenGate's role in facilitating data sharing, improving operational efficiency, and reducing downtime during outages.   Oracle GoldenGate 23ai: Fundamentals: https://mylearn.oracle.com/ou/course/oracle-goldengate-23ai-fundamentals/145884/237273 Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode. ---------------------------------------------------------------   Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:25 Nikita: Welcome to the Oracle University Podcast! I'm Nikita Abraham, Team Lead: Editorial Services with Oracle University, and with me is Lois Houston: Director of Innovation Programs. Lois: Hi everyone! Welcome to a new season of the podcast. This time, we're focusing on the fundamentals of Oracle GoldenGate. Oracle GoldenGate helps organizations manage and synchronize their data across diverse systems and databases in real time.  And with the new Oracle GoldenGate 23ai release, we'll uncover the latest innovations and features that empower businesses to make the most of their data. Nikita: Taking us through this is Nick Wagner, Senior Director of Product Management for Oracle GoldenGate. He's been doing database replication for about 25 years and has been focused on GoldenGate on and off for about 20 of those years.  01:18 Lois: In today's episode, we'll ask Nick to give us a general overview of the product, along with some use cases and benefits. Hi Nick! To start with, why do customers need GoldenGate? Nick: Well, it delivers continuous operations, being able to continuously move data from one database to another database or data platform in efficiently and a high-speed manner, and it does this with very low overhead. Almost all the GoldenGate environments use transaction logs to pull the data out of the system, so we're not creating any additional triggers or very little overhead on that source system. GoldenGate can also enable real-time analytics, being able to pull data from all these different databases and move them into your analytics system in real time can improve the value that those analytics systems provide. Being able to do real-time statistics and analysis of that data within those high-performance custom environments is really important. 02:13 Nikita: Does it offer any benefits in terms of cost?  Nick: GoldenGate can also lower IT costs. A lot of times people run these massive OLTP databases, and they are running reporting in those same systems. With GoldenGate, you can offload some of the data or all the data to a low-cost commodity hardware where you can then run the reports on that other system. So, this way, you can get back that performance on the OLTP system, while at the same time optimizing your reporting environment for those long running reports. You can improve efficiencies and reduce risks. Being able to reduce the amount of downtime during planned and unplanned outages can really make a big benefit to the overall operational efficiencies of your company.  02:54 Nikita: What about when it comes to data sharing and data security? Nick: You can also reduce barriers to data sharing. Being able to pull subsets of data, or just specific pieces of data out of a production database and move it to the team or to the group that needs that information in real time is very important. And it also protects the security of your data by only moving in the information that they need and not the entire database. It also provides extensibility and flexibility, being able to support multiple different replication topologies and architectures. 03:24 Lois: Can you tell us about some of the use cases of GoldenGate? Where does GoldenGate truly shine?  Nick: Some of the more traditional use cases of GoldenGate include use within the multicloud fabric. Within a multicloud fabric, this essentially means that GoldenGate can replicate data between on-premise environments, within cloud environments, or hybrid, cloud to on-premise, on-premise to cloud, or even within multiple clouds. So, you can move data from AWS to Azure to OCI. You can also move between the systems themselves, so you don't have to use the same database in all the different clouds. For example, if you wanted to move data from AWS Postgres into Oracle running in OCI, you can do that using Oracle GoldenGate. We also support maximum availability architectures. And so, there's a lot of different use cases here, but primarily geared around reducing your recovery point objective and recovery time objective. 04:20 Lois: Ah, reducing RPO and RTO. That must have a significant advantage for the customer, right? Nick: So, reducing your RPO and RTO allows you to take advantage of some of the benefits of GoldenGate, being able to do active-active replication, being able to set up GoldenGate for high availability, real-time failover, and it can augment your active Data Guard and Data Guard configuration. So, a lot of times GoldenGate is used within Oracle's maximum availability architecture platinum tier level of replication, which means that at that point you've got lots of different capabilities within the Oracle Database itself. But to help eke out that last little bit of high availability, you want to set up an active-active environment with GoldenGate to really get true zero RPO and RTO. GoldenGate can also be used for data offloading and data hubs. Being able to pull data from one or more source systems and move it into a data hub, or into a data warehouse for your operational reporting. This could also be your analytics environment too. 05:22 Nikita: Does GoldenGate support online migrations? Nick: In fact, a lot of companies actually get started in GoldenGate by doing a migration from one platform to another. Now, these don't even have to be something as complex as going from one database like a DB2 on-premise into an Oracle on OCI, it could even be simple migrations. A lot of times doing something like a major application or a major database version upgrade is going to take downtime on that production system. You can use GoldenGate to eliminate that downtime. So this could be going from Oracle 19c to Oracle 23ai, or going from application version 1.0 to application version 2.0, because GoldenGate can do the transformation between the different application schemas. You can use GoldenGate to migrate your database from on premise into the cloud with no downtime as well. We also support real-time analytic feeds, being able to go from multiple databases, not only those on premise, but being able to pull information from different SaaS applications inside of OCI and move it to your different analytic systems. And then, of course, we also have the ability to stream events and analytics within GoldenGate itself.  06:34 Lois: Let's move on to the various topologies supported by GoldenGate. I know GoldenGate supports many different platforms and can be used with just about any database. Nick: This first layer of topologies is what we usually consider relational database topologies. And so this would be moving data from Oracle to Oracle, Postgres to Oracle, Sybase to SQL Server, a lot of different types of databases. So the first architecture would be unidirectional. This is replicating from one source to one target. You can do this for reporting. If I wanted to offload some reports into another server, I can go ahead and do that using GoldenGate. I can replicate the entire database or just a subset of tables. I can also set up GoldenGate for bidirectional, and this is what I want to set up GoldenGate for something like high availability. So in the event that one of the servers crashes, I can almost immediately reconnect my users to the other system. And that almost immediately depends on the amount of latency that GoldenGate has at that time. So a typical latency is anywhere from 3 to 6 seconds. So after that primary system fails, I can reconnect my users to the other system in 3 to 6 seconds. And I can do that because as GoldenGate's applying data into that target database, that target system is already open for read and write activity. GoldenGate is just another user connecting in issuing DML operations, and so it makes that failover time very low. 07:59 Nikita: Ok…If you can get it down to 3 to 6 seconds, can you bring it down to zero? Like zero failover time?   Nick: That's the next topology, which is active-active. And in this scenario, all servers are read/write all at the same time and all available for user activity. And you can do multiple topologies with this as well. You can do a mesh architecture, which is where every server talks to every other server. This works really well for 2, 3, 4, maybe even 5 environments, but when you get beyond that, having every server communicate with every other server can get a little complex. And so at that point we start looking at doing what we call a hub and spoke architecture, where we have lots of different spokes. At the end of each spoke is a read/write database, and then those communicate with a hub. So any change that happens on one spoke gets sent into the hub, and then from the hub it gets sent out to all the other spokes. And through that architecture, it allows you to really scale up your environments. We have customers that are doing up to 150 spokes within that hub architecture. Within active-active replication as well, we can do conflict detection and resolution, which means that if two users modify the same row on two different systems, GoldenGate can actually determine that there was an issue with that and determine what user wins or which row change wins, which is extremely important when doing active-active replication. And this means that if one of those systems fails, there is no downtime when you switch your users to another active system because it's already available for activity and ready to go. 09:35 Lois: Wow, that's fantastic. Ok, tell us more about the topologies. Nick: GoldenGate can do other things like broadcast, sending data from one system to multiple systems, or many to one as far as consolidation. We can also do cascading replication, so when data moves from one environment that GoldenGate is replicating into another environment that GoldenGate is replicating. By default, we ignore all of our own transactions. But there's actually a toggle switch that you can flip that says, hey, GoldenGate, even though you wrote that data into that database, still push it on to the next system. And then of course, we can also do distribution of data, and this is more like moving data from a relational database into something like a Kafka topic or a JMS queue or into some messaging service. 10:24 Raise your game with the Oracle Cloud Applications skills challenge. Get free training on Oracle Fusion Cloud Applications, Oracle Modern Best Practice, and Oracle Cloud Success Navigator. Pass the free Oracle Fusion Cloud Foundations Associate exam to earn a Foundations Associate certification. Plus, there's a chance to win awards and prizes throughout the challenge! What are you waiting for? Join the challenge today by visiting visit oracle.com/education. 10:58 Nikita: Welcome back! Nick, does GoldenGate also have nonrelational capabilities?  Nick: We have a number of nonrelational replication events in topologies as well. This includes things like data lake ingestion and streaming ingestion, being able to move data and data objects from these different relational database platforms into data lakes and into these streaming systems where you can run analytics on them and run reports. We can also do cloud ingestion, being able to move data from these databases into different cloud environments. And this is not only just moving it into relational databases with those clouds, but also their data lakes and data fabrics. 11:38 Lois: You mentioned a messaging service earlier. Can you tell us more about that? Nick: Messaging replication is also possible. So we can actually capture from things like messaging systems like Kafka Connect and JMS, replicate that into a relational data, or simply stream it into another environment. We also support NoSQL replication, being able to capture from MongoDB and replicate it onto another MongoDB for high availability or disaster recovery, or simply into any other system. 12:06 Nikita: I see. And is there any integration with a customer's SaaS applications? Nick: GoldenGate also supports a number of different OCI SaaS applications. And so a lot of these different applications like Oracle Financials Fusion, Oracle Transportation Management, they all have GoldenGate built under the covers and can be enabled with a flag that you can actually have that data sent out to your other GoldenGate environment. So you can actually subscribe to changes that are happening in these other systems with very little overhead. And then of course, we have event processing and analytics, and this is the final topology or flexibility within GoldenGate itself. And this is being able to push data through data pipelines, doing data transformations. GoldenGate is not an ETL tool, but it can do row-level transformation and row-level filtering.  12:55 Lois: Are there integrations offered by Oracle GoldenGate in automation and artificial intelligence? Nick: We can do time series analysis and geofencing using the GoldenGate Stream Analytics product. It allows you to actually do real time analysis and time series analysis on data as it flows through the GoldenGate trails. And then that same product, the GoldenGate Stream Analytics, can then take the data and move it to predictive analytics, where you can run MML on it, or ONNX or other Spark-type technologies and do real-time analysis and AI on that information as it's flowing through.  13:29 Nikita: So, GoldenGate is extremely flexible. And given Oracle's focus on integrating AI into its product portfolio, what about GoldenGate? Does it offer any AI-related features, especially since the product name has “23ai” in it? Nick: With the advent of Oracle GoldenGate 23ai, it's one of the two products at this point that has the AI moniker at Oracle. Oracle Database 23ai also has it, and that means that we actually do stuff with AI. So the Oracle GoldenGate product can actually capture vectors from databases like MySQL HeatWave, Postgres using pgvector, which includes things like AlloyDB, Amazon RDS Postgres, Aurora Postgres. We can also replicate data into Elasticsearch and OpenSearch, or if the data is using vectors within OCI or the Oracle Database itself. So GoldenGate can be used for a number of things here. The first one is being able to migrate vectors into the Oracle Database. So if you're using something like Postgres, MySQL, and you want to migrate the vector information into the Oracle Database, you can. Now one thing to keep in mind here is a vector is oftentimes like a GPS coordinate. So if I need to know the GPS coordinates of Austin, Texas, I can put in a latitude and longitude and it will give me the GPS coordinates of a building within that city. But if I also need to know the altitude of that same building, well, that's going to be a different algorithm. And GoldenGate and replicating vectors is the same way. When you create a vector, it's essentially just creating a bunch of numbers under the screen, kind of like those same GPS coordinates. The dimension and the algorithm that you use to generate that vector can be different across different databases, but the actual meaning of that data will change. And so GoldenGate can replicate the vector data as long as the algorithm and the dimensions are the same. If the algorithm and the dimensions are not the same between the source and the target, then you'll actually want GoldenGate to replicate the base data that created that vector. And then once GoldenGate replicates the base data, it'll actually call the vector embedding technology to re-embed that data and produce that numerical formatting for you.  15:42 Lois: So, there are some nuances there… Nick: GoldenGate can also replicate and consolidate vector changes or even do the embedding API calls itself. This is really nice because it means that we can take changes from multiple systems and consolidate them into a single one. We can also do the reverse of that too. A lot of customers are still trying to find out which algorithms work best for them. How many dimensions? What's the optimal use? Well, you can now run those in different servers without impacting your actual AI system. Once you've identified which algorithm and dimension is going to be best for your data, you can then have GoldenGate replicate that into your production system and we'll start using that instead. So it's a nice way to switch algorithms without taking extensive downtime. 16:29 Nikita: What about in multicloud environments?  Nick: GoldenGate can also do multicloud and N-way active-active Oracle replication between vectors. So if there's vectors in Oracle databases, in multiple clouds, or multiple on-premise databases, GoldenGate can synchronize them all up. And of course we can also stream changes from vector information, including text as well into different search engines. And that's where the integration with Elasticsearch and OpenSearch comes in. And then we can use things like NVIDIA and Cohere to actually do the AI on that data.  17:01 Lois: Using GoldenGate with AI in the database unlocks so many possibilities. Thanks for that detailed introduction to Oracle GoldenGate 23ai and its capabilities, Nick.  Nikita: We've run out of time for today, but Nick will be back next week to talk about how GoldenGate has evolved over time and its latest features. And if you liked what you heard today, head over to mylearn.oracle.com and take a look at the Oracle GoldenGate 23ai Fundamentals course to learn more. Until next time, this is Nikita Abraham… Lois: And Lois Houston, signing off! 17:33 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

Zoekfunctionaliteit is een fundamenteel onderdeel van de moderne digitale wereld. Elastic, het bedrijf achter Elastic Search, voorziet daarin met een platform dat meer doet dan enkel door data bladeren. Elastic Search stelt gebruikers in staat razendsnel complexe queries uit te voeren op grote datasets, iets waar traditionele databases moeite mee hebben. Dat maakt het niet alleen geschikt voor webapplicaties en e-commerceplatforms, maar ook voor beveiligingssystemen en operationele monitoring. Bedrijven als Uber, Netflix en Wikipedia maken dan ook gebruik van Elastic-producten.

Zoekfunctionaliteit is een fundamenteel onderdeel van de moderne digitale wereld. Elastic, het bedrijf achter Elastic Search, voorziet daarin met een platform dat meer doet dan enkel door data bladeren. Elastic Search stelt gebruikers in staat razendsnel complexe queries uit te voeren op grote datasets, iets waar traditionele databases moeite mee hebben. Dat maakt het niet alleen geschikt voor webapplicaties en e-commerceplatforms, maar ook voor beveiligingssystemen en operationele monitoring. Bedrijven als Uber, Netflix en Wikipedia maken dan ook gebruik van Elastic-producten. De oorsprong van het bedrijf ligt in Amsterdam, waar de basis werd gelegd voor wat uiteindelijk een wereldwijd opererende speler is geworden. Het idee voor Elastic Search ontstond uit een persoonlijke behoefte: oprichter Shay Banon wilde een tool bouwen om recepten voor zijn vrouw makkelijk doorzoekbaar te maken. Daaruit groeide een open-sourceproject dat uitgroeide tot een infrastructuurproduct dat in duizenden bedrijven wordt ingezet. Toch brengt die open-source-aanpak ook uitdagingen met zich mee. Elastic heeft in 2021 een wijziging doorgevoerd in het open-sourcemodel, juist omdat grote cloudproviders hun technologie gebruikten zonder bij te dragen aan de ontwikkeling. Die stap was voor Elastic noodzakelijk om zichzelf duurzaam te kunnen blijven ontwikkelen. De balans tussen openheid en commerciële haalbaarheid blijft daarmee een voortdurende afweging. In een wereld waarin hyperscalers domineren, is de vraag gerechtvaardigd of puur open-sourcemodellen levensvatbaar blijven. Inmiddels is Elastic ook meegegaan in de golf van kunstmatige intelligentie. De toevoeging van vector search en AI-koppelingen aan de bestaande zoektechnologie maakt nieuwe toepassingen mogelijk, zoals semantisch zoeken of realtime analyses op ongeordende datasets. Daarmee sluit Elastic aan op de groeiende behoefte aan AI-native infrastructuur, zonder zijn kernproduct uit het oog te verliezen. De organisatie zelf is ingericht zonder traditioneel hoofdkantoor. Het bedrijf is meer dan 7 miljard dollar waard, maar kent een sterk gedistribueerd team. Dat blijkt goed te werken, zeker in een post-pandemische wereld waar locatie minder bepalend is voor samenwerking. Wel blijft de vraag hoe Europa zich verhoudt tot de mondiale techgiganten. Vanuit Nederland is Elastic uitgegroeid tot een voorbeeld van internationale schaalbaarheid, maar het Europese ecosysteem mist volgens Elastic soms de slagkracht om écht door te pakken. Gast Jeroen Berckenkamp Video YouTube Hosts Ben van der Burg & Daniël Mol Redactie Daniël MolSee omnystudio.com/listener for privacy information.

OpenSearch has evolved significantly since its 2021 launch, recently reaching a major milestone with its move to the Linux Foundation. This shift from company-led to foundation-based governance has accelerated community contributions and enterprise adoption, as discussed by NetApp's Amanda Katona in a New Stack Makers episode recorded at KubeCon + CloudNativeCon Europe. NetApp, an early adopter of OpenSearch following Elasticsearch's licensing change, now offers managed services on the platform and contributes actively to its development.Katona emphasized how neutral governance under the Linux Foundation has lowered barriers to enterprise contribution, noting a 56% increase in downloads since the transition and growing interest from developers. OpenSearch 3.0, featuring a Lucene 10 upgrade, promises faster search capabilities—especially relevant as data volumes surge. NetApp's ongoing investments include work on machine learning plugins and developer training resources.Katona sees the Linux Foundation's involvement as key to OpenSearch's long-term success, offering vendor-neutral governance and reassuring users seeking openness, performance, and scalability in data search and analytics.Learn more from The New Stack about OpenSearch: Report: OpenSearch Bests ElasticSearch at Vector ModelingAWS Transfers OpenSearch to the Linux Foundation OpenSearch: How the Project Went From Fork to FoundationJoin our community of newsletter subscribers to stay on top of the news and at the top of your game. 

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.

In this episode of Code with Jason, host Jason Swett interviews Prarthana Shiva, a senior software engineer at NexHealth, who shares how her team is handling massive database scaling challenges. Prarthana explains their PostgreSQL database's growth to 24 terabytes (with projections to triple within a year) and details their innovative solutions including read replicas, Elasticsearch implementation, Redis caching, external write-ahead logs, and optimized vacuuming processes. The conversation also touches on Jason's own database challenges with his CI platform and concludes with Prarthana's upcoming presentation at Sin City Ruby 2025, where she'll discuss their transition from schema-based to row-based multi-tenancy for better scalability.Prarthana Shiva on LinkedInSin City Ruby

How to speed up GenAI? Find out how on this episode of Six Five On the Road at AWS re:Invent with host Keith Townsend and Elastic's Ken Exner, CPO, for a conversation on how Elastic is at the forefront of accelerating generative AI (GenAI) innovation. Fast track this ⤵️ Insights into the adoption of generative AI applications among Elastic's customer base and how Elastic facilitates the acceleration of Gen AI initiatives. Future directions for Elastic's product portfolio with the integration of AI and machine learning. Developer feedback on Elasticsearch's usage in GenAI projects and its prominence as the top vector database. The launch of Elastic Cloud Serverless and Elastic's commitment to balancing usability with flexibility for both developers and end-users. A reflection on Elastic's product developments in the past year and anticipations for innovations in 2025.  

Fabrizio Papa Techera, Membre du Directoire de LexbaseDans cet épisode de Cheat Code, nous avons eu le plaisir d'accueillir Fabrizio Papa Techera, membre du directoire de Lexbase.Ancien avocat ayant prêté serment en 2005, Fabrizio a exercé jusqu'en 2008 avant de rejoindre Lexbase en 2017. Son rôle clé ? Mettre son expertise métier au service de la construction de produits innovants et fiables.Il pilote notamment la rédaction des cahiers des charges fonctionnels en identifiant les besoins pour les traduire en solutions concrètes, prêtes à être développées puis déployées.Chez Lexbase, l'objectif est simple : simplifier et optimiser vos recherches juridiques.Gagnez en efficacité, centralisez vos documents, collaborez plus facilement avec vos équipes et boostez vos performances budgétaires.‍Grâce à un moteur de recherche ultra-performant, basé sur Elasticsearch et alimenté par l'intelligence artificielle générative, vous obtenez des résultats précis et multisources en un temps record.‍Fini les heures perdues à chercher l'info ! Avec plus de 25 millions de documents juridiques (jurisprudence, textes de loi, revues…), Lexbase vous aide à trouver LA donnée qui fait la différence dans vos dossiers.Pour affiner vos recherches, des filtres intuitifs sont à votre disposition afin d'aller droit au but.Et parce qu'il est essentiel de se tenir informé pour se démarquer, Lexbase a développé une offre média juridique, notamment avec ses podcasts Lexradio. Disponibles sur Spotify, Deezer ou directement via Lexbase, ces podcasts s'adaptent parfaitement au rythme de vie des professionnels du droit.Comme l'explique Fabrizio : « Les avocats passent beaucoup de temps en voiture ; le format podcast leur permet de rester connectés à l'actualité juridique sans perdre une minute. »En conclusion, Lexbase, c'est bien plus qu'une base de données : c'est une solution complète pour optimiser vos recherches, structurer vos projets et faciliter votre quotidien juridique.

At All Things Open in October, Anandhi Bumstead, AWS's director of software engineering, highlighted OpenSearch's journey and the advantages of the Linux Foundation's stewardship. OpenSearch, an open source data ingestion and analytics engine, was transferred by Amazon Web Services (AWS) to the Linux Foundation in September 2024, seeking neutral governance and broader community collaboration. Originally forked from Elasticsearch after a licensing change in 2021, OpenSearch has evolved into a versatile platform likened to a “Swiss Army knife” for its broad use cases, including observability, log and security analytics, alert detection, and semantic and hybrid search, particularly in generative AI applications.Despite criticism over slower indexing speeds compared to Elasticsearch, significant performance improvements have been made. The latest release, OpenSearch 2.17, delivers 6.5x faster query performance and a 25% indexing improvement due to segment replication. Future efforts aim to enhance indexing, search, storage, and vector capabilities while optimizing costs and efficiency. Contributions are welcomed via opensearch.org.Learn more from The New Stack about deploying applications on OpenSearchAWS Transfers OpenSearch to the Linux FoundationFrom Flashpoint to Foundation: OpenSearch's Path ClearsSemantic Search with Amazon OpenSearch Serverless and TitanJoin our community of newsletter subscribers to stay on top of the news and at the top of your game. 

Philippe Noël is Co-Founder & CEO of ParadeDB, the modern Elasticsearch alternative built on Postgres. They're purpose-built for heavy, real-time workloads and their open source project, also called paradedb, has over 6K stars on GitHub. ParadeDB has raised $2M from investors including General Catalyst & YC. In this episode, we dig into the benefits of connecting search directly to the database (ie. no ETL), the types of users / use cases that really benefit from ParadeDB (e-commerce, FinTech, etc.), the decision to focus on Postgres, making adoption super easy, Philippe's learnings as a second-time founder & more!

Nicholas Knize discusses optimizing geospatial indexing and hybrid search using advanced data structures within the Lucene framework at FOSS4G NA 2024. He emphasizes reducing cloud infrastructure waste and improving geospatial data processing efficiency. Highlights

Shay Banon, the creator of Elasticsearch, joins us to discuss pulling off a reverse rug pull. Yes, Elasticsearch is open source, again! We discuss the complexities surrounding open source licensing and what made Elastic change their license, the implications of trademark law, the personal and business impact of moving away from open source, and ultimately what made them hit rewind and return to open source.

Shay Banon, the creator of Elasticsearch, joins us to discuss pulling off a reverse rug pull. Yes, Elasticsearch is open source, again! We discuss the complexities surrounding open source licensing and what made Elastic change their license, the implications of trademark law, the personal and business impact of moving away from open source, and ultimately what made them hit rewind and return to open source.

JVM summit, virtual threads, stacks applicatives, licences, déterminisme et LLMs, quantification, deux outils de l'épisode et bien plus encore. Enregistré le 13 septembre 2024 Téléchargement de l'épisode LesCastCodeurs-Episode–315.mp3 News Langages Netflix utilise énormément Java et a rencontré un problème avec les Virtual Thread dans Java 21. Les ingénieurs de Netflix analysent ce problème dans cet article : https://netflixtechblog.com/java–21-virtual-threads-dude-wheres-my-lock–3052540e231d Les threads virtuels peuvent améliorer les performances mais posent des défis. Un problème de locking a été identifié : les threads virtuels se bloquent mutuellement. Cela entraîne des performances dégradées et des instabilités. Netflix travaille à résoudre ces problèmes et à tirer pleinement parti des threads virtuels. Une syntax pour indiquer qu'un type est nullable ou null-restricted arriverait dans Java https://bugs.openjdk.org/browse/JDK–8303099 Foo! interdirait null Foo? indiquerait que null est accepté Foo?[]! serait un tableau non-null de valeur nullable Il y a aussi des idées de syntaxe pour initialiser les tableaux null-restricted JEP: https://openjdk.org/jeps/8303099 Les vidéos du JVM Language Summit 2024 sont en ligne https://www.youtube.com/watch?v=OOPSU4LnKg0&list=PLX8CzqL3ArzUEYnTa6KYORRbP3nhsK0L1 Project Leyden Update Project Babylon - Code Reflection Valhalla - Where Are We? An Opinionated Overview on Static Analysis for Java Rethinking Java String Concatenation Code Reflection in Action - Translating Java to SPIR-V Java in 2024 Type Specialization of Java Generics - What If Casts Have Teeth ? (avec notre Rémi Forax national !) aussi tip or tail pour tout l'ecosysteme quelques liens sur Babylon: Code reflection pour exprimer des langages etranger (SQL) dans Java: https://openjdk.org/projects/babylon/ et sont example en emulation de LINQ https://openjdk.org/projects/babylon/articles/linq Librairies Micronaut sort sa version 4.6 https://micronaut.io/2024/08/26/micronaut-framework–4–6–0-released/ essentiellement une grosse mise à jour de tonnes de modules avec les dernières versions des dépendances Microprofile 7 faire quelques changements et evolution incompatibles https://microprofile.io/2024/08/22/microprofile–7–0-release/#general enleve Metrics et remplace avec Telemetry (metrics, log et tracing) Metrics reste une spec mais standalone Microprofile 7 depende de Jakarta Core profile et ne le package plus Microprofile OpenAPI 4 et Telemetry 2 amenent des changements incompatibles Quarkus 3.14 avec LetsEncrypt et des serialiseurs JAckson sans reflection https://quarkus.io/blog/quarkus–3–14–1-released/ Hibernate ORM 6.6 Serialisateurs JAckson sans reflection installer des certificats letsencrypt simplement (notamment avec la ligne de commande qui aide sympa notamment avec ngrok pour faire un tunnel vers son localhost retropedalage sur @QuarkusTestResource vs @WithTestResource suite aux retour de OOME et lenteur des tests mieux isolés Les logs structurées dans Spring Boot 3.4 https://spring.io/blog/2024/08/23/structured-logging-in-spring-boot–3–4 Les logs structurées (souvent en JSON) vous permettent de les envoyer facilement vers des backends comme Elastic, AWS CloudWatch… Vous pouvez les lier à du reporting et de l'alerting. Spring Boot 3.4 prend en charge la journalisation structurée par défaut. Il prend en charge les formats Elastic Common Schema (ECS) et Logstash, mais il est également possible de l'étendre avec vos propres formats. Vous pouvez également activer la journalisation structurée dans un fichier. Cela peut être utilisé, par exemple, pour imprimer des journaux lisibles par l'homme sur la console et écrire des journaux structurés dans un fichier pour l'ingestion par machine. Infrastructure CockroachDB qui avait une approche Business Software License (source available puis ALS 3 ans apres), passe maintenant en license proprietaire avec source available https://www.cockroachlabs.com/blog/enterprise-license-announcement/ Polyform project offre des licences standardisees selon les besoins de gratuit vs payant https://polyformproject.org/ Cloud Azure fonctions, comment le demarrage a froid est optimisé https://www.infoq.com/articles/azure-functions-cold-starts/?utm_campaign=infoq_content&utm_source=twitter&utm_medium=feed&utm_term=Cloud fonctions ont une latence naturelle forte toutes les lantences longues ne sont aps impactantes pour le business les demarrages a froid peuvent etre mesures avec les outils du cloud provider donc faites en usage faites des decentilers de latences experience 381 ms cold et 10ms apres tracing pour end to end latence les strategies keep alive pings: reveiller la fonctione a intervalles reguliers pour rester “warm” dans le code de la fonction: initialiser les connections et le chargement des assemblies dans l'initialization configurer dans host.json le batching, desactiver file system logging etc deployer les fonctions as zips reduire al taille du code et des fichiers (qui sont copies sur le serveur froid) sur .net activer ready to run qui aide le JIT compiler instances azure avec plus de CPU et memoire sont plus cher amis baissent le cold start dedicated azure instances pour vos fonctions (pas aprtage avec les autres tenants) ensuite montre des exemples concrets Web Sortie de Vue.js 3.5 https://blog.vuejs.org/posts/vue–3–5 Vue.JS 3.5: Nouveautés clés Optimisations de performance et de mémoire: Réduction significative de la consommation de mémoire (–56%). Amélioration des performances pour les tableaux réactifs de grande taille. Résolution des problèmes de valeurs calculées obsolètes et de fuites de mémoire. Nouvelles fonctionnalités: Reactive Props Destructure: Simplification de la déclaration des props avec des valeurs par défaut. Lazy Hydration: Contrôle de l'hydratation des composants asynchrones. useId(): Génération d'ID uniques stables pour les applications SSR. data-allow-mismatch: Suppression des avertissements de désynchronisation d'hydratation. Améliorations des éléments personnalisés: Prise en charge de configurations d'application, d'API pour accéder à l'hôte et au shadow root, de montage sans Shadow DOM, et de nonce pour les balises. useTemplateRef(): Obtention de références de modèle via l'API useTemplateRef(). Teleport différé: Téléportation de contenu vers des éléments rendus après le montage du composant. onWatcherCleanup(): Enregistrement de callbacks de nettoyage dans les watchers. Data et Intelligence Artificielle On entend souvent parler de Large Language Model quantisés, c'est à dire qu'on utilise par exemple des entiers sur 8 bits plutôt que des floatants sur 32 bits, pour réduire les besoins mémoire des GPU tout en gardant une précision proche de l'original. Cet article explique très visuellement et intuitivement ce processus de quantisation : https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-quantization Guillaume continue de partager ses aventures avec le framework LangChain4j. Comment effectuer de la classification de texte : https://glaforge.dev/posts/2024/07/11/text-classification-with-gemini-and-langchain4j/ en utilisant la classe TextClassification de LangChain4j, qui utilise une approche basée sur les vector embeddings pour comparer des textes similaires en utilisant du few-shot prompting, sous différentes variantes, dans cet autre article : https://glaforge.dev/posts/2024/07/30/sentiment-analysis-with-few-shots-prompting/ et aussi comment faire du multimodal avec LangChain4j (avec le modèle Gemini) pour analyser des textes, des images, mais également des vidéos, du contenu audio, ou bien des fichiers PDFs : https://glaforge.dev/posts/2024/07/25/analyzing-videos-audios-and-pdfs-with-gemini-in-langchain4j/ Pour faire varier la prédictibilité ou la créativité des LLMs, certains hyperparamètres peuvent être ajustés, comme la température, le top-k et le top-p. Mais est-ce que vous savez vraiment comment fonctionnent ces paramètres ? Deux articles très clairs et intuitifs expliquent leur fonctionnement : https://medium.com/google-cloud/is-a-zero-temperature-deterministic-c4a7faef4d20 https://medium.com/google-cloud/beyond-temperature-tuning-llm-output-with-top-k-and-top-p–24c2de5c3b16 la tempoerature va ecraser la probabilite du prochain token mais il reste des variables: approximnation des calculs flottants, stacks differentes effectuants ces choix differemment, que faire en cas d'egalité de probabilité entre deux tokens mais il y a d'atures apporoches de configuiration des reaction du LLM: top-k (qui evite les tokens peu frequents), top-p pour avoir les n des tokens qui totalient p% des probabilités temperature d'abord puis top-k puis top-p explique quoi utiliser quand OSI propose une definition de l'IA open source https://www.technologyreview.com/2024/08/22/1097224/we-finally-have-a-definition-for-open-source-ai/ gros debats ces derniers mois utilisable pour tous usages sans besoin de permission chercheurs peuvent inspecter les components et etudier comment le system fonctionne systeme modifiable pour tout objectif y compris chager son comportement et paratger avec d'autres avec ou sans modification quelque soit l'usage Definit des niveaux de transparence (donnees d'entranement, code source, poids) Une longue rétrospective de PostgreSQL a des volumes de malades et les problèmes de lock https://ardentperf.com/2024/03/03/postgres-indexes-partitioning-and-lwlocklockmanager-scalability/ un article pour vous rassurer que vous n'aurez probablement jamais le problème histoire sous forme de post mortem des conseils pour éviter ces falaises Outillage Un premier coup d'oeil à la future notation déclarative de Gradle https://blog.gradle.org/declarative-gradle-first-eap un article qui explique à quoi ressemble cette nouvelle syntaxe déclarative de Gradle (en plus de Groovy et Kotlin) Quelques vidéos montrent le support dans Android Studio, pour le moment, ainsi que dans un outil expérimental, en attendant le support dans tous les IDEs L'idée est d'éviter le scripting et d'avoir vraiment qu'une description de son build Cela devrait améliorer la prise en charge de Gradle dans les IDEs et permettre d'avoir de la complétion rapide, etc c'est moi on on a Maven là? Support de Firefox dans Puppeteer https://hacks.mozilla.org/2024/08/puppeteer-support-for-firefox/ Puppeteer, la bibliothèque d'automatisation de navigateur, supporte désormais officiellement Firefox dès la version 23. Cette avancée permet aux développeurs d'écrire des scripts d'automatisation et d'effectuer des tests de bout en bout sur Chrome et Firefox de manière interchangeable. L'intégration de Firefox dans Puppeteer repose sur WebDriver BiDi, un protocole inter-navigateurs en cours de standardisation au W3C. WebDriver BiDi facilite la prise en charge de plusieurs navigateurs et ouvre la voie à une automatisation plus simple et plus efficace. Les principales fonctionnalités de Puppeteer, telles que la capture de journaux, l'émulation de périphériques, l'interception réseau et le préchargement de scripts, sont désormais disponibles pour Firefox. Mozilla considère WebDriver BiDi comme une étape importante vers une meilleure expérience de test inter-navigateurs. La prise en charge expérimentale de CDP (Chrome DevTools Protocol) dans Firefox sera supprimée fin 2024 au profit de WebDriver BiDi. Bien que Firefox soit officiellement pris en charge, certaines API restent non prises en charge et feront l'objet de travaux futurs. Guillaume a créé une annotation @Retry pour JUnit 5, pour retenter l'exécution d'un test qui est “flaky” https://glaforge.dev/posts/2024/09/01/a-retryable-junit–5-extension/ Guillaume n'avait pas trouvé d'extension par défaut dans JUnit 5 pour remplacer les Retry rules de JUnit 4 Mais sur les réseaux sociaux, une discussion intéressante s'ensuit avec des liens sur des extensions qui implémentent cette approche Comme JUnit Pioneer qui propose plein d'extensions utiles https://junit-pioneer.org/docs/retrying-test/ Ou l'extension rerunner https://github.com/artsok/rerunner-jupiter Arnaud a aussi suggéré la configuration de Maven Surefire pour relancer automatiquement les tests qui ont échoué https://maven.apache.org/surefire/maven-surefire-plugin/examples/rerun-failing-tests.html la question philosophique est: est-ce que c'est tolerable les tests qui ecouent de façon intermitente Architecture Un ancien fan de GraphQL en a fini avec la technologie GraphQL et réfléchit aux alternatives https://bessey.dev/blog/2024/05/24/why-im-over-graphql/ Problèmes de GraphQL: Sécurité: Attaques d'autorisation Difficulté de limitation de débit Analyse de requêtes malveillantes Performance: Problème N+1 (récupération de données et autorisation) Impact sur la mémoire lors de l'analyse de requêtes invalides Complexité accrue: Couplage entre logique métier et couche de transport Difficulté de maintenance et de tests Solutions envisagées: Adoption d'API REST conformes à OpenAPI 3.0+ Meilleure documentation et sécurité des types Outils pour générer du code client/serveur typé Deux approches de mise en œuvre d'OpenAPI: “Implementation first” (génération de la spécification à partir du code) “Specification first” (génération du code à partir de la spécification) retour interessant de quelqu'un qui n'utilise pas GraphQL au quotidien. C'était des problemes qui devaient etre corrigés avec la maturité de l'ecosysteme et des outils mais ca a montré ces limites pour cette personne. Prensentation de Grace Hoper en 1980 sur le future des ordinateurs. https://youtu.be/AW7ZHpKuqZg?si=w_o5_DtqllVTYZwt c'est fou la modernité de ce qu'elle décrit Des problèmes qu'on a encore aujourd'hui positive leadership Elle décrit l'avantage de systèmes fait de plusieurs ordinateurs récemment declassifié Leader election avec les conditional writes sur les buckets S3/GCS/Azure https://www.morling.dev/blog/leader-election-with-s3-conditional-writes/ L'élection de leader est le processus de choisir un nœud parmi plusieurs pour effectuer une tâche. Traditionnellement, l'élection de leader se fait avec un service de verrouillage distribué comme ZooKeeper. Amazon S3 a récemment ajouté le support des écritures conditionnelles, ce qui permet l'élection de leader sans service séparé. L'algorithme d'élection de leader fonctionne en faisant concourir les nœuds pour créer un fichier de verrouillage dans S3. Le fichier de verrouillage inclut un numéro d'époque, qui est incrémenté à chaque fois qu'un nouveau leader est élu. Les nœuds peuvent déterminer s'ils sont le leader en listant les fichiers de verrouillage et en vérifiant le numéro d'époque. attention il peut y avoir plusieurs leaders élus (horloges qui ont dérivé) donc c'est à gérer aussi Méthodologies Guillaume Laforge interviewé par Sfeir, où il parle de l'importance de la curiosité, du partage, de l'importance de la qualité du code, et parsemé de quelques photos des Cast Codeurs ! https://www.sfeir.dev/success-story/guillaume-laforge-maestro-de-java-et-esthete-du-code-propre/ Sécurité Comment crowdstrike met a genoux windows et de nombreuses entreprises https://next.ink/144464/crowdstrike-donne-des-details-techniques-sur-son-fiasco/ l'incident vient de la mise à jour de la configuration de Falcon l'EDR de crowdstrike https://www.crowdstrike.com/blog/falcon-update-for-windows-hosts-technical-details/ qu'est ce qu'un EDR? Un système Endpoint Detection and Response a pour but de surveiller votre machine ( access réseaux, logs, …) pour detecter des usages non habituels. Cet espion doit interagir avec les couches basses du système (réseau, sockets, logs systems) et se greffe donc au niveau du noyau du système d'exploitation. Il remonte les informations en live à une plateforme qui peut ensuite adapter les réponse en live si l'incident a duré moins de 1h30 coté crowdstrike plus de 8 millions de machines se sont retrouvées hors service bloquées sur le Blue Screen Of Death selon Microsoft https://blogs.microsoft.com/blog/2024/07/20/helping-our-customers-through-the-crowdstrike-outage/ cela n'est pas la première fois et était déjà arrivé il y a quelques mois sur Linux. Comme il s'agissait d'une incompatibilité de kernel il avait été moins important car les services ITs gèrent mieux ces problèmes sous Linux https://stackdiary.com/crowdstrike-took-down-debian-and-rocky-linux-a-few-months-ago-and-no-one-noticed/ Les benchmarks CIS, un pilier pour la sécurité de nos environnements cloud, et pas que ! (Katia HIMEUR TALHI) https://blog.cockpitio.com/security/cis-benchmarks/ Le CIS est un organisme à but non lucratif qui élabore des normes pour améliorer la cybersécurité. Les référentiels CIS sont un ensemble de recommandations et de bonnes pratiques pour sécuriser les systèmes informatiques. Ils peuvent être utilisés pour renforcer la sécurité, se conformer aux réglementations et normaliser les pratiques. Loi, société et organisation Microsoft signe un accord avec OVHCloud pour qu'il arretent leur plaine d'antitrust https://www.politico.eu/article/microsoft-signs-antitrust-truce-with-ovhcloud/ la plainte était en Europe mermet a des clients de plus facilement deployer les solutions Microsoft dans le fournisseur de cloud de leur choix la plainte avait ete posé à l'été 2021 ca rendait faire tourner les solutions MS plus cheres et non competitives vs MS ElasticSearch et Kibana sont de nouveau Open Source, en ajoutant la license AGPL à ses autres licences existantes https://www.elastic.co/fr/blog/elasticsearch-is-open-source-again le marché d'il y a trois ans et maintenant a changé AWS est une bon partenaire le flou Elasticsearch vs le produit d'AWS s'est clarifié donc retour a l'open source via AGPL Affero GPL Elastic n'a jamais cessé de croire en l'open source d'après Shay Banon son fondateur Le changement vers l'AGPL est une option supplémentaire, pas un remplacement d'une des autres licences existantes et juste apres, Elastic annonce des resultants decevants faisant plonger l'action de 25% https://siliconangle.com/2024/08/29/elastic-shares-plunge–25-lower-revenue-projections-amid-slower-customer-commitments/ https://unrollnow.com/status/1832187019235397785 et https://www.elastic.co/pricing/faq/licensing pour un résumé des licenses chez elastic Outils de l'épisode MailMate un client email Markdown et qui gere beaucoup d'emails https://medium.com/@nicfab/mailmate-a-powerful-client-email-for-macos-markdown-integrated-email-composition-e218fe2accf3 Emmanuel l'utilise sur les boites email secondaires un peu lent a demarrer (synchro) et le reste est rapide boites virtuelles (par requete) SpamSieve Que macOS je crois Trippy, un analyseur de réseau https://github.com/fujiapple852/trippy Il regroupe dans une CLI traceroute et ping Conférences La liste des conférences provenant de Developers Conferences Agenda/List par Aurélie Vache et contributeurs : 17 septembre 2024 : We Love Speed - Nantes (France) 17–18 septembre 2024 : Agile en Seine 2024 - Issy-les-Moulineaux (France) 19–20 septembre 2024 : API Platform Conference - Lille (France) & Online 20–21 septembre 2024 : Toulouse Game Dev - Toulouse (France) 25–26 septembre 2024 : PyData Paris - Paris (France) 26 septembre 2024 : Agile Tour Sophia-Antipolis 2024 - Biot (France) 2–4 octobre 2024 : Devoxx Morocco - Marrakech (Morocco) 3 octobre 2024 : VMUG Montpellier - Montpellier (France) 7–11 octobre 2024 : Devoxx Belgium - Antwerp (Belgium) 8 octobre 2024 : Red Hat Summit: Connect 2024 - Paris (France) 10 octobre 2024 : Cloud Nord - Lille (France) 10–11 octobre 2024 : Volcamp - Clermont-Ferrand (France) 10–11 octobre 2024 : Forum PHP - Marne-la-Vallée (France) 11–12 octobre 2024 : SecSea2k24 - La Ciotat (France) 15–16 octobre 2024 : Malt Tech Days 2024 - Paris (France) 16 octobre 2024 : DotPy - Paris (France) 16–17 octobre 2024 : NoCode Summit 2024 - Paris (France) 17–18 octobre 2024 : DevFest Nantes - Nantes (France) 17–18 octobre 2024 : DotAI - Paris (France) 30–31 octobre 2024 : Agile Tour Nantais 2024 - Nantes (France) 30–31 octobre 2024 : Agile Tour Bordeaux 2024 - Bordeaux (France) 31 octobre 2024–3 novembre 2024 : PyCon.FR - Strasbourg (France) 6 novembre 2024 : Master Dev De France - Paris (France) 7 novembre 2024 : DevFest Toulouse - Toulouse (France) 8 novembre 2024 : BDX I/O - Bordeaux (France) 13–14 novembre 2024 : Agile Tour Rennes 2024 - Rennes (France) 16–17 novembre 2024 : Capitole Du Libre - Toulouse (France) 20–22 novembre 2024 : Agile Grenoble 2024 - Grenoble (France) 21 novembre 2024 : DevFest Strasbourg - Strasbourg (France) 21 novembre 2024 : Codeurs en Seine - Rouen (France) 27–28 novembre 2024 : Cloud Expo Europe - Paris (France) 28 novembre 2024 : Who Run The Tech ? - Rennes (France) 2–3 décembre 2024 : Tech Rocks Summit - Paris (France) 3 décembre 2024 : Generation AI - Paris (France) 3–5 décembre 2024 : APIdays Paris - Paris (France) 4–5 décembre 2024 : DevOpsRex - Paris (France) 4–5 décembre 2024 : Open Source Experience - Paris (France) 5 décembre 2024 : GraphQL Day Europe - Paris (France) 6 décembre 2024 : DevFest Dijon - Dijon (France) 22–25 janvier 2025 : SnowCamp 2025 - Grenoble (France) 30 janvier 2025 : DevOps D-Day #9 - Marseille (France) 6–7 février 2025 : Touraine Tech - Tours (France) 3 avril 2025 : DotJS - Paris (France) 16–18 avril 2025 : Devoxx France - Paris (France) Nous contacter Pour réagir à cet épisode, venez discuter sur le groupe Google https://groups.google.com/group/lescastcodeurs Contactez-nous via twitter https://twitter.com/lescastcodeurs Faire un crowdcast ou une crowdquestion Soutenez Les Cast Codeurs sur Patreon https://www.patreon.com/LesCastCodeurs Tous les épisodes et toutes les infos sur https://lescastcodeurs.com/

Jerod & Adam share our Zulip first impressions, react to Elasticsearch going open source (again), discuss Christian Hollinger's blog post on why he still self-hosts & answer a listener question: how do we produce podcasts?

Jerod & Adam share our Zulip first impressions, react to Elasticsearch going open source (again), discuss Christian Hollinger's blog post on why he still self-hosts & answer a listener question: how do we produce podcasts?

Welcome to episode 274 of The Cloud Pod, where the forecast is always cloudy! Justin, Ryan and Matthew are your hosts this week as we explore the world of SnapShots, Maia, Open Source, and VMware – just to name a few of the topics. And stay tuned for an installment of our continuing Cloud Journey Series to explore ways to decrease tech debt, all this week on The Cloud Pod.   Titles we almost went with this week: The Cloud Pod in Parallel Cluster The Cloud Pod cringes at managing 1000 aws accounts The Cloud Pod welcomes Imagen 3 with less Wokeness The Cloud Pod wants to be instantly snapshotted The Cloud pod hates tech debt 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 00:32 Elasticsearch is Open Source, Again Shay Banon is pleased to call ElasticSearch and Kibana “open source” again.  He says everyone at Elastic is ecstatic to be open source again, it’s part of his and “Elastics DNA.”  They’re doing this by adding AGPL as another license option next to ELv2 and SSPL in the coming weeks.  They never stopped believing or behaving like an OSS company after they changed the license, but by being able to use the term open source and by using AGPL – an OSI approved license – removes any questions or fud people might have.  Shay says the change 3 years ago was because they had issues with AWS and the market confusion their offering was causing.  So, after trying all the other options, changing the license – all while knowing it would result in a fork with a different name – was the path they took.  While it was painful, they said it worked.  3 years later, Amazon is fully invested in their OpenSearch fork, the market confusion has mostly gone, and their partnership with AWS is stronger than ever. They are even being named partner of the year with AWS.  They want to “make life of our users as simple as possible,” so if you’re ok with the ELv2 or the SSPL, then you can keep using that license. They aren't removing anything, just giving you another option with AGPL. He calls out trolls and people who will pick at this announcement, so they are attempting to address the trolls in advance.  “Changing the license was a mistake, and Elastic now backtracks from it”. We removed a lot of market confusion when we changed our license 3 years ago. And because of our actions, a lot has changed. It's an entirely different landscape now. We aren't living in the past. We want to build a better future for our users. It's because we took action then, that we are in a position to take action now. “AGPL i

This week we dig back into home automation, we talk a bit about choosing cameras for a large camera system, and of course we answer your questions! -- During The Show -- 00:52 Intro Home automation Weekend of learning 03:48 Monitoring Remote Location (Cameras) - Rob Powerline adapters might work Ubiquiti Nano Beam Synology Surveillance Station (https://www.synology.com/en-global/surveillance) Frigate Do not put the NVR on the internet Privacy File server upload Home Assistant events 17:18 Camera Systems for Tribal Lands - William NDAA compliant cameras and NVRs ReoLink NVR banned ReoLink Cameras depends - bad idea NDAA compliant brands 360 Vision Technology (360 VTL) Avigilon Axis Communications BCD International Commend FLIR Geutebrück iryx JCI/Tyco Security Mobotix Pelco Rhombus Systems Seek Thermal Solink Vaion/Ava WatchGuard Main 3 NVR in use Exac Vision Avigilon Milestone NDAA conversation Noah's favorites Axis FLIR #### 25:09 Charlie Finds e-ink android - Charlie Boox Palma (https://shop.boox.com/products/palma) Why a camera? Nice for reading Lineage or Graphene will NOT work 27:57 ESPDevices for Light Switches - Avri Shelly's are ESP32 devices Devices can talk to each other 30:00 Beaming podcasts to Volumio and Roku - Tiny Pulse Audio Write in! 31:40 News Wire 4M Linux 46 - opensourcefeed.org (https://www.opensourcefeed.org/4mlinux-46-release/) Debain Bookwork 12.7 - debian.org (https://www.debian.org/News/2024/20240831) Porteus 1.6 - porteus.org (https://forum.porteus.org/viewtopic.php?t=11426) Rhino Linux 2nd Release - itsfoss.com (https://news.itsfoss.com/rhino-linux-2024-2-release/) GNU Screen 5 - theregister.com (https://www.theregister.com/2024/09/03/gnu_screen_5/) Wireshark 4.4 - wireshark.org (https://www.wireshark.org/docs/relnotes/wireshark-4.4.0) Bugzilla releases - bugzilla.org (https://www.bugzilla.org/blog/2024/09/03/release-of-bugzilla-5.2-5.0.4.1-and-4.4.14/) Armbian 24.8 - armbian.com (https://www.armbian.com/newsflash/armbian-24-8-yelt/) Elasticsearch and Kibana licensing - businesswire.com (https://www.businesswire.com/news/home/20240829537786/en/Elastic-Announces-Open-Source-License-for-Elasticsearch-and-Kibana-Source-Code) Xe2 Linux Support - wccftech.com (https://wccftech.com/intel-push-out-xe2-graphics-enablement-linux-6-12-kernel/) Cicada3301 - thehackernews.com (https://thehackernews.com/2024/09/new-rust-based-ransomware-cicada3301.html) New Phi-3.5 AI Models - infoq.com (https://www.infoq.com/news/2024/08/microsoft-phi-3-5/) Open-Source, EU AI Act Compliant LLMs - techzine.eu (https://www.techzine.eu/blogs/privacy-compliance/123863/aleph-alphas-open-source-llms-fully-comply-with-the-ai-act/) View on Why AI Models Should be Open and Free for All - businessinsider.com (https://www.businessinsider.com/anima-anandkumar-ai-climate-change-open-source-caltech-nvidia-2024-8) 33:53 Hoptodesk Comparison to Team Viewer Hoptodesk (https://www.hoptodesk.com/) Free & Open Source Cross platform E2E Encryption Can self host the server Wayland is not officially supported 38:05 EmuDeck ArsTechnica (https://arstechnica.com/gaming/2024/08/emudeck-machines-pack-popular-emulation-suite-in-linux-powered-plug-and-play-pc/) Seeking funding Already been doing this on the steamdeck For retro games Drawing unwanted attention Powered by Bazzite 41:05 Home Automation Zwave Great for nerds/tinkering Not for professional installs RadioRA 2 Licensed dedicated frequency Central planning Never had a failure Designed to be integrated Orbit Panels and Shelly Pro Line Game changer 100% reliable People don't want a wall of dimmers Seeed Studio mmWave Sensor (https://wiki.seeedstudio.com/mmwave_human_detection_kit/) I don't like WiFi for automation Steve's experience -- The Extra Credit Section -- For links to the articles and material referenced in this week's episode check out this week's page from our podcast dashboard! This Episode's Podcast Dashboard (http://podcast.asknoahshow.com/406) Phone Systems for Ask Noah provided by Voxtelesys (http://www.voxtelesys.com/asknoah) Join us in our dedicated chatroom #GeekLab:linuxdelta.com on Matrix (https://element.linuxdelta.com/#/room/#geeklab:linuxdelta.com) -- Stay In Touch -- Find all the resources for this show on the Ask Noah Dashboard Ask Noah Dashboard (http://www.asknoahshow.com) Need more help than a radio show can offer? Altispeed provides commercial IT services and they're excited to offer you a great deal for listening to the Ask Noah Show. Call today and ask about the discount for listeners of the Ask Noah Show! Altispeed Technologies (http://www.altispeed.com/) Contact Noah live [at] asknoahshow.com -- Twitter -- Noah - Kernellinux (https://twitter.com/kernellinux) Ask Noah Show (https://twitter.com/asknoahshow) Altispeed Technologies (https://twitter.com/altispeed)

The Cursor AI code editor raises $60 million, RedMonk's Rachel Stephens tries to determine if rug pulls are worth it, Caleb Porzio details how he made $1 million on GitHub Sponsors, Elastic founder Shay Banon announces that Elasticsearch is open source (again) & Tomas Stropus writes about the art of finishing.

The Cursor AI code editor raises $60 million, RedMonk's Rachel Stephens tries to determine if rug pulls are worth it, Caleb Porzio details how he made $1 million on GitHub Sponsors, Elastic founder Shay Banon announces that Elasticsearch is open source (again) & Tomas Stropus writes about the art of finishing.

The Cursor AI code editor raises $60 million, RedMonk's Rachel Stephens tries to determine if rug pulls are worth it, Caleb Porzio details how he made $1 million on GitHub Sponsors, Elastic founder Shay Banon announces that Elasticsearch is open source (again) & Tomas Stropus writes about the art of finishing.

Welcome to another episode of Category Visionaries — the show that explores GTM stories from tech's most innovative B2B founders. In today's episode, we're speaking with Robert Cowart, CEO & Co-Founder of ElastiFlow, a network performance and security analytics platform that's raised $8 Million in funding. Here are the most interesting points from our conversation: Network Dependency: Robert emphasizes the critical role of network infrastructure in today's world, impacting commerce, healthcare, entertainment, and social interactions. Genesis of ElastiFlow: The company started as an experiment to see how new data platforms like Elasticsearch could improve network observability, leading to a successful GitHub project. Community's Role: The initial success and growth of ElastiFlow were significantly boosted by a loyal community built around the GitHub project, highlighting the importance of community-led growth. Market Entry and Growth: ElastiFlow quickly transitioned from community support to paying customers, even before launching their beta product, showcasing the power of having a dedicated user base. Building a Marketing Strategy: Initially relying on inbound marketing, ElastiFlow has now invested in outbound sales and marketing, including paid ads and content creation, to increase brand awareness and drive growth. Future Vision: The company aims to continue enhancing network observability, adding more context to network traffic records, and ensuring comprehensive support for hybrid IT environments. //   Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe.  www.GlobalTalent.co    

Welcome to episode 265 of the Cloud Pod Podcast – where the forecast is always cloudy! It's a full house this week – Matthew, Jonathan, Ryan and Justin are all here to bring you the latest in cloud news – including FOCUS features in AWS Billing, Magic Quadrants, and AWS Metis. Plus, we have an Andoid vs. Apple showdown in the Aftershow, so be sure to stay tuned for that!  Titles we almost went with this week: Tech reports show Gartner leads in the BS quadrant  Oracle adds cloud and legal expenses to their FinOps hub AWS Metis: Great chatbot, or Greek tragedy waiting to happen?  The cloud pod rocks Cargo Pants  A sonnet is written for FOCUSing on spend A big thanks to this week's sponsor: We're sponsorless! Want to reach a dedicated audience of cloud engineers? Send us an email, or hit us up on our Slack Channel and let's chat!  General News 01:40 Finops X Recently Justin attended FinOps in beautiful and sunny San Diego – and if you weren't there, you really should plan on attending next year. This year's topics included: Focus 1.0 State of Vendors Conference size – they will most likely outgrow this particular conference center, seeing as how they're either selling out or pretty close to it.  Coolest thing about the conference – on stage all the biggies – TOGETHER.  It's great to see them all together talking about how they're making Finops better, and introducing new things for Finops and not just saving them for their own conferences.  Next Year  – Is Oracle going to be on stage next year?  08:22 Justin – “The shift left of FinOps was a big topic. You know, how do we get visibility? How do we show people what things are going to cost? How do we make sure that, you know, people are aware of what they’re doing? And so I think, you know, it’s just a recognition that is important and just as important as security is your cost. And in some ways security is part of your cost story. Because if you bankrupt your company, that’s a pretty bad security situation.” 10:17 Introducing Managed OpenSearch: Gain Control of Your Cloud with Powerful Log Analysis  Listen. We don't really *care* about OpenSearch – but the reality is it's taking over the world. Nobody is doing ElasticSearch anymore.  Digital Ocean is launching Managed OpenSearch offering, a comprehensive solution designed for in depth log analysis, simplifying troubleshooting, and optimizing application performance.  With Digital ocean you can Pinpoint and analyze log data with ease, customize log retention, enhance security and can scale with your business and receive forwarded logs from multiple sources including Digital Ocean droplets, managed databases, etc.  Interested in pricing? You can find that here. Or, if you'd like to take a product tour, you can do that

Redis is no longer open source. Just a few months ago, in March 2024, the project was relicensed, leaving its vast community confused. But the community did not give up, and started work to fork Redis to keep it open.  In this episode, we delve into the Valkey project, a prominent fork of Redis, established under the Linux Foundation, which brought together important figures from the Redis community, as well as leading industry giants including AWS, Google Cloud, Oracle and others. Valkey has rapidly gained momentum and just reached General Availability (GA).  Join us as we explore the motivations behind Valkey's creation, hear first-hand stories on its foundation and journey to GA, and learn of its Redis compatibility, roadmap and implications for the open-source community.  Valkey's first Contributor Summit is taking place June 5-6 in Seattle and we will bring you announcements and updates hot off the summit. Our guest is Kyle Davis, the Senior Developer Advocate on the Valkey project, and a past contributor for Redis.  Kyle currently works at AWS, a founding member of Valkey, and has a long history with open source and with forks. He was a founding contributor to the OpenSearch project, which started as a fork of Elasticsearch and Kibana after the latter's relicensing off OSS. Most recently Kyle worked to build a community around Bottlerocket OSS project.   The episode was live-streamed on 10 June 2024 and the video is available at youtube.com/live/HQ7TAdQpxu4 OpenObservability Talks episodes are released monthly, on the last Thursday of each month and are available for listening on your favorite podcast app and on YouTube. We live-stream the episodes on Twitch and YouTube Live - tune in to see us live, and chime in with your comments and questions on the live chat. ⁠⁠https://www.youtube.com/@openobservabilitytalks⁠   https://www.twitch.tv/openobservability⁠ Show Notes: 01:12 - Episode intro, Kyle Davis' Redis background  05:43 - Redis relicensing off open source  10:10 - Valkey vs. other Redis open source forks 16:50 - drop-in replacement of Redis 19:35 - Redis user experience during the relicensing 28:50 - From fork to GA in less than a month 34:00 - Valkey roadmap and Contributor Summit updates 40:00 - Valkey's Technical Steering Committee and leadership 44:14 - what Valkey latest GA is about  Resources: Valkey announced: https://www.linkedin.com/posts/horovits_redis-opensource-activity-7179186700470861824-Gghq Valkey first GA and new member companies: https://www.linkedin.com/posts/horovits_redis-valkey-valkey-activity-7186263342041198593-fsY3 Announcements from Valkey's first Contributor Summit: https://www.linkedin.com/posts/horovits_valkey-welcomes-new-partners-amid-growing-activity-7209084153718362112-OfdI/ For Kubernetes 10th anniversary - special episode with Kelsey Hightower: https://logz.io/blog/kubernetes-and-beyond-2023-reflection/?utm_source=devrel&utm_medium=devrel Socials: Twitter:⁠ https://twitter.com/OpenObserv⁠ YouTube: ⁠https://www.youtube.com/@openobservabilitytalks⁠ Dotan Horovits ============ Twitter: @horovits LinkedIn: in/horovits Mastodon: @horovits@fosstodon Kyle Davis ======== LinkedIn: linkedin.com/in/kyle-davis-linux/ Mastodon: @linux_mclinuxface@fosstodon.org

У дванадцятому випуску подкасту 1-2-3 Techno поговорили з Дмитром Чаплинським про культуру передачі знань в стартапах та великих компаніях, пошук мотивації, правильне делегування та вихід за межі стандартних рішень.

In this video I speak with Philippe Noël, about ParadeDB, which is an Elasticsearch alternative built on Postgres, modernizing the features of Elasticsearch's product suite, starting with real-time search and analytics. I hope you will enjoy and learn about the product. Chapters: 00:00 Introduction 01:12 Challenges with Elasticsearch and the Need for ParadeDB 02:29 Why Postgres? 06:30 Technical Details of ParadeDB's Search Functionality 18:25 Analytics Capabilities of ParadeDB 24:00 Understanding ParadeDB Queries and Transactions 24:22 Application Logic and Data Workflows 25:14 Using PG Cron for Data Migration 30:05 Scaling Reads and Writes in Postgres 31:53 High Availability and Distributed Systems 34:31 Isolation of Workloads 39:38 Database Upgrades and Migrations 41:21 Using ParadeDB Extensions and Distributions 43:02 Observability and Monitoring 44:42 Upcoming Features and Roadmap 46:34 Final Thoughts Important links: Links: GitHub: https://github.com/paradedb/paradedb Website: https://paradedb.com Docs: https://docs.paradedb.com/ Blog: https://blog.paradedb.com Follow me on Linkedin and Twitter: https://www.linkedin.com/in/kaivalyaapte/ and https://twitter.com/thegeeknarrator If you like this episode, please hit the like button and share it with your network. Also please subscribe if you haven't yet. Database internals series: https://youtu.be/yV_Zp0Mi3xs Popular playlists: Realtime streaming systems: https://www.youtube.com/playlist?list=PLL7QpTxsA4se-mAKKoVOs3VcaP71X_LA- Software Engineering: https://www.youtube.com/playlist?list=PLL7QpTxsA4sf6By03bot5BhKoMgxDUU17 Distributed systems and databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4sfLDUnjBJXJGFhhz94jDd_d Modern databases: https://www.youtube.com/playlist?list=PLL7QpTxsA4scSeZAsCUXijtnfW5ARlrsN Stay Curios! Keep Learning! #postgresql #datafusion #parquet #sql #OLAP #apachearrow #database #systemdesign #elasticsearch

PostgreSQL is an incredible general-purpose database, but it can't do everything. Every design decision is a tradeoff, and inevitably some of those tradeoffs get fundamentally baked into the way it's built. Take storage for instance - Postgres tables are row-oriented; great for row-by-row access, but when it comes to analytics, it can't compete with a dedicated OLAP database that uses column-oriented storage. Or can it?Joining me this week is Philippe Noël of ParadeDB, who's going to take us on a tour of Postgres' extension mechanism, from creating custom functions and indexes to Rust code that changes the way Postgres stores data on disk. In his journey to bring Elasticsearch's strengths to Postgres, he's gone all the way down to raw datafiles and back through the optimiser to teach a venerable old dog some new data-access tricks. –ParadeDB: https://paradedb.comParadeDB on Twitter: https://twitter.com/paradedbParadeDB on Github: https://github.com/paradedb/paradedbpgrx (Postgres with Rust): https://github.com/pgcentralfoundation/pgrxTantivy (Rust FTS library): https://github.com/quickwit-oss/tantivyPgMQ (Queues in Postgres): https://tembo.io/blog/introducing-pgmqApache Datafusion: https://datafusion.apache.org/Lucene: https://lucene.apache.org/Kris on Mastodon: http://mastodon.social/@krisajenkinsKris on LinkedIn: https://www.linkedin.com/in/krisjenkins/Kris on Twitter: https://twitter.com/krisajenkins

Explain my like i am five: Die Grundlagen moderner SuchenWir, als User, erwarten heutzutage ziemlich viel von einer Suchmaschine. Es soll “magisch” verstehen, was wir eigentlich finden möchten. Egal ob wir das richtige Wort dafür nutzen (aka Synonym-Suche) oder ob der Begriff einen Tippfehler hat (aka “Meinten Sie …?”).Oft werden Tools wie Elastic- oder OpenSearch, Solr, Algolia und Co. für sowas eingesetzt, denn eine einfache Volltext-Suche mittels eines Wildcard-SQL-SELECT Statement reicht dafür nicht mehr aus. Doch was steckt eigentlich dahinter? Wie funktionieren all diese modernen Suchen eigentlich im Inneren? In dieser Episode geht es um die Grundlagen moderner Suchmaschinen. Wir schmeißen mit Begriffen wie Stemming, Homonyme, BERT, Stopwords, Inverted Index, Suffixbäume, N-Grams, Term Frequency-Inverse Document Frequency, Vector Space Model und Co um uns und erklären das ganze im “Explain me Like I am five”-Stil.Bonus: Wie Konzepte des Information Retrieval mit Bälle-Bädern erklärt werden.**** Diese Episode wird von der HANDELSBLATT MEDIA GROUP gesponsert.Wirtschaft ist nicht immer einfach. Deswegen lautet die Mission der HANDELSBLATT MEDIA GROUP: „Wir möchten Menschen befähigen, die Wirtschaft zu verstehen.“ Mit ihren Kernprodukten, dem Handelsblatt und der WirtschaftsWoche, sowie 160.000 Abonnements, 15 Millionen Besuchern und 3 Milliarden Anfragen in einem Monat leisten sie einen wichtigen Beitrag zur Orientierung und Meinungsbildung in den Bereichen Wirtschaft und Politik und machen damit einen ausgezeichneten Job.Wenn du Teil dieser Mission sein möchtest, schau auf https://engineeringkiosk.dev/handelsblatt vorbei und werde ein Teil der HANDELSBLATT MEDIA GROUP.********Das schnelle Feedback zur Episode:

У восьмий випуск подкасту 1-2-3 Techno до нас завітав Всеволод Соловйов, CTO та co-founder Prophy Science. Він розповів про «надійність» Elasticsearch, роботу над проєктом для Збройних Сил України та співпрацю маленької компанії з бюрократичною Єврокомісією.

In Elixir Wizards Office Hours Episode 2, "Discovery Discoveries," SmartLogic's Project Manager Alicia Brindisi and VP of Delivery Bri LaVorgna join Elixir Wizards Sundi Myint and Owen Bickford on an exploratory journey through the discovery phase of the software development lifecycle. This episode highlights how collaboration and communication transform the client-project team dynamic into a customized expedition. The goal of discovery is to reveal clear business goals, understand the end user, pinpoint key project objectives, and meticulously document the path forward in a Product Requirements Document (PRD). The discussion emphasizes the importance of fostering transparency, trust, and open communication. Through a mutual exchange of ideas, we are able to create the most tailored, efficient solutions that meet the client's current goals and their vision for the future. Key topics discussed in this episode: Mastering the art of tailored, collaborative discovery Navigating business landscapes and user experiences with empathy Sculpting project objectives and architectural blueprints Continuously capturing discoveries and refining documentation Striking the perfect balance between flexibility and structured processes Steering clear of scope creep while managing expectations Tapping into collective wisdom for ongoing discovery Building and sustaining a foundation of trust and transparency Links mentioned in this episode: https://smartlogic.io/ Follow SmartLogic on social media: https://twitter.com/smartlogic Contact Bri: bri@smartlogic.io What is a PRD? https://en.wikipedia.org/wiki/Productrequirementsdocument Special Guests: Alicia Brindisi and Bri LaVorgna.

Today, we have Philipp Krenn on the show. He's the head of DevRel for Elastic, and we took a deep dive on all the Elasticsearch stuff like Indexes, Mappings, Shards and Replicas and how to think about performance and all that stuff. We also discussed the Use Cases and applications where Elastic is not suitable to use. This episode is packed with fundamentals and we think you'd love it. Timestamps 02:00 Introduction 04:13 What is Elasticsearch 05:33 Use Cases 11:25 Where not to use Elasticsearch 13:51 Index 16:44 Shards 23:29 Routing 33:57 Replicas 41:08 Bottlenecks 01:02:30 Upgrading an Elasticsearch Cluster 01:06:12 Rapid Fire

Episode #34 of "Can I get that software in blue?", a podcast by and for people engaged in technology sales. If you are in the technology presales, solution architecture, sales, support or professional services career paths then this show is for you! If you want to get into building AI products, first go to school and learn about antenna design! At least, that's how our guest for episode 34, Shane Connelly did it. Shane is a deep expert in the search and indexing space having started his career at Autonomy working on early search indexing algorithms and setting up solutions for customers before and after the HP acqusition, later leading Product for the Elasticsearch side of the Elastic product suite. Now he's Head of Product at Vectara building out the next generation of semantic search and retrieval augmented generation platforms. In this episode we touch on benchmarks for gauging the relative performance of difference search algorithms and how it applies to LLMs for doing things like preventing hallucinations in generative AI, what kinds of questions Elasticsearch customers were asking that led Shane to believe that vector based algorithms were the future for doing next generation semantic search, and why he believes Vectara is building the top tier solution to solve these problems. Our website: https://softwareinblue.com Twitter: https://twitter.com/softwareinblue LinkedIn: https://www.linkedin.com/showcase/softwareinblue Make sure to subscribe or follow us to get notified about our upcoming episodes: Youtube: https://www.youtube.com/channel/UC8qfPUKO_rPmtvuB4nV87rg Apple Podcasts: https://podcasts.apple.com/us/podcast/can-i-get-that-software-in-blue/id1561899125 Spotify: https://open.spotify.com/show/25r9ckggqIv6rGU8ca0WP2 Links mentioned in the episode: History of Lucene: https://www.elastic.co/celebrating-lucene Attention is all you need: https://arxiv.org/abs/1706.03762

Follow: https://stree.ai/podcast | Sub: https://stree.ai/sub | New episodes every Monday! In this episode, we continue our conversation with Ujwala Tulshigiri, Engineering Manager at Uber, focusing on the technical intricacies of migrating workloads and technology consolidation. Ujwala provides an in-depth look into Uber's strategic approach to infrastructure decisions, the challenges of technology migration, and how they contribute to and leverage the open-source community. She discusses the complexities of replacing systems like Elasticsearch with alternatives like Pinot, addressing the nuances of data management, search capabilities, and the importance of maintaining low-latency operations.

In this episode of the Laravel Podcast, we talk about the recent announcement of hiring a new head of engineering at Laravel and the impact it will have on the future of Laravel. We also dive into the upcoming conferences and events, including Laracon EU, Laracon US, and Laracon India. Additionally, we talk about Typesense, a potential alternative to Meilisearch and Algolia for self-hosted search functionality.Taylor Otwell's Twitter - https://twitter.com/taylorotwellMatt Stauffer's Twitter - https://twitter.com/stauffermattLaravel Twitter - https://twitter.com/laravelphpLaravel Website - https://laravel.com/Tighten.co - https://tighten.com/VP/Head of Engineering at Laravel - https://frequent-pick-a8d.notion.site/VP-Head-of-Engineering-at-Laravel-149b566a670841f7a74b3e904e261693Laracon EU - https://laracon.eu/Laracon US - https://laracon.us/Laravel Herd - https://herd.laravel.com/Laravel 11 - https://laravel.com/docs/master/releasesLaravel Live Denmark -https://laravellive.dk/Laravel Live UK - https://laravellive.uk/Laracon India - https://laracon.in/Caleb Porzio Twitter - https://twitter.com/calebporzioLivewire: https://laravel-livewire.com/ThePrimeagen Twitter - https://twitter.com/ThePrimeagenThe Factory - https://www.thefactoryindeepellum.com/Eric Barnes Twitter - https://twitter.com/ericlbarnesJoe Dixon Twitter - https://twitter.com/_joedixonJames Brooks - https://twitter.com/jbrooksukFreek VAn der Herten Twitter - https://twitter.com/freekmurze?lang=enPeter Suhm Twitter - https://twitter.com/petersuhmMichele Hansen Twitter - https://twitter.com/mjwhansenLaracon AU Twitter - https://twitter.com/LaraconAULaravel Scout - https://laravel.com/docs/10.x/scoutTypesense - https://typesense.org/Algolia -https://algolia.com/Meilisearch - https://www.meilisearch.com/Elasticsearch - https://www.elastic.co/elasticsearchLaravel Sail - https://laravel.com/docs/10.x/sailLaravel Vapor - https://vapor.laravel.com/Early Vapor Tweet - https://x.com/taylorotwell/status/1748782542663131442?s=20Tailwind CSS - https://tailwindcss.com/-----Editing and transcription sponsored by Tighten.

DShield Sensor Log Collection with Elasticsearch https://isc.sans.edu/forums/diary/DShield%20Sensor%20Log%20Collection%20with%20Elasticsearch/30616/ Anydesk Breach https://anydesk.com/en/public-statement Leaky Vessels https://snyk.io/blog/leaky-vessels-docker-runc-container-breakout-vulnerabilities/

DShield Sensor Log Collection with Elasticsearch https://isc.sans.edu/forums/diary/DShield%20Sensor%20Log%20Collection%20with%20Elasticsearch/30616/ Anydesk Breach https://anydesk.com/en/public-statement Leaky Vessels https://snyk.io/blog/leaky-vessels-docker-runc-container-breakout-vulnerabilities/

Jake and Michael discuss all the latest Laravel releases, tutorials, and happenings in the community.Show linksCompare Algolia vs ElasticSearch vs Meilisearch vs TypesenseAspen - The ultimate free API testing tool for macOS with AI integration(02:02) - Laravel 10.41 - Conditional Job Chains, a Number::spell() Threshold, Configurable model:prune Path, and More (06:46) - Laravel 10.42 - Global Defaults for the HTTP Client, a Max Validation Rule for Passwords, and more (09:43) - Laravel Scout Adds Typesense, A Lightening-fast Open-source Search (12:59) - Laravel 11 Introduces the Dumpable Trait (14:46) - Eager Load Limit is Coming to Laravel 11 (18:12) - Dive into the Streamlined Directory Structure in Laravel 11 (23:14) - Meet Aspen: Speedier & Smarter API Testing, Outshining Postman and Insomnia (26:53) - Laravel Live UK (28:25) - Write Tabular Assertions with Pest and PHPUnit (30:39) - Create Beautiful Charts in Filament With the Apex Charts Plugin (31:50) - Generate Tailwind Utility Stylesheets on Demand with Curlwind (34:16) - Download Over 1,500 Google Fonts in Your Laravel Project (35:17) - Create Dynamic Discounts with Custom Conditions on Laravel With the Discountify Package (37:46) - Handling Bulk Imports in Filament

In this podcast, Krish explores the various connectors available in Confluent Cloud. He starts by recapping the previous podcasts and the basics of Confluent Cloud. Krish then focuses on connectors, explaining their value and why they can reduce the need for writing code. He explores different connectors, such as the data gen source connector and the MongoDB Atlas connectors. Krish also discusses different data formats, including Avro, Protobuf, and JSON. He briefly touches on implementing custom connectors. Krish explores the topic of connectors in Confluent Cloud. He discusses the process of creating connectors and the different types of connectors available. Krish also delves into configuring connectors and defining configuration parameters. He explores the concept of custom connector configuration and the use of connector properties files. Krish then explores existing connectors, such as the HTTP source and sync connectors, and discusses the process of publishing custom connectors. He concludes by mentioning the Confluent CLI for managing connectors. Takeaways Connectors in Confluent Cloud provide value by reducing the need for writing code. Different connectors are available for various data sources and destinations, such as MongoDB, Amazon S3, and Elasticsearch. Data formats like Avro, Protobuf, and JSON can be used with connectors. Implementing custom connectors allows for more flexibility and integration with specific systems. Connectors enable seamless data integration and propagation between different systems. Connectors in Confluent Cloud allow for seamless integration with various systems and services. Custom connectors can be created and published to Confluent Cloud. Configuration parameters for connectors can be defined and managed. The Confluent CLI provides a command-line interface for managing connectors. Chapters 00:00 Introduction 00:35 Recap of Previous Podcasts 01:05 Focus on Connectors in Confluent Cloud 02:16 Exploring Data Gen Source Connector 03:43 Different Formats: Avro, Protobuf, JSON 08:07 Differences Between Avro and Protobuf 10:03 Exploring Other Connectors 11:14 Using MongoDB Atlas Connectors 12:08 Testing Different Formats with Connectors 13:36 Handling Avro Format with Consumer 16:58 Exploring More Connectors: Snowflake, Amazon S3, Elasticsearch 20:33 Implementing Custom Connectors 27:31 Exploring More Connectors: Salesforce, Oracle, Jira 35:16 Exploring More Connectors: SQL Server, MySQL 38:43 Implementing Custom Connectors 43:24 Exploring More Connectors: Kafka, File 46:20 Understanding Connector Implementation 49:06 Creating Custom Connectors 50:00 Summary and Conclusion 50:59 Creating Connectors 52:04 Configuring Connectors 54:00 Custom Connector Configuration 56:08 Defining Configuration Parameters 57:38 Configuration Properties 59:49 Self-Managed Connectors 01:00: 27 Connector Properties File 01:01:28 Creating Custom Connectors 01:02: 09 Publishing Custom Connectors 01:03: 37Existing Connectors 01:04: 14HTTP Source Connector 01:06:40 HTTP Sync Connector 01:08:34 Other Connectors 01:10:34 Managing Connectors 01:12:14 Confluent CLI Snowpal Products Backends as Services on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠AWS Marketplace⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Mobile Apps on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠App Store⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ and ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Play Store⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Web App⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Education Platform⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ for Learners and Course Creators

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.

It's the year of generative AI and every technology category changed as a result of access to new foundation models. In this podcast, I speak with Elastics's CPO Ken Exner about how enterprise search analytics and other categories, such as how security and observability are evolving.About Ken ExnerChief Product Officer, Elastic"Helping customers gain actionable insights from data is increasingly important in a world of ever-increasing volumes of data. At Elastic, I have the privilege of leading our cross-functional product teams. Nothing is more exciting than seeing engineering, product, and design teams working in rhythm to deliver great experiences for our customers. I am passionate about building customer-oriented solutions that balance flexibility and ease of use, and I don't believe customers should have to compromise for either."Exner  joined Elastic after three decades in various technology companies leading product and engineering teams. Most recently, he spent 16 years at Amazon Web Services (AWS), where he built and managed dozens of products used by millions of customers worldwide.He holds a bachelor of science degree from the Haas School of Business at the University of California, Berkeley. He and his family live on the outskirts of Seattle, where they spend time with their pets, which include dogs, cats, chickens, goats, and alpacas. Follow Ken at https://www.linkedin.com/in/ken-exner-b914542/ Follow Maribel at https://www.linkedin.com/in/maribellopez/About Elastic Elastic is a leading platform for search-powered solutions. Elastic understands it's the answers, not just the data. The Elasticsearch platform enables anyone to find the answers they need in real-time using all their data, at scale. Elastic delivers complete, cloud-based, AI-powered solutions for enterprise security, observability and search built on the Elasticsearch platform, the development platform used by thousands of companies, such as well-known brands Uber, Slack, Microsoft, and more than 50% of the Fortune 500.Elastic is a platform for search-powered solutions that helps everyone — organizations, their employees, and their customers — find what they need faster, while keeping applications running smoothly, and protecting against cyber threats.The company offers three main product categories that include Elastic Enterprise Search, Observability, and Security solutions. Some of its customers include well known brands such as Uber, Slack, Microsoft, and thousands of others who rely on us to accelerate results that matter.Follow Elastic at https://www.elastic.co/

At the AI Pioneers Summit we announced Latent Space Launchpad, an AI-focused accelerator in partnership with Decibel. If you're an AI founder of enterprise early adopter, fill out this form and we'll be in touch with more details. We also have a lot of events coming up as we wrap up the year, so make sure to check out our community events page and come say hi!We previously interviewed the founders of many developer productivity startups embedded in the IDE, like Codium AI, Cursor, and Codeium. We also covered Replit's (former) SOTA model, replit-code-v1-3b and most recently had Amjad and Michele announce replit-code-v1_5-3b at the AI Engineer Summit.Much has been speculated about the StackOverflow traffic drop since ChatGPT release, but the experience is still not perfect. There's now a new player in the “search for developers” arena: Phind.Phind's goal is to help you find answers to your technical questions, and then help you implement them. For example “What should I use to create a frontend for a Python script?” returns a list of frameworks as well as links to the sources. You can then ask follow up questions on specific implementation details, having it write some code for you, etc. They have both a web version and a VS Code integrationThey recently were top of Hacker News with the announcement of their latest model, which is now the #1 rated model on the BigCode Leaderboard, beating their previous version:TLDR Cheat Sheet:* Based on CodeLlama-34B, which is trained on 500B tokens* Further fine-tuned on 70B+ high quality code and reasoning tokens* Expanded context window to 16k tokens* 5x faster than GPT-4 (100 tok/s vs 20 tok/s on single stream)* 74.7% HumanEval vs 45% for the base modelWe've talked before about HumanEval being limited in a lot of cases and how it needs to be complemented with “vibe based” evals. Phind thinks of evals alongside two axis: * Context quality: when asking the model to generate code, was the context high quality? Did we put outdated examples in it? Did we retrieve the wrong files?* Result quality: was the code generated correct? Did it follow the instructions I gave it or did it misunderstand some of it?If you have bad results with bad context, you might get to a good result by working on better RAG. If you have good context and bad result you might either need to work on your prompting or you have hit the limits of the model, which leads you to fine tuning (like they did). Michael was really early to this space and started working on CommonCrawl filtering and indexing back in 2020, which led to a lot of the insights that now power Phind. We talked about that evolution, his experience at YC, how he got Paul Graham to invest in Phind and invite him to dinner at his house, and how Ron Conway connected him with Jensen Huang to get access to more GPUs!Show Notes* Phind* BigScience T0* InstructGPT Paper* Inception-V3* LMQL* Marginalia Nu* Mistral AI* People:* Paul Graham (pg)* Ron Conway* Yacine Jernite from HuggingFace* Jeff DelaneyTimestamps* [00:00:00] Intros & Michael's early interest in computer vision* [00:03:14] Pivoting to NLP and natural language question answering models* [00:07:20] Building a search engine index of Common Crawl and web pages* [00:11:26] Releasing the first version of Hello based on the search index and BigScience T0 model* [00:14:02] Deciding to focus the search engine specifically for programmers* [00:17:39] Overview of Phind's current product and focus on code reasoning* [00:21:51] The future vision for Phind to go from idea to complete code* [00:24:03] Transitioning to using the GPT-4 model and the impact it had* [00:29:43] Developing the Phind model based on CodeLlama and additional training* [00:32:28] Plans to continue improving the Phind model with open source technologies* [00:43:59] The story of meeting Paul Graham and Ron Conway and how that impacted the company* [00:53:02] How Ron Conway helped them get GPUs from Nvidia* [00:57:12] Tips on how Michael learns complex AI topics* [01:01:12] Lightning RoundTranscriptAlessio: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, partner and CTO of Residence and Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol AI. [00:00:19]Swyx: Hey, and today we have in the studio Michael Royzen from Phind. Welcome. [00:00:23]Michael: Thank you so much. [00:00:24]Alessio: It's great to be here. [00:00:25]Swyx: Yeah, we are recording this in a surprisingly hot October in San Francisco. And sometimes the studio works, but the blue angels are flying by right now, so sorry about the noise. So welcome. I've seen Phind blow up this year, mostly, I think since your launch in Feb and V2 and then your Hacker News posts. We tend to like to introduce our guests, but then obviously you can fill in the blanks with the origin story. You actually were a high school entrepreneur. You started SmartLens, which is a computer vision startup in 2017. [00:00:59]Michael: That's right. I remember when like TensorFlow came out and people started talking about, obviously at the time after AlexNet, the deep learning revolution was already in flow. Good computer vision models were a thing. And what really made me interested in deep learning was I got invited to go to Apple's WWDC conference as a student scholar because I was really into making iOS apps at the time. So I go there and I go to this talk where they added an API that let people run computer vision models on the device using far more efficient GPU primitives. After seeing that, I was like, oh, this is cool. This is going to have a big explosion of different computer vision models running locally on the iPhone. And so I had this crazy idea where it was like, what if I could just make this model that could recognize just about anything and have it run on the device? And that was the genesis for what eventually became SmartLens. I took this data set called ImageNet 22K. So most people, when they think of ImageNet, think of ImageNet 1K. But the full ImageNet actually has, I think, 22,000 different categories. So I took that, filtered it, pre-processed it, and then did a massive fine tune on Inception V3, which was, I think, the state of the art deep convolutional computer vision model at the time. And to my surprise, it actually worked insanely well. I had no idea what would happen if I give a single model. I think it ended up being 17,000 categories approximately that I collapsed them into. It worked so well that it actually worked better than Google Lens, which released its V1 around the same time. And on top of this, the model ran on the device. So it didn't need an internet connection. A big part of the issue with Google Lens at the time was that connections were slower. 4G was around, but it wasn't nearly as fast. So there was a noticeable lag having to upload an image to a server and get it back. But just processing it locally, even on the iPhones of the day in 2017, much faster. It was a cool little project. It got some traction. TechCrunch wrote about it. There was kind of like one big spike in usage, and then over time it tapered off. But people still pay for it, which is wild. [00:03:14]Swyx: That's awesome. Oh, it's like a monthly or annual subscription? [00:03:16]Michael: Yeah, it's like a monthly subscription. [00:03:18]Swyx: Even though you don't actually have any servers? [00:03:19]Michael: Even though we don't have any servers. That's right. I was in high school. I had a little bit of money. I was like, yeah. [00:03:25]Swyx: That's awesome. I always wonder what the modern equivalents kind of "Be my eyes". And it would be actually disclosed in the GPT-4 Vision system card recently that the usage was surprisingly not that frequent. The extent to which all three of us have our sense of sight. I would think that if I lost my sense of sight, I would use Be My Eyes all the time. The average usage of Be My Eyes per day is 1.5 times. [00:03:49]Michael: Exactly. I was thinking about this as well, where I was also looking into image captioning, where you give a model an image and then it tells you what's in the image. But it turns out that what people want is the exact opposite. People want to give a description of an image and then have the AI generate the image. [00:04:04]Alessio: Oh, the other way. [00:04:06]Michael: Exactly. And so at the time, I think there were some GANs, NVIDIA was working on this back in 2019, 2020. They had some impressive, I think, face GANs where they had this model that would produce these really high quality portraits, but it wasn't able to take a natural language description the way Midjourney or DALL-E 3 can and just generate you an image with exactly what you described in it. [00:04:32]Swyx: And how did that get into NLP? [00:04:35]Michael: Yeah, I released the SmartLens app and that was around the time I was a senior in high school. I was applying to college. College rolls around. I'm still sort of working on updating the app in college. But I start thinking like, hey, what if I make an enterprise version of this as well? At the time, there was Clarify that provided some computer vision APIs, but I thought this massive classification model works so well and it's so small and so fast, might as well build an enterprise product. And I didn't even talk to users or do any of those things that you're supposed to do. I was just mainly interested in building a type of backend I've never built before. So I was mainly just doing it for myself just to learn. I built this enterprise classification product and as part of it, I'm also building an invoice processing product where using some of the aspects that I built previously, although obviously it's very different from classification, I wanted to be able to just extract a bunch of structured data from an unstructured invoice through our API. And that's what led me to Hugnyface for the first time because that involves some natural language components. And so I go to Hugnyface and with various encoder models that were around at the time, I used the standard BERT and also Longformer, which came out around the same time. And Longformer was interesting because it had a much bigger context window than those models at the time, like BERT, all of the first gen encoder only models, they only had a context window of 512 tokens and it's fixed. There's none of this alibi or ROPE that we have now where we can basically massage it to be longer. They're fixed, 512 absolute encodings. Longformer at the time was the only way that you can fit, say, like a sequence length or ask a question about like 4,000 tokens worth of text. Implemented Longformer, it worked super well, but like nobody really kind of used the enterprise product and that's kind of what I expected because at the end of the day, it was COVID. I was building this kind of mostly for me, mostly just kind of to learn. And so nobody really used it and my heart wasn't in it and I kind of just shelved it. But a little later, I went back to HugMeFace and I saw this demo that they had, and this is in the summer of 2020. They had this demo made by this researcher, Yacine Jernite, and he called it long form question answering. And basically, it was this self-contained notebook demo where you can ask a question the way that we do now with ChatGPT. It would do a lookup into some database and it would give you an answer. And it absolutely blew my mind. The demo itself, it used, I think, BART as the model and in the notebook, it had support for both an Elasticsearch index of Wikipedia, as well as a dense index powered by Facebook's FAISS. I think that's how you pronounce it. It was very iffy, but when it worked, I think the question in the demo was, why are all boats white? When it worked, it blew my mind that instead of doing this few shot thing, like people were doing with GPT-3 at the time, which is all the rage, you could just ask a model a question, provide no extra context, and it would know what to do and just give you the answer. It blew my mind to such an extent that I couldn't stop thinking about that. When I started thinking about ways to make it better, I tried training, doing the fine tune with a larger BART model. And this BART model, yeah, it was fine tuned on this Reddit data set called Eli5. So basically... [00:08:02]Alessio: Subreddit. [00:08:03]Swyx: Yeah, subreddit. [00:08:04]Alessio: Yeah. [00:08:05]Michael: And put it into like a well-formatted, relatively clean data set of like human questions and human answers. And that was a really great bootstrap for that model to be able to answer these types of questions. And so Eli5 actually turned out to be a good data set for training these types of question answering models, because the question is written by a human, the answer is written by a human, and at least helps the model get the format right, even if the model is still very small and it can't really think super well, at least it gets the format right. And so it ends up acting as kind of a glorified summarization model, where if it's fed in high quality context from the retrieval system, it's able to have a reasonably high quality output. And so once I made the model as big as I can, just fine tuning on BART large, I started looking for ways to improve the index. So in the demo, in the notebook, there were instructions for how to make an Elasticsearch index just for Wikipedia. And I was like, why not do all of Common Crawl? So I downloaded Common Crawl, and thankfully, I had like 10 or $15,000 worth of AWS credits left over from the SmartLens project. And that's what really allowed me to do this, because there's no other funding. I was still in college, not a lot of money, and so I was able to spin up a bunch of instances and just process all of Common Crawl, which is massive. So it's roughly like, it's terabytes of text. I went to Alexa to get the top 1,000 websites or 10,000 websites in the world, then filtered only by those websites, and then indexed those websites, because the web pages were already included in Dump. [00:09:38]Swyx: You mean to supplement Common Crawl or to filter Common Crawl? [00:09:41]Michael: Filter Common Crawl. [00:09:42]Alessio: Oh, okay. [00:09:43]Michael: Yeah, sorry. So we filtered Common Crawl just by the top, I think, 10,000, just to limit this, because obviously there's this massive long tail of small sites that are really cool, actually. There's other projects like, shout out to Marginalia Nu, which is a search engine specialized on the long tail. I think they actually exclude the top 10,000. [00:10:03]Swyx: That's what they do. [00:10:04]Alessio: Yeah. [00:10:05]Swyx: I've seen them around, I just don't really know what their pitch is. Okay, that makes sense. [00:10:08]Michael: So they exclude all the top stuff. So the long tail is cool, but for this, that was kind of out of the question, and that was most of the data anyway. So we've removed that. And then I indexed the remaining approximately 350 million webpages through Elasticsearch. So I built this index running on AWS with these webpages, and it actually worked quite well. You can ask it general common knowledge, history, politics, current events, questions, and it would be able to do a fast lookup in the index, feed it into the model, and it would give a surprisingly good result. And so when I saw that, I thought that this is definitely doable. And it kind of shocked me that no one else was doing this. And so this was now the fall of 2020. And yeah, I was kind of shocked no one was doing this, but it costs a lot of money to keep it up. I was still in college. There are things going on. I got bogged down by classes. And so I ended up shelving this for almost a full year, actually. When I returned to it in fall of 2021, when BigScience released T0, when BigScience released the T0 models, that was a massive jump in the reasoning ability of the model. And it was better at reasoning, it was better at summarization, it was still a glorified summarizer basically. [00:11:26]Swyx: Was this a precursor to Bloom? Because Bloom's the one that I know. [00:11:29]Alessio: Yeah. [00:11:30]Michael: Actually coming out in 2022. But Bloom had other problems where for whatever reason, the Bloom models just were never really that good, which is so sad because I really wanted to use them. But I think they didn't turn on that much data. I think they used like the original, they were trying to replicate GPT-3. So they just use those numbers, which we now know are like far below Chinchilla Optimal and even Chinchilla Optimal, which we can like talk about later, like what we're currently doing with MIMO goes, yeah, it goes way beyond that. But they weren't trying enough data. I'm not sure how that data was clean, but it probably wasn't super clean. And then they didn't really do any fine tuning until much later. So T0 worked well because they took the T5 models, which were closer to Chinchilla Optimal because I think they were trained on also like 300 something billion tokens, similar to GPT-3, but the models were much smaller. I think T0 is the first model that did large scale instruction tuning from diverse data sources in the fall of 2021. This is before Instruct GPT. This is before Flan T5, which came out in 2022. This is the very, very first, at least well-known example of that. And so it came out and then I did, on top of T0, I also did the Reddit Eli5 fine tune. And that was the first model and system that actually worked well enough to where I didn't get discouraged like I did previously, because the failure cases of the BART based system was so egregious. Sometimes it would just miss a question so horribly that it was just extremely discouraging. But for the first time, it was working reasonably well. Also using a much bigger model. I think the BART model is like 800 million parameters, but T0, we were using 3B. So it was T0, 3B, bigger model. And that was the very first iteration of Hello. So I ended up doing a show HN on Hacker News in January 2022 of that system. Our fine tune T0 model connected to our Elasticsearch index of those 350 million top 10,000 common crawl websites. And to the best of my knowledge, I think that's the first example that I'm aware of a LLM search engine model that's effectively connected to like a large enough index that I consider like an internet scale. So I think we were the first to release like an internet scale LLM powered rag search system In January 2022, around the time me and my future co-founder, Justin, we were like, this seems like the future. [00:14:02]Alessio: This is really cool. [00:14:03]Michael: I couldn't really sleep even like I was going to bed and I was like, I was thinking about it. Like I would say up until like 2.30 AM, like reading papers on my phone in bed, go to sleep, wake up the next morning at like eight and just be super excited to keep working. And I was also doing my thesis at the same time, my senior honors thesis at UT Austin about something very similar. We were researching factuality in abstractive question answering systems. So a lot of overlap with this project and the conclusions of my research actually kind of helped guide the development path of Hello. In the research, we found that LLMs, they don't know what they don't know. So the conclusion was, is that you always have to do a search to ensure that the model actually knows what it's talking about. And my favorite example of this even today is kind of with chat GPT browsing, where you can ask chat GPT browsing, how do I run llama.cpp? And chat GPT browsing will think that llama.cpp is some file on your computer that you can just compile with GCC and you're all good. It won't even bother doing a lookup, even though I'm sure somewhere in their internal prompts they have something like, if you're not sure, do a lookup. [00:15:13]Alessio: That's not good enough. So models don't know what they don't know. [00:15:15]Michael: You always have to do a search. And so we approached LLM powered question answering from the search angle. We pivoted to make this for programmers in June of 2022, around the time that we were getting into YC. We realized that what we're really interested in is the case where the models actually have to think. Because up until then, the models were kind of more glorified summarization models. We really thought of them like the Google featured snippets, but on steroids. And so we saw a future where the simpler questions would get commoditized. And I still think that's going to happen with like Google SGE and like it's nowadays, it's really not that hard to answer the more basic kind of like summarization, like current events questions with lightweight models that'll only continue to get cheaper over time. And so we kind of started thinking about this trade off where LLM models are going to get both better and cheaper over time. And that's going to force people who run them to make a choice. Either you can run a model of the same intelligence that you could previously for cheaper, or you can run a better model for the same price. So someone like Google, once the price kind of falls low enough, they're going to deploy and they're already doing this with SGE, they're going to deploy a relatively basic glorified summarizer model that can answer very basic questions about like current events, who won the Super Bowl, like, you know, what's going on on Capitol Hill, like those types of things. The flip side of that is like more complex questions where like you have to reason and you have to solve problems and like debug code. And we realized like we're much more interested in kind of going along the bleeding edge of that frontier case. And so we've optimized everything that we do for that. And that's a big reason of why we've built Phind specifically for programmers, as opposed to saying like, you know, we're kind of a search engine for everyone because as these models get more capable, we're very interested in seeing kind of what the emergent properties are in terms of reasoning, in terms of being able to solve complex multi-step problems. And I think that some of those emerging capabilities like we're starting to see, but we don't even fully understand. So I think there's always an opportunity for us to become more general if we wanted, but we've been along this path of like, what is the best, most advanced reasoning engine that's connected to your code base, that's connected to the internet that we can just provide. [00:17:39]Alessio: What is Phind today, pragmatically, from a product perspective, how do people interact with it? Yeah. Or does it plug into your workflow? [00:17:46]Michael: Yeah. [00:17:47]Alessio: So Phind is really a system. [00:17:48]Michael: Phind is a system for programmers when they have a question or when they're frustrated or when something's not working. [00:17:54]Swyx: When they're frustrated. [00:17:55]Alessio: Yeah. [00:17:56]Michael: For them to get on block. I think like the single, the most abstract page for Phind is like, if you're experiencing really any kind of issue as a programmer, we'll solve that issue for you in 15 seconds as opposed to 15 minutes or longer. Phind has an interface on the web. It has an interface in VS code and more IDEs to come, but ultimately it's just a system where a developer can paste in a question or paste in code that's not working and Phind will do a search on the internet or they will find other code in your code base perhaps that's relevant. And then we'll find the context that it needs to answer your question and then feed it to a reasoning engine powerful enough to actually answer it. So that's really the philosophy behind Phind. It's a system for getting developers the answers that they're looking for. And so right now from a product perspective, this means that we're really all about getting the right context. So the VS code extension that we launched recently is a big part of this because you can just ask a question and it knows where to find the right code context in your code. It can do an internet search as well. So it's up to date and it's not just reliant on what the model knows and it's able to figure out what it needs by itself and answer your question based on that. If it needs some help, you can also get yourself kind of just, there's opportunities for you yourself to put in all that context in. But the issue is also like not everyone wants these VS code. Some people like are real Neovim sticklers or they're using like PyCharm or other IDEs, JetBrains. And so for those people, they're actually like okay with switching tabs, at least for now, if it means them getting their answer. Because really like there's been an explosion of all these like startups doing code, doing search, etc. But really who everyone's competing with is ChatGPT, which only has like that one web interface. Like ChatGPT is really the bar. And so that's what we're up against. [00:19:50]Alessio: And so your idea, you know, we have Amman from Cursor on the podcast and they've gone through the we need to own the IDE thing. Yours is more like in order to get the right answer, people are happy to like go somewhere else basically. They're happy to get out of their IDE. [00:20:05]Michael: That was a great podcast, by the way. But yeah, so part of it is that people sometimes perhaps aren't even in an IDE. So like the whole task of software engineering goes way beyond just running code, right? There's also like a design stage. There's a planning stage. A lot of this happens like on whiteboards. It happens in notebooks. And so the web part also exists for that where you're not even coding it and you're just trying to get like a more conceptual understanding of what you're trying to build first. The podcast with Amman was great, but somewhere where I disagree with him is that you need to own the IDE. I think like he made some good points about not having platform risk in the long term. But some of the features that were mentioned like suggesting diffs, for example, those are all doable with an extension. We haven't yet seen with VS Code in particular any functionality that we'd like to do yet in the IDE that we can't either do through directly supported VS Code functionality or something that we kind of hack into there, which we've also done a fair bit of. And so I think it remains to be seen where that goes. But I think what we're looking to be is like we're not trying to just be in an IDE or be an IDE. Like Phind is a system that goes beyond the IDE and like is really meant to cover the entire lifecycle of a developer's thought process in going about like, hey, like I have this idea and I want to get from that idea to a working product. And so then that's what the long term vision of Phind is really about is starting with that. In the future, I think programming is just going to be really just the problem solving. Like you come up with an idea, you come up with like the basic design for the algorithm in your head, and you just tell the AI, hey, just like just do it, just make it work. And that's what we're building towards. [00:21:51]Swyx: I think we might want to give people an impression about like type of traffic that you have, because when you present it with a text box, you could type in anything. And I don't know if you have some mental categorization of like what are like the top three use cases that people tend to coalesce around. [00:22:08]Alessio: Yeah, that's a great question. [00:22:09]Michael: The two main types of searches that we see are how-to questions, like how to do X using Y tool. And this historically has been our bread and butter, because with our embeddings, like we're really, really good at just going over a bunch of developer documentation and figuring out exactly the part that's relevant and just telling you, OK, like you can use this method. But as LLMs have gotten better, and as we've really transitioned to using GPT-4 a lot in our product, people organically just started pasting in code that's not working and just said, fix it for me. [00:22:42]Swyx: Fix this. [00:22:43]Alessio: Yeah. [00:22:44]Michael: And what really shocks us is that a lot of the people who do that, they're coming from chat GPT. So they tried it in chat GPT with chat GPT-4. It didn't work. Maybe it required like some multi-step reasoning. Maybe it required some internet context or something found in either a Stack Overflow post or some documentation to solve it. And so then they paste it into find and then find works. So those are really those two different cases. Like, how can I build this conceptually or like remind me of this one detail that I need to build this thing? Or just like, here's this code. Fix it. And so that's what a big part of our VS Code extension is, is like enabling a much smoother here just like fix it for me type of workflow. That's really its main benefits. Like it's in your code base. It's in the IDE. It knows how to find the relevant context to answer that question. But at the end of the day, like I said previously, that's still a relatively, not to say it's a small part, but it's a limited part of the entire mental life cycle of a programmer. [00:23:47]Swyx: Yep. So you launched in Feb and then you launched V2 in August. You had a couple other pretty impactful posts slash feature launches. The web search one was massive. So you were mostly a GPT-4 wrapper. We were for a long time. [00:24:03]Michael: For a long time until recently. Yeah. [00:24:05]Alessio: Until recently. [00:24:06]Swyx: So like people coming over from ChatGPT were saying, we're going to say model with your version of web search. Would that be the primary value proposition? [00:24:13]Michael: Basically yeah. And so what we've seen is that any model plus web search is just significantly better than [00:24:18]Alessio: that model itself. Do you think that's what you got right in April? [00:24:21]Swyx: Like so you got 1500 points on Hacking News in April, which is like, if you live on Hacking News a lot, that is unheard of for someone so early on in your journey. [00:24:31]Alessio: Yeah. [00:24:32]Michael: We're super, super grateful for that. Definitely was not expecting it. So what we've done with Hacker News is we've just kept launching. [00:24:38]Alessio: Yeah. [00:24:39]Michael: Like what they don't tell you is that you can just keep launching. That's what we've been doing. So we launched the very first version of Find in its current incarnation after like the previous demo connected to our own index. Like once we got into YC, we scrapped our own index because it was too cumbersome at the time. So we moved over to using Bing as kind of just the raw source data. We launched as Hello Cognition. Over time, every time we like added some intelligence to the product, a better model, we just keep launching. And every additional time we launched, we got way more traffic. So we actually silently rebranded to Find in late December of last year. But like we didn't have that much traffic. Nobody really knew who we were. [00:25:18]Swyx: How'd you pick the name out of it? [00:25:19]Michael: Paul Graham actually picked it for us. [00:25:21]Swyx: All right. [00:25:22]Alessio: Tell the story. Yeah. So, oh boy. [00:25:25]Michael: So this is the biggest side. Should we go for like the full Paul Graham story or just the name? [00:25:29]Swyx: Do you want to do it now? Or do you want to do it later? I'll give you a choice. [00:25:32]Alessio: Hmm. [00:25:33]Michael: I think, okay, let's just start with the name for now and then we can do the full Paul Graham story later. But basically, Paul Graham, when we were lucky enough to meet him, he saw our name and our domain was at the time, sayhello.so and he's just like, guys, like, come on, like, what is this? You know? And we were like, yeah, but like when we bought it, you know, we just kind of broke college students. Like we didn't have that much money. And like, we really liked hello as a name because it was the first like conversational search engine. And that's kind of, that's the angle that we were approaching it from. And so we had sayhello.so and he's like, there's so many problems with that. Like, like, like the say hello, like, what does that even mean? And like .so, like, it's gotta be like a .com. And so we did some time just like with Paul Graham in the room. We just like looked at different domain names, like different things that like popped into our head. And one of the things that popped into like Paul Graham said was fine with the Phind spelling in particular. [00:26:33]Swyx: Yeah. Which is not typical naming advice, right? Yes. Because it's not when people hear it, they don't spell it that way. [00:26:38]Michael: Exactly. It's hard to spell. And also it's like very 90s. And so at first, like, we didn't like, I was like, like, ah, like, I don't know. But over time it kept growing on us. And eventually we're like, okay, we like the name. It's owned by this elderly Canadian gentleman who we got to know, and he was willing to sell it to us. [00:26:57]Michael: And so we bought it and we changed the name. Yeah. [00:27:01]Swyx: Anyways, where were you? [00:27:02]Alessio: I had to ask. [00:27:03]Swyx: I mean, you know, everyone who looks at you is wondering. [00:27:06]Michael: And a lot of people actually pronounce it Phind, which, you know, by now it's part of the game. But eventually we want to buy Phind.com and then just have that redirect to Phind. So Phind is like definitely the right spelling. But like, we'll just, yeah, we'll have all the cases addressed. [00:27:23]Swyx: Cool. So Bing web search, and then August you launched V2. Is V2 the Phind as a system pitch? Or have you moved, evolved since then? [00:27:31]Michael: Yeah, so I don't, like the V2 moniker, like, I don't really think of it that way in my mind. There's like, there's the version we launched during, last summer during YC, which was the Bing version directed towards programmers. And that's kind of like, that's why I call it like the first incarnation of what we currently are. Because it was already directed towards programmers. We had like a code snippet search built in as well, because at the time, you know, the models we were using weren't good enough to generate code snippets. Even GPT, like the text DaVinci 2 was available at the time, wasn't that good at generating code and it would generate like very, very short, very incomplete code snippets. And so we launched that last summer, got some traction, but really like we were only doing like, I don't know, maybe like 10,000 searches a day. [00:28:15]Alessio: Some people knew about it. [00:28:16]Michael: Some people used it, which is impressive because looking back, the product like was not that good. And every time we've like made an improvement to the way that we retrieve context through better embeddings, more intelligent, like HTML parsers, and importantly, like better underlying models. Every major version after that was when we introduced a better underlying answering model. Like in February, we had to swallow a bit of our pride when we were like, okay, our own models aren't good enough. We have to go to open AI. And actually that did lead to kind of like our first decent bump of traffic in February. And people kept using it, like our attention was way better too. But we were still kind of running into problems of like more advanced reasoning. Some people tried it, but people were leaving because even like GPT 3.5, both turbo and non-turbo, like still not that great at doing like code related reasoning beyond the how do you do X, like documentation search type of use case. And so it was really only when GPT 4 came around in April that we were like, okay, like this is like our first real opportunity to really make this thing like the way that it should have been all along. And having GPT 4 as the brain is what led to that Hacker News post. And so what we did was we just let anyone use GPT 4 on Fyne for free without a login, [00:29:43]Alessio: which I actually don't regret. [00:29:45]Michael: So it was very expensive, obviously. But like at that stage, all we needed to do was show like, we just needed to like show people here's what Fyne can do. That was the main thing. And so that worked. That worked. [00:29:58]Alessio: Like we got a lot of users. [00:29:59]Michael: Do you know Fireship? [00:30:01]Swyx: Yeah. YouTube, Jeff Delaney. [00:30:03]Michael: Yeah. He made a short about Fyne. [00:30:06]Alessio: Oh. [00:30:07]Michael: And that's on top of the Hacker News post. And that's what like really, really made it blow up. It got millions of views in days. And he's just funny. Like what I love about Fireship is like he like you guys, yeah, like humor goes a long a long way towards like really grabbing people's attention. And so that blew up. [00:30:25]Swyx: Something I would be anxious about as a founder during that period, so obviously we all remember that pretty closely. So there were a couple of people who had access to the GPT-4 API doing this, which is unrestricted access to GPT-4. And I have to imagine OpenAI wasn't that happy about that because it was like kind of de facto access to GPT-4 before they released it. [00:30:46]Alessio: No, no. [00:30:47]Michael: GPT-4 was in chat GPT from day one. I think. OpenAI actually came to our support because what happened was we had people building unofficial APIs around to try to get free access to it. And I think OpenAI actually has the right perspective on this where they're like, OK, people can do whatever they want with the API if they're paying for it, like they can do whatever they want, but it's like not OK if, you know, paying customers are being exploite by these other actors. They actually got in touch with us and they helped us like set up better Cloudflare bot monitoring controls to effectively like crack down on those unofficial APIs, which we're very happy about. But yeah, so we launched GPT-4. A lot of people come to the product and yeah, for a long time, we're just we're figuring out like what do we make of this, right? How do we a make it better, but also deal with like our costs, which have just like massively, massively ballooned. Over time, it's become more clear with the release of Llama 2 and Llama 3 on the horizon that we will once again see a return to vertical applications running their own models. As was true last year and before, I think that GPT-4, my hypothesis is that the jump from 4 to 4.5 or 4 to 5 will be smaller than the jump from 3 to 4. And the reason why is because there were a lot of different things. Like there was two plus, effectively two, two and a half years of research that went into going from 3 to 4. Like more data, bigger model, all of the instruction tuning techniques, RLHF, all of that is known. And like Meta, for example, and now there's all these other startups like Mistral too, like there's a bunch of very well-funded open source players that are now working on just like taking the recipe that's now known and scaling it up. So I think that even if a delta exists, the delta between in 2024, the delta between proprietary and open source won't be large enough that a startup like us with a lot of data that we've collected can take the data that we have, fine tune an open source model, and like be able to have it be better than whatever the proprietary model is at the time. That's my hypothesis.Michael: But we'll once again see a return to these verticalized models. And that's something that we're super excited about because, yeah, that brings us to kind of the fine model because the plan from kind of the start was to be able to return to that if that makes sense. And I think now we're definitely at a point where it does make sense because we have requests from users who like, they want longer context in the model, basically, like they want to be able to ask questions about their entire code base without, you know, context and retrieval and taking a chance of that. Like, I think it's generally been shown that if you have the space to just put the raw files inside of a big context window, that is still better than chunking and retrieval. So there's various things that we could do with longer context, faster speed, lower cost. Super excited about that. And that's the direction that we're going with the fine model. And our big hypothesis there is precisely that we can take a really good open source model and then just train it on absolutely all of the high quality data that we can find. And there's a lot of various, you know, interesting ideas for this. We have our own techniques that we're kind of playing with internally. One of the very interesting ideas that I've seen, I think it's called Octopack from BigCode. I don't think that it made that big waves when it came out, I think in August. But the idea is that they have this data set that maps GitHub commits to a change. So basically there's all this really high quality, like human made, human written diff data out there on every time someone makes a commit in some repo. And you can use that to train models. Take the file state before and like given a commit message, what should that code look like in the future? [00:34:52]Swyx: Got it. [00:34:53]Alessio: Do you think your HumanEval is any good?Michael: So we ran this experiment. We trained the Phind model. And if you go to the BigCode leaderboard, as of today, October 5th, all of our models are at the top of the BigCode leaderboard by far. It's not close, particularly in languages other than Python. We have a 10 point gap between us and the next best model on JavaScript. I think C sharp, multilingual. And what we kind of learned from that whole experience releasing those models is that human eval doesn't really matter. Not just that, but GPT-4 itself has been trained on human eval. And we know this because GPT-4 is able to predict the exact docstring in many of the problems. I've seen it predict like the specific example values in the docstring, which is extremely improbable. So I think there's a lot of dataset contamination and it only captures a very limited subset of what programmers are actually doing. What we do internally for evaluations are we have GPT-4 score answers. GPT-4 is a really good evaluator. I mean, obviously it's by really good, I mean, it's the best that we have. I'm sure that, you know, a couple of months from now, next year, we'll be like, oh, you know, like GPT-4.5, GPT-5, it's so much better. Like GPT-4 is terrible, but like right now it's the best that we have short of humans. And what we found is that when doing like temperature zero evals, it's actually mostly deterministic GPT-4 across runs in assigning scores to two different answers. So we found it to be a very useful tool in comparing our model to say, GPT-4, but yeah, on our like internal real world, here's what people will be asking this model dataset. And the other thing that we're running is just like releasing the model to our users and just seeing what they think. Because that's like the only thing that really matters is like releasing it for the application that it's intended for, and then seeing how people react. And for the most part, the incredible thing is, is that people don't notice a difference between our model and GPT-4 for the vast majority of searches. There's some reasoning problems that GPT-4 can still do better. We're working on addressing that. But in terms of like the types of questions that people are asking on find, there's not that much difference. And in fact, I've been running my own kind of side by side comparisons, shout out to GodMode, by the way. [00:37:16]Michael: And I've like myself, I've kind of confirmed this to be the case. And even sometimes it gives a better answer, perhaps like more concise or just like better implementation than GPT-4, which that's what surprises me. And by now we kind of have like this reasoning is all you need kind of hypothesis where we've seen emerging capabilities in the find model, whereby training it on high quality code, it can actually like reason better. It went from not being able to solve world problems, where riddles were like with like temporal placement of objects and moving and stuff like that, that GPT-4 can do pretty well. We went from not being able to do those at all to being able to do them just by training on more code, which is wild. So we're already like starting to see like these emerging capabilities. [00:37:59]Swyx: So I just wanted to make sure that we have the, I guess, like the model card in our heads. So you started from Code Llama? [00:38:07]Alessio: Yes. [00:38:08]Swyx: 65, 34? 34. [00:38:10]Michael: So unfortunately, there's no Code Llama 70b. If there was, that would be super cool. But there's not. [00:38:15]Swyx: 34. And then, which in itself was Llama 2, which is on 2 trillion tokens and the added 500 billion code tokens. Yes. [00:38:22]Michael: And you just added a bunch more. [00:38:23]Alessio: Yeah. [00:38:24]Michael: And they also did a couple of things. So they did, I think they did 500 billion, like general pre-training and then they did an extra 20 billion long context pre-training. So they actually increased the like max position tokens to 16k up from 8k. And then they changed the theta parameter for the ROPE embeddings as well to give it theoretically better long context support up to 100k tokens. But yeah, but otherwise it's like basically Llama 2. [00:38:50]Swyx: And so you just took that and just added data. [00:38:52]Michael: Exactly. [00:38:53]Swyx: You didn't do any other fundamental. [00:38:54]Michael: Yeah. So we didn't actually, we haven't yet done anything with the model architecture and we just trained it on like many, many more billions of tokens on our own infrastructure. And something else that we're taking a look at now is using reinforcement learning for correctness. One of the interesting pitfalls that we've noticed with the Phind model is that in cases where it gets stuff wrong, it sometimes is capable of getting the right answer. It's just, there's a big variance problem. It's wildly inconsistent. There are cases when it is able to get the right chain of thought and able to arrive [00:39:25]Alessio: at the right answer, but not always. [00:39:27]Michael: And so like one of our hypotheses is something that we're going to try is that like we can actually do reinforcement learning on, for a given problem, generate a bunch of completions and then like use the correct answer as like a loss basically to try to get it to be more correct. And I think there's a high chance I think of this working because it's very similar to the like RLHF method where you basically show pairs of completions for a given question except the criteria is like which one is like less harmful. But here we have a different criteria. But if the model is already capable of getting the right answer, which it is, we're just, we just need to cajole it into being more consistent. [00:40:06]Alessio: There were a couple of things that I noticed in the product that were not strange but unique. So first of all, the model can talk multiple times in a row, like most other applications is like human model, human model. And then you had outside of the thumbs up, thumbs down, you have things like have DLLM prioritize this message and its answers or then continue from this message to like go back. How does that change the flow of the user and like in terms of like prompting it, yeah, what are like some tricks or learnings you've had? [00:40:37]Michael: So yeah, that's specifically in our pair programmer mode, which is a more conversational mode that also like asks you clarifying questions back if it doesn't fully understand what you're doing and it kind of it holds your hand a bit more. And so from user feedback, we had requests to make more of an auto GPT where you can kind of give it this problem that might take multiple searches or multiple different steps like multiple reasoning steps to solve. And so that's the impetus behind building that product. Being able to do multiple steps and also be able to handle really long conversations. Like people are really trying to use the pair programmer to go from like sometimes really from like basic idea to like complete working code. And so we noticed was is that we were having like these very, very long threads, sometimes with like 60 messages, like 100 messages. And like those become really, really challenging to manage the appropriate context window of what should go inside of the context and how to preserve the context so that the model can continue or the product can continue giving good responses, even if you're like 60 messages deep in a conversation. So that's where the prioritized user messages like comes from. It's like people have asked us to just like let them pin messages that they want to be left in the conversation. And yeah, and then that seems to have like really gone a long way towards solving that problem, yeah. [00:41:54]Alessio: And then you have a run on Replit thing. Are you planning to build your own repl? Like learning some people trying to run the wrong code, unsafe code? [00:42:03]Michael: Yes. Yes. So I think like in the long term vision of like being a place where people can go from like idea to like fully working code, having a code sandbox, like a natively integrated code sandbox makes a lot of sense. And replit is great and people use that feature. But yeah, I think there's more we can do in terms of like having something a bit closer to code interpreter where it's able to run the code and then like recursively iterate on it. Exactly. [00:42:31]Swyx: So you're working on APIs to enable you to do that? Yep. So Amjad has specifically told me in person that he wants to enable that for people at the same time. He's also working on his own models, and Ghostwriter and you know, all the other stuff. So it's going to get interesting. Like he wants to power you, but also compete with you. Yeah. [00:42:47]Michael: And like, and we love replit. I think that a lot of the companies in our space, like we're all going to converge to solving a very similar problem, but from a different angle. So like replit approaches this problem from the IDE side. Like they started as like this IDE that you can run in the browser. And they started from that side, making coding just like more accessible. And we're approaching it from the side of like an LLM that's just like connected to everything that it needs to be connected to, which includes your code context. So that's why we're kind of making inroads into IDEs, but we're kind of, we're approaching this problem from different sides. And I think it'll be interesting to see where things end up. But I think that in the long, long term, we have an opportunity to also just have like this general technical reasoning engine product that's potentially also not just for, not just for programmers. It's also powered in this web interface, like where there's potential, I think other things that we will build that eventually might go beyond like our current scope. [00:43:49]Swyx: Exciting. We'll look forward to that. We're going to zoom out a little bit into sort of AI ecosystem stories, but first we got to get the Paul Graham, Ron Conway story. [00:43:59]Alessio: Yeah. [00:44:00]Michael: So flashback to last summer, we're in the YC batch. We're doing the summer batch, summer 22. So the summer batch runs from June to September, approximately. And so this was late July, early August, right around the time that many like YC startups start like going out, like during up, here's how we're going to pitch investors and everything. And at the same time, me and my co-founder, Justin, we were planning on moving to New York. So for a long time, actually, we were thinking about building this company in New York, mainly for personal reasons, actually, because like during the pandemic, pre-ChatGPT, pre last year, pre the AI boom, SF unfortunately really kind of, you know, like lost its luster. Yeah. Like no one was here. It was far from clear, like if there would be an AI boom, if like SF would be like... [00:44:49]Alessio: Back. [00:44:50]Michael: Yeah, exactly. Back. As everyone is saying these days, it was far from clear. And so, and all of our friends, we were graduating college because like we happened to just graduate college and immediately start YC, like we didn't even have, I think we had a week in between. [00:45:06]Swyx: You didn't bother looking for jobs. You were just like, this is what we want to do. [00:45:08]Michael: Well, actually both me and my co-founder, we had jobs that we secured in 2021 from previous internships, but we both, funny enough, when I spoke to my boss's boss at the company at where I reneged my offer, I told him we got into YC, they actually said, yeah, you should do YC. [00:45:27]Swyx: Wow. [00:45:28]Alessio: That's very selfless. [00:45:29]Swyx: That was really great that they did that. But in San Francisco, they would have offered to invest as well. [00:45:33]Michael: Yes, they would have. But yeah, but we were both planning to be in New York and all of our friends were there from college at this point, like we have this whole plan where like on August 1st, we're going to move to New York and we had like this Airbnb for the month of New York. We're going to stay there and we're going to work and like all of that. The day before we go to New York, I called Justin and I just, I tell him like, why are we doing this? Because in our batch, by the time August 1st rolled around, all of our mentors at YC were saying like, hey, like you should really consider staying in SF. [00:46:03]Swyx: It's the hybrid batch, right? [00:46:04]Michael: Yeah, it was the hybrid batch, but like there were already signs that like something was kind of like afoot in SF, even if like we didn't fully want to admit it yet. And so we were like, I don't know, I don't know. Something kind of clicked when the rubber met the road and it was time to go to New York. We're like, why are we doing this? And like, we didn't have any good reasons for staying in New York at that point beyond like our friends are there. So we still go to New York because like we have the Airbnb, like we don't have any other kind of place to go for the next few weeks. We're in New York and New York is just unfortunately too much fun. Like all of my other friends from college who are just, you know, basically starting their jobs, starting their lives as adults. They just stepped into these jobs, they're making all this money and they're like partying and like all these things are happening. And like, yeah, it's just a very distracting place to be. And so we were just like sitting in this like small, you know, like cramped apartment, terrible posture, trying to get as much work done as we can, too many distractions. And then we get this email from YC saying that Paul Graham is in town in SF and he is doing office hours with a certain number of startups in the current batch. And whoever signs up first gets it. And I happen to be super lucky. I was about to go for a run, but I just, I saw the email notification come across the street. I immediately clicked on the link and like immediately, like half the spots were gone, but somehow the very last spot was still available. And so I picked the very, very last time slot at 7 p.m. semi-strategically, you know, so we would have like time to go over. And also because I didn't really know how we're going to get to SF yet. And so we made a plan that we're going to fly from New York to SF and back to New York in one day and do like the full round trip. And we're going to meet with PG at the YC Mountain View office. And so we go there, we do that, we meet PG, we tell him about the startup. And one thing I love about PG is that he gets like, he gets so excited. Like when he gets excited about something, like you can see his eyes like really light up. And he'll just start asking you questions. In fact, it's a little challenging sometimes to like finish kind of like the rest of like the description of your pitch because like, he'll just like asking all these questions about how it works. And I'm like, you know, what's going on? [00:48:19]Swyx: What was the most challenging question that he asked you? [00:48:21]Michael: I think that like really how it worked. Because like as soon as like we told him like, hey, like we think that the future of search is answers, not links. Like we could really see like the gears turning in his head. I think we were like the first demo of that. [00:48:35]Swyx: And you're like 10 minutes with him, right? [00:48:37]Michael: We had like 45, yeah, we had a decent chunk of time. And so we tell him how it works. Like he's very excited about it. And I just like, I just blurted out, I just like asked him to invest and he hasn't even seen the product yet. We just asked him to invest and he says, yeah. And like, we're super excited about that. [00:48:55]Swyx: You haven't started your batch. [00:48:56]Michael: No, no, no. This is about halfway through the batch or two, two, no, two thirds of the batch. [00:49:02]Swyx: And you're like not technically fundraising yet. We're about to start fundraising. Yeah. [00:49:06]Michael: So we have like this demo and like we showed him and like there was still a lot of issues with the product, but I think like it must have like still kind of like blown his mind in some way. So like we're having fun. He's having fun. We have this dinner planned with this other friend that we had in SF because we were only there for that one day. So we thought, okay, you know, after an hour we'll be done, you know, we'll grab dinner with our friend and we'll fly back to New York. But PG was like, like, I'm having so much fun. Do you want to have dinner? Yeah. Come to my house. Or he's like, I gotta go have dinner with my wife, Jessica, who's also awesome, by the way. [00:49:40]Swyx: She's like the heart of YC. Yeah. [00:49:42]Michael: Jessica does not get enough credit as an aside for her role. [00:49:46]Swyx: He tries. [00:49:47]Michael: He understands like the technical side and she understands people and together they're just like a phenomenal team. But he's like, yeah, I got to go see Jessica, but you guys are welcome to come with. Do you want to come with? And we're like, we have this friend who's like right now outside of like literally outside the door who like we also promised to get dinner with. It's like, we'd love to, but like, I don't know if we can. He's like, oh, he's welcome to come too. So all of us just like hop in his car and we go to his house and we just like have this like we have dinner and we have this just chat about the future of search. Like I remember him telling Jessica distinctly, like our kids as kids are not going to know what like a search result is. Like they're just going to like have answers. That was really like a mind blowing, like inflection point moment for sure. [00:50:34]Swyx: Wow, that email changed your life. [00:50:35]Michael: Absolutely. [00:50:36]Swyx: And you also just spoiled the booking system for PG because now everyone's just going to go after the last slot. Oh man. [00:50:42]Michael: Yeah. But like, I don't know if he even does that anymore. [00:50:46]Swyx: He does. He does. Yeah. I've met other founders that he did it this year. [00:50:49]Michael: This year. Gotcha. But when we told him about how we did it, he was like, I am like frankly shocked that YC just did like a random like scheduling system. [00:50:55]Alessio: They didn't like do anything else. But, um. [00:50:58]Swyx: Okay. And then he introduces Duron Conway. Yes. Who is one of the most legendary angels in Silicon Valley. [00:51:04]Michael: Yes.So after PG invested, the rest of our round came together pretty quickly. [00:51:10]Swyx: I'm, by the way, I'm surprised. Like it's, it might feel like playing favorites right within the current batch to be like, yo, PG invested in this one. Right. [00:51:17]Alessio: Too bad for the others. [00:51:18]Swyx: Too bad for the others, I guess. [00:51:19]Michael: I think this is a bigger point about YC and like these accelerators in general is like YC gets like a lot of criticism from founders who feel like they didn't get value out of it. But like, in my view, YC is what you make of it. And YC tells you this. They're like, you really got to grab this opportunity, like buy the balls and make the most of it. And if you do, then it could be the best thing in the world. And if you don't, and if you're just kind of like a passive, even like an average founder in YC, you're still going to fail. And they tell you that. They're like, if you're average in your batch, you're going to fail. Like you have to just be exceptional in every way. With that in mind, perhaps that's even part of the reason why we asked PG to invest. And so yeah, after PG invested, the rest of our round came together pretty quickly, which I'm very fortunate for. And yeah, he introduced us to Ron. And after he did, I get a call from Ron. And then Ron says like, hey, like PG tells me what you're working on. I'd love to come meet you guys. And I'm like, wait, no way. And then we're just holed up in this like little house in San Mateo, which is a little small, but you know, it had a nice patio. In fact, we had like a monitor set up outside on the deck out there. And so Ron Conway comes over, we go over to the patio where like our workstation is. And Ron Conway, he's known for having like this notebook that he goes around with where he like sits down with the notebook and like takes very, very detailed notes. So he never like forgets anything. So he sits down with his notebook and he asks us like, hey guys, like, what do you need? And we're like, oh, we need GPUs. Back then, the GPU shortage wasn't even nearly as bad as it is now. But like even then, it was still challenging to get like the quota that we needed. And he's like, okay, no problem. And then like he leaves a couple hours later, we get an email and we're CC'd on an email that Ron wrote to Jensen, the CEO of Nvidia, saying like, hey, these guys need GPUs. [00:53:02]Swyx: You didn't say how much? It was just like, just give them GPUs. [00:53:04]Alessio: Basically, yeah. [00:53:05]Michael: Ron is known for writing these like one-liner emails that are like very short, but very to the point. And I think that's why like everyone responds to Ron. Everyone loves Ron. And so Jensen responds. He responds quickly, like tagging this VP of AI at Nvidia. And we start working with Nvidia, which is great. And something that I love about Nvidia, by the way, is that after that intro, we got matched with like a dedicated team. And at Nvidia, they know that they're going to win regardless. So they don't care where you get the GPUs from. They're like, they're truly neutral, unlike various sales reps that you might encounter at various like clouds and, you know, hardware companies, et cetera. They actually just want to help you because they know they don't care. Like regardless, they know that if you're getting Nvidia GPUs, they're still winning. So I guess that's a tip is that like if you're looking for GPUs like Nvidia, they'll help you do it. [00:53:54]Swyx: So just to tie up this thing, because so first of all, that's a fantastic story. And I just wanted to let you tell that because it's special. That is a strategic shift, right? That you already decided to make by the time you met Ron, which is we are going to have our own hardware. We're going to rack him in a data center somewhere. [00:54:11]Michael: Well, not even that we need our own hardware because actually we don't. Right. But we just we just need GPUs, period. And like every cloud loves like they have their own sales tactics and like they want to make you commit to long terms and like very non-flexible terms. And like there's a web of different things that you kind of have to navigate. Nvidia will kind of be to the point like, OK, you can do this on this cloud, this on this cloud. Like this is your budget. Maybe you want to consider buying as well. Like they'll help you walk through what the options are. And the reason why they're helpful is because like they look at the full picture. So they'll help you with the hardware. And in terms of software, they actually implemented a custom feature for us in Faster Transformer, which is one of their libraries.Swyx: For you? [00:54:53]Michael: For us. Yeah. Which is wild. I don't think they would have done it otherwise. They implemented streaming generation for T5 based models, which we were running at the time up until we switched to GPT in February, March of this year. So they implemented that just for us, actually, in Faster Transformer. And so like they'll help you like look at the complete picture and then just help you get done what you need to get done. I know one of your interests is also local models, open source models and hardware kind of goes hand in hand.Alessio: Any fun projects, explorations in the space that you want to share with local llamas and stuff? [00:55:27]Michael: Yeah, it's something that we're very interested in because something that kind of we're hearing a lot about is like people want something like find, especially comp

Victoria is joined by guest co-host Joe Ferris, CTO at thoughtbot, and Seif Lotfy, the CTO and Co-Founder of Axiom. Seif discusses the journey, challenges, and strategies behind his data analytics and observability platform. Seif, who has a background in robotics and was a 2008 Sony AIBO robotic soccer world champion, shares that Axiom pivoted from being a Datadog competitor to focusing on logs and event data. The company even built its own logs database to provide a cost-effective solution for large-scale analytics. Seif is driven by his passion for his team and the invaluable feedback from the community, emphasizing that sales validate the effectiveness of a product. The conversation also delves into Axiom's shift in focus towards developers to address their need for better and more affordable observability tools. On the business front, Seif reveals the company's challenges in scaling across multiple domains without compromising its core offerings. He discusses the importance of internal values like moving with urgency and high velocity to guide the company's future. Furthermore, he touches on the challenges and strategies of open-sourcing projects and advises avoiding platforms like Reddit and Hacker News to maintain focus. Axiom (https://axiom.co/) Follow Axiom on LinkedIn (https://www.linkedin.com/company/axiomhq/), X (https://twitter.com/AxiomFM), GitHub (https://github.com/axiomhq), or Discord (https://discord.com/invite/axiom-co). Follow Seif Lotfy on LinkedIn (https://www.linkedin.com/in/seiflotfy/) or X (https://twitter.com/seiflotfy). Visit his website at seif.codes (https://seif.codes/). Follow thoughtbot on X (https://twitter.com/thoughtbot) or LinkedIn (https://www.linkedin.com/company/150727/). Become a Sponsor (https://thoughtbot.com/sponsorship) of Giant Robots! Transcript: VICTORIA: This is the Giant Robots Smashing Into Other Giant Robots Podcast, where we explore the design, development, and business of great products. I'm your host, Victoria Guido, and with me today is Seif Lotfy, CTO and Co-Founder of Axiom, the best home for your event data. Seif, thank you for joining me. SEIF: Hey, everybody. Thanks for having me. This is awesome. I love the name of the podcast, given that I used to compete in robotics. VICTORIA: What? All right, we're going to have to talk about that. And I also want to introduce a guest co-host today. Since we're talking about cloud, and observability, and data, I invited Joe Ferris, thoughtbot CTO and Director of Development of our platform engineering team, Mission Control. Welcome, Joe. How are you? JOE: Good, thanks. Good to be back again. VICTORIA: Okay. I am excited to talk to you all about observability. But I need to go back to Seif's comment on competing with robots. Can you tell me a little bit more about what robots you've built in the past? SEIF: I didn't build robots; I used to program them. Remember the Sony AIBOs, where Sony made these dog robots? And we would make them compete. There was an international competition where we made them play soccer, and they had to be completely autonomous. They only communicate via Bluetooth or via wireless protocols. And you only have the camera as your sensor as well as...a chest sensor throws the ball near you, and then yeah, you make them play football against each other, four versus four with a goalkeeper and everything. Just look it up: RoboCup AIBO. Look it up on YouTube. And I...2008 world champion with the German team. VICTORIA: That sounds incredible. What kind of crowds are you drawing out for a robot soccer match? Is that a lot of people involved with that? SEIF: You would be surprised how big the RoboCup competition is. It's ridiculous. VICTORIA: I want to go. I'm ready. I want to, like, I'll look it up and find out when the next one is. SEIF: No more Sony robots but other robots. Now, there's two-legged robots. So, they make them play as two-legged robots, much slower than four-legged robots, but works. VICTORIA: Wait. So, the robots you were playing soccer with had four legs they were running around on? SEIF: Yeah, they were dogs [laughter]. VICTORIA: That's awesome. SEIF: We all get the same robot. It's just a competition on software, right? On a software level. And some other competitions within the RoboCup actually use...you build your own robot and stuff like that. But this one was...it's called the Standard League, where we all have a robot, and we have to program it. JOE: And the standard robot was a dog. SEIF: Yeah, I think back then...we're talking...it's been a long time. I think it started in 2001 or something. I think the competition started in 2001 or 2002. And I compete from 2006 to 2008. Robots back then were just, you know, simple. VICTORIA: Robots today are way too complicated [laughs]. SEIF: Even AI is more complicated. VICTORIA: That's right. Yeah, everything has gotten a lot more complicated [laughs]. I'm so curious how you went from being a world-champion robot dog soccer player [laughs] programmer [laughs] to where you are today with Axiom. Can you tell me a little bit more about your journey? SEIF: The journey is interesting because it came from open source. I used to do open source on the side a lot–part of the GNOME Project. That's where I met Neil and the rest of my team, Mikkel Kamstrup, the whole crowd, basically. We worked on GNOME. We worked on Ubuntu. Like, most of them were working professionally on it. I was working for another company, but we worked on the same project. We ended up at Xamarin, which was bought by Microsoft. And then we ended up doing Axiom. But we've been around each other professionally since 2009, most of us. It's like a little family. But how we ended up exactly in observability, I think it's just trying to fix pain points in my life. VICTORIA: Yeah, I was reading through the docs on Axiom. And there's an interesting point you make about organizations having to choose between how much data they have and how much they want to spend on it. So, maybe you can tell me a little bit more about that pain point and what you really found in the early stages that you wanted to solve. SEIF: So, the early stages of what we wanted to solve we were mainly dealing with...so, the early, early stage, we were actually trying to be a Datadog competitor, where we were going to be self-hosted. Eventually, we focused on logs because we found out that's what was a big problem for most people, just event data, not just metric but generally event data, so logs, traces, et cetera. We built out our own logs database completely from scratch. And one of the things we stumbled upon was; basically, you have three things when it comes to logging, which is low cost, low latency, and large scale. That's what everybody wants. But you can't get all three of them; you can only get two of them. And we opted...like, we chose large scale and low cost. And when it comes to latency, we say it should be just fast enough, right? And that's where we focused on, and this is how we started building it. And with that, this is how we managed to stand out by just having way lower cost than anybody else in the industry and dealing with large scale. VICTORIA: That's really interesting. And how did you approach making the ingestion pipeline for masses amount of data more efficient? SEIF: Just make it coordination-free as possible, right? And get rid of Kafka because Kafka just, you know, drains your...it's where you throw in money. Like maintaining Kafka...it's like back then Elasticsearch, right? Elasticsearch was the biggest part of your infrastructure that would cost money. Now, it's also Kafka. So, we found a way to have our own internal way of queueing things without having to rely on Kafka. As I said, we wrote everything from scratch to make it work. Like, every now and then, I think that we can spin this out of the company and make it a new product. But now, eyes on the prize, right? JOE: It's interesting to hear that somebody who spent so much time in the open-source community ended up rolling their own solution to so many problems. Do you feel like you had some lessons learned from open source that led you to reject solutions like Kafka, or how did that journey go? SEIF: I don't think I'm rejecting Kafka. The problem is how Kafka is built, right? Kafka is still...you have to set up all these servers. They have to communicate, et cetera, etcetera. They didn't build it in a way where it's stateless, and that's what we're trying to go to. We're trying to make things as stateless as possible. So, Kafka was never built for the cloud-native era. And you can't really rely on SQS or something like that because it won't deal with this high throughput. So, that's why I said, like, we will sacrifice some latency, but at least the cost is low. So, if messages show after half a second or a second, I'm good. It doesn't have to be real-time for me. So, I had to write a couple of these things. But also, it doesn't mean that we reject open source. Like, we actually do like open source. We open-source a couple of libraries. We contribute back to open source, right? We needed a solution back then for that problem, and we couldn't find any. And maybe one day, open source will have, right? JOE: Yeah. I was going to ask if you considered open-sourcing any of your high latency, high throughput solutions. SEIF: Not high latency. You make it sound bad. JOE: [laughs] SEIF: You make it sound bad. It's, like, fast enough, right? I'm not going to compete on milliseconds because, also, I'm competing with ClickHouse. I don't want to compete with ClickHouse. ClickHouse is low latency and large scale, right? But then the cost is, you know, off the charts a bit sometimes. I'm going the other route. Like, you know, it's fast enough. Like, how, you know, if it's under two, three seconds, everybody's happy, right? If the results come within two, three seconds, everybody is happy. If you're going to build a real-time trading system on top of it, I'll strongly advise against that. But if you're building, you know, you're looking at dashboards, you're more in the observability field, yeah, we're good. VICTORIA: Yeah, I'm curious what you found, like, which customer personas that market really resonated with. Like, is there a particular, like, industry type where you're noticing they really want to lower their cost, and they're okay with this just fast enough latency? SEIF: Honestly, with the current recession, everybody is okay with giving up some of the speed to reduce the money because I think it's not linear reduction. It's more exponential reduction at this point, right? You give up a second, and you're saving 30%. You give up two seconds, all of a sudden, you're saving 80%. So, I'd say in the beginning, everybody thought they need everything to be very, very fast. And now they're realizing, you know, with limitations you have around your budget and spending, you're like, okay, I'm okay with the speed. And, again, we're not slow. I'm just saying people realize they don't need everything under a second. They're okay with waiting for two seconds. VICTORIA: That totally resonates with me. And I'm curious if you can add maybe a non-technical or a real-life example of, like, how this impacts the operations of a company or organization, like, if you can give us, like, a business-y example of how this impacts how people work. SEIF: I don't know how, like, how do people work on that? Nothing changed, really. They're still doing the, like...really nothing because...and that aspect is you run a query, and, again, as I said, you're not getting the result in a second. You're just waiting two seconds or three seconds, and it's there. So, nothing really changed. I think people can wait three seconds. And we're still like–when I say this, we're still faster than most others. We're just not as fast as people who are trying to compete on a millisecond level. VICTORIA: Yeah, that's okay. Maybe I'll take it back even, like, a step further, right? Like, our audience is really sometimes just founders who almost have no formal technical training or background. So, when we talk about observability, sometimes people who work in DevOps and operations all understand it and kind of know why it's important [laughs] and what we're talking about. So, maybe you could, like, go back to -- SEIF: Oh, if you're asking about new types of people who've been using it -- VICTORIA: Yeah. Like, if you're going to explain to, like, a non-technical founder, like, why your product is important, or, like, how people in their organization might use it, what would you say? SEIF: Oh, okay, if you put it like that. It's more of if you have data, timestamp data, and you want to run analytics on top of it, so that could be transactions, that could be web vitals, rather than count every time somebody visits, you have a timestamp. So, you can count, like, how many visitors visited the website and what, you know, all these kinds of things. That's where you want to use something like Axiom. That's outside the DevOps space, of course. And in DevOps space, there's so many other things you use Axiom for, but that's outside the DevOps space. And we actually...we implemented as zero-config integration with Vercel that kind of went viral. And we were, for a while, the number one enterprise for self-integration because so many people were using it. So, Vercel users are usually not necessarily writing the most complex backends, but a lot of things are happening on the front-end side of things. And we would be giving them dashboards, automated dashboards about, you know, latencies, and how long a request took, and how long the response took, and the content type, and the status codes, et cetera, et cetera. And there's a huge user base around that. VICTORIA: I like that. And it's something, for me, you know, as a managing director of our platform engineering team, I want to talk more to founders about. It's great that you put this product and this app out into the world. But how do you know that people are actually using it? How do you know that people, like, maybe, are they all quitting after the first day and not coming back to your app? Or maybe, like, the page isn't loading or, like, it's not working as they expected it to. And, like, if you don't have anything observing what users are doing in your app, then it's going to be hard to show that you're getting any traction and know where you need to go in and make corrections and adjust. SEIF: We have two ways of doing this. Right now, internally, we use our own tools to see, like, who is sending us data. We have a deployment that's monitoring production deployment. And we're just, you know, seeing how people are using it, how much data they're sending every day, who stopped sending data, who spiked in sending data sets, et cetera. But we're using Mixpanel, and Dominic, our Head of Product, implemented a couple of key metrics to that for that specifically. So, we know, like, what's the average time until somebody starts going from building its own queries with the builder to writing APL, or how long it takes them from, you know, running two queries to five queries. And, you know, we just start measuring these things now. And it's been going...we've been growing healthy around that. So, we tend to measure user interaction, but also, we tend to measure how much data is being sent. Because let's keep in mind, usually, people go in and check for things if there's a problem. So, if there's no problem, the user won't interact with us much unless there's a notification that kicks off. We also just check, like, how much data is being sent to us the whole time. VICTORIA: That makes sense. Like, you can't just rely on, like, well, if it was broken, they would write a [chuckles], like, a question or something. So, how do you get those metrics and that data around their interactions? So, that's really interesting. So, I wonder if we can go back and talk about, you know, we already mentioned a little bit about, like, the early days of Axiom and how you got started. Was there anything that you found in the early discovery process that was surprising and made you pivot strategy? SEIF: A couple of things. Basically, people don't really care about the tech as much as they care [inaudible 12:51] and the packaging, so that's something that we had to learn. And number two, continuous feedback. Continuous feedback changed the way we worked completely, right? And, you know, after that, we had a Slack channel, then we opened a Discord channel. And, like, this continuous feedback coming in just helps with iterating, helps us with prioritizing, et cetera. And that changed the way we actually developed product. VICTORIA: You use Slack and Discord? SEIF: No. No Slack anymore. We had a community Slack. We had a community [inaudible 13:19] Slack. Now, there's no community Slack. We only have a community Discord. And the community Slack is...sorry, internally, we use Slack, but there's a community Discord for the community. JOE: But how do you keep that staffed? Is it, like, everybody is in the Discord during working hours? Is it somebody's job to watch out for community questions? SEIF: I think everybody gets involved now just...and you can see it. If you go on our Discord, you will just see it. Just everyone just gets involved. I think just people are passionate about what they're doing. At least most people are involved on Discord, right? Because there's, like, Discord the help sections, and people are just asking questions and other people answering. And now, we reached a point where people in the community start answering the questions for other people in the community. So, that's how we see it's starting to become a healthy community, et cetera. But that is one of my favorite things: when I see somebody from the community answering somebody else, that's a highlight for me. Actually, we hired somebody from that community because they were so active. JOE: Yeah, I think one of the biggest signs that a product is healthy is when there's a healthy ecosystem building up around it. SEIF: Yeah, and Discord reminds me of the old days of open sources like IRC, just with memes now. But because all of us come from the old IRC days, being on Discord and chatting around, et cetera, et cetera, just gives us this momentum back, gave us this momentum back, whereas Slack always felt a bit too businessy to me. JOE: Slack is like IRC with emoji. Discord is IRC with memes. SEIF: I would say Slack reminds me somehow of MSN Messenger, right? JOE: I feel like there's a huge slam on MSN Messenger here. SEIF: [laughs] What do you guys use internally, Slack or? I think you're using Slack, right? Or Teams. Don't tell me you're using Teams. JOE: No, we're using Slack. SEIF: Okay, good, because I shit talk. Like, there is this, I'll sh*t talk here–when I start talking about Teams, so...I remember that one thing Google did once, and that failed miserably. JOE: Google still has, like, seven active chat products. SEIF: Like, I think every department or every, like, group of engineers just uses one of them internally. I'm not sure. Never got to that point. But hey, who am I to judge? VICTORIA: I just feel like I end up using all of them, and then I'm just rotating between different tabs all day long. You maybe talked me into using Discord. I feel like I've been resisting it, but you got me with the memes. SEIF: Yeah, it's definitely worth it. It's more entertaining. More noise, but more entertaining. You feel it's alive, whereas Slack is...also because there's no, like, history is forever. So, you always go back, and you're like, oh my God, what the hell is this? VICTORIA: Yeah, I have, like, all of them. I'll do anything. SEIF: They should be using Axiom in the background. Just send data to Axiom; we can keep your chat history. VICTORIA: Yeah, maybe. I'm so curious because, you know, you mentioned something about how you realized that it didn't matter really how cool the tech was if the product packaging wasn't also appealing to people. Because you seem really excited about what you've built. So, I'm curious, so just tell us a little bit more about how you went about trying to, like, promote this thing you built. Or was, like, the continuous feedback really early on, or how did that all kind of come together? SEIF: The continuous feedback helped us with performance, but actually getting people to sign up and pay money it started early on. But with Vercel, it kind of skyrocketed, right? And that's mostly because we went with the whole zero-config approach where it's just literally two clicks. And all of a sudden, Vercel is sending your data to Axiom, and that's it. We will create [inaudible 16:33]. And we worked very closely with Vercel to do this, to make this happen, which was awesome. Like, yeah, hats off to them. They were fantastic. And just two clicks, three clicks away, and all of a sudden, we created Axiom organization for you, the data set for you. And then we're sending it...and the data from Vercel is being forwarded to it. I think that packaging was so simple that it made people try it out quickly. And then, the experience of actually using Axiom was sticky, so they continued using it. And then the price was so low because we give 500 gigs for free, right? You send us 500 gigs a month of logs for free, and we don't care. And you can start off here with one terabyte for 25 bucks. So, people just start signing up. Now, before that, it was five terabytes a month for $99, and then we changed the plan. But yeah, it was cheap enough, so people just start sending us more and more and more data eventually. They weren't thinking...we changed the way people start thinking of “what am I going to send to Axiom” or “what am I going to send to my logs provider or log storage?” To how much more can I send? And I think that's what we wanted to reach. We wanted people to think, how much more can I send? JOE: You mentioned latency and cost. I'm curious about...the other big challenge we've seen with observability platforms, including logs, is cardinality of labels. Was there anything you had to sacrifice upfront in terms of cardinality to manage either cost or volume? SEIF: No, not really. Because the way we designed it was that we should be able to deal with high cardinality from scratch, right? I mean, there's open-source ways of doing, like, if you look at how, like, a column store, if you look at a column store and every dimension is its own column, it's just that becomes, like, you can limit on the amount of columns you're creating, but you should never limit on the amount of different values in a column could be. So, if you're having something like stat tags, right? Let's say hosting, like, hostname should be a column, but then the different hostnames you have, we never limit that. So, the cardinality on a value is something that is unlimited for us, and we don't really see it in cost. It doesn't really hit us on cost. It reflects a bit on compression if you get into technical details of that because, you know, high cardinality means a lot of different data. So, compression is harder, but it's not repetitive. But then if you look at, you know, oh, I want to send a lot of different types of fields, not values with fields, so you have hostname, and latency, and whatnot, et cetera, et cetera, yeah, that's where limitation starts because then they have...it's like you're going to a wide range of...and a wider dimension. But even that, we, yeah, we can deal with thousands at this point. And we realize, like, most people will not need more than three or four. It's like a Postgres table. You don't need more than 3,000 to 4000 columns; else, you know, you're doing a lot. JOE: I think it's actually pretty compelling in terms of cost, though. Like, that's one of the things we've had to be most careful about in terms of containing cost for metrics and logs is, a lot of providers will...they'll either charge you based on the number of unique metric combinations or the performance suffers greatly. Like, we've used a lot of Prometheus-based solutions. And so, when we're working with developers, even though they don't need more than, you know, a few dozen metric combinations most of the time, it's hard for people to think of what they need upfront. It's much easier after you deploy it to be able to query your data and slice it retroactively based on what you're seeing. SEIF: That's the detail. When you say we're using Prometheus, a lot of the metrics tools out there are using, just like Prometheus, are using the Gorilla data structure. And the real data structure was never designed to deal with high cardinality labels. So, basically, to put it in a simple way, every combination of tags you send for metrics is its own file on disk. That's, like, the very simple way of explaining this. And then, when you're trying to search through everything, right? And you have a lot of these combinations. I actually have to get all these files from this conversion back together, you know, and then they're chunked, et cetera. So, it's a problem. Generally, how metrics are doing it...most metrics products are using it, even VictoriaMetrics, et cetera. What they're doing is they're using either the Prometheus TSDB data structure, which is based on Gorilla. Influx was doing the same thing. They pivoted to using more and more like the ones we use, and Honeycomb uses, right? So, we might not be as fast on metrics side as these highly optimized. But then when it comes to high [inaudible 20:49], once we start dealing with high cardinality, we will be faster than those solutions. And that's on a very technical level. JOE: That's pretty cool. I realize we're getting pretty technical here. Maybe it's worth defining cardinality for the audience. SEIF: Defining cardinality to the...I mean, we just did that, right? JOE: What do you think, Victoria? Do you know what cardinality is now? [laughs] VICTORIA: All right. Now I'm like, do I know? I was like, I think I know what it means. Cardinality is, like, let's say you have a piece of data like an event or a transaction. SEIF: It's like the distinct count on a property that gives you the cardinality of a property. VICTORIA: Right. It's like how many pieces of information you have about that one event, basically, yeah. JOE: But with some traditional metrics stores, it's easy to make mistakes. For example, you could have unbounded cardinality by including response time as one of the labels -- SEIF: Tags. JOE: And then it's just going to -- SEIF: Oh, no, no. Let me give you a better one. I put in timestamp at some point in my life. JOE: Yeah, I feel like everybody has done that one. [laughter] SEIF: I've put a system timestamp at some point in my life. There was the actual timestamp, and there was a system timestamp that I would put because I wanted to know when the...because I couldn't control the timestamp, and the only timestamp I had was a system timestamp. I would always add the actual timestamp of when that event actually happened into a metric, and yeah, that did not scale. MID-ROLL AD: Are you an entrepreneur or start-up founder looking to gain confidence in the way forward for your idea? At thoughtbot, we know you're tight on time and investment, which is why we've created targeted 1-hour remote workshops to help you develop a concrete plan for your product's next steps. Over four interactive sessions, we work with you on research, product design sprint, critical path, and presentation prep so that you and your team are better equipped with the skills and knowledge for success. Find out how we can help you move the needle at tbot.io/entrepreneurs. VICTORIA: Yeah. I wonder if you could maybe share, like, a story about when it's gone wrong, and you've suddenly charged a lot of money [laughs] just to get information about what's happening in the system. Any, like, personal experiences with observability that kind of informed what you did with Axiom? SEIF: Oof, I have a very bad one, like, a very, very bad one. I used to work for a company. We had to deploy Elasticsearch on Windows Servers, and it was US-East-1. So, just a combination of Elasticsearch back in 2013, 2014 together with Azure and Windows Server was not a good idea. So, you see where this is going, right? JOE: I see where it's going. SEIF: Eventually, we had, like, we get all these problems because we used Elasticsearch and Kibana as our, you know, observability platform to measure everything around the product we were building. And funny enough, it cost us more than actually maintaining the infrastructure of the product. But not just that, it also kept me up longer because most of the downtimes I would get were not because of the product going down. It's because my Elasticsearch cluster started going down, and there's reasons for that. Because back then, Microsoft Azure thought that it's okay for any VM to lose connection with the rest of the VMs for 30 seconds per day. And then, all of a sudden, you have Elasticsearch with a split-brain problem. And there was a phase where I started getting alerted so much that back then, my partner threatened to leave me. So I bought a...what I think was a shock bracelet or a shock collar via Bluetooth, and I connected it to phone for any notification. And I bought that off Alibaba, by the way. And I would charge it at night, put it on my wrist, and go to sleep. And then, when alert happens, it will fully discharge the battery on me every time. JOE: Okay, I have to admit, I did not see where that was going. SEIF: Yeah, did that for a while; definitely did not save my relationship either. But eventually, that was the point where, you know, we started looking into other observability tools like Datadog, et cetera, et cetera, et cetera. And that's where the actual journey began, where we moved away from Elasticsearch and Kibana to look for something, okay, that we don't have to maintain ourselves and we can use, et cetera. So, it's not about the costs as much; it was just pain. VICTORIA: Yeah, pain is a real pain point, actual physical [chuckles] and emotional pain point [laughter]. What, like, motivates you to keep going with Axiom and to keep, like, the wind in your sails to keep working on it? SEIF: There's a couple of things. I love working with my team. So, honestly, I just wake up, and I compliment my team. I just love working with them. They're a lot of fun to work with. And they challenge me, and I challenge them back. And I upset them a lot. And they can't upset me, but I upset them. But I love working with them, and I love working with that team. And the other thing is getting, like, having this constant feedback from customers just makes you want to do more and, you know, close sales, et cetera. It's interesting, like, how I'm a very technical person, and I'm more interested in sales because sales means your product works, the product, the technical parts, et cetera. Because if technically it's not working, you can't build a product on top of it. And if you're not selling it, then what's the point? You only sell when the product is good, more or less, unless you're Oracle. VICTORIA: I had someone ask me about Oracle recently, actually. They're like, "Are you considering going back to it?" And I'm maybe a little allergic to it from having a federal consulting background [laughs]. But maybe they'll come back around. I don't know. We'll see. SEIF: Did you sell your soul back then? VICTORIA: You know, I feel like I just grew up in a place where that's what everyone did was all. SEIF: It was Oracle, IBM, or HP back in the day. VICTORIA: Yeah. Well, basically, when you're working on applications that were built in, like, the '80s, Oracle was, like, this hot, new database technology [laughs] that they just got five years ago. So, that's just, yeah, interesting. SEIF: Although, from a database perspective, they did a lot of the innovations. A lot of first innovations could have come from Oracle. From a technical perspective, they're ridiculous. I'm not sure from a product perspective how good they are. But I know their sales team is so big, so huge. They don't care about the product anymore. They can still sell. VICTORIA: I think, you know, everything in tech is cyclical. So, you know, if they have the right strategy and they're making some interesting changes over there, there's always a chance [laughs]. Certain use cases, I mean, I think that's the interesting point about working in technology is that you know, every company is a tech company. And so, there's just a lot of different types of people, personas, and use cases for different types of products. So, I wonder, you know, you kind of mentioned earlier that, like, everyone is interested in Axiom. But, you know, I don't know, are you narrowing the market? Or, like, how are you trying to kind of focus your messaging and your sales for Axiom? SEIF: I'm trying to focus on developers. So, we're really trying to focus on developers because the experience around observability is crap. It's stupid expensive. Sorry for being straightforward, right? And that's what we're trying to change. And we're targeting developers mainly. We want developers to like us. And we'll find all these different types of developers who are using it, and that's the interesting thing. And because of them, we start adding more and more features, like, you know, we added tracing, and now that enables, like, billions of events pushed through for, you know, again, for almost no money, again, $25 a month for a terabyte of data. And we're doing this with metrics next. And that's just to address the developers who have been giving us feedback and the market demand. I will sum it up, again, like, the experience is crap, and it's stupid expensive. I think that's the [inaudible 28:07] of observability is just that's how I would sum it up. VICTORIA: If you could go back in time and talk to yourself when you were still a developer, now that you're CTO, what advice would you give yourself? JOE: Besides avoiding shock collars. VICTORIA: [laughs] Yes. SEIF: Get people's feedback quickly so you know you're on the right track. I think that's very, very, very, very important. Don't just work in the dark, or don't go too long into stealth mode because, eventually, people catch up. Also, ship when you're 80% ready because 100% is too late. I think it's the same thing here. JOE: Ship often and early. SEIF: Yeah, even if it's not fully ready, it's still feedback. VICTORIA: Ship often and early and talk to people [laughs]. Just, do you feel like, as a developer, did you have the skills you needed to be able to get the most out of those feedback and out of those conversations you were having with people around your product? SEIF: I still don't think I'm good enough. You're just constantly learning, right? I just accepted I'm part of a team, and I have my contributions. But as an individual, I still don't think I know enough. I think there's more I need to learn at this point. VICTORIA: I wonder, what questions do you have for me or Joe? SEIF: How did you start your podcast, and why the name? VICTORIA: Oh, man, I hope I can answer. So, the podcast was started...I think it's, like, we're actually about to be at our 500th Episode. So, I've only been a host for the last year. Maybe Joe even knows more than I do. But what I recall is that one person at thoughtbot thought it would be a great idea to start a podcast, and then they did it. And it seems like the whole company is obsessed with robots. I'm not really sure where that came from. There used to be a tiny robot in the office, is what I remember. And people started using that as, like, the mascot. And then, yeah, that's it, that's the whole thing. SEIF: Was the robot doing anything useful or just being cute? JOE: It was just cute, and it's hard to make a robot cute. SEIF: Was it a real robot, or was it like a -- JOE: No, there was, at one point, a toy robot. The name...I actually forget the origin–origin of the name, but the name Giant Robots comes from our blog. So, we named the podcast the same as the blog: Giant Robots Smashing Into Other Giant Robots. SEIF: Yes, it's called transformers. VICTORIA: Yeah, I like it. It's, I mean, now I feel like -- SEIF: [laughs] VICTORIA: We got to get more, like, robot dogs involved [laughs] in the podcast. SEIF: Like, I wanted to add one thing when we talked about, you know, what gets me going. And I want to mention that I have a six-month-old son now. He definitely adds a lot of motivation for me to wake up in the morning and work. But he also makes me wake up regardless if I want to or not. VICTORIA: Yeah, you said you had invented an alarm clock that never turns off. Never snoozes [laughs]. SEIF: Yes, absolutely. VICTORIA: I have the same thing, but it's my dog. But he does snooze, actually. He'll just, like, get tired and go back to sleep [laughs]. SEIF: Oh, I have a question. Do dogs have a Tamagotchi phase? Because, like, my son, the first three months was like a Tamagotchi. It was easy to read him. VICTORIA: Oh yeah, uh-huh. SEIF: Noisy but easy. VICTORIA: Yes, yes. SEIF: Now, it's just like, yeah, I don't know, like, the last month he has opinions at six months. I think it's because I raised him in Europe. I should take him back to the Middle East [laughs]. No opinions. VICTORIA: No, dogs totally have, like, a communication style, you know, I pretty much know what he, I mean, I can read his mind, obviously [laughs]. SEIF: Sure, but that's when they grow a bit. But what when they were very...when the dog was very young? VICTORIA: Yeah, they, I mean, they also learn, like, your stuff, too. So, they, like, learn how to get you to do stuff or, like, I know she'll feed me if I'm sitting here [laughs]. SEIF: And how much is one dog year, seven years? VICTORIA: Seven years. SEIF: Seven years? VICTORIA: Yeah, seven years? SEIF: Yeah. So, basically, in one year, like, three months, he's already...in one month, he's, you know, seven months old. He's like, yeah. VICTORIA: Yeah. In a year, they're, like, teenagers. And then, in two years, they're, like, full adults. SEIF: Yeah. So, the first month is basically going through the first six months of a human being. So yeah, you pass...the first two days or three days are the Tamagotchi phase that I'm talking about. VICTORIA: [chuckles] I read this book, and it was, like, to understand dogs, it's like, they're just like humans that are trying to, like, maximize the number of positive experiences that they have. So, like, if you think about that framing around all your interactions about, like, maybe you're trying to get your son to do something, you can be like, okay, how do I, like, I don't know, train him that good things happen when he does the things I want him to do? [laughs] That's kind of maybe manipulative but effective. So, you're not learning baby sign language? You're just, like, going off facial expressions? SEIF: I started. I know how Mama looks like. I know how Dada looks like. I know how more looks like, slowly. And he already does this thing that I know that when he's uncomfortable, he starts opening and closing his hands. And when he's completely uncomfortable and basically that he needs to go sleep, he starts pulling his own hair. VICTORIA: [laughs] I do the same thing [laughs]. SEIF: You pull your own hair when you go to sleep? I don't have that. I don't have hair. VICTORIA: I think I do start, like, touching my head though, yeah [inaudible 33:04]. SEIF: Azure took the last bit of hair I had! Went away with Azure, Elasticsearch, and the shock collar. VICTORIA: [laughs] SEIF: I have none of them left. Absolutely nothing. I should sue Elasticsearch for this shit. VICTORIA: [laughs] Let me know how that goes. Maybe there's more people who could join your lawsuit, you know, with a class action. SEIF: [laughs] Yeah. Well, one thing I wanted to also just highlight is, right now, one of the things that also makes the company move forward is we realized that in a single domain, we proved ourselves very valuable to specific companies, right? So, that was a big, big thing, milestone for us. And now we're trying to move into a handful of domains and see which one of those work out the best for us. Does that make sense? VICTORIA: Yeah. And I'm curious: what are the biggest challenges or hurdles that you associate with that? SEIF: At this point, you don't want just feedback. You want constructive criticism. Like, you want to work with people who will criticize the applic...and you iterate with them based on this criticism, right? They're just not happy about you and trying to create design partners. So, for us, it was very important to have these small design partners who can work with us to actually prove ourselves as valuable in a single domain. Right now, we need to find a way to scale this across several domains. And how do you do that without sacrificing? Like, how do you open into other domains without sacrificing the original domain you came from? So, there's a lot of things [inaudible 34:28]. And we are in the middle of this. Honestly, I Forrest Gumped my way through half of this, right? Like, I didn't know what I was doing. I had ideas. I think it's more of luck at this point. And I had luck. No, we did work. We did work a lot. We did sleepless nights and everything. But I think, in the last three years, we became more mature and started thinking more about product. And as I said, like, our CEO, Neil, and Dominic, our head of product, are putting everything behind being a product-led organization, not just a tech-led organization. VICTORIA: That's super interesting. I love to hear that that's the way you're thinking about it. JOE: I was just curious what other domains you're looking at pushing into if you can say. SEIF: So, we are going to start moving into ETL a bit more. We're trying to see how we can fit in specific ML scenarios. I can't say more about the other, though. JOE: Do you think you'll take the same approaches in terms of value proposition, like, low cost, good enough latency? SEIF: Yes, that's definitely one thing. But there's also...so, this is the values we're bringing to the customer. But also, now, our internal values are different. Now it's more of move with urgency and high velocity, as we said before, right? Think big, work small. The values in terms of values we're going to take to the customers it's the same ones. And maybe we'll add some more, but it's still going to be low-cost and large-scale. And, internally, we're just becoming more, excuse my French, agile. I hate that word so much. Should be good with Scrum. VICTORIA: It's painful, but everyone knows what you're talking about [laughs], you know, like -- SEIF: See, I have opinions here about Scrum. I think Scrum should be only used in terms of iceScrum [inaudible 36:04], or something like that. VICTORIA: Oh no [laughter]. Well, it's a Rugby term, right? Like, that's where it should probably stay. SEIF: I did not know it's a rugby term. VICTORIA: Yeah, so it should stay there, but -- SEIF: Yes [laughs]. VICTORIA: Yeah, I think it's interesting. Yeah, I like the being flexible. I like the just, like, continuous feedback and how you all have set up to, like, talk with your customers. Because you mentioned earlier that, like, you might open source some of your projects. And I'm just curious, like, what goes into that decision for you when you're going to do that? Like, what makes you think this project would be good for open source or when you think, actually, we need to, like, keep it? SEIF: So, we open source libraries, right? We actually do that already. And some other big organizations use our libraries; even our competitors use our libraries, that we do. The whole product itself or at least a big part of the product, like database, I'm not sure we're going to open source that, at least not anytime soon. And if we open source, it's going to be at a point where the value-add it brings is nothing compared to how well our product is, right? So, if we can replace whatever's at the back with...the storage engine we have in the back with something else and the product doesn't get affected, that's when we open source it. VICTORIA: That's interesting. That makes sense to me. But yeah, thank you for clarifying that. I just wanted to make sure to circle back. Since you have this big history in open source, yeah, I'm curious if you see... SEIF: Burning me out? VICTORIA: Burning you out, yeah [laughter]. Oh, that's a good question. Yeah, like, because, you know, we're about to be in October here. Do you have any advice or strategies as a maintainer for not getting burned out during the next couple of weeks besides, like, hide in a cave and without internet access [laughs]? SEIF: Stay away from Reddit and Hacker News. That's my goal for October now because I'm always afraid of getting too attached to an idea, or too motivated, or excited by an idea that I drift away from what I am actually supposed to be doing. VICTORIA: Last question is, is there anything else you would like to promote? SEIF: Yeah, check out our website; I think it's at axiom.co. Check it out. Sign up. And comment on Discord and talk to me. I don't bite, sometimes grumpy, but that's just because of lack of sleep in the morning. But, you know, around midday, I'm good. And if you're ever in Berlin and you want to hang out, I'm more than willing to hang out. VICTORIA: Whoo, that's awesome. Yeah, Berlin is great. I was there a couple of years ago but no plans to go back anytime soon, but maybe I'll keep that in mind. You can subscribe to the show and find notes along with a complete transcript for this episode at giantrobots.fm. If you have questions or comments, email us at hosts@giantrobots.fm. And you could find me on Twitter @victori_ousg. And this podcast is brought to you by thoughtbot and produced and edited by Mandy Moore. Thanks for listening. See you next time. Did you know thoughtbot has a referral program? If you introduce us to someone looking for a design or development partner, we will compensate you if they decide to work with us. More info on our website at tbot.io/referral. Or you can email us at referrals@thoughtbot.com with any questions. Special Guests: Joe Ferris and Seif Lotfy.

Seif Lotfy, Co-Founder and CTO at Axiom, joins Corey on Screaming in the Cloud to discuss how and why Axiom has taken a low-cost approach to event data. Seif describes the events that led to him helping co-found a company, and explains why the team wrote all their code from scratch. Corey and Seif discuss their views on AWS pricing, and Seif shares his views on why AWS doesn't have to compete on price. Seif also reveals some of the exciting new products and features that Axiom is currently working on. About SeifSeif is the bubbly Co-founder and CTO of Axiom where he has helped build the next generation of logging, tracing, and metrics. His background is at Xamarin, and Deutche Telekom and he is the kind of deep technical nerd that geeks out on white papers about emerging technology and then goes to see what he can build.Links Referenced: Axiom: https://axiom.co/ Twitter: https://twitter.com/seiflotfy 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. This promoted guest episode is brought to us by my friends, and soon to be yours, over at Axiom. Today I'm talking with Seif Lotfy, who's the co-founder and CTO of Axiom. Seif, how are you?Seif: Hey, Corey, I am very good, thank you. It's pretty late here, but it's worth it. I'm excited to be on this interview. How are you today?Corey: I'm not dead yet. It's weird, I see you at a bunch of different conferences, and I keep forgetting that you do in fact live half a world away. Is the entire company based in Europe? And where are you folks? Where do you start and where do you stop geographically? Let's start there. We over—everyone dives right into product. No, no, no. I want to know where in the world people sit because apparently, that's the most important thing about a company in 2023.Seif: Unless you ask Zoom because they're undoing whatever they did. We're from New Zealand, all the way to San Francisco, and everything in between. So, we have people in Egypt and Nigeria, all around Europe, all around the US… and UK, if you don't consider it Europe anymore.Corey: Yeah, it really depends. There's a lot of unfortunate naming that needs to get changed in the wake of that.Seif: [laugh].Corey: But enough about geopolitics. Let's talk about industry politics. I've been a fan of Axiom for a while and I was somewhat surprised to realize how long it had been around because I only heard about you folks a couple of years back. What is it you folks do? Because I know how I think about what you're up to, but you've also gone through some messaging iteration, and it is a near certainty that I am behind the times.Seif: Well, at this point, we just define ourselves as the best home for event data. So, Axiom is the best home for event data. We try to deal with everything that is event-based, so time-series. So, we can talk metrics, logs, traces, et cetera. And right now predominantly serving engineering and security.And we're trying to be—or we are—the first cloud-native time-series platform to provide streaming search, reporting, and monitoring capabilities. And we're built from the ground up, by the way. Like, we didn't actually—we're not using Parquet [unintelligible 00:02:36] thing. We're completely everything from the ground up.Corey: When I first started talking to you folks a few years back, there were two points to me that really stood out, and I know at least one of them still holds true. The first is that at the time, you were primarily talking about log data. Just send all your logs over to Axiom. The end. And that was a simple message that was simple enough that I could understand it, frankly.Because back when I was slinging servers around and you know breaking half of them, logs were effectively how we kept track of what was going on, where. These days, it feels like everything has been repainted with a very broad brush called observability, and the takeaway from most company pitches has been, you must be smarter than you are to understand what it is that we're up to. And in some cases, you scratch below the surface and realize it no, they have no idea what they're talking about either and they're really hoping you don't call them on that.Seif: It's packaging.Corey: Yeah. It is packaging and that's important.Seif: It's literally packaging. If you look at it, traces and logs, these are events. There's a timestamp and just data with it. It's a timestamp and data with it, right? Even metrics is all the way to that point.And a good example, now everybody's jumping on [OTel 00:03:46]. For me, OTel is nothing else, but a different structure for time series, for different types of time series, and that can be used differently, right? Or at least not used differently but you can leverage it differently.Corey: And the other thing that you did that was interesting and is a lot, I think, more sustainable as far as [moats 00:04:04] go, rather than things that can be changed on a billboard or whatnot, is your economic position. And your pricing has changed around somewhat, but I ran a number of analyses on your cost that you were passing on to customers and my takeaway was that it was a little bit more expensive to store data for logs in Axiom than it was to store it in S3, but not by much. And it just blew away the price point of everything else focused around logs, including AWS; you're paying 50 cents a gigabyte to ingest CloudWatch logs data over there. Other companies are charging multiples of that and Cisco recently bought Splunk for $28 billion because it was cheaper than paying their annual Splunk bill. How did you get to that price point? Is it just a matter of everyone else being greedy or have you done something different?Seif: We looked at it from the perspective of… so there's the three L's of logging. I forgot the name of the person at Netflix who talked about that, but basically, it's low costs, low latency, large scale, right? And you will never be able to fulfill all three of them. And we decided to work on low costs and large scale. And in terms of low latency, we won't be low as others like ClickHouse, but we are low enough. Like, we're fast enough.The idea is to be fast enough because in most cases, I don't want to compete on milliseconds. I think if the user can see his data in two seconds, he's happy. Or three seconds, he's happy. I'm not going to be, like, one to two seconds and make the cost exponentially higher because I'm one second faster than the other. And that's, I think, that the way we approached this from day one.And from day one, we also started utilizing the idea of existence of Open—Object Storage, we have our own compressions, our own encodings, et cetera, from day one, too, so and we still stick to that. That's why we never converted to other existing things like Parquet. Also because we are a Schema-On-Read, which Parquet doesn't allow you really to do. But other than that, it's… from day one, we wanted to save costs by also making coordination free. So, ingest has to be coordination free, right, because then we don't run a shitty Kafka, like, honestly a lot—a lot of the [logs 00:06:19] companies who running a Kafka in front of it, the Kafka tax reflects in what they—the bill that you're paying for them.Corey: What I found fun about your pricing model is it gets to a point that for any reasonable workload, how much to log or what to log or sample or keep everything is no longer an investment decision; it's just go ahead and handle it. And that was originally what you wound up building out. Increasingly, it seems like you're not just the place to send all the logs to, which to be honest, I was excited enough about that. That was replacing one of the projects I did a couple of times myself, which is building highly available, fault-tolerant, rsyslog clusters in data centers. Okay, great, you've gotten that unlocked, the economics are great, I don't have to worry about that anymore.And then you started adding interesting things on top of it, analyzing things, replaying events that happen to other players, et cetera, et cetera, it almost feels like you're not just a storage depot, but you also can forward certain things on under a variety of different rules or guises and format them as whatever on the other side is expecting them to be. So, there's a story about integrating with other observability vendors, for example, and only sending the stuff that's germane and relevant to them since everyone loves to charge by ingest.Seif: Yeah. So, we did this one thing called endpoints, the number one. Endpoints was a beginning where we said, “Let's let people send us data using whatever API they like using, let's say Elasticsearch, Datadog, Honeycomb, Loki, whatever, and we will just take that data and multiplex it back to them.” So, that's how part of it started. This allows us to see, like, how—allows customers to see how we compared to others, but then we took it a bit further and now, it's still in closed invite-only, but we have Pipelines—codenamed Pipelines—which allows you to send data to us and we will keep it as a source of truth, then we will, given specific rules, we can then ship it anywhere to a different destination, right, and this allows you just to, on the fly, send specific filter things out to, I don't know, a different vendor or even to S3 or you could send it to Splunk. But at the same time, you can—because we have all your data, you can go back in the past, if the incident happens and replay that completely into a different product.Corey: I would say that there's a definite approach to observability, from the perspective of every company tends to visualize stuff a little bit differently. And one of the promises of OTel that I'm seeing that as it grows is the idea of oh, I can send different parts of what I'm seeing off to different providers. But the instrumentation story for OTel is still very much emerging. Logs are kind of eternal and the only real change we've seen to logs over the past decade or so has been instead of just being plain text and their positional parameters would define what was what—if it's in this column, it's an IP address and if it's in this column, it's a return code, and that just wound up being ridiculous—now you see them having schemas; they are structured in a variety of different ways. Which, okay, it's a little harder to wind up just cat'ing a file together and piping it to grep, but there are trade-offs that make it worth it, in my experience.This is one of those transitional products that not only is great once you get to where you're going, from my playing with it, but also it meets you where you already are to get started because everything you've got is emitting logs somewhere, whether you know it or not.Seif: Yes. And that's why we picked up on OTel, right? Like, one of the first things, we now support… we have an OTel endpoint natively bec—or as a first-class citizen because we wanted to build this experience around OTel in general. Whether we like it or not, and there's more reasons to like it, OTel is a standard that's going to stay and it's going to move us forward. I think of OTel as will have the same effect if not bigger as [unintelligible 00:10:11] back of the day, but now it just went away from metrics, just went to metrics, logs, and traces.Traces is, for me, very interesting because I think OTel is the first one to push it in a standard way. There were several attempts to make standardized [logs 00:10:25], but I think traces was something that OTel really pushed into a proper standard that we can follow. It annoys me that everybody uses a different bits and pieces of it and adds something to it, but I think it's also because it's not that mature yet, so people are trying to figure out how to deliver the best experience and package it in a way that it's actually interesting for a user.Corey: What I have found is that there's a lot that's in this space that is just simply noise. Whenever I spend a protracted time period working on basically anything and I'm still confused by the way people talk about that thing, months or years later, I'm starting to get the realization that maybe I'm not the problem here. And I'm not—I don't mean this to be insulting, but one of the things I've loved about you folks is I've always understood what you're saying. Now, you can hear that as, “Oh, you mean we talk like simpletons?” No, it means what you're talking about resonates with at least a subset of the people who have the problem you solve. That's not nothing.Seif: Yes. We've tried really hard because one of the things we've tried to do is actually bring observability to people who are not always busy or it's not part of their day to day. So, we try to bring into [Versal 00:11:37] developers, right, with doing a Versal integration. And all of a sudden, now they have their logs, and they have a metrics, and they have some traces. So, all of a sudden, they're doing the observability work. Or they have actual observability, for their Versal based, [unintelligible 00:11:54]-based product.And we try to meet the people where they are, so we try to—instead of actually telling people, “You should send us data.”—I mean, that's what they do now—we try to find, okay, what product are you using and how can we grab data from there and send it to us to make your life easier? You see that we did that with Versal, we did that with Cloudflare. AWS, we have extensions, Lambda extensions, et cetera, but we're doing it for more things. For Netlify, it's a one-click integration, too, and that's what we're trying to do to actually make the experience and the journey easier.Corey: I want to change gears a little bit because something that we spent a fair bit of time talking about—it's why we became friends, I would think anyway—is that we have a shared appreciation for several things. One of which, at most notable to anyone around us is whenever we hang out, we greet each other effusively and then immediately begin complaining about costs of cloud services. What is your take on the way that clouds charge for things? And I know it's a bit of a leading question, but it's core and foundational to how you think about Axiom, as well as how you serve customers.Seif: They're ripping us off. I'm sorry [laugh]. They just—the amount of money they make, like, it's crazy. I would love to know what margins they have. That's a big question I've always had. I'm like, what are the margins they have at AWS right now?Corey: Across the board, it's something around 30 to 40%, last time I looked at it.Seif: That's a lot, too.Corey: Well, that's also across the board of everything, to be clear. It is very clear that some services are subsidized by other services. As it should be. If you start charging me per IAM call, we're done.Seif: And also, I mean, the machine learning stuff. Like, they won't be doing that much on top of it right now, right, [else nobody 00:13:32] will be using it.Corey: But data transfer? Yeah, there's a significant upcharge on that. But I hear you. I would moderate it a bit. I don't think that I would say that it's necessarily an intentional ripoff. My problem with most cloud services that they offer is not usually that they're too expensive—though there are exceptions to that—but rather that the dimensions are unpredictable in advance. So, you run something for a while and see what it costs. From where I sit, if a customer uses your service and then at the end of usage is surprised by how much it cost them, you've kind of screwed up.Seif: Look, if they can make egress free—like, you saw how Cloudflare just did the egress of R2 free? Because I am still stuck with AWS because let's face it, for me, it is still my favorite cloud, right? Cloudflare is my next favorite because of all the features that are trying to develop and the pace they're picking, the pace they're trying to catch up with. But again, one of the biggest things I liked is R2, and R2 egress is free. Now, that's interesting, right?But I never saw anything coming back from S3 from AWS on S3 for that, like you know. I think Amazon is so comfortable because from a product perspective, they're simple, they have the tools, et cetera. And the UI is not the flashiest one, but you know what you're doing, right? The CLI is not the flashiest one, but you know what you're doing. It is so cool that they don't really need to compete with others yet.And I think they're still dominantly the biggest cloud out there. I think you know more than me about that, but [unintelligible 00:14:57], like, I think they are the biggest one right now in terms of data volume. Like, how many customers are using them, and even in terms of profiles of people using them, it's very, so much. I know, like, a lot of the Microsoft Azure people who are using it, are using it because they come from enterprise that have been always Microsoft… very Microsoft friendly. And eventually, Microsoft also came in Europe in these all these different weird ways. But I feel sometimes ripped off by AWS because I see Cloudflare trying to reduce the prices and AWS just looking, like, “Yeah, you're not a threat to us so we'll keep our prices as they are.”Corey: I have it on good authority from folks who know that there are reasons behind the economic structures of both of those companies based—in terms of the primary direction the traffic flows and the rest. But across the board, they've done such a poor job of articulating this that, frankly, I think the confusion is on them to clear up, not us.Seif: True. True. And the reason I picked R2 and S3 to compare there and not look at Workers and Lambdas because I look at it as R2 is S3 compatible from an API perspective, right? So, they're giving me something that I already use. Everything else I'm using, I'm using inside Amazon, so it's in a VPC, but just the idea. Let me dream. Let me dream that S3 egress will be free at some point.Corey: I can dream.Seif: That's like Christmas. It's better than Christmas.Corey: What I'm surprised about is how reasonable your pricing is in turn. You wind up charging on the basis of ingest, which is basically the only thing that really makes sense for how your company is structured. But it's predictable in advance, the free tier is, what, 500 gigs a month of ingestion, and before people think, “Oh, that doesn't sound like a lot,” I encourage you to just go back and think how much data that really is in the context of logs for any toy project. Like, “Well, our production environment spits out way more than that.” Yes, and by the word production that you just used, you probably shouldn't be using a free trial of anything as your critical path observability tooling. Become a customer, not a user. I'm a big believer in that philosophy, personally. For all of my toy projects that are ridiculous, this is ample.Seif: People always tend to overestimate how much logs they're going to be sending. Like so, there's one thing. What you said it right: people who already have something going on, they already know how much logs they'll be sending around. But then eventually they're sending too much, and that's why we're back here and they're talking to us. Like, “We want to ttry your tool, but you know, we'll be sending more than that.” So, if you don't like our pricing, go find something else because I think we are the cheapest out there right now. We're the competitive the cheapest out there right now.Corey: If there is one that is less expensive, I'm unaware of it.Seif: [laugh].Corey: And I've been looking, let's be clear. That's not just me saying, “Well, nothing has skittered across my desk.” No, no, no, I pay attention to this space.Seif: Hey, where's—Corey, we're friends. Loyalty.Corey: Exactly.Seif: If you find something, you tell me.Corey: Oh, if I find something, I'll tell everyone.Seif: Nononon, you tell me first and you tell me in a nice way so I can reduce the prices on my site [laugh].Corey: This is how we start a price was, industry-wide, and I would love to see it.Seif: [laugh]. But there's enough channels that we share at this point across different Slacks and messaging apps that you should be able to ping me if you find one. Also, get me the name of the CEO and the CTO while you're at it.Corey: And where they live. Yes, yes, of course. The dire implications will be awesome.Seif: That was you, not me. That was your suggestion.Corey: Exactly.Seif: I will not—[laugh].Corey: Before we turn into a bit of an old thud and blunder, let's talk about something else that I'm curious about here. You've been working on Axiom for something like seven years now. You come from a world of databases and events and the like. Why start a company in the model of Axiom? Even back then, when I looked around, my big problem with the entire observability space could never have been described as, “You know what we need? More companies that do exactly this.” What was it that you saw that made you say, “Yeah, we're going to start a company because that sounds easy.”Seif: So, I'll be very clear. Like, I'm not going to, like, sugarcoat this. We kind of got in a position where it [forced counterweighted 00:19:10]. And [laugh] by that I mean, we came from a company where we were dealing with logs. Like, we actually wrote an event crash analytics tool for a company, but then we ended up wanting to use stuff like Datadog, but we didn't have the budget for that because Datadog was killing us.So, we ended up hosting our own Elasticsearch. And Elasticsearch, it costs us more to maintain our Elasticsearch cluster for the logs than to actually maintain our own little infrastructure for the crash events when we were getting, like, 1 billion crashes a month at this point. So eventually, we just—that was the first burn. And then you had alert fatigue and then you had consolidating events and timestamps and whatnot. The whole thing just seemed very messy.So, we started off after some company got sold, we started off by saying, “Okay, let's go work on a new self-hosted version of the [unintelligible 00:20:05] where we do metrics and logs.” And then that didn't go as well as we thought it would, but we ended up—because from day one, we were working on cloud na—because we d—we cloud ho—we were self-hosted, so we wanted to keep costs low, we were working on and making it stateless and work against object store. And this is kind of how we started. We realized, oh, our cost, we can host this and make it scale, and won't cost us that much.So, we did that. And that started gaining more attention. But the reason we started this was we wanted to start a self-hosted version of Datadog that is not costly, and we ended up doing a Software as a Service. I mean, you can still come and self-hosted, but you'll have to pay money for it, like, proper money for that. But we do as a SaaS version of this and instead of trying to be a self-hosted Datadog, we are now trying to compete—or we are competing with Datadog.Corey: Is the technology that you've built this on top of actually that different from everything else out there, or is this effectively what you see in a lot of places: “Oh, yeah, we're just going to manage Elasticsearch for you because that's annoying.” Do you have anything that distinguishes you from, I guess, the rest of the field?Seif: Yeah. So, very just bluntly, like, I think Scuba was the first thing that started standing out, and then Honeycomb came into the scene and they start building something based on Scuba, the [unintelligible 00:21:23] principles of Scuba. Then one of the authors of actual Scuba reached out to me when I told him I'm trying to build something, and he's gave me some ideas, and I start building that. And from day one, I said, “Okay, everything in S3. All queries have to be serverless.”So, all the queries run on functions. There's no real disks. It's just all on S3 right now. And the biggest issue—achievement we got to lower our cost was to get rid of Kafka, and have—let's say, in behind the scenes we have our own coordination-free mechanism, but the idea is not to actually have to use Kafka at all and thus reduce the costs incredibly. In terms of technology, no, we don't use Elasticsearch.We wrote everything from the ground up, from scratch, even the query language. Like, we have our own query language that's based—modeled after Kusto—KQL by Microsoft—so everything we have is built from absolutely from the ground up. And no Elastic. I'm not using Elastic anymore. Elastic is a horror for me. Absolutely horror.Corey: People love the API, but no, I've never met anyone who likes managing Elasticsearch or OpenSearch, or whatever we're calling your particular flavor of it. It is a colossal pain, it is subject to significant trade-offs, regardless of how you work with it, and Amazon's managed offering doesn't make it better; it makes it worse in a bunch of ways.Seif: And the green status of Elasticsearch is a myth. You'll only see it once: the first time you start that cluster, that's what the Elasticsearch cluster is green. After that, it's just orange, or red. And you know what? I'm happy when it's orange. Elasticsearch kept me up for so long. And we had actually a very interesting situation where we had Elasticsearch running on Azure, on Windows machines, and I would have server [unintelligible 00:23:10]. And I'd have to log in and every day—you remember, what's it called—RP… RP Something. What was it called?Corey: RDP? Remote Desktop Protocol, or something else?Seif: Yeah, yeah. Where you have to log in, like, you actually have visual thing, and you have to go in and—Corey: Yep.Seif: And visually go in and say, “Please don't restart.” Every day, I'd have to do that. Please don't restart, please don't restart. And also a lot of weird issues, and also at that point, Azure would decide to disconnect the pod, wanted to try to bring in a new pod, and all these weird things were happening back then. So, eventually, end up with a [unintelligible 00:23:39] decision. I'm talking 2013, '14, so it was back in the day when Elasticsearch was very young. And so, that was just a bad start for me.Corey: I will say that Azure is the most cost-effective cloud because their security is so clown shoes, you can just run whatever you want in someone else's account and it's free to you. Problem solved.Seif: Don't tell people how we save costs, okay?Corey: [laugh]. I love that.Seif: [laugh]. Don't tell people how we do this. Like, Corey, come on [laugh], you're exposing me here. Let me tell you one thing, though. Elasticsearch is the reason I literally use a shock collar or a shock bracelet on myself every time it went down—which was almost every day, instead of having PagerDuty, like, ring my phone.And, you know, I'd wake up and my partner back then would wake up. I bought a Bluetooth collar off of Alibaba that would tase me every time I'd get a notification, regardless of the notification. So, some things are false alarm, but I got tased for at least two, three weeks before I gave up. Every night I'd wake up, like, to a full discharge.Corey: I would never hook myself up to a shocker tied to outages, even if I owned a company. There are pleasant ways to wake up, unpleasant ways to wake up, and even worse. So, you're getting shocked for some—so someone else can wind up effectively driving the future of the business. You're, more or less, the monkey that gets shocked awake to go ahead and fix the thing that just broke.Seif: [laugh]. Well, the fix to that was moving from Azure to AWS without telling anybody. That got us in a lot of trouble. Again, that wasn't my company.Corey: They didn't notice that you did this, or it caused a lot of trouble because suddenly nothing worked where they thought it would work?Seif: They—no, no, everything worked fine on AWS. That's how my love story began. But they didn't notice for, like, six months.Corey: That's kind of amazing.Seif: [laugh]. That was specta—we rewrote everything from C# to Node.js and moved everything away from Elasticsearch, started using Redshift, Redis and a—you name it. We went AWS all the way and they didn't even notice. We took the budget from another department to start filling that in.But we cut the costs from $100,000 down to, like, 40, and then eventually down to $30,000 a month.Corey: More than a little wild.Seif: Oh, God, yeah. Good times, good times. Next time, just ask me to tell you the full story about this. I can't go into details on this podcast. I'll get in a lot—I think I'll get in trouble. I didn't sign anything though.Corey: Those are the best stories. But no, I hear you. I absolutely hear you. Seif, I really want to thank you for taking the time to speak with me. If people want to learn more, where should they go?Seif: So, axiom.co—not dot com. Dot C-O. That's where they learn more about Axiom. And other than that, I think I have a Twitter somewhere. And if you know how to write my name, you'll—it's just one word and find me on Twitter.Corey: We will put that all in the [show notes 00:26:33]. Thank you so much for taking the time to speak with me. I really appreciate it.Seif: Dude, that was awesome. Thank you, man.Corey: Seif Lotfy, co-founder and CTO of Axiom, who has brought this promoted guest episode our way. 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 angry comment that one of these days, I will get around to aggregating in some horrifying custom homebrew logging system, probably built on top of rsyslog.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.

Today is a very special episode. It is our 150th episode! To celebrate, Laura and Kevin welcome Tim Tutt, CEO & cofounder at Night Shift Development. We talk about his company's origins, bootstrapping vs. taking on funding, startup best practices, augmented intelligence, the ribs at Earl's Kitchen and Bar and more!Tim Tutt is a proven technical leader with over a decade of software engineering experience focused specifically on developing and deploying large-scale search and discovery and data analytics solutions in both the public and private sectors. He has a strong passion for operationalizing deep technical capabilities for the benefit of non-technical business users.As an Elastic Certified Engineer Tim has deep experience using Elasticsearch for a variety of use cases. As the Chief Technology officer of Bogart Associates, he led the development and creation of a best-in-class, easy to use, secure communications solution ultimately leading to Bogart's first issued patent. Tim is currently the CEO and Cofounder of Night Shift Development, Inc., which focuses on helping organizations make sense of their data. Using his background in natural language processing, machine learning, and data analytics, he created and productized a solution aimed at allowing non-technical users to have conversational experience with their data.Thank you to all of our listeners!-The Entire That Tech Pod Team

As OSS projects continue to look for ways to balance community, sustainability and profitability, let's explore some alternative considerations for companies and communities. SHOW: 754CLOUD NEWS OF THE WEEK - http://bit.ly/cloudcast-cnotwCHECK OUT OUR NEW PODCAST - "CLOUDCAST BASICS"SHOW SPONSORS:Datadog Synthetic Monitoring: Frontend and Backend Modern MonitoringEnsure frontend issues don't impair user experience by detecting user-facing issues with API and browser tests with a free 14 day Datadog trial. Listeners of The Cloudcast will also receive a free Datadog T-shirt. AWS Insiders is an edgy, entertaining podcast about the services and future of cloud computing at AWS. Listen to AWS Insiders in your favorite podcast player. Cloudfix HomepageCloudZero – Cloud Cost Visibility and Savings​​CloudZero provides immediate and ongoing savings with 100% visibility into your total cloud spendSHOW NOTES:Troubles with Open Source Gig Economy (Chris Aniszczyk, CNCF)Open Source Needs Maintainers, How Do They Get Paid? (TNS)A New Way to Think about Open Source Sustainability (InfoWorld)OPEN SOURCE HAS BECOME CRITICAL INFRASTRUCTURE FOR BUSINESSESDocker, Kubernetes, Terraform, Ansible, Kafka, MongoDB, ElasticSearch, Java are all mission-critical to businesses. Cloud providers have disrupted the previous OSS models - operationalize vs. contributionsHow to pay contributors?How to avoid unpredictable changes to licensing?How to provide better transparency to customers and communities?DO OSS PROJECT NEED MORE STRUCTURE TO SUCCEED OR SUSTAIN?Establish “types” of projects: Business, Community, Any - Years to ActionEstablish some timelines around various types of projectsPatents have a timeline to explore - can there be OSS licenses that have timelinesGovernment tax credits for paying OSS contributorsShould governance foundations create forks at established times?Should groups like CNCF be more active in recruiting forks after a period of time?FEEDBACK?Email: show at the cloudcast dot netTwitter: @thecloudcastnet

Welcome episode 227 of the Cloud Pod podcast - where the forecast is always cloudy! This week your hosts are Justin, Jonathan, Matthew and Ryan - and they're REALLY excited to tell you all about the 161 one things announced at Google Next. Literally, all the things. We're also saying farewell to EC2 Classic, Amazon SES, and Azure's Explicit Proxy - which probably isn't what you think it is.  Titles we almost went with this week:

Elasticsearch is the most established solution today to search and analyze large amounts of logs. However, it can be costly and complex to manage. Quickwit searches large amounts of append only cloud data like logs or ledgers in a fraction time with significantly less cost than Elasticsearch. In this episode, we interview Paul Masurel, one The post Cloud-native Search with Paul Masurel appeared first on Software Engineering Daily.

Mach5 Search is a slide-in, cloud-native replacement for Elasticsearch and OpenSearch that immediately saves up to 90% in operating cost. Mach5 Search can run on top of Google BigQuery, Snowflake, and Databricks, or natively on Object Stores in all the major clouds. Vinayak Borkar is the CEO and Co-Founder of Mach5 Software and he joins The post Cloud Native Search with Vinayak Borkar appeared first on Software Engineering Daily.