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Meta moves for the social network for AI bots. Code Review for Claude Code seems to be like another revolution for the software development industry. Yan LeCun raises the biggest European seed round of all time. And the MacBook Neo… worth investing in or not? Exclusive: Meta hires duo behind Moltbook (Axios) OpenAI and Google employees rush to Anthropic's defense in DOD lawsuit (TechCrunch) This new Claude Code Review tool uses AI agents to check your pull requests for bugs - here's how (ZDNet) Amazon holds engineering meeting following AI-related outages (FT) Yann LeCun's AI start-up raises more than $1bn in Europe's largest seed round (FT) MacBook Neo review: the Mac for the masses (The Verge) Learn more about your ad choices. Visit megaphone.fm/adchoices

The reception to our recent post on Code Reviews has been strong. Catch up!Amid a maelstrom of discussion on whether or not AI is killing SaaS, one of the top publicly listed SaaS companies in the world has just reported record revenues, clearing well over $1.1B in ARR for the first time with a 28% margin. As we comment on the pod, Aaron Levie is the rare public company CEO equally at home in both worlds of Silicon Valley and Wall Street/Main Street, by day helping 70% of the Fortune 500 with their Enterprise Advanced Suite, and yet by night is often found in the basements of early startups and tweeting viral insights about the future of agents.Now that both Cursor, Cloudflare, Perplexity, Anthropic and more have made Filesystems and Sandboxes and various forms of “Just Give the Agent a Box” cool (not just cool; it is now one of the single hottest areas in AI infrastructure growing 100% MoM), we find it a delightfully appropriate time to do the episode with the OG CEO who has been giving humans and computers Boxes since he was a college dropout pitching VCs at a Michael Arrington house party.Enjoy our special pod, with fan favorite returning guest/guest cohost Jeff Huber!Note: We didn't directly discuss the AI vs SaaS debate - Aaron has done many, many, many other podcasts on that, and you should read his definitive essay on it. Most commentators do not understand SaaS businesses because they have never scaled one themselves, and deeply reflected on what the true value proposition of SaaS is.We also discuss Your Company is a Filesystem:We also shoutout CTO Ben Kus' and the AI team, who talked about the technical architecture and will return for AIE WF 2026.Full Video EpisodeTimestamps* 00:00 Adapting Work for Agents* 01:29 Why Every Agent Needs a Box* 04:38 Agent Governance and Identity* 11:28 Why Coding Agents Took Off First* 21:42 Context Engineering and Search Limits* 31:29 Inside Agent Evals* 33:23 Industries and Datasets* 35:22 Building the Agent Team* 38:50 Read Write Agent Workflows* 41:54 Docs Graphs and Founder Mode* 55:38 Token FOMO Culture* 56:31 Production Function Secrets* 01:01:08 Film Roots to Box* 01:03:38 AI Future of Movies* 01:06:47 Media DevRel and EngineeringTranscriptAdapting Work for AgentsAaron Levie: Like you don't write code, you talk to an agent and it goes and does it for you, and you may be at best review it. That's even probably like, like largely not even what you're doing. What's happening is we are changing our work to make the agents effective. In that model, the agent didn't really adapt to how we work.We basically adapted to how the agent works. All of the economy has to go through that exact same evolution. Right now, it's a huge asset and an advantage for the teams that do it early and that are kinda wired into doing this ‘cause you'll see compounding returns. But that's just gonna take a while for most companies to actually go and get this deployed.swyx: Welcome to the Lane Space Pod. We're back in the chroma studio with uh, chroma, CEO, Jeff Hoover. Welcome returning guest now guest host.Aaron Levie: It's a pleasure. Wow. How'd you get upgraded to, uh, to that?swyx: Because he's like the perfect guy to be guest those for you.Aaron Levie: That makes sense actually, for We love context. We, we both really love context le we really do.We really do.swyx: Uh, and we're here with, uh, Aaron Levy. Welcome.Aaron Levie: Thank you. Good to, uh, good to be [00:01:00] here.swyx: Uh, yeah. So we've all met offline and like chatted a little bit, but like, it's always nice to get these things in person and conversation. Yeah. You just started off with so much energy. You're, you're super excited about agents.I loveAaron Levie: agents.swyx: Yeah. Open claw. Just got by, got bought by OpenAI. No, not bought, but you know, you know what I mean?Aaron Levie: Some, some, you know, acquihire. Executiveswyx: hire.Aaron Levie: Executive hire. Okay. Executive hire. Say,swyx: hey, that's my term. Okay. Um, what are you pounding the table on on agents? You have so many insightful tweets.Why Every Agent Needs a BoxAaron Levie: Well, the thing that, that we get super excited by that I think is probably, you know, should be relatively obvious is we've, we've built a platform to help enterprises manage their files and their, their corporate files and the permissions of who has access to those files and the sharing collaboration of those files.All of those files contain really, really important information for the enterprise. It might have your contracts, it might have your research materials, it might have marketing information, it might have your memos. All that data obviously has, you know, predominantly been used by humans. [00:02:00] But there's been one really interesting problem, which is that, you know, humans only really work with their files during an active engagement with them, and they kind of go away and you don't really see them for a long time.And all of a sudden, uh, with the power of AI and AI agents, all of that data becomes extremely relevant as this ongoing source of, of answers to new questions of data that will transform into, into something else that, that produces value in your organization. It, it contains the answer to the new employee that's onboarding, that needs to ramp up on a project.Um, it contains the answer to the right thing to sell a customer when you're having a conversation to them, with them contains the roadmap information that's gonna produce the next feature. So all that data. That previously we've been just sort of storing and, and you know, occasionally forgetting about, ‘cause we're only working on the new active stuff.All of that information becomes valuable to the enterprise and it's gonna become extremely valuable to end users because now they can have agents go find what they're looking for and produce new, new [00:03:00] value and new data on that information. And it's gonna become incredibly valuable to agents because agents can roam around and do a bunch of work and they're gonna need access to that data as well.And um, and you know, sometimes that will be an agent that is sort of working on behalf of, of, of you and, and effectively as you as and, and they are kind of accessing all of the same information that you have access to and, and operating as you in the system. And then sometimes there's gonna be agents that are just.Effectively autonomous and kind of run on their own and, and you're gonna collaborate and work with them kind of like you did another person. Open Claw being the most recent and maybe first real sort of, you know, kind of, you know, up updating everybody's, you know, views of this landscape version of, of what that could look like, which is, okay, I have an agent.It's on its own system, it's on its own computer, it has access to its own tools. I probably don't give it access to my entire life. I probably communicate with it like I would an assistant or a colleague and then it, it sort of has this sandbox environment. So all of that has massive implications for a platform that manage that [00:04:00] enterprise data.We think it's gonna just transform how we work with all of the enterprise content that we work with, and we just have to make sure we're building the right platform to support that.swyx: The sort of shorthand I put it is as people build agents, everybody's just realizing that every agent needs a box. Yes.And it's nice to be called box and just give everyone a box.Aaron Levie: Hey, I if I, you know, if we can make that go viral, uh, like I, I think that that terminology, I, that's theswyx: tagline. Every agentAaron Levie: needs a box. Every agent needs a box. If we can make that the headline of this, I'm fine with this. And that's the billboard I wanna like Yeah, exactly.Every agent needs a box. Um, I like it. Can we ship this? Like,swyx: okay, let's do it. Yeah.Aaron Levie: Uh, my work here is done and I got the value I needed outta this podcast Drinks.swyx: Yeah.Agent Governance and IdentityAaron Levie: But, but, um, but, but, you know, so the thing that we, we kind of think about is, um, is, you know, whether you think the number 10 x or a hundred x or whatever the number is, we're gonna have some order of magnitude more agents than people.That's inevitable. It has to happen. So then the question is, what is the infrastructure that's needed to make all those agents effective in the enterprise? Make sure that they are well governed. Make sure they're only doing [00:05:00] safe things on your information. Make sure that they're not getting exposed. The data that they shouldn't have access to.There's gonna be just incredibly spectacularly crazy security incidents that will happen with agents because you'll prompt, inject an agent and sort of find your way through the CRM system and pull out data that you shouldn't have access to. Oh, weJeff Huber: have God,Aaron Levie: right? I mean, that's just gonna happen all over the place, right?So, so then the thing is, is how do you make sure you have the right security, the permissions, the access controls, the data governance. Um, we actually don't yet exactly know in many cases how we're gonna regulate some of these agents, right? If you think about an agent in financial services, does it have the exact same financial sort of, uh, requirements that a human did?Or is it, is the risk fully on the human that was interacting or created the agent? All open questions, but no matter what, there's gonna need to be a layer that manages the, the data they have access to, the workflows that they're involved in, pulling up data from multiple systems. This is the new infrastructure opportunity in the era of agents.swyx: You have a piece on agent identities, [00:06:00] which I think was today, um, which I think a lot of breaking news, the security, security people are talking about, right? Like you basically, I, I always think of this as like, well you need the human you and then there you need the agent. YouAaron Levie: Yes.swyx: And uh, well, I don't know if it's that simple, but is box going to have an opinion on that or you're just gonna be like, well we're just the sort of the, the source layer.Yeah. Let's Okta of zero handle that.Aaron Levie: I think we're gonna have an opinion and we will work with generally wherever the contours of the market end up. Um, and the reason that we're gonna have an opinion more than other topics probably is because one of the biggest use cases for why your agent might need it, an identity is for file system access.So thus we have to kind of think about this pretty deeply. And I think, uh, unless you're like in our world thinking about this particular problem all day long, it might be, you know, like, why is this such a big deal? And the reason why it's a really big deal is because sometimes sort of say, well just give the agent an, an account on the system and it just treats, treat it like every other type of user on the system.The [00:07:00] problem is, is that I as Aaron don't really have any responsibility over anybody else's box account in our organization. I can't see the box account of any other employee that I work with. I am not liable for anything that they do. And they have, I have, I have, you know, strict privacy requirements on everything that they're able to, you know, that, that, that they work on.Agents don't have that, you know, don't have those properties. The person who creates the agent probably is gonna, for the foreseeable future, take on a lot of the liability of what that agent does. That agent doesn't deserve any privacy because, because it's, you know, it can't fully be autonomously operated and it doesn't have any legal, you know, kind of, you know, responsibility.So thus you can't just be like, oh, well I'll just create a bunch of accounts and then I'll, I'll kind of work with that agent and I'll talk to it occasionally. Like you need oversight of that. And so then the question is, how do you have a world where the agent, sometimes you have oversight of, but what if that agent goes and works with other people?That person over there is collaborating with the agent on something you shouldn't have [00:08:00] access to what they're doing. So we have all of these new boundaries that we're gonna have to figure out of, of, you know, it's really, really easy. So far we've been in, in easy mode. We've hit the easy button with ai, which is the agent just is you.And when you're in quad code and you're in cursor, and you're in Codex, you're just, the agent is you. You're offing into your services. It can do everything you can do. That's the easy mode. The hard mode is agents are kind of running on their own. People check in with them occasionally, they're doing things autonomously.How do you give them access to resources in the enterprise and not dramatically increased the security risk and the risk that you might expose the wrong thing to somebody. These are all the new problems that we have to get solved. I like the identity layer and, and identity vendors as being a solution to that, but we'll, we'll need some opinions as well because so many of the use cases are these collaborative file system use cases, which is how do I give it an agent, a subset of my data?Give it its own workspace as well. ‘cause it's gonna need to store off its own information that would be relevant for it. And how do I have the right oversight into that? [00:09:00]Jeff Huber: One thing, which, um, I think is kind interesting, think about is that you know, how humans work, right? Like I may not also just like give you access to the whole file.I might like sit next to you and like scroll to this like one part of the file and just show you that like one part and like, you know,swyx: partial file access.Jeff Huber: I'm just saying I think like our, like RA does seem to be dead, right? Like you wanna say something is dead uhhuh probably RA is dead. And uh, like the auth story to me seems like incredibly unsolved and unaddressed by like the existing state of like AI vendors.ButAaron Levie: yeah, I think, um, we're, I mean you're taking obviously really to level limit that we probably need to solve for. Yeah. And we built an access control system that was, was kind of like, you know, its own little world for, for a long time. And um, and the idea was this, it's a many to many collaboration system where I can give you any part of the file system.And it's a waterfall model. So if I give you higher up in the, in the, in the system, you get everything below. And that, that kind of created immense flexibility because I can kind of point you to any layer in the, in the tree, but then you're gonna get access to everything kind of below it. And that [00:10:00] mostly is, is working in this, in this world.But you do have to manage this issue, which is how do I create an agent that has access to some of my stuff and somebody else's stuff as well. Mm-hmm. And which parts do I get to look at as the creator of the agent? And, and these are just brand new problems? Yeah. Crazy. And humans, when there was a human there that was really easy to do.Like, like if the three of us were all sharing, there'd be a Venn diagram where we'd have an overlapping set of things we've shared, but then we'd have our own ways that we shared with each other. In an agent world, somebody needs to take responsibility for what that agent has access to and what they're working on.These are like the, some of the most probably, you know, boring problems for 98% of people on, on the internet, but they will be the problems that are the difference between can you actually have autonomous agents in an enterprise contextswyx: Yeah.Aaron Levie: That are not leaking your data constantly.swyx: No. Like, I mean, you know, I run a very, very small company for my conference and like we already have data sensitivity issues.Yes. And some of my team members cannot see Yes. Uh, the others and like, I can't imagine what it's like to run a Fortune 500 and like, you have to [00:11:00] worry about this. I'm just kinda curious, like you, you talked to a lot like, like 70, 80% of your cus uh, of the Fortune 500, your customers.Aaron Levie: Yep. 67%. Just so we're being verySEswyx: precise.So Yeah. I'm notAaron Levie: Okay. Okay.swyx: Something I'm rounding up. Yes. Round up. I'm projecting to, forAaron Levie: the government.swyx: I'm projecting to the end of the year.Aaron Levie: Okay.swyx: There you go.Aaron Levie: You do make it sound like, like we, we, well we've gotta be on this. Like we're, we're taking way too long to get to 80%. Well,swyx: no, I mean, so like. How are they approaching it?Right? Because you're, you don't have a, you don't have a final answer yet.Why Coding Agents Took Off FirstAaron Levie: Well, okay, so, so this is actually, this is the stark reality that like, unfortunately is the kinda like pouring the water on the party a little bit.swyx: Yes.Aaron Levie: We all in Silicon Valley are like, have the absolute best conditions possible for AI ever.And I think we all saw the dke, you know, kind of Dario podcast and this idea of AI coding. Why is that taken off? And, and we're not yet fully seeing it everywhere else. Well, look, if you just like enumerated the list of properties that AI coding has and then compared it to other [00:12:00] knowledge work, let's just, let's just go through a few of them.Generally speaking, you bring on a new engineer, they have access to a large swath of the code base. Like, there's like very, like you, just, like new engineer comes on, they can just go and find the, the, the stuff that they, they need to work with. It's a fully text in text out. Medium. It's only, it's just gonna be text at the end of the day.So it's like really great from a, from just a, uh, you know, kinda what the agent can work with. Obviously the models are super trained on that dataset. The labs themselves have a really strong, kind of self-reinforcing positive flywheel of why they need to do, you know, agent coding deeply. So then you get just better tooling, better services.The actual developers of the AI are daily users of the, of the thing that they're we're working on versus like the, you know, probably there's only like seven Claude Cowork legal plugin users at Anthropic any given day, but there's like a couple thousand Claude code and you know, users every single day.So just like, think about which one are they getting more feedback on. All day long. So you just go through this list. You have a, you know, everybody who's a [00:13:00] developer by definition is technical so they can go install the latest thing. We're all generally online, or at least, you know, kinda the weird ones are, and we're all talking to each other, sharing best practices, like that's like already eight differences.Versus the rest of the economy. Every other part of the economy has like, like six to seven headwinds relative to that list. You go into a company, you're a banker in financial services, you have access to like a, a tiny little subset of the total data that's gonna be relevant to do your job. And you're have to start to go and talk to a bunch of people to get the right data to do your job because Sally didn't add you to that deal room, you know, folder.And that that, you know, the information is actually in a completely different organization that you now have to go in and, and sort of run into. And it's like you have this endless list of access controls and security. As, as you talked about, you have a medium, which is not, it's not just text, right? You have, you have a zoom call that, that you're getting all of the requirements from the customer.You have a lot of in-person conversations and you're doing in-person sales and like how do you ever [00:14:00] digitize all of that information? Um, you know, I think a lot of people got upset with this idea that the code base has all the context, um, that I don't know if you follow, you know, did you follow some of that conversation that that went viral?Is like, you know, it's not that simple that, that the code base doesn't have all the knowledge, but like it's a lot, you're a lot better off than you are with other areas of knowledge work. Like you, we like, we like have documentation practices, you write specifications. Those things don't exist for like 80% of work that happens in the enterprise.That's the divide that we have, which is, which is AI coding has, has just fully, you know, where we've reached escape velocity of how powerful this stuff is, and then we're gonna have to find a way to bring that same energy and momentum, but to all these other areas of knowledge work. Where the tools aren't there, the data's not set up to be there.The access controls don't make it that easy. The context engineering is an incredibly hard problem because again, you have access control challenges, you have different data formats. You have end users that are gonna need to kind of be kind of trained through this as opposed to their adopting [00:15:00] these tools in their free time.That's where the Fortune 500 is. And so we, I think, you know, have to be prepared as an industry where we are gonna be on a multi-year march to, to be able to bring agents to the enterprise for these workflows. And I think probably the, the thing that we've learned most in coding that, that the rest of the world is not yet, I think ready for, I mean, we're, they'll, they'll have to be ready for it because it's just gonna inevitably happen is I think in coding.What, what's interesting is if you think about the practice of coding today versus two years ago. It's probably the most changed workflow in maybe the history of time from the amount of time it's changed, right? Yeah. Like, like has any, has any workflow in the entire economy changed that quickly in terms of the amount of change?I just, you know, at least in any knowledge worker workflow, there's like very rarely been an event where one piece of technology and work practice has so fundamentally, you know, changed, changed what you do. Like you don't write code, you talk to an agent and it goes and [00:16:00] does it for you, and you may be at best review it.And even that's even probably like, like largely not even what you're doing. What's happening is we are changing our work to make the agents effective. In that model, the agent didn't really adapt to how we work. We basically adapted to how the agent works. Mm-hmm. All of the economy has to go through that exact same evolution.The rest of the economy is gonna have to update its workflows to make agents effective. And to give agents the context that they need and to actually figure out what kind of prompting works and to figure out how do you ensure that the agent has the right access to information to be able to execute on its work.I, you know, this is not the panacea that people were hoping for, of the agent drops in, just automates your life. Like you have to basically re-engineer your workflow to get the most out of agents and, uh, and that, that's just gonna take, you know, multiple years across the economy. Right now it's a huge asset and an advantage for the teams that do it early and that are kinda wired into doing this.‘cause [00:17:00] you'll see compounding returns, but that's just gonna take a while for most companies to actually go and get this deployed.swyx: I love, I love pushing back. I think that. That is what a lot of technology consultants love to hear this sort of thing, right? Yeah, yeah, yeah. First to, to embrace the ai. Yes. To get to the promised land, you must pay me so much money to a hundred percent to adopt the prescribed way of, uh, conforming to the agents.Yes. And I worry that you will be eclipsed by someone else who says, no, come as you are.Aaron Levie: Yeah.swyx: And we'll meet you where you are.Aaron Levie: And, and, and and what was the thing that went viral a week ago? OpenAI probably, uh, is hiring F Dees. Yeah. Uh, to go into the enterprise. Yeah. Yeah. And then philanthropic is embedded at Goldman Sachs.Yeah. So if the labs are having to do this, if, if the labs have decided that they need to hire FDE and professional services, then I think that's a pretty clear indication that this, there's no easy mode of workflow transformation. Yeah. Yeah. So, so to your point, I think actually this is a market opportunity for, you know, new professional services and consulting [00:18:00] firms that are like Agent Build and they, and they kind of, you know, go into organizations and they figure out how to re-engineer your workflows to make them more agent ready and get your data into the right format and, you know, reconstruct your business process.So you're, you're not doing most of the work. You're telling agents how to do the work and then you're reviewing it. But I haven't seen the thing that can just drop in and, and kinda let you not go through those changes.swyx: I don't know how that kind of sales pitch goes over. Yeah. You know, you're, you're saying things like, well, in my sort of nice beautiful walled garden, here's, there's, uh, because here's this, here's this beautiful box account that has everything.Yes. And I'm like, well, most, most real life is extremely messy. Sure. And like, poorly named and there duplicate this outdated s**tAaron Levie: a hundred percent. And so No, no, a hundred percent. And so this is actually No. So, so this is, I mean, we agree that, that getting to the beautiful garden is gonna be tough.swyx: Yeah.Aaron Levie: There's also the other end of the spectrum where I, I just like, it's a technical impossibility to solve. The agent is, is truly cannot get enough context to make the right decision in, in the, in the incredibly messy land. Like there's [00:19:00] no a GI that will solve that. So, so we're gonna have to kind of land in somewhere in between, which is like we all collectively get better at.Documentation practices and, and having authoritative relatively up-to-date information and putting it in the right place like agents will, will certainly cause us to be much better organized around how we work with our information, simply because the severity of the agent pulling the wrong data will be too high and the productivity gain of that you'll miss out on by not doing this will be too high as well, that you, that your competition will just do it and they'll just have higher velocity.So, uh, and, and we, we see this a lot firsthand. So we, we build a series of agents internally that they can kind of have access to your full box account and go off and you give it a task and it can go find whatever information you're looking for and work with. And, you know, thank God for the model progress, but like, if, if you gave that task to an agent.Nine months ago, you're just gonna get lots of bogus answers because it's gonna, it's gonna say, Hey, here's, here are fi [00:20:00] five, you know, documents that all kind of smell like the right thing. And I'm gonna, but I, but you're, you're putting me on the clock. ‘cause my assistant prompt says like, you know, be pretty smart, but also try and respond to the user and it's gonna respond.And it's like, ah, it got the wrong document. And then you do that once or twice as a knowledge worker and you're just neverswyx: again,Aaron Levie: never again. You're just like done with the system.swyx: Yeah. It doesn't work.Aaron Levie: It doesn't work. And so, you know, Opus four six and Gemini three one Pro and you know, whatever the latest five 3G BT will be, like, those things are getting better and better and it's using better judgment.And this sort of like the, all of these updates to the agentic tool and search systems are, are, we're seeing, we're seeing very real progress where the agent. Kind of can, can almost smell some things a little bit fishy when it's getting, you know, we, we have this process where we, we have it go fan out, do a bunch of searches, pull up a bunch of data, and then it has to sort of do its own ranking of, you know, what are the right documents that, that it should be working with.And again, like, you know, the intelligence level of a model six months ago, [00:21:00] it'd be just throwing a dart at like, I'm just, I'm gonna grab these seven files and I, I pray, I hope that that's the right answer. And something like an opus first four five, and now four six is like, oh, it's like, no, that one doesn't seem right relative to this question because I'm seeing some signal that is making that, you know, that's contradicting the document where it would normally be in the tree and who should have access.Like it's doing all of that kind of work for you. But like, it still doesn't work if you just have a total wasteland of data. Like, it's just not, it's just not possible. Partly ‘cause a human wouldn't even be able to do it. So basically if a, if a really, really smart human. Could not do that task in five or 10 minutes for a search retrieval type task.Look, you know, your agent's not gonna be able to do it any better. You see this all day long. SoContext Engineering and Search Limitsswyx: this touches on a thing that just passionate about it was just context engineering. I, I'm just gonna let you ramble or riff on, on context engineering. If, if, if there's anything like he, he did really good work on context fraud, which has really taken over as like the term that people use and the referenceAaron Levie: a hundred percent.We, we all we think about is, is the context rob problem. [00:22:00]Jeff Huber: Yeah, there's certainly a lot of like ranking considerations. Gentech surgery think is incredibly promising. Um, yeah, I was trying to generate a question though. I think I have a question right now. Swyx.Aaron Levie: Yeah, no, but like, like I think there was this moment, um, you know, like, I don't know, two years ago before, before we knew like where the, the gotchas were gonna be in ai and I think someone was like, was like, well, infinite context windows will just solve all of these problems and ‘cause you'll just, you'll just give the context window like all the data and.It's just like, okay, I mean, maybe in 2035, like this is a viable solution. First of all, it, it would just, it would just simply cost too much. Like we just can't give the model like the 5,000 documents that might be relevant and it's gonna read them all. And I've seen enough to, to start believing in crazy stuff.So like, I'm willing to just say, sure. Like in, in 10 years from now,swyx: never say, never, never.Aaron Levie: In, in 10 years from now, we'll have infinite context windows at, at a thousandth of the price of today. Like, let's just like believe that that's possible, but Right. We're in reality today. So today we have a context engineering [00:23:00] problem, which is, I got, I got, you know, 200,000 tokens that I can work with, or prob, I don't even know what the latest graph is before, like massive degradation.16. Okay. I have 60,000 tokens that I get to work with where I'm gonna get accurate information. That's not a lot of tokens for a corpus of 10 million documents that a knowledge worker might have across all of the teams and all the projects and all the people they work with. I have, I have 10 million documents.Which, you know, maybe is times five pages per document or something like that. I'm at 50 million pages of information and I have 60,000 tokens. Like, holy s**t. Yeah. This is like, how do I bridge the 50 million pages of information with, you know, the couple hundred that I get to work with in that, in that token window.Yeah. This is like, this is like such an interesting problem and that's why actually so much work is actually like, just like search systems and the databases and that layer has to just get so locked in, but models getting better and importantly [00:24:00] knowing when they've done a search, they found the wrong thing, they go back, they check their work, they, they find a way to balance sort of appeasing the user versus double checking.We have this one, we have this one test case where we ask the agent to go find. 10 pieces of information.swyx: Is this the complex work eval?Aaron Levie: Uh, this is actually not in the eval. This is, this is sort of just like we have a bunch of different, we have a bunch of internal benchmark kind of scenarios. Every time we, we update our agent, we have one, which is, I ask it to find all of our office addresses, and I give it the list of 10 offices that we have.And there's not one document that has this, maybe there should be, that would be a great example of the kind of thing that like maybe over time companies start to, you know, have these sort of like, what are the canonical, you know, kind of key areas of knowledge that we need to have. We don't seem to have this one document that says, here are all of our offices.We have a bunch of documents that have like, here's the New York office and whatever. So you task this agent and you, you get, you say, I need the addresses for these 10 offices. Okay. And by the way, if you do this on any, you know, [00:25:00] public chat model, the same outcome is gonna happen. But for a different kind of query, you give it, you say, I need these 10 addresses.How many times should the agent go and do its search before it decides whether or not, there's just no answer to this question. Often, and especially the, the, let's say lower tier models, it'll come back and it'll give you six of the 10 addresses. And it'll, and I'll just say I couldn't find the otherswyx: four.It, it doesn't know what It doesn't know. ItAaron Levie: doesn't know what It doesn't know. Yeah. So the model is just like, like when should it stop? When should it stop doing? Like should it, should it do that task for literally an hour and just keep cranking through? Maybe I actually made up an office location and it doesn't know that I made it up and I didn't even know that I made it up.Like, should it just keep, re should it read every single file in your entire box account until it, until it should exhaust every single piece of information.swyx: Expensive.Aaron Levie: These are the new problems that we have. So, you know, something like, let's say a new opus model is sort of like, okay, I'm gonna try these types of queries.I didn't get exactly what I wanted. I'm gonna try again. I'm gonna, at [00:26:00] some point I'm gonna stop searching. ‘cause I've determined that that no amount of searching is gonna solve this problem. I'm just not able to do it. And that judgment is like a really new thing that the model needs to be able to have.It's like, when should it give up on a task? ‘cause, ‘cause you just don't, it's a can't find the thing. That's the real world of knowledge, work problems. And this is the stuff that the coding agents don't have to deal with. Because they, it just doesn't like, like you're not usually asking it about, you're, you're always creating net new information coming right outta the model for the most part.Obviously it has to know about your code base and your specs and your documentation, but, but when you deploy an agent on all of your data that now you have all of these new problems that you're dealing withJeff Huber: our, uh, follow follow-up research to context ride is actually on a genetic search. Ah. Um, and we've like right, sort of stress tested like frontier models and their ability to search.Um, and they're not actually that good at searching. Right. Uh, so you're sort of highlighting this like explore, exploit.swyx: You're just say, Debbie, Donna say everything doesn't work. Like,Aaron Levie: well,Jeff Huber: somebody has to be,Aaron Levie: um, can I just throw out one more thing? Yeah. That is different from coding and, and the rest [00:27:00] of the knowledge work that I, I failed to mention.So one other kind of key point is, is that, you know, at the end of the day. Whether you believe we're in a slop apocalypse or, or whatever. At the end of the day, if you, if you build a working product at the end of, if you, if you've built a working solution that is ultimately what the customer is paying for, like whether I have a lot of slop, a little slop or whatever, I'm sure there's lots of code bases we could go into in enterprise software companies where it's like just crazy slop that humans did over a 20 year period, but the end customer just gets this little interface.They can, they can type into it, it does its thing. Knowledge work, uh, doesn't have that property. If I have an AI model, go generate a contract and I generate a contract 20 times and, you know, all 20 times it's just 3% different and like that I, that, that kind of lop introduces all new kinds of risk for my organization that the code version of that LOP didn't, didn't introduce.These are, and so like, so how do you constrain these models to just the part that you want [00:28:00] them to work on and just do the thing that you want them to do? And, and, you know, in engineering, we don't, you can't be disbarred as an engineer, but you could be disbarred as a lawyer. Like you can do the wrong medical thing In healthcare, you, there's no, there's no equivalent to that of engineering.Like, doswyx: you want there to be, because I've considered softwareJeff Huber: engineer. What's that? Civil engineering there is, right? NotAaron Levie: software civil engineer. Sure. Oh yeah, for sure. But like in any of our companies, you like, you know, you'll be forgiven if you took down the site and, and we, we will do a rollback and you'll, you'll be in a meeting, but you have not been disbarred as an engineer.We don't, we don't change your, you know, your computer science, uh, blameJeff Huber: degree, this postmortem.Aaron Levie: Yeah, exactly. Exactly. So, so, uh, now maybe we collectively as an industry need to figure out like, what are you liable for? Not legally, but like in a, in a management sense, uh, of these agents. All sorts of interesting problems that, that, that, uh, that have to come out.But in knowledge work, that's the real hostile environments that we're operating in. Hmm.swyx: I do think like, uh, a lot of the last year's, 2025 story was the rise of coding agents and I think [00:29:00] 2026 story is definitely knowledge work agents. Yes. A hundredAaron Levie: percent.swyx: Right. Like that would, and I think open claw core work are just the beginning.Yes. Like it's, the next one's gonna just gonna be absolute craziness.Aaron Levie: It it is. And, and, uh, and it's gonna be, I mean, again, like this is gonna be this, this wave where we, we are gonna try and bring as many of the practices from coding because that, that will clearly be the forefront, which is tell an agent to go do something and has an access to a set of resources.You need to be responsible for reviewing it at the end of the process. That to me is the, is the kind of template that I just think goes across knowledge, work and odd. Cowork is a great example. Open Closet's a great example. You can kind of, sort of see what Codex could become over time. These are some, some really interesting kind of platforms that are emerging.swyx: Okay. Um, I wanted to, we touched on evals a little bit. You had, you had the report that you're gonna go bring up and then I was gonna go into like, uh, boxes, evals, but uh, go ahead. Talk about your genetic search thing.Jeff Huber: Yeah. Mostly I think kinda a few of the insights. It's like number one frontier model is not good at search.Humans have this [00:30:00] natural explore, exploit trade off where we kinda understand like when to stop doing something. Also, humans are pretty good at like forgetting actually, and like pruning their own context, whereas agents are not, and actually an agent in their kind of context history, if they knew something was bad and they even, you could see in the trace the reason you trace, Hey, that probably wasn't a good idea.If it's still in the trace, still in the context, they'll still do it again. Uhhuh. Uh, and so like, I think pruning is also gonna be like, really, it's already becoming a thing, right? But like, letting self prune the con windowsswyx: be a big deal. Yeah. So, so don't leave the mistake. Don't leave the mistake in there.Cut out the mistake but tell it that you made a mistake in the past and so it doesn't repeat it.Jeff Huber: Yeah. But like cut it out so it doesn't get like distracted by it again. ‘cause really, you know, what is so, so it will repeat its mistake just because it's been, it's inswyx: theJeff Huber: context. It'sAaron Levie: in the context so much.That's a few shot example. Even if it, yeah.Jeff Huber: It's like oh thisAaron Levie: is a great thing to go try even ifJeff Huber: it didn't work.Aaron Levie: Yeah,Jeff Huber: exactly.Aaron Levie: SoJeff Huber: there's like a bunch of stuff there. JustAaron Levie: Groundhogs Day inside these models. Yeah. I'm gonna go keep doing the same wrongJeff Huber: thing. Covering sense. I feel like, you know, some creator analogy you're trying like fit a manifold in latent space, which kind is doing break program synthesis, which is kinda one we think about we're doing right.Like, you know, certain [00:31:00] facts might be like sort of overly pitting it. There are certain, you know, sec sectors of latent space and so like plug clean space. Yeah. And, uh, andswyx: so we have a bell, our editor as a bell every time you say that. SoJeff Huber: you have, you have to like remove those, likeswyx: you shoulda a gong like TPN or something.IfJeff Huber: we gong, you either remove those links to like kinda give it the freedom, kind of do what you need to do. So, but yeah. We'll, we'll release more soon. That'sAaron Levie: awesome.Jeff Huber: That'll, that'll be cool.swyx: We're a cerebral podcast that people listen to us and, and sort of think really deep. So yeah, we try to keep it subtle.Okay. We try to keep it.Aaron Levie: Okay, fine.Inside Agent Evalsswyx: Um, you, you guys do, you guys do have EVs, you talked about your, your office thing, but, uh, you've been also promoting APEX agents and complex work. Uh, yeah, whatever you, wherever you wanna take this just Yeah. How youAaron Levie: Apex is, is obviously me, core's, uh, uh, kind of, um, agent eval.We, we supported that by sort of. Opening up some data for them around how we kind of see these, um, data workspaces in, in the, you know, kind of regular economy. So how do lawyers have a workspace? How do investment bankers have a workspace? What kind of data goes into those? And so we, [00:32:00] we partner with them on their, their apex eval.Our own, um, eval is, it's actually relatively straightforward. We have a, a set of, of documents in a, in a range of industries. We give the agent previously did this as a one shot test of just purely the model. And then we just realized we, we need to, based on where everything's going, it's just gotta be more agentic.So now it's a bit more of a test of both our harness and the model. And we have a rubric of a set of things that has to get right and we score it. Um, and you're just seeing, you know, these incredible jumps in almost every single model in its own family of, you know, opus four, um, you know, sonnet four six versus sonnet four five.swyx: Yeah. We have this up on screen.Aaron Levie: Okay, cool. So some, you're seeing it somewhere like. I, I forget the to, it was like 15 point jump, I think on the main, on the overall,swyx: yes.Aaron Levie: And it's just like, you know, these incredible leaps that, that are starting to happen. Um,swyx: and OP doesn't know any, like any, it's completely held out from op.Aaron Levie: This is not in any, there's no public data which has, you know, Ben benefits and this is just a private eval that we [00:33:00] do, and then we just happen to show it to, to the world. Hmm. So you can't, you can't train against it. And I think it's just as representative of. It's obviously reasoning capabilities, what it's doing at, at, you know, kind of test time, compute capabilities, thinking levels, all like the context rot issues.So many interesting, you know, kind of, uh, uh, capabilities that are, that are now improvingswyx: one sector that you have. That's interesting.Industries and Datasetsswyx: Uh, people are roughly familiar with healthcare and legal, but you have public sector in there.Aaron Levie: Yeah.swyx: Uh, what's that? Like, what, what, what is that?Aaron Levie: Yeah, and, and we actually test against, I dunno, maybe 10 industries.We, we end up usually just cutting a few that we think have interesting gains. All extras, won a lot of like government type documents. Um,swyx: what is that? What is it? Government type documents?Aaron Levie: Government filings. Like a taxswyx: return, likeAaron Levie: a probably not tax returns. It would be more of what would go the government be using, uh, as data.So, okay. Um, so think about research that, that type of, of, of data sets. And then we have financial services for things like data rooms and what would be in an investment prospectus. Uhhuh,swyx: that one you can dog food.Aaron Levie: Yeah, exactly. Exactly. Yes. Yes. [00:34:00] So, uh, so we, we run the models, um, in now, you know, more of an agent mode, but, but still with, with kinda limited capacity and just try and see like on a, like, for like basis, what are the improvements?And, and again, we just continue to be blown away by. How, how good these models are getting.swyx: Yeah, I mean, I think every serious AI company needs something like that where like, well, this is the work we do. Here's our company eval. Yeah. And if you don't have it, well, you're not a serious AI company.Aaron Levie: There's two dimensions, right?So there's, there's like, how are the models improving? And so which models should you either recommend a customer use, which one should you adopt? But then every single day, we're making changes to our agents. And you need to knowswyx: if you regressed,Aaron Levie: if you know. Yeah. You know, I've been fully convinced that the whole agent observability and eval space is gonna be a massive space.Um, super excited for what Braintrust is doing, excited for, you know, Lang Smith, all the things. And I think what you're going to, I mean, this is like every enter like literally every enterprise right now. It's like the AI companies are the customers of these tools. Every enterprise will have this. Yeah, you'll just [00:35:00] have to have an eval.Of all of your work and like, we'll, you'll have an eval of your RFP generation, you'll have an eval of your sales material creation. You'll have an eval of your, uh, invoice processing. And, and as you, you know, buy or use new agentic systems, you are gonna need to know like, what's the quality of your, of your pipeline.swyx: Yeah.Aaron Levie: Um, so huge, huge market with agent evals.swyx: Yeah.Building the Agent Teamswyx: And, and you know, I'm gonna shout out your, your team a bit, uh, your CTO, Ben, uh, did a great talk with us last year. Awesome. And he's gonna come back again. Oh, cool. For World's Fair.Aaron Levie: Yep.swyx: Just talk about your team, like brag a little bit. I think I, I think people take these eval numbers in pretty charts for granted, but No, there, I mean, there's, there's lots of really smart people at work during all this.Aaron Levie: Biggest shout out, uh, is we have a, we have a couple folks at Dya, uh, Sidarth, uh, that, that kind of run this. They're like a, you know, kind of tag tag team duo on our evals, Ben, our CTO, heavily involved Yasha, head of ai, uh, you know, a bunch of folks. And, um, evals is one part of the story. And then just like the full, you know, kind of AI.An agent team [00:36:00] is, uh, is a, is a pretty, you know, is core to this whole effort. So there's probably, I don't know, like maybe a few dozen people that are like the epicenter. And then you just have like layers and layers of, of kind of concentric circles of okay, then there's a search team that supports them and an infrastructure team that supports them.And it's starting to ripple through the entire company. But there's that kind of core agent team, um, that's a pretty, pretty close, uh, close knit group.swyx: The search team is separate from the infra team.Aaron Levie: I mean, we have like every, every layer of the stack we have to kind of do, except for just pure public cloud.Um, but um, you know, we, we store, I don't even know what our public numbers are in, you know, but like, you can just think about it as like a lot of data is, is stored in box. And so we have, and you have every layer of the, of the stack of, you know, how do you manage the data, the file system, the metadata system, the search system, just all of those components.And then they all are having to understand that now you've got this new customer. Which is the agent, and they've been building for two types of customers in the past. They've been building for users and they've been building for like applications. [00:37:00] And now you've got this new agent user, and it comes in with a difference of it, of property sometimes, like, hey, maybe sometimes we should do embeddings, an embedding based, you know, kind of search versus, you know, your, your typical semantic search.Like, it's just like you have to build the, the capabilities to support all of this. And we're testing stuff, throwing things away, something doesn't work and, and not relevant. It's like just, you know, total chaos. But all of those teams are supporting the agent team that is kind of coming up with its requirements of what, what do we need?swyx: Yeah. No, uh, we just came from, uh, fireside chat where you did, and you, you talked about how you're doing this. It's, it's kind of like an internal startup. Yeah. Within the broader company. The broader company's like 3000 people. Yeah. But you know, there's, there's a, this is a core team of like, well, here's the innovation center.Aaron Levie: Yeah.swyx: And like that every company kind of is run this way.Aaron Levie: Yeah. I wanna be sensitive. I don't call it the innovation center. Yeah. Only because I think everybody has to do innovation. Um, there, there's a part of the, the, the company that is, is sort of do or die for the agent wave.swyx: Yeah.Aaron Levie: And it only happens to be more of my focus simply because it's existential that [00:38:00] we get it right.swyx: Yeah.Aaron Levie: All of the supporting systems are necessary. All of the surrounding adjacent capabilities are necessary. Like the only reason we get to be a platform where you'd run an agent is because we have a security feature or a compliance feature, or a governance feature that, that some team is working on.But that's not gonna be the make or break of, of whether we get agents right. Like that already exists and we need to keep innovating there. I don't know what the right, exact precise number is, but it's not a thousand people and it's not 10 people. There's a number of people that are like the, the kind of like, you know, startup within the company that are the make or break on everything related to AI agents, you know, leveraging our platform and letting you work with your data.And that's where I spend a lot of my time, and Ben and Yosh and Diego and Teri, you know, these are just, you know, people that, that, you know, kind of across the team. Are working.swyx: Yeah. Amazing.Read Write Agent WorkflowsJeff Huber: How do you, how do you think about, I mean, you talked a lot about like kinda read workflows over your box data. Yep.Right. You know, gen search questions, queries, et cetera. But like, what about like, write or like authoring workflows?Aaron Levie: Yes. I've [00:39:00] already probably revealed too much actually now that I think about it. So, um, I've talked about whatever,Jeff Huber: whatever you can.Aaron Levie: Okay. It's just us. It's just us. Yeah. Okay. Of course, of course.So I, I guess I would just, uh, I'll make it a little bit conceptual, uh, because again, I've already, I've already said things that are not even ga but, but we've, we've kinda like danced around it publicly, so I, yeah, yeah. Okay. Just like, hopefully nobody watches this, um, episode. No.swyx: It's tidbits for the Heidi engaged to go figure out like what exactly, um, you know, is, is your sort of line of thinking.Sure. They can connect the dots.Aaron Levie: Yeah. So, so I would say that, that, uh, we, you know, as a, as a place where you have your enterprise content, there's a use case where I want to, you know, have an agent read that data and answer questions for me. And then there's a use case where I want the agent to create something.And use the file system to create something or store off data that it's working on, or be able to have, you know, various files that it's writing to about the work it's doing. So we do see it as a total read write. The harder problem has so far been the read only because, because again, you have that kind of like 10 [00:40:00] million to one ratio problem, whereas rights are a lot of, that's just gonna come from the model and, and we just like, we'll just put it in the file system and kinda use it.So it's a little bit of a technically easier problem, but the only part that's like, not necessarily technically hard, it is just like it's not yet perfected in the state of the ecosystem is, you know, building a beautiful PowerPoint presentation. It's still a hard problem for these models. Like, like we still, you know, like, like these formats are just, we're not built for.They'reswyx: working on it.Aaron Levie: They're, they're working on it. Everybody's working on it.swyx: Every launch is like, well, we do PowerPoint now.Aaron Levie: We're getting, yeah, getting a lot, getting a lot of better each time. But then you'll do this thing where you'll ask the update one slide and all of a sudden, like the fonts will be just like a little bit different, you know, on two of the slides, or it moved, you know, some shape over to the left a little bit.And again, these are the kind of things that, like in code, obviously you could really care about if you really care about, you know, how beautiful is the code, but at the end, user doesn't notice all those problems and file creation, the end user instantly sees it. You're [00:41:00] like, ah, like paragraph three, like, you literally just changed the font on me.Like it's a totally different font and like midway through the document. Mm-hmm. Those are the kind of things that you run into a lot of in the, in the content creation side. So, mm-hmm. We are gonna have native agents. That do all of those things, they'll be powered by the leading kind of models and labs.But the thing that I think is, is probably gonna be a much bigger idea over time is any agent on any system, again, using Box as a file system for its work, and in that kind of scenario, we don't necessarily care what it's putting in the file system. It could put its memory files, it could put its, you know, specification, you know, documents.It could put, you know, whatever its markdown files are, or it could, you know, generate PDFs. It's just like, it's a workspace that is, is sort of sandboxed off for its work. People can collaborate into it, it can share with other people. And, and so we, we were thinking a lot about what's the right, you know, kind of way to, to deliver that at scale.Docs Graphs and Founder Modeswyx: I wanted to come into sort of the sort of AI transformation or AI sort of, uh, operations things. [00:42:00] Um, one of the tweets that you, that you wanted to talk about, this is just me going through your tweets, by the way. Oh, okay. I mean, like, this is, you readAaron Levie: one by one,swyx: you're the, you're the easiest guest to prep for because you, you already have like, this is the, this is what I'm interested in.I'm like, okay, well, areAaron Levie: we gonna get to like, like February, January or something? Where are we in the, in the timelines? How far back are we going?swyx: Can you, can you describe boxes? A set of skills? Right? Like that, that's like, that's like one of the extremes of like, well if you, you just turn everything into a markdown file.Yeah. Then your agent can run your company. Uh, like you just have to write, find the right sequence of words toAaron Levie: Yes.swyx: To do it.Aaron Levie: Sorry, isthatswyx: the question? So I think the question is like, what if we documented everything? Yes. The way that you exactly said like,Aaron Levie: yes.swyx: Um, let's get all the Fortune five hundreds, uh, prepared for agents.Yes. And like, you know, everything's in golden and, and nicely filed away and everything. Yes. What's missing? Like, what's left, right? LikeAaron Levie: Yeah.swyx: You've, you've run your company for a decade. LikeAaron Levie: Yeah. I think the challenge is that, that that information changes a week later. And because something happened in the market for that [00:43:00] customer, or us as a company that now has to go get updated, and so these systems are living and breathing and they have to experience reality and updates to reality, which right now is probably gonna be humans, you know, kinda giving those, giving them the updates.And, you know, there is this piece about context graphs as as, uh, that kinda went very viral. Yeah. And I, I, I was like a, i, I, I thought it was super provocative. I agreed with many parts of it. I disagree with a few parts around. You know, it's not gonna be as easy as as just if we just had the agent traces, then we can finally do that work because there's just like, there's so much more other stuff that that's happening that, that we haven't been able to capture and digitize.And I think they actually represented that in the piece to be clear. But like there's just a lot of work, you know, that that has to, you just can't have only skills files, you know, for your company because it's just gonna be like, there's gonna be a lot of other stuff that happens. Yeah. Change over time.Yeah. Most companies are practically apprenticeships.swyx: Most companies are practically apprenticeships. LikeJeff Huber: every new employee who joins the team, [00:44:00] like you span one to three months. Like ramping them up.Aaron Levie: Yes. AllJeff Huber: that tat knowledgeAaron Levie: isJeff Huber: not written down.Aaron Levie: Yes.Jeff Huber: But like, it would have to be if you wanted to like give it to an Asian.Right. And so like that seems to me like to beAaron Levie: one is I think you're gonna see again a premium on companies that can document this. Mm-hmm. Much. There'll be a huge premium on that because, because you know, can you shorten that three month ramp cycle to a two week ramp cycle? That's an instant productivity gain.Can you re dramatically reduce rework in the organization because you've documented where all the stuff is and where the answers are. Can you make your average employee as good as your 90th percentile employee because you've captured the knowledge that's sort of in the heads of, of those top employees and make that available.So like you can see some very clear productivity benefits. Mm-hmm. If you had a company culture of making sure you know your information was captured, digitized, put in a format that was agent ready and then made available to agents to work with, and then you just, again, have this reality of like add a 10,000 person [00:45:00] company.Mapping that to the, you know, access structure of the company is just a hard problem. Is like, is like, yeah, well, you just, not every piece of information that's digitized can be shared to everybody. And so now you have to organize that in a way that actually works. There was a pretty good piece, um, this, this, uh, this piece called your company as a file is a file system.I, did you see that one?swyx: Nope.Aaron Levie: Uh, yes. You saw it. Yeah. And, and, uh, I actually be curious your thoughts on it. Um, like, like an interesting kind of like, we, we agree with it because, because that's how we see the world and, uh,swyx: okay. We, we have it up on screen. Oh,Aaron Levie: okay. Yeah. But, but it's all about basically like, you know, we've already, we, we, we already organized in this kind of like, you know, permission structure way.Uh, and, and these are the kind of, you know, natural ways that, that agents can now work with data. So it's kind of like this, this, you know, kind of interesting metaphor, but I do think companies will have to start to think about how they start to digitize more, more of that data. What was your take?Jeff Huber: Yeah, I mean, like the company's probably like an acid compliant file system.Aaron Levie: Uh,Jeff Huber: yeah. Which I'm guessing boxes, right? So, yeah. Yes.swyx: Yeah. [00:46:00]Jeff Huber: Which you have a great piece on, but,swyx: uh, yeah. Well, uh, I, I, my, my, my direction is a little bit like, I wanna rewind a little bit to the graph word you said that there, that's a magic trigger word for us. I always ask what's your take on knowledge graphs?Yeah. Uh, ‘cause every, especially at every data database person, I just wanna see what they think. There's been knowledge graphs, hype cycles, and you've seen it all. So.Aaron Levie: Hmm. I actually am not the expert in knowledge graphs, so, so that you might need toswyx: research, you don't need to be an expert. Yeah. I think it's just like, well, how, how seriously do people take it?Yeah. Like, is is, is there a lot of potential in the, in the HOVI?Aaron Levie: Uh, well, can I, can I, uh, understand first if it's, um, is this a loaded question in the sense of are you super pro, super con, super anti medium? Iswyx: see pro, I see pros and cons. Okay. Uh, but I, I think your opinion should be independent of mine.Aaron Levie: Yeah. No, no, totally. Yeah. I just want to see what I'm stepping into.swyx: No, I know. It's a, and it's a huge trigger word for a lot of people out Yeah. In our audience. And they're, they're trying to figure out why is that? Because whyAaron Levie: is this such aswyx: hot item for them? Because a lot of people get graph religion.And they're like, everything's a graph. Of course you have to represent it as a graph. Well, [00:47:00] how do you solve your knowledge? Um, changing over time? Well, it's a graph.Aaron Levie: Yeah.swyx: And, and I think there, there's that line of work and then there's, there's a lot of people who are like, well, you don't need it. And both are right.Aaron Levie: Yeah. And what do the people who say you don't need it, what are theyswyx: arguing for Mark down files. Oh, sure, sure. Simplicity.Aaron Levie: Yeah.swyx: Versus it's, it's structure versus less structure. Right. That's, that's all what it is. I do.Aaron Levie: I think the tricky thing is, um, is, is again, when this gets met with real humans, they're just going to their computer.They're just working with some people on Slack or teams. They're just sharing some data through a collaborative file system and Google Docs or Box or whatever. I certainly like the vision of most, most knowledge graph, you know, kind of futuristic kind of ways of thinking about it. Uh, it's just like, you know, it's 2026.We haven't seen it yet. Kind of play out as as, I mean, I remember. Do you remember the, um, in like, actually I don't, I don't even know how old you guys are, but I'll for, for to show my age. I remember 17 years ago, everybody thought enterprises would just run on [00:48:00] Wikis. Yeah. And, uh, confluence and, and not even, I mean, confluence actually took off for engineering for sure.Like unquestionably. But like, this was like everything would be in the w. And I think based on our, uh, our, uh, general style of, of, of what we were building, like we were just like, I don't know, people just like wanna workspace. They're gonna collaborate with other people.swyx: Exactly. Yeah. So you were, you were anti-knowledge graph.Aaron Levie: Not anti, not anti. Soswyx: not nonAaron Levie: I'm not, I'm not anti. ‘cause I think, I think your search system, I just think these are two systems that probably, but like, I'm, I'm not in any religious war. I don't want to be in anybody's YouTube comments on this. There's not a fight for me.swyx: We, we love YouTube comments. We're, we're, we're get into comments.Aaron Levie: Okay. Uh, but like, but I, I, it's mostly just a virtue of what we built. Yeah. And we just continued down that path. Yeah.swyx: Yeah.Aaron Levie: And, um, and that, that was what we pursued. But I'm not, this is not a, you know, kind of, this is not a, uh, it'sswyx: not existential for you. Great.Aaron Levie: We're happy to plug into somebody else's graph.We're happy to feed data into it. We're happy for [00:49:00] agents to, to talk to multiple systems. Not, not our fight.swyx: Yeah.Aaron Levie: But I need your answer. Yeah. Graphs or nerd Snipes is very effective nerd.swyx: See this is, this is one, one opinion and then I've,Jeff Huber: and I think that the actual graph structure is emergent in the mind of the agent.Ah, in the same way it is in the mind of the human. And that's a more powerful graph ‘cause it actually involved over time.swyx: So don't tell me how to graph. I'll, I'll figure it out myself. Exactly. Okay. All right. AndJeff Huber: what's yours?swyx: I like the, the Wiki approach. Uh, my, I'm actually

Two words that make most engineers shudder: code refactoring. Now raise the stakes — refactoring decades of legacy systems inside a large enterprise. A tech debt-heavy project of this scale needs a leader who has driven complex digital transformations, like Gayatri Narayan (formerly PepsiCo, Microsoft, Amazon). Now, as President of Technology at Builders FirstSource, Gayatri Narayan is achieving a 3–4x increase in engineering velocity since joining less than a year ago.  Gayatri joins host Yousuf Khan to unpack the strategy behind those results, including how to deploy AI across the SDLC, how to rigorously evaluate ROI on AI investments, and how to lead change across complex enterprise tech stacks.Key Moments: 01:30 – Why Construction Technology Is Ready for Transformation 04:05 – AI Strategy: Elevating UX and Customer Experience 08:20 – Evaluating AI Investments: ROI, NPV, and Operating Costs 12:45 – Achieving 3–4x Engineering Velocity 16:05 – Humans in the Loop: Craft, Code Review, and AI Amplification 18:35 – Where the Industry Gets AI Adoption Wrong 20:30 – Leadership Advice: Start with the Customer About Gayatri: Gayatri Narayan is a general management executive with more than 15 years of experience leading product, engineering, data science, and operations across global enterprises, with full P&L responsibility and a track record of driving profitable growth through digital transformation.  She currently serves as President of Technology at Builders FirstSource, where she leads enterprise technology strategy, modernizes legacy systems, and embeds AI into the software development lifecycle to accelerate innovation across the residential construction value chain.  Previously, she served as Senior Vice President of Digital Products and Services at PepsiCo and held multiple general management roles at Microsoft, including leading Product and Engineering for Intelligent Communications across Teams and Skype as well as Enterprise PaaS and SaaS businesses; she also held leadership roles at Amazon spanning Marketplace Transportation and Logistics and several major retail categories. Guest Highlights: “We've seen a three to four times increase in engineering velocity — especially in refactoring legacy systems where historically there was very little knowledge of how the system actually worked.” “With generative AI, companies that have existed for 20 or 30 years don't have to get bogged down by legacy stacks. They can embrace emerging technologies without spending 18 to 24 months just refactoring.” “It really comes down to efficiency of time. The developer's surface area of impact expands dramatically — it's not just about writing code anymore, it's about delivering business value faster.” Visit ciopod.com for more episodes. Subscribe on YouTube or follow on your favorite podcast platform so you never miss a conversation with today's top technology leaders. Our Sponsor:  Want to accelerate software development by 500%? Meet Blitzy, the only autonomous code generation platform with infinite code context, purpose-built for large, complex enterprise-scale codebases.  While other AI coding tools provide snippets of code and struggle with context, Blitzy ingests millions of lines of code and orchestrates thousands of agents that reason for hours to map every line-level dependency.  With a complete contextual understanding of your codebase, Blitzy is ready to be deployed at the beginning of every sprint. Blitzy handles the heavy lifting, delivering over 80% of the work autonomously. The platform plans, builds, and validates premium-quality code at the speed of compute, turning months of engineering into a matter of days. It's the secret weapon for Fortune 500 companies globally.  To hear how engineering leaders are transforming the way they deliver software, visit blitzy.com. Schedule a meeting with their consultants to enable an AI-Native SDLC in your organization today. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Alexander Embiricos is the Head of Codex at OpenAI, leading the development of the company's flagship AI coding systems that power automated software generation, debugging and developer workflows. Under his leadership, Codex has become one of the most widely adopted AI developer platforms.  AGENDA: 05:13 Will Coding Be Automated? Why AI Could Create More Engineers, Not Fewer 07:17 Do We Need PMs? The "Undefined" Product Role and When It Matters 08:06 The Real AGI Bottleneck: Human Prompting, Validation, and "Too Much Effort" 13:04 Three Phases of Agents: Coding → Computer Use → Productized Workflows 13:52 Enterprise Reality Check: Security, Permissions, and Safe Agentic Browsing 17:57 Is Inference the New Sales and Marketing?  18:49 What % of Codex Was Written by AI? 21:33 Do OpenAI Use AI for Code Review? 23:31 Is there any stickiness to AI coding tools? 28:22 What Does "Winning" Mean at OpenAI? Mission, Competition, and Moats 32:04 The Future UI: Chat or Voice 34:10 Agent-to-Agent Workflows: Designing for Approvals, Compliance, and Automation 35:39 Do Coding Models Have a Data Moat? 36:50 How does Codex View Data: Will They Build Their Own Mercor and Turing? 37:27 How Does Codex View Consumer: Will They Compete with Lovable? 41:56 Benchmarks vs "Vibes": How People Actually Judge Models 42:43 Cursor's Edge and the Case for Building Your Own Models 47:37 Is SaaS Dead? What Still Defends Value (Humans + Systems of Record) 51:28 Talent Wars and Career Advice for New Engineers in the AI Era 01:01:03 Guardrails, the Fully AI-Managed Stack, and a 10-Year Vision for Everyone      

Od ostatnich odcinków minęło trochę czasu, ale świat IT nie stał w miejscu – wręcz przeciwnie, przyspieszył tak, że momentami trudno nadążyć. Dlatego w tym odcinku, wspólnie z Łukaszem Szydło i Marcinem Markowskim, próbujemy po prostu głośno zastanowić się, co tak naprawdę dzieje się z pracą architekta oprogramowania i ogólnie architekturą software'u w dobie wszechobecnego Generative AI.Gdy kolejne modele wychodzą w coraz szybszym tempie, w zasadzie trochę trudno rozmawiać o tym, jakie 10 narzędzi zmieni Twoje życie architekta, z których warto korzystać już teraz. Zamiast tego usiedliśmy, żeby porozmawiać o naszych spostrzeżeniach i obserwacjach z placu boju. AI wpędza nas po trochu w pułapkę: kod powstaje błyskawicznie, ale nasze ludzkie moce przerobowe do jego czytania i weryfikacji pozostają w zasadzie bez zmian. Czy przez to nie zmieniamy się powoli w redaktorów kodu i czy Code Review nie stanie się zaraz największym wąskim gardłem w naszych projektach? Ale Code Review jest tylko jednym z etapów procesu Software Development Lifecycle, na którym widać wpływ narzędzi AI.Ogłoszenie!Już niedługo, bo 17 lutego, będziemy mogli się spotkać na otwartym warsztacie DevHours: Fullstack x EventStorming, który mam przyjemność współorganizować z Capgemini. Jeśli interesujesz się oprogramowaniem i chcesz podnieść swoje umiejętności w projektowaniu software'u, zapraszam do rejestracji.

In dieser Folge ist endlich Ivo zu Gast – langjähriger Softwareentwickler, Sparringspartner im Hintergrund und jetzt auch vor dem Mikrofon. Wir sprechen über virtuelle Büros wie aus „Die Sims“, Remote-Teamgefühl ohne Kaffeemaschine und warum Avatare Hemmschwellen abbauen können. Der zweite große Block dreht sich um KI in der Softwareentwicklung: Wird der Developer überflüssig? Haben Junioren noch eine Zukunft? Was verändert sich wirklich – und was ist nur Hype? Ivo gibt tiefe Einblicke in Vibe Coding, Side Projects, Testing mit KI, Code Reviews durch Maschinen und warum sich Code heute „fremder“ anfühlt als noch 2004. Eine Folge über Geschwindigkeit, Verantwortung, Vertrauen – und dass „tatsächlich“ immer noch Geld kostet. -- Links zur Folge immer auf https://podcast.ichglaubeeshackt.de/ Wenn Euch unser Podcast gefallen hat, freuen wir uns über eine Bewertung! Feedback wie z.B. Themenwünsche könnt Ihr uns über sämtliche Kanäle zukommen lassen: Email: podcast@ichglaubeeshackt.de Web: podcast.ichglaubeeshackt.de Instagram: http://instagram.com/igehpodcast

רק מספר 509 של רברס עם פלטפורמה - באמפרס מספר 90, שהוקלט ב-1 בינואר 2026, שנה אזרחית חדשה טובה! רן, דותן ואלון באולפן הוירטואלי (עם Riverside) בסדרה של קצרצרים וחדשות (ולפעמים קצת ישנות) מרחבי האינטרנט: הבלוגים, ה-GitHub-ים, ה-Rust-ים וה-LLM-ים החדשים מהתקופה האחרונה.

Wann kommt der Moment, an dem man als Entwickler:in weniger Code schreibt – und ist das überhaupt ein Ziel, das man anstreben sollte? In diesem Deep Dive sprechen wir mit Mirko Seifert, selbsternannter Devfluencer, CEO und Entwickler aus Überzeugung, über genau diese Frage und über den oft schleichenden Übergang von der reinen Entwicklung hin zu Rollen mit mehr Verantwortung.Mirko beschreibt sehr offen, wie stark sein eigener Coding-Anteil über die Jahre geschwankt hat und warum es ihn unzufrieden macht, wenn er zu lange keinen Code mehr anfasst. Dennis reflektiert seinen Weg vom iOS-Entwickler über Product Ownership bis zum Head of Development und erklärt, warum er heute bewusst Abstand zur Codebasis hält, obwohl ihn das Entwickeln immer noch reizt. Jan ergänzt diese Perspektiven um seine Erfahrungen aus Tech Leadership, Developer Relations und Community-Arbeit.Gemeinsam diskutieren die drei, ob technisches Up-to-date-Sein in Führungsrollen Pflicht ist oder ob es reicht, die grundlegenden Probleme der Softwareentwicklung einmal wirklich verstanden zu haben. Es geht um Vertrauen und Credibility in Teams, um Machtgefälle bei Code-Reviews, um das Risiko von Micromanagement und um die Frage, wie viel Nähe zum Code hilfreich ist und ab wann sie eher schadet. Auch Recruiting, große technologische Shifts und der Einfluss von AI auf aktuelle Entwicklungsarbeit spielen dabei eine Rolle.Ein wiederkehrendes Motiv ist die Versuchung, sich in das zu flüchten, was leicht fällt und Spaß macht – und warum genau das für Menschen mit Verantwortung gefährlich werden kann. Am Ende steht keine einfache Antwort, sondern eine ehrliche Bestandsaufnahme: Wie viel Code gut tut, hängt stark von Rolle, Umfeld und Teamdynamik ab.Dieser Deep Dive richtet sich an alle, die zwischen Coding, Verantwortung und Selbstverständnis stehen, egal, ob frisch befördert, schon lange in einer Lead-Rolle oder gerade mitten in der Frage, wie die eigene Zukunft in der Softwareentwicklung eigentlich aussehen soll.Schreibt uns! Schickt uns eure Themenwünsche und euer Feedback: podcast@programmier.barFolgt uns! Bleibt auf dem Laufenden über zukünftige Folgen und virtuelle Meetups und beteiligt euch an Community-Diskussionen. BlueskyInstagramLinkedInMeetupYouTubeMusik: Hanimo

פרק מספר 505 של רברס עם פלטפורמה - באמפרס מספר 89, שהוקלט ב-13 בנובמבר 2025, רגע אחרי כנס רברסים 2025 [יש וידאו!]: רן, דותן ואלון (והופעת אורח של שלומי נוח!) באולפן הוירטואלי עם סדרה של קצרצרים מרחבי האינטרנט: הבלוגים, ה-GitHub-ים, ה-Claude-ים וה-GPT-ים החדשים מהתקופה האחרונה.

After 20+ years as a software developer, AI coding assistants revealed a shocking truth: I never actually loved coding—I loved what code could accomplish. In this episode, I explore how transitioning from hand-crafting every line at Podscan to orchestrating AI-generated code exposed the fundamental difference between developers who cherish solving technical puzzles and entrepreneurs who prioritize shipping features that drive business value. This shift from programmer to orchestrator isn't just about tools; it's about letting go of a carefully constructed identity and embracing that for software entrepreneurs, pristine code was never the goal—rapid deployment, customer value, and business growth always were. If you're struggling with AI coding tools or clinging to perfectionist coding standards, this perspective might fundamentally change how you view your role as a technical founder.This episode of The Bootstraped Founder is sponsored by Paddle.comYou'll find the Black Friday Guide here: https://www.paddle.com/learn/grow-beyond-black-fridayThe blog post: https://thebootstrappedfounder.com/i-never-really-loved-coding-and-only-ai-made-me-realize-it/ The podcast episode: https://tbf.fm/episodes/424-i-never-really-loved-coding-and-only-ai-made-me-realize-it Check out Podscan, the Podcast database that transcribes every podcast episode out there minutes after it gets released: https://podscan.fmSend me a voicemail on Podline: https://podline.fm/arvidYou'll find my weekly article on my blog: https://thebootstrappedfounder.comPodcast: https://thebootstrappedfounder.com/podcastNewsletter: https://thebootstrappedfounder.com/newsletterMy book Zero to Sold: https://zerotosold.com/My book The Embedded Entrepreneur: https://embeddedentrepreneur.com/My course Find Your Following: https://findyourfollowing.comHere are a few tools I use. Using my affiliate links will support my work at no additional cost to you.- Notion (which I use to organize, write, coordinate, and archive my podcast + newsletter): https://affiliate.notion.so/465mv1536drx- Riverside.fm (that's what I recorded this episode with): https://riverside.fm/?via=arvid- TweetHunter (for speedy scheduling and writing Tweets): http://tweethunter.io/?via=arvid- HypeFury (for massive Twitter analytics and scheduling): https://hypefury.com/?via=arvid60- AudioPen (for taking voice notes and getting amazing summaries): https://audiopen.ai/?aff=PXErZ- Descript (for word-based video editing, subtitles, and clips): https://www.descript.com/?lmref=3cf39Q- ConvertKit (for email lists, newsletters, even finding sponsors): https://convertkit.com?lmref=bN9CZw

In this special crossover episode with the brand-new Embedded AI Podcast, Luca and Jeff are joined by Ryan Torvik, Luca's co-host on the Embedded AI podcast, to explore the intersection of AI-powered development tools and agile embedded systems engineering. The hosts discuss practical strategies for using Large Language Models (LLMs) effectively in embedded development workflows, covering topics like context management, test-driven development with AI, and maintaining code quality standards in safety-critical systems. The conversation addresses common anti-patterns that developers encounter when first adopting LLM-assisted coding, such as "vibe coding" yourself off a cliff by letting the AI generate too much code at once, losing control of architectural decisions, and failing to maintain proper test coverage. The hosts emphasize that while LLMs can dramatically accelerate prototyping and reduce boilerplate coding, they require even more rigorous engineering discipline - not less. They discuss how traditional agile practices like small commits, continuous integration, test-driven development, and frequent context resets become even more critical when working with AI tools. For embedded systems engineers working in safety-critical domains like medical devices, automotive, and aerospace, the episode provides valuable guidance on integrating AI tools while maintaining deterministic quality processes. The hosts stress that LLMs should augment, not replace, static analysis tools and human code reviews, and that developers remain fully responsible for AI-generated code. Whether you're just starting with AI-assisted development or looking to refine your approach, this episode offers actionable insights for leveraging LLMs effectively while keeping the reins firmly in hand. ## Key Topics * [03:45] LLM Interface Options: Web, CLI, and IDE Plugins - Choosing the Right Tool for Your Workflow* [08:30] Prompt Engineering Fundamentals: Being Specific and Iterative with LLMs* [12:15] Building Effective Base Prompts: Learning from Experience vs. Starting from Templates* [16:40] Context Window Management: Avoiding Information Overload and Hallucinations* [22:10] Understanding LLM Context: Files, Prompts, and Conversation History* [26:50] The Nature of Hallucinations: Why LLMs Always Generate, Never Judge* [29:20] Test-Driven Development with AI: More Critical Than Ever* [35:45] Avoiding 'Vibe Coding' Disasters: The Importance of Small, Testable Increments* [42:30] Requirements Engineering in the AI Era: Becoming More Specific About What You Want* [48:15] Extreme Programming Principles Applied to LLM Development: Small Steps and Frequent Commits* [52:40] Context Reset Strategies: When and How to Start Fresh Sessions* [56:20] The V-Model Approach: Breaking Down Problems into Manageable LLM-Sized Chunks* [01:01:10] AI in Safety-Critical Systems: Augmenting, Not Replacing, Deterministic Tools* [01:06:45] Code Review in the AI Age: Maintaining Standards Despite Faster Iteration* [01:12:30] Prototyping vs. Production Code: The Superpower and the Danger* [01:16:50] Shifting Left with AI: Empowering Product Owners and Accelerating Feedback Loops* [01:19:40] Bootstrapping New Technologies: From Zero to One in Minutes Instead of Weeks* [01:23:15] Advice for Junior Engineers: Building Intuition in the Age of AI-Assisted Development ## Notable Quotes > "All of us are new to this experience. Nobody went to school back in the 80s and has been doing this for 40 years. We're all just running around, bumping into things and seeing what works for us." — Ryan Torvik > "An LLM is just a token generator. You stick an input in, and it returns an output, and it has no way of judging whether this is correct or valid or useful. It's just whatever it generated. So it's up to you to give it input data that will very likely result in useful output data." — Luca Ingianni > "Tests tell you how this is supposed to work. You can have it write the test first and then evaluate the test. Using tests helps communicate - just like you would to another person - no, it needs to function like this, it needs to have this functionality and behave in this way." — Ryan Torvik > "I find myself being even more aggressively biased towards test-driven development. While I'm reasonably lenient about the code that the LLM writes, I am very pedantic about the tests that I'm using. I will very thoroughly review them and really tweak them until they have the level of detail that I'm interested in." — Luca Ingianni > "It's really forcing me to be a better engineer by using the LLM. You have to go and do that system level understanding of the problem space before you actually ask the LLM to do something. This is what responsible people have been saying - this is how you do engineering." — Ryan Torvik > "I can use LLMs to jumpstart me or bootstrap me from zero to one. Once there's something on the screen that kind of works, I can usually then apply my general programming skill, my general engineering taste to improve it. Getting from that zero to one is now not days or weeks of learning - it's 20 minutes of playing with it." — Jeff Gable > "LLMs are fantastic at small-scale stuff. They will be wonderful at finding better alternatives for how to implement a certain function. But they are absolutely atrocious at large-scale stuff. They will gleefully mess up your architecture and not even notice because they cannot fit it into their tiny electronic brains." — Luca Ingianni > "Don't be afraid to try it out. We're all noobs to this. This is the brave noob world of AI exploration. Be curious about it, but also be cautious about it. Don't ever take your hands off the reins. Trust your engineering intuition - even young folks that are just starting, trust your engineering intuition." — Ryan Torvik > "As the saying goes, good judgment comes from experience. Experience comes from bad judgment. You'll find spectacular ways of messing up - that is how you become a decent engineer. LLMs do not change that. Junior engineers will still be necessary, will still be around, and they will still evolve into senior engineers eventually after they've fallen on their faces enough times." — Luca Ingianni  You can find Jeff at https://jeffgable.com.You can find Luca at https://luca.engineer.Want to join the agile Embedded Slack? Click hereAre you looking for embedded-focused trainings? Head to https://agileembedded.academy/Ryan Torvik and Luca have started the Embedded AI podcast, check it out at https://embeddedaipodcast.com/

Blockiert dein Code Review gerade mal wieder den Release oder ist es der unsichtbare Klebstoff, der Wissen im Team verteilt? In dieser Episode gehen wir der Frage auf den Grund, warum Reviews weit mehr sind als ein einfaches “looks good to me” und was sie mit sozialer Interaktion, Teamdynamik und Wissensverteilung zu tun haben. Wir sprechen mit Prof. Michael Dorner, Professor für Software Engineering an der TH Nürnberg, der seit Jahren zur Rolle von Code Reviews in der Softwareentwicklung forscht: mit Code Review Daten von Microsoft, Spotify oder trivago. Überall zeigt sich: Pull Requests sind mehr als technische Checks, sie sind Kommunikationsnetzwerke. Gemeinsam beleuchten wir, warum Tooling oft zweitrangig ist, wie sich Review-Praktiken historisch entwickelt haben und was das alles mit Ownership, Architektur und sogar Steuern zu tun hat. Ein Blick auf Code Reviews, der dir definitiv eine neue Perspektive eröffnet.Bonus: Wir erklären, warum alle Informatiker Doktoren auch Philosophen sind ;)Unsere aktuellen Werbepartner findest du auf https://engineeringkiosk.dev/partnersDas schnelle Feedback zur Episode:

Technische Schulden: Code veröffentlichen und weiterziehen oder doch erst aufräumen?Technische Schulden fühlen sich oft nach Ballast an, können aber dein stärkster Hebel für Speed sein. Der Knackpunkt ist, sie bewusst und sichtbar einzugehen und konsequent wieder abzubauen. In dieser Episode sprechen wir darüber, wie wir technische Schulden strategisch nutzen, ohne uns langfristig festzufahren.Ward Cunningham sagt: Technische Schulden sind nicht automatisch schlechter Code. Wir ordnen ein, was wirklich als “Debt” zählt und warum Provisorien oft länger leben als geplant. Dann erweitern wir die Perspektive von der Code‑ und Architektur‑Ebene auf People und Prozesse: Knowledge Silos, fehlendes Code Review und organisatorische Entscheidungen können genauso Schulden sein wie ein any in TypeScript. Wir diskutieren sinnvolle Indikatoren wie DORA Metriken, zyklomatische Komplexität und den CRAP Index, aber auch ihre Grenzen. Warum Trends über Releases hilfreicher sind als Einzelwerte oder wie Teamskalierung die Kennzahlen beeinflusst. Dazu die Business Seite: reale Kosten, Produktivitätsverluste, Frust im Team und Fluktuation. Als Anschauung dient der Sonos App Rewrite als teures Lehrstück für akkumulierte Schulden.Wenn du wissen willst, wie du in deinem Team Technical Debt als Werkzeug nutzt, Metriken und Kultur klug kombinierst und den Business Impact sauber argumentierst, dann ist diese Episode für dich.Bonus: Wir verraten, warum Legacy allein keine Schuld ist und wie Open Source, Plattformteams und Standardisierung dir echte Zinsen sparen können.Unsere aktuellen Werbepartner findest du auf https://engineeringkiosk.dev/partnersDas schnelle Feedback zur Episode:

I know you're out there. The developer who watches their colleagues enthusiastically embrace Claude Code and Cursor, having AI write entire feature sets while you proudly type every semicolon by hand. The founder who sees AI-generated code as a ticking time bomb of bugs and security vulnerabilities. The software entrepreneur who believes that real code comes from human minds, not language models.This one's for you.This episode of The Bootstraped Founder is sponsored by Paddle.comYou'll find the Black Friday Guide here: https://www.paddle.com/learn/grow-beyond-black-fridayThe blog post: https://thebootstrappedfounder.com/ai-for-the-code-writing-purist-how-to-use-ai-without-surrendering-your-keyboard/The podcast episode:  https://tbf.fm/episodes/420-ai-for-the-code-writing-purist-how-to-use-ai-without-surrendering-your-keyboardCheck out Podscan, the Podcast database that transcribes every podcast episode out there minutes after it gets released: https://podscan.fmSend me a voicemail on Podline: https://podline.fm/arvidYou'll find my weekly article on my blog: https://thebootstrappedfounder.comPodcast: https://thebootstrappedfounder.com/podcastNewsletter: https://thebootstrappedfounder.com/newsletterMy book Zero to Sold: https://zerotosold.com/My book The Embedded Entrepreneur: https://embeddedentrepreneur.com/My course Find Your Following: https://findyourfollowing.comHere are a few tools I use. Using my affiliate links will support my work at no additional cost to you.- Notion (which I use to organize, write, coordinate, and archive my podcast + newsletter): https://affiliate.notion.so/465mv1536drx- Riverside.fm (that's what I recorded this episode with): https://riverside.fm/?via=arvid- TweetHunter (for speedy scheduling and writing Tweets): http://tweethunter.io/?via=arvid- HypeFury (for massive Twitter analytics and scheduling): https://hypefury.com/?via=arvid60- AudioPen (for taking voice notes and getting amazing summaries): https://audiopen.ai/?aff=PXErZ- Descript (for word-based video editing, subtitles, and clips): https://www.descript.com/?lmref=3cf39Q- ConvertKit (for email lists, newsletters, even finding sponsors): https://convertkit.com?lmref=bN9CZw

Bug-Management muss man wollen … und können.Jede:r von uns kennt sie: Bugs in der Software. Sie verstecken sich nicht nur in tiefen Architekturentscheidungen oder Skurrilitäten des Nutzerverhaltens. Sie sind Alltag, egal wie viel Testautomatisierung, KI-Unterstützung oder Code-Reviews wir in unseren Prozessen haben. Doch wie gehst du damit um, wenn die Bugliste immer länger wird, dein Team über Jira-Tickets stöhnt und die Frage im Raum steht: Lohnt es sich überhaupt, Bugs systematisch zu managen?In dieser Episode nehmen wir dich mit durch alle Facetten des modernen Bug-Managements. Wir diskutieren, wie Bugs überhaupt entstehen, warum 'Zero Bug'-Versprechen ein Mythos sind und welche Strategien es gibt, Fehler möglichst früh zu finden. Ob durch Beta-Channels, Dogfooding im eigenen Unternehmen oder kreatives Recruiting. Wir tauchen ein in die Welt der Bug Reports: Wie sieht ein richtig guter aus? Welche Infos braucht das Engineering und wie senkst du die Hürden, damit dein Team (und auch die Community) wirklich meldet? Klartext gibt's auch zur Priorisierung: Wie klassifizierst du Bugs nach User-Impact, Komplexität und Business-Wert, anstatt an zu vielen bunten Jira-Feldern zu verzweifeln?Neugierig? Dann bleib dran.Bonus: Unerwartete Funfact-Challenge → Ist schlechte UX ein Bug oder ein Feature?Unsere aktuellen Werbepartner findest du auf https://engineeringkiosk.dev/partnersDas schnelle Feedback zur Episode:

AI Assisted Coding: From Deterministic to AI-Driven—The New Paradigm of Software Development, With Markus Hjort In this BONUS episode, we dive deep into the emerging world of AI-assisted coding with Markus Hjort, CTO of Bitmagic. Markus shares his hands-on experience with what's being called "vibe coding" - a paradigm shift where developers work more like technical product owners, guiding AI agents to produce code while focusing on architecture, design patterns, and overall system quality. This conversation explores not just the tools, but the fundamental changes in how we approach software engineering as a team sport. Defining Vibecoding: More Than Just Autocomplete "I'm specifying the features by prompting, using different kinds of agentic tools. And the agent is producing the code. I will check how it works and glance at the code, but I'm a really technical product owner." Vibecoding represents a spectrum of AI-assisted development approaches. Markus positions himself between pure "vibecoding" (where developers don't look at code at all) and traditional coding. He produces about 90% of his code using AI tools, but maintains technical oversight by reviewing architectural patterns and design decisions. The key difference from traditional autocomplete tools is the shift from deterministic programming languages to non-deterministic natural language prompting, which requires an entirely different way of thinking about software development. The Paradigm Shift: When AI Changed Everything "It's a different paradigm! Looking back, it started with autocomplete where Copilot could implement simple functions. But the real change came with agentic coding tools like Cursor and Claude Code." Markus traces his journey through three distinct phases. First came GitHub Copilot's autocomplete features for simple functions - helpful but limited. Next, ChatGPT enabled discussing architectural problems and getting code suggestions for unfamiliar technologies. The breakthrough arrived with agentic tools like Cursor and Claude Code that can autonomously implement entire features. This progression mirrors the historical shift from assembly to high-level languages, but with a crucial difference: the move from deterministic to non-deterministic communication with machines. Where Vibecoding Works Best: Knowing Your Risks "I move between different levels as I go through different tasks. In areas like CSS styling where I'm not very professional, I trust the AI more. But in core architecture where quality matters most, I look more thoroughly." Vibecoding effectiveness varies dramatically by context. Markus applies different levels of scrutiny based on his expertise and the criticality of the code. For frontend work and styling where he has less expertise, he relies more heavily on AI output and visual verification. For backend architecture and core system components, he maintains closer oversight. This risk-aware approach is essential for startup environments where developers must wear multiple hats. The beauty of this flexibility is that AI enables developers to contribute meaningfully across domains while maintaining appropriate caution in critical areas. Teaching Your Tools: Making AI-Assisted Coding Work "You first teach your tool to do the things you value. Setting system prompts with information about patterns you want, testing approaches you prefer, and integration methods you use." Success with AI-assisted coding requires intentional configuration and practice. Key strategies include: System prompts: Configure tools with your preferred patterns, testing approaches, and architectural decisions Context management: Watch context length carefully; when the AI starts making mistakes, reset the conversation Checkpoint discipline: Commit working code frequently to Git - at least every 30 minutes, ideally after every small working feature Dual AI strategy: Use ChatGPT or Claude for architectural discussions, then bring those ideas to coding tools for implementation Iteration limits: Stop and reassess after roughly 5 failed iterations rather than letting AI continue indefinitely Small steps: Split features into minimal increments and commit each piece separately In this segment we refer to the episode with Alan Cyment on AI Assisted Coding, and the Pachinko coding anti-pattern.  Team Dynamics: Bigger Chunks and Faster Coordination "The speed changes a lot of things. If everything goes well, you can produce so much more stuff. So you have to have bigger tasks. Coordination changes - we need bigger chunks because of how much faster coding is." AI-assisted coding fundamentally reshapes team workflows. The dramatic increase in coding speed means developers need larger, more substantial tasks to maintain flow and maximize productivity. Traditional approaches of splitting stories into tiny tasks become counterproductive when implementation speed increases 5-10x. This shift impacts planning, requiring teams to think in terms of complete features rather than granular technical tasks. The coordination challenge becomes managing handoffs and integration points when individuals can ship significant functionality in hours rather than days. The Non-Deterministic Challenge: A New Grammar "When you're moving from low-level language to higher-level language, they are still deterministic. But now with LLMs, it's not deterministic. This changes how we have to think about coding completely." The shift to natural language prompting introduces fundamental uncertainty absent from traditional programming. Unlike the progression from assembly to C to Python - all deterministic - working with LLMs means accepting probabilistic outputs. This requires developers to adopt new mental models: thinking in terms of guidance rather than precise instructions, maintaining checkpoints for rollback, and developing intuition for when AI is "hallucinating" versus producing valid solutions. Some developers struggle with this loss of control, while others find liberation in focusing on what to build rather than how to build it. Code Reviews and Testing: What Changes? "With AI, I spend more time on the actual product doing exploratory testing. The AI is doing the coding, so I can focus on whether it works as intended rather than syntax and patterns." Traditional code review loses relevance when AI generates syntactically correct, pattern-compliant code. The focus shifts to testing actual functionality and user experience. Markus emphasizes: Manual exploratory testing becomes more important as developers can't rely on having written and understood every line Test discipline is critical - AI can write tests that always pass (assert true), so verification is essential Test-first approach helps ensure tests actually verify behavior rather than just existing Periodic test validation: Randomly modify test outputs to verify they fail when they should Loosening review processes to avoid bottlenecks when code generation accelerates dramatically Anti-Patterns and Pitfalls to Avoid Several common mistakes emerge when developers start with AI-assisted coding: Continuing too long: When AI makes 5+ iterations without progress, stop and reset rather than letting it spiral Skipping commits: Without frequent Git checkpoints, recovery from AI mistakes becomes extremely difficult Over-reliance without verification: Trusting AI-generated tests without confirming they actually test something meaningful Ignoring context limits: Continuing to add context until the AI becomes confused and produces poor results Maintaining traditional task sizes: Splitting work too granularly when AI enables completing larger chunks Forgetting exploration: Reading about tools rather than experimenting hands-on with your own projects The Future: Autonomous Agents and Automatic Testing "I hope that these LLMs will become larger context windows and smarter. Tools like Replit are pushing boundaries - they can potentially do automatic testing and verification for you." Markus sees rapid evolution toward more autonomous development agents. Current trends include: Expanded context windows enabling AI to understand entire codebases without manual context curation Automatic testing generation where AI not only writes code but also creates and runs comprehensive test suites Self-verification loops where agents test their own work and iterate without human intervention Design-to-implementation pipelines where UI mockups directly generate working code Agentic tools that can break down complex features autonomously and implement them incrementally The key insight: we're moving from "AI helps me code" to "AI codes while I guide and verify" - a fundamental shift in the developer's role from implementer to architect and quality assurance. Getting Started: Experiment and Learn by Doing "I haven't found a single resource that covers everything. My recommendation is to try Claude Code or Cursor yourself with your own small projects. You don't know the experience until you try it." Rather than pointing to comprehensive guides (which don't yet exist for this rapidly evolving field), Markus advocates hands-on experimentation. Start with personal projects where stakes are low. Try multiple tools to understand their strengths. Build intuition through practice rather than theory. The field changes so rapidly that reading about tools quickly becomes outdated - but developing the mindset and practices for working with AI assistance provides durable value regardless of which specific tools dominate in the future. About Markus Hjort Markus is Co-founder and CTO of Bitmagic, and has over 20 years of software development expertise. Starting with Commodore 64 game programming, his career spans gaming, fintech, and more. As a programmer, consultant, agile coach, and leader, Markus has successfully guided numerous tech startups from concept to launch. You can connect with Markus Hjort on LinkedIn.

Sally and Aji discuss their experiences with invisible mentorship when it comes to code review. Together they question when is the right time to have conversations with your team in a bid to chase improvement, the importance of understanding your co-workers perspectives, as well as the best ways to initiate a mentoring moment. — Check out some of the things mentioned in this episode - The Coding Train (https://thecodingtrain.com) - Sarah Mel's Livable Code (https://www.youtube.com/watch?v=lI77oMKr5EY&pp=ygUTc2FyYWggbWVpIHJhaWxzY29uZg==) Thanks to our sponsors for this episode Judoscale - Autoscale the Right Way (https://judoscale.com/bikeshed) (check the link for your free gift!), and Scout Monitoring (https://www.scoutapm.com/). Your hosts for this episode have been thoughtbot's own Sally Hall (https://www.linkedin.com/in/sallyannahall) and Aji Slater (https://www.linkedin.com/in/doodlingdev/) If you would like to support the show, head over to our GitHub page (https://github.com/sponsors/thoughtbot), or check out our website (https://bikeshed.thoughtbot.com). Got a question or comment about the show? Why not write to our hosts: hosts@bikeshed.fm This has been a thoughtbot (https://thoughtbot.com/) podcast. Stay up to date by following us on social media - YouTube (https://www.youtube.com/@thoughtbot/streams) - LinkedIn (https://www.linkedin.com/company/150727/) - Mastodon (https://thoughtbot.social/@thoughtbot) - BlueSky (https://bsky.app/profile/thoughtbot.com) © 2025 thoughtbot, inc.

OpenAI's Codex has already shipped hundreds of thousands of pull requests in its first month. But what is it really, and how will coding agents change the future of software?In this episode, General Partner Anjney Midha goes behind the scenes with one of Codex's product leads- Alexander Embiricos - to unpack its origin story, why its PR success rate is so high, the safety challenges of autonomous agents, and what this all means for developers, students, and the future of coding. Timecodes:0:00 Intro: The Vision for AI Agents1:25 Codex's Origin and Naming3:20 Early Prototypes and Agent Form Factors6:00 Cloud Agents: Safety and Security9:40 Prompt Injection and Attack Vectors12:00 PR Merging: Metrics and Transparency17:00 The Future of Code Review and Automation20:00 User Adoption: Internal vs. External Surprises22:00 Multi-Turn Interactions and Product Learnings29:30 Best-of-N, Slot Machine Analogy, and Creativity33:00 Human Taste, Iteration, and Collaboration40:00 AI's Impact on Software Engineering Careers45:00 Education, CS Degrees, and AI Integration49:00 Prototyping, Hackathons, and Speed to Magic55:00 Legacy Code, Modernization, and Global Adoption1:00:00 Enterprise, Security, and Air-Gapped Environments1:05:00 Product Roadmap and Future of Codex1:10:00 Advice for Founders and Startups1:15:00 Education Reform and Project-Based Learning1:20:00 Hiring, Building, and New Grad Advice Resources: Find Alex on X: https://x.com/embiricoFind Anjney on X: https://twitter.com/AnjneyMidha Stay Updated: If you enjoyed this episode, be sure to like, subscribe, and share with your friends!Find a16z on X: https://x.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zListen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYXListen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711Follow our host: https://x.com/eriktorenbergPlease note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Stay Updated:Find a16z on XFind a16z on LinkedInListen to the a16z Podcast on SpotifyListen to the a16z Podcast on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

How To Perform Technical Due Diligence Hello, this is Hall T. Martin with the Startup Funding Espresso -- your daily shot of startup funding and investing. Investors perform diligence on a startup before investing. Most of the diligence focuses on the financial aspects of the business. Technical diligence is just as important. For startups, it's also important to focus on the technical aspects. Here's a list of areas to review for technical due diligence: Architecture Review the technical architecture for scalability and robustness. Check the architecture for fit with the application. Code Review the code for quality and documentation. Are there processes for testing and verification? Security Review the code for security measures. Perform penetration exercises to check its strength. People Interview the technical team for their technical background and skills. See if the skills match the project requirements. Intellectual property Review the intellectual property to see if the key technical features are covered. Consider these steps in performing technical due diligence on a startup.   Thank you for joining us for the Startup Funding Espresso where we help startups and investors connect for funding. Let's go startup something today. _______________________________________________________ For more episodes from Investor Connect, please visit the site at:   Check out our other podcasts here:   For Investors check out:   For Startups check out:   For eGuides check out:   For upcoming Events, check out    For Feedback please contact info@tencapital.group    Please , share, and leave a review. Music courtesy of .

This interview was recorded for GOTO Unscripted.https://gotopia.techRead the full transcription of this interview herePaul Slaughter - Staff Fullstack Engineer at GitLab & Creator of Conventional CommentsAdrienne Braganza Tacke - Senior Developer Advocate at Viam Robotics & Author of "Looks Good To Me: Constructive Code Reviews"RESOURCESPaulhttps://x.com/souldzinhttps://github.com/souldzinhttps://gitlab.com/pslaughterhttps://gitlab.com/souldzinhttps://souldzin.comAdriennehttps://bsky.app/profile/abt.bsky.socialhttps://x.com/AdrienneTackehttps://github.com/AdrienneTackehttps://www.linkedin.com/in/adriennetackehttps://www.instagram.com/adriennetackehttps://www.adrienne.iohttps://blog.adrienne.ioLinkshttps://conventionalcomments.orgDESCRIPTIONCan "Conventional Comments" transform code reviews from frustrating experiences into productive collaborations?Paul Slaughter shares his experience developing and practicing "Conventional Comments", a structured approach to improving code review communications through labeled feedback. The conversation explores clear communication patterns with labels (e.g. 'suggestion:', 'issue:' or 'question:').Paul and Adrienne discuss the importance of empathy in the review process, the balance between politeness and efficiency, and how GitLab's Code Review Weekly Workshops have helped normalize review experiences across their remote teams. The interview highlights that effective code reviews depend not just on technical evaluations but on thoughtful communication that acknowledges developers' emotional investment in their work while fostering a culture of collaborative ownership.RECOMMENDED BOOKSAdrienne Braganza Tacke • "Looks Good to Me": Constructive Code ReviewsAdrienne Braganza Tacke • Coding for KidsGrace Huang • Code Reviews in TechMartin Fowler • RefactoringMatthew Skelton & Manuel Pais • Team TopologiesDave Thomas & Andy Hunt • The Pragmatic ProgrammerBlueskyTwitterInstagramLinkedInFacebookCHANNEL MEMBERSHIP BONUSJoin this channel to get early access to videos & other perks:https://www.youtube.com/channel/UCs_tLP3AiwYKwdUHpltJPuA/joinLooking for a unique learning experience?Attend the next GOTO conference near you! Get your ticket: gotopia.techSUBSCRIBE TO OUR YOUTUBE CHANNEL - new videos posted daily!

AI is rewriting the rules of code review and CodeRabbit is leading the charge. In this featured episode of Screaming in the Cloud, Harjot Gill shares with Corey Quinn how his team built the most-installed AI app on GitHub and GitLab, nailed positive unit economics, and turned code review into a powerful guardrail for the AI era.Show Highlights(0:00) Entrepreneurial Journey and Code Rabbit's Origin (3:06) The Broken Nature of Code Reviews (5:47) Developer Feedback and the Future of Code Review (9:50) AI-Generated Code and the Code Review Burden (11:46) Traditional Tools vs. AI in Code Review (13:41) Keeping Up with State-of-the-Art Models (16:16) Cloud Architecture and Google Cloud Run(18:21) Context Engineering for Large Codebases (20:52) Taming LLMs and Balancing Feedback (22:30) Business Model and Open Source Strategy About Harjot Gill Harjot is the CEO of CodeRabbit, a leading AI-first developer tools company. LinksHarjot on LinkedIn: https://www.linkedin.com/in/harjotsgill/SponsorCodeRabbit: https://coderabbit.link/corey

Are long code review cycles killing your engineering team's velocity? Learn how top engineering teams are shipping code faster without sacrificing quality.In this episode, Greg Foster, CTO and co-founder of Graphite, discusses the evolution of code review practices, from the fundamentals of pull requests to the future of AI in code review workflows. He shares the secrets behind how the Graphite team became one of the most productive engineering teams by leveraging techniques like small code changes and stacked PRs (pull requests).Key topics discussed:The evolution of code review from bug-hunting to knowledge sharingBest practices for PRs and why small PRs get better feedbackHow stacked PRs eliminate waiting time in development workflowsThe rise of AI in the code review processWhy AI code review works best as an automated CI checkHow Graphite achieves P99 engineering productivityHiring engineers in the age of AI-assisted codingTimestamps:(00:00) Trailer & Intro(02:21) Career Turning Points(05:11) Now is The Golden Time to Be in Software Engineering(09:08) The Evolution of Code Review in Software Development(14:59) The Popularity of Pull Request Workflow(21:01) Pull Request Best Practices(26:17) The Stacked PR and Its Benefits(34:07) How Graphite Ships Code Remarkably Fast(40:03) The Cool Things About AI Code Review(45:23) Graphite's Unique Recipes for Engineering Productivity(50:55) Hiring Engineers in the Age of AI(55:31) 2 Tech Lead Wisdom_____Greg Foster's BioGreg Foster is the CTO and co-founder of Graphite, an a16z and Anthropic-backed company helping teams like Snowflake, Figma, and Perplexity ship faster and scale AI-generated code with confidence. Prior to Graphite, Greg was a dev tools engineer at Airbnb. There, he experienced the impact of robust internal tooling on developer velocity and co-founded Graphite to bring powerful, AI-powered code review to every team. Greg holds a BS in Computer Science from Harvard University.Follow Greg:LinkedIn – linkedin.com/in/gregmfosterX – x.com/gregmfosterEmail – greg@graphite.devGraphite – graphite.devGraphite X – x.com/withgraphiteLike this episode?Show notes & transcript: techleadjournal.dev/episodes/231.Follow @techleadjournal on LinkedIn, Twitter, and Instagram.Buy me a coffee or become a patron.

Show DescriptionIdentifying where we are in the AI hype cycle, a quick #davegoeshairy update, what has been the impact of AI on tech creators, Chris is making his own CSS starter on stream, and Item flow / masonry discussions. Listen on WebsiteLinks Introducing GPT-5 - YouTube Simon Willison on ai Orion Browser by Kagi VisBug Chrome Canary Features For Developers - Google Chrome Download Microsoft Edge Zen Browser Google Backtracks On Plans For URL Shortener Service Impact of AI on Tech Content Creators Pre-commit Hooks, requestAnimationFrame, Code Reviews, and More - Syntax #922 CodePen Radio CSS Tools: Reset CSS Item Flow – Part 2: next steps for Masonry | WebKit

Cybersecurity Today: July Review - Massive Lawsuits, AI Warnings, and Major Breaches In this episode of Cybersecurity Today: The Month in Review, host Jim Love and an expert panel, including David Shipley, Anton Levaja, and Tammy Harper, discuss the most significant cybersecurity stories from July. Key topics include the $380 million lawsuit between Clorox and Cognizant following a massive ransomware attack, the ongoing legal battle between Delta and CrowdStrike, and breached forums like XSS leading to significant law enforcement actions. The panel also dives into AI-related risks in software development, recent supply chain attacks, and legislative developments in Europe affecting cybersecurity. Watch to stay informed about the latest trends and challenges in the cybersecurity landscape. 00:00 Introduction and Panelist Introductions 01:28 Major Cybersecurity Lawsuits: Clorox vs. Cognizant and Delta vs. CrowdStrike 04:11 Reflections on Legal Implications and Industry Impact 13:01 Tammy Harper on XSS Forum Seizure 17:52 Law Enforcement Tactics and Dark Web Trust Issues 23:47 Anton Levaja on Supply Chain Attacks 30:18 AI Wiping Code and Backup Issues 31:18 Security Concerns with Model Control Protocol 31:56 Challenges with AI in Code Review 34:02 The Problem with AI-Generated Code 40:43 The SharePoint Apocalypse 43:36 Impact of Business Decisions on Technology 49:16 Final Thoughts and Upcoming Stories 49:25 Current and Upcoming Tech Legislation

This interview was recorded for GOTO Unscripted.https://gotopia.techRead the full transcription of this interview hereAdrienne Braganza Tacke - Senior Developer Advocate at Viam Robotics & Author of "Looks Good To Me: Constructive Code Reviews"Saša Jurić - Author of "Elixir in Action" & The Ultimate Beacon in the Elixir SpaceRESOURCESAdriennehttps://bsky.app/profile/abt.bsky.socialhttps://x.com/AdrienneTackehttps://github.com/AdrienneTackehttps://www.linkedin.com/in/adriennetackehttps://www.instagram.com/adriennetackehttps://www.adrienne.iohttps://blog.adrienne.ioSašahttps://bsky.app/profile/sasajuric.bsky.socialhttps://twitter.com/sasajurichttps://github.com/sasa1977https://linkedin.com/in/sa%C5%A1a-juri%C4%87-21b23186https://www.theerlangelist.comDESCRIPTIONAdrienne Braganza, author of "Looks Good to Me: Constructive Code Reviews", and Saša Jurić, author of “Elixir in Action”, explore best practices for effective code reviews. They discuss how smaller, well-organized pull requests lead to better feedback, the importance of comment classification, and when to take discussions offline. Both emphasize that code reviews aren't just about catching bugs—they're crucial for knowledge transfer and creating cohesive codebases.While AI tools can help with routine aspects, human judgment remains essential, especially as AI-generated code becomes more common. The speakers agree that when done well, code reviews Digital Disruption with Geoff Nielson Discover how technology is reshaping our lives and livelihoods.Listen on: Apple Podcasts Spotify Inspiring Tech Leaders - The Technology PodcastInterviews with Tech Leaders and insights on the latest emerging technology trends.Listen on: Apple Podcasts SpotifyBlueskyTwitterInstagramLinkedInFacebookCHANNEL MEMBERSHIP BONUSJoin this channel to get early access to videos & other perks:https://www.youtube.com/channel/UCs_tLP3AiwYKwdUHpltJPuA/joinLooking for a unique learning experience?Attend the next GOTO conference near you! Get your ticket: gotopia.techSUBSCRIBE TO OUR YOUTUBE CHANNEL - new videos posted daily!

In this episode of Founded & Funded, Madrona Investor Rolanda Fu is joined by Dedy Kredo, the co-founder and chief product officer of QodoAI — formerly CodiumAI, a 2024 IA40 winner and one of the most exciting AI companies shaping the future of software development. Dedy and his co-founder, Itamar, are entrepreneurs who have spent their careers building for developers, and with Qodo, they're tackling one of the most frustrating problems in software engineering — testing and verifying code. As AI generates more code, the challenge shifts to ensuring quality, maintaining standards, and managing complexity across the entire software development lifecycle. In this conversation, Dedy and Rolanda discuss how Qodo's agentic architecture and deep code-based understanding are helping enterprises leverage AI speed while ensuring code integrity and governance. They get into what it takes to build enterprise-ready AI platforms, the strategy behind scaling from a developer-first approach to major enterprise partnerships, and how AI agents might reshape software engineering teams altogether. Transcript: https://www.madrona.com/engineering-ai-era-qodo-dedy-kredo-on-ai-powered-sdlc Chapters:  (00:00) Introduction  (01:12) The Future of AI in Software Development (01:58) Dedy's Journey in Tech (03:02) The Genesis of Qodo (03:53) Qodo's Unique Approach to AI Coding (05:13) Exploring Qodo's Product Features (06:42) Code Review and Verification (08:53) Customizing AI Agents (11:02) Vibe Coding and Code Review (13:27) Developer Love vs. Enterprise Needs (15:33) Enterprise Adoption (17:51) Future of Software Engineering (22:13) Balancing Developer Love and Enterprise Sales (24:05) Advice for Founders 

One of the most immediate and high-impact applications of LLMs has been in software development. The models can significantly accelerate code writing, but with that increased velocity comes a greater need for thoughtful, scalable approaches to codereview. Integrating AI into the development workflow requires rethinking how to ensure quality,security, and maintainability at scale. CodeRabbit is The post CodeRabbit and RAG for Code Review with Harjot Gill appeared first on Software Engineering Daily.

One of the most immediate and high-impact applications of LLMs has been in software development. The models can significantly accelerate code writing, but with that increased velocity comes a greater need for thoughtful, scalable approaches to codereview. Integrating AI into the development workflow requires rethinking how to ensure quality,security, and maintainability at scale. CodeRabbit is The post CodeRabbit and RAG for Code Review with Harjot Gill appeared first on Software Engineering Daily.

One of the most immediate and high-impact applications of LLMs has been in software development. The models can significantly accelerate code writing, but with that increased velocity comes a greater need for thoughtful, scalable approaches to codereview. Integrating AI into the development workflow requires rethinking how to ensure quality,security, and maintainability at scale. CodeRabbit is The post CodeRabbit and RAG for Code Review with Harjot Gill appeared first on Software Engineering Daily.

Cette semaine, on reçoit Sandra Gomes, responsable du contenu musical à Konbini et coanimatrice du Code Review, pour une longue entrevue.   Rosaliedu38 - NOT ALONE Theodora - 243 km/h (feat. Zoomy) SadBoi & Blanco - L's Bob Marlich - Les deux Souffrance - Barbecue En Hiver (feat. Chilly Gonzales) Medine - Thalys (feat. Isha) Okis & Mani Deiz - TÉLÉ BOCAL (feat. Gen) Yvnnis - BARA Hamza - DRAGONS (feat. Werenoi) AAMO - CHF (feat. Mairo & Kenzy) Nunca - Passage Nicholas Craven & Boldy James - Spider Webbing Windshields Boldy James & Your Boy Posca - Nancy Botwin

LINKS:  https://distrust.co/software.html - Software page with OSS software Linux distro: https://codeberg.org/stagex/stagex Milksad vulnerability:  https://milksad.info/ In this episode of Cybersecurity Today on the Weekend, host Jim Love engages in a captivating discussion with Anton Livaja  from Distrust. Anton shares his unique career transition from obtaining a BA in English literature at York University to delving into cybersecurity and tech. Anton recounts how he initially entered the tech field through a startup and quickly embraced programming and automation. The conversation covers Anton's interest in Bitcoin and blockchain technology, including the importance of stablecoins, and the frequent hacking incidents in the crypto space. Anton explains the intricacies of blockchain security, emphasizing the critical role of managing cryptographic keys. The dialogue also explores advanced security methodologies like full source bootstrapping and deterministic builds, and Anton elaborates on the significance of creating open-source software for enhanced security. As the discussion concludes, Anton highlights the need for continual curiosity, teamwork, and purpose-driven work in the cybersecurity field. 00:00 Introduction to Cybersecurity Today 00:17 Anton's Journey from Literature to Cybersecurity 01:08 First Foray into Programming and Automation 02:35 Blockchain and Its Real-World Applications 04:36 Security Challenges in Blockchain and Cryptocurrency 13:21 The Rise of Insider Threats and Social Engineering 16:40 Advanced Security Measures and Supply Chain Attacks 22:36 The Importance of Deterministic Builds and Full Source Bootstrapping 29:35 Making Open Source Software Accessible 31:29 Blockchain and Supply Chain Traceability 33:34 Ensuring Software Integrity and Security 38:20 The Role of AI in Code Review 40:37 The Milksad Incident 46:33 Introducing Distrust and Its Mission 52:23 Final Thoughts and Encouragement

More info: https://docs.anthropic.com/en/docs/claude-code/overviewThe AI coding wars have now split across four battlegrounds:1. AI IDEs: with two leading startups in Windsurf ($3B acq. by OpenAI) and Cursor ($9B valuation) and a sea of competition behind them (like Cline, Github Copilot, etc).2. Vibe coding platforms: Bolt.new, Lovable, v0, etc. all experiencing fast growth and getting to the tens of millions of revenue in months.3. The teammate agents: Devin, Cosine, etc. Simply give them a task, and they will get back to you with a full PR (with mixed results)4. The cli-based agents: after Aider's initial success, we are now seeing many other alternatives including two from the main labs: OpenAI Codex and Claude Code. The main draw is that 1) they are composable 2) they are pay as you go based on tokens used.Since we covered all three of the first categories, today's guests are Boris and Cat, the lead engineer and PM for Claude Code. If you only take one thing away from this episode, it's this piece from Boris: Claude Code is not a product as much as it's a Unix utility.This fits very well with Anthropic's product principle: “do the simple thing first.” Whether it's the memory implementation (a markdown file that gets auto-loaded) or the approach to prompt summarization (just ask Claude to summarize), they always pick the smallest building blocks that are useful, understandable, and extensible. Even major features like planning (“/think”) and memory (#tags in markdown) fit the same idea of having text I/O as the core interface. This is very similar to the original UNIX design philosophy:Claude Code is also the most direct way to consume Sonnet for coding, rather than going through all the hidden prompting and optimization than the other products do. You will feel that right away, as the average spend per user is $6/day on Claude Code compared to $20/mo for Cursor, for example. Apparently, there are some engineers inside of Anthropic that have spent >$1,000 in one day!If you're building AI developer tools, there's also a lot of alpha on how to design a cli tool, interactive vs non-interactive modes, and how to balance feature creation. Enjoy!Full Video EpisodeTimestamps[00:00:00] Intro[00:01:59] Origins of Claude Code[00:04:32] Anthropic's Product Philosophy[00:07:38] What should go into Claude Code?[00:09:26] Claude.md and Memory Simplification[00:10:07] Claude Code vs Aider[00:11:23] Parallel Workflows and Unix Utility Philosophy[00:12:51] Cost considerations and pricing model[00:14:51] Key Features Shipped Since Launch[00:16:28] Claude Code writes 80% of Claude Code[00:18:01] Custom Slash Commands and MCP Integration[00:21:08] Terminal UX and Technical Stack[00:27:11] Code Review and Semantic Linting[00:28:33] Non-Interactive Mode and Automation[00:36:09] Engineering Productivity Metrics[00:37:47] Balancing Feature Creation and Maintenance[00:41:59] Memory and the Future of Context[00:50:10] Sandboxing, Branching, and Agent Planning[01:01:43] Future roadmap[01:11:00] Why Anthropic Excels at Developer Tools Get full access to Latent.Space at www.latent.space/subscribe

More info: https://docs.anthropic.com/en/docs/claude-code/overview The AI coding wars have now split across four battlegrounds: 1. AI IDEs: with two leading startups in Windsurf ($3B acq. by OpenAI) and Cursor ($9B valuation) and a sea of competition behind them (like Cline, Github Copilot, etc). 2. Vibe coding platforms: Bolt.new, Lovable, v0, etc. all experiencing fast growth and getting to the tens of millions of revenue in months. 3. The teammate agents: Devin, Cosine, etc. Simply give them a task, and they will get back to you with a full PR (with mixed results) 4. The cli-based agents: after Aider's initial success, we are now seeing many other alternatives including two from the main labs: OpenAI Codex and Claude Code. The main draw is that 1) they are composable 2) they are pay as you go based on tokens used. Since we covered all three of the first categories, today's guests are Boris and Cat, the lead engineer and PM for Claude Code. If you only take one thing away from this episode, it's this piece from Boris: Claude Code is not a product as much as it's a Unix utility. This fits very well with Anthropic's product principle: “do the simple thing first.” Whether it's the memory implementation (a markdown file that gets auto-loaded) or the approach to prompt summarization (just ask Claude to summarize), they always pick the smallest building blocks that are useful, understandable, and extensible. Even major features like planning (“/think”) and memory (#tags in markdown) fit the same idea of having text I/O as the core interface. This is very similar to the original UNIX design philosophy: Claude Code is also the most direct way to consume Sonnet for coding, rather than going through all the hidden prompting and optimization than the other products do. You will feel that right away, as the average spend per user is $6/day on Claude Code compared to $20/mo for Cursor, for example. Apparently, there are some engineers inside of Anthropic that have spent >$1,000 in one day! If you're building AI developer tools, there's also a lot of alpha on how to design a cli tool, interactive vs non-interactive modes, and how to balance feature creation. Enjoy! Timestamps [00:00:00] Intro [00:01:59] Origins of Claude Code [00:04:32] Anthropic's Product Philosophy [00:07:38] What should go into Claude Code? [00:09:26] Claude.md and Memory Simplification [00:10:07] Claude Code vs Aider [00:11:23] Parallel Workflows and Unix Utility Philosophy [00:12:51] Cost considerations and pricing model [00:14:51] Key Features Shipped Since Launch [00:16:28] Claude Code writes 80% of Claude Code [00:18:01] Custom Slash Commands and MCP Integration [00:21:08] Terminal UX and Technical Stack [00:27:11] Code Review and Semantic Linting [00:28:33] Non-Interactive Mode and Automation [00:36:09] Engineering Productivity Metrics [00:37:47] Balancing Feature Creation and Maintenance [00:41:59] Memory and the Future of Context [00:50:10] Sandboxing, Branching, and Agent Planning [01:01:43] Future roadmap [01:11:00] Why Anthropic Excels at Developer Tools

News includes the release of Elixir 1.18.2 with various enhancements and bug fixes, a new experimental SQL sigil for Ecto that brings automatic parameterized queries, a recent GOTO 2025 talk featuring Saša Jurić on code reviews. We talked with Jonatan Kłosko about his work on PythonX, a new library for executing Python code inside Elixir, the Fine library for working with C++ NIFs, and much more! Show Notes online - http://podcast.thinkingelixir.com/244 (http://podcast.thinkingelixir.com/244) Elixir Community News https://gigalixir.com/thinking (https://gigalixir.com/thinking?utm_source=thinkingelixir&utm_medium=shownotes) – Visit Gigalixir.com to sign up and get 20% off your first year. Or use the promo code "Thinking" during signup. https://github.com/elixir-lang/elixir/releases/tag/v1.18.2 (https://github.com/elixir-lang/elixir/releases/tag/v1.18.2?utm_source=thinkingelixir&utm_medium=shownotes) – Elixir 1.18.2 was released with enhancements to Code.Fragment and Regex, plus bug fixes for CLI, ExUnit, IEx.Autocomplete, and mix deps.update. https://github.com/elixir-lang/elixir/releases/tag/v1.18.1 (https://github.com/elixir-lang/elixir/releases/tag/v1.18.1?utm_source=thinkingelixir&utm_medium=shownotes) – Elixir 1.18.1 included bug fixes for Kernel, ExUnit.Case, mix compile.elixir, mix escript.build, and Mix.Shell, especially related to error handling and Windows compatibility. https://www.erlang.org/news/174 (https://www.erlang.org/news/174?utm_source=thinkingelixir&utm_medium=shownotes) – Erlang OTP 28 RC-1 is out with a new source Software Bill of Materials (SBOM) on the Github Releases page. https://github.com/elixir-dbvisor/sql (https://github.com/elixir-dbvisor/sql?utm_source=thinkingelixir&utm_medium=shownotes) – A new experimental SQL sigil for Ecto brings an extensible SQL parser to Elixir with automatic parameterized queries. https://groups.google.com/g/elixir-ecto/c/8MOkRFAdLZc (https://groups.google.com/g/elixir-ecto/c/8MOkRFAdLZc?utm_source=thinkingelixir&utm_medium=shownotes) – The experimental SQL sigil for Ecto is being discussed on the Elixir-Ecto mailing list. https://www.youtube.com/watch?v=AYUNI2Pm6_w (https://www.youtube.com/watch?v=AYUNI2Pm6_w?utm_source=thinkingelixir&utm_medium=shownotes) – New talk from GOTO 2025 with Saša Jurić and Adrienne Braganza Tacke on "Small PRs, Big Impact - The Art of Code Reviews." https://alchemyconf.com/ (https://alchemyconf.com/?utm_source=thinkingelixir&utm_medium=shownotes) – AlchemyConf is coming up March 31 - April 3 in Braga, Portugal. https://www.gigcityelixir.com/ (https://www.gigcityelixir.com/?utm_source=thinkingelixir&utm_medium=shownotes) – GigCity Elixir and NervesConf are happening in Chattanooga, TN, USA with NervesConf on May 8 and the main event on May 9-10. https://www.elixirconf.eu/ (https://www.elixirconf.eu/?utm_source=thinkingelixir&utm_medium=shownotes) – ElixirConf EU will be held May 15-16, 2025 in Kraków & Virtual. https://goatmire.com/#tickets (https://goatmire.com/#tickets?utm_source=thinkingelixir&utm_medium=shownotes) – Goatmire tickets are on sale now for the event happening September 10-12, 2025 in Varberg, Sweden. Do you have some Elixir news to share? Tell us at @ThinkingElixir (https://twitter.com/ThinkingElixir) or email at show@thinkingelixir.com (mailto:show@thinkingelixir.com) Discussion Resources https://dashbit.co/blog/dashbit-plans-2025 (https://dashbit.co/blog/dashbit-plans-2025?utm_source=thinkingelixir&utm_medium=shownotes) https://github.com/thewca/wca-live (https://github.com/thewca/wca-live?utm_source=thinkingelixir&utm_medium=shownotes) – Speed cubing software https://dashbit.co/blog/running-python-in-elixir-its-fine (https://dashbit.co/blog/running-python-in-elixir-its-fine?utm_source=thinkingelixir&utm_medium=shownotes) https://hexdocs.pm/pythonx/Pythonx.html (https://hexdocs.pm/pythonx/Pythonx.html?utm_source=thinkingelixir&utm_medium=shownotes) https://github.com/livebook-dev/pythonx (https://github.com/livebook-dev/pythonx?utm_source=thinkingelixir&utm_medium=shownotes) https://bsky.app/profile/josevalim.bsky.social/post/3liyrfvlth22c (https://bsky.app/profile/josevalim.bsky.social/post/3liyrfvlth22c?utm_source=thinkingelixir&utm_medium=shownotes) – Jose said “We said we will focus on interoperability for 2025 and we are ready to share the first results.” https://github.com/elixir-nx/fine (https://github.com/elixir-nx/fine?utm_source=thinkingelixir&utm_medium=shownotes) – “Fine” is a new package related to the elixir-nx organization. It's a C++ library enabling more ergonomic NIFs, tailored to Elixir. https://peps.python.org/pep-0703/ (https://peps.python.org/pep-0703/?utm_source=thinkingelixir&utm_medium=shownotes) – Discussion about removing the Python GIL Find us online - Message the show - Bluesky (https://bsky.app/profile/thinkingelixir.com) - Message the show - X (https://x.com/ThinkingElixir) - Message the show on Fediverse - @ThinkingElixir@genserver.social (https://genserver.social/ThinkingElixir) - Email the show - show@thinkingelixir.com (mailto:show@thinkingelixir.com) - Mark Ericksen on X - @brainlid (https://x.com/brainlid) - Mark Ericksen on Bluesky - @brainlid.bsky.social (https://bsky.app/profile/brainlid.bsky.social) - Mark Ericksen on Fediverse - @brainlid@genserver.social (https://genserver.social/brainlid) - David Bernheisel on Bluesky - @david.bernheisel.com (https://bsky.app/profile/david.bernheisel.com) - David Bernheisel on Fediverse - @dbern@genserver.social (https://genserver.social/dbern)

Roy Derks, Developer Experience at IBM, talks about the integration of Large Language Models (LLMs) in web development. We explore practical applications such as building agents, automating QA testing, and the evolving role of AI frameworks in software development. Links https://www.linkedin.com/in/gethackteam https://www.youtube.com/@gethackteam https://x.com/gethackteam https://hackteam.io We want to hear from you! How did you find us? Did you see us on Twitter? In a newsletter? Or maybe we were recommended by a friend? Let us know by sending an email to our producer, Emily, at emily.kochanekketner@logrocket.com (mailto:emily.kochanekketner@logrocket.com), or tweet at us at PodRocketPod (https://twitter.com/PodRocketpod). Follow us. Get free stickers. Follow us on Apple Podcasts, fill out this form (https://podrocket.logrocket.com/get-podrocket-stickers), and we'll send you free PodRocket stickers! What does LogRocket do? LogRocket provides AI-first session replay and analytics that surfaces the UX and technical issues impacting user experiences. Start understand where your users are struggling by trying it for free at [LogRocket.com]. Try LogRocket for free today.(https://logrocket.com/signup/?pdr) Special Guest: Roy Derks.

In this episode, I sit down with Stephen Smith, founder of the Center for Building, to uncover the labyrinthine world of building codes—how they're made, who really influences them, and why they end up stifling the very innovation and affordability we need. From the peculiarities of elevator requirements to the often overlooked role of local politics and special interests, we unpack how these complex rules shape our homes, buildings, and cities more than we might realize.TAKEAWAYSWhy Building Codes Matter: Even if you're not a builder or developer, codes dictate your home's layout, the price of housing, and whether your favorite small condo project can even get off the ground.The ICC (International Code Council) Isn't Really “International”: You'll learn how this non-governmental body, which writes most U.S. building codes, can be both extremely influential and surprisingly insular.Over-Regulation's Real-World Costs: We break down how elevator mandates, fire codes, and accessibility requirements—though well-intended—sometimes create perverse incentives that drive up costs or discourage better solutions.Local Politics & Hidden Interests: Discover how “government members” and private manufacturers shape these codes, and why your mayor or city council may have little say in regulations that affect everyone.A Path Forward: Stephen shares practical ways policymakers and citizens can get involved in reforming the system, focusing on a more human-centered approach that balances safety, cost, and beautyCHAPTERS00:00 The Challenge of Building Codes in the U.S.03:06 The Role of Building Codes in Urban Development05:46 Understanding the American Way of Building09:09 The Impact of Building Codes on Housing Costs12:09 Elevator Regulations and Their Consequences14:52 Harmonization of Codes and Standards18:11 Over-Dimensioning in American Construction21:05 Labor Issues in the Construction Industry23:57 The Need for Code Review and Justification26:49 The Tyranny of Bureaucracy in Building Codes38:49 The Grenfell Fire and Its Aftermath45:05 Design Innovations in Building Codes48:25 Understanding the ICC and Code Writing58:09 The Revolving Door: Industry and Regulation01:07:26 The Role of Government in Building Codes01:15:20 Getting Involved: Supporting Change in Building CodesCONNECT WITH STEPHENCenter for Building https://www.centerforbuilding.org/BlueSky: https://bsky.app/profile/stephenjacobsmith.com Email: stephen@centerforbuilding.orgMENTIONED RESOURCESElevator research: https://admin.centerforbuilding.org/wp-content/uploads/2024/12/Elevators.pdf CONNECT WITH AUSTIN TUNNELLNewsletter: https://playbook.buildingculture.com/https://www.instagram.com/austintunnell/https://www.linkedin.com/in/austin-tunnell-2a41894a/https://twitter.com/AustinTunnellCONNECT WITH BUILDING CULTUREhttps://www.buildingculture.com/https://www.instagram.com/buildingculture/https://twitter.com/build_culturehttps://www.facebook.com/BuildCulture/SPONSORSThank you so much to the sponsors of The Building Culture Podcast!Sierra Pacific Windows: https://www.sierrapacificwindows.com/One Source Windows: https://onesourcewindows.com/

Merrill Lutsky is the cofounder and CEO of Graphite, an AI-powered code reviewer that's used by tens of thousands of users. They are backed by amazing investors including Andreessen Horowitz.Merrill's favorite book: Never Split the Difference (Author: Chris Voss)(00:01) Introduction(00:06) Teaching AI to Understand Code(02:40) AI-Assisted Code Generation and Code Review(06:20) Current Landscape of AI-Assisted Code Review(09:04) Motivation Behind Launching Graphite(16:52) Landing the First Paying Users and Early Learnings(21:42) Growth Experiments: Wins and Misses(26:27) Current Scale of Graphite(29:12) Tech Stack Behind Graphite(33:12) Future of AI-Assisted Coding and Graphite's Role(35:37) Rapid Fire Round--------Where to find Merrill Lutsky: LinkedIn: https://www.linkedin.com/in/merrill-lutsky/--------Where to find Prateek Joshi: Newsletter: https://prateekjoshi.substack.com Website: https://prateekj.com LinkedIn: https://www.linkedin.com/in/prateek-joshi-91047b19 X: https://x.com/prateekvjoshi 

This interview was recorded for the GOTO Book Club.http://gotopia.tech/bookclubRead the full transcription of the interview hereAdrienne Braganza Tacke - Senior Developer Advocate at Cisco & Author of "Looks Good To Me: Constructive Code Reviews"Paul Slaughter - Staff Fullstack Engineer at GitLab & Creator of Conventional CommentsRESOURCESAdriennehttps://x.com/AdrienneTackehttps://github.com/AdrienneTackehttps://www.linkedin.com/in/adriennetackehttps://www.instagram.com/adriennetackehttps://www.adrienne.iohttps://blog.adrienne.ioPaulhttps://x.com/souldzinhttps://github.com/souldzinhttps://gitlab.com/pslaughterhttps://gitlab.com/souldzinhttps://souldzin.comDESCRIPTIONPaul Slaughter and Adrienne Braganza Tacke delve into the critical role of communication in code reviews, emphasizing how soft skills can significantly enhance the engineering process. Adrienne, drawing insights from her upcoming book, explores the expectations for software engineers in code reviews, offers practical tips for improving communication, and shares her unique perspective on the parallels between writing and reviewing code.Their conversation highlights the importance of fostering a positive feedback culture and leading by example to create a collaborative environment within teams.RECOMMENDED BOOKSAdrienne Braganza Tacke • "Looks Good to Me": Constructive Code ReviewsAdrienne Braganza Tacke • Coding for KidsGrace Huang • Code Reviews in TechMartin Fowler • RefactoringMatthew Skelton & Manuel Pais • Team TopologiesDave Thomas & Andy Hunt • The Pragmatic ProgrammerBlueskyTwitterInstagramLinkedInFacebookCHANNEL MEMBERSHIP BONUSJoin this channel to get early access to videos & other perks:https://www.youtube.com/channel/UCs_tLP3AiwYKwdUHpltJPuA/joinLooking for a unique learning experience?Attend the next GOTO conference near you! Get your ticket: gotopia.techSUBSCRIBE TO OUR YOUTUBE CHANNEL - new videos posted daily!

In this episode, we dive deep into the dynamics of working solo versus being part of a development team. From the ideal team composition at large companies to the challenges of maintaining open source projects, our hosts share their experiences and insights. Learn about the crucial roles of designers and product managers, the importance of documentation, and why even senior developers still Google Git commands. Whether you're a solo developer looking to collaborate or a team player wanting to improve your workflow, this episode has something for everyone. Chapter Marks00:00 - Introduction01:16 - The Perfect Team Composition02:44 - Different Approaches to Team Building04:37 - Working Without Designers: The FedEx Experience08:10 - Documentation and Project Requirements12:30 - The Role of Documentation in Team Success14:47 - Documentation's Impact on Career Growth15:14 - Onboarding and Documentation Connection16:51 - Open Source Project Management19:45 - Automation in Open Source22:34 - Deals for Devs: Managing Contributors25:29 - Branch Management and PR Workflows29:59 - Solo Development Practices31:21 - Git Commands and Team Workflows35:14 - Open Source Knowledge Barriers38:02 - The Importance of Admitting What You Don't Know39:15 - Episode Wrap-up LinksNick Taylor's Blog Post about GitHub Code Owners - https://dev.to/opensauced/supercharge-your-repository-with-code-owners-4clgB Dougie's GitHub Action for the "Take" command - https://github.com/bdougie/take-action/blob/main/action.ymlChantastic's Git Course on Epic Web - https://www.epicweb.dev/tutorials/git-fundamentalsGitHub Documentation on Squash Merging vs Rebase Merging - https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/incorporating-changes-from-a-pull-request/about-pull-request-mergesMerge vs Rebase vs Squash - https://gist.github.com/mitchellh/319019b1b8aac9110fcfb1862e0c97fbGitHub Issue Forms Documentation - https://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/syntax-for-issue-formsGitHub Pull Request Templates Guide - https://docs.github.com/en/communities/using-templates-to-encourage-useful-issues-and-pull-requests/creating-a-pull-request-template-for-your-repositoryGitHub Code Owners Documentation - https://docs.github.com/en/repositories/managing-your-repositorys-settings-and-features/customizing-your-repository/about-code-ownersVirtual Coffee's Hacktoberfest Resources - https://hacktoberfest.virtualcoffee.io/OpenSauce - https://opensauced.pizza/The "Working Genius" Assessment - https://www.workinggenius.com/Gun.io Work Personality Quiz - https://gun.io/workstyle/Deals for Devs Project - https://www.dealsfordevs.com/GitHub Actions Documentation on Release Management - https://docs.github.com/en/actions/sharing-automations/creating-actions/releasing-and-maintaining-actionsConventional Commits Documentation - https://www.conventionalcommits.org/en/v1.0.0/

Are you helping or holding your team back? In this episode, I explore why sharing technical answers with your development team might not be the best way to help them grow. Instead, learn how to use resourceful questions to empower your team members to think critically, solve problems independently, and build long-term confidence. This coaching strategy is rooted in co-active coaching principles and can transform how you mentor junior developers or lead your software team. Grow Faster in Your Tech Career: https://thrivingtechnologist.com/coaching Join the Thriving Tech Community: https://thrivingtechnologist.com/community As a tech lead, manager, or mentor, it's natural to want to provide answers when your team is stuck. But constantly solving problems for them can create dependency and stifle their growth. By shifting from a directive approach to a coaching mindset, you'll help your team develop essential skills, foster ownership, and reduce your own workload in the long run. In this video, I'll share practical examples and techniques you can start using today to guide your team effectively without always giving them the solution. If you've ever felt the pressure to be the “go-to expert” on your team, this episode will challenge that belief and show you a better way to lead. Coaching your team to solve their own problems not only benefits them but also makes you a stronger leader. Watch now to learn how to implement these strategies and take your leadership to the next level! You can also watch this episode on YouTube.  Chapter markers / timelinks: (0:00) Introduction (1:14) 1 Bringing Out The Best in Your Software Team (1:31) 1.1 Solving Problems For People Holds Them Back (2:48) 1.2 Let People Struggle (4:36) 1.3 The Role of Resourceful Questions (11:30) 1.3.1 Examples of Resourceful Questions (11:35) 1.3.1.1 "Have You Broken This Up Into Smaller Pieces?" (12:33) 1.3.1.2 "Have You Really Considered ALL Your Options?" (14:07) 1.3.1.3 "What if I Wasn't Available?" (15:09) 1.4 Shifting From Expert to Coach (22:36) 1.5 The Long-Term Benefits of Coaching (26:16) 2 How To Start Leading Like a Coach (26:48) 2.1 Start Small (28:50) 2.2 You Don't Need Formal Training (29:50) 2.3 Where to Start Coaching (30:02) 2.3.1 Code Reviews (31:08) 2.3.2 Design Reviews  (32:46) 2.3.3 Project Planning (33:30) 2.3.4 Debugging Sessions (34:30) Get Help with Leadership Visit me at thrivingtechnologist.com

Exploring AI, Cybersecurity, and Open Source with Chris Lindsey from Mend.io In this episode of the FINOS podcast, Grizz Griswold interviews Chris Lindsey, a Security Evangelist from Mend.io. They discuss the intersection of AI, cybersecurity, and open-source software. Chris shares his extensive experience in software development and application security, providing insights into secure coding practices, the critical role of open source in development, and practical approaches to managing AI technology within highly regulated industries. Tune in to learn about the challenges and strategies in the evolving landscape of AI and cybersecurity. 00:00 Introduction to AI and Security 01:07 Meet Chris Lindsey of Mend.io 02:34 Chris Lindsey's Origin Story 04:39 Challenges in Secure Software Development 07:00 Open Source Security Concerns 15:28 AI in Code Reviews and Security 20:09 The Role of AI in Cybersecurity 29:25 About Mend.io and Its Tools 32:18 Final Thoughts and Future Discussions Chris Lindsey: https://www.linkedin.com/in/chris-lindsey-39b3915/ Mend.io: https://www.mend.io/ Grizz's Info | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.linkedin.com/in/aarongriswold/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠grizz@finos.org⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Find more info about FINOS: On the web: https://www.finos.org/ Twitter: https://twitter.com/FINOSFoundation LinkedIn: https://www.linkedin.com/company/finosfoundation/ 2024 State of Open Source in Financial Services Download: ⁠https://www.finos.org/state-of-open-source-in-financial-services-2024⁠ FINOS Current Newsletter Here: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.finos.org/newsletter About FINOS FINOS (The Fintech Open Source Foundation) is a nonprofit whose mission is to foster the adoption of open source, open standards, and collaborative software development practices in financial services. It is the center for open source developers and the financial services industry to build new technology projects that have a lasting impact on business operations. As a regulatory compliant platform, the foundation enables developers from these competing organizations to collaborate on projects with a strong propensity for mutualization. It has enabled codebase contributions from both the buy- and sell-side firms and counts over 50 major financial institutions, fintechs and technology consultancies as part of its membership. FINOS is also part of the Linux Foundation, the largest shared technology organization in the world. Get involved and join FINOS as a Member.

In this engaging episode, Mon-Chaio and Andy dive deep into the topic of peer code reviews. They begin by providing historical context, tracing back code review practices to IBM's research in the 70s and 80s, and examine the efficacy of modern-day peer reviews. The hosts debate the true benefits of code reviews, discussing whether they genuinely enhance quality or merely serve as a process ritual. They also explore alternative methods like pair programming, and propose innovative ideas for improving peer review processes, such as detailed walkthroughs and code review checklists. Listeners are encouraged to experiment with different tactics and to consider feedback on their own peer review approaches. Join Mon-Chaio and Andy as they navigate the intricacies of peer reviews and share actionable insights for refining this critical practice in software engineering organizations. References Investigating the effectiveness of peer code review in distributed software development based on objective and subjective data Advances in Software Inspections The Impact of Design and Code Reviews on Software Quality: An Empirical Study Based on PSP Data An empirical study of the impact of modern code reviewpractices on software quality Evaluating Pair Programming with Respect to System Complexity and Programmer Expertise Are Two Heads Better than One? On the Effectiveness of Pair Programming From Async Code Reviews to Co-Creation Patterns

In this episode, Dominik Dorfmeister, TanStack maintainer, joins us to discuss component composition in React. He discusses breaking components apart, managing conditional rendering, and the benefits of early returns in improving code readability and maintainability. Links https://tkdodo.eu/blog/component-composition-is-great-btw https://tkdodo.eu/blog https://github.com/TkDodo https://www.dorfmeister.cc https://x.com/TkDodo https://www.linkedin.com/in/dominik-dorfmeister-8a71051b9 We want to hear from you! How did you find us? Did you see us on Twitter? In a newsletter? Or maybe we were recommended by a friend? Let us know by sending an email to our producer, Emily, at emily.kochanekketner@logrocket.com (mailto:emily.kochanekketner@logrocket.com), or tweet at us at PodRocketPod (https://twitter.com/PodRocketpod). Follow us. Get free stickers. Follow us on Apple Podcasts, fill out this form (https://podrocket.logrocket.com/get-podrocket-stickers), and we'll send you free PodRocket stickers! What does LogRocket do? LogRocket provides AI-first session replay and analytics that surfaces the UX and technical issues impacting user experiences. Start understand where your users are struggling by trying it for free at [LogRocket.com]. Try LogRocket for free today.(https://logrocket.com/signup/?pdr) Special Guest: Dominik Dorfmeister.

To kick off Elixir Wizards Season 13, The Creator's Lab, we're joined by Zach Daniel, the creator of Igniter and the Ash framework. Zach joins hosts Owen Bickford and Charles Suggs to discuss the mechanics and aspirations of his latest brainchild, Igniter—a code generation and project patching framework designed to revolutionize the Elixir development experience. Igniter isn't just about generating code; it's about generating smarter code. By leveraging tools like Sourcerer and Rewrite, Igniter allows developers to modify source code and batch updates by directly interacting with Elixir's AST instead of regex patching. This approach streamlines new project setup and package installations and enhances overall workflow. They also discuss the strategic implications of Igniter for the broader Elixir community. Zach hopes Igniter will foster a more interconnected and efficient ecosystem that attracts new developers to Elixir and caters to the evolving needs of seasoned Elixir engineers. Topics discussed in this episode: Advanced package installation and code generation improve the developer experience Scripting and staging techniques streamline project updates Innovative methods for smoother installation processes in Elixir packages High-level tools apply direct patches to source code Progressive feature additions simplify the mix phx.new experience Chaining installers and composing tasks for more efficient project setup Continuous improvement in developer experiences to boost Elixir adoption Encourage listeners to collaborate by sharing code generation patterns Introduction of a new mix task aimed at removing the "unless" keyword in preparation for Elixir 1.18 You can learn more in the upcoming book "Building Web Applications with Ash Framework" by Zach and Rebecca Links mentioned: https://smartlogic.io/ https://alembic.com.au/blog/igniter-rethinking-code-generation-with-project-patching https://hexdocs.pm/igniter/readme.html https://github.com/ash-project/igniter https://www.zachdaniel.dev/p/serialization-is-the-secret https://www.zachdaniel.dev/p/welcome-to-my-substack https://ash-hq.org/ https://hexdocs.pm/sourceror/readme.html https://smartlogic.io/podcast/elixir-wizards/s10-e09-hugo-lucas-future-of-elixir-community/ https://github.com/hrzndhrn/rewrite https://github.com/zachdaniel https://github.com/liveshowy/webauthn_components https://hexdocs.pm/elixir/Regex.html https://github.com/msaraiva/vscode-surface https://github.com/swoosh/swoosh https://github.com/erlef/oidcc https://alembic.com.au/ https://www.zachdaniel.dev/ Special Guest: Zach Daniel.

Code reviews can be highly beneficial but tricky to execute well due to the human factors involved, says Adrienne Braganza Tacke, author of *Looks Good to Me: Actionable Advice for Constructive Code Review.* In a recent conversation with *The New Stack*, Tacke identified three challenges teams must address for successful code reviews: ambiguity, subjectivity, and ego.Ambiguity arises when the goals or expectations for the code are unclear, leading to miscommunication and rework. Tacke emphasizes the need for clarity and explicit communication throughout the review process. Subjectivity, the second challenge, can derail reviews when personal preferences overshadow objective evaluation. Reviewers should justify their suggestions based on technical merit rather than opinion. Finally, ego can get in the way, with developers feeling attached to their code. Both reviewers and submitters must check their egos to foster a constructive dialogue.Tacke encourages programmers to first review their own work, as self-checks can enhance the quality of the code before it reaches the reviewer. Ultimately, code reviews can improve code quality, mentor developers, and strengthen team knowledge. Learn more from The New Stack about code reviews:The Anatomy of Slow Code Reviews One Company Rethinks Diff to Cut Code Review TimesHow Good Is Your Code Review Process?Join our community of newsletter subscribers to stay on top of the news and at the top of your game. 

In this episode of the Modern Web Podcast, we sit down with Shashi Lo, Senior UX Engineer at Microsoft and the founder of the Gridiron Survivor project. Shashi shares his approach to mentoring junior developers by helping them bridge the gap between boot camp and their first job in tech. We cover the challenges of onboarding, the importance of code reviews, and how companies can better support new talent by investing in mentorship and training. Shashi also talks about his experience with building a community of learners, the process of de-risking junior candidates, and why companies should be more proactive in nurturing the next generation of developers.  00:00 - Meet Shashi Lo 02:25 - The Gridiron Survivor Project 05:02 - The Importance of Code Reviews 07:25 - Teaching the Basics of Project Communication 09:47 - Code Reviews as a Learning Tool 12:06 - Why Shashi Mentors: Giving Back to the Community 14:26 - The Importance of De-Risking Junior Candidates 16:41 - Building in Public: Transparency and Learning 19:00 - Assessing Candidates for the Gridiron Survivor Project 21:25 - The Power of Simple Coding Tests 23:45 - Scaling Up Skills: From Small Tasks to Big Projects 26:07 - Should Companies Be Doing This? 28:25 - Finding Hidden Gems in the Job Market 30:47 - The Challenges of Filtering Candidates 33:02 - Where to Find Shashi Online 34:38 - Closing Remarks Follow Shashi Lo on Social Media Twitter: https://x.com/shashiwhocodes Linkedin: https://www.linkedin.com/in/shashilo/ Github: https://github.com/shashilo Sponsored by This Dot.

“A lot of problems that we are facing in code review are due to the interface. We are not actually giving feedback to a person, but we are just filling in text boxes and looking at code." Dr. Michaela Greiler is a code review advocate and runs the “Awesome Code Reviews” workshops. In this episode, we discuss the importance of making code reviews awesome. We discuss the common challenges of code reviews, emphasizing the need for a balanced approach that considers both the technical and social aspects. Michaela also shares insights on how to assess and improve code review practices within teams, highlighting the code review quadrant of review speed and feedback value. Our discussion further explores the correlation between code reviews and developer experience, emphasizing the role of psychological safety and a positive feedback culture. Finally, Michaela provides valuable tips on code review tools and techniques, including the use of checklists and strategies for managing context switching. If you're looking to enhance your team's code review process and foster a positive developer experience, listen now and take your code reviews to the next level!   Listen out for: Career Journey - [00:01:53] Awesome Code Review - [00:05:33] Assessing Code Review Practices - [00:11:41] Code Review Speed - [00:18:23] Code Review & Developer Experience - [00:23:31] Improving Code Review Cultural Aspect - [00:29:13] Code Review Tools - [00:35:36] Code Review Techniques - [00:42:11] Context Switching - [00:48:15] 3 Tech Lead Wisdom - [00:54:14] _____ Michaela Greiler's BioFor over 15 years, Michaela Greiler has helped software teams build high-quality software in an efficient and effective way. Her mission is to lead teams to unlock their full potential during company workshops and team coaching sessions. She shares her findings through articles on her blog or in scientific journals, in publications such as freecodecamp and at conferences. She also runs a weekly newsletter that more than 2500 people enjoy. In this newsletter, she shares her thoughts on relevant software engineering topics and helps you stay up-to-date. She's also the host of the software engineering unlocked podcast. Here, she interviews experienced developers, product managers and CTOs about how they build software. Follow Michaela: Awesome Code Reviews – awesomecodereviews.com Personal Website – michaelagreiler.com LinkedIn – linkedin.com/in/mgreiler Twitter – @mgreiler SE Unlocked Podcast – softwareengineeringunlocked.com _____ Our Sponsors Enjoy an exceptional developer experience with JetBrains. Whatever programming language and technology you use, JetBrains IDEs provide the tools you need to go beyond simple code editing and excel as a developer.Check out FREE coding software options and special offers on jetbrains.com/store/#discounts.Make it happen. With code. Manning Publications is a premier publisher of technical books on computer and software development topics for both experienced developers and new learners alike. Manning prides itself on being independently owned and operated, and for paving the way for innovative initiatives, such as early access book content and protection-free PDF formats that are now industry standard.Get a 40% discount for Tech Lead Journal listeners by using the code techlead24 for all products in all formats. Like this episode?Show notes & transcript: techleadjournal.dev/episodes/189.Follow @techleadjournal on LinkedIn, Twitter, and Instagram.Buy me a coffee or become a patron.

Guest: Abraham Aranguren, Managing Director at 7ASecurity [@7aSecurity]On LinkedIn | https://www.linkedin.com/in/abrahamaranguren/____________________________Hosts: Sean Martin, Co-Founder at ITSPmagazine [@ITSPmagazine] and Host of Redefining CyberSecurity Podcast [@RedefiningCyber]On ITSPmagazine | https://www.itspmagazine.com/sean-martinMarco Ciappelli, Co-Founder at ITSPmagazine [@ITSPmagazine] and Host of Redefining Society PodcastOn ITSPmagazine | https://www.itspmagazine.com/itspmagazine-podcast-radio-hosts/marco-ciappelli____________________________Episode NotesIn this On Location episode recorded in Lisbon at the OWASP AppSec Global event, Sean Martin engages in a comprehensive discussion with Abraham Aranguren, a cybersecurity trainer skilled at hacking IoT, iOS, and Android devices. The conversation delves into the intricacies of mobile application security, touching on both the technical and procedural aspects that organizations must consider to build and maintain secure apps.Abraham Aranguren, known for his expertise in cybersecurity training, shares compelling insights into identifying IoT vulnerabilities without physically having the device. By reverse engineering applications, one can uncover potential security flaws and understand how apps communicate with their IoT counterparts. For instance, Aranguren describes exercises where students analyze mobile apps to reveal hardcoded passwords and unsecured Wi-Fi connections used to manage devices like drones.A significant portion of the discussion revolves around real-world examples of security lapses in mobile applications. Aranguren details an incident involving a Chinese government app that harvests personal data from users' phones, highlighting the serious privacy implications of such vulnerabilities. Another poignant example is Hong Kong's COVID-19 contact-tracing app, which stored sensitive user information insecurely, revealing how even high-budget applications can suffer from critical security flaws if not properly tested.Sean Martin, drawing from his background in software quality assurance, emphasizes the importance of establishing clear, repeatable processes and workflows to ensure security measures are consistently applied throughout the development and deployment phases. He and Aranguren agree that while developers need to be educated in secure coding practices, organizations must also implement robust processes, including code reviews, automated tools for static analysis, and third-party audits to identify and rectify potential vulnerabilities.Aranguren stresses the value of pentests, noting that organizations often show significant improvement over multiple tests. He shares experiences of clients who, after several engagements, greatly reduced the number of exploitable vulnerabilities. Regular, comprehensive testing, combined with a proactive approach to fixing identified issues, helps create a robust security posture, ultimately making applications harder to exploit and dissuading potential attackers.For businesses developing apps, this episode underscores the necessity of integrating security from the ground up, continuously educating developers, enforcing centralized security controls, and utilizing pentests as a tool for both validation and education. The ultimate goal is to make applications resilient enough to deter attackers, ensuring both the business and its users are protected.Be sure to follow our Coverage Journey and subscribe to our podcasts!____________________________Follow our OWASP AppSec Global Lisbon 2024 coverage: https://www.itspmagazine.com/owasp-global-2024-lisbon-application-security-event-coverage-in-portugalOn YouTube:

Key Insights:Importance of Code Reviews: Code reviews are essential for error detection, understanding new features, adhering to coding standards, and ensuring only reviewed code is deployed.Emotional Impact: Emotional dynamics play a significant role, with 30% of developers reviewing code from less favored colleagues, which can lead to biased judgments and negative feelings.Striving for Objectivity: Despite personal feelings, approximately 76% of developers strive for objectivity to maintain professionalism.Impact of Developer Experience: The experience level of a developer also influences the depth of code reviews and the manner in which feedback is provided.Perceptions Formed: Reviewers' perceptions of code quality can affect their views on the author's skills or character.Strategies to Mitigate Bias: The episode outlines multiple strategies to reduce bias in code reviews, such as involving multiple reviewers, standardizing review criteria, and implementing anonymous reviews.Additional Resources: Read the full paper called "How social interactions can affect Modern Code Review"Visit awesomecodereviews.com to discover Dr. McKayla's latest article on the top 10 code review techniques and methodologies, including systematic approaches like using checklists and change-impact analysis.Conclusion: The podcast sheds light on both the positive and negative impacts of human factors in code reviews and emphasizes the need for strategies to minimize bias, enhancing both code quality and team dynamics.

In Office Hours Episode 6, SmartLogic Developers Anna Dorigo and Bilal Hankins join Elixir Wizards Sundi and Dan to discuss their experiences maintaining a decade-old Ruby on Rails codebase. They delve into the critical importance of deeply understanding the codebase, keeping dependencies current, and adapting to the original application's evolving priorities and design choices. The conversation spans a range of topics, including accessibility, testing, monitoring, and the challenges of deploying database migrations in production environments. The guests share effective strategies for sustaining and enhancing older codebases, such as employing automated tools, performing code audits, and adhering to clean coding principles. Key topics discussed in this episode: Grasping the legacy codebase and its historical context Overcoming accessibility issues in older applications Safe dependency management and upgrades The effects of application scaling on database performance The critical role of comprehensive test suites in legacy systems Using tools like Sentry for error tracking and performance monitoring The benefits of automated security and dependency scans Juggling client needs with budget constraints Local simulation techniques for large datasets The value of iterative code reviews and maintaining clean code Utilizing git history for contextual understanding Onboarding strategies for legacy projects Removing obsolete code and avoiding "magic numbers" Importance of descriptive naming for better code clarity Leveraging a rich repository of example code for learning and reference Proactive code audits to anticipate issues Managing pull request sizes for smoother reviews Communicating effectively about upgrades and potential impacts Strategies for handling large databases efficiently Ensuring thorough test coverage Keeping open lines of communication with clients regarding ongoing maintenance Links mentioned: COBOL programming language https://developer.ibm.com/languages/cobol/ Ruby on Rails https://rubyonrails.org/ ARIA Rules (Accessible Rich Internet Applications) https://www.w3.org/TR/using-aria/ Shawn Vo on Elixir as a Competitive Advantage https://smartlogic.io/podcast/elixir-wizards/s5e5-vo/ Bundler Audit Ruby Gem https://rubygems.org/gems/bundler-audit/ Sentry application monitoring and error tracking software  https://sentry.io/ Dependabot Github automated dependency updates Mix hex.audit https://hexdocs.pm/hex/Mx.Tasks.Hex.Audit.html Git Blame https://git-scm.com/docs/git-blame Cow hoof trimming videos - The Hoof GP on YouTube (TW graphic imagery) Special Guests: Anna Dorigo and Bilal Hankins.