Podcasts about Devins

Hey folks, Alex here, let me catch you up! I've had a feeling that this week is going to be crazy, as it started on the weekend MiniMax M3, then with Jensen announcing new RTX Spark, NVIDIA's first PC chip packing 1 petaflop of local AI power into thin laptops.A few days later at Microsoft BUILD, Satya & Mustafa from MAI dropped 7 AI models, completely pre-trained from scratch, including a new MAI-thinking-1, MAI-code and MAI-image 2.5 that started topping the image gen charts. Then other image models started racing to the top of the Arena benchmarks, IdeoGram 4 hitting becoming SOTA open weights image-gen model, and Reve 2 beating Nano Banana just a few hours after that. And then today, NVIDIA dropped Nemotron 3 Ultra, their latest 550B open weights model, data and training and Arena published a new agentic eval leaderboard and we got a new Gemma 4 12B. I've had the great pleasure to host Chris (@llm_wizard) from Nvidia, Peter Gostev from Arena and Karan from Nous Research (who were featured prominently by Jensen!) all on the show. Def don't miss this one! Let's get into the details. ThursdAI - Join the flock of folks who know what is happening in AI before everyone else.Open Source LLMs

The new AIEWF website is live! CFPs close in 2 days and we will run our first New Engineer Orientation this weekend, get your tickets booked ASAP as they -will- sell out. Take the AI Engineering Survey and get >$2k in credits and free AIE WF tickets!One of the central tensions in the agents industry is that even while there are major decacorn agent labs like Sierra, Decagon, Notion and Cursor being built up, it is also true that it has never been easier to DIY agents, with a plethora of agent frameworks like LangGraph and Pydantic and Flue, and managed agents from Anthropic and Gemini and Amazon. There has been a wave of companies building their own background agents from Shopify to Stripe to Paradigm to Razorpay, and even Cognition's friends Ramp have built their own coding agent with other friend Modal.You'd think Cognition might feel a bit threatened, but they're not - even after all this, they were way oversubscribed for the $1B Series D they just announced:Walden Yan, coiner of context engineering and Chief Product Officer/Cofounder of Cognition, invited OpenInspect's Cole Murray to talk about why the Devin is in the Details.Full conversation live on the pod today: In retrospect, async agents were the most AGI pilled bet you could make in 2024 - the models weren't good enough yet to vibecode, and people didn't trust AI enough to let it rip, nobody (including early Cognition) was sure about the form factors. Now it is obvious:* The first wave of AI coding tools made the developer faster but remain heavily in the loop. Copilor and Cursor's tab autocomplete are prime examples However, the workflow was still heavily centered around and bottlenecked by the developer's local workflow: a developer in an IDE, watching the model, accepting or rejecting changes, and pushing code one interaction at a time.* The second wave was local agents: Claude Code, Windsurf, Cursor's agents pane: first one and increasingly many terminals all running concurrently.* The current Age of Async Agents points to a different future focused more on agent orchestration which drives end-to-end development.According to previous guest Steve Yegge, there are finer-grained 8 levels to agent adoption, but we have collapsed it into three.As Cursor's Michael Truell put it in The third era of AI software development:Cursor is no longer primarily about writing code. It is about helping developers build the factory that creates their software. This factory is made up of fleets of agents that they interact with as teammates: providing initial direction, equipping them with the tools to work independently, and reviewing their work.The agent should not sit solely inside the developer's flow. It should be setup to work in the background so that you can give it a task, a repo, a machine, a shell, a browser, tests, memory, and review loops to go do the work somewhere else.In less than a year, the sentiment has shifted from avoiding multi-agent systems:to suggesting approaches that actually work:From coining “context engineering” to building the infrastructure behind Devin's 7x PR growth and jump from 16% to 80% of commits across Cognition repos, Walden Yan has had a front-row seat to the background-agent shift. In this episode, Cognition co-founder and CPO Walden Yan joins swyx alongside Cole Murray, creator of OpenInspect, to unpack why everyone is building their own Devin, what changed after the December 2025 model inflection, and why “spec to pull request” is now becoming a real production workflow.We go deep on the architecture of background agents: harness-in-the-box vs out-of-the-box, why Devin separates the “brain” from the machine, why repo setup is still one of the hardest problems, why Docker is not always enough, and how full VMs, snapshots, scoped secrets, GitHub bots, Slack integrations, and video-based testing all fit together. Walden and Cole also dig into memory, MCP limitations, multi-agent orchestration, AI code review, SRE auto-triage, PMs shipping code from Slack, Windsurf 2.0, hybrid frontier/sub-frontier systems, and the real failure mode of uncontrolled vibe coding: your codebase regressing to your worst engineer.And as agents eat software… and software eats the world… you can draw the conclusion on what is next:We discuss:* Why the engineering world is waking up to background agents and cloud agents* The December 2025 model inflection that made spec-to-PR workflows practical* Devin's 7x merged PR growth and rise from 16% to 80% of commits* Why Cole built OpenInspect as an open-source background-agent system* The economics of $20/seat agent products and why monetization is tricky* What Cognition actually sells beyond Devin: infra, onboarding, integrations, and adoption* Harness in the box vs out of the box, and why architecture matters* Why Devin separates the brain from the machine for security and permissions* Repo setup, scoped secrets, Docker Compose, and agent-ready dev environments* Why full VMs matter when agents need to run real applications and test them* Android, macOS, Windows, nested virtualization, and machine-specific agent work* Why testing is much harder than “computer use”* Screenshots, video verification, and the “I know it works” merge moment* GitHub UX, Devin Review, AI reviewers, and agents responding to PR comments* Why MCP alone is not enough for first-class Slack and enterprise integrations* Memory, Knowledge, skills, Claude.md, and why retrieval is still unsolved* Devin's auto-generated memories and the challenge of memory pruning* Always-on agents as permanent PMs for issues, tickets, and product areas* Sub-agents, meta-Devin management, and what multi-agent systems actually add* Why pure auto-merge vibe coding breaks down after about two weeks* AI code smells, lint rules, reward hacking, and Semgrep for agent-written code* GitAI, inline context, and preserving the “why” behind code changes* Local testing, mock servers, older codebases, and preparing companies for agents* Windsurf 2.0 and the handoff between local foreground agents and cloud background agents* SRE auto-triage, support workflows, and agents as first responders* PMs, marketing, and non-engineers creating pull requests from Slack* AI agent budgets, $1k-$5k per engineer spend, and hybrid frontier/sub-frontier systems* The rise of autonomous coding factories and who Cognition is hiringWalden Yan* X: https://x.com/walden_yan* LinkedIn: https://www.linkedin.com/in/waldenyan/Cole Murray* X: https://x.com/_colemurray* LinkedIn: https://www.linkedin.com/in/colemurray/* OpenInspect / Background Agents: https://github.com/ColeMurray/background-agentsTimestamps00:00:00 Introduction00:00:43 Why Everyone Is Building Their Own Devin00:01:57 Devin's 2025 Ramp: 7x PR Growth and 80% of Commits00:03:49 OpenInspect and the Rise of Open-Source Background Agents00:07:59 What Cognition Actually Sells Beyond Devin00:09:56 Background Agent Architecture: Harness In vs Out of the Box00:12:08 Separating the Brain from the Machine00:14:07 Repo Setup, Secrets, Docker, and Full VMs00:19:13 Why Testing Is Harder Than Computer Use00:22:40 Video Verification and the “I Know It Works” Merge Moment00:23:19 GitHub UX, Devin Review, and AI Code Review00:25:42 MCP, Slack, and Enterprise Agent Integrations00:28:59 Memory, Knowledge, and Always-On Agents00:36:16 Sub-Agents, Multi-Agent Orchestration, and Meta-Devin00:43:55 Vibe Coding, Auto-Merge, and Codebase Decay00:48:38 Agent Infra, VPCs, Cloud Providers, and Fast VM Restore00:52:25 AI Code Smells, Reward Hacking, and Code Review Systems00:56:10 Making Codebases Agent-Ready00:58:30 Windsurf 2.0 and the Local-to-Cloud Agent Handoff01:01:15 SRE Auto-Triage, PMs Shipping Code, and Agent Use Cases01:04:32 Agent Budgets, Hybrid Models, and Autonomous Coding Factories01:06:51 Hiring at Cognition and OpenInspect Consulting01:07:45 OutroTranscriptIntroduction: Walden Yan, Cole Murray, and Context EngineeringSwyx [00:00:00]: All right, we're in the studio with Walden Yan, co-founder of Cognition, CPO.Walden [00:00:08]: Happy to be here.Swyx [00:00:09]: Which is a cool title. And coiner of context engineering.Walden [00:00:15]: Although I think there are many people who'd used the terms in various ways beforehand, but I did find that people, both internally and externally, enjoyed the upgrade from prompt engineering or model wrapping into maybe a more thoughtful way to build agents.Swyx [00:00:33]: For those who haven't caught up on that, I have on screen the Don't Build Multi-Agents post, which you should go read on and we might refer to, and Cole Murray, who created OpenInspect.Cole [00:00:43]: Great to be here.Swyx [00:00:43]: So let's talk about it. Everyone is building their own Devins. What's going on?The December Shift: From Handholding Models to Autonomous PRsCole [00:00:51]: So I think the engineering world is waking up to this idea of background agents, cloud agents, whatever you'd like to call it. And I think we saw a shift around the December timeframe of 2025, where the models Opus 4.5 and GPT 5.2, they reached a capability where we moved away from handholding the model and being able to actually more or less autonomously drive the model. And what I mean by that is that we could pretty much go from a specification to a completed pull request, assuming the spec was good enough, with very little friction. And that paradigm alone, I think, changed a lot of how we interact with agents, and opened this world where background agents became more practical.Swyx [00:01:41]: I think for Cole, everyone experienced this in December, but I feel like there was just this increasing ramp, right? There was this moment which was, I think, Sonnet 3.7, where, You guys rewrote Devin in one night or something. So describe 2025 or how it felt from your side.Walden [00:02:01]: In retrospect, we always thought it was ramping up, but then even now, over the last three, four months from today, it's been ramping up even faster. So it's almost funny to be talking about how, big of a leap Sonnet 3.7 was, and honestly, a lot of it was stripping out parts of Devin that were no longer needed with that jump in of intelligence. But I also just think that a lot of the recent leaps, especially, you look at, models like Opus and the latest GPT models, they are reaching levels of autonomy where people are actually finding that they actually can just be hands-off. And people who were once debating, “Oh, do I need to be in the weeds with my model in the IDE? Can I just completely move it off into the cloud?” That's a more serious conversation, and we've seen that in all of our growth charts. Internally there's this funny graph where our usage has, of PRs, our merged PRs, has grown 7X since I forget what it was called.Swyx [00:02:57]: I think Dev, maybe tweeted that. Yes.Walden [00:03:01]: it grew like 7X over, the last, I think it was, two months, three months, something like that. And then you see our engineering headcount growth. It's, gone up by, 10% or something.Swyx [00:03:11]: We were, we were afraid To release this. So this is Devin commit percentages on all Devin repos, was 16% in January and now 80% in March.Walden [00:03:25]: It's a big shift right now. And so it makes sense that a lot of people are now thinking about, buying Devin, but also maybe, trying to build their own and there's Lots of I have a lot of fun building Devin, so I can see why other people would want to build their own cloud agents as well. Matt, well, maybe it's good to hear, what initially inspired you to try to build OpenInspect?OpenInspect: Ramp, Cloud Agents, and Open SourceCole [00:03:49]: OpenInspect came about, through primarily my clients observing how they were using tools like Claude, OpenAI's Codex at the time, and seeing some of the friction that they were having with it. Primarily the Claude was being used through Slack, and a big issue they ran into was that the sessions that were launched were specific to whoever called it via Slack. And so if a PM was the one who invoked the session and they would then go to pass context to engineering can't see the session. And that in itself was a deal breaker because the PM, “Hey, engineering, can you jump in?” But there's nothing to jump in on unless they're copy-pasting out or the single response that came back. And so seeing some of these problems, I had built a similar architecture internally, just to experiment with, test out different ideas as this trend of moving off of localhost was starting to become, And as Ramp released their blog post, I had a lot of the pieces for this already in place, and just thought it would be funny to, see what Claude could do just purely from the blog post. And on my X account, there's actually a thread of where I live tweeted, going through thisCole [00:05:14]: comparing GPT and Claude as both of them are going through it.Swyx [00:05:17]: On the announcement thing or something else?Cole [00:05:19]: right after it got released. We can put it in the show notes. Yeah, it was helpful that I had already knew how to verify the system. I knew what I was looking for. I think Ramp did a great job of really illustrating, the technical aspects of how to build something. It was much more than just like, “Hey, we built a great system.” It was, “And here's how you can build it too.” And so, I resonated a lot with that, just with the problems that I was already seeing, and I thought that, looking around, I didn't really see anything in the open source community that, met this type of system. I think there's a lot that run, in localhost like Superset, Conductor, and many others.But nothing that was actually running in the cloud. And so, I built it, and I thought it was interesting to just open source it and allow anyone to then have a foundation that they can mix and match on top of.The Business of Background Agents: Open Source vs. DevinSwyx [00:06:16]: So literally after Devin was launched was, there was OpenDevin Which became All Hands. I don't know if you tried that orWalden [00:06:22]: I was going to say, one of the things that interested me a lot with OpenInspect was, you didn't try to go make it then something you monetize. There are a lot of, I think, these open source projects would then go and really try to, raise VSwyx [00:06:36]: That's why no OpenDevin. Yeah.Walden [00:06:38]: yeah, and how did you think about that? I thought that was very interesting.Cole [00:06:44]: I thought, and just what I had seen across my clients, was that having a background agent system is going to become a critical infrastructure within their company. And so because of that, I think that I wanted to open source it so that they could fork it and put in whatever customization they wanted. To that question though, I get asked all, “Oh, are you going to raise? Are you going to turn this into a service?”Walden [00:07:08]: I'm sure you've gotten offers.Cole [00:07:09]: but primarily I don't want to do that for a few reasons. One, I think that I don't want to compete for, $20 a seat. I think that is just a really difficult business. I think it's very easy to copy the main pieces of it. Again, I built this fairly quickly. And I think because you are not owning, I guess, the entire stack, it's hard to monetize. You have money being made at the sandbox layer with Daytona, E2b, many other players. You have money being made at the model layer. And you sit in this weird in-between gray area where what are you actually selling? You're selling, I guess, the infrastructure. You're selling, the integrations maybe.Swyx [00:07:55]: let's ask the guy. What are you What are you selling?Walden [00:07:59]: Well, yeah, there's multiple layers to this in practice, and actually it's funny you mentioned the infrastructure, ‘cause when we got started building Devin as well, we had to go figure out how to make the infrastructure as well because,Swyx [00:08:10]: You had to build this two years before everyone else,?Swyx [00:08:15]: Including, the model sideWalden [00:08:17]: It was not, it was not very polished at the start, when we just built it off of raw VMs from cloud providers like EC2, the boot up time was so slow, I think, And especially then, turning off the machines, saving them, and then to be able to bring them back up again when the, when you want Devin to wake up again later. It would just be out cold for like 10 minutes because that's just how long these systems took. They were not built for this repeated down and up usage. And so we actually had to go do all of that. And as a result now, one thing we offer when we go and sell Devin to people is, you don't have to worry about all the compute side of things. We'll make it work. We'll make it work in your cloud if you want it to. But aside from the product, and I want to go into the agents and the tuning of the intelligence part later, but I think a big part of what we do at Cognition as well is to just make sure that your company learns and uses and adopts these coding agents. ‘Cause I think for especially the largest enterprises in the world, you find that there is a lot of people who want to move over to using AI for their day-to-day workloads. But because of the way projects are planned, because, not everyone is literate in using AI in these ways, having a team of engineers who can actually go in and onboard you, set up all the integrations you need, the automations you need to really get to that level of, leverage with AI, is super helpful. And so We do that. We show thought partners to the customers that we work with as well.Swyx [00:09:56]: So let's talk about, architectural stuff. I think that's always, that is something that was the topic of conversation between the two of you. Is this, the mental model that you want to start with or something else? I'll just leave the floor open to you guys.Agent Architecture: Harness in the Box vs. Out of the BoxCole [00:10:11]: I think, maybe we can start here as just a general what are the pieces of a background agent system. And then maybe we can go into some of the nuances of, Decisions that you can make.Swyx [00:10:22]: But I guess I also Like, what, maybe what Walden is saying is the agent is like in this open code box, I guess. Right? This is infra, and then there's, that's the agent. And you had this discussion about whether you put the agent in here or in Out externally. Can you tease that out?Cole [00:10:39]: In a background agent systems, you have a decision to make of where the agent is actually going to run. This is typically described as the harness in the box or out of the box. With running the agent in the box, you're making some trade-offs by doing that. The negative trade-off you're making is primarily security. Because the agent is running in that box, unless you otherwise design it, all of your secrets need to go into that box as well. And given the nature of AI, it can be unpredictable, and you could very easily end up accidentally exfilling your secrets, or other unintended behavior. Now, the out of the box is the idea that we are going to have the actual agent running not directly in the sandbox, and we will have, quote-unquote, the brain of the agent running in some type of worker, control plane. That sandbox then is going to serve as the hands where the brain is basically operating and making tool calls into that environment to manipulate it. I guess other trade-off that you're making between the two systems is that, in my opinion, running it out of the box is much more complex because, you have state that has to be managed, whereas if you're running it in the box, all of the state of that agent is actually in the box, and yes, it's you could persist it elsewhere, but it's all localized and you have less concerns to worry about.Walden [00:12:08]: I think a lot of that, what you mentioned, is why we actually from the start built Devin to what we called separate the brain from the machine. The other thing that this allows you to do is reuse any existing infrastructure you have for dev boxes Perhaps. And so you don't have to worry as much about making a new type of dev box that has all the dependencies the brain needs, as you mentioned, the secrets the brain needs as well. One thing that we've seen some customers run into is, you have a GitHub app and you want Devin, your agent, whatever, be able to interact with GitHub through this application, but then you have different users with different actual permissions. If they are all interacting through the same GitHub app and there's no actual, separation between the system that decides, what it does and the actual secrets on the machine, then you run into an issue where, okay, it's hard to do the separation. But in practice, with Devin, it's much easier because we just say whatever you put on the machine, that is, the scope of basically what the user is free to do, what the agent is free to do. So only put the most scoped secrets on that machine, and then the brain is fully not accessible from the machine. So you don't have to worry about messing with the, any of the most secure parts of the brain if the user is free to do whatever they want with the machine.Swyx [00:13:31]: I was going to just bring, I have this, chart from OpenAI, where I don't know if this is, in the box, out of the box. That is something that they do use to describe it. And then also recently Anthropic did, managed agentsSwyx [00:13:44]: Which is, this is their thing. I don't know. It's all, it's all variations of the same pattern, right?Cole [00:13:49]: So this would be out of the box.Swyx [00:13:51]: Which, is preferable for them because it's less work?Cole [00:13:56]: I would say it's more work.Swyx [00:13:58]: It's more work?Cole [00:13:58]: But it, in my opinion, it is the better architecture of the two. It's just, you're taking on a bit of complexity by doing that.Repo Setup, Docker, and VM-Based Development EnvironmentsWalden [00:14:07]: One thing I've not seen a lot of other players do well is how do you manage what's actually on the box? And this can be complex for many reasons. Let's say you have a big repository that's changing and updating a lot with changing dependencies. How do you make sure that the working environment of the agent actually stays up to date, has all the credentials it needs to, let's say, run the app and test it, and all the things you want your autonomousSwyx [00:14:34]: So a repo setup.Walden [00:14:35]: Exactly. So in, internally At Cognition, we call this repo setup.Cole [00:14:39]: The hardest part ofWalden [00:14:40]: It's been a perennial problem since the start of the company, of how do we help people get this set up? Because not everyone just has, working cloud environments working out of the box. And do you find this to be a common problem withSwyx [00:14:53]: How do you solve it?Walden [00:14:53]: Your clients?Cole [00:14:54]: This is a very common problem, and through my consulting, this is a lot of what I help teams do. A lot of teams don't really have great developer environment setups, if any. A lot of the times it's, “Go talk to Bob and get the secrets,” and that obviously doesn't work when the agent needs to actually set this up. And so a lot of that, most teams are using Docker Compose or some type of microservices. And so for theSwyx [00:15:19]: Even in prod?Cole [00:15:20]: Not in prod. With the OpenInspect, you are using this primarily to interact, and make code changes. There is other use cases, but you can hook, whether through CLI, MCPs, other tools, you can then hook that into your production systems primarily for, SRE type use cases. But you are not, necessarily, trying to test your prod internal microservice through the system.Walden [00:15:48]: And you mentioned Docker Compose. I think one direction we saw some of our friends take early on was, using Docker containers as the level of abstraction for their models. There's lots of reasons, I think, why Docker containers are not great. One thing is, Docker container's not really a true security boundary, for one. But the other is, if you are running real applications, a lot of times those applications use Docker, and then you have to think about Docker in Docker, which is, really weird. And so I think part of, the really hard challenge of getting VMs to work, why did we do that? Well, it was because we realized that you actually needed, full VMs to be able to do these types of things. And especially nowadays where there's actually value in running the application and clicking around and sending you screen recordings of these things. The value just, keeps adding on top of that. But it is a decision I see people run into when they try to build their own systems, is, “Oh, do we, in addition to this, do we put the agent in the machine or out of the machine? Do we use Docker? Do we use something else?” What do you recommend people nowadays?Cole [00:16:57]: I think Docker is a good solution for maybe not running the agent, but running your infrastructure, because that is more or less the same setup your engineers are probably already using. If they're not, then I don't know what they're using. But they're probably already using Docker Compose.Swyx [00:17:14]: I've always had a small candle for web containers. I don't know if you guys have tried them before.Swyx [00:17:19]: To me, they were, supposed to be like Docker Light.Cole [00:17:22]: Is it?Swyx [00:17:22]: I don't know.Cole [00:17:22]: No, I haven't tried it. But yeah, I think any environment that you've set up that is a good experience for your developer naturally lends itself to being easy to set up for the agent. And once you figure out that local developer story, you've more or less solved the agent in a sandbox, environment setup. OpenInspect does have hooks as well, where you can, run a setup SH script that will pre-install everything. You can then pre-snapshot that build so it starts instantly, and then there is a second hook to actually then, restore the state of the sandbox when it comes back. And so you can already have all of those microservices running and basically get the same experience that you would on your machine within the sandbox.Testing Agents: Computer Use, Screenshots, and Real App WorkflowsWalden [00:18:08]: Another thing that we've been thinking a lot about is like Different VM service offerings. Have you had customers where they needed like macOS specific VMs or like Windows specificWalden [00:18:20]: VMs?Walden [00:18:22]: There are like many technologies in the world that only work on specific types of machines, right? If you're building a.NET application that has to run on Windows or like, maybe more commonly if you want to build iOS or macOS Does that workSwyx [00:18:32]: Does Commission supportSwyx [00:18:33]: Choices like that?Walden [00:18:35]: The fundamental architecture we do, because we do the separation, it does support, but the actual work in progress is happening right now on these. Another thing that we've actually recently added support now for, it's in beta, is doing Android development. To do that, we needed to support, I think, nested virtualization within our machines because the VM itself is like a, is a virtualized Firecracker instance, and then you had to then run another Android emulator inside. And there's like weird performance issues that like, it, which is why it's like still in beta. We have to think through these problems, but it unlocks a lot for anyone who wants to do Android development.Swyx [00:19:13]: I was trying to find like a reference video for the testing thing. I couldn't find it, but I think you worked on the testing, capability. Why call it testing and not like computer use or I don't know, it's, what's the general Category of problem?Walden [00:19:26]: I think that when people think about the ability of an AI to run your app and test it, I think they actually over-index on the computer use part of it because computer use in my mind is the literal, okay, you want what button you want to click. Can you emit the right coordinates to go click that button? I think testing is actually a really interesting likeWalden [00:19:48]: Problem-solving, challenge for these AIs because if you wanted to do arbitrary testing, imagine you make a change that spans the frontend and the backend, maybe, even some other like even more deeply nested service. To actually test that change, we have to reason through what-- how do you first run these applications to orchestrate with each other with the right version of the code? Then, okay, how do I trigger the feature or how do I make the thing actually happen? And this can get arbitrarily hard, maybe you have to be an admin. Maybe a certain thing has to be feature flagged on. Maybe, you have to like run two sessions and then send us a very specific word into one of them to trigger a specific behavior. And figuring out how do you do that requires a lot of code base context, requires, a lot of orchestration that we've specifically done. And in some cases, we found that you actually, no one frontier model can actually do this full end-to-end task itself.Walden [00:20:42]: We've seen cases where we actually had to orchestrate different frontier models together to solve this problem together. That is where we spend most of our time when we think about this testing problem, not so much the computer use part. Computer use for what it's worth has gotten a lot better with recent models and it's made that part of the job certainly easier.Swyx [00:20:58]: Especially with like even 4.7, that they released yesterday, apparently like way better in terms of the vision stuff, which is going to be encompassing computer use.Walden [00:21:08]: Having evals for all these as well is something that like takes a while to build up. And having the evals be right is tricky as well. Do you ever see like, clients who are building their own agents have to start standing up evals to make sure things don't regress?Swyx [00:21:25]: Not so much evals in the traditional sense, but specific to the testing part that has just gone in. I just added support for screenshots And in theory you can also do video. I need to put in a plugin to do that. But they do show up natively, and it was a very heavily requested feature, especially after Cursor's recording came out. I think that was very enlightening for everyone of like, “Oh, this is a very good feature to actually have.”, I think with Devin you guys have had this for a while.Swyx [00:21:57]: Oh, yeah. See how screenshots work. Yeah, I don't know if there's anything, super and not obvious. It's like once what feature to build, you can just prompt it and it Will mostly work.Walden [00:22:09]: I think to Walden's point, though, the computer use is a subset of the larger testing problem, and I think that's very specific to the code base that you're working and it's not something that, out of the box that you could just solve it. The-- you do need the code base context to actually know how to test it. And I think in the case of a background agent system, you fortunately do have that code base locally that what is changing and could then inspect it and use that to drive the model.Swyx [00:22:40]: For those who haven't seen it before, this is an example of how it works. You, after the PR is done, you click testing approved, and then it sends you back a video. What I really like is that it labels, It's very small here, but it actually labels what it's testing. And then it-- and then you actually see the cursor and everything. So I don't know, yeah, the engineering in this, just Whatever you want to show. ‘cause this is like, this is one of those like, oh, few of the AGI moments, right? ‘cause Once I look at this, I actually don't I wish I can just merge inside Of Slack instead of going to GitHub ‘cause I don't need to see the code. I know it works.Walden [00:23:19]: Maybe a new feature in Cursor. Yeah, the annotations at the bottom was also a big difference for me when I, when I added those.Swyx [00:23:27]: It's just like, what am I looking at? What are you trying to demonstrate?Walden [00:23:30]: Exactly. There's a surprisingly long tail of small details that ends up making a big difference for this end metric of like how fast do you actually merge the code in. One experience that we spent a lot of time tuning early on was what is the right experience on GitHub for these tools. Because I think, most tools out there when you build the agent, you'll think about, oh, it'll create the PR for you. We try to take that a step further and say, “Oh, what if we actually made sure you could interact Devin, with direct Devin directly on GitHub?” And so we made sure that you can comment on GitHub, and Devin would actually receive those comments and address them back. But there's actually quite a bit of tuning you have to do here because you can imagine that actually like-We recently have Devin Review, for example. Devin Review will post comments on his own PR And then Devin has to then goGitHub Workflows: Devin Review, Comments, and PR AutomationSwyx [00:24:23]: He answers his own comments, which is Really loopy. So like, yeah, I like that it just updates here that it's, that I have commented But usually it's just me saying like, “Hey, merged, fix any merge conflicts.”Walden [00:24:37]: The, so when Devin fixes his own comments, you might be scared that, oh, maybe I'll infinite loop. But we've put a lot of work into making sure it doesn't, both by making sure that the comments are high signal, but also that the agent is thoughtful about what comments it immediately goes and tries to fix, and what comments it's like, “Wait a second, I think you're wrong.” Actually, that's one of my favorite moments is when Devin tells me that I'm wrong, when I try to get it to do something different. But tuning that behavior, actually makes a big difference in terms of how useful the actual GitHub experience is.Cole [00:25:06]: I think to touch on that as well, I think having the AI reviewer integrated into the system is a critical part of this background system. OpenInspect does have that. It has a GitHub code reviewer that you can control the prompt. It does do comments as well. It doesn't do them automatically yet. The capability is there, but it's not fully used.Swyx [00:25:27]: So you have to ask for it?Cole [00:25:28]: you do, yeah. You can tag it on GitHub, and then whatever you named your, GitHub bot, it will then follow up on it. It will then, if you have merge conflicts or whatever you have asked it to resolve, it will then resolve it, but it doesn't do it automatically yet.Integrations: Slack, MCP, and First-Party Agent InterfacesWalden [00:25:42]: Well, I'm curious, what is, the most common thing that people end up requesting, that they still need on top of OpenInspect when you help them go implement it?Cole [00:25:52]: I think a lot of it comes down to actually integrating it into the company. It's one thing to have the background agent system set up, but if it isn't actually integrated into your larger ecosystem, it isn't that useful. It is useful to be able to kick off sessions, but what we really want to be able to do is hook it into all of our other systems, whether that is the production database with read-only credentials, the logs, a Confluence or internal knowledge-based system. I think that is where I see the huge leap for companies, and that can be a challenge for companies as well who are maybe not familiar with exactly how to approach it, especially if they're in environments that have more compliance type things where, access control can be pretty big and how do you deliberately think about these problems, I find to be, one of the problems that comes with a system like this.Walden [00:26:46]: The thing we found is So, MCPs, obviously it has been like this, really big explosion of, oh, you can go, integrate it with all these different things. But to actually get the integration right and the and get the right experience, oftentimes we found that we had to go build our own ad hoc things. I think Slack is a great example of this. You could give your agent a Slack MCP and okay, it can post messages back to you on Slack. But we actually use Devin like a coworker in Slack, and that's how it's been built from the ground up. But to do that, you actually need to, support webhooks that come back, right? And then Devin has to respond in a natural way and then hopefully don't spam your threads too much and annoy the people in your company. So you got to tune that experience just right. Especially when there's a lot of back and forths, we find that we actually have to go beyond the simple MCP integrations in these places.Swyx [00:27:39]: I just pulled up the MCP marketplace. I know this is a Fair amount of work. Is the answer to eventually take first party control of all the top MCPs? Is that theWalden [00:27:48]: I would love a world where you could have something that's more expressive than MCP. That, goes both ways, not just a set of tools, but a proper system that interacts back and lets it Have the right experience with all these interfaces.Swyx [00:28:03]: So there actually is sampling in the MCP spec, but nobody Uses it, right?Walden [00:28:07]: And so I think that's the other part is, actually we found that when the MCP spec starts to get too complicated, it starts to lose its original promise of Being like a simple one-step connect. Now then we have to go figure out how to support all these different variations of things and It starts to look a lot like just building the first party integrations in a lot of these cases now.Cole [00:28:29]: I think it matters, too, how critical it is to your company, right? If this is something that nearly every session is going through, it probably makes sense to own it so that you can make optimizations on top of it Versus just whatever is off the shelf.Swyx [00:28:43]: Awesome. Other than MCPs, what else, sorry, well, I don't know if that's Narrowing in too much on, integrations. But what else? What other elements of building OpenInspect or Devin that you guys really sink on?Memory and Knowledge: What Agents Should RememberCole [00:28:59]: I think, a problem that comes up very frequently is this idea of memories or knowledge base.Swyx [00:29:05]: Oh, boy. How do you solve it?Cole [00:29:08]: so not solved yet, is the short answer.Cole [00:29:11]: it's something, there's a open issue for it, someone asking about it.Swyx [00:29:16]: There's, I, D Wiki hasn't indexed anything about memory yet.Cole [00:29:20]: how I'm seeing it solved across my clients is primarily through skills. I find that skills can be a good gap within that or updating Claude MD, but I think memory as a whole is a pretty unsolved problem, and it is why I've been hesitant to add it. I think there is parts of memory and that can be addressed, but I think as a whole it's a very difficult retrieval problem.Swyx [00:29:44]: Oh my God. RAMP didn't write anything about memory? I see zero search results.Walden [00:29:50]: No. Memory can be quite tricky to get right because it's the retrieval, but also the generation of the memories that can be really tricky. You don't want it to just like Remember very specific details.Swyx [00:29:59]: Walk us through the Devin memory journey because I know there's been a journey.Walden [00:30:03]: the first version of memory that like stuck around for a while was A system we have called Knowledge. And the idea was we wanted it to pick up things over time and not need the user to be proactive about teaching Devin things. So, okay, any time you remind Devin, “Wait, no, that's not quite the way you're supposed to use Git”Like, we actually want Devin to say, “Hey, do you want me to actually just remember this for the future?” And for you to just basically quickly approve or reject and for it to build up over time. ‘Cause I find that, 95%, I think, or some crazy stat like that of the memories that Devin has are all through these auto-generated things. Very few people actually just want to sit down and write big docs on Here's how you're supposed to work with the technology, et cetera. The generation and the retrieval has been something that we've been trying to tune a lot over the years. Generation, you don't want it to remember something like, if you asked one time to like, “Oh, please open as a draft PR,” you don't want to be like, “Oh, everyone forever now should get their PRs as draft PRs.” But you do want some, conveyor. Maybe you want to say like, “Oh, Cole generally likes, things to be created as draft PRs.” Same with retrieval, if you have thousands of these memories, how do you actually make sure they're retrieved at the right time? And that can be quite tricky to do right without exploding the context with a bunch of useful yeah, useless information. Surprising amount of just, eval work to just make sure that, memory is, remains a reliable system as new models come and go.Cole [00:31:31]: Do you have anything that you could share on, memory pruning? And like the temporal aspect of memory?Swyx [00:31:36]: Deleting and forgetting?Walden [00:31:39]: The, today, the, So the things they could do is it could edit memories. And so if your memory used to say like, “Oh, Cole likes to open everything as like a draft PR,” then you can imagine, “No, don't do that.” And then it'll say, “Oh, do you want me to update the memory to be Cole now want everything as, open PRs?” I think that at the same time we don't know if this is going to be the final version of the system. Whatever we have here will probably, translate into the new system that we'll be coming up with. But I think one big difference between two years ago and today is these agents are really good at using anything that resembles a file system natively. And so part of us are, is thinking, “Oh, should we rebuild memories to feel more like a file system that we let the agent navigate on its own?” That's been an interesting exploration. Also similar ideas in the scale space.Swyx [00:32:35]: I am pulling up OpenClaude's memory thing right now. So memory, OpenClaude has like this like daily memory journal thing, right? And you can I mean, that is a file system you can grep through and is a source of truth. I don't know if it's the best. It's probably super noisy, but at least, if you lose something you can discover it or you can apply some, forgetting algorithm to, more ancient memories that don't get recalled again or something. I don't know.Walden [00:33:01]: One thing we've been trying to do to push the boundaries of how you use agents at your company is letting an agent basically have a very similar file, a memory.md or something, and just like be your permanent PM for a specific set of issues maybe. So we have like some Slack channels internally, maybe a Slack channel dedicated to, a specific product like DeepWiki maybe. And you can imagine that, or you want a Devin that never stops, it's just always awake, but it has this like memory dock that it can just maintain for itself about, okay, what are like the number one priorities of what we have to fix and prioritize? Who is responsible for some upcoming work? Maybe they'll even Devin will even tag you on some recurring basis. And so it's been an interesting move to see, okay, how can we actually use Devin for more than just engineering? Can we actually upstream above the engineering process and maybe it's just Devin creating tickets, which then maybe some humans do, but then maybe other Devins do.Swyx [00:34:00]: One of my more fun automations is go research competitors and just suggest stuff to me on a weekly basis. That's the automation. I can't find it right now, but basically it just like, “Look at competitors and suggest things.” “And here are three things that you've suggested that I don't want any more of,” and you just stick that in the prompts. But like I wish actually So for like when I, for example, when I reject a PR, I wish that it updated memory so that I can then just not have to go up, go back and update the scheduled, sync, but anyway, feature request.Walden [00:34:31]: what? We might change it soon. I guess OpenInspect, in the time you've been around, has there been anything you tried to implement but then you had to like undo and like do a different way?OpenInspect Architecture: Webhooks, Control Planes, and Agent StateCole [00:34:41]: Nothing yet, but something that is on my mind. The initial way that I built it was that each of the integrations lives as its own package. And so you have The Slack bot, which is what's handling the webhooks, and then is basically interacting with the control plane. As I'm seeing the system starting to be more integrated, specifically with the GitHub bot integration, I'm considering bringing that all into the central control plane because especially now I want to start, And a request that I'm getting is the ability to monitor, the actual, pull requests being merged, as well as just tracking ofSwyx [00:35:19]: What do I have open?Cole [00:35:21]: What do I have open? How many of these are getting merged? How many comments are showing up? To just understand the health of the system. And so in the case of a GitHub app, you only have one webhook. And so then it's a question of do I put that webhook in that GitHub bot package? That's weird. It doesn't really make sense to live there because that package is more for like the code reviewer. Or do I like centralize it? So that's something that's on my mind of, making that decision. I think the other one we touched on earlier is the harness in the box versus out of the box. I think long term the architecture will eventually come back out of the box. Some of the newer tools that I've added are calling back into the control plane so that you don't have the secrets in the sandbox. And so I think long term I probably will pull the actual, agent out of the box, but I think for now it's fine.Subagents and Multi-Agent Systems: When Parallelism Helps or HurtsSwyx [00:36:16]: Just, a quick question on pulling the agent out of the box. I'm One thing I'm very bullish on this year is agents calling other agents or spawning sub-agents or Whatever you want to call it. Does that make it harder or easier? I can't tell. Because if the harness is in the box, you can just spin up more boxes. If the harness is outside the box, then you're, it's less easy because you are, you have a unicorn pet of a, of a harness that's, living outside the box.Cole [00:36:45]: In theory it would be the same way, right? Whether, one agent has launched many, sub-sessions within it, OpenInspect, for example, can launch sub-sessions and actually create other environments and then monitor them. In the case where it is out of the box, that would basically just be an additional session that's running. And so that session is also running outside of the box. It's running in your worker plane, wherever you're running this. And then you really just have to think about how does your top level agent then interact with it. I do think it can be more complex, just ‘cause again, you have now a more difficult architecture. But I think if you figured it out once, it's probably fine.Swyx [00:37:26]: Well, then I'm just, throwing it open to you in terms of, I call this like meta Devin management. Which is like the, Devin's calling Devins or Devin scheduling Devins or querying trajectories or anything like that. What have you built or unshipped, anything?Cole [00:37:46]: I think one of the surprising things we've seen is that a lot of the ways that, these, separate agents work with each other, and you want them to, parallelize their work, has still mostly followed the same manager sub-agents regime. And a lot of people I think are excited about this world where you have swarms of agents that, talk with each other all over the place. We've actually given Devin an MCP so they can just go arbitrarily message other Devins And create new Devins, et cetera. But I guess, it somehow creates, a really chaotic world in that sense. And so we've still found that most practical use on a day-to-day basis has been one single Devin.Cole [00:38:33]: Figuring out how to segregate the work and get, have other Devins work on it in, a relatively isolated sense, each with their own boxes Not sharing machines, so there's, a very little room for conflict is the regime that you have to create today.Swyx [00:38:50]: I'll call out, the experiments from Cursor, right? This is Wilson Lin's work on Single agent to multi-agent, and you're obviously famously on the side of don't build multi-agent. But they went through the whole thing, only to arrive at, this Which is exactly what Devin has, I think.Cole [00:39:08]: I think there will be a revision to that post at some point AboutSwyx [00:39:12]: Tell us about itCole [00:39:12]: I think multi-agents were very much not at all possible a year ago. You do see more multi-agent experiments today, but you can argue, are they really multi-agents, or are they just just, tool calls,? There are people who, will create sub-agents to go look for XYZ file, XYZ implementation. Has really nice context management benefits because all of the tool calls and tokens that it spends then get collapsed back to just the answer for the main agent. There's a lot of benefits to doing this. We basically have Devin do this with Deep Bookie, make a call out to Deep Bookie, give you back the results, but that feels like a tool call,? It's not like these, two collaborators actually talking back with each, back and forth with each other. But I think the thing that gives me the most bullishness that multi-agents might actually be possible is actually what I said earlier about Devin will actually sometimes tell me I'm wrong and push back, and I think that demonstrates a level of maturity and communication today that makes a multi-agent world possible. One, can two agents who have seen different information come back to each other and actually figure out who is right, what is the correct implementation? They're not just, yes men. Claude, I guess is like, used to just say, what is it? “You're right,” or,Swyx [00:40:25]: “You're absolutely right.”Cole [00:40:26]: “You're absolutely right.” Yeah.Swyx [00:40:28]: The Have you seen, did you seeCole [00:40:29]: The age is overSwyx [00:40:30]: The Codex app troll in Topic? This is the Codex app. Inside of Settings, there's a little, there's a little Easter egg, right? So if you go to, the Themes or Appearance, right? There's all these, color codes, and the top is absolutely, and it's the Topic's colors. Which is such a troll. Anyway.Model Behavior: Pushback, Adversarial Prompts, and Agent SkepticismCole [00:40:53]: I love that Easter egg. Did you discover that yourself?Swyx [00:40:54]: No, it was, someone was, tweeting about it And I was like, I was like, “Is this true?” Because, sometimes people just tweet stuff to, get a rise out of you. But yeah, there you go, in Topic colors.Cole [00:41:06]: Yeah. So yeah, we're out of this regime where, it just says you're absolutely right, and they can have real conversations and real back and forths.Swyx [00:41:13]: You can prompt it as well to be more adversarial or whatever. Yeah. Okay. Yeah, that, I mean, to me, that is more intelligence, right? That is not just something that's, a dumb tool, it's actually pushing back on you I think. Yeah.Cole [00:41:24]: when you mentioned, of course, the blog posts. There was one blog they had where they fed a swarm of agents together and built a browser.Swyx [00:41:34]: That was I think that was the one.Cole [00:41:36]: You can have, likeSwyx [00:41:37]: I think it's the same oneCole [00:41:37]: Creation of it. We found a surprising success of, don't do a swarm or anything, just have one Devin, it does its own context management. Just let it keep running for a while and give it some crazy tasks. I think we asked it to, rebuild, a Windows OS system. And it managed to do it just like, going on for long enough. It'sSwyx [00:41:55]: Was this Andrew's thing?Cole [00:41:58]: there were lots of demos that we ended up not posting, ‘cause at some point we'd just be posting way too much a bunch of, Demos. But I love that because it shows that I think the multi-agent thing still has, a bit of exciting sexiness to it, which is maybe still beyond still, the actual delta it adds to the capabilities of these systems. But it's absolutely the future. I think we're heading in that direction and we can see the progress being made there already.Swyx [00:42:25]: If I were to, make one super minor pushback because I don't feel that confident about it yetCole [00:42:33]: Go for itSwyx [00:42:33]: But I've had Ryan Lopopolo from OpenAI on the pod And he's a super slop cannon, right? Oh my God, that's my coding agent being done. I downloaded this, Peon Ping. I don't know if you guys have heard this. It takes like-, sound packs from popular games like, Command and Conquer and Warcraft, and then it plays it whenever it's done. And so it's like, “Work,” or whatever, “At your command,” or something. Anyway, what I got from the Cursor code base and from Ryan's thing was that there's a slop cannon approach where you try to loosen the single agent's, bottleneck, and I feel like that is, probably an, a very important thing to try to figure out. I don't think anyone's, really solved it. Because then you just have more reviewer slop on top of the agent slop To try to wrangle it all. Ryan will probably very strongly object that I say that he hasn't solved it, but he thinks he's He thinks he's completely solved it. But I think it's still I think it's, very important, ‘cause, that is a bottleneck, right? I feel Devin is slow sometimes Because I'm like, well, yeah, this is very readable and very sensible, but also it is slower than it could be if I just, I want a button to just say, “Just ramp this up 1,000 next parallel, in parallel and just, see what happens,”? And I don't know if that's, feasible at some point in the future.Code Review, Entropy, and AI SlopWalden [00:43:55]: I And we've also run experiments internally where we've basically tried to build entire products, true products that we knew we would eventually ship, but for now, let's try to see if we can do it just by purely, vibe coding on top of each other, auto merge, no code review at all. And then there's this benchmark of how many weeks can you go onto this for Before you say, “We have the trashiest code base.”Walden [00:44:18]: “Let's actually rewrite it from scratch.”Swyx [00:44:19]: Start a new factory, yeah. What'd you find?Walden [00:44:21]: I think we found that the state-of-the-art in December was you can probably, run this for about two weeks. By the end of those two weeks, you'd find that, hey, you want to, change the color of a button. Well, it turns out this button is implemented in, 10 different places, and they, have All these different variations, and oh, you forgot one of them, and actually it's a slightly different color in one spot. And you're like, “Okay, this is too much to work with. Let's actually try to do code review at the same time.” And make sure that we're on top of our software, actually cleaning it up a bit And making sure it's done in a scalable way.Cole [00:44:54]: I think building on that, the idea of, you don't have to look at code, I think is generally a bad idea. And the meme that I have for thatWalden [00:45:03]: What timeline, all right, is Do you think that statement will be true on?Cole [00:45:06]: I think probably for a while it'll be true that you should continue to look at your code. A problem that I see a lot of teams run into that I work with who are embracing AI native, AI first coding, is The meme that I have is that your code base regresses to your worst engineer, because that engineer who is, very gung-ho about AI and is not auditing their code, their pattern starts cementing into the code, and now the AI is referencing their patterns. And so now their if/else block that, is 20 if/elses back and forth, the AI is seeing that as the pattern of how things are done and starts to then exponentially grow this slop. And I find to your point, a pretty good approach to that is having scheduled cleanup, whether by humans or through systems, that are looking for duplication. They then address that. You'll end up with like 12 helpers for how to format a date. And you need to address that, because otherwise it will continue to sprawl.Swyx [00:46:09]: Within balance, I think it's fine to have some duplication, and then sometimes To have garbage collection, right? Yeah. The What I've been, talking about with a lot of engineering leaders is that you want to be very strict about the boundaries between modules, and it's your job as an architect, as a CTO, whatever, to say like, “Okay, here's the hard contract between you guys and you guys. Whatever you do inside this black box is your business. You do whatever. But between these guys, let's be, really damn clear, and any movement must be signed off by a human or me,” or. Then, and like that's that. I don't know if you have any other modifications or advice.Walden [00:46:44]: Well, I guess generally on the topic of, where humans can be useful, I found that ‘cause, some of these, really deep infra problems, sometimes just having a human that just has, really deep expertise can make a big difference. I've actually seen this come into play when actually building agents. So we've had a few friends now, try building their own coding agents, and I think one same problem that I recurringly heard a lot of them run into was this problem of like, “Oh, Grep is really slow on our agents' machines.” And so a lot of them, I assume because they're using AI and they themselves don't have, super deep infra background knowledge, say, “Okay, we're going to go build our own custom Grep index. It's going to be really fast,” and use that as a way around this problem. When we ran into this problem About like, maybe like a year and a half ago when we were, in the early days of building Devin, we obviously didn't have AI then. We just asked our, how to, how to do this. You can just swap out a new Grep index, so.Infrastructure Details: Grep, File Systems, and SandboxesSwyx [00:47:45]: What do you mean you hand-coded Devin? What?Walden [00:47:48]: It's like, can you believe we hand-wrote this code? And we had, our infra people who are really amazing, they were looking into it and they're like, “Oh, what? We realized that actually the root cause of this problem is actually super simple, but like fine-grain detail,” which is that a lot of these virtual machines actually underlying them don't use real file systems. They use these, network file systems where things are actually cached over the network actually in S3. So when you're Grepping, you're actually making network calls Every time you're doing these things, and that's why Grep is extremely slow on these machines. And so again, goes back to, what is all of the crazy infra work that we had to do to actually get these machines working. If you try to do this yourself, there are tons of small details like this, and so we had to eventually go swap out that network file system. ButSwyx [00:48:35]: I think there's a write-up about it, right? Silas did one about the virtual file system.Walden [00:48:38]: Oh, that was a whole other thing. TheSwyx [00:48:39]: Oh, that's a different thingWalden [00:48:40]: The BlockDev file storage formatSwyx [00:48:42]: I'll bring it upWalden [00:48:42]: Which is, a file system format that we built so that the VMs could be spun up and down very quickly. Basically, the intuition behind this is-Imagine you have, a terabyte of disk, and your agent only, wrote, a hundred lines of code on top of that disk. How long does it, say, take to, save and re-bring up that disk? And most systems, because you're not optimizing for this case, it's just, on the order of a terabyte of work because you have to Save all of that and bring it back up. In our system, we try to build a file system that incrementally builds on top of each other. So every time you save and bring the machine back up, you're only doing work that is proportional to effectively the diff in the file system. And so this, shaves off a lot of time in the boot-up process of Devin. I think we This is actually now outdated. We have a newer system inside of Devin. But yeah, there's a lot of tiny details you have to get right here to actually get the day-to-day experience of Devin to be good.Swyx [00:49:39]: It's, not technically agents, but it is agent infra, and when you sell an agent as a company, you sell agent plus agent infra.Walden [00:49:46]: At least the way we do it be And the other The nice thing about having the agent infra being done together is, you We get to deploy Devin in whatever environment we want now. We don't need to wait for some underlying infra provider to also go and support VPC or on-prem or FedGovCloud, for instance. So we can actually go and figure out, okay, since we own the infrastructure, how can we get that set up for you?Cloud Providers: Modal, Daytona, and Enterprise SandboxesSwyx [00:50:12]: Whereas you're Cloudflare dependent.Cole [00:50:15]: so Cloudflare runs the control plane. The sandboxes, Modal is supported. A contributor just added Daytona. E2B is on the roadmap, and I think there's an abstraction in place that if any contributor wants to add a new provider, they can add that in.Walden [00:50:32]: Well, what are, How are the customers you work with Do they generally try to then go set up a contract with another one of these third-party providers? Do they try to do the VMs in-house?Cole [00:50:44]: most of them I see using Modal. I think Modal has a greatWalden [00:50:48]: Shout out Modal.Swyx [00:50:48]: Shout out Modal.Cole [00:50:50]: I think Modal has a great offering. It captures all of the sandbox pieces you need, snapshots being a pretty big piece of that, and given that they also offer GPUs, I think it's a pretty nice offering as a whole.Swyx [00:51:04]: no debate there.Walden [00:51:07]: Modal is great, especially, I think their container offering is, the most natural, and so especially if you are willing to, forego, the full VM requirements Modal is, a really vast place you can spin something up on.Swyx [00:51:20]: Is there a point So Modal's very Python, and I feel like most workload, has really shifted to JavaScript. I don't know if you guys Get the same feeling. So, okay, when I started Landspace and IE and all these things, I was like 50/50 Python and JS, right? That's roughly. I think that's wrong now. I think JS has won. I don't know if you guys Like, I Maybe I'm overstating it, and maybe for cognition, there's, C# and Java and what have you. But for, new greenfield apps, do you feel that Do you get that sense? Does it matter?Cole [00:51:52]: I think that most of the libraries that I see in this space are Python native first, especially in theCole [00:51:58]: Observability space. That said, I think that there is a pretty big appeal of having your entire system in one language. Especially when you have both your frontend and backend communicating, you can have one central type Which is very nice.Swyx [00:52:11]: That's my case against Modal, which is Then you have to run JS. You can run JS inside Modal. It's just, one extra step That, isn't native to the runtime. I don't know ifWalden [00:52:22]: I don't knowSwyx [00:52:23]: Reviews. Do you have numbers? I don't know.Walden [00:52:25]: the one thing I don't like about Python is whenever AI, whenever it writes Python, it always does, the weirdest patterns, andSwyx [00:52:32]: Oh, because it's, mixing two and three or what?Walden [00:52:34]: I think it's something mixing two and three, yeah. The I don't know if you see this. It always tries to do, has attribute on objects as likeCole [00:52:41]: Oh, my God.Walden [00:52:41]: But it's like But that you shouldn't be doing that. It should error if there wasSwyx [00:52:45]: Because it's training on library code?Cole [00:52:47]: I think it's more of, likeCole [00:52:48]: From what I've seen, it's more of, a reward hacking mechanism where it doesn't want to basicallyWalden [00:52:54]: It'll never error.Cole [00:52:54]: It doesn't want the code to fail. And so it Even when it knows it has the attribute, it'll call getattr on a, and for a lot of my clients who have moved towards more autonomous coding, we've put that in as a lint rule That if you do getattr, your pull request is going to fail.Slop Signatures: Comments, Backwards Compatibility, and TypesSwyx [00:53:12]: Ooh, this is a fun topic. Can you tell me more about this? What else is a sign of AI coding that you have to put guards in?Walden [00:53:21]: So we were talking just before this about Opus 4.7. One of the things this new model likes to do is it writes lots of comments. Not like, it'll, comment every line, but it'll write, paragraph, PRDs, on top of every function. But I will say, to its credit, these aren't slop, descriptions like they were before. “Oh, here's what this function does.” It's like, “Oh, here's actually the r

Think flow is just a "zone" for athletes and artists? Gaëlle Devins says it's survival for leaders. Forget chasing perks, employees don't want ping pong tables. They want to be seen, valued, and led by someone who actually knows their own emotions. In this episode, we unpack why yodeling (yes, yodeling) is a leadership lesson, how twins + bedrest turned into a corporate framework, and why flow is more than KPIs… it's people, purpose, and performance working in harmony. Gaëlle doesn't just talk theory; she lived it across continents and industries. From luxury brands to global teams, she reveals why most leaders are scared to ask the right questions (hint: they don't want the real answers). Plus, how disrespect poisons culture, and why she gave 100% of her book profits to children fighting cancer. This isn't about lowering the bar. It's about daring to care, building authentic flow, and leading in a way that makes Monday feel a little less Monday. Website: https://gaelledevins.com/ LinkedIn: https://www.linkedin.com/in/gaelle-devins/ Book: Flow Leadership: Unleash the Power of People, Purpose, and Performance: https://a.co/d/3JCb2us Timestamps 00:00 Cold Intro & Start 06:15 Cowbells & Yodeling: The Swiss Surprise Nobody Saw Coming 07:08 "Not Just Swiss": Why Flow Starts with a Big Heart 08:54 Bedridden with Twins... and Suddenly Writing a Book 12:59 Flow Defined: Seen, Valued, and At Your Best 17:36 Leaders Don't Know Flow (and That's the Problem) 21:05 Goodbye Command-and-Control, Hello Emotional Connection 25:55 The First Barrier to Flow? You 33:44 People & Purpose: The Two P's Leaders Keep Ignoring 43:41 The Poison of Disrespect: How Toxic Workplaces Begin 44:52 A Magic Wand: Why Every Book Sale Goes to Kids Fighting Cancer

In this episode, I introduce you to Devin R. who is married with three children; two are bonus kids that came with marrying her husband. They have 2 dogs and two cats. In 2018, Devin was looking for help to get organized. She was mowing her lawn when I caught her ear mentioning that I lived in the Cincinnati area. Then Devin's life hit rock bottom with her work insinuating that she was not a good teacher and then she and her husband decided to separate. She could resonate with my story from 2012. Devin looked at her life and decided she needed to get control of her life. Once she grabbed a bag and gathered all the papers she felt better. It was like since her physical chaos was getting under control her mental chaos could be addressed. We had a great discussion that all marriages go through rough patches and Devin was vulnerable to share a little bit about her marriage and how they are reconciling after about three years of separation. She shared how her mindset has shifted about organization. She looks around and asks what do I have? Why is it here? And do we even need it? She remembers making her daughter's lunch recently and didn't have to trip over the cat to get the things for the lunch. It was all in the lunch making station.  Devin had tried other systems but they never felt like life long solutions to her. The podcast and videos work as reminders to keep up with the systems.  They are like a life partner to the systems. And they help with the cognitive load. We talked about the Sunday Basket® and how when you have Prospective Memory and you write something down it's cognitive off loading to free up your working memory.  This is something Devins struggles with because of MS but lets her neurologist know at each visit how much the Sunday Basket® helps and how it could help others with her diagnosis. And then we got into the weeds a little bit about qualitative studies that I want to do based off the information from my (unbeknownst to myself when I started) unofficial quantitative or phenomenological study AKA the Wednesday Transformation episodes.  Devin has been on her organizational journey for about three years now. She's coped with her MS diagnosis, separated from her husband, and is entering the coaching years of parenting. Because of all her systems, she has more time to really invest in her daughter and being present. She really wishes she knew someone taught organization sooner and found Organize 365® sooner.  It takes time to get organized. Devin pointed out that she values the Power of Positivity episode. “Life is not happening to you, it's happening for you.”  With the right attitude you can apply positivity to all journeys, not just organization journeys, no matter how insurmountable they seem. Devin started working upholstering for her sister after she left teaching. She shared how she got the Friday Workbox®, worked on it with Monique at a paper retreat and how it helps her to stay organized on customers' jobs.  I got to offer a little advice on how she could slowly incorporate a part time worker for her to get back some CEO time. A good balance of responsibilities is to spend 80% on production and 20% on admin and lead generation.  With the addition of a part time worker, she could add another client and make money to cover the hours of that part time worker. Devin's advice is, “Jump in one step at a time. Listen to all the podcast episodes and then the videos from The Productive Home Solution™”  EPISODE RESOURCES: The Sunday Basket® The Productive Home Solution™ Home Planning Day The Friday Workbox® Sign Up for the Organize 365® Newsletter  On the Wednesday podcast, I get to talk with members of the Organize 365­® community as they share the challenges, progress, missteps and triumphs along their organizing journey. I am grateful that you are reaching out to share with me and with this community. You can see and hear transformation in action. If you are ready to share your story with us, please apply at https://organize365.com/wednesday. Did you enjoy this episode? Please leave a rating and review in your favorite podcast app. Share this episode with a friend and be sure to tag Organize 365® when you share on social media!

This week on The New P&L - Principles & Leadership and Business podcast we speak with Gaelle Devins, Chief Customer Officer & Member of the Executive Board at Breitling – the Swiss luxury watchmaker. Gaelle is alsothe Founder of FLOW@WORK  and Author of the brand new book, Flow Leadership – unleash the power of people, purpose and performance – which was officially released earlier this month. Key discussion points:- Overview of Flow Leadership - How to measure 'Flow at Work'- Challenges of achieving Flow Leadership - Self-awareness and leadership - Purpose as a commercial imperative- Methodology for finding personal purpose - Barriers to Flow Leadership - The role of curiosity in business Connect with Gaelle: Home - Gaelle Devins - Flow Leadership & FLOW@WORKhttps://www.linkedin.com/in/gaelle-devins/ To order Flow Leadership: https://www.amazon.com/dp/1394344864To book Paul for keynotes: Connect on LinkedIn: https://www.linkedin.com/in/paulspiers/Contact form: https://www.principlesandleadership.com/contact

In this riveting session of Vigilantes Radio Live

In this riveting session of Vigilantes Radio Live

Any manager knows the best work happens when a team is all pulling in the same direction. Gaëlle Devins' new book 'Flow Leadership: Unleash the Power of People, Purpose, and Performance' offers an easy way to ensure this happens. She joins Bobby to tell him more about it.

Scott Wu is the co-founder and CEO of Cognition, the company behind Devin—the world's first autonomous AI software engineer. Unlike other AI coding tools, Devin works like an autonomous engineer that you can interact with through Slack, Linear, and GitHub, just like with a remote engineer. With Scott's background in competitive programming and a previous AI-powered startup, Lunchclub, teaching AI to code has become his ultimate passion.What you'll learn:1. How a team of “Devins” are already producing 25% of Cognition's pull requests, and they are on track to hit 50% by year's end2. How each engineer on Cognition's 15-person engineering team works with about five Devins each3. How Devin has evolved from a “high school CS student” to a “junior engineer” over the past year4. Why engineering will shift from “bricklayers” to “architects”5. Why AI tools will lead to more engineering jobs rather than fewer6. How Devin creates its own wiki to understand and document complex codebases7. The eight pivots Cognition went through before landing on their current approach8. The cultural shifts required to successfully adopt AI engineers—Brought to you by:Enterpret—Transform customer feedback into product growthParagon—Ship every SaaS integration your customers wantAttio—The powerful, flexible CRM for fast-growing startups—Where to find Scott Wu:• X: https://x.com/scottwu46• LinkedIn: https://www.linkedin.com/in/scott-wu-8b94ab96/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Scott Wu and Devin(09:13) Scaling and future prospects(10:23) Devin's origin story(17:26) The idea of Devin as a person(22:19) How a team of “Devins” are already producing 25% of Cognition's pull requests(25:17) Important skills in the AI era(30:21) How Cognition's engineering team works with Devin's(34:37) Live demo(42:20) Devin's codebase integration(44:50) Automation with Linear(46:53) What Devin does best(52:56) The future of AI in software engineering(57:13) Moats and stickiness in AI(01:01:57) The tech that enables Devin(01:04:14) AI will be the biggest technology shift of our lives(01:07:25) Adopting Devin in your company(01:15:13) Startup wisdom and hiring practices(01:22:32) Lightning round and final thoughts—Referenced:• Devin: https://devin.ai/• GitHub: https://github.com/• Linear: https://linear.app/• Waymo: https://waymo.com/• GitHub Copilot: https://github.com/features/copilot• Cursor: https://www.cursor.com/• Anysphere: https://anysphere.inc/• Bolt: https://bolt.new/• StackBlitz: https://stackblitz.com/• Cognition: https://cognition.ai/• v0: https://v0.dev/• Vercel: https://vercel.com/• Everyone's an engineer now: Inside v0's mission to create a hundred million builders | Guillermo Rauch (founder and CEO of Vercel, creators of v0 and Next.js): https://www.lennysnewsletter.com/p/everyones-an-engineer-now-guillermo-rauch• Inside Bolt: From near-death to ~$40m ARR in 5 months—one of the fastest-growing products in history | Eric Simons (founder and CEO of StackBlitz): https://www.lennysnewsletter.com/p/inside-bolt-eric-simons• Assembly: https://en.wikipedia.org/wiki/Assembly_language• Pascal: https://en.wikipedia.org/wiki/Pascal_(programming_language)• Python: https://www.python.org/• Jevons paradox: https://en.wikipedia.org/wiki/Jevons_paradox• Datadog: https://www.datadoghq.com/• Bending the universe in your favor | Claire Vo (LaunchDarkly, Color, Optimizely, ChatPRD): https://www.lennysnewsletter.com/p/bending-the-universe-in-your-favor• OpenAI's CPO on how AI changes must-have skills, moats, coding, startup playbooks, more | Kevin Weil (CPO at OpenAI, ex-Instagram, Twitter): https://www.lennysnewsletter.com/p/kevin-weil-open-ai• Behind the product: Replit | Amjad Masad (co-founder and CEO): https://www.lennysnewsletter.com/p/behind-the-product-replit-amjad-masad• Windsurf: https://windsurf.com/• COBOL: https://en.wikipedia.org/wiki/COBOL• Fortran: https://en.wikipedia.org/wiki/Fortran• Magic the Gathering: https://magic.wizards.com/en• Aura frames: https://auraframes.com/• AirPods: https://www.apple.com/airpods/• Steven Hao on LinkedIn: https://www.linkedin.com/in/steven-hao-160b9638/• Walden Yan on LinkedIn: https://www.linkedin.com/in/waldenyan/—Recommended books:• How to Win Friends & Influence People: https://www.amazon.com/How-Win-Friends-Influence-People/dp/0671027034• The Power Law: Venture Capital and the Making of the New Future: https://www.amazon.com/Power-Law-Venture-Capital-Making/dp/052555999X• The Great Gatsby: https://www.amazon.com/Great-Gatsby-F-Scott-Fitzgerald/dp/0743273567—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

What does Jesus Christ have to do with "Rainbow Body" and what does that mean for people of faith? Tom Devins joins the show to chat about his book "Christianity's Dirty Little Secrets: The Truth About Resurrection, The Rainbow Body, Religion and Reality," as well as what a Rainbow Body is...

Vi fortsätter vår interkontinentala bevakning av Love is Blind. I det åttonde avsnittet snackar vi Daves, typ, slutshaming av Lauren, att Laurens ”ex” bor i huset dem flyttat in i, Devins löjliga skovana och mål att en dag vilja uppnå saker, Virginias prenup, Fam Jam och faktumet att vi har ännu en hatande syster. Plus mycket mer. Enjoy!  Stötta oss på Patreon för regelbundna bonusavsnitt + mer! Hosted on Acast. See acast.com/privacy for more information.

Oh boy. Det är dags för avsnitt 4 av senaste Love is Blind-säsongen! Vi pratar Saras Black Lives Matter-diskussion, Ben som inte kan ta ställning i frågor om mänskliga rättigheter, Alex tunghäfta, Davids fortsatta jonglerande mellan Molly och Lauren samt självklart allt om Devins dömande av Brittanys sexualitet och *checks notes* iprenberoende.  Stötta oss på Patreon för regelbundna bonusavsnitt + mer! Hosted on Acast. See acast.com/privacy for more information.

Vi tar oss an ännu en Love is Blind-säsong! I det första avsnittet snackar vi strategier för short kings (och vad som en räknas som kort), Mollys störiga ansikte, vad som kännetecknet folk från Minnesota, trauma bombing, JD Vance-kopian, Devins hairline och motbevisande av ”black don't crack”-föreställningen, överdrivna skratt, att skämmas för att jobba med plastikoperationer, folk som vill bli ”led”, ”I was listening to this Joe Rogan podcast” som ingång + mycket mer . Och just det, Jon kommer tidigt ut som den största Madison-hataren. Enjoy!  Stötta oss på Patreon för regelbundna bonusavsnitt + mer! Hosted on Acast. See acast.com/privacy for more information.

The holidays are upon us, and with them come shorter days, endless to-do lists, and the temptation to cozy up instead of lacing up. But fear not—this episode is here to inspire you to bust through those running slumps and find joy in your miles!Join me as I chat with two extraordinary runners who bring fun, flair, and purpose to every race they tackle. Whether it's a runDisney event or a World Marathon Major, Ian Hirth and Pearl Devins have a knack for turning every start line into an opportunity to make memories and give back.We'll dive into the unique tradition of the Dead Last Start—a way of embracing the race day experience with humor and heart, all while raising awareness and funds for an incredible cause: the American Cancer Society. Both Ian and Pearl are gearing up to bring this signature move to the Chicago Marathon, and they need your help to hit their fundraising goals!About Our Guests:

Devin asked if bach nation was done yet, and to that I say...nope. Because I am back to have a more fluid and deeper convo with a friend of the pod and advocate- Natalie- together we get into what all this mess means for the franchise. Regarding the pattern it has enabled or even sought to create, the damage it does, and how we got here. CW: open discussion about DV and SA and the systems that they exist in Thank you Natalie!! You rock Follow us on insta @datecardpod Twitter @datecardpodcast Tiktok @datecardpodcast @jennawithasmile  Art: Olivia Zakes Green https://www.oliviazakesgreen.com/ Music: Jed Overly @jeddyboyjames  https://www.flowcode.com/page/jeddyjames Proud podcast of So Below Media

Es ist Halbzeit bei der Bachelorette! In „RTL erleben – der RTL Deutschland Podcast“ lässt Herzkönigin Stella gemeinsam mit Host Dajana Pürsten die ersten sechs Folgen der neuen Staffel Revue passieren – es gibt also ordentlich Gesprächsstoff. Denn dieses Mal ist alles anders: Stella ist bisexuell und sucht in Thailand unter 20 Männern und Frauen ihren Herzensmenschen. Bereits das große Kennenlernen lief dieses Mal anders ab. Wie kamen die Überraschungen bei Stella an? Im Podcast spricht die Bachelorette über ihr Gefühlschaos, Max‘ plötzlichen Abschied und was das bei ihr ausgelöst hat, und vor welchen Herausforderungen sie sich aktuell stehen sieht. Außerdem verrät Stella ihren Geheimtipp für guten Sex, was es mit dem Handy für Leila auf sich hat, wie schwer Devins rauchiges Laster wirklich wiegt, und was sie sich neben der großen Liebe von ihrer einzigartigen Bachelorette-Staffel erhofft. Die neue Folge ist ab sofort auf RTL+ sowie weiteren Podcast-Plattformen kostenfrei abrufbar.

Betteridge's law says no: with seemingly infinite flavors of RAG, and >2million token context + prompt caching from Anthropic/Deepmind/Deepseek, it's reasonable to believe that "in context learning is all you need".But then there's Cosine Genie, the first to make a huge bet using OpenAI's new GPT4o fine-tuning for code at the largest scale it has ever been used externally; resulting in what is now the #1 coding agent in the world according to SWE-Bench Full, Lite, and Verified:SWE-Bench has been the most successful agent benchmark of the year, receiving honors at ICLR (our interview here) and recently being verified by OpenAI. Cognition (Devin) was valued at $2b after reaching 14% on it. So it is very, very big news when a new agent appears to beat all other solutions, by a lot:While this number is self reported, it seems to be corroborated by OpenAI, who also award it clear highest marks on SWE-Bench verified:The secret is GPT-4o finetuning on billions of tokens of synthetic data. * Finetuning: As OpenAI says:Genie is powered by a fine-tuned GPT-4o model trained on examples of real software engineers at work, enabling the model to learn to respond in a specific way. The model was also trained to be able to output in specific formats, such as patches that could be committed easily to codebases. Due to the scale of Cosine's finetuning, OpenAI worked closely with them to figure out the size of the LoRA:“They have to decide how big your LoRA adapter is going to be… because if you had a really sparse, large adapter, you're not going to get any signal in that at all. So they have to dynamically size these things.”* Synthetic data: we need to finetune on the process of making code work instead of only training on working code.“…we synthetically generated runtime errors. Where we would intentionally mess with the AST to make stuff not work, or index out of bounds, or refer to a variable that doesn't exist, or errors that the foundational models just make sometimes that you can't really avoid, you can't expect it to be perfect.”Genie also has a 4 stage workflow with the standard LLM OS tooling stack that lets it solve problems iteratively:Full Video Podlike and subscribe etc!Show Notes* Alistair Pullen - Twitter, Linkedin* Cosine Genie launch, technical report* OpenAI GPT-4o finetuning GA* Llama 3 backtranslation* Cursor episode and Aman + SWEBench at ICLR episodeTimestamps* [00:00:00] Suno Intro* [00:05:01] Alistair and Cosine intro* [00:16:34] GPT4o finetuning* [00:20:18] Genie Data Mix* [00:23:09] Customizing for Customers* [00:25:37] Genie Workflow* [00:27:41] Code Retrieval* [00:35:20] Planning* [00:42:29] Language Mix* [00:43:46] Running Code* [00:46:19] Finetuning with OpenAI* [00:49:32] Synthetic Code Data* [00:51:54] SynData in Llama 3* [00:52:33] SWE-Bench Submission Process* [00:58:20] Future Plans* [00:59:36] Ecosystem Trends* [01:00:55] Founder Lessons* [01:01:58] CTA: Hiring & CustomersDescript Transcript[00:01:52] AI Charlie: Welcome back. This is Charlie, your AI cohost. As AI engineers, we have a special focus on coding agents, fine tuning, and synthetic data. And this week, it all comes together with the launch of Cosign's Genie, which reached 50 percent on SWE Bench Lite, 30 percent on the full SWE Bench, and 44 percent on OpenAI's new SWE Bench Verified.[00:02:17] All state of the art results by the widest ever margin recorded compared to former leaders Amazon Q and US Autocode Rover. And Factory Code Droid. As a reminder, Cognition Devon went viral with a 14 percent score just five months ago. Cosign did this by working closely with OpenAI to fine tune GPT 4. 0, now generally available to you and me, on billions of tokens of code, much of which was synthetically generated.[00:02:47] Alistair Pullen: Hi, I'm Ali. Co founder and CEO of Cosign, a human reasoning lab. And I'd like to show you Genie, our state of the art, fully autonomous software engineering colleague. Genie has the highest score on SWBench in the world. And the way we achieved this was by taking a completely different approach. We believe that if you want a model to behave like a software engineer, it has to be shown how a human software engineer works.[00:03:15] We've designed new techniques to derive human reasoning from real examples of software engineers doing their jobs. Our data represents perfect information lineage, incremental knowledge discovery, and step by step decision making. Representing everything a human engineer does logically. By actually training Genie on this unique dataset, rather than simply prompting base models, which is what everyone else is doing, we've seen that we're no longer simply generating random code until some works.[00:03:46] It's tackling problems like[00:03:48] AI Charlie: a human. Alistair Pullen is CEO and co founder of Kozen, and we managed to snag him on a brief trip stateside for a special conversation on building the world's current number one coding agent. Watch out and take care.[00:04:07] Alessio: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, partner and CTO of Resonance at Decibel Partners, and I'm joined by my co host Swyx, founder of Small. ai.[00:04:16] swyx: Hey, and today we're back in the studio. In person, after about three to four months in visa jail and travels and all other fun stuff that we talked about in the previous episode.[00:04:27] But today we have a special guest, Ali Pullen from Cosign. Welcome. Hi, thanks for having me. We're very lucky to have you because you're on a two day trip to San Francisco. Yeah, I wouldn't recommend it. I would not[00:04:38] Alistair Pullen: recommend it. Don't fly from London to San Francisco for two days.[00:04:40] swyx: And you launched Genie on a plane.[00:04:42] On plain Wi Fi, um, claiming state of the art in SuiteBench, which we're all going to talk about. I'm excited to dive into your whole journey, because it has been a journey. I've been lucky to be a small angel in part of that journey. And it's exciting to see that you're launching to such acclaim and, you know, such results.[00:05:01] Alistair and Cosine intro[00:05:01] swyx: Um, so I'll go over your brief background, and then you can sort of fill in the blanks on what else people should know about you. You did your bachelor's in computer science at Exeter.[00:05:10] Speaker 6: Yep.[00:05:10] swyx: And then you worked at a startup that got acquired into GoPuff and round about 2022, you started working on a stealth startup that became a YC startup.[00:05:19] What's that? Yeah. So[00:05:21] Alistair Pullen: basically when I left university, I, I met my now co founder, Sam. At the time we were both mobile devs. He was an Android developer. iOS developer. And whilst at university, we built this sort of small consultancy, sort of, we'd um, be approached to build projects for people and we would just take them up and start with, they were student projects.[00:05:41] They weren't, they weren't anything crazy or anything big. We started with those and over time we started doing larger and larger projects, more interesting things. And then actually, when we left university, we just kept doing that. We didn't really get jobs, traditional jobs. It was also like in the middle of COVID, middle of lockdown.[00:05:57] So we were like, this is a pretty good gig. We'll just keep like writing code in our bedrooms. And yeah, that's it. We did that for a while. And then a friend of ours that we went to Exeter with started a YC startup during COVID. And it was one of these fast grocery delivery companies. At the time I was living in the deepest, darkest countryside in England, where fast grocery companies are still not a thing.[00:06:20] So he, he sort of pitched me this idea and was like, listen, like I need an iOS dev, do you fancy coming along? And I thought, absolutely. It was a chance to get out of my parents house, chance to move to London, you know, do interesting things. And at the time, truthfully, I had no idea what YC was. I had no idea.[00:06:34] I wasn't in the startup space. I knew I liked coding and building apps and stuff, but I'd never, never really done anything in that area. So I said, yes, absolutely. I moved to London just sort of as COVID was ending and yeah, worked at what was fancy for about a year and a half. Then we brought Sam along as well.[00:06:52] So we, Sam and I, were the two engineers at Fancy for basically its entire life, and we built literally everything. So like the, the front, the client mobile apps, the, the backends, the internal like stock management system, the driver routing, algorithms, all those things. Literally like everything. It was my first.[00:07:12] You know, both of us were super inexperienced. We didn't have, like, proper engineering experience. There were definitely decisions we'd do differently now. We'd definitely buy a lot of stuff off the shelf, stuff like that. But it was the initial dip of the toe into, like, the world of startups, and we were both, like, hooked immediately.[00:07:26] We were like, this is so cool. This sounds so much better than all our friends who were, like, consultants and doing, like, normal jobs, right? We did that, and it ran its course, and after, I want to say, 18 months or so, GoPuff came and acquired us. And there was obviously a transitionary period, an integration period, like with all acquisitions, and we did that, and as soon as we'd vested what we wanted to vest, and as soon as we thought, okay, this chapter is sort of done, uh, in about 2022, We left and we knew that we wanted to go alone and try something like we'd had this taste.[00:07:54] Now we knew we'd seen how a like a YC startup was managed like up close and we knew that we wanted to do something similar ourselves. We had no idea what it was at the time. We just knew we wanted to do something. So we, we tried a small, um, some small projects in various different areas, but then GPT 3.[00:08:12] He'd seen it on Reddit and I'm his source of all knowledge. Yeah, Sam loves Reddit. I'd actually heard of GPT 2. And obviously had like loosely followed what OpenAI had done with, what was the game they trained a model to play? Dota. Was it Dota? Yeah. So I'd followed that and, I knew loosely what GPT 2 was, I knew what BERT was, so I was like, Okay, this GPT 3 thing sounds interesting.[00:08:35] And he just mentioned it to me on a walk. And I then went home and, like, googled GPT was the playground. And the model was DaVinci 2 at the time. And it was just the old school playground, completions, nothing crazy, no chat, no nothing. I miss completions though. Yeah. Oh, completion. Honestly, I had this conversation in open hours office yesterday.[00:08:54] I was like, I just went. I know. But yeah, so we, we, um, I started playing around with the, the playground and the first thing I ever wrote into it was like, hello world, and it gave me some sort of like, fairly generic response back. I was like, okay, that looks pretty cool. The next thing was. I looked through the docs, um, also they had a lot of example prompts because I had no idea.[00:09:14] I didn't know if the, if you could put anything in, I didn't know if you had to structure in a certain way or whatever, and I, and I saw that it could start writing like tables and JSON and stuff like that. So I was like, okay, can you write me something in JSON? And it did. And I was like, Oh, wow, this is, this is pretty cool.[00:09:28] Um, can it, can it just write arbitrary JSON for me? And, um, immediately as soon as I realized that my mind was racing and I like got Sam in and we just started messing around in the playground, like fairly innocently to start with. And then, of course, both being mobile devs and also seeing, at that point, we learned about what the Codex model was.[00:09:48] It was like, this thing's trained to write code, sounds awesome. And Copilot was start, I think, I can't actually remember if Copilot had come out yet, it might have done. It's round about the same time as Codex. Round about the same time, yeah. And we were like, okay, as mobile devs, let's see what we can do.[00:10:02] So the initial thing was like, okay, let's see if we can get this AI to build us a mobile app from scratch. We eventually built the world's most flimsy system, which was back in the day with like 4, 000 token context windows, like chaining prompts, trying to keep as much context from one to the other, all these different things, where basically, Essentially, you'd put an app idea in a box, and then we'd do, like, very high level stuff, figuring out what the stack should be, figuring out what the frontend should be written in, backend should be written in, all these different things, and then we'd go through, like, for each thing, more and more levels of detail, until the point that you're You actually got Codex to write the code for each thing.[00:10:41] And we didn't do any templating or anything. We were like, no, we're going to write all the code from scratch every time, which is basically why it barely worked. But there were like occasions where you could put in something and it would build something that did actually run. The backend would run, the database would work.[00:10:54] And we were like, Oh my God, this is insane. This is so cool. And that's what we showed to our co founder Yang. I met my co founder Yang through, through fancy because his wife was their first employee. And, um, we showed him and he was like, You've discovered fire. What is this? This is insane. He has a lot more startup experience.[00:11:12] Historically, he's had a few exits in the past and has been through all different industries. He's like our dad. He's a bit older. He hates me saying that. He's your COO now? He's our COO. Yeah. And, uh, we showed him and he was like, this is absolutely amazing. Let's just do something. Cause he, he, at the time, um, was just about to have a child, so he didn't have anything going on either.[00:11:29] So we, we applied to YC, got an interview. The interview was. As most YC interviews are short, curt, and pretty brutal. They told us they hated the idea. They didn't think it would work. And that's when we started brainstorming. It was almost like the interview was like an office hours kind of thing. And we were like, okay, given what you know about the space now and how to build things with these LLMs, like what can you bring out of what you've learned in building that thing into Something that might be a bit more useful to people on the daily, and also YC obviously likes B2B startups a little bit more, at least at the time they did, back then.[00:12:01] So we were like, okay, maybe we could build something that helps you with existing codebases, like can sort of automate development stuff with existing codebases, not knowing at all what that would look like, or how you would build it, or any of these things. And They were like, yeah, that sounds interesting.[00:12:15] You should probably go ahead and do that. You're in, you've got two weeks to build us an MVP. And we were like, okay, okay. We did our best. The MVP was absolutely horrendous. It was a CLI tool. It sucked. And, um, at the time we were like, we, we don't even know. How to build what we want to build. And we didn't really know what we wanted to build, to be honest.[00:12:33] Like, we knew we wanted to try to help automate dev work, but back then we just didn't know enough about how LLM apps were built, the intricacies and all those things. And also, like, the LLMs themselves, like 4, 000 tokens, you're not going very far, they're extremely expensive. So we ended up building a, uh, a code based retrieval tool, originally.[00:12:51] Our thought process originally was, we want to build something that can do our jobs for us. That is like the gold star, we know that. We've seen like there are glimpses of it happening with our initial demo that we did. But we don't see the path of how to do that at the moment. Like the tech just wasn't there.[00:13:05] So we were like, well, there are going to be some things that you need to build this when the tech does catch up. So retrieval being one of the most important things, like the model is going to have to build like pull code out of a code base somehow. So we were like, well, let's just build the tooling around it.[00:13:17] And eventually when the tech comes, then we'll be able to just like plug it into our, our tooling and then it should work basically. And to be fair, that's basically what we've done. And that's basically what's happened, which is very fortunate. But in the meantime, whilst we were waiting for everything to sort of become available, we built this code base retrieval tool.[00:13:34] That was the first thing we ever launched when we were in YC like that, and it didn't work. It was really frustrating for us because it was just me and Sam like working like all hours trying to get this thing to work. It was quite a big task in of itself, trying to get like a good semantic search engine working that could run locally on your machine.[00:13:51] We were trying to avoid sending code to the cloud as much as possible. And then for very large codebases, you're like, you know, millions of lines of code. You're trying to do some sort of like local HNSW thing that runs inside your VS Code instance that like eats all your RAM as you've seen in the past.[00:14:05] All those different things. Yep. Yeah.[00:14:07] swyx: My first call with[00:14:07] Alistair Pullen: you, I had trouble. You were like, yeah, it sucks, man. I know, I know. I know it sucks. I'm sorry. I'm sorry. But building all that stuff was essentially the first six to eight months of what at the time was built. Which, by the way, build it. Build it. Yeah, it was a terrible, terrible name.[00:14:25] It was the worst,[00:14:27] swyx: like, part of trying to think about whether I would invest is whether or not people could pronounce it.[00:14:32] Alistair Pullen: No, when we, so when we went on our first ever YC, like, retreat, No one got the name right. They were like, build, build, well, um, and then we actually changed the names, cosign, like, although some people would spell it as in like, as if you're cosigning for an apartment or something like that's like, can't win.[00:14:49] Yeah. That was what built was back then. But the ambition, and I did a talk on this back in the end of 2022, the ambition to like build something that essentially automated our jobs was still very much like core to what we were doing. But for a very long time, it was just never apparent to us. Like. How would you go about doing these things?[00:15:06] Even when, like, you had 3. suddenly felt huge, because you've gone from 4 to 16, but even then 16k is like, a lot of Python files are longer than 16k. So you can't, you know, before you even start doing a completion, even then we were like, eh, Yeah, it looks like we're still waiting. And then, like, towards the end of last year, you then start, you see 32k.[00:15:28] 32k was really smart. It was really expensive, but also, like, you could fit a decent amount of stuff in it. 32k felt enormous. And then, finally, 128k came along, and we were like, right, this is, like, this is what we can actually deal with. Because, fundamentally, to build a product like this, you need to get as much information in front of the model as possible, and make sure that everything it ever writes in output can be read.[00:15:49] traced back to something in the context window, so it's not hallucinating it. As soon as that model existed, I was like, okay, I know that this is now going to be feasible in some way. We'd done early sort of dev work on Genie using 3. 5 16k. And that was a very, very like crude way of proving that this loop that we were after and the way we were generating the data actually had signal and worked and could do something.[00:16:16] But the model itself was not useful because you couldn't ever fit enough information into it for it to be able to do the task competently and also the base intelligence of the model. I mean, 3. 5, anyone who's used 3. 5 knows the base intelligence of the model is. is lacking, especially when you're asking it to like do software engineering, this is quite quite involved.[00:16:34] GPT4o finetuning[00:16:34] Alistair Pullen: So, we saw the 128k context model and um, at that point we'd been in touch with OpenAI about our ambitions and like how we wanted to build it. We essentially are, I just took a punt, I was like, I'm just going to ask to see, can we like train this thing? Because at the time Fortobo had just come out and back then there was still a decent amount of lag time between like OpenAI releasing a model and then allowing you to fine tune it in some way.[00:16:59] They've gotten much better about that recently, like 4. 0 fine tuning came out either, I think, a day, 4. 0 mini fine tuning came out like a day after the model did. And I know that's something they're definitely like, optimising for super heavily inside, which is great to see.[00:17:11] swyx: Which is a little bit, you know, for a year or so, YC companies had like a direct Slack channel to open AI.[00:17:17] We still do. Yeah. Yeah. So, it's a little bit of a diminishing of the YC advantage there. Yeah. If they're releasing this fine tuning[00:17:23] Alistair Pullen: ability like a day after. Yeah, no, no, absolutely. But like. You can't build a startup otherwise. The advantage is obviously nice and it makes you feel fuzzy inside. But like, at the end of the day, it's not that that's going to make you win.[00:17:34] But yeah, no, so like we'd spoken to Shamul there, Devrel guy, I'm sure you know him. I think he's head of solutions or something. In their applied team, yeah, we'd been talking to him from the very beginning when we got into YC, and he's been absolutely fantastic throughout. I basically had pitched him this idea back when we were doing it on 3.[00:17:53] 5, 16k, and I was like, this is my, this is my crazy thesis. I want to see if this can work. And as soon as like that 128k model came out, I started like laying the groundwork. I was like, I know this definitely isn't possible because he released it like yesterday, but know that I want it. And in the interim, like, GPT 4, like, 8K fine tuning came out.[00:18:11] We tried that, it's obviously even fewer tokens, but the intelligence helped. And I was like, if we can marry the intelligence and the context window length, then we're going to have something special. And eventually, we were able to get on the Experimental Access Program, and we got access to 4Turbo fine tuning.[00:18:25] As soon as we did that, because in the entire run up to that we built the data pipeline, we already had all that set up, so we were like, right, we have the data, now we have the model, let's put it through and iterate, essentially, and that's, that's where, like, Genie as we know it today, really was born. I won't pretend like the first version of Gene that we trained was good.[00:18:45] It was a disaster. That's where you realize all the implicit biases in your data set. And you realize that, oh, actually this decision you made that was fairly arbitrary was the wrong one. You have to do it a different way. Other subtle things like, you know, how you write Git diffs in using LLMs and how you can best optimize that to make sure they actually apply and work and loads of different little edge cases.[00:19:03] But as soon as we had access to the underlying tool, we were like, we can actually do this. And I was I breathed a sigh of relief because I didn't know it was like, it wasn't a done deal, but I knew that we could build something useful. I mean, I knew that we could build something that would be measurably good on whatever eval at the time that you wanted to use.[00:19:23] Like at the time, back then, we weren't actually that familiar with Swift. But once Devin came out and they announced the SBBench core, I like, that's when my life took a turn. Challenge accepted. Yeah, challenge accepted. And that's where like, yes, that's where my friendships have gone. My sleep has gone. My weight.[00:19:40] Everything got into SweeBench and yeah, we, we, it was actually a very useful tool in building GeniX beforehand. It was like, yes, vibe check this thing and see if it's useful. And then all of a sudden you have a, an actual measure to, to see like, couldn't it do software engineering? Not, not the best measure, obviously, but like it's a, it's the best that we've got now.[00:19:57] We, we just iterated and built and eventually we got it to the point where it is now. And a little bit beyond since we actually Like, we actually got that score a couple of weeks ago, and yeah, it's been a hell of a journey from the beginning all the way now. That was a very rambling answer to your question about how we got here, but that's essentially the potted answer of how we got here.[00:20:16] Got the full[00:20:16] swyx: origin story[00:20:17] Alessio: out. Yeah, no, totally.[00:20:18] Genie Data Mix[00:20:18] Alessio: You mentioned bias in the data and some of these things. In your announcement video, you called Genie the worst verse AI software engineering colleague. And you kind of highlighted how the data needed to train it needs to show how a human engineer works. I think maybe you're contrasting that to just putting code in it.[00:20:37] There's kind of like a lot more than code that goes into software engineering. How do you think about the data mixture, you know, and like, uh, there's this kind of known truth that code makes models better when you put in the pre training data, but since we put so much in the pre training data, what else do you add when you turn to Genium?[00:20:54] Alistair Pullen: Yeah, I think, well, I think that sort of boils down fundamentally to the difference between a model writing code and a model doing software engineering, because the software engineering sort of discipline goes wider, because if you look at something like a PR, that is obviously a Artifact of some thought and some work that has happened and has eventually been squashed into, you know, some diffs, right?[00:21:17] What the, very crudely, what the pre trained models are reading is they're reading those final diffs and they're emulating that and they're being able to output it, right? But of course, it's a super lossy thing, a PR. You have no idea why or how, for the most part, unless there are some comments, which, you know, anyone who's worked in a company realizes PR reviews can be a bit dodgy at times, but you see that you lose so much information at the end, and that's perfectly fine, because PRs aren't designed to be something that perfectly preserves everything that happened, but What we realized was if you want something that's a software engineer, and very crudely, we started with like something that can do PRs for you, essentially, you need to be able to figure out why those things happened.[00:21:58] Otherwise, you're just going to rely, you essentially just have a code writing model, you have something that's good at human eval, but But, but not very good at Sweet Eng. Essentially that realization was, was part of the, the kernel of the idea of of, of the approach that we took to design the agent. That, that is genie the way that we decided we want to try to extract what happened in the past, like as forensically as possible, has been and is currently like one of the, the main things that we focus all our time on, because doing that as getting as much signal out as possible, doing that as well as possible is the biggest.[00:22:31] thing that we've seen that determines how well we do on that benchmark at the end of the day. Once you've sorted things out, like output structure, how to get it consistently writing diffs and all the stuff that is sort of ancillary to the model actually figuring out how to solve a problem, the core bit of solving the problem is how did the human solve this problem and how can we best come up with how the human solved these problems.[00:22:54] So all the effort went in on that. And the mix that we ended up with was, as you've probably seen in the technical report and so on, all of those different languages and different combinations of different task types, all of that has run through that pipeline, and we've extracted all that information out.[00:23:09] Customizing for Customers[00:23:09] Alessio: How does that differ when you work with customers that have private workflows? Like, do you think, is there usually a big delta between what you get in open source and maybe public data versus like Yeah,[00:23:19] Alistair Pullen: yeah, yeah. When you scrape enough of it, most of open source is updating readmes and docs. It's hilarious, like we had to filter out so much of that stuff because when we first did the 16k model, like the amount of readme updating that went in, we did like no data cleaning, no real, like, we just sort of threw it in and saw what happened.[00:23:38] And it was just like, It was really good at updating readme, it was really good at writing some comments, really good at, um, complaining in Git reviews, in PR reviews, rather, and it would, again, like, we didn't clean the data, so you'd, like, give it some feedback, and it would just, like, reply, and, like, it would just be quite insubordinate when it was getting back to you, like, no, I don't think you're right, and it would just sort of argue with you, so The process of doing all that was super interesting because we realized from the beginning, okay, there's a huge amount of work that needs to go into like cleaning this, getting it aligned with what we want the model to do to be able to get the model to be useful in some way.[00:24:12] Alessio: I'm curious, like, how do you think about the customer willingness? To share all of this historical data, I've done a lot of developer tools investing in my career and getting access to the code base is always one of the hard things. Are people getting more cautious about sharing this information? In the past, it was maybe like, you know, you're using static analysis tool, like whatever else you need to plug into the code base, fine.[00:24:35] Now you're building. A model based on it, like, uh, what's the discussion going into these companies? Are most people comfortable with, like, letting you see how to work and sharing everything?[00:24:44] Alistair Pullen: It depends on the sector, mostly. We've actually seen, I'd say, people becoming more amenable to the idea over time, actually, rather than more skeptical, because I think they can see the, the upside.[00:24:55] If this thing could be, Does what they say it does, it's going to be more help to us than it is a risk to our infosec. Um, and of course, like, companies building in this space, we're all going to end up, you know, complying with the same rules, and there are going to be new rules that come out to make sure that we're looking at your code, that everything is safe, and so on.[00:25:12] So from what we've seen so far, we've spoken to some very large companies that you've definitely heard of and all of them obviously have stipulations and many of them want it to be sandbox to start with and all the like very obvious things that I, you know, I would say as well, but they're all super keen to have a go and see because like, despite all those things, if we can genuinely Make them go faster, allow them to build more in a given time period and stuff.[00:25:35] It's super worth it to them.[00:25:37] Genie Workflow[00:25:37] swyx: Okay, I'm going to dive in a little bit on the process that you have created. You showed the demo on your video, and by the time that we release this, you should be taking people off the waitlist and launching people so people can see this themselves. There's four main Parts of the workflow, which is finding files, planning action, writing code and running tests.[00:25:58] And controversially, you have set yourself apart from the Devins of the world by saying that things like having access to a browser is not that important for you. Is that an accurate reading of[00:26:09] Alistair Pullen: what you wrote? I don't remember saying that, but At least with what we've seen, the browser is helpful, but it's not as helpful as, like, ragging the correct files, if that makes sense.[00:26:20] Like, it is still helpful, but obviously there are more fundamental things you have to get right before you get to, like, Oh yeah, you can read some docs, or you can read a stack overflow article, and stuff like that.[00:26:30] swyx: Yeah, the phrase I was indexing on was, The other software tools are wrappers around foundational models with a few additional tools, such as a web browser or code interpreter.[00:26:38] Alistair Pullen: Oh, I see. No, I mean, no, I'm, I'm not, I'm not, I'm not deri, I'm deriding the, the, the approach that, not the, not the tools. Yeah, exactly. So like, I would[00:26:44] swyx: say in my standard model of what a code agent should look like, uh, Devon has been very influential, obviously. Yeah. Yeah. Because you could just add the docs of something.[00:26:54] Mm-Hmm. . And like, you know, now I have, now when I'm installing a new library, I can just add docs. Yeah, yeah. Cursor also does this. Right. And then obviously having a code interpreter does help. I guess you have that in the form[00:27:03] Alistair Pullen: of running tests. I mean, uh, the Genie has both of those tools available to it as well.[00:27:08] So, yeah, yeah, yeah. So, we have a tool where you can, like, put in URLs and it will just read the URLs. And you can also use this Perplexities API under the hood as well to be able to actually ask questions if it wants to. Okay. So, no, we use both of those tools as well. Like, those tools are Super important and super key.[00:27:24] I think obviously the most important tools to these agents are like being able to retrieve code from a code base, being able to read Stack Overflow articles and what have you and just be able to essentially be able to Google like we do is definitely super useful.[00:27:38] swyx: Yeah, I thought maybe we could just kind of dive into each of those actions.[00:27:41] Code Retrieval[00:27:41] swyx: Code retrieval, one of the core indexer that Yes. You've worked on, uh, even as, as built, what makes it hard, what approach you thought would work, didn't work,[00:27:52] Alistair Pullen: anything like that. It's funny, I had a similar conversation to this when I was chatting to the guys from OpenAI yesterday. The thing is that searching for code, specifically semantically, at least to start with, I mean like keyword search and stuff like that is a, is a solved problem.[00:28:06] It's been around for ages, but at least being able to, the phrase we always used back in the day was searching for what code does rather than what code is. Like searching for functionality is really hard. Really hard. The way that we approached that problem was that obviously like a very basic and easy approach is right.[00:28:26] Let's just embed the code base. We'll chunk it up in some arbitrary way, maybe using an AST, maybe using number of lines, maybe using whatever, like some overlapping, just chunk it up and embed it. And once you've done that, I will write a query saying, like, find me some authentication code or something, embed it, and then do the cosine similarity and get the top of K, right?[00:28:43] That doesn't work. And I wish it did work, don't get me wrong. It doesn't work well at all, because fundamentally, if you think about, like, semantically, how code looks is very different to how English looks, and there's, like, not a huge amount of signal that's carried between the two. So what we ended up, the first approach we took, and that kind of did well enough for a long time, was Okay, let's train a model to be able to take in English code queries and then produce a hypothetical code snippet that might look like the answer, embed that, and then do the code similarity.[00:29:18] And that process, although very simple, gets you so much more performance out of the retrieval accuracy. And that was kind of like the start of our of our engine, as we called it, which is essentially like the aggregation of all these different heuristics, like semantic, keyword, LSP, and so on. And then we essentially had like a model that would, given an input, choose which ones it thought were most appropriate, given the type of requests you had.[00:29:45] So the whole code search thing was a really hard problem. And actually what we ended up doing with Genie is we, um, let The model through self play figure out how to retrieve code. So actually we don't use our engine for Genie. So instead of like a request coming in and then like say GPT 4 with some JSON output being like, Well, I think here we should use a keyword with these inputs and then we should use semantic.[00:30:09] And then we should like pick these results. It's actually like, A question comes in and Genie has self played in its training data to be able to be like, okay, this is how I'm going to approach finding this information. Much more akin to how a developer would do it. Because if I was like, Shawn, go into this new code base you've never seen before.[00:30:26] And find me the code that does this. You're gonna probably, you might do some keywords, you're gonna look over the file system, you're gonna try to figure out from the directories and the file names where it might be, you're gonna like jump in one, and then once you're in there, you're probably gonna be doing the, you know, go to definition stuff to like jump from file to file and try to use the graph to like get closer and closer.[00:30:46] And that is exactly what Genie does. Starts on the file system, looks at the file system, picks some candidate files, is this what I'm looking for, yes or no, and If there's something that's interesting, like an import or something, it can, it can command click on that thing, go to definition, go to references, and so on.[00:31:00] And it can traverse the codebase that way.[00:31:02] swyx: Are you using the VS Code, uh, LSP, or? No,[00:31:05] Alistair Pullen: that's not, we're not like, we're not doing this in VS Code, we're just using the language servers running. But, we really wanted to try to mimic the way we do it as best as possible. And we did that during the self play process when we were generating the dataset, so.[00:31:18] Although we did all that work originally, and although, like, Genie still has access to these tools, so it can do keyword searches, and it can do, you know, basic semantic searches, and it can use the graph, it uses them through this process and figures out, okay, I've learned from data how to find stuff in codebases, and I think in our technical report, I can't remember the exact number, but I think it was around 65 or 66 percent retrieval accuracy overall, Measured on, we know what lines we need for these tasks to find, for the task to actually be able to be completed, And we found about 66 percent of all those lines, which is one of the biggest areas of free performance that we can get a hold of, because When we were building Genie, truthfully, like, a lot more focus went on assuming you found the right information, you've been able to reproduce the issue, assuming that's true, how do you then go about solving it?[00:32:08] And the bulk of the work we did was on the solving. But when you go higher up the funnel, obviously, like, the funnel looks like, have you found everything you need for the task? Are you able to reproduce the problem that's seen in the issue? Are you then able to solve it? And the funnel gets narrower as you go down.[00:32:22] And at the top of the funnel, of course, is rank. So I'm actually quite happy with that score. I think it's still pretty impressive considering the size of some of the codebases we're doing, we're using for this. But as soon as that, if that number becomes 80, think how many more tasks we get right. That's one of the key areas we're going to focus on when we continue working on Genie.[00:32:37] It'd be interesting to break out a benchmark just for that.[00:32:41] swyx: Yeah, I mean, it's super easy. Because I don't know what state of the art is.[00:32:43] Alistair Pullen: Yeah, I mean, like, for a, um, it's super easy because, like, for a given PR, you know what lines were edited. Oh, okay. Yeah, you know what lines were[00:32:50] swyx: you can[00:32:51] Alistair Pullen: source it from Cbench, actually.[00:32:52] Yeah, you can do it, you can do it super easily. And that's how we got that figure out at the other end. Um, for us being able to see it against, um, our historic models were super useful. So we could see if we were, you know, actually helping ourselves or not. And initially, one of the biggest performance gains that we saw when we were work, when we did work on the RAG a bit was giving it the ability to use the LSP to like go to definition and really try to get it to emulate how we do that, because I'm sure when you go into an editor with that, where like the LSP is not working or whatever, you suddenly feel really like disarmed and naked.[00:33:20] You're like, Oh my god, I didn't realize how much I actually used this to get about rather than just find stuff. So we really tried to get it to do that and that gave us a big jump in performance. So we went from like 54 percent up to like the 60s, but just by adding, focusing on that.[00:33:34] swyx: One weird trick. Yes.[00:33:37] I'll briefly comment here. So this is the standard approach I would say most, uh, code tooling startups are pursuing. The one company that's not doing this is magic. dev. So would you do things differently if you have a 10 million[00:33:51] Alistair Pullen: token context window? If I had a 10 million context window and hundreds of millions of dollars, I wouldn't have gone and built, uh, it's an LTM, it's not a transformer, right, that they're using, right?[00:34:03] If I'm not mistaken, I believe it's not a transformer. Yeah, Eric's going to come on at some point. Listen, they obviously know a lot more about their product than I do. I don't know a great deal about how magic works. I don't think he knows anything yet. I'm not going to speculate. Would I do it the same way as them?[00:34:17] I like the way we've done it because fundamentally like we focus on the Active software engineering and what that looks like and showing models how to do that. Fundamentally, the underlying model that we use is kind of null to us, like, so long as it's the best one, I don't mind. And the context windows, we've already seen, like, you can get transformers to have, like, million, one and a half million token context windows.[00:34:43] And that works perfectly well, so like, as soon as you can fine tune Gemini 1. 5, then you best be sure that Genie will run on Gemini 1. 5, and like, we'll probably get very good performance out of that. I like our approach because we can be super agile and be like, Oh, well, Anthropic have just released whatever, uh, you know, and it might have half a million tokens and it might be really smart.[00:35:01] And I can just immediately take my JSONL file and just dump it in there and suddenly Genie works on there and it can do all the new things. Does[00:35:07] swyx: Anthropic have the same fine tuning support as OpenAI? I[00:35:11] Alistair Pullen: actually haven't heard any, anyone do it because they're working on it. They are partner, they're partnered with AWS and it's gonna be in Bedrock.[00:35:16] Okay. As far as, as far as I know, I think I'm, I think, I think that's true. Um, cool. Yeah.[00:35:20] Planning[00:35:20] swyx: We have to keep moving on to, uh, the other segments. Sure. Uh, planning the second piece of your four step grand master plan, that is the frontier right now. You know, a lot of people are talking about strawberry Q Star, whatever that is.[00:35:32] Monte Carlo Tree Search. Is current state of the art planning good enough? What prompts have worked? I don't even know what questions to ask. Like, what is the state of planning?[00:35:41] Alistair Pullen: I think it's fairly obvious that with the foundational models, like, you can ask them to think by step by step and ask them to plan and stuff, but that isn't enough, because if you look at how those models score on these benchmarks, then they're not even close to state of the art.[00:35:52] Which ones are[00:35:52] swyx: you referencing? Benchmarks? So, like,[00:35:53] Alistair Pullen: just, uh, like, SweetBench and so on, right? And, like, even the things that get really good scores on human evalor agents as well, because they have these loops, right? Yeah. Obviously these things can reason, quote unquote, but the reasoning is the model, like, it's constrained by the model as intelligence, I'd say, very crudely.[00:36:10] And what we essentially wanted to do was we still thought that, obviously, reasoning is super important, we need it to get the performance we have. But we wanted the reasoning to emulate how we think about problems when we're solving them as opposed to how a model thinks about a problem when we're solving it.[00:36:23] And that was, that's obviously part of, like, the derivation pipeline that we have when we, when we, when we Design our data, but the reasoning that the models do right now, and who knows what Q star, whatever ends up being called looks like, but certainly what I'm excited on a small tangent to that, like, what I'm really excited about is when models like that come out, obviously, the signal in my data, when I regenerate, it goes up.[00:36:44] And then I can then train that model. It's already better at reasoning with it. improved reasoning data and just like I can keep bootstrapping and keep leapfrogging every single time. And that is like super exciting to me because I don't, I welcome like new models so much because immediately it just floats me up without having to do much work, which is always nice.[00:37:02] But at the state of reasoning generally, I don't see it going away anytime soon. I mean, that's like an autoregressive model doesn't think per se. And in the absence of having any thought Maybe, uh, an energy based model or something like that. Maybe that's what QSTAR is. Who knows? Some sort of, like, high level, abstract space where thought happens before tokens get produced.[00:37:22] In the absence of that for the moment, I think it's all we have and it's going to have to be the way it works. For what happens in the future, we'll have to see, but I think certainly it's never going to hinder performance to do it. And certainly, the reasoning that we see Genie do, when you compare it to like, if you ask GPT 4 to break down step by step and approach for the same problem, at least just on a vibe check alone, looks far better.[00:37:46] swyx: Two elements that I like, that I didn't see in your initial video, we'll see when, you know, this, um, Genie launches, is a planner chat, which is, I can modify the plan while it's executing, and then the other thing is playbooks, which is also from Devin, where, here's how I like to do a thing, and I'll use Markdown to, Specify how I do it.[00:38:06] I'm just curious if, if like, you know,[00:38:07] Alistair Pullen: those things help. Yeah, no, absolutely. We're a hundred percent. We want everything to be editable. Not least because it's really frustrating when it's not. Like if you're ever, if you're ever in a situation where like this is the one thing I just wish I could, and you'd be right if that one thing was right and you can't change it.[00:38:21] So we're going to make everything as well, including the code it writes. Like you can, if it makes a small error in a patch, you can just change it yourself and let it continue and it will be fine. Yeah. So yeah, like those things are super important. We'll be doing those two.[00:38:31] Alessio: I'm curious, once you get to writing code, is most of the job done?[00:38:35] I feel like the models are so good at writing code when they're like, And small chunks that are like very well instructed. What's kind of the drop off in the funnel? Like once you get to like, you got the right files and you got the right plan. That's a great question[00:38:47] Alistair Pullen: because by the time this is out, there'll be another blog, there'll be another blog post, which contains all the information, all the learnings that I delivered to OpenAI's fine tuning team when we finally got the score.[00:38:59] Oh, that's good. Um, go for it. It's already up. And, um, yeah, yeah. I don't have it on my phone, but basically I, um, broke down the log probs. I basically got the average log prob for a token at every token position in the context window. So imagine an x axis from 0 to 128k and then the average log prob for each index in there.[00:39:19] As we discussed, like, The way genie works normally is, you know, at the beginning you do your RAG, and then you do your planning, and then you do your coding, and that sort of cycle continues. The certainty of code writing is so much more certain than every other aspect of genie's loop. So whatever's going on under the hood, the model is really comfortable with writing code.[00:39:35] There is no doubt, and it's like in the token probabilities. One slightly different thing, I think, to how most of these models work is, At least for the most part, if you ask GPT4 in ChatGPT to edit some code for you, it's going to rewrite the entire snippet for you with the changes in place. We train Genie to write diffs and, you know, essentially patches, right?[00:39:55] Because it's more token efficient and that is also fundamentally We don't write patches as humans, but it's like, the result of what we do is a patch, right? When Genie writes code, I don't know how much it's leaning on the pre training, like, code writing corpus, because obviously it's just read code files there.[00:40:14] It's obviously probably read a lot of patches, but I would wager it's probably read more code files than it has patches. So it's probably leaning on a different part of its brain, is my speculation. I have no proof for this. So I think the discipline of writing code is slightly different, but certainly is its most comfortable state when it's writing code.[00:40:29] So once you get to that point, so long as you're not too deep into the context window, another thing that I'll bring up in that blog post is, um, Performance of Genie over the length of the context window degrades fairly linearly. So actually, I actually broke it down by probability of solving a SWE bench issue, given the number of tokens of the context window.[00:40:49] It's 60k, it's basically 0. 5. So if you go over 60k in context length, you are more likely to fail than you are to succeed just based on the amount of tokens you have on the context window. And when I presented that to the fine tuning team at OpenAI, that was super interesting to them as well. And that is more of a foundational model attribute than it is an us attribute.[00:41:10] However, the attention mechanism works in, in GPT 4, however, you know, they deal with the context window at that point is, you know, influencing how Genie is able to form, even though obviously all our, all our training data is perfect, right? So even if like stuff is being solved in 110, 000 tokens, sort of that area.[00:41:28] The training data still shows it being solved there, but it's just in practice, the model is finding it much harder to solve stuff down that end of the context window.[00:41:35] Alessio: That's the scale with the context, so for a 200k context size, is 100k tokens like the 0. 5? I don't know. Yeah, but I,[00:41:43] Alistair Pullen: I, um, hope not. I hope you don't just take the context length and halve it and then say, oh, this is the usable context length.[00:41:50] But what's been interesting is knowing that Actually really digging into the data, looking at the log probs, looking at how it performs over the entire window. It's influenced the short term improvements we've made to Genie since we did the, got that score. So we actually made some small optimizations to try to make sure As best we can without, like, overdoing it, trying to make sure that we can artificially make sure stuff sits within that sort of range, because we know that's our sort of battle zone.[00:42:17] And if we go outside of that, we're starting to push the limits, we're more likely to fail. So just doing that sort of analysis has been super useful without actually messing with anything, um, like, more structural in getting more performance out of it.[00:42:29] Language Mix[00:42:29] Alessio: What about, um, different languages? So, in your technical report, the data makes sense.[00:42:34] 21 percent JavaScript, 21 percent Python, 14 percent TypeScript, 14 percent TSX, um, Which is JavaScript, JavaScript.[00:42:42] Alistair Pullen: Yeah,[00:42:42] swyx: yeah, yeah. Yes,[00:42:43] Alistair Pullen: yeah, yeah. It's like 49 percent JavaScript. That's true, although TypeScript is so much superior, but anyway.[00:42:46] Alessio: Do you see, how good is it at just like generalizing? You know, if you're writing Rust or C or whatever else, it's quite different.[00:42:55] Alistair Pullen: It's pretty good at generalizing. Um, obviously, though, I think there's 15 languages in that technical report, I think, that we've, that we've covered. The ones that we picked in the highest mix were, uh, the ones that, selfishly, we internally use the most, and also that are, I'd argue, some of the most popular ones.[00:43:11] When we have more resource as a company, and, More time and, you know, once all the craziness that has just happened sort of dies down a bit, we are going to, you know, work on that mix. I'd love to see everything ideally be represented in a similar level as it is. If you, if you took GitHub as a data set, if you took like how are the languages broken down in terms of popularity, that would be my ideal data mix to start.[00:43:34] It's just that it's not cheap. So, um, yeah, trying to have an equal amount of Ruby and Rust and all these different things is just, at our current state, is not really what we're looking for.[00:43:46] Running Code[00:43:46] Alessio: There's a lot of good Ruby in my GitHub profile. You can have it all. Well, okay, we'll just train on that. For running tests It sounds easy, but it isn't, especially when you're working in enterprise codebases that are kind of like very hard to spin up.[00:43:58] Yes. How do you set that up? It's like, how do you make a model actually understand how to run a codebase, which is different than writing code for a codebase?[00:44:07] Alistair Pullen: The model itself is not in charge of like setting up the codebase and running it. So Genie sits on top of GitHub, and if you have CI running GitHub, you have GitHub Actions and stuff like that, then Genie essentially makes a call out to that, runs your CI, sees the outputs and then like moves on.[00:44:23] Making a model itself, set up a repo, wasn't scoped in what we wanted Genie to be able to do because for the most part, like, at least most enterprises have some sort of CI pipeline running and like a lot of, if you're doing some, even like, A lot of hobbyist software development has some sort of like basic CI running as well.[00:44:40] And that was like the lowest hanging fruit approach that we took. So when, when Genie ships, like the way it will run its own code is it will basically run your CI and it will like take the, um, I'm not in charge of writing this. The rest of the team is, but I think it's the checks API on GitHub allows you to like grab that information and throw it in the context window.[00:44:56] Alessio: What's the handoff like with the person? So, Jeannie, you give it a task, and then how long are you supposed to supervise it for? Or are you just waiting for, like, the checks to eventually run, and then you see how it goes? Like, uh, what does it feel like?[00:45:11] Alistair Pullen: There are a couple of modes that it can run in, essentially.[00:45:14] It can run in, like, fully headless autonomous modes, so say you assign it a ticket in linear or something. Then it won't ask you for anything. It will just go ahead and try. Or if you're in like the GUI on the website and you're using it, then you can give it a task and it, it might choose to ask you a clarifying question.[00:45:30] So like if you ask it something super broad, it might just come back to you and say, what does that actually mean? Or can you point me in the right direction for this? Because like our decision internally was, it's going to piss people off way more if it just goes off and has, and makes a completely like.[00:45:45] ruined attempt at it because it just like from day one got the wrong idea. So it can ask you for a lot of questions. And once it's going much like a regular PR, you can leave review comments, issue comments, all these different things. And it, because you know, he's been trained to be a software engineering colleague, responds in actually a better way than a real colleague, because it's less snarky and less high and mighty.[00:46:08] And also the amount of filtering has to do for When you train a model to like be a software engineer, essentially, it's like you can just do anything. It's like, yeah, it looks good to me, bro.[00:46:17] swyx: Let's[00:46:17] Alistair Pullen: ship it.[00:46:19] Finetuning with OpenAI[00:46:19] swyx: I just wanted to dive in a little bit more on your experience with the fine tuning team. John Allard was publicly sort of very commentary supportive and, you know, was, was part of it.[00:46:27] Like, what's it like working with them? I also picked up that you initially started to fine tune what was publicly available, the 16 to 32 K range. You got access to do more than that. Yeah. You've also trained on billions of tokens instead of the usual millions range. Just, like, take us through that fine tuning journey and any advice that you might have.[00:46:47] Alistair Pullen: It's been so cool, and this will be public by the time this goes out, like, OpenAI themselves have said we are pushing the boundaries of what is possible with fine tuning. Like, we are right on the edge, and like, we are working, genuinely working with them in figuring out how stuff works, what works, what doesn't work, because no one's doing No one else is doing what we're doing.[00:47:06] They have found what we've been working on super interesting, which is why they've allowed us to do so much, like, interesting stuff. Working with John, I mean, I had a really good conversation with John yesterday. We had a little brainstorm after the video we shot. And one of the things you mentioned, the billions of tokens, one of the things we've noticed, and it's actually a very interesting problem for them as well, when you're[00:47:28] How big your peft adapter, your lore adapter is going to be in some way and like figuring that out is actually a really interesting problem because if you make it too big and because they support data sets that are so small, you can put like 20 examples through it or something like that, like if you had a really sparse, large adapter, you're not going to get any signal in that at all.[00:47:44] So they have to dynamically size these things and there is an upper bound and actually we use. Models that are larger than what's publicly available. It's not publicly available yet, but when this goes out, it will be. But we have larger law adapters available to us, just because the amount of data that we're pumping through it.[00:48:01] And at that point, you start seeing really Interesting other things like you have to change your learning rate schedule and do all these different things that you don't have to do when you're on the smaller end of things. So working with that team is such a privilege because obviously they're like at the top of their field in, you know, in the fine tuning space.[00:48:18] So we're, as we learn stuff, they're learning stuff. And one of the things that I think really catalyzed this relationship is when we first started working on Genie, like I delivered them a presentation, which will eventually become the blog post that you'll love to read soon. The information I gave them there I think is what showed them like, oh wow, okay, these guys are really like pushing the boundaries of what we can do here.[00:48:38] And truthfully, our data set, we view our data set right now as very small. It's like the minimum that we're able to afford, literally afford right now to be able to produce a product like this. And it's only going to get bigger. So yesterday while I was in their offices, I was basically, so we were planning, we were like, okay, how, this is where we're going in the next six to 12 months.[00:48:57] Like we're, Putting our foot on the gas here, because this clearly works. Like I've demonstrated this is a good, you know, the best approach so far. And I want to see where it can go. I want to see what the scaling laws like for the data. And at the moment, like, it's hard to figure that out because you don't know when you're running into like saturating a PEFT adapter, as opposed to actually like, is this the model's limit?[00:49:15] Like, where is that? So finding all that stuff out is the work we're actively doing with them. And yeah, it's, it's going to get more and more collaborative over the next few weeks as we, as we explore like larger adapters, pre training extension, different things like that.[00:49:27] swyx: Awesome. I also wanted to talk briefly about the synthetic data process.[00:49:32] Synthetic Code Data[00:49:32] swyx: One of your core insights was that the vast majority of the time, the code that is published by a human is encrypted. In a working state. And actually you need to fine tune on non working code. So just, yeah, take us through that inspiration. How many rounds, uh, did you, did you do? Yeah, I mean, uh,[00:49:47] Alistair Pullen: it might, it might be generous to say that the vast majority of code is in a working state.[00:49:51] I don't know if I don't know if I believe that. I was like, that's very nice of you to say that my code works. Certainly, it's not true for me. No, I think that so yeah, no, but it was you're right. It's an interesting problem. And what we saw was when we didn't do that, obviously, we'll just hope you have to basically like one shot the answer.[00:50:07] Because after that, it's like, well, I've never seen iteration before. How am I supposed to figure out how this works? So what the what you're alluding to there is like the self improvement loop that we started working on. And that was in sort of two parts, we synthetically generated runtime errors. Where we would intentionally mess with the AST to make stuff not work, or index out of bounds, or refer to a variable that doesn't exist, or errors that the foundational models just make sometimes that you can't really avoid, you can't expect it to be perfect.[00:50:39] So we threw some of those in with a, with a, with a probability of happening and on the self improvement side, I spoke about this in the, in the blog post, essentially the idea is that you generate your data in sort of batches. First batch is like perfect, like one example, like here's the problem, here's the answer, go, train the model on it.[00:50:57] And then for the second batch, you then take the model that you trained before that can look like one commit into the future, and then you let it have the first attempt at solving the problem. And hopefully it gets it wrong, and if it gets it wrong, then you have, like, okay, now the codebase is in this incorrect state, but I know what the correct state is, so I can do some diffing, essentially, to figure out how do I get the state that it's in now to the state that I want it in, and then you can train the model to then produce that diff next, and so on, and so on, and so on, so the model can then learn, and also reason as to why it needs to make these changes, to be able to learn how to, like, learn, like, solve problems iteratively and learn from its mistakes and stuff like that.[00:51:35] Alessio: And you picked the size of the data set just based on how much money you could spend generating it. Maybe you think you could just make more and get better results. How, what[00:51:42] Alistair Pullen: multiple of my monthly burn do I spend doing this? Yeah. Basically it was, it was very much related to Yeah. Just like capital and um, yes, with any luck that that will be alleviated to[00:51:53] swyx: very soon.[00:51:54] Alistair Pullen: Yeah.[00:51:54] SynData in Llama 3[00:51:54] swyx: Yeah. I like drawing references to other things that are happening in, in the, in the wild. So, 'cause we only get to release this podcast once a week. Mm-Hmm. , the LAMA three paper also had some really interesting. Thoughts on synthetic data for code? I don't know if you have reviewed that. I'll highlight the back translation section.[00:52:11] Because one of your dataset focuses is updating documentation. I think that translation between natural language, English versus code, and

Last week on Crime Weekly News we talked about Bianca's Law, a law passed in New York that criminalizes the posting of photographs of victims of a crime on the internet. Bianca Devins was only 17 when she was murdered by a man she met on Instagram who took photos of the scene and spread them online - photos that are still used to harass her friends and family to this day. This week, we are honored to sit down and speak with Kim Devins, Bianca's mother, about her efforts towards making this a federal law, as well as giving consumers the power to hold social media sites more accountable. If you'd like to write to your representatives about Bianca's Law, you may use this email template shared by Kim here: Bianca's Law Email Template Try our coffee!! - www.CriminalCoffeeCo.com Become a Patreon member -- > https://www.patreon.com/CrimeWeekly Shop for your Crime Weekly gear here --> https://crimeweeklypodcast.com/shop Youtube: https://www.youtube.com/c/CrimeWeeklyPodcast Website: CrimeWeeklyPodcast.com Instagram: @CrimeWeeklyPod Twitter: @CrimeWeeklyPod Facebook: @CrimeWeeklyPod ADS: 1. CozyEarth.com/CrimeWeekly - Use code CRIMEWEEKLY for 30% off!

During Most Wanted Car Clubs banger, we catch up with Clutch Marles and Devin Schmidt and talk about everything from the current paint job on Devins van, to if we've ever stepped foot on the moon! This is an epsiode you dont want to miss!

Today on The Salty Podcast, Devins speaks with Sophia Watts about growing up rooted in Jesus, the hardest time in her life (Hint: breaking up with Taylor) and how God can change transform the ones we love for the better! She also speaks on filling voids in our lives with the wrong things, how to ground our children in Christ, and being an influence for young ladies. Taylor and Sohpia TikTok - https://www.tiktok.com/@taylorrandsoph Taylor and Sophia YT - https://www.youtube.com/@TaylorandSoph Sophia IG - https://www.instagram.com/sophiahillll Taylor IG - https://www.instagram.com/taylorrwatts

This week on the Murder Diaries we're talking to Kim Devins, the mother of Bianca Devins. We wanted to share this conversation so our listeners can get more insight into who Bianca was, Kim's experience as the mother of a murder victim, and her new pursuit of justice in the name of her daughter. Bianca's Law Petition: https://www.change.org/p/bianca-s-law-stop-the-spread-of-violent-and-graphic-images-on-the-internet Bianca Devins Memorial Scholarship: https://foundationhoc.org/give/funds/bianca-michelle-devins-memorial-scholarship-fund To further help, contact your local congress representatives and say we need to repeal section 230. We need a law to hold social media companies responsible for the content on their platforms. Music Used: Pop Guitar Intro 01 by TaigaSoundProd Link: https://filmmusic.io/song/10472-pop-guitar-intro-01 Licensed under CC BY 4.0: https://filmmusic.io/standard-license Lobby Time by Kevin MacLeod Link: https://incompetech.filmmusic.io/song/3986-lobby-time License: https://filmmusic.io/standard-license Our Links: Website: https://themurderdiariespodcast.com/ Patreon: https://www.patreon.com/themurderdiariespod Buy Us a Coffee: https://www.buymeacoffee.com/mdiariespod Instagram: https://www.instagram.com/themurderdiariespod/ TikTok: https://www.tiktok.com/@themurderdiariespod Threads: https://www.threads.net/@themurderdiariespod Edited by: https://www.landispodcastediting.com/ Learn more about your ad choices. Visit megaphone.fm/adchoices

Devin Brown is set to take the stage as Ohio State's starting quarterback. Devin Sanchez is set to make a commitment that has Ohio State on the edge of its seat. Who better to guide us through than The Dean and Grand Poobah? Yep, Bill Kurelic and Mark Porter medicated their holiday hangovers to be here with us. Is Devin Brown ready to shine on a national stage? Is Devin Sanchez a future Buckeye? While we're at it ... is Will Howard a future Buckeye? Topics aplenty today. Spend 5ish with us this a.m., 'Nutters! To learn more about listener data and our privacy practices visit: https://www.audacyinc.com/privacy-policy Learn more about your ad choices. Visit https://podcastchoices.com/adchoices

La secció més fosca del magazín "Fem Campana" de Ràdio Vilafant. Bianca Michelle Devins, una jove nord-americana amb certa repercussió a les xarxes socials, va ser assassinada el juliol del 2019 després d'haver assistit a un concert amb un amic anomenat Brandon Andrew Clark, al qual va conèixer a través d'Instagram. Durant el concert, Devins va fer un petó a un altre noi, fet que va molestar Clark i això suposadament va servir d'excusa perquè aquest l'atacés i l'apunyalés fins a la mort. Després de cometre el crim Brandon Clark va trucar a la seva família i a la policia i va confessar els fets. En directe cada dijous a les 16 h a Ràdio Vilafant. 107.7 FM i a radiovilafant.net

The Mulligan Stew Podcast  Guest is Devin Cuddy. (Devin Cuddy band) Son of Jim Cuddy and very definitely his own man. Jim has said, on this very podcast, that he offered to school Devin and son Sam on the fine art of songwriting and playing but the guys went their separate ways. Now, Jim says he's very impressed with their growth and creativity. They've been singing together for years but just casually at fundraising events. Devin talks about a maritime tour they just did. Devin has released his first new music in 5 years. The album is called Dear Jane – with a reference to Hotlips Houlihan on the Mash TV series. The album is a soulful mix of New Orleans, honky tonk, roots and Randy Newman. Devins originals are storytelling taken to another level. He's become his own storyteller. Devin is creating his place in Canada's music world, just like his Father. One of the great surprises is Devin's cover of Barney Bentall's hit Come Back to Me. Barney sings on the track. Barney, Jim and Devin have been friends a long time. Devin says he's been waiting to sing these songs for 5 years. Should be a great 2024 for The Devin Cuddy Band. Tracks are included in the podcast.

Your hosts Kyle Stelter and Women Shaping Conservation Chair, Rebecca Peters welcome Courtney Devins to the podcast. Courtney is the Director of communications and marketing for the Saskatchewan Wildlife Federation (SWF).We talk Advancing Women in Conservation and their recent conference held in Fredericton, New Brunswick. Courtney dives into the disparity of women in leadership roles in the conservation space and what SWF and AWIC are doing to tackle that issue. Courtney is a fierce advocate for women in the outdoor space and this is a conversation you do not want to miss.Talk is Sheep is brought to you by our Title Sponsor, Mountain Tough Fitness Labs.We partnered with MTNTOUGH Fitness Lab to help get you in shape and mentally stronger. Whether you are a veteran hunter or just starting, the MTNTOUGH app will take you to the next level. We've personally trained using the MTNTOUGH programs and we believe in it so much that we want to give you 6 weeks for free using code: SHEEPBCTalk is Sheep is the Official Podcast of the Wild Sheep Society of BC. The official sponsor of the Wild Sheep Society of BC is SITKA Gear and our Conservation Partners - Frontiersmen Gear, Gunwerks, Precision Optics, Schnee's, Stone Glacier, Swarovski Optik and YETI.er now at 

Patreon and Paypal link – In this episode, I am chatting with Devin Cecil-Wishing in New York. Devin answers your questions on a variety of subjects. Devin's Instagram @devin_cecil_wishing Devins's Website https://www.devincecil-wishing.com     Thanks for listening! Click here to support the podcast. Subscribe on iTunes Theme music by The Argyle Pimps. Thanks lads.

Bianca Devins byla mladá influencerka, která byla brutálně zavražděna svým kamarádem. Ten následně sdílel fotky jejího těla online.

Dans cet épisode, nous allons parler de cette tendance marketing qui consiste à toujours vouloir nous prédire ce que sera demain.

I teach elementary special education in Dyer, Indiana at Protsman Elementary School. My room is a self-contained classroom for students with moderate to severe disabilities, that we call Applied Skills.  Here is the link to my wishlist! Thank you!:) https://www.amazon.com/hz/wishlist/ls/E6CM7IG3Q8GK?ref_=wl_share

She was a young girl from Louisiana, who followed her heart and chance opportunity to become an accomplished Hollywood model, actress and producer.  Getting her start as Playboy's Playmate of the month in 1985, Devin DeVasquez went on to win Star Search and appear in popular movies like, "Can't Buy Me Love," and over 100 commercials!  Throughout her journey, Devin had numerous high profile romantic relationships, such as Sylvester Stallone and current husband, Ronn Moss.   However, one relationship had a significant spiritual and transformative impact in her early days-- her romance and friendship with the musical icon, Prince.  This episode tells Devins journey personally and professionally, but we also deep dive into her time with Prince, as told in her memoir "The Day It Snowed in April."   Devin and her spouse/partner Ronn have gone on to produce Emmy award winning content and are just getting started!  Up next...Oscar? Get Ready To Be Inspired, Educated, Empowered and Entertained! For more information visit us @shesa10times5. https://instagram.com/shesa10times5

Welcome to the 108th episode of The One Podcast To (Eventually) Rule Them All! Grab your favorite piece of furniture to sit on, your favorite loungewear, and an ice cold crisp beverage and enjoy! We're a group of longtime friends getting together and just hanging out and talking about things that interest us. We like to talk about video games, sports, wrestling, and anything else that might catch our attention. Let us know what you liked/disliked, topics you'd like us to discuss, or any questions you might want answered. Episodes come out every Monday on any of the podcasting platforms that you desire at 12pm EST! Do You Knowuh, TOPTERTA? is our opening segment in which we ask a question for us and you guys to get to know us a little better. This week we ask the question: If you could go back and witness any historical event, what would it be? News of The Week Devin brings us the latest and best news from the worlds of gaming, entertainment, wrestling, pop culture, and more. Jake's Power Hour is a segment in which Jake plays a game for at least an hour and gives his review on it along with the rest of the boyz talking about their recent games. This week, Jake talks a game he's been hyping up for awhile. This game is Car Mechanic Simulator! Jordi is also back and talking about Survivor.io. Devins tells us about his recent adventures in Naruto To Boruto: Shinobi Striker. The Shadow Knows... is a segment in which Cody covers the latest show he is watching at the moment. This week, Cody reviews the first few episodes of the new Chainsaw Man. Jake isn't too fond of the horror movie As Above, So Below. Let's Talk Some Wrestling This week we discuss Shibata wrestling Orange Cassidy, Nakamura vs Great Muta, and Triple H possibly making some changes to the Money In The Bank. We also talk about what changes we'd like to see Triple H make concerning matches like Hell In A Cell and Elimination Chamber. Jordi's Words of Wisdom IS BACK! -------------------------------------------------------------------------------------------------------------------------------------------------------------- TOPTERTA Social Media Twitter: @TOPTERTA Youtube: https://www.youtube.com/@topterta Facebook: https://www.facebook.com/TOPTERTA Chongo aka Jake Twitter: @ChongoPro TikTok: @Chongopro YouTube: https://www.youtube.com/c/FriendlyPlays Cody Twitter: @TGingerbeardMan Check out the The TableTop Library for all your table top gaming needs! Twitter: @TTTLibrary TikTok: @TheTableTopLibrary Also go support Cody's wife who is an amazing artist: Facebook: https://www.facebook.com/savesthedayartwork/ Etsy: https://www.etsy.com/shop/Savesthedayartwork Snow Twitter: @TheBsnow TikTok: @TheBsnow Devin Bliss Twitter: @SexyDevieB Check out my amazing art! https://www.deviantart.com/devinbliss I don't do anything else Caleb Twitter: @CalebsXe44 Jordi Twitter: @WISE_talk_ --- Send in a voice message: https://anchor.fm/topterta/message Support this podcast: https://anchor.fm/topterta/support

This Episode we interview Brian Devins, Jobin Zapico, Beau Chavez about their take on being a Gym Owner. Welcome to the Gym Lords Podcast, where we talk with successful gym owners to hear what they're doing that is working RIGHT NOW, and to hear lessons and failures they've learned along the way. We would love to share your story! If you'd like to be featured on the podcast, fill out the form on the link below. https://gymlaunchsecrets.com/podcast

This Episode we interview Brian Devins, Jobin Zapico, Beau Chavez about their take on being a Gym Owner. Welcome to the Gym Lords Podcast, where we talk with successful gym owners to hear what they're doing that is working RIGHT NOW, and to hear lessons and failures they've learned along the way. We would love to share your story! If you'd like to be featured on the podcast, fill out the form on the link below. https://gymlaunchsecrets.com/podcast

One of the nicest people in New York City and also one of the funniest comedians, Cara Devins joins Caitlin to discuss the time her entire family was catfished in person by a lonely soldier. Did he want to marry Cara's dad or did he just want to buy everyone a round of drinks? Maybe if your recent wedding was held at The Four Seasons and has a trending hashtag, it's not the best decision to start dating. It's the tale as old as time: lonely soldier meets a wonderful family but is hiding his own bride in a neighboring state.  Plus hear exclusive episodes on our Patreon.  RESOURCES:  @c_devs @caitybrodnick  @scamwowpodcast Scamwowpodcast.com Get those couch DISCOUNTS!!! https://allform.com/lp/scamwow/podcasts  DISCLAIMER: We are comedians and this is satire. C'mon Send us your scams! scamwowpodcast@gmail.com Or call: 347-509-9414 Learn more about your ad choices. Visit megaphone.fm/adchoices

Liz Devins, a Brand Ambassador for St. Jude Research Hospital and a patient mom, is joined by Jennifer Castell, Senior Advisor -- Area Development for Southern California and Hawaii, to discuss the mission and work of the renowned children's cancer research hospital. Devins, whose daughter Claire was diagnosed with a rare brain tumor, talks about how the hospital added four more years to Claire's life before she passed away in 2011. Castell chats about the annual St. Jude Walk/Run on September 24 at NTC Park in Liberty Station.

EL caso de Bianca Devins, una joven que fue a un concierto con un amigo y jamás regresó...

EL caso de Bianca Devins, una joven que fue a un concierto con un amigo y jamás regresó...

When it comes to mental health and disability, sometimes we don't seek out the support and resources we need. a member of United Spinal talks about the personal struggles she encountered in effectively addressing her mental health after her spinal cord injury.

2 Years 10 Months Undercover Business Evaluation For the past two years and ten months Devin has been very quiet about his job with a Micro Mobility company. That was for good reason he was hired by their parent company to go undercover to evaluate the business and how it could be made better or if the parent company just wanted to keep the data and sell it off. Well after a ton of work and reporting back they decided to sell it and end the contract. Here is Devins take on the entire 2 year process. --- Send in a voice message: https://anchor.fm/theyoungbloodlife/message Support this podcast: https://anchor.fm/theyoungbloodlife/support

This episode we chat with Devin Hunter. In part 2 we touch on Crystals as well as Devins traditions of Sacred Fires and the Black Rose tradition of Witchcraft.  You can find Devin here http://devinhunter.net/ and follow him on instagram here https://www.instagram.com/mr.devinhunter Did you enjoy this podcast? Leave a rating and review on Apple podcasts and please consider supporting it on Patreon! www.patreon.com/suburbanwitchery You can also provide a donation directly via PayPal https://paypal.me/suburbanwitchery To book an astrology reading or tarot reading with Hana the Suburban Witch visit www.suburbanwitchery.com Follow along with our social media accounts too! https://www.instagram.com/suburban_witchery https://www.instagram.com/witchtalkspodcast https://www.facebook.com/suburbanwitchery https://www.twitter.com/hanathesw https://www.youtube.com/suburbanwitchery https://www.tiktok.com/@suburbanwitchery https://www.amazon.com.au/hz/wishlist/ls/2EOSTO589ZAZ5?ref_=wl_share

This week, host Kelsey Bainter talks with Joy Devins, a multi-hyphenate who shares her thoughts on balancing life, and stressing better.  Get involved --> http://www.goingon30pod.comSupport the show (https://www.buymeacoffee.com/goingon30pod)

Get To Know Our GuestMichaela Devins is a mental health counselor who has been a C4-C5 quadriplegic for eleven years; in that time, she has lived in Upstate New York, Philadelphia, and now Cambridge, Massachusetts with her husband Kyle and service dog Chicory.  She attended graduate school at the University of Pennsylvania in Philadelphia and completed her Masters there in professional mental health counseling in 2019. Since then, she has worked in outpatient mental health in Center City Philadelphia and now in the Boston area. She is working toward her licensure and hopes to someday build a private practice that will specifically cater to the mental health needs of disabled people. Michaela is also involved in advocacy through United Spinal's tech access group and as a member of the consumer advisory board for Neuralink. In her free time, she loves to read, sing with the Metropolitan Chorale of Brookline, and explore new places to travel! Instagram:  https://www.instagram.com/thewheelchairdiaries/Links: https://unitedspinal.org/michaela-devins/https://unitedspinal.org/dont-be-ashamed-to-ask-for-help-with-anxiety/Click the link below to register for the United Spinal Association's TechTalkhttps://unitedspinal.org/events/techtalks/Stay Connected to the PodcastInstagram: https://www.instagram.com/ramp.it.up.podcast/Facebook: https://www.facebook.com/ZoeOnWheelz/Email: rampituppodcast@gmail.comSpecial Thanks: JWoods CompanyContact JWoods Company for all of your catering and special event needs.Website: https://www.jwoodscompany.com/Instagram: https://www.instagram.com/jwoodscompany/Email: contact@jwoodscompany.comBoss Girl Trucking: ROAD MAP TO BUSINESS CREDITA Step by Step Guide on building Business Credit. Get the strategies on how to access $50K to $100k in funding!Website: https://bossgirltruckin.samcart.com/products/roadmap-to-business-credit/Instagram: https://www.instagram.com/bossgirltrucking/HandiCup Get 15% OFF when you buy your HandiCup with Discount Code RAMPITUP15Disclaimer: This post contains affiliate links. If you make a purchase, I may receive a commission at no extra cost to you.Support the show

IWC Schaffhausen presents episode six of Creator's Time, in which host Abraxas Higgins talks to IWC's Gaelle Devins and thy.self founder Chloe Pierre about motherhood, the value of community and why it's important to have mums in leadership roles in businesses. All thoughts and opinions are of those speaking and do not necessarily reflect IWC Schaffhausen

On this episode the boys start off their month of Spoopyness, or Spooktober interviewing 2 real life Paranormal Investigators, Devin and Tosha AKA BeardedKing33 and QueenOfBK33. The boys learn how Devin and Tosha met (Hint, it was not in a graveyard like Chris originally thought), how they got into ghost hunting, what it takes to be a hunter, locations they have been to and what type of activity they have seen, ghost equipment, demons and Tosha has heard a deeper voice than Devins??!?!?!!? Join us! https://anchor.fm/egtn Follow the Devin AKA BeardedKing33 on Twitch! https://www.twitch.tv/beardedking33 Follow Tosha AKA Wifey Or QueenofBK33 on Twitch! https://www.twitch.tv/QueenOfBK33 Follow their ghost hunting adventures on the Facebooks! Family Paranormal On the Discords! https://discord.gg/WyRcK5cf6F and the Facebooks! https://www.facebook.com/EGTNerdom/ Or the Instagram! https://www.instagram.com/egtnerdom/ Follow Kyle on Twitch! https://www.twitch.tv/k_sig Follow Chris on Twitch! https://www.twitch.tv/guyverunt01 Everything will be fiiiiiiiiinnnneeee

https://www.youtube.com/channel/UChVM... I apologize for the length as there is much content and important facts to cover... If you have not watched this episode I recommend you please do. As we see Aluntra and Bryce relationship start to leave the fantasy and get into reality we can see the pressures that are put on returning citizens and why our recidivism rate is so high.. We watch Isa and Devins relationship fall apart due to misinformation and mistrust was allowed to set in. And more of MIchael.. In this episode @discoveryplus @spotify @appletunes @itunes @iheartradio @pandora @anchor.fm #prison #stigma #discoveryplus #prisoneroflove #mentalhealth --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app --- Send in a voice message: https://anchor.fm/thethomasfreemepodcast/message Support this podcast: https://anchor.fm/thethomasfreemepodcast/support

This week were playing fetch with Devin Tuomala and his kennel of Labs. Devin is a Breeder, a guide, a trainer, and a great guy to know if you want to get into Special Bred Labs. He walks us through some of the nuances of hunting dogs, from things to avoid while training to stories of his fabled Sire of 13 years, and how he will be sorely missed this upcoming duck season. If you have ever considered a Bird dog, shed dog, or service dog, look no further. Devins dogs are used across board by law enforcement, top outfitting guides, and many notable waterfowlers. Join us in the kennel as we dig further into realm of Bird Dogging. The Outdoor Drive Podcast Official Website: Theoutdoordrive.com Be Sure To Check Out Our Partners- Gator Outdoors: gatoroutdoors.com   Promo Code: Outdoordrive25 for 25% off your entire order Zues Broadheads: neweraarchery.com Latitude Outdoors: latitudeoutdoors.com Timbered Tumblers: timberedtumblers.com Nor'easter Game Calls: nor-eastergamecalls.com Out on a Limb MFG: outonalimbmfg.com

WIBX First News with Keeler in the Morning features newsmakers, hot topics and great conversation about everything that matters to the Utica-Rome area and the Mohawk Valley. Host Bill Keeler is joined each weekday morning from 6:00-9:00 a.m. by Jeff Monaski and Andrew Derminio on Your News, Talk and Sports Leader WIBX 950.

This episode had me in tears while recording and once again while editing. Devin Jones is an International Women's Self Love Coach and Creator of the SACRED program where she leads women to dive into self-love. She has many qualifications and endorsements, but nothing parallels Devins sovereign wisdom that she allows herself to lead from. The integrity of her work is displayed through her full embodiment of self-love and the service she shows up in. As we dive into her transition from evangelical Christianity to spiritual mysticism, you will be invited into a very vulnerable and encouraging story that leads us home to the powerful force of love that is woven throughout life. I can't wait for you to listen! Devin's Instagram: @_devinjones Devin's Website: devinjones.co Girls Gotta Meditate Instagram: @girlsgottameditate Host: @itscarissanichole

This week, Webby and Devin sit down and talk about Cyberpunk, one of Devins favorite genres.  Host: Webby- @JaxForestwalker Featuring: Devin- @DMP_Pookie   Music by Ross Malcolm Boyd Please give us a rating and review wherever you listen to us, and as always check out our Website for links to other things we do!

This week, Webby and Devin sit down, talk about Devins almost meeting with the Devil, Some Love, Death and Robots, then Webby vents.    Host: Webby- @JaxForestwalker Featuring: Devin- @DMP_Pookie   Music by Ross Malcom Boyd Please give us a rating and review wherever you listen to us, and as always check out our Website for links to other things we do! as part of our anniversary we have a follower contest starting today. The winner receives a 5th anniversary shirt, poster, and an opportunity to be in an RPG game with some of the cast. Winner will be drawn at random, deadline is March 23rd. https://gleam.io/kneQd/come-play-an-rpg-with-us