Beyond Vibe Coding with Addy Osmani

What does vibe coding mean to you? >> VIP coding is not the same as AI assisted engineering and I feel like that distinction is kind of critical because we don't want to devalue the discipline of engineering. >> How do you personally use these tools? >> I have more recently been focusing on the idea of specdriven development. Having a very clear plan of what it is that I want to build. If you are open to tests, it can be a great way of derisking your use of LLMs in coding. One thing that I'm kind of noticing on myself already is losing a little bit of criticality. >> It's going to continue to be very important for us to be able to think through how things work, be able to problem solve without necessarily relying on the AI. Testing and retesting your critical thinking skills are going to be important. >> You're working inside a larger team, the Chrome team, other teams as well. What are things that you're observing >> at a company like Google with AI? What we've realized is >> how do professional software engineers and the likes of Google go beyond vibe coding to speed up their day-to-day work with AI? Eddie Osmani has worked on the Chrome team for 13 years. And if you ever opened up the developer tab on Chrome, you've definitely used the stuff he built. He is also a prolific author. His latest book is titled Beyond VIP Coding and it's aimed at professional software engineers. Today we go into VIP coding versus AI assisted engineering and why vibe coding isn't useful for much more than just prototyping something quick and dirty. the importance of understanding what the model does. Why Addie always reads to the thinking log of the model he uses to make sure he fully understands what it did and why before approving changes. New development workflows with AI how things like speci development asynchronous coding background agents and parallel coding with several agents are new and unexplored areas of software engineering and many more. If you're a software engineer who wants to work better with AI coding tools on the day-to-day and wants to build reliable software with AI tools, then this episode is for you. This podcast episode is presented by Statsic, the unified platform for flags, analytics, experiments, and more. Check out the show notes to learn more about them and our other season sponsor. So, Addy, welcome to the podcast. Thank you. It's a pleasure to be here. You're an author of a lot of different books, leading effective engineering teams. This came out about a year ago, and now your latest book is is called Beyond Vibe Coding. And on on your on your Substack, you also write a lot about your your learnings about VIP coding/ AI assisted development. In this book and also on your blog, you talk about VI coding versus AI assisted software engineering. In your mind, what does VIP coding mean to you specifically and how is it different or similar to AI assisted software engineering? >> Yeah. So, I've um I've I've been I tend to tell folks that I personally think the vibe coding is not the same as AI assisted engineering. And I feel like that distinction is kind of critical um because we don't want to devalue the discipline of engineering um and give you know folks that are new to the industry an incomplete picture of what it takes to build um robust production ready software. But I I kind of have I guess two two definitions for them. Um I think the vibe coding is really about um fully giving into the creative flow within AI. So very much focused on high level prompting and in many ways forgetting that the code exists. Um so you would you know this this goes back to Andraj Karpathi's like original um definition but it's about accepting AI suggestions without necessarily having a deep review and focusing on rapid iterative experimentation. Personally, I find it really great for prototypes um MVPs and learning um and uh I think it's you know what what I've seen in um production teams is that this has been very useful for them when it comes to trying out ideas rapidly and building out an intuition for what you know the shape of an idea might look like, what a component might look like, what an MVP might look like and that tends to prioritize speed and exploration over things like correctness and maintainability, things that we perhaps care a little bit um about when it comes to building things for large production audiences. And I would say that there's a little bit of a spectrum between vibe coding and uh doing what falls a little bit closer into traditional software engineering. You know, doing more planning, more um specificationdriven development uh including sufficient context uh and uh really what is AI assisted engineering across the full software development life cycle. So to me AI assisted engineering is where AI is this powerful collaborator but it's not a replacement for engineering principles and so in that model it is a force multiplier but it can help you across that whole cycle um whether it is with boilerplate debugging deployment but the big difference is that the human engineer um remains firmly in control you are responsible for thinking about the architecture for reviewing the mode and for understanding um every line if not most of what AI is generating for you and you're on the hook really for making sure that the final product is secure um scalable and maintainable. Maybe AI is helping you with with velocity, which is which is great, but that doesn't mean that you can uh you know, sort of shrug off your uh your responsibility for quality at the end of the day. And I tend to think that um you know a lot of people have said that they found uh AI and coding to be a force multiplier but um I found that the greater expertise that you have in software engineering um the better the results you can get when you're using uh LLMs. And I think sometimes uh if you're new to the industry or you're a junior, maybe there are some what we would consider to be traditional best practices that uh you know, maybe you haven't had to experience them yet or think about them. Like you know, if if you care about production quality programming, you should probably only be committing code to your repo that you can fully explain to somebody else because just expecting that the AI is going to help you untangle whatever mess um happens later on is probably not going to work out. um long term >> and how do you you personally use uh these tools may that be vibe coding and especially AI assisted uh engineering? >> I have uh more recently been focusing on the idea of specdriven development um having a very clear plan um of what it is that I want to build. I think that um you know there are definitely places where I still vibe code um if it's for a personal tool something throwaway or you know in the old days if an engineer or a PM had an idea maybe would we would put together you know a quick mock maybe we would put together a wireframe or a sketch or something like that um perhaps work with UX to come up with like something a little bit more polished these days the fact that you can vibe code a prototype and actually show somebody in a pull request or in a chat like, "Hey, here's here's an even more clear version of the vision that I had in mind for this." I think there's something very powerful to that and I'm loving vibe coding for that. Just being able to give me a slightly higher quality way to deliver uh sharing an idea. Vibe coding is having its moment. Describe an app in a prompt and boom, you've got something running. It feels like magic. But here's what VIP coding doesn't account for. institutional knowledge. Every prompt stems from what you tell it, not what your team already knows. Real product development is the opposite. It's accumulated context. For example, every buck has a kind of history. Every feature connects to customer requests. Every PR fits into your team's road map. A short prompt will not share all this additional context for the agent to use. Linear agents work inside this additional team context. They live inside your development system where actual work happens. They see the issue blocking your sprint, the related PRs, the project goals, the discussions that your team already had about this exact problem. And because linear is your team's shared workspace, agents don't only see your context, they see your entire org's context, the bug report from support, the design spec from your PM, the architecture knows from the tech lead. So when you ask an agent to draft PR or build a feature, it's not improvising just from a prompt. It's using the same context that your team already uses. This is what AI powered development looks like when you're building something real. Not just building fast, but building intentional. See how agents work in linear at linear.app/agents. And if you want to try them yourself, get linear business for free by visiting linear.app/pragmatic. That doesn't mean that we take the vibecoded um prototype or that code and just stick it in production. Once you have clarity around the vision that you have for for a component, for a feature, for a view, you should probably be writing out like, okay, well, what are the actual expectations around this? What do we actually consider to be the requirements? And that will give you a much higher quality outcome typically from the LLM. Um, because otherwise, if you're giving into the vibes, you're also sort of giving giving into, okay, well, you figure out the architecture, you figure out like what this should do. And while that's fine for ideiation, it's probably not sufficient for um production sort of product engineering. So for me, specd driven development has been a big thing. Um I think that tests are uh are great. uh and uh if if you are open to tasks, it can, you know, be a great way of derisking your use of LMS and coding because sometimes, you know, even if you're using state-of-the-art models, you can end up in a situation where maybe the code looks more convoluted um than you would expect or maybe your first couple of uh you know, prompts have been generating really good code and then for whatever reason things go off the rails. But if you can prove that uh things are working with tests um and that uh if something did go off the rails, it's clearer to you what that was, I think that that can help you keep your project green the whole time. Um and that that's been, you know, something that's helped me quite a lot as well. So, um spectriven development testing, I uh try to also make sure that I'm leveraging, you know, this is I guess this is uh this is a shout out. We my my team just released Chrome DevTools MCP. So I use >> Yeah. So we just released Chrome DevTools MCP. I care a lot about quality and I think that uh you know in the last couple of years we've seen a number of cases where if you notice that something is broken. Um I will see a lot of engineers that will just say okay well hey LM it looks like this button is off or it looks like this thing is not quite exactly what it should be. Go fix it. And that that's again going a little bit closer to just you know uh rolling with the vibes. But if you can keep things like um a browser in the loop or something that can actually see the page that is that is what kind of Chrome DevTools MCP and related solutions do. They give your LLM eyes in many ways. So it can see what the browser sees. It can see what's rendering or what isn't. It can detect if there are warnings, errors in the console. Maybe even get a deeper sense of what is broken. And that can just improve the feedback loop that you have. And so I've been just excited about MCP for being able to help us sort of evolve our workflows a lot more thanks to this idea of being able to call into other tools. So that has been that has been great. Um otherwise uh for me generally speaking um I've found that you know uh you really do need to put in the effort to get proficient with these tools and and I still find myself doing that if there are new models, new tools, new platforms coming out. I'm generally um finding that I experiment a lot um every week. Um I try to you know encourage my teams to share with each other with me like how how are things going? Are there any insights that are worth us bubbling up or things that we should try out as a team? And if your team see that you are very open to learning together, I think that that can just create this nice culture of psychological safety that can set your team up for success as we're as we're all going through this big period of change. >> Yeah. And I I I feel, you know, what you said about it takes time to learn this thing. I have so many like smaller and larger aha moments just by using it. And I get a lot there's plenty of people who are skeptical especially including engineers who are skeptical about LLMs whether may be the theory may that be energy footprint other things but I found that a lot of people who are skeptical have either not tried it or they haven't given it some time because it does take some time some playing around and you know like reners it doesn't work everywhere it it it it makes mistakes uh it screws up but it does like I I think if you've not Like I can only tell for myself, but I found a bunch of like ways where it helps even my smaller projects. And as you said, like it's it's about what works for you and and what works for others. Speaking of which, you're working in a larger inside a larger team, larger production team. You know, you've got the the Chrome team, other teams as well. What are things that you're observing on how others are using it and interesting ways maybe un unexpected ways or or even ways that maybe didn't work out for those specific people or engineers? I would say that at a company like Google, you know, we we have this um this very long-term very battle tested way of thinking about um software engineering at scale. And um with AI, what we've realized is, you know, a lot of that doesn't go away. Uh you you still want to care about quality. You still want to care about um doing due diligence. The things that I think we've found interesting kind of mimic um what people have seen in in startups and other kinds of companies. So um the importance of mastering understanding like what is prompt engineering, right? like um making sure that you are constructing the right set of incantations um to get the best outcome from an LLM and then context engineering um more recently like how can you make sure that you are um optimizing the context window to uh increase the chances that those those outcomes are higher quality. We spend a lot of time thinking about that. Um making sure that the right sets of descriptions, details, files, examples, any additional content um that is specific to a project that the LLM may not necessarily have in its training data. Um that has been very interesting and and important for us as we've been working through. I have been somebody that has been uh trying to explore using AI in every facet of my life in many ways over the last couple of years. Um and I that that's been that's been very eye opening in terms of places where there is meaningful productivity gain to be had as well as places where either model quality or you know system prompts and tool quality are not quite there just yet. And so um I've been also sort of nudging my teams in that direction as well. Like if if uh if you are thinking about this idea of us eventually all being AI native engineers, then one prompt for you is before you try to solve a problem yourself, if you gave this to a model, you gave this to AI, uh what would it do? Would it actually help you accomplish your goal much faster or would it likely slow you down? And if it it would slow you down like why why is that? But even that prompt is something that I think helps us to learn a lot about what is possible and what isn't. Many of us have been on this journey where you know if if you're if you're you know classic software engineer if you're a web developer whatever um you probably don't have this very deep understanding of AI. for myself and and for some of my leads, I set us this task of like okay well let's start to become slightly deeper experts in these areas so that we can guide our team um towards well where where would it be useful for you to build up this expertise too. So like in in the last year I've been spending a lot more time on thinking about things like eval benchmarks and uh how much should we be caring about rag versus fine-tuning and so on and this ends up contributing as well because we're at the same time that we are talking about AI assisted engineering many of the products that we work on also have this consideration for well is AI going to help you deliver a better customer experience in some And so I'm trying to look at like how can the work that we're doing for our coding workflow also benefit the product work that we're going to have to do. So that that's been a good learning for us. Um I think honestly just the importance of always maintaining human oversight has been one of the bigger learnings. Uh we have of course like I know a lot of people have run into this. We've of course seen you know cases where people um very often like external contributors um will sometimes be very passionate about like hey I want to contribute to your project but then they'll use an LLM >> know where you're going with yeah use an LLM and submit something that I think Machal uh Hashimoto was blowing up on social media because he just had enough that people were submitting things that as you said out of good intention but putting way more load on maintainers. I love reading studies about how different segments of the developer population are finding AI and and what what that means for their teams. And one of the studies that I recently read um highlighted that uh if you are increasing the velocity of uh code and improvements that can land in the team and you are doing human oversight, the human review is going to become the bottleneck. And that is what teams are starting to realize like, oh wait, we're starting to see a lot more PRs come through, but who's going to review those, right? So, I'm glad that teams are, you know, at least some teams seem to be caring about that quality dimension sufficiently to actually hand review it themselves. But, uh, that does also mean that our, uh, our workflows may have to evolve. I have seen, you know, a lot of talk about like, yeah, why why aren't we using, you know, LLMs to also do the code review and that, you know, that that's a little bit of a slippery slope because if the AI is writing the code and you haven't studied it carefully, uh, but the AI is also reviewing the code, are you actually sure about what's what's landing? So, I think that the best practices around code review are still something that are evolving and I'm I'm excited to see where that goes. For me personally, there are a lot of tools that I use. Um, some of my favorite ones include using uh Klein in VS Code. A lot of people >> you're you're a big fan if people follow your writing on Klein. >> Yeah, I love I love Klein. I I think that you know people can do a lot with cursor and co-pilot in VS Code these days as well. But one of the things that I enjoy doing is uh you know most of these tools will show you the thinking that is happening behind the scenes as a solution is being built out. And even if that happens quickly, like I will try to go back, scroll through, expand and read through, okay, what was your thinking process through actually being able to build this out? What decisions did you make? What did you generate? And I will review that code before it ends up in a pull request. There's going to be some likelihood later on that I may have to maintain that code, that I may have to make some tweaks to that code that the LM is not going to be able to help me with. Uh just last night I was I was working through a problem and uh the code looked you know on paper code looked looked right. It wasn't working the way it was supposed to. I asked the LLM a few times to like hey can you go use your tools go figure out what the problem is. It continued to make changes. Didn't actually fix the underlying problem. And so tonight I'll have to roll up my sleeves and go and manually debug it. If I didn't understand how that code worked or I hadn't been reading it, I would be, you know, feeling like I'd just been dropped into a jungle and having to navigate it myself. >> Well, plus I I I mean, I mean, reflecting on this, right? Like I feel this is the difference between a software engineer who's worth their money just in terms of, you know, being employed and one that's not not if if all you can do is just prompt and prompt. I mean, anyone can do that. I'm exaggerating, but but a new can can do that or or or someone still in college. But not everyone will have taken the effort to understand to know how it works and be able to roll up their sleeves and when these models fail, which they they fail, they can fix it and also they can also explain it to people, they can explain it in meeting uh coherently without rambling. So I I feel you know like this is what a professional is right like in in any industry it's like they know how the things work. Same with the car mechanic. Same with anything else. So maybe it's just a reminder that if you're not doing this, if you're kind of letting go and like, oh, it it solves my problems, you're you're kind of at the risk of, well, if if you don't know how it works, I mean, why do we need you? Anyone can prompt another LLM. >> Exactly. Exactly. And you know as we start to talk more about you know in the last year uh you know people working in their terminal their CLIs has become more of a thing once again with cloud code and Gemini CLI and open code and so on. And then people started talking about okay well how do we orchestrate um multiple parallel agents being able to complete work for us. As we start thinking about this idea of each engineer almost having a virtual team of their own being able to go off and work through your backlog and get all of these different tasks implemented concurrently. I think that you start to quickly realize well that all sounds great in in the abstract and in theory but then you're going to compound all of these other problems where a lack of manual review is going to probably lead you to some level of tech debt. um maybe not immediately, but at some point it very likely will. And um my my experience of this kind of led me at one point to to write about what I called the 70% uh kind of problem. >> Let's talk about that cuz >> yeah, you you wrote about it extensively. We actually published a shared article, a guest article based on your article which we'll also link in the the notes below. what is this the 70% problem and how did you you know come across it and how do you think it might have changed since you published it which was about six months ago >> yeah ob obviously you know model quality tooling quality continues to to get better um I so the the 70% problem is really about uh this idea that LLMs can produce very roughly like 70% of a working application very quickly but they tend to struggle with that last 30% and that last 30% uh you can consider it like the the the last mile, but it includes lots of different kinds of patterns that you know your audience will probably run across or maybe they will run across things like the two steps back pattern. So, you know, you have uh you know, used a couple of prompts to build up something. You give an LLM one more prompt and it happens to uh go in a completely different direction. Maybe it has fully rewritten your UI or the functionality behind your component. Things like that. Uh there are very often hidden maintainability costs places where you're not being specific. You are delegating the responsibility back to the LLM. Um and you may end up getting diminishing returns. you know, as as we've seen time and time again on hacker news, uh security vulnerabilities, uh you know, this this is exactly where we see people accidentally, you know, leaking API keys, uh where there are excss issues, where there are all kinds of problems because people um did not think as holistically about the problem that they were solving and just sort of gave into the vibes. So a vi coded proof of concept, you know, is fine for for MVP for that prototyping phase, but it likely needs to be rewritten with production quality in mind. Uh if you're going to be landing this in a codebase where you're working with other people, working with a team and um you know dealing with a real user base um of people, I think that the security and quality aspects uh you know re speak to the need for for keeping the human in the loop there. >> Yeah. And we keep hearing stories about product managers and less technical founders who get really into vibe coding excited, you know, they spin up a prototype or a better version and they and then they want to build something that they can put out into production and there's a bunch of stories. I I'll link one in the the show notes on how they just get stuck or it takes them a very long time. What what they thought would take a day or two ends up being 10 20 30 days. And in the 70% problem, you went into something relevant for this, which is you said that more experienced engineers can finish the last 30% a lot easier and then less experienced engineers get actually a lot more stuck or or get this false confidence. Yes. Yes. Absolutely. I think that um with that last 30% what you will often see uh junior engineers or interns or new grads doing is you know they they will not really know what to do next beyond just constantly reprompting the LLM to fix the issues and if it's not able to do it they're not necessarily going to have all of those skills just yet for debugging the problem or understanding where the issues are and so what this speaks to is the importance of having a really good critical thinking problem solving mindset. You know, we always talked about the importance of this for people who are going into computer science. I think that that remains now. But that diligence required to actually read the code, understand the system, understand how all of those pieces connect together, I I don't think that that necessarily goes away. as you said earlier um with the tools that we have today and the models we have today almost anybody can give um a highlevel prompt to a tool and have something um you know that that seems like it works come out the back of it but um I wouldn't necessarily trust that in production um see too too many stories at this point of things uh just going uh a little bit off the rails >> which reminds me when this was before AI tools I I had an engineer who joined Uber who came from a smaller startup and I uh new joiner. So after a week I was this person's manager I asked like how are things going and he was really distressed. I'm like oh it's really difficult. I like what's difficult. He's like I'm I'm trying to read all the code and it's just too much. And I was like why why are you trying to read all the code of Uber which was like you know our backend system. It was like you like just so it was mobile app. It was like more than a million lines of code. He's like well I do that because whenever I join new company I read all the code to understand everything. And I was like I was like I get where you're coming from and I think that's great. I was just like let me let me explain to you how the codebase works is you shouldn't read all the code but you should understand the structure you know where you can find things because this is you know on a code base where we had I think two or 300 engineers working on it and I I thought this person had a really great intention which is I'm coming to a new place I want to understand and I'm going to spend the first few weeks understanding and those people excel and I I'm kind of thinking we keep getting back to this thing but what what I'm hearing from you is is If you can actually to some extent outsource this to an LLM which is a bit of a hit or miss or might it might or might not succeed but if you do you're now hopelessly dependent on it and at some point when the context window fills up or when the model is not having a good day because these things are nondeterministic you're kind of stuck you know your best thing is to try a new model or or empty the context window or I don't know do do something else but it doesn't feel really feel like you're in control. >> Yes. Yes. You're absolutely right and I think that whether your gateway into this new world was vibe coding or you are a senior engineer and you've been evolving your workflow for AI there there are a few things I think that everybody should keep in mind that can help you get the best outcomes. Addy just mentioned getting the best outcomes. Here's what I'm seeing with AI and Vive coding. These tools can give you incredible velocity. You can ship features a lot faster than before. But velocity without precision means you're just shipping more things, not necessarily the right things. How do you know if what you built actually works? Did that new checkout flow improve conversion or hurt it? Is a feature you shipped helping retention or causing a drop off? Without precision in how you roll out a measure, you're making decisions blind. That's where static comes in. Static is our presenting partner for the season and they built a precision toolkit that matches your AI accelerated velocity. Here's what precision looks like. You ship a feature to 10% of users in a controlled environment and see if it's moving your metrics up or down. If conversion drops, you catch it early and roll back before it affects everyone. You're making precise datadriven decisions at the same pace you're shipping code. Take graphite as an example. They build granular control and rapid iteration into their development workflow using statig. When they roll out a new feature, built-in analytics show exactly how it affects their key metrics. They can see if engagement is up, if the feature is causing errors, or if users are dropping off. They do this with feature gates and metrics working together. And they're running over 300s of these controls rollouts at any given time. During production incidents, this approach cut their resolution times by over 50% because they could quickly identify which feature flag was causing issues and fix it instantly. Most teams stitch together separate systems, wait on queries, and try to correlate user segments that don't match. By the time they know if something worked, they've already moved on to the next feature. With Static, you have everything in one place. Feature flags, experimentation, and analytics with the same user data. Static has a generous free tier to get started, and Proing for Team starts at $150 per month. To learn more and get a 30-day enterprise trial, go to stats.com/pragmatic. With this, let's get back to the conversation about AI and development workflows. Understand that, you know, models tend to have finite context windows. They've been getting larger over time. you probably need to at some level adopt um sort of a project manager mindset. You know, break tasks into small verifiable chunks. I've seen a lot of people who will like throw throw, you know, the kitchen sink at the LLM and say like, "Hey, yes, build build all these requirements at once." And that doesn't necessarily work the best. So, you know, small verifiable chunks, um clear expectations, um and be prepared to iterate with the AI. Um, don't just feel like oneshotting is is going to give you the best outcome because it it probably isn't. Um, and that decomposition is really pretty similar to planning a sprint or writing pseudo code, the type of thing that we would do in the the older days. And it reduces the risk of context loss and compounding errors. I think that a lot of best practices in software engineering remain timeless even with AI coding. So um caring about modular testable code enforcing those code reviews you know AI is going to introduce a few new habits like thinking about the input and output constraints and seating enough context but that doesn't mean that you can't get good output from it. It does mean that you're going to have to apply a level of diligence with that human in the loop to make sure that uh you are setting yourself up for success. um the more that you sort of uh give give up your responsibilities to the the LLM, the higher the risk is that something's going to go off the rails. >> Yeah. And one thing that I heard from an engineer uh actually this was Armen Ronacher who's longtime century engineer uh the creator of Flask and a bunch of open source frameworks and he was telling me that he's observed something interesting with engineers who are firmly in control and very confident in their work. uh he said that he sees they're just seeing a lot more success with AI and the people who feel that they don't have that much control over their work over their tasks they're just assigned tickets and and they also have a bit of a like you know the world's control I mean mindset they're freaking out a lot more about AI and what it will do and this was so interesting of of it just came to me as as we're talking about it because what what I'm sensing from a car conversation is again a lot of your advice boils down to be in charge understand everything. Be confident, you know, that if this thing gets taken away, you can you can make progress like no problem. It'll just be a bit slower. And as long as you have this mindset, it feels like everything is easier. And and and keeping this like, you know, you're as you said, like you still read through the thinking, you prefer the models where it explains to you and then you can go back if you wish and you often do it. You read through to make sure to learn. Plus, it's kind of fun. You keep learning professionally. You keep getting better every day. >> Absolutely. Absolutely right. I I'm I'm reminded of, you know, AI is just another tool in your tool belt. You know, we we've gone we've gone through so many different moments over time of um engineering uh and developer experience getting a little bit better um with with every generation. You know, when uh when I when I was coming up, I remembered getting excited when templates became a thing. >> You could you could generate code with templates, you mean? >> Oh, no. just just downloading templates for ideas for UI for um you know starting off >> on on the web, right? >> Yeah. On the web and I like this this was just the the simplest of things. It was like a zip file, right? Somebody else had created it. But okay, so I' I've improved my starting point and then we got stronger um command line interfaces, scaffolding, um that type of thing. And then you didn't have to worry about the exact starting point. And I feel like this is just taking us a few steps forward. like it's making it easier for us to bootstrap a solution um that that kind of works but still requires you to really understand what you are putting into your codebase shipping out to users and and not uh you know uh getting rid of your responsibilities at the end of the day I find that um at least for me in the last year or two uh going back to first principles as always like has helped a lot. I've been uh working a little bit more closely uh with the Google DeepMind team um on Gemini and uh its coding capabilities and that has been a really good reminder for me of just understanding how a lot of these things work behind the scenes. If you remember that you know what is what is training data? Well, it's very likely going to be looking at permissively licensed um you know code that's on GitHub um or or out on the open web. um the patterns in that code are probably going to be reflective of in many case like lowest common denominator um things that maybe just work. Are they going to be the most secure, the most high performance, the most accessible? Possibly not. And so if you remember that the training data itself um still requires a lot of work to like actually get things to a place where you know it maybe is of production quality, you sort of start start to set your expectations a little bit lower. um when when working with LLMs um you realize like yeah it will probably do a better job than me just copying and pasting snippets of code from somewhere else um obviously but um I still very likely need to do um a level of manual work or a level of diligence on top of this to get the best outputs. Yeah, it reminds me a little bit 10 years ago or so, 10 5 10 years ago, we use a lot of Stack Overflow because when you search for something, it was there. And so, for example, for email validation, like you would search like how do I validate an email address? There was a Stack Overflow question and it had like 20 answers. One of them was top rated. It was a regular expression. And what what I did and I think what a lot of people did is I just copied and pasted because I cannot be bothered to define exactly the regular expression of emails which is a lot more complicated than just things. So I just kind of and if it was for nothing important it it kind of worked but there were then complaints uh and like flags raised that a few of them not this specific example but they were insecure or they didn't account for edge cases and a lot of devs again when you were in the mindless mode or it didn't matter or you know better you did that and I guess we're we're going to have the same thing at larger scale like why did the bug bug happen and you're going to look back at the history yeah someone just looked looks good to me for for that big pull request that was in the end generated by an AI. >> I think that this is this is very much a smaller um point, a side discussion point, but um I I've seen a number of cases where people will now say, you know, hey, do I do I still need to use third party libraries if I can just prompt a very slightly smaller version of a solution myself? Which reminded me exactly of this Stack Overflow copy pasting the top response thing. And what you re if you're a senior engineer what you realize is well hold on that also means that you are now taking on the responsibility of making sure that this is future proof for security issues for all sorts of platform issues that could happen. If you're relying on a library it's perhaps easier for there to be a central point of leverage where those fixes can be made and then deployed out to people. If you own all of these different patterns in your codebase yourself, that also means that you're taking on that responsibility can be totally okay, but it's something that I I sometimes see people just not being mindful of of requiring a little bit of extra work. >> And I I feel this is where we just need to remind oursel that it's just engineering, right? It's trade-off. Do you take on the responsibility and the risk of of maintaining this thing and the fact that it might have missing edge cases or whatever or might not work correctly or securely or do you take on a dependency which has its own problems right there's now dependency security etc. But speaking of the need for software engineering, what are some new workflows, some some things that we have not been able to do before as software engineers that we can now experiment with that you you might be trying out with with these AI tools that is it's just like is brand new. >> Yeah. I think for me the the thing that I'm most excited about um seeing evolve is this idea of a synchronous background coding agents. a number of teams playing around with these ideas at the moment. Uh jewels, codec u codeex uh we also see uh you know GitHub um experimenting with some of these ideas as well. I think that the idea of you being able to delegate um parts of your backlog and have a system be able to uh implement that uh in you know asynchronously is is a very interesting idea. um if we can do that without merge conflicts in a way that is easily human verifiable. So again going back to that human in the loop I think that's very interesting. I have been finding that that is uh very very very much already in a place where if you are having um you know one agent work on uh writing or updating your tests or you have a number of agents working together on uh trying to migrate your codebase from one version of a library to another or you know a version of a dependency to another that they're pretty okay at doing that kind of work right now. smaller changes like there there are lots of things that you know we we all have to do like adding dark mode for for example the smaller kinds of changes where maybe being able to delegate uh those kinds of changes to uh as agents are are going to you know get get very very good um I'm excited about that I think that the jury is still out on exactly what is the surface for being able to you know if you are the conductor of this orchestra um what is the right surface for you being able to manage all of these things and what is a realistic number of tasks for you to be managing at the same time because you know even I like a number of people have shown off like hey yeah I've got like 20 different terminals open and I have quad code running in half of them and then Gemini running and and it's it looks it's funny but the reality is that you only have a finite amount of attention and if you are going to be actually putting in um diligence into your code reviews and into each of these like workflows you are probably only going to be able to do a couple of things um at the same time. You know, it all comes back to to multitasking best practices, but I'm interested in that. I'm interested in seeing where that goes. Another thing that's very interesting is is vibe designing. Um I'm seeing that part uh of product uh you know, product engineering, product development uh evolving a little bit. Um was very exciting to see you know, Figma's MCP moving um a little bit closer in this direction. um things that allow designers and developers to work more closely together or to at least enable designers to be able to take their vision and turn it into a functional prototype, something perhaps a little bit closer to code that can then be um productionized uh and is not just a one-off demo. Yeah, I I I heard uh an engineering lead or design leader at Shopify said that her team, every designer on her team, so not all of Shopify has cursor and what they do is they create a Figma design and then they ask cursor to implement it and then they show it to engineers and this is not saying ship it. It's just more we now have something interactive that we can work with as opposed to just what they used to do which is sharing a Fibbit design. And I was like, huh, this is something I've not heard before. Like truly like like all designers or at least on a team using the developer tool like I I couldn't imagine designers using Visual Studio Code for I mean I can imagine them being able to open it but I couldn't imagine them it's just not built for them. So this was really really interesting to hear and again this this wasn't some vendor advertisement or anything. Yeah, I've I've a shout out to the Shopify team. Honestly, I I feel like um a number of them are have been very open to sharing what's been working out well for their teams and I've I've enjoyed following their story. Um I think that uh you know more of these patterns spreading is going to depend a little bit on on training on governance on like clear boundaries between what is prototype code and production. But already being able to turn something um static into a semifunctional like prototype is very cool. Are we going to see like everybody using cursor? I I don't I don't know. I I think that um I at least for me I I see folks being able to accomplish the same outcomes from the tools that they spend the most time in or the bridges between tools getting better over time. But I still think it's very very exciting. In the same way, you know, we're also seeing lots of good conversations about PM and EM roles uh changing. PMs maybe, you know, spending uh more time on problem framing and metrics and policies for agents. EM spending more time on um evals and safety reviews and really enabling their teams to work with AI confidently. Um it won't change uh accountability for outcomes, but um I am seeing a lot of good discussions about the need for taste in product engineering. um where that's going to be what differentiates people because if anybody can you know look at what you've built and can use prompts to accomplish um you know a similar set of functionality the taste piece is going to continue to be very interesting um for folks to focus on in the future. You and I have talked about sort of uh juniors and seniors. Um, I think that, you know, there's going to be interesting times for new grads, uh, versus seniors. You know, uh, AI definitely raises the floor, but it also raises the ceiling, too. And, uh, juniors are going to be able to get moving faster, but, uh, seniors who can, you know, write specs, decompose work, understand system architecture, review effectively, I think that they're going to become even more valuable. And that last 30% that we talked about, I think it's leverage. Um, not just busy work, but I actually think it's leverage. And um, you know, a lot of surveys have been showing that uh trust is in a a cautious but optimistic place at the moment. But um, that cautious piece speaks to the need for that human oversight remaining pretty central. >> Yeah. And if I think about I was just thinking that when it comes to this idea of parallel agent on one I don't think it's ever been done in the sense that as a developer you were never able to do this. I mean we we couldn't even kick off and talk with natural language to a machine that would spit out code that actually compiles like I think this is new as well but the fact that you can do it with multiple on it's not happened before in programming is a very you know you're in the flow you work on one problem when you stop working on it you switch over you get your context almost like the stack clears you know you load the new thing it it kind of feels like it right but on the other when I think back to who are the best senior engineers I know and what their days look like well they're on a team with a few mid-level may maybe some interns or new grads and they're working on their stuff and then they're the ones who get the Slack ping saying hey could you please unblock me so they context switch review the code you know like like criticize it or they block out time and they review review review and like for every kind of senior slash often these are tech leads they have like a few people who are you know they're not agents they're people but they they just review their work and they kind of orchestrate them a little bit on the standup, you know, they're in the planning meeting, they nudge them, they mentor them. So, in some sense, I feel like we seniors already do this. And if if I had a magic wand saying like who who would I expect in a few years time to be able to manage multiple agents? Well, the senior for sure like new grads probably like I mean I I feel there will be a stretch for them, but they're not expected to have that. They don't have the expertise. So I I wonder if some of these skills are somewhat transferable in a sense that and you know that senior why could that senior do that well because they understood the codebase. They knew what good looked like. They were always thorough in their reviews. They never let things slip. They call out even the smallest thing. >> I completely agree with you and and I think that there's a lot to be said about how um developer education in teams may also evolve um for this moment. Um historically I I remember when I was coming up you know um mentorship was always a big topic and we talked about you know especially for folks uh who are joining a new team um the importance of like pair programming. I think we're going to see, you know, perhaps even situations of of trio programming where it's a junior senior and the AI and maybe the senior is going to be there uh asking you to explain um the code that the AI is generating in some way or walking you through how that code perhaps connects to other parts of the system and really again using it as an additional tool um in their arsenal uh to be able to help uh build up confidence and awareness um of how of how the full app actually works. We're also seeing some interesting discussion about um you know potentially new roles or or refinements of roles, things like forward deployed engineers. Um I'm seeing interest in you know developers who are a little bit more embedded with customers who can build features rapidly with AI while feeding back requirements. And that type of thing might blur the lines between um you know the developer PM and designer roles a little bit. I'm interested to see where where that um may end up going. And I'm also interested in how, you know, just generally speaking, AI engineering is going to evolve how um you know, we approach education, whether you're in high school or you're in college. Like, are are we going to be teaching people prompt and context engineering best practices? What what does that all actually look like? How do we continue to enable people to think um with systems uh design and engineering in mind? But I'm I'm very excited where the education side of this goes. >> So, one area that we've talked about was code reviews and how it's really important. It's it's a bottleneck and and we should review the code. One thing that I'm kind of noticing on myself already is when I use these AI tools, uh may that be cloud code or or agents or or even just the autocomplete, I have a tendency to do tap tab or accept or just like accept all the things, especially when I'm working on something that I mean it's it's not the most critical thing in the world. And it's kind of it's easy and especially after I started to trust that it gets it right most of the time. In the end, I know I'm going to review, but I find myself losing a little bit of criticality. I'm not as critical, you know, the second day and the third day and and when I was the first time when I didn't trust this thing. And I worry a little bit that with code reviews, the same could happen. You know, LGTM looks good to me. I mean, I understand that's a Google has it in in built in as a as as a feature in in your code review tool, which is a really fun tool as I understand. But there is this risk. There's always been this risk of course that that like you just kind of like it's a lot of work. It kind of looks good and you're not looking critically. How do you think especially on on on you know like proper teams we could battle or or or we could do this kind of fighting against like yes giving it a proper review especially if we haven't written the code that much because I feel with writing the code you have like two reviews. One is you're writing your own code. You're typing it out, which we're not doing it as much anymore. And then someone else gives a review knowing that it was you who typed it out. >> I mean, it's always it's always more fun to to write code than to to read and and review it. But >> a little bit, right? >> Yeah. And I I I think more and more of our job is going to become about reading and reviewing code. There are a few ideas that I've I've tried to suggest to folks. um things like uh you know for certain features or certain days of the week maybe you intentionally try to not lean on uh using AI or an LLM and just try to see like okay well can I can I still solve some of these problems myself um to preserve your critical thinking skills and force you to think about okay well let's say that all of the top LLM providers were down for the day uh what would you do being cheeky, you know, I would probably say like, yeah, I'm just I'm just going to go, you know, use Olama and a local model and I'll be totally fine. I will find some fallback. But the reality is that I do think that it's going to continue to be very important for us to be able to think through um how things work, be able to problem solve without necessarily, you know, uh relying on the AI. The the models are going to continue getting better. their ability to take in sufficient context from your codebase is going to continue getting better. But it is going to take quite some time in my opinion before uh you're going to be able to fully trust that in every situation whatever requirements you throw at it is going to be able to get it right. Um and if you get stuck, if you're trying things out and you've tried it five, 10 times and it still hasn't solved the problem, you're going to have to solve the problem yourself. So, I think that um being able to force yourself into situations where you're testing and retesting your critical thinking skills are going to be important. I also think that there's a little bit of value in, you know, doing some game theory around this. Teams or individual people are probably going to start leaning on AI to do more of the code reviews to keep up with the, you know, pace and and velocity of change coming in. What are those workflows going to look like? If you have an agent that's saying like, "Yeah, I reviewed this PR and it looks good to merge in, are you actually going to trust it or are you going to go and do a human level review, even if it take, you know, is maybe more shallow than you historically would have done?" Or maybe you're okay with spending 10, 20 minutes or something on on the review, like what does that work will look like? >> In my case, even if an agent an agent telling me that something um looks good to merge is a signal. It's similar to a signal of like, okay, well, maybe a junior person on my team has also, you know, given this in LGTM. I'm still going to probably go back if it's critical enough >> or the CICD passes all the tests, right? It's it's green. All test pass. >> Exactly. Exactly. It's it's a signal. Signals are useful, but you still want to apply, you know, your lens on quality uh to it and and take a look through and just make sure that you're you're confident. things like having those tests, having, you know, whatever quality gates that you you and your team discuss, like having those in place, all of these are good signals for building up confidence. So, the more signals that you have can build up to build up confidence that things aren't going to go off the rails when you merge in, I think that's good. But, um, I try to, you know, be very intentional with making sure that I'm not leaning on AI for absolutely everything. I may use it for many aspects of my life on a day-to-day including AI coding, but I still try to make sure that I'm, you know, whether it's 20 or 30% or whatever amount of my tasks don't necessarily require AI, I still try to make sure that I'm using my brain um to to solve those problems myself. And I think that intentionality like just be proactive with maintaining your critical thinking skills. I think that's going to help people. >> Yeah. And one of these things of like I you're still hands on two things came to my mind. One was a cloud code actually recently shipped. You can change modes. You can have explanatory mode where it explains things and you have a thing called learning mode which is it pauses and says you do this part which I thought was really clever. I actually turned it on for for something that I was building. It didn't work as I expected because it gave me a really weird task to do but I I see the potential and I I actually I actually am tempted to lean a bit more on that and I think it's I hope other tools will also do this as part of the developer. like I I feel I do want to know that I can do it and I I can do it, but how am I going to know if I'm if I'm not doing it? Of course, you fiddle around a little bit with it. >> I think that is such a fantastic idea. And if somebody is a junior or you're coming in to a codebase that you're not familiar with, fight that uh feeling of the first thing I'm going to do is try to provide value and I'm going to just prompt a new feature into existence. Maybe use the LLM to just explain how the codebase works and just spend time like soaking yourself up in the richness of like what is the codebase, how does it work, how does everything connect together before you start prompting things into existence. I think that using it as a learning tool is is a very powerful thing we need we need more of and this is also something what you said new new new joiners using a learning tool. I'm hearing from a few companies that they're seeing new joiners on board faster. The ones where they can use these AI tools to explain stuff to them out like the cloud-based is is just a a kind of a trusted person 24/7. You can ask questions on like a sen the senior engineer who I mean you can ask questions anytime but obviously you don't want to do it like you know 8 hours of the 8 hour working day like you're going to keep your limits and it's it's interesting how that that is changing the dynamics at the at a bunch of workplaces that do have these tools. >> Absolutely. I I I think that you know uh my my hope is that it's going to become a much more standard thing uh training AI as a learning tool and it's it's not just for understanding um you know a new codebase. Uh it can be very useful for understanding programming concepts or frameworks or architectural patterns. Um there are times when I want to be able to bring a feature over from one codebase that is very different or written in a different programming language over to another. And I have found AI uh very much indispensable for those situations of helping me understand one codebase and the path to being able to bring over something um to to a new one. And so encouraging your team to experiment with AI tools, sharing their best practices, making it a regular thing that people know it's okay to use it as a learning tool, I I think it's just going to be great for you as as as leads and and and managers. >> Yeah. And you're you're also a manager of of a team and as part of this obviously you do performance reviews, promotions, help people basically professionally get better. How do you think the kind of your definition or what you're seeing on the team the definition of a really standout solid software engineer has changed in the last 3 to four years since these tools have come out? What is new and and what is the same? I think that what is new is the importance you know we one of the things that I have found has always held true is the importance of being a lifelong learner um that has always remained true doesn't matter if frameworks come and go tools change you know the industry evolves being a lifelong learner and being very open to um trying out new things failing building up those skills I think that that remains very very important um the people on my team that I think were uh the most successful early on at leveraging AI for coding and for product engineering were the ones that went in with this mindset of I am happy to learn new things to try out and to have this growth mindset and if it doesn't work that's fine at least I've tried it out at least I understand um the constraints and maybe I'll try out different models for these different use cases in the future I think that that has been very much a consistent thing that's been important um I feel like if you are a lead, uh, now is the time for you to be helping your team through this moment in terms of showing that you're okay with learning as well. One of the things that I do every week actually is I spend a lot of time I love reading. I spend a lot of time reading. I will of course uh read uh your newsletter a lot. Um I will read a number of different papers, white papers, um blogs, watch videos, watch courses, you know, read announcements about what is new in AI, what is new in AI engineering, um during the week and I will surface those things to my team um in a newsletter. I I will also surface >> Oh, you have like an internal newsletter for your team? >> Yeah, I've got an internal newsletter. I write it every Monday. So I'll be I'll be writing it after after this and I will include like what am I hacking on? What am I writing? What am I thinking? um what are the things that I think are important for our team to actually pay attention to and in this time where you know you you see so many people struggling to stay up to date if you follow along on on Twitter or other social networks it can feel um very overwhelming >> because every few hours it feels like something has changed in a fundamental way >> and being able to sift through that and help people to just pay attention to like what is actually important I think that's a great thing for leadership to be doing right now especally if you can guide folks towards, hey, maybe spend a little bit more time poking at this rather than that. >> I just want to say I think it's Islam dunk because you're also staying closer to, you know, the the industry like to being technical, maybe it's a stretch to say hands-on, but you're pretty close to that just by keeping up with all of this. >> Yeah. Yeah. And I've, you know, I I I'll I'll defer to, you know, people on my my team to to to say things, but I think that um with I've generally had good feedback about this. I've even had like um other execs saying, "Hey, actually, I'm finding it really hard to stay on top of this and your newsletter is helping um me navigate this moment as well." And so I I think that as a lead being able to stay on top of what is happening at whatever level you're comfortable with or that your bandwidth allows, I think that that can be a really powerful thing for your team and will help you just keep your your own skills relatively sharp. You know, we've been talking a lot about AI assisted engineering. If your team do have to build out like AI features, a lot of this stuff is also going to continue to be relevant um for them too because you can help connect the dots between what is happening in the industry where AI is concerned that is actually important to their work versus what is not. And that's especially I think it's exceptionally important in this moment where maybe there are things that they have not had to historically think about like oh hey well model X is really good at image generation but model Y is really good at generating sound. I'm just making things up but I think that it it is really useful for you to be able to um maybe highlight some of these opportunities in a more concrete way and then your team can go off and and spend time digging into it themselves but they're not starting from a blank slate. Plus, I guess you're you're just showing that look look I'm learning. I'm spending time reading, understanding, digesting. I'm doing some stuff on the side, which kind of gives the permission that it's okay to do it. And if anything, I mean, you know, everyone for themselves, but I would assume that right now because we are having a big change in in this technology. It's it's a new tool, a lot of capabilities. It should be okay at most companies at and most teams unless you're like has down crunch time shipping something like on Friday to for for engineers to spend some time experimenting figuring out, hey, can I use this thing? Will it work for me? You know, when I talk with again the folks at Shopify, they do a lot of this and and in the end actually what h what some people think might happen is like, well, people do less work. Actually, what happens is they do the same work as they did before, but they're way more motivated. They tried a bunch of new stuff. Some of it doesn't work, and it's kind of a a you would think it's a waste, but it's actually not. It was learning, and now they're a lot more confident about what works, what doesn't. You know, they'll they'll confidently say like, okay, we're we're not going to use uh AI for to generate this part of the codebase for prototyping is great, and so on. So, I I feel like everyone's figuring things out. So, it's it's almost a waste to not give permission. And how how how better way to get permission than to show that you're doing this as well. >> Yes. Yes. Absolutely. A AI is a tool tools that can compress work I think can can also lead to good moments of clarity. And it is faster than ever to try out certain classes of ideas. And I think that that efficiency can also sometimes highlight the importance of our human qualities when it comes to like judging what what should we be moving forward with, what should we be ignoring, what should we be minimizing. I think that trying to uh embrace AI in this moment and figure out what you know what works well for you you and your teams is is a great use of time. Um I did want to say if there are folks in your audience who are in enterprise especially I know that having gone through this journey um there may be teams who feel like oh man it feels like startups are able to move uh you know so much faster than we are or they're able to use all of these great new tools and these models but we are still waiting on the enterprise friendly version of these things and I've talked to teams where they can sometimes feel like we're waiting we're waiting we're waiting to be able to try out more of these tools What I ended up doing in my team was we were similarly, you know, a couple of years ago now, we we were similarly waiting for the company embraced official way of doing things. But that didn't stop us from being able to learn. So I encouraged people well a lot of us are hacking on side projects right at the weekends. We can try out whatever you know third party tools, third party models, our own models like we can try out and and learn and that doesn't have to be a big blocker for you. So you can still, you know, help your team along this journey um without necessarily having uh to to do a lot of that waiting. So just wanted to let folks know there there are many paths to being able to embrace this moment. >> Yeah. And I I think the consensus is pretty much that these tools will be widespread. They keep changing and they're already contributing to a lot of teams code bases and people are using it. So might as well just, you know, use it and and because it takes time in the end. So the earlier you start the you'll have plenty of learning to do regardless. >> Yes. Absolutely right. >> So as closing I just have a few wrap-up questions. So I'll just fire them and then you you tell me what what comes to mind. What's your favorite programming language and why? >> My favorite programming language is JavaScript. This is where >> I thought you might say this but I wasn't sure. >> Of course >> you wrote books about this. >> My favorite programming language is JavaScript. And it's it's not for the personal reasons. Um, there are probably better programming languages. I like JavaScript because it enables anybody on the planet to be able to build and ship something to the web in a way that doesn't require gatekeepers. It it's very open. And I think that there's something very liberating about that idea. So that that is one of the reasons why I like JavaScript the probably the most. I I like it. I I'm I'm always surprised when uh the software engineer uh says JavaScript because we know that compared to other languages, it it has a lot of limitations and you know there's been books written about it. as well. But I I cannot disagree with it. I I I love it. It is true. Uh what's a tool that you really like using and what does it do? >> I would say that um one of my favorite tools at the moment and I I'm biased. I have a I have a book out about this as well is probably Bolt um Bolt. Uh Bolt is one of those uh vibe coding scaffolding tools uh that people can use. Um they very recently added support actually for being able to use custom agents. So you can use clawed code for example um to build your vicoded app and um the the output is generally very high quality great designs. So I've I've been really enjoying that and and the team have been I would say on on the edge of of offering some really great integrations. So I've been I've I've been liking this idea that many vibe coding platforms are now starting to think about the integration layer. So, how can we automate the idea of you needing to use, you know, like a superb basease or an authentication provider or these other things? You still need to pay, of course, attention to all of the code that's being generated, but being able to remove more of that setup friction, I think, is amazing. And I guess the fun fact on Bolt.new is they started off as a company called Stack Blitz who built a really cool online editor, really advanced, and then they moved over from that to Bold. So, and there are software engineers behind it again who really know their craft. >> Yes. Yes. Absolutely. >> And uh finally, what's a book that you would recommend? >> So, the book I'm going to recommend um is one that I think covers uh I would say career paths and best practices for software engineers in a in a way that I found very compelling. It is by you. It's the software engineer guide book. Um and of course I'm I'm playing to the crowd, but it it is genuinely genu genuinely um a very very sharp book. Um uh from from all the reviews that I've read about it, other people have also found it to be an excellent read. >> Well, it's it's on my desk, but we we didn't talk about this by the way, so this recommendation came just as a surprise to me as Tyler. Thank you. >> Genuinely an an excellent uh an excellent book. Um I guess if I if I wasn't uh going to of course you know recommend that one I think that if you are trying to uh learn more about the foundational aspects um of AI engineering in this moment there's a great book called AI engineering um by Chip Huin uh that is also worth taking a look at. Uh very very well reviewed a very very thorough book and I I also recommend folks check that out. >> It's such a good book. I I I feel if if you know the concepts there, you're pretty well off. And if you don't know them, you're probably going to be, you know, still finding your way. So, big plus one for that. Addy, this has been great. Thanks so much for for doing this and this really awesome conversation. >> Well, thank you. I I really appreciate it. Um I hope the folks, you know, if you are interested in this space, Beyond Vibe Coding is out now um from O'Reilly. Hopefully, it'll be useful to some people. But it's been a pleasure having this conversation with you. >> Yeah. and I I' I've been reading the book. I I really like how it goes into the practicality. So I I can also very much recommend it. >> Thank you. >> I hope you enjoyed going beyond vibe coding, pun intended, with Addios Manny. One of the things I took away from this conversation is how it's really important that as an engineer, we keep understanding exactly what the LLM does and why. And if we don't understand it, we just stop, understand it, and then move on. As long as you understand the LM, you are in charge. >> >> But once you stop understanding, it's in charge and you can kind of become helpless and out of your depth. For more tips on how to get up to speed with AI engineering, check out Deep Dives in the Pragmatic Engineer that I wrote, which are linked in the show notes below. If you've enjoyed this podcast, please do subscribe on your favorite podcast platform and on YouTube. A special thank you if you also leave a rating for the show. Thanks and see you in the next one.