Interviewer (1:56)

So you want to so Addy, welcome to the podcast.

Addy Osmani (1:58)

Thank you. It's a pleasure to be here.

Interviewer (1:59)

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 called Beyond Vibe Coding and on your substack you also write a lot about your learnings about Vibe coding, AI assisted development. In this book and also on your blog you talk about Vibe coding versus AI assisted software engineering. In your mind, what does Vibe coding mean to you specifically and how is it different or similar to AI assisted software engineering?

Addy Osmani (2:29)

Yeah, so I tend to tell folks that I personally think that Vibe 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 and give folks that are new to the industry an incomplete picture of what it takes to build robust production ready software. But I kind of have, I guess, two definitions for them. I think the Vibe coding is really about 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. So you would, you know, this goes back to Andrej Karpathy's like original 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, MVPs and learning. And I think it's, you know, what I've seen in 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 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 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 doing what falls a little bit closer into traditional software engineering. You know, doing more planning, more specification driven development, including sufficient context. And really what is AI assisted engineering across the full software development lifecycle. 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, whether it is with boilerplate, debugging, deployment. But the big difference is that the human engineer remains firmly in control. You are responsible for thinking about the architecture, for reviewing the code, and for understanding 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, scalable and maintainable. Maybe AI is helping you with velocity, which is great, but that doesn't mean that you can Sort of shrug off your responsibility for quality at the end of the day. And I tend to think that a lot of people have said that they found AI encoding to be a force multiplier. But I found that the greater expertise that you have in software engineering, the better the results you can get when you're using LLMs. And I think sometimes 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, 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, to somebody else because just expecting that the AI is going to help you untangle whatever mess happens later on is probably not going to work out long term.

Interviewer (6:06)

And how do you personally use these tools? May that be Vibe coding and especially AI assisted engineering?

Addy Osmani (6:14)

I have more recently been focusing on the idea of spec driven development, having a very clear plan of what it is that I want to build. I think that, you know, there are definitely places where I still vibe code if it's for a personal tool, something throw away, or, you know, in the old days, if an engineer or a PM had an idea, maybe we would put together, you know, a quick mock, maybe we would put together a wireframe or a sketch or something like that. 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. Sharing an idea.

Host (7:13)

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 Vive coding doesn't account for institutional knowledge. Every prompt starts from what you tell it, not what your team already knows. Real product development is the opposite. It's accumulated context. For example, every bug has a kind of history. Every feature connects to customer requests. Every PR fits into your team's roadmap. 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.

Interviewer (8:32)

Visiting Linear App Pragmatic.

Addy Osmani (8:36)

That doesn't mean that we take the Vibe coded prototype or that code and just stick it in production. Once you have clarity around the vision that you have 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. Because otherwise if you're giving into the vibes, you're also sort of giving into, okay, well, you figure out the architecture, you figure out like what this should do. And while that's fine for ideation, it's probably not sufficient for production sort of product engineering. So for me, spec driven development has been a big thing. I think that tests are great and if you are open to tests, it can be a great way of de risking your use of LLMs and coding. Because sometimes even if you're using state of the art models, you can end up in a situation where maybe the code looks more convoluted than you would expect, or maybe your first couple of, 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 things are working with tests and that 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. And that that's been, you know, something that's helped me quite a lot as well. So spectrum development, testing, I try to also make sure that I'm leveraging, you know, this is, I guess this is, 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, you know, in the last couple of years, we've seen a number of cases where if you notice that something is broken, I will see a lot of engineers that will just say, okay, well, hey, LLM, 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's again, going a little bit closer to just rolling with the vibes. But if you can keep things like a browser in the loop or something that can actually see the page, that is what kind of Chrome, DevTools, MCP and related solutions do. They give your LLM eyes in many ways so we can see what the browser sees and 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 in to other tools. So that has been great otherwise for me. Generally speaking, I've found that, you know, you really do need to put in the effort to get proficient with these tools. And I still find myself doing that. If there are new models, new tools, new platforms coming out, I'm generally finding that I experiment a lot every week. I try to, you know, encourage my teams to share with each other with me, like, 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.

Interviewer (12:10)

And I feel what you said about it takes time to learn this thing. I have had so many 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, we're engineers, it doesn't work everywhere. It makes mistakes, it screws up, but it does. Like, I think if you've not found, 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 about what works for you 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 Chrome team, other teams as well. What are things that you're observing on how others are using it, and interesting ways, maybe unexpected ways or even ways that maybe didn't work out for those specific people or engineers.

Addy Osmani (13:22)

I would say that at a company like Google, you know, we have this very long term, very battle tested way of thinking about software engineering at scale. And with AI, what we've realized is, you know, a lot of that doesn't go away. You still want to care about quality, you still want to care about doing due diligence. The things that I think we've found interesting kind of mimic what people have seen in startups and other kinds of companies. So the importance of mastering understanding, like what is prompt engineering, right? Like making sure that you are constructing the right set of incantations to get the best outcome from an LLM. And then context engineering more recently, like how can you make sure that you are optimizing the context window to increase the chances that those outcomes are higher quality? We spend a lot of time thinking about that, making sure that the right sets of descriptions, details, files, examples, any additional content that is specific to a project that the LLM may not necessarily have in its training data that has been very interesting and important for us as we've been working through. I have been somebody that has been trying to explore using AI in every facet of my life in many ways over the last couple of years. And 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 system prompts and tool quality or are not quite there just yet. And so I've been also sort of nudging my teams in that direction as well. Like if, if, 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, what would it do? Would it actually help you accomplish your goal much faster or would it likely slow you down? And if 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, you probably don't have this very deep understanding of AI. For myself 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 towards, well, where would it be useful for you to build up this expertise too? So in the last year I've been spending a lot more time on thinking about things like evals and benchmarks and 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 way? 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's been a good learning for us. I think honestly, just the importance of always maintaining human oversight has been one of the bigger learnings we have. Of course, I know a lot of people have run into this. We've of course seen cases where people, very often external contributors will sometimes be very passionate about like, hey, I want to contribute to your project, but then they'll use an LLM, where you're going.

Interviewer (17:11)

With, yeah, use an LLM and submit something that I think Mitchell 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.

Addy Osmani (17:28)

I love reading studies about how different segments of the developer population are finding AI and what that means for their teams. And one of the studies that I recently read highlighted that if you are increasing the velocity of 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 that does also mean that our workflows may have to evolve. I have seen, you know, a lot of talk about, like, yeah, why aren't we using, you know, LLMs to also do the code review. And that, you know, that's a little bit of a slippery slope because if the AI is writing the code and you haven't studied it carefully, 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 excited to see where that goes. For me personally, there are a lot of tools that I use. Some of my favorite ones include using Klein in VS code.

Interviewer (18:52)

A lot of people use. You're a big fan of people follow your writing on Klein.

Addy Osmani (18:56)

Yeah, I love Klein. I think that, you know, people can do a lot with Cursor and Copilot and VS code these days as well. But one of the things that I enjoy doing is, 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 LLM is not going to be able to help me with. Just last night I was, I was working through a problem and 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.

Interviewer (20:13)

Well, plus, I mean, I'm reflecting on this, right? Like, I feel this is the difference between a software engineer who's worth their money just in terms of being employed and one that's not. If all you can do is just prompt and prompt. I mean, anyone can do that. I'm exaggerating, but a new grad can do that, 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 fail, they can fix it. And also they can also explain it to people. They can explain it in a meeting coherently, without rambling. So I feel this is what a professional is in any industry. It's like they know how the things work. Same with the car mechanics, 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 solves my problems, you're kind of at the risk of, well, if you don't know how it works, I mean, why do we need you? Anyone can prompt another LLM.

Addy Osmani (21:14)

Exactly, exactly. And, you know, as we start to talk more about, you know, in the last year, you know, people working in their terminal, their CLIS has become more of a thing once again with Claude code and Gemini CLI and open code and so on. And then people started talking about, okay, well, how do we orchestrate 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. Maybe not immediately, but at some point it very likely will. And my experience of this kind of led me at one point to write about what I called the 70% kind of problem.

Interviewer (22:19)

Let's talk about that. Because you wrote about that extensively. We actually published a shared article, a guest article based on your article, which we'll also link in the notes below. What is the 70% problem and how did you come across it, and how do you think it might have changed since you published it, which was about six months ago?

Addy Osmani (22:38)

Yeah, obviously, you know, model quality, tooling quality continues to get better. So the 70% problem is really about 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%. You can consider it like 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, you know, used a couple of prompts to build up something, you give an LLM one more prompt and it happens to go in a completely different direction. Maybe it has fully rewritten your UI or the functionality behind your component. Things like that, they're very often hidden, maintain ability costs, places where you're not being specific, you are delegating the responsibility back to the LLM and you may end up getting diminishing returns. As we've seen time and time again on hacker news security vulnerabilities. This is exactly where we see people accidentally leaking API keys, where there are XSS issues, where there are all kinds of problems because people did not think as holistically about the problem that they were solving and just sort of gave in to the Vibes. So a Vibe 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. If you are going to be landing this in a code base where you're working with other people, working with a team and, you know, dealing with a real user base of people, I think that the security and quality aspects really speak to the need for keeping the human in the loop there.

Host (24:31)

Yeah.

Interviewer (24:31)

And we keep hearing stories about product managers and less technical founders who get really into Vibe coding. Excited, they spin up a prototype or a better version and then they want to build something that they can put out into production. And there's a bunch of stories, I'll link one in the show notes on how they just get stuck or it takes them a very long time. 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 get this false confidence.

Addy Osmani (25:14)

Yes, yes, absolutely. I think that with that last 30%, what you will often see junior engineers or interns or new grads doing is, you know, they, they will not really know what to do next beyond just constantly repronting 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 don't think that that necessarily goes away. As you said earlier, with the tools that we have today and the models we have today, almost anybody can give a high level prompt to a tool and have something, you know, that that seems like it works, come out the back of it. But I wouldn't necessarily trust that in production. Seeing too many stories at this point of things just going a little bit.

Interviewer (26:28)

Off the rails, it reminds me when this was before AI tools, I had an engineer who joined Uber who came from a smaller startup and a 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 was like, what's difficult? He's like, I'm trying to read all the code and it's just too much. And I was like, why are you trying to read all the code of Uber? Which is like our backend system. It was 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 get what you're coming from and I think that's great. I was just like, let me explain to you how the code base works. You shouldn't read all the code, but you should understand the structure where you can find things. Because this is, you know, on the code base where we had, I think two or 300 engineers working on it. And I thought this person had a really great intention, which is, I'm coming to a new place, I want to understand it. I'm going to spend the first few weeks understanding and those people excel. And I'm kind of thinking we keep getting back to this thing. But what I'm hearing from you is if you can actually to some extent outsource this to an LLM, which is a bit of a hit or miss, or 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 non deterministic, you're kind of stuck. You know, your best thing is to try a new model or empty the context window or, I don't know, do something else. But it doesn't really feel like you're in control.

Addy Osmani (28:01)

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 are a few things I think that everybody should keep in mind that can help you get the best outcomes.

Host (28:19)

Addy just mentioned getting the best outcomes. Here's what I'm seeing with AI in Vive coding. These tools can give you incredible velocity. You can ship features a lot faster than before, but velocity with that precision means you're just shipping more things, not necessarily the right things. How do you know if what you build 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 Statsic comes in. Statsic 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, data driven 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 statsig. 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 three hundreds of these controlled 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 stitched 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 statsic, 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 propricing for teams starts at $150 per month. To learn more and get a 30 day enterprise trial, go to statsic.com Pragmatic with this, let's get back to the conversation about AI and development workflows.

Addy Osmani (30:12)

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 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 of these requ requirements at once. And that doesn't necessarily work the best. So small verifiable chunks, clear expectations and be prepared to iterate with the AI. Don't just feel like one shotting is going to give you the best outcome, because it probably isn't. And that decomposition is really pretty similar to planning a sprint or writing pseudocode, the type of thing that we would do in 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 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 seeding 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 and to make sure that you are setting yourself up for success. The more that you sort of give, give up your responsibilities to the LLM, the higher the risk is that something's going to go off the rails.

Interviewer (31:40)

Yeah, and one thing that I heard from an engineer, actually, this is Armin Ronacher, who's longtime Sentry engineer, 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. 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 they also have a bit of 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. It just came to me as we're talking about it, because what I'm sensing from a 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 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.

Addy Osmani (32:55)

Absolutely. Absolutely right. I'm reminded of AI is just another tool in your tool belt. We've gone through so many different moments over time of engineering and developer experience getting a little bit better with every generation. When I was coming up, I remembered getting excited when templates became a thing.

Interviewer (33:20)

You could generate code with. Templates, you mean?

Addy Osmani (33:23)

Oh, no, just downloading templates for ideas for ui. For starting off on the web, right? Yeah, on the web. And this was just the simplest of things. It was like a zip file, right? Somebody else had created it, but okay, so I've improved my starting point and then we got stronger command line interfaces, scaffolding, 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 that, that kind of works, but still requires you to really understand what you are putting into your code base. Shipping out to users and not, you know, getting rid of your responsibilities at the end of the day. I find that, at least for me in the last year or two, going back to first principles, as always, like, has helped a lot. I've been working a little bit more closely with the Google DeepMind team on Gemini and 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, you know, code that's on GitHub or out on the open web. The patterns in that code are probably going to be reflective of in many cases, like lowest common denominator 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 still requires a lot of work to actually get things to a place where it maybe is of production quality, you start to set your expectations a little bit lower when working with LLMs. You realize like, yeah, it will probably do a better job than me just copying and pasting snippets of code from somewhere else, obviously, but I still very likely need to do a level of manual work or a level of diligence on top of this to get the best outputs.

Interviewer (35:32)

Yeah, it reminds me a little bit. Ten years ago or so, five, ten years ago, we used 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 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 kind of worked. But there were then complaints 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, 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 looks good to me for, for that big pull request that was in the end generated by an AI.

Addy Osmani (36:36)

I think that this is very much a smaller point, a side discussion point. But 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, if you were 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 code base yourself, that also means that you're taking on that responsibility can be totally okay. But it's something that I sometimes see people just not being mindful of, requiring a little bit of extra work.

Interviewer (37:33)

And I feel this is where we just need to remind ourselves that it's just engineering. Right? It's trade off. Do you take on the responsibility and the risk 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 risk, et cetera. 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 might be trying out with with these AI tools that it's. Is just like, is brand new?

Addy Osmani (38:11)

Yeah. I think for me that the thing that I'm most excited about seeing evolve is this idea of asynchronous background coding agents, a number of teams playing around with these ideas at the moment, Jules, Codecs, Codex. We also see GitHub experimenting with some of these ideas as well. I think that the idea of you being able to delegate parts of your backlog and have a system be able to implement that asynchronously is a very interesting idea 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 very, very, very much already in a place where if you are having, you know, one agent work on writing or updating your tests, or you have a number of agents working together on trying to migrate your code base 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 are lots of things that, you know, we all have to do, like adding dark mode, for example, like smaller kinds of changes where maybe being able to delegate those kinds of changes to synchronous agents are going to get very good. I'm excited about that. I think that the jury's still out on exactly what is the surface for being able to, you know, if you are the conductor of this orchestra, 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 in. And 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 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 at the same time. You know, all comes back 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 vibe designing. I'm seeing that part of product, you know, product engineering, product development evolving a little bit. It was very exciting to see, you know, Figma's MCP moving a little bit closer in this direction. 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 productionized and it's not just a one off demo.

Interviewer (41:12)

Yeah, I heard 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 fiber 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 well they used to do, which is sharing a Figma design. And I was like, huh, this is something I've not heard before. Like truly like all designers or at least on a team using the developer tool. 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 wasn't some vendor advertisement or anything.

Addy Osmani (42:00)

Yeah, I've shout out to the Shopify team. Honestly, I feel like a number of them have been very open to sharing what's been working out well for their teams and I've enjoyed following their story. I think that more of these patterns spreading is going to depend a little bit on training, on governance, on clear boundaries between what is prototype code and production. But already being able to turn something static into a semifunctional prototype is very cool. Are we going to see like everybody using Cursor? I don't, I don't know. I think that I, at least for me, 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 changing PMs, maybe, you know, spending more time on problem framing and metrics and policies for agents. EM spending more time on evals and safety reviews and really enabling their teams to work with AI confidently. It won't change accountability for outcomes. But I am seeing a lot of good discussions about the need for taste in product engineering, where that's going to be what differentiates people. Because if anybody can look at what you've built and can use prompts to accomplish, you know, a similar set of functionality, the taste piece is going to continue to be very interesting for folks to focus on in the future. You and I have talked about sort of juniors and seniors. I think that, you know, there's going to be interesting times for new grads versus seniors that, you know, AI definitely raises the floor, but it also raises the ceiling too. And juniors are going to be able to get moving faster, but seniors who can 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. Not just busy work, but actually I think it's leverage. And a lot of surveys have been showing that trust is in a cautious but optimistic place at the moment. But that cautious piece speaks to the need for that human oversight remaining pretty central.

Interviewer (44:26)

Yeah, and if I think about, I was just thinking that when it comes to this idea of parallel agent on one end, 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 couldn't even kick off and talk with natural language to machine that would spit out code that actually compiles. Like I think this is new as well. But the fact you can do it with multiple on one end, it's not happened before. And 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 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 do their days look like? Well, they're on a team with a few mid level, 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 criticize it, or they block out time and they Review, review, review. And like for every kind of senior, often these are tech leads. They have like a few people who are, you know, they're not agents, they're people, but 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, they nudge them, they mentor them. So in some sense, I feel like seniors already do this and if I had a magic one saying, 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. I feel there will be a stretch for them, but they're not expected to have that and they don't have the expertise. So I wonder if some of these skills are somewhat transferable in a sense. That, and that senior, why could that senior do that? Well, because they understood the code base, they knew what good looked like, they were always thorough on their reviews, they never let things slip, they call out even the smallest thing.

Addy Osmani (46:15)

I completely agree with you and I think that there's a lot to be said about how developer education in teams may also evolve for this moment. Historically, I remember when I was coming up, mentorship was always a big topic and we talked about, especially for folks who are joining a new team, the importance of pair programming. I think we're going to see perhaps even situations of trio programming where it's a junior senior and the AI, and maybe the senior is going to be there asking you to explain 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 in their arsenal to be able to help build up confidence and awareness of how the full app actually works. We're also seeing some interesting discussion about, you know, potentially new roles or refinements of roles, things like forward deployed engineers. 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, you know, the developer, PM and designer roles a little bit. I'm interested to see where that may end up going and I'm also interested in how, just generally speaking, AI engineering is going to evolve how we approach education. Whether you're in high school or you're in college. Are we going to be teaching people prompt and context engineering best practices? What does that all actually look like? How do we continue to enable people to think with systems design and engineering in mind, but I'm very excited where the education side of this goes.

Interviewer (48:06)

So one area that we've talked about was code reviews and how it's really important, it's a bottleneck and we should review the code. One thing that I'm kind of noticing on myself already is when I use these AI tools, may that be cloud code or agents or even just the autocomplete, I have a tendency to do tap, tap or accept or just like accept all the things, especially when I'm working on something that, I mean, 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 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 built in as a, as a feature 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 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 proper teams, we could battle or we could do this kind of fighting against. 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 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.

Addy Osmani (49:38)

I mean, it's always, it's always more fun to write code than to read and review it.

Interviewer (49:43)

Yeah, a little bit.

Addy Osmani (49:44)

Right? Yeah. And 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 tried to suggest to folks. Things like, you know, for certain features or certain days of the week, maybe you intentionally try to not lean on using AI or an LLM and just try to see like, okay, well, can I, can I still solve some of these problems myself? 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, what would you do? Being cheeky? You know, I would probably say like, yeah, I'm just going to go, you know, use Ollama 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 how things work, be able to problem solve without necessarily, you know, relying on the AI. The models are going to continue getting better. Their ability to take in sufficient context from your code base is going to continue getting better. But it is going to take quite some time, in my opinion, before you're going to be able to fully trust that in every situation, whatever requirements you throw at it, it's going to be able to get it right. And if you get stuck, if you're trying things out and you've tried it five, ten times and it still hasn't solved the problem, you're going to have to solve the problem yourself. So I think that 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 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's 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 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 analogy. Tm, I'm still going to probably go back if it's critical enough or the.

Interviewer (52:15)

CICD passes all the tests, right? It's green. All tests pass.

Addy Osmani (52:19)

Exactly, exactly. It's a signal. Signals are useful but you still want to apply your lens on quality to it and take a look through and just make sure that you're confident. Things like having those tests, having whatever quality gates that 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 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. Still try to make sure that I'm using my brain 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.

Interviewer (53:18)

Yeah. And one of these things of like 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 something that I was building. It didn't work as I expected because it gave me a really weird task to do. But I see the potential and 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 feel I do want to know that I can do it, I can do it, but how am I going to know if I'm not doing it? Of course you fiddle around a little bit with it.

Addy Osmani (54:00)

I think that is such a fantastic idea. And if somebody is a junior or you are coming in to a code base that you're not familiar with, fight that 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 code base works and just spend time like soaking yourself up in the richness of like what is the code base, 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 a very powerful thing we need more of.

Interviewer (54:35)

And this is also something, what you said, new joiners using a learning tool. I'm hearing from a few companies that they're seeing new joiners onboard faster. The ones where they can use these AI tools to explain stuff to them outlay the cloud base, it's just kind of a trusted person 24, 7. You can ask questions on like the senior engineer who. I mean, you can ask questions anytime, but obviously you don't want to do it like eight hours of the eight hour working day, like you're going to keep your limits. And it's interesting how that has changed the dynamics at a bunch of workplaces that do have these tools.

Addy Osmani (55:12)

Absolutely. I think that my hope is that it's going to become a much more standard thing. Training AI as a learning tool. And it's not just for understanding a new code base. It can be very useful for understanding programming concepts or frameworks or architectural patterns. There are times when I want to be able to bring a feature over from one code base that is very different or written in a different programming language over to another. And I have found AI very much indispensable for those situations of helping me understand one code base and the path to being able to bring over something 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 think is just going to be great for you as leads and managers.

Interviewer (56:07)

And you're also a manager 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 team a definition of a really standout solid software engineer has changed in the last three to four years since these tools have come out? What is new and what is the same?

Addy Osmani (56:30)

I think that what is new is the importance. You know, one of the things that I have found has always held true is the importance of being a lifelong learner. That has always remained true. It doesn't matter if frameworks come and go, tools change. You know, the industry evolves. Being a lifelong learner and being very open to trying out new things, failing, building up those skills, I think that that remains very, very important. The people on my team that I think were 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 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. I feel like if you are a lead, 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 read your newsletter a lot. I will read a number of different papers, white papers, blogs, watch videos, watch courses, you know, read announcements about what is new in AI, what is new in AI engineering during the week. And I will surface those things to my team in a newsletter. I will also surface.

Interviewer (58:06)

Oh, you have like an internal newsletter for your team?

Addy Osmani (58:07)

Yeah, I 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, 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 see so many people struggling to stay up to date, if you follow along on Twitter or other social networks, it can feel very overwhelming.

Interviewer (58:34)

Yeah.

Addy Osmani (58:34)

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 leaders, leadership to be doing right now, especially if you can guide folks towards, hey, maybe spend a little bit more time poking at this rather than that.

Interviewer (58:53)

I just want to say I think it's not done 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.

Addy Osmani (59:06)

Yeah, yeah. And I've, you know, I, I'll, I'll defer to, you know, people on my team to say things, but I think that I've generally had good feedback about this. I've even had like other execs saying, hey, actually I'm finding it really hard to stay on top of this. And your newsletter is helping me navigate this moment as well. And so I think that as a lead, being able to stay on top of what is happening at whatever level you are 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 own skills relatively sharp. You know, we've been talking a lot about AI assisted engine 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 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 is really useful for you to be able to maybe highlight some of these opportunities in a more concrete way. And then your team can go off and spend time digging into it themselves. But they're not starting from a blank slate.

Interviewer (60:29)

Plus, I guess you're just showing that, 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 everyone for themselves. But I would assume that right now, because we are having a big change in this technology. It's a new tool, a lot of capabilities. It should be okay at most companies and most teams, unless you're like, heads down, crunch time, shipping something on Friday for engineers to spend some time experimenting, figuring out, hey, can I use this thing? Will it work for me? When I talk with again, the folks at Shopify, they do a lot of this. And in the end, actually, 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.

Host (61:23)

You would think it's a waste, but it's actually not.

Interviewer (61:25)

It was learning and now they're a lot more confident about what works, what doesn't. They'll confidently say, like, okay, we're not going to use AI to generate this part of the code base for prototyping is great, and so on. So I feel like everyone's figuring things out. So it's almost a waste to not give permission. And how better way to give permission than to show that you're doing this as well?

Addy Osmani (61:48)

Yes, yes, absolutely. AI is a tool. Tools that can compress work, I think 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 embrace AI in this moment and figure out what, you know, what works well for you and your teams is a great use of time. 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, there may be teams who feel like, oh man, it feels like startups are able to move, 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 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 encourage 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 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 without necessarily having to do a lot of that waiting. So just wanted to let folks know there are many paths to being able to embrace this moment.

Interviewer (63:26)

Yeah, and 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 use it because it takes time in the end. So the earlier you start, you'll have plenty of learning to do regardless.

Addy Osmani (63:45)

Yes, absolutely right.

Interviewer (63:46)

So as closing, I just have a few rapid questions, so I'll just fire them and then you tell me what comes to mind. What's your favorite programming language and why?

Addy Osmani (63:55)

My favorite programming language is JavaScript. This is very biased.

Interviewer (63:59)

I thought you might say this, but I wasn't sure. Of course, you wrote books about this.

Addy Osmani (64:04)

My favorite programming language is JavaScript and it's not for the personal reasons. 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's very open and I think that there's something very liberating about that idea. So that is one of the reasons why I like JavaScript probably the most.

Interviewer (64:28)

I like it. I'm always surprised when a software engineer says JavaScript, because we know that compared to other languages it has a lot of limitations and there's been books written about it as well, but I cannot disagree with it. I love it. It is true. What's a tool that you really like using and what does it do?

Addy Osmani (64:48)

I would say that one of my favorite tools at the moment, and I'm biased, I have a book out about this as well. It's probably Bolt Bolt New. Bolt New is one of those Vibe coding scaffolding tools that people can use. They very recently added support actually for being able to use custom agents, so you can use Claude code, for example, to build your vite coded app. And the output is generally very high quality. Great designs. So I've been really enjoying that. And the team have been, I would say, on the edge of offering some really great integrations. 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 a Supabase 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.

Interviewer (65:46)

And I guess the fun fact on Bolt New is they started off as a company called Stackblitz who built a really cool online editor, really advanced, and then they moved over from that to Bolt New. So. And there are software engineers behind it, again, who really know their craft.

Addy Osmani (66:02)

Yes, yes, absolutely.

Interviewer (66:04)

And finally, what's a book that you would recommend?

Addy Osmani (66:08)

So the book I'm going to recommend is one that I think covers, 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, is the software engineer's guidebook. And of course I'm playing to the crowd, but it is generally genuinely a very, very sharp book from, from all the reviews that I've read about it. Other people have also found it to be an excellent read.

Interviewer (66:32)

It's. It's on my desk. But we didn't talk about this, by the way, so this recommendation came just as a surprise to me as Tyler.

Addy Osmani (66:38)

Than genuinely an excellent book, I guess if I wasn't going to, of course, reckon with that one. I think that if you are trying to learn more about the foundational aspects of AI engineering in this moment. There's a great book called AI Engineering by Chip Huyen that is also worth taking a look at. Very, very well reviewed, a very, very thorough book. And I also recommend folks check that out.

Interviewer (67:04)

It's such a good book. I feel if you, 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. Addie, this has been great. Thanks so much for doing this and this really awesome conversation.

Addy Osmani (67:21)

Well, thank you. I really appreciate it. I hope the folks you know, if you are interested in this space Beyond Vibe Coding is out now from O'Reilly. Hopefully it'll be useful to some people, but it's been a pleasure having this conversation with you.

Interviewer (67:33)

Yeah and I've been reading the book. I really like how it goes into the practicality, so I can also very much recommend it.

Addy Osmani (67:40)

Thank you.

Host (67:41)

I hope you enjoyed Going Beyond Vibe Coding, pun intended with Adi Osmani. 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 and then move on. As long as you understand the alum.

Interviewer (68:01)

You are in charge.

Host (68:02)

But once you stop understanding, it's in.

Interviewer (68:05)

Charge and you can kind of become helpless and out of your depth.

Host (68:08)

For more tips on how to get up to speed with AI engineering, check out Deep Dives into 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.