Armin Ronacher (2:30)

Yeah. So fun fact this year was the Python documentary, which Guido invited me to be part of, which was very nice. And that documentary had a segment on it, on that Python 2 to 3 and 3 migration. And that basically made me go back in time just to remember all of it, because in many ways my memory was also incredibly fuzzy and hazy. I also remember certain parts more than others. So if you really want to go into the deeps of Python, Python 3, look at the Python documentary. It has a whole segment on it, but I think in retrospective, I think you can't do it anymore. It was just at this moment in time, it didn't kill Python, but I think it could have. If people didn't put a lot of energy in to actually make that migration work. It could have been quite problematic for the language because if you go Back to Python 3.0 and 3.1 and 3.2, there were so many missteps in the original library designs and decision to be made. And so as someone who maybe hasn't participated in this originally, you have to consider that Python started out with just basic byte strings like C. Eventually it gained Unicode strings and as an option, so you did a U in front of the string and it became a unicode string. And Python 3's biggest motivation of existence was to be a lot more strict about strings and move everything to Unicode. What wasn't quite anticipated is that that migration was a little bit more nuanced in practice than designed. Right. There was a very simplistic view of what Unicode looks in the real world and, and that just. It didn't look like that. And I actually think that Python's messy Python 3 migration also had a really positive impact on other ecosystems, like Ubuntu for instance, because there were like two things sort of happening simultaneously. Python 3 got a little bit more restrict, a bit more lenient to working with non Unicode data. But also a bunch of the situations where Python made it very hard to work with Unicode was actually all kinds of configuration issues. So like I remember not every Linux had UTF 8 everywhere. It was still very common for you to connect to university network and not find a UTF8. Linux which had all kinds of really funny things on a file system, right? Sort of these things converged a little bit. So the Unicode story got better. Um, but, but you had millions of lines of Python 2 code that couldn't work on Python 3. And the initial assumption was you can just, you just migrate once and then in Python 3 and you're good. And that's, that's not what happened. We had to maintain libraries simultaneously for Python 2 and Python 3 for many years. And I, for instance, I, I remember meeting at the Python Language Summit in Florence, where I proposed to bring back the U prefix on Unicode strings so you can have them optionally, because I noticed that if they were there, I could write code that supports both 5 and 2 and Python 3. And I still remember there was a huge pushback. It's like, why would you even want to maintain a library for both 2 and 3? You should just move to Python 3 and then make everybody move like that. But that just doesn't work like that. So what really made the Python 3 migration ultimately work was a lot of effort put in by a lot of people and just a more realistic look into that. You have to. It took 10 years, right? Or more even. You have to just support both versions in one form or another. That is what made it work. But I think the Python 3 migration also left a pretty important data point for future languages to approach this better. And I remember Rust early on specifically pointed at the Python 3 migration to demonstrate why it wants something like an additional system, or I forgot what it's called originally, but basically a very targeted opting into new features or opting out of old features so that you can have code bases even within the same project from different versions.

Host (6:50)

Nice. And speaking of the differences in programming languages you mentioned, Python has been, was your favorite language for a very, very long time. Later you helped introduce Rust into Sentry, partially for performance reasons as well. And now you're also playing with Go. How would you compare these languages? And when you think of a language, what is your mental model of what is this language good for, not good for. What would I use it for?

Armin Ronacher (7:18)

I wrote a bunch of blog posts that are not quite about languages, but they are sort of about my general split brain in a way, because I have two programmers in myself. One is I like building cool open source software that hopefully thousands of people use and put a lot of handcrafted efforts into it. It's like the Swiss watchmaking of source code writing. And there is like, I care a lot about the language, I care about a lot of the API and everything. But then when you build a company, when you build a product, none of that matters. Like is a write once, run many times code, but not write many times against your code code. Right. And so what makes Rust amazing, I think for crafting really cool open source code, also makes it a suboptimal programming language. For a startup because there's much more friction in it, right? It's a much more precise language. It's a great lift up compared to C, which is in many ways what the alternative would have looked at at Sentry for binary file processing. But it's a much less capable language for a startup when it comes to rapid iteration.

Host (8:30)

Can you give examples of this friction?

Armin Ronacher (8:32)

Compile times like Rust compiles so incredibly slow.

Narrator (8:37)

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Armin Ronacher (10:15)

You write a lot more code compared to Python. You spend a lot of time thinking about types. I love types. I think they're great. But certain things are incredibly hard to express with types. And I don't even have to think about it in Python, right? Working with like and I think one thing to consider here is like dynamic and static Languages have aligned a little bit more over the years. I remember the dynamic keyword landing in C was a great thing because it showed people they can have aesthetically compiled language with like opt in dynamic runtime typing for instance, and TypeScript took that entire idea and sort of applied it to JavaScript. So you have, you can have both worlds in a way. But, but Rust is like very straightforward. Like there's just static types and if your problem is a dynamic type problem, then you're basically going to create your own dynamic typewrapper left and right. It is also that the borrow checker, as great as it is, is a huge problem because it doesn't allow to express certain things that other programmers expect there to be like adjacent bors. So you cannot have a struct that bores into itself coming from C, there's like, why the hell, like why does it do that? Here's my problem, I want to solve it. And the compiler says no. It's like, no, you're wrong, it should work. So that is like you can program yourself in the corner where it's incredibly hard to come out. So I think from that perspective there are a lot of problems which are not rust shaped. There are a lot of problems which are in fact rust shaped, but a lot of them are not. And so one of the reasons I'm using Gove right now for the new company I'm working on is because it is a very pragmatic language and if pragmatism is what you're after, then why not? And you can expect it to hang around and it's not super sexy. And worst case, even if Google stopped maintaining it, you can imagine there will be dozens of other people who want to keep it alive. Even modern Java is actually an awesome language. It has virtual threads, it makes me happy. Like I don't have to deal with promises all the time. So I have, I think, a much more pragmatic view when it comes to what a company should build its stuff with, because the focus is not so much the code, it is the product that you're building.

Host (12:27)

So you're, you're now doing a startup. Is it, is it a few people? Is it yourself?

Armin Ronacher (12:35)

So at the moment it's me and a co founder, but we just started hiring and I also like, it's me and a co founder, a bunch of cloths and a bunch of codexes and everything. So the world has definitely changed in a sense that there's an army of interns that also now writes code and that has also Changed the way I'm looking at things.

Host (12:53)

So now it's you and your co founder with the startup. You've got an army of clauds and codexes and you're expecting to to grow the team to hire as you've settled on the technologies. You said in this case, this is a lot of it will be go, but what does a programming language need to give you? And for example, why was Python is very flexible. Okay, it's a bit slow, but you said it was a good choice. What made you say like, yeah, go. Is this good middle ground between Python and Rust and so on?

Armin Ronacher (13:28)

Yeah. So right now I think if you build any company at all, you're going to end up with Python in there if you want it or not. It will be impossible for you to build a company that doesn't have Python code in it. Do you want to build your main service with it? Probably not. But if you do anything with ML, if you do anything with data processing, Python is going to be in there and you can write nice code in Python too. Right. So it's like that's going to be there. Good luck not having JavaScript in a company. And if you have JavaScript, you have TypeScript, so those languages are going to be there. Another question is like how much of that code that you write overall is in those languages. And I actually felt that I don't want to build backend services in either one of those languages and not even for performance reasons, but just for ecosystem like what the ecosystems are good at. In my particular case right now, a big part of what I'm doing at the company is parsing emails and that's just not something JavaScript is amazing at. That's something actually Python is quite good at. But I think at scale is Python a good choice here. And I looked at it and I made the decision that I wouldn't. And from the experience of sort of doing Rust in that space at Sentry, I felt that I can totally imagine it at scale, like Rust will make its way back in, but at the moment I don't think that that's the right trade off.

Host (14:54)

When you say at scale, this is just thinking about when you will have to process huge amounts of data, lots of processes running and so on. We're talking specifically about the performance capabilities of the language.

Armin Ronacher (15:05)

I think it's at scale of a company and that is not necessarily measured by how much data goes through it. If you scale a company either by the number of people working on it or the complexity of problem or the complexity of how much data goes through it. You have to make trade offs. And sometimes that means introducing a new language, either because of performance reasons or because you integrate into some ecosystem that's particularly strong in some certain environment. And then all of a sudden the language is here. Why did Rust exist at Century? Well, it didn't just exist because I wanted to write Rust. It also was like, well, now we have a problem with performance, how are we going to solve it? And on the one hand it was like, well, we could probably write a Go service here, but I didn't want to maintain another service. So Rust was a pragmatic choice of embedding it in Python. And then later on when we did native symbol processing, the alternative to Rust basically would have been C. And from my experience of working with C, we used C on that project first. We just had a lot of crashes and I just didn't feel like I want to maintain that. And so then there was no choice of using Go because there was no Go ecosystem. So unless I would have built everything from zero, it wouldn't have worked. But Rust was growing and Rust had the same problem in the compiler. So the Rust ecosystem for debug files was really good. And there was another company also working on it, which was Mozilla at the time. So there was already two shared interests in this and that was a really pragmatic choice. Even though the language was Rust, then maybe we overdid it a little bit by putting Rust into places where it wasn't the most optimal choice, like the ingestion system. I think at this point probably wouldn't make that decision again, but it would have been non pragmatic at that moment to say like, you know what we're going to do, we're going to go into corner for like nine months and we're going to build this all in this now, because for the size of the team at the time, the problem was too big, right? And so we had to reuse something. So that's one of the reasons, I think, why I can imagine that my company is going to again end up with adopting a language further down the line that it kind of initially set no to, just because the situation makes it necessary. And it doesn't necessarily have to mean that performance is the reason. It cannot just be ecosystem stability or anything like that.

Host (17:15)

And speaking about ecosystem stability ecosystems, how do you think about Python ecosystem today? The Rust ecosystem and the Go ecosystem.

Armin Ronacher (17:23)

Python ecosystem is a lot of infrastructure, a lot of provisioning machines. So that's what I'M using it for right now using preliminary with Python to get the infrastructure up. It is really well entrenched in machine learning. So that's. I have a little data pipeline going on that uses Python. It is also pretty good still at bringing services web services to some of the things Python's good at. Would I necessarily write then like the main application logic in it maybe? Right, right. Because if I'm doing a whole AI first company and all it does is inference, I'm just waiting on a network layer anyway. So maybe putting some things together with Python is great. Particularly also if you start to do agentic things where you want to have code execution on demand, maybe that's a good choice. So I think Python has a great future in it. I just don't think it's the most natural language to pick for a service where you think it's going to be a lot of that like higher throughput. I think at this point Go is an easier language to write than Python for a bunch of engineers because Python's increased in complexity and Go didn't quite as much. So I find it to just be a better trade off for complexity too. So Rust I think if you work with binary data, if you're. Maybe if you build a load balancer you build a database, maybe databases now people talk about sig and stuff but if you build a Rust shaped problem which I think is largely defined as you single binary to distribute for some reason or another you can't deal with the garbage collector. You want very predictable performance. You really care about data layout. Although Sega also is good perhaps is the benefit that if you also then work in something that requires concurrency and where you're afraid of mismanaging memory and crashes like modules that they might load into some other high security environment like a browser seems like a pretty good choice working with again like dwarf files like working with just data in general. I love Rust and I think it's such a pragmatic language also for if you extend Python. So if you already have a Python problem and now you have a performance problem or you have an ecosystem integration problem and Rust happens to fit good into that. Writing extension modules in Python for Python in Rust beats everything else out there, right? It's a really good choice for that. Go in particular I think is just a good language for building web services and really kind of only web services. Maybe some command line tools. One of the other reasons why Rust I think is impossible to get rid of is is webassembly never really took off but the use Cases of webassembly are impossible to get rid of at this point. There's so many things you actually have to bring into the browser and Rust can do that. I tried it with Go, it's not a great experience because of the garbage collector and some of the complications that come with running Go in a browser. So you wouldn't do that, but with Rust you can. Right? So I think you kind of have to approaches from a perspective of like what am I doing here? Like what's the problem that I have? And then you said like I want to have not more than 3,4 technologies in the beginning, what did I use it for?

Host (20:47)

And then finally I guess the one that we didn't talk about, which is popular JavaScript tyscript ecosystem.

Armin Ronacher (20:54)

Well, in the browser you can't get rid of it on the server. I think there's. I'm very conflicted on this because I actually think that it's a pretty good language environment at this point. Particularly hypothetically if someone were to make the JavaScript too, like the Python 3 of it and get rid of some of the naughty stuff. I don't think anyone should be doing this because I've seen a migration, but there's a lot of good stuff in the language now that's just also some really old dumb stuff. Why am I not building backend services with Typescript right now? Actually it's because of the NPM ecosystem that ruins it for me. I'm a low dependency kind of guy. I want to control my shit and I feel like it's impossible for me to build productive in the JavaScript ecosystem with under 500 dependencies. And that makes me uneasy. On the browser I can live with it because I don't have much choice but on the server, do I really have to do it? Do I have to deal with the fear of all of it and managing it? So it's like that honestly ruins it for me more than anything else. And so maybe that will change as that ecosystem matures in other ways. But that really I think is the biggest reason and it doesn't have enough benefits for me on the server that I wouldn't just say there's this idea that you have a unified code base. And every time I'm trying to make a unified code base work, I just realize that the browser and the server are actually sufficiently different that it's very hard to actually have that unified code base. And so then it just be explicit like do some open API channel and again some code generation and it actually feels much better yeah.

Host (22:31)

The biggest argument I keep hearing from developers for TypeScript is the unified code base that you can have a React and TypeScript and then you use, let's say Node or Express or whatever on the backend. And now you have the same language and people can contribute. Which I think was very, very compelling, especially before we had AI tools, because now you didn't have to learn a new language. Now it might change.

Armin Ronacher (22:57)

I said this recently in a sort of talk I gave at a meetup, which is, I think right now the floor is racing the ceiling. Not really in the sense that the expectations that everybody has in everything is much higher than it was before, but also because the expectations are higher and more tooling to enable it. And so code generation, for instance, right now is in a much better state than it was even just two to three years ago. You can even buy off the shelf open API to SDK generators now. Right. I don't even know what the companies are called, but Stainless, for instance, is one of them, which just generates all the APIs for all the AI cloud providers right now.

Host (23:33)

Yeah, for SDKs. Yeah, they're one of them. Yeah.

Armin Ronacher (23:35)

That was a huge cost of doing anything at Sentry. It's like, how do you keep these SDKs around? It's now it's like, well, I have an open API shenanigan, somehow magically on GitHub and SDK appears. Is it then really so important for me to have a unified code base if the code generation is so damn good, like all of a sudden it doesn't matter quite as much anymore. So I think that there's definitely some value to it. But you also have to imagine that if you have the unified code base, then the, the boundaries are sort of accidentally very undefined. Particularly with rsc, I noticed this a lot where, like, it's very hard to actually reason your head around what's on the server, what's not. So actually having this extra boundary for me is almost like a feature.

Host (24:16)

Speaking about AI agentic coding, one thing that was very interesting that you mentioned is there's two of you in the startup right now and you have just a lot of kind of, you know, like these AI interns running around cloud code codecs. How are you using them? What are you seeing for them? And by the way, can you also share what you're building with them?

Narrator (24:39)

Speaking of how Armin and his co founder use an army of AI interns shows just how much software engineering is changing. Our work as a software engineer is no longer sequential. Doing one thing after the other. But we can kick off parallel work with AI agents. This is one of the biggest changes in the software industry in a long time. A leading example of this change comes from our seasoned sponsor, Linear. Their cursor integration lets you add Cursor as an agent to your workspace. This agent that works alongside you and your team to make code changes and answer questions. Say you have an issue in Linear like a bug fix or a small feature request. You just mention cursor and ask to take a look at it. Or you can simply assign the cursor agent to a ticket and follow along its progress. This delegates the issue and off cursor goes. Cursor then runs in the background, writes the necessary code, opens a pull request, and once you read the changes, you can merge it. It might not always one shot tasks, but it's pretty good. Mostly because the agent had access to context that's already in Linear. Project plans, backlogs, issue descriptions, discussions. It's so useful you can now delegate bug fixes, trivial work or tedious tasks to the agent, then come back when it finishes. Linear sort of feels like ground zero. You've got to try it out. Head to Linear App Pragmatic.

Armin Ronacher (25:49)

Yeah, so we scoped out the email to be a fascinating space to work in. And so you can kind of imagine what the kind of problems are that you have when you work with emails in one form or another. And maybe a little bit of why we went into email is because one of the things that is very evident now of the biggest shift in at least my lifetime what's capable of computers is that natural language processing is now available at scale in many languages for a reasonable amount of money. And that finds it's a good reason to go into email because email is all about natural language and there's a lot of good data in it. But it has been very hard to work with the data in the past. If you read my blog, you know that my opinion on AI stuff for programming was incredibly negative up until February, March where it slowly moved around. And then in May I was like, holy shit, nothing is going to be the same anymore. And one big reason why I'm actually so bullish on this is because when you think into and actually think it doesn't really work that well for teams today. I think maybe there are ways and I'm going to see as I'm scaling out this company, like how it's going to work at scale. But one of the reasons why I think it will actually be really good is because If I look over the years of all the stuff that I wish I would have been able to do, just didn't have the time for it. And I had to make this explicit decision not to do it. I have too many cases of this. I remember we were reworking the grooving algorithm at Sentry.

Host (27:20)

The grooving algorithm, what is that?

Armin Ronacher (27:22)

It's like making sure that two errors which are very, very similar are grouped into the same grouping. Yeah, it's a data problem at scale, but it's also one very specific data. And so we spent like three weeks building just a basic visualizer for ourselves to figure out if the new algorithm works better than the old one. I know for a fact that I get this tool in 30 minutes on the site from Claude. It's around 5,000 lines of code and it looks better and has pretty UI and everything. Right. So these sort of. There are many projects that didn't go anywhere because the legwork needed to build yourself this bespoke tooling. To even be able to pull this project off, like a migration system or something like this, it's just too much. Right. And so that's. I think one of the big changes I'm noticing for myself is that I have so many better tools around now that just make it much better to work with. I have like at the moment, I moved from Terraform to Pulumi for one of the services just to see how that goes. And I had Claude build me like my perfect control system to get my logs and visualize what's going on in production. And I would never have done this before just because it wouldn't have worked. Now the big question is like, how well does it work on the main code base? Right. My co founder is not a technical co founder, but that doesn't stop him from using Claude or Codex now. And it's actually great because it means that even someone who maybe you normally in certain stages of the company, there's a bunch of stuff where you wish you could do something, but it actually requires maybe more coding skills than otherwise. Right. And so there's an entire code base where I basically don't look at, which actually validates a core part of the feature that we're working on. And it's just vibes and it works. It's not pretty, but it's not necessary because the whole point of it is figure out is this the experience that we want? And this is great. And then things like how much of the code that I'm writing, which I feel like this is Going to be a foundation of what we're doing is at this point agentic. And I guarantee you it's more than 80%. And it's like there's very deliberate code which is not written this way or where really put a lot of effort into it. But most of the code that exists in this code base is not so important. It's a bunch of API endpoints, it's a bunch of open API specifications, it's a bunch of run of the mill pieces of code. They should look nice, they should pass all the tests, they should follow exactly the architecture that they want. But if you were to compress them down and say like, what's the actual information contained within them? It's very little. Right. And so with the right approach, you're actually very productive with this.

Host (30:06)

What made you so negative or like just like pretty negative up to around February or so? The things that you were just really skeptical about and then why have you become so much more positive about like these AI coding tools or agenda coding? Right.

Armin Ronacher (30:21)

The biggest thing is that it actually now starts doing work that I hated doing, but I know I had to do. So that is like, as an example yesterday I really had to figure out why one of the endpoints that I have work running there in production didn't work quite as it should. And it was not a problem of the error in the code, it was a problem of AWS permissioning. And like there was like there was a chain of three things that I did wrong.

Host (30:53)

Yeah.

Armin Ronacher (30:53)

That all resulted in it couldn't do what it was supposed to be doing. Right. Like first there was an IM permissioning error and then there was a white listing error in a different system. And then the final fuck up was I forgot what it was, I think. But the final fuck up was also something with maybe that was actually logic. But the point is it was one error and one error and another error and you couldn't see them all at once. And I was also busy doing something else and I was looking at this like, I know I need to debug this, but if I'm going to do this now, it's going to take me at least two hours. But it didn't matter because Claude was doing a lot of it. Like I still had to copy paste a bunch of things from the logs, but it knows so much because it has world knowledge access, it can combine a bunch of things together and I, and I could make progress on the thing I was doing while it was also fixing and debugging this production issue. And there's a significant improvement. The same thing with one of the things I hated the most was creating repo cases. But I know that every time I got a good repro case, I enjoyed it so much more. Right? And now I can say, claude, make me the repro case for this. This is roughly what it looks like. Let's not try to fix it. I so figure out, like, how do we get this one function that it can keep calling in a loop? That is what we're trying to figure out here, Right. So I was not able to get that experience out of Cursor, even with the Enchanting mode prior to May April. But that was really what changed my opinion on it. And then as I started going deeper and deeper into, okay, now that I know that I can do this, and I can do this, and I can do this, I also got incredibly adventurous in giving it more things to do. And now I have a new sense of where it could go just by giving it the things I don't want to do, but staying in control of the things I do want to do. And that was the big shift for me, was recognizing that it can do that in a way.

Host (32:59)

And so far, what are you seeing and what these agentic tools change about software engineering and what are the things that don't change? Especially you've been writing code for what.

Armin Ronacher (33:12)

20 years or so, system architecture and overall complexity and how you build things in a way that something stays maintainable as it scales into a larger and larger company. There's a lot of experience in it. And I also didn't have all of that experience at any point in time. So I feel like I. This is one of the things where I really like. As time went on, I learned a lot more, which gives me a benefit over my prior self that didn't have that. That is going to continue to be true. Now. There's a. There's an argument to be made that maybe Claude is like, systems like this will help you and maybe can even bootstrap these sort of things around it. If you fully delegate everything that you're doing to a machine, then the person that doesn't do that has an edge on you. Right? Because there's going to be some innovation that is not in these things yet. So right now, what these systems spit out is sort of our best understanding of how stuff worked up to this point. And so having something that maybe at scale is not in these corpuses yet is where some of the edge comes from. It also creates a better culture in a company if you're not completely delegating everything to a machine. Right. It's like because what's the point if it's just a bunch of like this? This is one of the reasons why I want to hire more people now. It's just because Claude is not a human and it's just there's an energy to a well running company and to motivated people that just a computer can't replace. So I think that is a big part that sticks with you as an engineer is like understanding how to create your systems. Understanding when the code that it produces is, is more right versus more wrong. So I don't think much of this has changed. I feel the same programming with Claude even if I physically write less code. It's just that I don't know. The physical act of punching in the keyboard is not quite the same anymore.

Host (35:09)

What is different though?

Armin Ronacher (35:10)

What has changed with it? I think the cost of building your custom tools went down. The the ability to make better decisions now for me is heavily improved in parts. It is because it Googles very well, it searches the Internet very well. If I now have a problem of a type that I didn't have before, it can teach me this problem. This is one of the things I use it a lot with is I don't actually want to get the answer. I just want to. I also want the answer, but I also want to understand like why that's the answer. And it can do that and it can put it into the perspective of it. I'm not very good with a lot of very complex math and I found that every once in a while I need to do that. And having ChatGPT dumb it down for me helps me a lot. Right. It's like there's seemingly. If a problem is presented in a certain way, it works very well for me if the problem is presented in a different way, particularly a very abstract way in which you have it like in many papers, I'm not nearly as good as I should be, I think for what I'm trying to do. But ChatGPT helps me grasp it quicker. Right? So I think that's a huge change. The other thing that definitely has changed and I even wrote about this recently is that it brings a whole bunch of people into programming that didn't do this before. I wrote about this before, but I had a four hour conversation with air traffic controller on a train who talks about how he uses ChatGPT and how he can even do some programming with it. That's a programmer now. He has a Very specific problem he wouldn't have programmed before. But because there's codecs in the ChatGPT subscription, he's able to solve some problems that he wasn't able to solve before. And that is a huge unlock. Right? We are going to have many more programmers just because as a byproduct of asking a question, ChatGPT is programming and a human is going to keep running that code and maybe it wants to do some changes to it. One of the side effects of Flask being so popular is that it has been in university courses. It has been in onboarding for new programmers in boot camps and things like that. So I've been exposed in one way or another through how long it takes for someone to learn programming. That's not a thing that you do in a day. So for you to be a productive programmer that's capable of spitting out the program, that's a couple of months of work. And now someone can press a button and get something and then maybe they enjoy programming for that. Maybe there's like, well, now I see the effect of having produced something. Maybe you didn't do all the things, but it's still the same gratification I get from it. And then maybe that's a way for you to, for people that wouldn't have gone into programming, to actually go into programming because the onboarding has been so much easier.

Host (38:03)

We talked a lot about programming languages and you have very specific, you know, you have your opinions on them. But with AI and cloud code codecs and all these more and more capable agents being here, how important, how do you think the importance of programming languages will change? Do you think it'll be limited to a few people or just fewer and fewer people like yourself and others who actually have spent a lot of time understanding the pros and cons of programming languages, thinking well beyond, because it sounds like you can just tell AI to solve it in whatever, give it an existing code base, it'll just do it, right. So there is an argument to be made that programming languages might not be that important anymore. What is your take?

Armin Ronacher (38:42)

Well, I think I will be consistent with the prior thing that I said, which is I think I have a lot of respect for programming languages because of the trade offs that are in there. And with AI now, I think you might have to start looking to different kind of trade offs. Another reason why I ended up with GO in my startup is because I just noticed how good GO code scales for AI because the abstractions are very thin, it understands the code better. So clearly there's some. I did the measurement, I made it write a certain type of program in different languages 10 times and see like how often did it pass. And I just noticed that it did so much better on Go than it did on Python and much better than at Rust. Clearly languages matter because the quality of what the agent is going to do will matter. And then is any of the languages they have right now, is that the perfect language for both a human and a computer combined work environment? I don't know. Maybe. Maybe we found the pinning of programming languages, but maybe that's also exactly the moment in time where someone will come and say like, hey, I actually have a brilliant idea of what the better trade off is here because the cost of doing this kind of stuff is going down with AI, but the cost of this and this stuff is going up because of AI. So let me rebalance this in a different way. So actually I think that program language will continue to matter a lot, particularly the trade offs that the programming language implies on the runtime environment matters a lot more. So I don't think that's going to change. I think it's just going to. Everything accelerates, everything is going so much quicker right now, or it feels to go so much quicker because the fundamentals are shifting around. And this is not just with now. Everybody wants to build all the new things. It's also like you feel like if you're not working all the time now, you're missing out on some of the changes. And I don't think that's just on the product side. I think it's on the tech side too. I'm pretty sure a bunch of people who are feeling like I want to be the first person to build the programming language that works really well for agents and humans. But it's not just one person, probably a lot of them who are looking into this at the moment. It just feels so like there is in fact a moment right now to build the perfect language or a better language that works in that environment because we are probably not going to get rid of coding. And what is the likelihood that all of the languages that we have right now are the best ones? Probably not that high. Right? So it will be the same and different in many ways. And one thing not to be underestimated is that the likelihood that computers are just going to program and no human is going to be in a loop I don't think is very high. I think the human will stay in the loop longer than we want and in many more cases than we want. And so you cannot go down to say like what the optimal output is, write a bunch of assembly code, because then nobody can review this anymore. Right? And so if anything, the trade offs will have to be maybe higher level so that reviewing gets easier.

Host (41:39)

Also don't forget that the thing with assembly, right. Like one of the reasons we have higher language languages is assembly needs to be different for every architecture type or every CPU type. Which is why Java was so popular in the early 90s. It ran on both Macs.

Armin Ronacher (41:56)

But if I can just have the AI ported to my other operating system, they could make the argument that maybe that is not needed anymore. For myself, I don't think that's the opinion that I hold. But it changes the cost of some of those things. Right.

Host (42:10)

But you mentioned an interesting thing, which is how everyone seems to just be working all the time, which is this interesting contraction how AI or these agents, you can kick off and you could in theory go to sleep and have it just run in loops for you. And so you could work less. But in fact we're seeing working more and we now have this thing which is very amusing to observe that AI startups in particular are increasing demanding or advertising this 996 from 9am to 9pm six days a week, especially in NSF. They're putting it in the job adverts, they're posting it proudly online that in the office at midnight or after midnight, people are still there and working hard. You share some thoughts on this as well on your blog. It caused quite a bit of stir. But what is your take on this? Because you did say there is a lot of energy right now. There's a feeling of not wanting to be stamped. Clearly that's why we, why these people are doing it.

Armin Ronacher (43:08)

Yeah. So a handful of thoughts, one of which is I actually have to credit Peter Steinberger quite a lot of pushing me into HNT coding because he worked at a company called PSPDFkit, which is sold. And then I don't want to tell his story, but long story short, at one point he kind of stopped programming. And then he fell in and said like, hey, I found this computer which programs for me. And he said he doesn't sleep anymore. He's like, I'm so addicted to it. You should try this in a way he didn't quite say it like this, but I'm skeptical. Right.

Host (43:45)

But the computer that does your work, you cannot sleep because of this. That's kind of so amusing.

Armin Ronacher (43:49)

Yeah. I realized it does something with your brain if you do this. A trendy coding thing because in the beginning I felt like any minute I'm not having it running and doing something, I'm just wasting time right now. It was a huge part about it. Like it's almost like a drug, right? It has this instant gratification of something happened and you can kick it off again and kick it off again. It's like this, it is slot machine, right? And so I think I attribute some of it to that. And it took me a while actually to come out of this way of thinking because it's not very sustainable. Like there were a bunch of nights where I just did through the night with decoding, not because I felt like I'm incredibly productive on something using from a startup. It was just like, this is blowing my mind. Just one more prompt, one more prompt. So I think to some degree, because if you're so AI close, that probably also contributes. The other one is I think everybody sees the change and wants to do as much as possible. I think the tricky bit with the 9 and 6 thing is that 9 and 6 defines a very, very specific kind of work regimen, which is you work 12 hours a day and you basically don't have a weekend. No, I never did something like that, even though I probably worked sometimes 80 hour weeks. And the reason for this is that I know that I cannot be productive all the time. If I. If I work late at night, I will shit in the morning. I have three kids, I have a wife, I. Family is very important for me. It's the most important thing. So we'll always arrange my work around that. You can work with intensity, but you can still do it in a way that is not crazy hours at crazy times where it's just like you're optimizing to the optimal output that you can produce somehow. And I also want to say there's a huge difference between if you are the founder or if you're a very, very early engineer with a really good compensation system where equity means something to you from most of the people who join a company later on, or where the company has no realistic path of their equity being worth something, there's absolutely no point in putting a ton of effort into something where only one person benefits from. And there's a lot of subtlety to all of this. And what annoys me about the 996 body in particular is that they pick that word which if you go back to why that naming even exists is because of. Basically there's no subtlety to it. It's like you work 12 hours a day, six days a week, and you give up what is otherwise in your life. And I don't think it's a trade off that anyone should make at any point in time, because there's more to life than working in a company. And that is not the thing that you should be putting on your flag, which has such a negative connotation because of exactly what it did, where people literally killed themselves over it. Right? And if you want to be a high intensity work employment, then say that, but be more. Because in practical terms you cannot be a company that runs like that. You can only be a company where high energy people work and to build themselves up. But there's a limit to which you can do it. And you have to be transparent about what the cost of this is. And I've been in this industry long enough that I've seen many, many, many bad outcomes. And in particular through my community, a lot of people even went down like psychosis and schizophrenia and a bunch of things where your likelihood of going down that also increases during certain years with unhealthy behavior.

Host (47:39)

Now, having worked at accentu, which is all about error handling or at core it's there you've probably seen tons of stats on most common errors that are coming up in different servers. What have you learned about error handling and how to build better software with fewer errors from a developer's perspective?

Armin Ronacher (48:02)

So the biggest thing that I learned, and ironically hasn't really dramatically shifted despite the existence of tools like Sentry and their popularity, is that many of the ways in which errors are worked with just doesn't carry enough information or only carries useful information for debugging in debug builds. And that's problematic because the most interesting errors don't happen in debug right now. Maybe this is changing a little bit with AI because the cost of producing a reproducer from not perfect information might go down. Maybe there's a counter move to that. But I really felt that as a language designer, as a VM creator, you should put should put a lot more emphasis on making sure that the errors can carry really useful information rather cheaply, even in redes and in production runs. And so not a lot of ecosystems don't do that, which is, which is very sad. And I think that the fact that Python had such great introspectability without further like, Python is a slow language, so if you already throw all your debugging tools at it, it doesn't get significantly slower. That actually was the reason Sentry existed, because Sentry was able to show Local variables. For Python, it had all this rich, powerful data that for many languages, even today we're not able to do because the runtime doesn't support it, or the runtime effect of doing that is too high. And so that's the thing that I learned the most, is like your experience of debugging issues is so much better if you have rich information errors. And unfortunately both languages, language creators runtimes often neglect errors so they don't carry the right information. And the application and library developers often don't think about errors at all or they capture them down in the wrong places and then the useful stack trace is gone, all that sort of stuff because you think they're exceptional, they're not happening all that often, but unfortunately every time they did happen, you didn't have enough data. So just in general, the design of errors is completely not where it should be, given how important they are.

Host (50:30)

So that's one thing. But Sentry works across a bunch of different languages, almost all languages right these days, or at least the ones that are widely used. Have you seen interesting patterns between the types of errors or the frequency of errors of using languages? Basically, I'm trying to get to how important is it to choose your language in terms of having more correct programs?

Armin Ronacher (50:56)

Well, different languages crash in different ways, and not just because of the language, but also what you build with it. For instance, JavaScript at scale, if you look at a lot of websites errors all the time, the percentage of errors in JavaScript that actually are like meaningful is very, very low.

Host (51:18)

Yeah, like whenever I open up my deep developer tools and then it shows you the errors and warnings and like every single website or almost every.

Armin Ronacher (51:29)

And that makes sense because very few errors that you can actually cause in JavaScript and crash your browser tab, for instance. Right? So it's like on error resume, next you're hobbling along like something is broken, but doesn't really. But the existence of an error in the console log doesn't imply that the website is broken. But for instance, now the event listener might not fire anymore. So the click is a dead click, which is something that Sentry needed a session replay product to find because the actual error that you would find in Sentry was insufficiently linked to user not being advanced on the page. On the other hand, if you have a computer game, it's written in C and it crashes, your session is over. Right. And so there are comparatively fewer crashes in computer games compared to all the nonsense going in JavaScript. But when they do happen, they're much more Meaningful. And so it's very hard to say error rates in different languages and so forth because the impact that these errors have is very, very different. So C code crashes in computer games shockingly little. Like the amount of traffic that Sentry gets from these is very, very low. But the usefulness of each individual crash report in C is so much higher. So it's just, it's a very complex topic at scale to say like how does something error? Because it really depends on like what does it take down. Obviously there are certain types of errors that if you see them long enough you feel like they really shouldn't happen to some degree. There was a large scale realization in JavaScript ecosystem that type checkers could get rid of a whole bunch of class of errors because at the very least you have to explicitly check if this thing is nullable or not. But I also didn't ever get the feeling that the adoption of TypeScript would dramatically change anything about like Sentry's JavaScript error rates. If anything, none of that adoption had any meaningful impact on how crashy.

Host (53:21)

Yeah, well, because what I would have expected, right, like we know and I think it's pretty commonly assumed. I'm not sure if it's proven that type safe languages will reduce certain kind of more obvious errors. TypeScript does this with JavaScript. I mean it has a compiler, but in the end you do get a check before it all gets compiled to JavaScript. So we should have fewer. That's what I think. So you're saying that you didn't really see too much change?

Armin Ronacher (53:51)

No, if there was an impact, it's unmeasurable. And of course you have other sort of frequency functions that might happen that offset maybe whatever improvement that you have on capturing some of those. Maybe that sort of makes it just more adventurous to build more complex code in comparison. Right. A lot of the time of Sentry was the SERP era of crazy complexity. And so then many of the errors were related to not like was this nullable or not. But now all of a sudden it's a microservice that I'm talking to and they have misaligned versions and my type check actually didn't help me at all because someone decided that on a network layer it's now null anyways. Right. So the increased complexity of many of the software that sent you dealt with over the years. I think you could have probably measured that. Especially I remember the adoption of more and more complex things in the REACT ecosystem just dramatically increased the types of errors at one point, like hydration Errors were not a thing for many years. And then all of a sudden there's a whole class of new errors coming in. Because now all of a sudden the view stability between initial server render and with the JavaScript dynamically loaded on time does. That was a whole class of errors that didn't exist before and all of a sudden does exist. Right. So it's very hard to measure these things just because of how we change and how much more complex our apps are.

Host (55:23)

Yeah. So you're saying that's true. Before React and Hydration, errors are specific to React, but that whole error category just didn't exist. And then as React spread, they're kind of pretty common in reaction. So now you get. Technically you get more errors.

Armin Ronacher (55:40)

It turns out to be a very safe business to be in errors, because they're not really going down.

Host (55:44)

Yeah, well, and then I think we're going to extrapolate it, obviously with AI tools using, typically using the most popular frameworks or the ones that has the most training data, which is the most popular one. So we're probably. And we'll likely see an explosion of code and deployments. So, yeah, it's probably good business. But having been in the errors business, how did you change your OR at the team at Sentry, how did you change your approach to building software with errors in mind? Were you more aware of it? Did anything change at all? Because it's interesting you've had such good insight into all the, I'm going to say mistakes. They might not be mistakes, but we can say error is generally not a positive thing. But you've seen all these things come through.

Narrator (56:30)

Did you change how we develop software?

Armin Ronacher (56:31)

Yeah. You would think that working in Observability company makes it great at building observable products. And the reality is that there's a reason errors are an afterthought. Right. It's like you almost have to force yourself to take care of this 5% case or 1% case. Right. And that's a continued problem. Like every pull request, in a way you are now like, okay, now it passes. So we have dealt with this. Like, are you going to put the extra time in to also make sure that even if it fails that it reports the error correctly? I don't think that Sentry's code is more correct for error reporting than any random other code out there is. Because the act of taking care of good error reporting is a deliberate act. The act of good logging is a deliberate act. The act of good metric supporting is a deliberate act. And you will Train that muscle a little bit over time. But I think like you maybe get 50% better, 100% better at it, which is still very, very low. We should get like a thousand percent better. Like, it's like that's where we would see the improvements. Like if, if everything, like, if 90% of errors are really, like, error cases are really well reported, like, like that would be really good. But like, that's a really massive lift. And you would only really get this lift if you stop programming like we are. And maybe the languages themselves have like pulling this into every stack frame, into every sort of corner case. So I think that the real lift would come if a bunch of people that now build these observability companies say like, okay, now that we know what our problem is, which is data collection in the first place, how do we build a programming environment that makes this native right? For many, many years, a biggest challenge for just bringing awareness of what we're doing was context local.

Host (58:23)

What is context local?

Armin Ronacher (58:25)

There's an excellent talk by Ron Pressler, who wrote, I think the original implementation of virtual threads for Java, who later worked on the LOOM project to bring virtual threads straight into Java, basically made a strong argument for the stacked frame. Like, what's so great about stack frames? Like stack frames are amazing because they carry information basically implicitly with it. And he sort of demonstrated what you give up if you move to Async await, where you basically chain together a bunch of promises to each other. And the immediate problem that we have if you start moving towards promises is that now it's very hard to carry through information through all of the potential paths that this promised chain would take you. So for instance, if you want to say, like, I have a correlation id, which should be attached to every single log event, it should be attached to every single error event that might happen as a result of this original thing I'm doing with stack frames. Super trivial, it's sitting somewhere in the stack. I will not lose it. I can walk up the stack and find it again. But with promises now I need to have this chain together. And so context locals is what is called in some languages in. Net is called the execution context. It's basically a hidden piece of almost like a thread local, but a much more narrow scoped one which flows with the logical flow of execution. And why is this necessary? Because if you do something like opentelemetry, if you do tracing, if you do things like you want to have a correlation id, you want to have it show up everywhere, right? And for many Years at century, it was like just talking to people like, hey, you know, it would be really great to have context locals everywhere. And JavaScript, I think in a browser still doesn't have them in the backend. It eventually got domains initially, then it got async hooks and nowadays it's called Async Local Storage.

Host (60:19)

It sounds like to me, as you said, as an engineering team, let's say even at Sentry, your focus is to get the thing done. You test that it works correctly and then when it comes to errors, as you said, it's kind of usually an afterthought, either when it blows up or if you've got a lot of time or you really need to make sure that it's going to work correctly. But with languages, programming languages, it almost sounds ironically that in many languages it's an afterthought because as you said, if you thought it through better saying, hey, at some point the program will crash or there will be an error. What can I do? And there's a context local thing that you talked about.

Armin Ronacher (60:56)

I don't think it's an afterthought. I think it's deliberately not done because you have to imagine there are different kind of forces in a programming language that say what should the language do? And some of them are very clearly at odds with each other. So what's the problem with context locals? Well, they make every call slower. So now it's like all the people that want a faster language, they're like, well now you got to make a really strong argument why you should be doing this. I remember one of the biggest fights over the years that sort of hidden was hidden behind the scenes was on native platforms. Compilers at one point made the decision that we can get one extra register by basically using the, it's called the stack register. You would give up the last register to recover the stack frame for one extra register for other users and then you would have to use a very complex dwarf unwinding system to recover this. Right. So you basically say like we're moving some of the complexity of being able to just add runtime but walk through the stack to doing it delayed through a very complicated binary program which is embedded in this debug information file. But what do you lose for this? Well, you lose runtime profiling capabilities because that's too slow to do at scale. So I can't do an in process profiler which turns out to be incredibly useful because you will find information you wouldn't find through tracing. Right. You can do like basically you can run a sampling profiler. It also made it incredibly hard for Sentry to do proper stack reports for native code because I might not be able to recover the debug symbol because it's a binary driver from someone and nobody ever gave me the dll.

Host (62:38)

Yeah, you don't have a source code or the mappings.

Armin Ronacher (62:40)

I know. Like Facebook, for instance. Facebook's app secretly uploaded Android system symbols all the time. Like your device were randomly assigned if it should upload it or not, and they were harvesting all the system symbols to increase the quality of the error reporting. Like if you Facebook scaler can do that. Like Sentry couldn't do that. Like we can't just magically have every Sentry SDK just upload a bunch of files. That would just destroy user trust. But eventually, and I think that only took place two years ago, someone at, I think it was Debian or Red Hat was like, well, this is nonsense, we're going to change this now. We're going to bring the stack pointer back. And it was a fight. It was a fight to do it. And they was like, well, we benchmarked all of it, we're just losing 5% except for Python, where the Python interpreter, for whatever reason got like 20% slower. So they didn't do it for Python. But it was one flag, right? It's one flag. But what matters is that flag is set by default to everything, right? So it should be on by default. But this 5% of performance difference, which was assumed to be more than that, there was a year long pressuring, right? So I think it's hard to do because you have these different interests in it and some people want the performance and some people want the debugability and they might live in completely different worlds. Like maybe this one person that really cares about performance just never has to look at any crashes because that's not the area of responsibility. And then you have someone who maintains a fleet of complex services and every one of them has a small percentage of crashing and often doesn't run the code that they have. And then once they have the crash, they don't have the data that they need.

Host (64:17)

This is so interesting because it reminds you of how difficult it must be to design a language because again, you have so many users and as you say, the person who just had a crash, they actually want, ideally the language support to get that back in a very easy way. But now it will by default slow it down, either through performance or through higher memory usage. Like you cannot have a free lunch, right?

Armin Ronacher (64:41)

Yeah, it looks like the lunch comes with some price. But look, I have a lot more respect now, I think for language designers just because of, especially if you start out programming, you have this naive idea of it has to work this way, right. And then over time you realize, oh, there are all these trade offs you have to make and some of them are really hard to quantify and you might even have to make some very unpopular decisions. And there is no real right or wrong, there's just a lot of trade offs. And maybe this is also why we have so many languages to, to some degree. But it is very fascinating to think about what some of these seemingly little defaults have in terms of what you can do with it. And then maybe what kind of people are going to pick your language? I thought Python was the most amazing language ever for building web services because I could inspect every process and it didn't make it any slower. But now I know that's also why Python was slow to begin with. Right.

Host (65:35)

And so what are some things that you've learned being an early engineer, almost like a founding engineer at a startup. And what is advice that you would give to an engineer joining a startup, a fast growing startup right now, early on?

Armin Ronacher (65:48)

Well, I can give the advice from someone who would have joined the company 10 years ago. Right. Look, the situation changes all the time, I think, but my life in many ways is a very linear path where it's like there's an opportunity, I make a decision if I take it or not. It's like the secretary problem, but I'm kind of taking not too many attempts of it. Right. Which is like I just want to. The passing bar for me is do I think that this is great, do I want to work on it? And then maybe I take the opportunity to work on it. And for me in particular, I'm shaped like a person that is actually not a great employee because I don't necessarily go and fit a particular mold. My titles at Sentry in many ways over the years were I didn't even associate with them in a way.

Host (66:38)

Right.

Armin Ronacher (66:38)

It's like at one point it said I don't know software engineer on it, but I was responsible for an office here with people underneath where I sometimes felt like I'm a payroll provider. Right? Like there were so many other things in it because like a small startup has this many now someone needs to deal with this stuff too, right? Like you need to hire someone to even just bring dumb things like furniture into the place. If you join a company early, everything is in Flux. Nothing is well defined. And some people like that, some people don't like that. And I think you have to be just willing to experience new things. If you start at a scrappy startup, that particularly maybe comes from someone who hasn't founded a company before, hasn't been there when a company sort of grew from small to big. Because even now for me, I just realized again, oh, I kind of forgot I need to do all of this and this again. But there are people that like that and people don't like that. And you just have to be aware of the consequence of this. You have to path the way for yourself somehow. Either by being okay with just doing whatever comes up, or being incredibly ambitious and deliberately taking certain paths towards where you want to go, even if that might temporarily set you back in one form or another.

Host (67:58)

And now you're the one who started a startup as a software engineer. You founded your startup with your co founder. So far, how's it going and how is it different to what you've expected? Especially that you were on the other side, right? You were an early employee inside a startup. You've seen a lot of things. Is it kind of like exactly as you expected or is it just a different feeling?

Armin Ronacher (68:22)

In some ways I postponed the founding of the company many times, right? In my mind at least. My recollection of early century was like, well, there could have been a path where we didn't do century, we did something else, right? And then after almost like four or five years already I remember when my first all my shares wasted, I was like, is there a path where, because there was also Vercel was coming out at one point where basically took next JS and built an entire business around. It's like, hey, is there a path where you could take actually something like flask and build a thing around it? So in some ways that was like a multi year preparation for me. So now it doesn't feel like, oh, it's actually different, it's just like, okay, that's the next logical step, kind of. No, I wouldn't call them missteps, but knowing some of the decision we have made at Sentry, I can now look at them and sort of look them from a different perspective. I have different opinions now about the equity, about the consequences of equity, how to structure certain things. Like this time around, basically I didn't start with here's an idea for like a product I want to build. The reason I ended up starting this company with a co founder is like we actually sat down and said like hey, what do we want a company to be? Like, why are we even in here for it? We already know that we like, we're the kind of people that want to start a company. But this is just like, that's not an end on itself, like that something should come out of it. Right. And so I didn't do that last time. I just rolled into it now. You can overthink it obviously, right. But it's also, there were many moments over the years at Century where I felt like, wow, actually it would have been great for us to think about some of those things. So now I'm thinking about them. In the beginning, the experience primarily puts you there. I don't think you can sort of start out and say like, I'm going to do this. Obviously you can read a bunch of stories of people that started startups and then you're listening to all the podcasts and everything and you hopefully get a better sense of it and maybe to some degree over prepared. But having gone through it yourself versus just hearing from someone, like, I read a lot of stuff about startups, even 2015, right. I was like, I was sort of addicted to this, to that thing. And still it feels very different now doing it versus if I would have done it five years ago just because of my own inner. The experience. Sort of like how I feel about all of this. Like which when I have a level of urgency versus where I just sit it out, like the experience kind of puts you in a different position. I would have done otherwise. I don't know if that's helpful, but that's, I think mostly how I feel.

Host (70:54)

Well, I mean, it sounds like you can like, you know, get all the opinions, read about it, listen to stories. But it'll be different when you do it in the end. And it's hard to tell what ways until you do it.

Armin Ronacher (71:05)

Yeah, and it works for some people, but it wouldn't have worked for me. Like no amount of reading of anything would have sufficiently prepared me. Some people it might. Right. Particularly if you're well educated, a person grew up with the right values to have this intrinsically sort of put onto you. But that was not my education. My education was just standard schooling, bunch of university. And then there's a whole world that I haven't been exposed to that sort of. I had to learn in one form or another.

Host (71:35)

And then just to close with some rapid questions. So I'll just ask and then you tell me what comes up. Of the programming languages, which one is your favorite? And I'm really Interested to hear it's.

Armin Ronacher (71:46)

Python and two answers for why. One, because it gave me the career that I had and so there's obviously a lot of emotional attachment to it. But also it is a bad programming language for many, many aspects of like misdesigned and whatever. But it is incredibly pragmatic and I just like that. And I kind of want to point towards many of the things that Cal Henderson did over the years. He was the CTO at Flickr originally and then Slack PHP guy, right? But the pragmatism in which he used PHP was in many ways a pragmatism which I always like building product with Python and it's like you don't really care if it's good or bad in that sense what you can do with it. And I could do a lot with Python and I appreciate it for this.

Host (72:34)

And then what's the tool that you love using and what does it do?

Armin Ronacher (72:37)

I will answer this as a NOD programmer. A tool I love using is a screwdriver. It screws. But the reason why I just love it so much, I think it's one of the things that I learned over the years. Like I never really had a good electric screwdriver and then when we bought an apartment, I just bought really good tools, including a screwdriver. And it has increased my willingness to drill holes and assemble furniture and everything. And, and that by far is probably the most favorite thing that I have now is just really, really good, well manufactured screwdriver.

Host (73:16)

I wonder if there's a metaphor there that will apply to development as well. Like, you know, when you, when you have good tools, you're actually a lot more motivated and willing and you're more adventurous as well. Well, thank you for this chat. This was really fun.

Armin Ronacher (73:32)

It was, it was really nice talking about it.

Narrator (73:34)

One of the most interesting parts about talking with Armin was how he went from being very negative about AI six months ago and now he's building his startup with AI agents like Cloud Code and OpenAI codecs.

Host (73:44)

I see.

Narrator (73:44)

Armin is a very pragmatic engineer and if he went through this much of a change after pushing himself to use these tools more, it tells me that it's worth giving AI coding tools some time and see how they work for you. For more observations on AI tooling trends, check out Deep Dives in the Pragmatic Engineer, which are linked in the show notes. 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.