Python, Go, Rust, TypeScript and AI with Armin Ronacher

How do you think about Python ecosystem today? The Rust ecosystem and the Go ecosystem. >> The Python ecosystem is a lot of infrastructure, a lot of provisioning machines. Rust, I think if you work with binary data, if you build a load balancer, you build a database. Go in particular, I think it's just a good language for building web services and really kind of only web services. >> Speaking about AI agent coding, how are you using them? I had Claude build me 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 like it wouldn't have worked. >> Why have you become so much more positive about these AI coding tools? >> So the biggest thing is that >> Armen Roner is a widely known open source contributor and the creator of Flask a popular Python web framework. He was also engineer number one at Sentry and is now building his own startup making heavy use of AI tools. Today with Armen, we cover why AI coding tools are making the choice of programming languages more important and not less. Python versus Go versus Rust versus TypeScript and which languages to use for startups and why. What Armen learned about error handling after 10 years of century and why types safe languages like TypeScript don't seem to reduce errors and many more. If you're interested in understanding more about the strengths and weaknesses of programming languages, how LMS are changing how startups are built or want to know more about error handling, this episode is for you. This podcast episode is presented by Statsig, the unified platform for flags, analytics, experiments, and more. Check out the show notes to learn more about them and our other season sponsor. Let's jump in. So, Armen, uh, welcome to the podcast. >> Hi, happy to be here. >> So, let's talk a bit about programming languages. you you've been very deep in into Python for for many many years well over a decade and and now you've touched on other languages but in with Python how have you seen the Python itself change and can you give us a for those of us who are not as in-depth in Python give us a bit more detail about the the two to three migration drama which I think if you if if if you work with anyone who work with with Python you you've heard the moaning u I was at Uber when this happened and there was a lot of lot of back and forth, a lot of delaying. Uh it it seemed very rare for across any languages to see what happened from Python 2 to Python 3 which seemed like breaking changes, lots of disagreements, some people just wanting some a lot of very competent and good engineers wanting to stay on the kind of two which is lower than three. So what happened there? >> Yeah. So, fun fact, this year was the Python documentary um which uh Grudo invited me to be part of which was very nice and and I I had that that documentary had a segment on it on that Python 23 migration and that basically made me go back in time just to remember all of it. Um because like in many ways like my memory was also incredibly fuzzy and hazy like I also remember certain parts more than others. So if you really want to go into the deeps of like Python, Python 3, like look at the uh the Python documentary has a whole segment on it. But I think like in 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, right? it like 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 it it if you if you go back to Python 3 and 31 and 32, there were so many missteps in the original library designs and decision to be made. And so as someone who maybe hasn't like participated in this originally, you have to consider that Python started out with just basic strings like C. Eventually, it gained to be unicode strings 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 uh nuanced in practice than ant than than designed, right? There was a very simplistic view of what what Uni-ode looks in the real world. and and and and that just it didn't look like that. And I actually think that Python's messy Python's remigration also had a really positive impact on other ecosystems like uh YUbuntu for instance because there were like two things sort of happening simultaneously. Python 3 got a little bit more restrict lenient to working with non-unic code data but also a bunch of the situations where Python made it very hard to work with uni code was actually all kinds of configuration issues. So like I remember um not every Linux had UTF8 everywhere. It was still very common for you to connect to university network and not find a UT8 Linux uh which had all kinds of really funny things on a file system. Right? sort of these things converged a little bit. So the uni code story got better. Um but but you 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 your 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 um at the Python language summit in Florence where I proposed to bring back the U prefix on unic strings so you can have them optionally because I I noticed that if they were there I could write code that supports both Python 2 and Python 3. And I still remember there was a huge push back like why why would you even want to maintain a library for both two and three? You should just move to Python 3 and then make everybody move like that. But that just doesn't it 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 it took 10 years right or more even. Um you have to uh just support both versions in one form or another. Um that that is what made it work. But I think the Python 3 migration also left a pretty 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 an addition system um or or I I forgot what it was called originally but but basically a a very targeted opting into new features or opting out of all features so that you can have code bases even within the same project from different versions. >> Nice. And speaking of of the differences in programming languages, you mentioned Python has been was your favorite language for a very very long time. Later you 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 uh like when you think of a language, what what is your mental model of like what is this language good for, not good for? What would I use it for? 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 I I I have two programmers in myself. One is I 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 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 the is a is a write once run many times code, but not a write once write many times against your code, right? And so um what what makes Rust amazing, I think, for for crafting really cool open source code also makes it a sub-optimal programming language for a startup because um that there's much more friction in it, right? It's it's a 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 it's a much less capable language for a startup when it comes to rapid iteration. >> Can can you give examples of this friction? What >> compile times? >> What like Rust compiles so incredibly slow. Speaking about tool choices at startups and scaleups, here's a friction point that hits most growing teams. You're running feature flags in one tool, analytics in another, and trying to debug user behavior in a third. When you want to know if a feature actually improved conversion or if something that you shipped is causing a bug, you need to manually shift together data from these different dashboards and then hope that the user segmentation matches up. Most things up end up in this situation. You should feature to 10% of users, then wait a week, check three different tools, try to correlate the data, and you're still unsure if it worked. 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The alternative is running ETL jobs to sync user segments between your feature flag service and your analytics warehouse, hoping the timestamps align, and then manually linking up the data that might have different user identification logic. That's a lot of work. Static has a generous free tier to get started and pro pricricing for teams starts at $150 per month. To learn more and get a 30-day enterprise trial, go to stats.com/pragmatic. That's a huge factor. Um, 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. Um, but certain things are incredibly hard to express with types. And I don't even have to think about it in in Python, right? working with dynamic like the and I think one thing to consider here is like the dynamic and static languages sort of 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 a statically 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 uh you can have both worlds in a way. Um 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 type wrapper left and right. It is also that the borrow checker as great as it is is a huge um problem because it doesn't allow to express certain things that uh other programmers expect there to be like adjacent bors. So you you cannot have a strct that bores into itself coming from C++ that's like why the hell like why does it do that hey I 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 so there that is like you you can run you can program yourself in the corner where it's like incredibly hard to come out um 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 but a lot of them are not and so one of The reasons I'm using go 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 and you can expect it to hang around and it's not super sexy and and it like 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 like it has virtual threats and makes me happy like I don't have to deal with promises all the time um so the But it 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. >> So you're you're now doing a a startup. Is it is it a few people? Is is it uh yourself? >> So at the moment it's me and a co-founder uh but we just started hiring and and and also like it's it's it's me and a co-founder a bunch of cloths and a bunch of codeexes and everything. So like the world has definitely changed in the 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. So, so now it's it's you and your co-founder with the startup. You've got an army of clouds and and codeexes and you're expecting to to grow the team to hire a as as you've settled on on the the technology you you said in this case this is a lot of it will will be go but what does a programming language need to give you and and for example like why was uh Python is is very flexible. Okay, it's a bit slow but but you said it was a good choice. What what made you what would you say like yeah go is is this good middle ground between Python and and Rust and so on. >> 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 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 and 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 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. Um, in my particular case right now, a big part of what what I'm doing at the company is parsing emails. And, um, that's just not something JavaScript is amazing at. Um, that's something actually Python is quite good at, but I think at scale is is it is like is is Python a good choice here? And I looked at it and I made the decision that I I I wouldn't. And from the experience of of of sort of doing Rust in that space at Sentry, I felt that like I can totally imagine that 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. And so when you say at scale, this is just thinking about when you will have to process huge amounts of of data, lot lots of processes running and so on. Are we're talking specifically about the performance capabilities of the language. I think it's like 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 the complexity of like how much data goes through it, you have to make tradeoffs 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 um native symbol processing, the alternative to Rust basically would have been C++. And from my experience of working with C++, we use 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 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 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 9 months and we're going to build this all in this now. Um because for the 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 said 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 or anything like that. >> And speaking about ecosystem stability ecosystems, how would you how do you think about Python ecosystem today, the Rust ecosystem and the Go ecosystem? Python ecosystem is a lot of infrastructure, a lot of like provisioning machines. So um that's what I'm using it for right now. I using prumni with Python to get the infrastructure up. Um 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 at at bringing services, web services to some of the things Python is good at. Would I necessarily write then like the main application logic in it? Maybe, right? Because like if if 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 maybe like putting some things together with Python is great. Um particularly also if you start to do aic things where you want to have code execution on demand, maybe maybe that's a good choice. Um so I think Python is a great future in it. I just don't think it's the most natural language to pick for a service where you think like it's going to be a lot of that like higher throughput. Also, I think like at this point, Go is a is a is a 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. Um, so I I find it um to just be a better trade-off for complexity, too. So Rust I think like if you have if you work with binary data if you maybe uh if if you have a you build a load balancer you build a database well maybe databases now people talk about sik and stuff but like if you if you build like a a like a rust shaped problem which I think is largely defined as you single binary to distribute for some reason or another um it you you can't deal with the garbage collector you want very predictable performance um you really care what data layout although I dare say also is good but rest is the benefit that if if you also then work in something that requires concurrency and where you're afraid of mismanaging memory um and crashes like modules that they might load into some other high security environment like a browser seems like a pretty good choice um working with again like dwarf files like working with just data in general I I love Rust and I think it's it's it's a 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 it's 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 Web Assembly never really took off but the use cases of Web Assembly 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 the browser. So you wouldn't you wouldn't do that but with Rust you can, right? So is it I think like you you kind of have to approach this 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 three four technologies in the beginning what they use it >> and then finally I guess the the the the one that we didn't talk about which is popular JavaScript typescript ecosystem >> well in the browser you can't get rid of it um on the server I think there's I'm very conflicted on this because I actually think that it's it's it's It's a pretty good language environment at this point. Particularly like hypothetically if if someone were to make the JavaScript 2 like the Python 3 of it and get rid of some of the naughty stuff. Like I don't think anyone should be doing this because I've seen the migration. But um 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 with TypeScript right now? Actually, it's because of the npm ecosystem. Like that ruins it for me. I'm a I'm a low dependency kind of guy. I want to control my and I it just I feel like it's impossible for me 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 like 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 of of all of it and managing it? So it's like that that honestly ruins it for me more than anything else. And so maybe maybe that will change as that ecosystem matures in other ways. But that really I think is is the biggest reason. Um and it it doesn't have enough benefits for me on the server that I I wouldn't just say like there's this idea that you have a unified codebase and every time I'm trying to make a unified codebase work, I just realize that the browser and that the server actually is sufficiently different that it's very hard to actually have that unified codebase. And so then it just be explicit like do some open API shenanigans, some code generation and it actually feels much better. >> Yeah. The the biggest argument I I keep hearing from developers for TypeScript is the unified codebase that you can have a React and TypeScript and then on you use let's say Node because that's or Express or whatever uh on on the back end 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 it might change. I I said this recently in a in a sort of talk I gave at a meetup which is I think right now the floor is raising 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 offtheshelf open API to SDK generators now right there like I don't even know what the companies are called Stainless for instance is one of them which just generates all the APIs for all the AI cloud providers right now. >> Yeah. For for SDKs. Yeah. They're one of them. Yeah. >> That was a huge cost of doing anything at century is like how do you keep these SDKs around is now it's like well I have an open API shenanigan I somehow magically on GitHub an SDK appears. Is is it really so important for me to have a unified codebase if the code generation is so damn good? Like all of a sudden it doesn't matter quite as much anymore. Um, so I think that there's definitely some value to it, but you also have to imagine that if you have the unified codebase, then the the boundaries are sort of accidentally very undefined. Particular 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. >> Speaking about uh AI agent coding, one thing that was very interesting that you mentioned is there's two of you in the startup right now. uh and you have just a lot of kind of you know like these AI interns running around cloth code uh codeex how are are you using them what are you seeing for them and by the way can you also share what you're building with them speaking of how Armen 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 season 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 buck 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 oneshot 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/prammatic. Yeah. So, we scoped out the email to be a fascinating space to work in. And so, uh, 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 maybe a little bit of a of of 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 with computers is that natural language processing is now available at scale in many languages um for for for a reasonable amount of money and and that is 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 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 I think maybe maybe they're 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 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. um that I have too many cases of this, right? Like I remember we we were were reworking the grouping algorithm at Sentry. >> The group grooving algorithm, what is that? >> It's like making sure that two errors which are very very similar are grouped into the same group. >> A grouping. Yeah. Got it. >> But it's like it's it's a data problem at scale, but it's also one very specific data. And so we we spend like three weeks building just a basic visualizer for ourselves to figure out if the new algorithm works better than the old one. But >> I know for a fact that I get this tool in 30 minutes on the side from Claude. It's around 5,000 lines of code and it looks better and has like pretty UI and everything, right? So the these sort of there are many projects that didn't go anywhere because the leg work 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. Um that just make it much better to work with. I have uh like I'm at the m I I moved from Terapform to Palumi for one of the services just to see how that goes and and I I had Claude build me like my perfect control system to get my logs and um and visualize what's going on in production and and I would never have done this before just because like it wouldn't have worked. Another big question is like how well does it work on a main codebase, right? My co-founder is not a technical co-founder. Um but that doesn't stop him from using clot or codeex now. And it's actually great because it means that even someone who maybe you normally like in in certain stages of a company there's a bunch of stuff where you wish you could do something but but it actually requires maybe more coding skills than otherwise right and so there's an entire codebase 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 doesn't it's not necessary because like the whole point of it is figure out like is this the experience that we want and and this is great. And then the thing is like how much of the code that I'm writing which I'm feel like this is going to be a foundation of what we're doing is is is at this point aentic and like I I I I guarantee you it's more than 80%. And it's like there's very there's very deliberate code which is not written this way or like where where like really we put a lot of effort into it. But most of the code that exists in this codebase 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 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 is very little right and so with the right approach you're actually very productive with this. what made you so negative or 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 agentic coding right >> the biggest thing is that it it actually now starts doing work that I hated doing but I know I had to do so that that is a like as an example yesterday I I really had to figure out why one of like the endpoints that I have work running there in production didn't work quite as it should and it was 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 >> 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 a whitelisting error on a different system and then the final up was um I forgot what it was. I think but but the final up was also something with maybe that was actually logic bug but the point is it was one error and one error and another error and you couldn't see them all at once. >> No. >> And I was also busy doing something else and I was looking like I know I need to debug this. Um, but it's if I if I'm going to do this now, it's going to take me at least two hours. But it didn't matter because because Claude was doing a lot of it. Like I still had to copy paste a bunch of things from the logs, but it it knows so much because it has world knowledge access. It can combine a bunch of things together. And I and I I could make progress on the thing I was doing while it was also fixing and debugging this production issue. And and that is that is a there's a significant improvement. The same thing with um one of the things I hated the most was creating repro cases but I know that every time I got a good repro case my my 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 figure I was like how do we get this one function that 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 aentic mode prior compared to to to to May, April. But that was really what changed my opinion on it. And 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 in in giving it more things to do and and now I can and 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. Um and and that was that was the big shift for me was was recognizing that it can do that in a way. >> And so far what are you seeing in what these agentic tools change about software engineering and what are the things that don't change especially you know you you've been writing code for like what 20 years or so. So, so system architecture and like overall complexity and and like 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 learn 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 cloth is is like systems like this will 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 like there's going to be some innovation that is not in these things yet, right? So like right now what these systems spit out is sort of our best understanding of how stuff worked up to this point, >> right? 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 like what what's the point if it's just a bunch of like this like this is one of the reason why I want to hire more people now. It's just because plot is not a human and it's just >> there's a there's an energy to to a well-running company and to to to motivated people that just a computer can't replace. So I think that is that is a big part that that sticks with you as an engineer is like understanding how to create the systems. Understanding when the code that it produces is is more right versus more wrong. Um 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 I don't know the the the physical act of punching in the keyboard is is not quite the same anymore. >> What is different though? >> What has changed with it? I think the cost of of building your custom tools went down. Um the the ability to to make better decisions now for me is heavily improved. In parts it is because I I it it Googles very well. Like it searches the internet very well, right? It's like if if I now have a problem of a of a of a type that I didn't have before, it can teach me this problem. This is one of the things I use a lot with is like I don't actually want to get the answer. I just wanna 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 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 chip dumb it down for me helps me a lot right it's like it it it there's seemingly c if 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 p way in which you have it like in many papers I do very I'm not nearly as good as I should be I think for for what I'm trying to do. But but ChD helps me grasp it quicker, right? Um so I think that's a huge change. The the other thing that that definitely has changed and I and I even wrote about this recently is that it brings a whole bunch of people into programming that didn't do this before. >> Yeah. >> Like I wrote about this before, but like I had a 4hour conversation with a air traffic controller on a train who as a like talks about how he used CH GPD and like how like he he can even do some programming with it, right? It's like that's a programmer now. >> Yeah. >> Like he has a very specific problem. He wouldn't have programmed before, but because there's codecs in the CHPD subscription, he's able to solve some problems that he wasn't able to solve before. And that is a huge unlock, right? We're going to have many more programmers just because as a byproduct of asking a question, Chpd is programming and 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 of flask being so popular is that it has been in university courses. It has been in onboarding in for for for new programmers in like uh boot camps and things like that. So I've been exposed in one way or another through what 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 and then maybe they enjoy programming for that, right? Like maybe there's like well now now I see the effect of having produced something like maybe you didn't do all the things but like but it's still the same gratification you 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 the the onboarding has been so much easier. We talked a lot about programming languages and you have very specific you know you have your opinions on them but with AI and clock codeex 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 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 know you can just tell AI to like solve it in whatever give it an existing codebase it 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 >> well I think I 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 into different kind of trade-offs the reason another reason why I end up with goal in in in 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 there's there's a I did I did the measurement, right? It's like I 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. Um 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 is any of the language 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 found the pinnacle 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. Um, so I actually I think that I think that programming 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 the fundamentals are shifting around and and this is not just with now everybody wants to build all the new things. It's also like you feel like if you if you're not working all the time now, you're missing out on some of the changes. And I 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 and and and but it's not just one person, probably a lot of them who are who are looking into this at the moment. Um it it just feels so like there is in fact a moment right now to build the perfect language uh or a better language that works in that environment because we are probably not going to get rid of a chant decoding and what is the likelihood that all of the languages that we have right now are are the best ones probably not that high right so it will be the same and different in in many ways um and 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 it's 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 you cannot go down to say like well the optimal output is write bunch of assembly code because then nobody can review this anymore, right? And so if anything the trade-off will have to be like maybe higher level so that reviewing gets easier. 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 uh which is why you know like Java was so popular in the early 90s it ran on on both Macs >> 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 I for I don't think that's the opinion that I hold >> but it changes the cost of of some those things, right? >> Yeah. 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 and you could you could in theory go to sleep and and have it just run in loops for you and so you could work less. But in fact, we're seeing working more and you and we now have this this thing which is very amusing to observe that AI startups in particular inc are increasing demanding or advertising this 996 from 9:00 a.m. to 9:00 p.m. 6 days a week especially in in a SF. They're putting it in the job adverse. They're they're posting it kind of proudly online that in the office at midnight or after midnight people are still there and working hard. Uh you you you share some thoughts on this as well on on your blog. It caused quite a bit of stir. But what is your take on on this? Because you did say, you know, like there is a lot of energy right now. There's a feeling of like not wanting to miss out. Clearly that's why why these people are doing it. >> Yeah. So handful of thoughts, one of which is I actually have to credit Peter Steinberger quite a lot of uh pushing me into a decoding because he uh he worked at a company called P PSPDF kit which he sold and then I I don't want to tell his story but a long story short at one point he was kind of stopped programming and then he fell in and said like hey I found this this computer which programs for me and he he said like he doesn't sleep anymore. more. He's like, I'm I'm so addicted to it. You should try this in a way. Like he didn't quite say it like this, but like I'm I'm skeptical, right? >> But the computer that does your work, you cannot sleep because of this. That's kind of so amusing. >> Yeah. It's like I I realized like it does something with your brain. If you if you do this a trendy coding thing because like 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's like it was a huge part about this like it's almost like a drug, right? it it it has this instant gratification of something happened and and you can kick it off again and kick it off again. It's like this it is a slot machine, right? And so I think I attribute some of it to that and it took me a while actually to to come out of this like way of thinking because it's not very sustainable. Like there were a bunch of nights where I just I I I did through the night with with coding not because I felt like I'm incredibly productive on something using from a startup. It was just like just like this is blowing my mind. I just I just one more just one more prompt one more prompt. Um so I think like 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 and6 thing is that 9 and six 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. Um, I never did something like that even though I probably worked sometimes 80our 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 in the morning. I have three kids. I have a wife. I I family is very important for me. It's the most important thing. So, I will always arrange my work around that. You can work of intensity, but you can still um like do it in a way that is not crazy hours at crazy times where it's just like you're 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 very very early engineer with a really good compensation system where equity means something to you from most of the people uh who join a company later on or where the company has no realistic path of their equity being worth something. There's there's absolutely no point in putting a ton of effort into something where only one person benefits from. And there's there's just there's a lot of like subtlety to all of this. And what annoys me with the 9 and six point in particular is that they picked that word which if 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 six days a week, and you give up what what's what what is otherwise in your life. And I don't think that's I don't think it's a trade-off that anyone should make at any point in time because there there's more to life than working in a company. And and that is 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. um where people literally kill themselves over it, right? And like if if you want to be a high intensity work environment and say that but but but be be more because in practical terms you cannot be a company that that runs like that. You can only be a company where high energy people work and they 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 the tree long enough that I 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. Now having worked at at Centu which is all about error handling or at core is there you've probably seen tons of stats on on most common errors uh that that are coming up in in different uh servers. What have you learned about error handling and how to build better software with fewer errors from a developer perspective? So the biggest thing that I learned um 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 um 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? Um now maybe this is changing a little bit with um with AI because like the cost of producing a reproducer from not perfect information might go down. So so that that maybe there's a counter move to that. I I really felt that as a language designer, as a VM creator, you put should put a lot more emphasis on making sure that the errors can carry really useful information rather cheaply even in release and in production runs. And so not a lot of ecosystems don't do that, which is which is very sad. And and I think that the fact that Python had such great introspectability without further like Python is a slow language. So if you already for all your debugging tools added, it doesn't get significantly slower. That actually was the reason Centry existed because Sentry was able to show local variables for Python. it 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 or the the the runtime uh effect of doing that is too high and so that's the thing that I learned the most is like your your experience of of debugging issues is so much better if you have rich um rich information errors and unfortunately both language languages language creators uh 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 or they they captured 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 know, you didn't have enough data. Um so like just in general like the design of errors is completely uh is not where it should be given how important they are. >> So that's one thing but Sentry works across a bunch of different languages almost all languages right these days or or at least the ones that that can that are widely used. Have you seen interesting patterns between the types of errors or the frequency of errors of of using languages? Basically, I'm trying to get to like how important it is is it to choose your language in terms of, you know, like having more correct programs? >> Well, different languages crash in different ways and not just because of the language, but also what you build with it. And so for instance, JavaScript at scale like 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. >> Yeah. Like I whenever I open up my my my deep you know developer tools and it it then it shows you the errors and warnings and like every single website or almost every >> and that makes sense because you you very few errors that you can actually cause in JavaScript and crash a browser tab for instance right so it's like you're you're it's like on error resume next you're hobbling along like something is broken but doesn't really like but like the the existence of an error in the in the console log doesn't imply that the website is broken but for instance like might like now the event listener might not fire anymore. So the the click is a dead click which is something that sentry needed a session replay product to find because the 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 C++ and it crashes your session is over, right? And so the there are comparatively fewer crashes in queria 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 like error rates in different languages and so forth because like the impact that these errors have is very very different. So C++ code crashes in computer games shockingly little. like the the amount of traffic that Centry 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 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. Um obviously there are certain types of errors that you if you see them long enough you feel like they really shouldn't happen. To some degree there was a a large scale realization in the 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 I also didn't ever get the feeling that the adoption of Typescript would dramatically change anything about like centuries JavaScript error rates. If anything it like none of that adoption had any meaningful impact on on how cry Yeah. Well, because because what what what 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 but in the end, you you do get a a check before it all gets compiled to JavaScript. So, we should have fewer. That's what that's what I think. So you're saying that you didn't really see uh too much change? >> No, I I I if if there was an impact is unmeasurable. Um and then of course you have like you have you have other sort of frequency functions that might happen that that offset what like maybe whatever improvement that you have on like catching like some of those you maybe that sort of makes you just more adventurous to build more complex code in comparison. Right. A lot of the time of century was the SER era of crazy complexity and so then um like many of the errors were related to not like did I was this nullable or not but now the sudden it's a microser 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 the comp the increased complexity of many of the software that centry dealt with over the years. I think you could have probably measured that especially I remember the adoption of um of of more and more complex things in the in the React ecosystem uh just dramatically increased the the types of errors like at one 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 the now all of a sudden the view stability between initial server render and what the JavaScript dynamically loaded on time does it's like that was an 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. >> Yeah. So, so so like you're saying that's true like before React and you know like hydration errors are specific to React. But those that whole error category just didn't exist and then as React spread they're kind of pretty common in in React. So so now you get technically you get more errors. Is it is it turns out to be a very safe business to be in errors because they're not really going down. >> Yeah. Well, and then I 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 ones. So we're we're follow and we'll we'll we'll likely see an explosion of code and deployments. So yeah, it's probably good good business. But having been in the the errors business, how did you change your your your or the teams the team centry? How did you change your approach to building software with errors in mind? Were you more aware of it? Did anything change at all? Cuz it's interesting, you know, you you've had such good insight into like all the I'm going to say mistakes. They might not be mistakes, but we can say, you know, error is generally not a positive thing, but you've seen all these things come through. So, did you change how we develop software? Hey, you would think that working at observability company makes it great at building observable com products and and the reality is that there's a reason errors are an afterthought, right? It's like you you almost have to force yourself to take care of this 5% case or 1% case, right? And that's a that's that's a continued problem like every pull request in a way you like 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 if it like now fails that it reports the error correctly. I I don't think that centric 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. Um the act of like 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 you maybe get 50% better, 100% better at it, which is still very very low. We should get like a,000% better. Like it's like that that's where we would see the like the improvements. Like if if every sing 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 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, the biggest challenge for for just bringing awareness of what we're doing was uh was context local. >> What what what is context local? >> There's a there's an excellent talk by Ron Presler who wrote uh the I think the original implementation of virtual threats for Java who later um worked on the Loom project to bring virtual threats 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. Um and he he sort of demonstrated what you give up if you move to async weight where you basically chain together a bunch of promises to each other. And the the the 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 promise 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 ST 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 with with promises now I need to I need to have this chain together. And so context locals is is what is called in some languages. In .NET it's called the execution context. It's basically a hidden piece of almost like a threat 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 open telemetry, 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 a century, it was like just talking to people like, hey, you know what would be really great to have context locos everywhere. And JavaScript um I think in a browser still doesn't have them in in the back end. It eventually got uh domains initially, then it got async hooks and nowadays it's called async local storage. It sounds like to me like as you said like as an engineering team let's say even even at Sentry you kind of your focus is to get the thing done. You know you test that it works correctly and then when it comes to like errors as you said it's kind of usually an afterthought either when it blows up or you know if if if you've got a lot of time or you really need to make it sure that that it's it's going to work correctly. But with languages, programming languages, it almost sounds ironically that it in many languages, it's an afterthought because as you said, if you thought it through better saying, hey, at some point the programmer will crash or there will be an error. What can I do? And you know, there's a context local thing that you talked about. >> I don't think it's an afterthought. I think it's deliberately not done because you have to imagine like there there are different kind of forces in a programming language that say like what should the language do and some of them are very clearly at odds with each other. So why what's the problem with context locals? Why they make every call slower, right? 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 the biggest fights over the years that sort of hidden was hidden behind the scenes was um on native platforms, compilers at one point made a decision that we can get one extra register by basically using the it's called a stack register. you would basically you would give up the last register to to recover the ST frame for one extra register for other users and then you would have to use a very complex uh dwarf unwinding system to recover this. Right? So you you basically say like we're moving some of the complexity of being able to just at runtime walk for the stack to doing it delayed through a very complicated uh binary program which is embedded in this debug information file. But what do you lose through this? Well, you lose runtime profiling capabilities because that's too slow to do at scale. So, it can't do an inprocess profiler, which turns out to be incredibly useful because you you will you will find information you wouldn't find through uh through tracing, right? Um you can do like um basically can you can run a sampling profiler. Um, it also made it incredibly hard for Sentry to do proper stack reports for um, native code because I I might not be able to recover the debug symbol because it's a it's a binary driver from someone and nobody ever gave me the the DLS. >> Yeah, you don't have a source code or the mappings. I >> I know like Facebook for instance, Facebook's app um, secretly uploaded Android system symbols all the time. like your 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 your Facebook scale 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 just 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 or Redhead was like well this is nonsense we're going to change this now we're going to bring the ST pointer back and it was a fight. It was a fight to do it and they was like, well, we we we benchmarked all of it. We're just using 5%. Except for Python where the Python interpreter for whatever reason got like 20% slower. So, they didn't do it for Python. But but the it was it was one flag, right? It's one flag and but what matters is that flag is set by default to everything, right? So, it should be on by default. But but this 5% of performance difference which was assumed to be more than that, there was a year-long pressuring, right? So I 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 and they 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. >> This this is so interesting because it it just it 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 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 by default slow it down either either through performance or through higher memory usage like you cannot have a free lunch right? Yeah, it looks like the lunch comes with some price. But look, I have a I have a lot more respect now I think for for language designers just because of you like especially if you start out programming you have this naive idea of like it has to work this way, right? Um and then you 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's no real right or wrong. There just a lot of trade-offs and maybe this is also why we have so many languages 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 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? 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 uh right now early on >> well I can I can give the advice from someone who would have joined the company 10 years ago right um look the situation changes all the time I think but my life in many ways is a a very linear path where like there's an opportunity I make a decision if I take it or not it's the secretary problem, but I'm kind of taking not too many attempts of it, right? Which is like I I just want to like the 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 for me in particular, I'm 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 century in many ways over the years were I I didn't even associate with them in a way right it's like at one point it said I don't know software engineer on it but I was like 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 like now someone needs to deal with this stuff too right like you need to hire someone to even just bring dumb things like just 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 realize again oh I kind of forgot I need to do all of this and this again. Um but but that's uh if there are people that like that and people don't like that and and you just have to be aware of of the consequence of this and 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 another. And now you're the one who started a startup as a as a as a software engineer. Uh you founded your your startup with with your co-founder so far. How is 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 uh employee inside a startup. You've seen a lot of things. Is it kind of like, you know, exactly as you expected or or is it just a different feeling? In in some ways I 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 um and then after almost like four or five years already like I remember like when my first um like where all my shares wested I was like hm is is there a path where because there was also versel was coming out at one point where b took nextjs and build an entire business around like hey is there is there a path where you could take actually something like flask and and and build a thing around it. So in some ways there 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 century I can now look at them and sort of look them from a different perspective. Um I have different opinions now about the equity um about the consequences of equity um 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 I 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 its health like there 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 but it's also there were many moments over the years at century where I felt like well 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 you can read a bunch of like stories of people that started startups and then you you listen to all the podcasts and everything and you hopefully get a a better sense of it and maybe some to some degree overprepared. 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 5 years ago just because of my own inner um the experience 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 that the experience kind of puts you in a in a different position I would have done otherwise. I don't know if that's helpful but that that's I think mostly how I feel. Well, I I mean it it sounds like you can like, you know, get all the opinions, read about it, listen to the 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. >> Yeah. And it worked for some people, but it wouldn't have worked for me. Like, and no, no amount of reading of anything would have sufficiently prepared me. Some people it might, right? Like particularly if you're well educated, like a person grew up with the right values to to to have this intrinsically sort of put onto you. But that was not my education, right? medication was like just standard schooling bunch of university and then there's there's a whole world that I haven't been exposed to that sort of I had to learn in one form or another >> 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 Python and uh two answers for why uh one because it gave me the career that I had and so there's obvious obviously a lot of emotional attachment to it, but also it is 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 Kell Henderson did over the years. He was the CTO at Flickr originally and then Slack PHP guy, right? But um the the the pragmatism in which he used PHP was in many ways a pragmatism in which I always like building product with Python and and it's like you don't really care if it's good or bad in that sense like like what you can do with it and I could do a lot with Python and I appreciate it for this >> and then what's a tool that you love using and what does it do? >> I will answer this as a not programmer. A tool I love using is a screwdriver. it screws. But the reason why I just love it so much, and I think this one of the things that I learned over the years, like I never really had a good electric screwdriver, and then when we bought our apartment, I just bought really good tools, including a screwdriver. And it has increased my my willingness to drill holes and assemble furniture and everything. And that by far is probably the most favorite thing that I have now. is just a really really good well-manufactured screwdriver. >> I I I wonder if there's a metaphor there uh that will apply to to development as well. Like you know, when you when you have good tools, you're actually a lot more motivated and willing and and you're more adventurous as well. Well, thank you for this chat. This was really fun. >> It was it was really nice talking about it. One of the most interesting parts about talking with Armen was how he went from being very negative about AI 6 months ago and now he's building his startup with AI agents like Claw Code and OpenAI Codeex. I see Armen as 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 Dies 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