The Peterman Pod
OpenAI Eng & Dev Tools Founder: How Software Engineering Is Changing | Charlie Marsh
22 Jun 2026
Transcript generated automatically by AI and may contain errors.
Chapter 1: What is Charlie Marsh's origin story in software engineering?
It's such an interesting time to be building software because things are just changing so fast.
This is Charlie Marsh, founder of Astral, the Python DevTools startup that was acquired by OpenAI, and I interviewed him about how software engineering is changing.
Now it's like when you put up a PR, I actually have to review it really closely because you're not writing it anymore. It's like the agent. The cost of putting up a plausible PR has gone to zero, while the cost to review has remained the same. If they're building so heavily with agents, to what degree do they need to understand different parts of the code?
I do think it would be really hard to be an early career software engineer right now.
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What was the space like when you were first getting into building these dev tools? Why did you think it could be better?
Before I started Astral, I was at a computational biology company and I was like the second engineer. I had no bio background and no like ML background. And so I was building all the like software systems to like do machine learning. And so that was like all Python.
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Chapter 2: Why did Charlie Marsh choose Rust for his projects?
So I was like learning Python and we had some Go and some Rust. And so I just worked across lots of different ecosystems. And I think Python at the time, like when I was in that position, we were trying to do a lot as a small team. So we had like a very, I mean, not compared to large code bases, but we had a comparatively large code base.
And it was really a small group of us that were software engineers trying to serve the rest of the team, whether they were like machine learning researchers or scientists. So I had kind of worked across a bunch of different ecosystems, seen a lot of the tooling.
Chapter 3: How did Charlie migrate his codebase from Zig to Rust?
And now I was working on Python. And we kept trying to get lots of leverage out of the tools, whether it was the type checker or the linter or the package manager. Things were scaling up. We were a small team. We wanted to have really good static analysis, really good tooling. And we kept running into limitations around it.
I mean, I think there were some... I kind of looked at the Python ecosystem and I just didn't see the same amount of experimentation that I saw in other ecosystems. Like in the web ecosystem, there were a lot of things going on that seemed pretty crazy.
Chapter 4: What role do LLMs play in code generation and open source?
And there was a very intense focus on... I don't know about very intense, but there was more of a focus on performance. And that started with ESBuild, which was in Go. And then you had SWC and Rust and then Bun and Deno and all these other tools that came around.
And this idea that you could have native tooling for JavaScript became very accepted, especially as applications became bigger and people were doing more and more stuff on their machine. And that wasn't really happening in Python. And so that to me was kind of like the key question was like, why can't we have that?
Chapter 5: How can AI optimize performance in software development?
Because I was seeing all this stuff that was happening in web and I had done some Rust, I'd done some Go, I'd like seen these different tool chains and like how they work with the experiences, like the user experience, right? Mostly in those cases. And then I saw Python and it was like, We had a smaller set of tools.
There were lots of interesting ideas from other ecosystems that I didn't see represented. And they were really all written in Python. And so that to me was kind of like the interesting question. And it was the first...
it was sort of like the first thing I asked when I started working on this, like when I released Ruff, the title of the blog post was something like Python tooling could be like much, much faster. And for me, it was really like, this is a hypothesis, like could Python tooling be much faster? And then I built a prototype and then that prototype was like, yes, I think it could be.
You wrote a post kind of like exploring MyPi or publishing your results using MyPi. And the date on that, like nine days later, you published the post, that one that you mentioned, like it could be so much faster.
Oh, that's funny. I didn't even realize that. Yeah, those were so close together.
Did you build that proof of concept in those nine days?
Yeah, I wrote that blog post about basically type checking at scale because I was trying to think of what are things that I learned that people haven't written much about. And then I started with building a linter because I thought it would be much easier. I think it actually is a lot easier than building a type checker. We're now building a type checker and we have a type checker.
And so I can say that I think building a type checker is much harder. But I started with a linter because I thought it would let me prove a lot of the same ideas, but in a smaller form factor. Like building a startup or just building like a tool, you want to try to like get something into people's hands that they can actually use and that actually proves out value like as quickly as possible.
And also that lets you iterate very quickly. And a linter weirdly ended up being like kind of the perfect form factor for it because it it has like a pretty simple core, but it's you know, then you have like tons and tons of rules. Right.
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Chapter 6: What insights did Charlie gain from starting his own company?
And so it's like extensible in a lot of different ways. But people can get value very quickly from a small core plus some number of rules. And so we were able to get something out there and then iterate and ship more rules and more functionality and people could use it alongside other tools. It was useful immediately, which I think is extremely helpful, as opposed to something like...
I mean, actually, the type checker would be a good example. Like, a type checker that's, like, 75% done, like, isn't very useful. Same with the package manager. Like, these things have to be, I mean, there are ways, like, I think I would challenge those as, like, blanket statements. But the point is it's harder to ship something iteratively that's, like, still useful to users.
And I think that's, like, one of the key properties in actually, like, building momentum as, like, a tool or something else.
When you posted that writing, what was the reception?
I mean, the blog post itself, I published it, and then it was on Hacker News, and it got tweeted about a lot, and it just created some amount of excitement and interest around it. And those things, I mean, especially being on the front page of Hacker News, I have a lot of thoughts about. I mean, it's certainly not, it's like some percent luck and then some percent skill.
And it is like a helpful thing, but not a requisite to success. And it also doesn't like guarantee success just because you got something on the front page of Hacker News.
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Chapter 7: What advice would Charlie give to his younger self?
Um, like it's great. It's great if you can, but, um, there's a lot of randomness to it. Um, I do think though that, uh, well, maybe a couple of things. So one,
Chapter 8: What are the challenges faced by early career software engineers today?
After that happened, some people started actually paying attention to the project. And so I really tried to capitalize on that basically by being very engaged. And so when people would file issues, I would aggressively try to acknowledge the issue as quickly as possible, fix it, and ship a release. If you can do that within one day, it's a very, very powerful loop.
You gain supporters and create more momentum around the project. And there were also a couple high-profile people in the ecosystem that started paying attention to the project, like Sebastian Ramirez, who does FastAPI and a bunch of other things. And he's been a great supporter of our projects, too. But early on, he was like, oh, this is interesting. I'd like to use it.
And I was like, I basically asked myself, well, what would it take for him to use it? And then I just tried to do all those things. So I tried to capitalize on that. I think the other is, though... I do think a lot about like developer marketing and, um, It's sort of like a dirty word or a dirty expression. I think a lot of engineers want technical products and tools to just win on their merits.
The best technical solution should just be the one that wins and grows. But I actually, I mean, I have this sort of stupid hypothesis that there's all these, there's thousands of really amazing projects on GitHub that basically don't know how to market themselves and so never get discovered and never go anywhere.
And I don't actually know if that's true, but it's like sort of how I feel about a lot of technical work where it's like, it's actually worth looking at a project like rough.
And, um, when I think about like what to put in the blog post or this extends to like what to put in the read me or like what to put in the, you don't need like a million emojis and like, and like a million screenshots of like everything. What you need is like, okay.
Someone's going to like like land on this page and I have like 10 seconds to get them interested in what I'm doing and help them understand like why it might matter to them and like why it's useful. And so like that was like that's like the key idea that I try to bring in. basically to everything.
I mean, it's a little bit depressing because it's like, okay, I only have like 10, but like people, people really don't have like, like this is like the attention economy, right? It's like, um, it's same thing with a blog post.
Like, um, ironically, I used to look at a lot of the open AI blog posts when I was at spring and we were trying to, we were doing like machine learning in bio and we were trying to publish material to like explain what we do. But we had all these interesting insights. Like we looked at the OpenAI blog posts, especially like the Dolly ones, like the older ones.
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