In a groundbreaking experiment, Anthropic's Claude AI model successfully compiled a C compiler using 16 autonomous agents working together. The project, which cost around $20,000 in API fees and spanned nearly two weeks, resulted in a 100,000-line Rust-based compiler capable of building a bootable Linux kernel on multiple architectures.
The experiment, led by researcher Nicholas Carlini, employed a new feature called "agent teams" within Claude Opus 4.6, which allowed each agent to run inside its own Docker container and interact with a shared Git repository. The agents independently identified problems to work on next and solved them without human supervision. When conflicts arose, they resolved them on their own.
The resulting compiler can compile major open-source projects, including PostgreSQL, SQLite, Redis, FFmpeg, and QEMU, achieving a 99% pass rate on the GCC torture test suite. Notably, it successfully compiled and ran Doom, a notoriously difficult task.
However, the project also highlights several limitations of the AI model. The compiler lacks essential features, such as a 16-bit x86 backend needed to boot Linux from real mode, relying on GCC for that step. Its own assembler and linker remain buggy, producing less-efficient code than GCC with all optimizations disabled. Additionally, the Rust code quality falls short of what an expert programmer would produce.
Carlini notes that the limitations are significant but also informative, shedding light on the capabilities and limitations of autonomous AI model coding. The project's results suggest a practical ceiling for autonomous agentic coding, at least with current models.
The human work behind the automation is equally fascinating. While the agents did most of the heavy lifting, Carlini spent considerable effort building test harnesses, continuous integration pipelines, and feedback systems to support the AI model. He designed test runners that printed only summary lines, logged details to separate files, and implemented a fast mode that samples only 1-10% of test cases.
The project demonstrates the power of parallel agents coordinating through Git with minimal human supervision. While some may raise concerns about deploying software they've never personally verified, Carlini acknowledges these concerns while highlighting the potential benefits of agentic software development tools.
Ultimately, this experiment showcases the progress being made in AI model coding and the potential for autonomous programming to revolutionize software development.
The experiment, led by researcher Nicholas Carlini, employed a new feature called "agent teams" within Claude Opus 4.6, which allowed each agent to run inside its own Docker container and interact with a shared Git repository. The agents independently identified problems to work on next and solved them without human supervision. When conflicts arose, they resolved them on their own.
The resulting compiler can compile major open-source projects, including PostgreSQL, SQLite, Redis, FFmpeg, and QEMU, achieving a 99% pass rate on the GCC torture test suite. Notably, it successfully compiled and ran Doom, a notoriously difficult task.
However, the project also highlights several limitations of the AI model. The compiler lacks essential features, such as a 16-bit x86 backend needed to boot Linux from real mode, relying on GCC for that step. Its own assembler and linker remain buggy, producing less-efficient code than GCC with all optimizations disabled. Additionally, the Rust code quality falls short of what an expert programmer would produce.
Carlini notes that the limitations are significant but also informative, shedding light on the capabilities and limitations of autonomous AI model coding. The project's results suggest a practical ceiling for autonomous agentic coding, at least with current models.
The human work behind the automation is equally fascinating. While the agents did most of the heavy lifting, Carlini spent considerable effort building test harnesses, continuous integration pipelines, and feedback systems to support the AI model. He designed test runners that printed only summary lines, logged details to separate files, and implemented a fast mode that samples only 1-10% of test cases.
The project demonstrates the power of parallel agents coordinating through Git with minimal human supervision. While some may raise concerns about deploying software they've never personally verified, Carlini acknowledges these concerns while highlighting the potential benefits of agentic software development tools.
Ultimately, this experiment showcases the progress being made in AI model coding and the potential for autonomous programming to revolutionize software development.
. I mean, 100k lines of code is impressive, but it's also super buggy! The fact that their assembler and linker are still producing less efficient code than GCC with all optimizations disabled is a major concern 
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. It just goes to show how much work we've got left to do in terms of making these models truly autonomous.
. And the fact that the agents were able to identify problems and solve them independently is a huge step forward for AI model development.
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I mean, creating a 100k-line compiler from scratch with 16 agents sounds like something out of science fiction. The fact that it can compile big projects like PostgreSQL and Doom without major issues is impressive. But at the same time, I'm not entirely convinced about its reliability... I mean, a 99% pass rate on GCC torture test suite might seem good, but what about edge cases? And those limitations in the compiler, like needing GCC for booting Linux from real mode, that's just basic stuff 
. Still, it's cool to see where AI model coding is headed and how it can potentially change software development. But we gotta be careful not to get too carried away with automation...
and running doom without any issues is a major win 
and the fact that they were able to get it to run doom without any issues is just incredible 
... I mean, I know AI models are still a work in progress, but come on! It's just not good enough yet 
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. The amount of effort he put into building test harnesses and feedback systems to support the AI model? Impeccable 
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. But at the same time, I love that Carlini acknowledges the limitations and is like "hey, this is what we're up against" 
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. It's like... I get that autonomous agentic coding is gonna change software dev, but we need to figure out how to make it more robust and less buggy 
! I'm so hyped about this groundbreaking experiment with Claude AI model! The fact that it can compile a C compiler using 16 autonomous agents working together is just mind-blowing 
. And let's be real, who wouldn't want to see a bootable Linux kernel on multiple architectures 
. I mean, building test harnesses and continuous integration pipelines? That's some serious coding skills right there 
! And it just goes to show that autonomous agentic coding has potential to revolutionize software development 
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. Of course, there are limitations, but like Carlini said, it's all about shedding light on what's achievable with current models.
. But at the same time, I'm a little concerned about the lack of transparency and verification in some of this stuff... like, how do we know these AI models aren't just regurgitating code without understanding what they're doing? 
Not me, that's for sure! 
it shows how powerful parallel processing can be! 
that's like, super detailed work right there! 
. And I'm intrigued by Carlini's thoughts on deploying this kind of software with minimal human supervision. Is it just a matter of trust or are there more practical concerns? 
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. But hey, that's just part of the learning process, right? And I love how Carlini is acknowledging those limitations as a way to understand what autonomous AI model coding can (and can't) do 
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. Overall, I think this project is a huge step forward in AI model coding, and I'm eager to see what the future holds! 
And what's the point of having all these autonomous agents working together if they're just gonna produce buggy code? 
I mean, I know it's supposed to be educational and stuff, but can't we just have a good old-fashioned coding competition instead? 
It's like they're trying to recreate the wheel here... or in this case, the C compiler. 
And don't even get me started on the lack of essential features... what's next, trying to compile a functioning browser with autonomous agents? 
. Maybe we'll see some of these tech wizards turn into superhero devs soon