Researchers at Anthropic have just achieved a remarkable breakthrough in AI coding: 16 Claude Opus 4.6 agents, an AI model designed to write code, worked together to create a fully functional C compiler from scratch. The project, which took nearly two weeks and cost around $20,000 in API fees, has significant implications for the future of autonomous software development.
The experiment involved releasing 16 instances of Claude Opus 4.6 into a shared codebase with minimal supervision, tasking them with building a C compiler from scratch. The agents worked independently, each identifying what seemed like the most obvious problem to work on next and solving it on their own. When merge conflicts arose, they resolved them on their own, without any human intervention.
The resulting compiler, released on GitHub, is a 100,000-line Rust-based compiler capable of compiling a range of major open source projects, including PostgreSQL, SQLite, Redis, FFmpeg, and QEMU. It achieved a 99 percent pass rate on the GCC torture test suite and successfully compiled and ran the classic game Doom.
However, it's essential to note that this achievement comes with caveats. The compiler has significant limitations, including a lack of a 16-bit x86 backend needed to boot Linux from real mode, and its own assembler and linker remain buggy. Even with all optimizations enabled, the compiler produces less efficient code than GCC running with all optimizations disabled.
Moreover, the $20,000 figure only covers API token costs and excludes the billions spent training the model, human labor, and decades of work by compiler engineers who created test suites and reference implementations that made the project possible. This highlights the significant investment required for such an ambitious project.
Anthropic's approach to this experiment also raises questions about the role of humans in AI development. While the headline result is a compiler written without human pair-programming, much of the real work involved designing the environment around the AI model agents rather than writing compiler code directly. The researchers spent considerable effort building test harnesses, continuous integration pipelines, and feedback systems tailored to the specific ways language models fail.
The project demonstrates that novel methodologies for parallel agent coordination through Git with minimal human supervision can be successful. However, it also raises concerns about the deployment of software that has never been personally verified by programmers. As one researcher noted, "the thought of programmers deploying software they've never personally verified is a real concern."
In conclusion, Anthropic's achievement in creating a C compiler using AI agents marks an important milestone in autonomous software development. While it comes with limitations and caveats, the experiment highlights the potential for AI to augment human capabilities in coding and demonstrates innovative approaches to parallel agent coordination. As researchers continue to explore these boundaries, it will be essential to consider the implications of such advancements on the software industry and programming practices.
The experiment involved releasing 16 instances of Claude Opus 4.6 into a shared codebase with minimal supervision, tasking them with building a C compiler from scratch. The agents worked independently, each identifying what seemed like the most obvious problem to work on next and solving it on their own. When merge conflicts arose, they resolved them on their own, without any human intervention.
The resulting compiler, released on GitHub, is a 100,000-line Rust-based compiler capable of compiling a range of major open source projects, including PostgreSQL, SQLite, Redis, FFmpeg, and QEMU. It achieved a 99 percent pass rate on the GCC torture test suite and successfully compiled and ran the classic game Doom.
However, it's essential to note that this achievement comes with caveats. The compiler has significant limitations, including a lack of a 16-bit x86 backend needed to boot Linux from real mode, and its own assembler and linker remain buggy. Even with all optimizations enabled, the compiler produces less efficient code than GCC running with all optimizations disabled.
Moreover, the $20,000 figure only covers API token costs and excludes the billions spent training the model, human labor, and decades of work by compiler engineers who created test suites and reference implementations that made the project possible. This highlights the significant investment required for such an ambitious project.
Anthropic's approach to this experiment also raises questions about the role of humans in AI development. While the headline result is a compiler written without human pair-programming, much of the real work involved designing the environment around the AI model agents rather than writing compiler code directly. The researchers spent considerable effort building test harnesses, continuous integration pipelines, and feedback systems tailored to the specific ways language models fail.
The project demonstrates that novel methodologies for parallel agent coordination through Git with minimal human supervision can be successful. However, it also raises concerns about the deployment of software that has never been personally verified by programmers. As one researcher noted, "the thought of programmers deploying software they've never personally verified is a real concern."
In conclusion, Anthropic's achievement in creating a C compiler using AI agents marks an important milestone in autonomous software development. While it comes with limitations and caveats, the experiment highlights the potential for AI to augment human capabilities in coding and demonstrates innovative approaches to parallel agent coordination. As researchers continue to explore these boundaries, it will be essential to consider the implications of such advancements on the software industry and programming practices.
It's just a bunch of fancy math that can't even create a fully functional compiler without a ton of human input 
. And don't even get me started on the costs – $20,000 is peanuts compared to the real investment that goes into training these models and creating test suites 
. Plus, it's just a bunch of code that's been verified by humans, what's the point of deploying unverified software? 
It's just a case of AI masquerading as innovation when really it's just a tool for automating more work 
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. I mean, imagine an AI model that can write code like a human, without even having someone sitting next to it guiding its every move! 
The idea that these 16 agents worked together to create a fully functional C compiler from scratch is just mind-blowing. And the fact that they were able to compile Doom and get a 99% pass rate on the GCC torture test suite is just insane 
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It's a valid concern, and one that needs to be addressed ASAP.
. I mean, we're talking about an AI model that can write code that's almost indistinguishable from human-written code 
. The potential implications for software development and programming practices are huge, and I'm excited to see where researchers take this technology next 
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. That's like, a whole different level of innovation right there 
. It just goes to show that even in areas where we thought humans were the only ones who could excel, AI can sometimes surprise us with its capabilities 
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... they barely scratched the surface of what it means to be able to work independently. Have you seen the list of projects they compiled? PostgreSQL and SQLite are legit tools we use every day 
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... somehow it works?! 
And the fact that they didn't even need human supervision for most of it? Mind. Blown. 
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. We're not even writing the code anymore? 
It's more like we're setting up a playground for these agents to play in 
... and that raises some interesting questions 
I don't know, but it's definitely something to think about 
. Anyway, kudos to Anthropic for pushing the boundaries of AI development 
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16 agents working together and all that. i mean its not like they made a perfect compiler or anything, its got some limitations and stuff, but still... $20k is wild though 
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And what's with the $20,000 API fee? How much did it cost to train that model anyway? Thousands? Millions? The researchers are gonna make bank off this thing 
. And what about the bugs in their assembler and linker? That's just a ticking time bomb waiting to happen 
