AI Built a Compiler! 🤯 Code Revolution 🔥
AI
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In early 2026, an experiment at Anthropic yielded a surprising result: the autonomous construction of a C compiler. A research scientist, Nicholas Carlin, deployed sixteen instances of the Claude Opus 4.6 AI model to work on a shared codebase, tasked with building a compiler with minimal human oversight. Over a period of two weeks, the AI agents, operating independently within Docker containers, generated a 100,000-line Rust-based compiler capable of compiling a bootable Linux 6.9 kernel across x86, ARM, and RISC-V architectures. The experiment highlighted the potential of AI agents in software development, demonstrating an ability to resolve merge conflicts and achieve a high pass rate on a benchmark suite, including compiling the reference compiler, Doom. While the compiler’s code quality was noted as less sophisticated than human-written code, the demonstration represents a significant step in the exploration of AI’s role in complex software projects.
AI-Driven Compiler Development: A Novel Experiment
The $20,000 experiment compiled a Linux kernel but needed deep human management. Amid apush toward AI agents, with both Anthropic and OpenAI shipping multi-agent tools this week, Anthropic is more than ready to show off some of its more daring AI coding experiments. But as usual with claims of AI-related achievement, you’ll find some key caveats ahead.
Autonomous Agent Coordination: A Multi-Instance Approach
On Thursday, Anthropic researcher Nicholas Carlinipublished a blog postdescribing how he set 16 instances of the company’s Claude Opus 4.6 AI model loose on a shared codebase with minimal supervision, tasking them with building a C compiler from scratch. Over two weeks and nearly 2,000 Claude Code sessions costing about $20,000 in API fees, the AI model agents reportedly produced a 100,000-line Rust-based compiler capable of building a bootable Linux 6.9 kernel on x86, ARM, and RISC-V architectures.
Technical Specifications and Implementation
Carlini, a research scientist on Anthropic’s Safeguards team who previously spent seven years at Google Brain and DeepMind, used a new feature launched with Claude Opus 4.6 called “agent teams.” In practice, each Claude instance ran inside its own Docker container, cloning a shared Git repository, claiming tasks by writing lock files, then pushing completed code back upstream. No orchestration agent directed traffic. Each instance independently identified whatever problem seemed most obvious to work on next and started solving it. When merge conflicts arose, the AI model instances resolved them on their own.
Compiler Functionality and Performance
The resulting compiler, which Anthropic hasreleased on GitHub, can compile 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, in what Carlini called “the developer’s ultimate litmus test,” compiled and ranDoom. known-good reference compiler to check against. Most real-world software projects have none of these advantages. The hard part of most development isn’t writing code that passes tests; it’s figuring out what the tests should be in the first place.
Limitations and Challenges
The compiler also has clear limitations that Carlini was upfront about. It lacks a 16-bit x86 backend needed to boot Linux from real mode, so it calls out to GCC for that step. Its own assembler and linker remain buggy. Even with all optimizations enabled, it produces less efficient code than GCC running with all optimizations disabled. And the Rust code quality, while functional, does not approach what an expert Rust programmer would produce. “The resulting compiler has nearly reached the limits of Opus’s abilities,” Carlini wrote. “I tried (hard!) to fix several of the above limitations but was not fully successful. New features and bugfixes frequently broke existing functionality.”
Agentic Development Strategies
To address this, Carlini designed test runners that printed only a few summary lines and logged details to separate files. He also found that Claude has no sense of time and will spend hours running tests without making progress, so he built a fast mode that samples only 1 percent to 10 percent of test cases. When all 16 agents got stuck trying to fix the same Linux kernel bug simultaneously, he used GCC as a reference oracle, randomly compiling most kernel files with GCC and only a subset with Claude’s compiler, so each agent could work on different bugs in different files.
Parallelization and Coordination Techniques
The project highlighted novel agentic development strategies, including parallelization through Git with minimal human supervision and coordination techniques. The methodology of parallel agents coordinating through Git with minimal human supervision is novel, and the engineering tricks Carlini developed to keep the agents productive (context-aware test output, time-boxing, the GCC oracle for parallelization) could potentially represent useful contributions to the wider use of agentic software development tools.
Refining the Approach: Addressing Instability
Carlini himself acknowledged feeling conflicted about his own results. “Building this compiler has been some of the most fun I’ve had recently, but I did not expect this to be anywhere near possible so early in 2026,” he wrote. He also raised concerns rooted in his previous career in penetration testing, noting that “the thought of programmers deploying software they’ve never personally verified is a real concern.”
This article is AI-synthesized from public sources and may not reflect original reporting.