AI Agents Decompile First-Person Shooter After 500 B Tokens
agents
| Source: HN | Original article
After spending three months and over 500 billion tokens, AI agents have been used to decompile a popular first‑person shooter.
A developer known only as Maurice spent the better part of three months orchestrating a swarm of sixteen autonomous AI agents to reverse‑engineer a popular first‑person shooter. By feeding the models roughly 500 billion tokens – an estimated 600‑700 billion when accounting for prompts and context – the agents parsed disassembly, rebuilt functions and data structures, and produced a C++ codebase that compiles back to a binary matching the original in 99 % of its functions and 83 % of its bytes.
The experiment, detailed in a blog post that has since been mirrored on sites such as Hacker News, moves beyond the proof‑of‑concept stage that has dominated most LLM‑agent demos. Instead of a toy example, Maurice aimed for a “accurate, stable and feature‑complete recreation” of a commercial game, and the token budget reflects the scale required for such fidelity.
Why it matters is twofold. First, it showcases that LLM agents can handle the long‑range context and intricate reasoning needed for large‑scale reverse engineering, a task traditionally reserved for expert human analysts. Second, the token consumption highlights the economic barrier: even with today’s most capable models, a project of this size costs hundreds of billions of tokens, raising questions about sustainability and accessibility for smaller teams.
Looking ahead, the community will watch for improvements in token efficiency, better orchestration frameworks, and cost‑reduction strategies that could make similar workflows viable for security audits, legacy code migration, or modding. As we reported on AI coding agents and their bottlenecks on Oct 10, this decompilation effort underscores both the promise and the practical limits of current LLM‑driven development tools.
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