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Maurice Heumann says he and community collaborators spent three months using AI agents to decompile a commercial first-person shooter into C++. He estimates the project consumed more than 500 billion tokens, but that figure is his reported estimate—not an independently audited total. Although the current post does not name the game, preserved earlier wording identifies it as Call of Duty: Modern Warfare 2 (2009).
What the project set out to do
In an October 9, 2026 post, project author Maurice Heumann described an effort to reconstruct a commercial shooter as readable, compiling C++ code while accurately reproducing the original program’s behavior. Modernization and portability were later deferred. The undertaking was also an experiment in coordinating autonomous AI agents over months, not a one-prompt attempt to generate a game.
Heumann summarized the technical aim this way: “We aimed at an accurate decompilation of the game to C++.” He also described the broader goal as learning “how to effectively orchestrate autonomous AI agents over the course of months.” Heumann’s October 9, 2026 account
Did AI agents really rebuild Modern Warfare 2’s source code?
The careful answer is that Heumann says the team used AI agents to decompile the game into C++ and worked toward a behaviorally accurate reconstruction. That is not the same as recovering the original developers’ source files: decompilation reconstructs source-like code from compiled software, and the project’s stated target was a new C++ representation.
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The game identification also needs qualification. Heumann’s current post leaves the shooter unnamed. Earlier wording preserved in contemporaneous discussion identifies it as Call of Duty: Modern Warfare 2 (2009); that identification comes from the earlier wording and linked community material, not from the current post itself. Preserved earlier wording and discussion
How many tokens did the project use?
Heumann’s October 2026 headline estimate is more than 500 billion tokens. Treat this as the project’s reported consumption, not a verified measurement: the available secondary coverage repeats the figure but provides no independent telemetry. No audited total or reliable dollar-cost figure is established, and token counts alone cannot determine cost across different models and subscription arrangements. Project account Secondary coverage
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How the agents and tools were coordinated
Heumann reports using Claude Max and Codex Pro subscriptions, with agents running in Claude Code CLI and Codex CLI. The work was divided and tracked through GitHub issues organized around translation units, while a shared Discord channel supported coordination. Hex-Rays’ ida-mcp was used for disassembly and decompilation. These details describe the reported workflow; they do not by themselves establish how much independently verified progress the agents produced.
How the team checked whether the code was right
A reconstructed program can compile and appear to run while still differing internally or behaviorally from the original. Heumann’s account describes a harness that compared the reconstruction against the original in machine-checkable ways. The discussion around the project highlights a key choice: checking for byte-identical output is stricter than checking functional equivalence, but exact matching also makes compiler behavior and other sources of variation important. Heumann’s account Discussion of verification approaches
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Byte equality and functional equivalence answer different questions
- Byte-identical output: asks whether outputs match exactly. It can expose differences that a finite collection of functional tests misses, but demands careful control of exactness and compiler effects.
- Functional equivalence: asks whether the reconstructed code behaves like the original under chosen tests or conditions. It can be a useful target, but the result depends on how comprehensive and independent the harness is.
The project’s machine-checkable comparison is evidence of a verification approach, not universal proof that every behavior is equivalent. A reader assessing similar claims should distinguish the test method from the conclusion it can support.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What this experiment demonstrates—and what it does not
The reported work shows the scale and coordination involved in a long-running decompilation effort using AI agents. Its central lesson is that agent throughput and token consumption are not substitutes for verified correctness. A meaningful assessment depends on the reconstruction target, the strength and independence of the comparison harness, and whether the goal is readable or portable code, fidelity to original behavior, or exact output matching.
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The available account does not establish an independent audit of token use, nor does it provide a definitive legal ruling about this particular project. Those limits do not erase the reported workflow, but they do mean the headline figure and claims of fidelity should be attributed rather than treated as independently confirmed.
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