February 16, 2026
Speedrun meets boss fight
The Long Tail of LLM-Assisted Decompilation
AI races through N64 game code, then hits a boss fight — the comments go wild
TLDR: An AI blew through much of a Nintendo 64 game’s code and, with “find-similar” tricks, reached ~75% before stalling. Commenters celebrate a path to revive lost classics, while skeptics question correctness, note big-function limits, and push for pairing old-school tools with AI—fueling a lively preservation debate.
The dev behind an AI-powered attempt to decode a Nintendo 64 game says the bot sprinted from about 25% to 58% matching code, then crawled until a new trick—prioritizing similar functions—pushed it to ~75%. After that? A stall. And the internet showed up with popcorn and hot takes.
Hype squad first: fans are giddy, dreaming of resurrecting lost classics. One commenter cheered that this could help games “with lost source,” name-dropping Red Alert 2. Meanwhile, the skeptics brought receipts. One user pounced on the line that Claude quits on jumbo-sized functions, dubbing it the final boss of retro code. Another asked the big question: can this technique guarantee correctness, or is it just confident guessing dressed as C?
Then came the pragmatists: why not tag-team with traditional decompilers and let AI clean up the messy parts? The author’s own tweaks—finding lookalike code via similarity scores (think: code doppelgängers), plus tools like a graphics microcode helper and a code-permuter—got applause for being clever, not magic.
The community vibe: AI speedruns old games until it hits the boss, preservationists are ecstatic, engineers demand proof, and everyone agrees this is high-drama retro archaeology. One fan dubbed it “AI that helps and harms no one”—and yes, the memes wrote themselves.
Key Points
- •Initial one-shot LLM decompilation rapidly increased matched code from ~25% to 58% for a Nintendo 64 game.
- •Progress slowed, prompting a workflow shift that ultimately reached ~75% matched code before stalling.
- •Early prioritization by estimated difficulty (via logistic regression) worked until only hard functions remained.
- •Prioritizing unmatched functions with similar matched counterparts proved highly effective for decompilation.
- •Exact similarity methods, including the Coddog tool using Levenshtein distance, complemented a handcrafted metric; specialized tools like gfxdis.f3dex2 and decomp-permuter further aided results.