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nadeauglenn1-max/README.md

Glenn Nadeau

I build systems that hold up — databases, platforms, and lately open infrastructure for training AI agents.

Currently building Crucible — turn any real software (a database, a CLI, a codebase, an API) into a trainable, gradable, replayable RL environment. The reward is your real software; every episode replays byte-for-byte, so every reward is auditable. MIT.

→ A 0.5B model went 5%→100% on a real SQL task with a Crucible environment as the only reward — then generalized across shell, code, and database agents. The proof.

How I build: one source of truth, reuse over rebuild. Tests ship with the change (100% coverage, CI gates). Docs move in the same commit as the code. One production path — no MVP-vs-real branches.

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  1. crucible crucible Public

    Turn any real software into a replayable RL environment for training AI agents — deterministic replay, verifiable rewards, TRL & verifiers adapters.

    Python