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Every Correction You Give Your Agent Is Data - Brian Douglas, Paper Compute

AI Engineer

29 views10 Nov 2026

YouTube

A Rust program can compile and still violate the decisions an experienced teammate would catch immediately. Brian Douglas finds that missing knowledge in moments when engineers push back on their coding agents. At Paper Compute, he captures agent sessions, labels corrections and uses those traces to recover the reasoning that otherwise disappears inside a local coding harness. His example starts with a gap in his own Rust expertise: accepting plausible generated code leads to objections later, when a teammate reviews the pull request. Those earlier objections become useful training material only if someone preserves and labels them. Brian demonstrates session capture with Tapes and Paper Console, then runs a Twin Review prototype against a pull request using examples of a teammate's feedback. He discusses labels for pushback, successful sessions and regressions, and shows how the same records can support skills, evaluations or model adaptation. The prototype is a demonstration, not a measured comparison against an independent evaluation set, and Brian corrects himself during the recording to clarify that Twin Review is not publicly released because its data is sensitive. His practical starting point is simpler than training a model: collect the traces you already generate, find the corrections that encode your team's conventions, and turn those into a skill you can test. The durable asset is the connection between a decision, the expert's objection and the resulting outcome. Speaker info: Brian Douglas, Paper Compute - https://b.dougie.dev/ - https://briandouglas.me/ - https://tapes.dev/ Timestamps: 0:00 - Engineering judgment and agent feedback 1:47 - Capturing sessions with Tapes 3:43 - Labeling expert pushback in Paper Console 5:59 - Turning traces into a review dataset 6:28 - The Twin Review prototype 8:06 - Useful labels and recurring failure patterns 9:32 - Start with skills and evaluations 10:48 - Model adaptation experiments and closing advice

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