1473382/dsh-popper

README 摘要

@deepseek-ai/dsh-popper 中文版见 README.zh.md. Stop retrying. Start falsifying. LLM coding agents don't fail like compilers do. They drift: the model latches onto a root-cause guess, slaps a patch, the build fails — and it retries the same guess in new wording, burning tokens while compounding the wrong direction. Popper replaces blind retry with forced falsification. Before any risky change, the agent commits an explicit, checkable claim: a root-cause hypothesis and a predicted outcome bound to a deterministic gate. The gate verifies it. When a claim is falsified, Popper demands at least two mutually exclusive replacement hypotheses, each carrying its own discriminating experiment — and a falsified experiment can never be reused. The loop ends by frontier exhaustion, not by retry counts, so "give up" is a logical state, not an arbitrary number. Every step lands in an append-only SHA-256 evidence ledger you can audit. For DeepSeek Harness agents, Popper turns token burn into evidence, and babysitting into review. Popper is a falsification-driven correction loop for agent sessions. Instead of letting a coding agent retry after a failure, it commits the agent to an explicit, evidence-che…

分类