
local-llm
A comprehensive guide for building and configuring a high-end local machine to run state-of-the-art LLMs, with detailed hardware choices, BIOS tuning, and Docker-based model serving.
它不仅写出规则,还通过确定性钩子、独立子代理和行为评估来执行规则,并坦率承认局限性(如令牌开销、尚未验证的对比优势),而非过度宣传。
A transferable, self-enforcing software-engineering methodology for AI coding agents — written by Claude Fable 5 to make a less advanced model work more like it.
It began as a single directive to Fable 5: document your complete working methodology so a less capable model can execute it cold. The result is an attempt to extract the concrete procedures, decision rules, reasoning strategies, and quality standards an advanced model actually uses — and hand them to a weaker successor (or to the same model after a context reset, with no goodwill assumed).
The central bet is that writing the methodology down is not enough, because written rules decay. So it ships in four layers — prose you read, skills that load on demand, subagents with strict contracts, and hooks that block at lifecycle events — arranged so that the parts a script can enforce are enforced by a script, and only genuine judgement is left to prose. Everything is written as executable instruction — imperative, concrete, with decision rules ("if X, do Y"), worked examples, and a "done when" criterion — never abstract advice.
This repository is the output of an unusual exercise: asking an advanced model to explain itself well enough to be replaced by a lesser one.
Claude Fable 5 — a model in Anthropic's Claude 5 family, positioned above the Opus tier in raw capability — was given a single standing directive:
Document your complete working methodology so
A transferable, self-enforcing software-engineering methodology for AI coding agents — playbook, skills, contracted subagents, lifecycle hooks, and evals.
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A comprehensive guide for building and configuring a high-end local machine to run state-of-the-art LLMs, with detailed hardware choices, BIOS tuning, and Docker-based model serving.
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