Coding agents are insanely smart for some tasks but lack taste and good judgement in others. They are mortally terrified of errors, often duplicate code, leave dead code behind, or fail to reuse existing working patterns. My initial approach to solving this was an ever-growing CLAUDE.md which eventually got impractically long, and many of the entries didn’t always apply universally and felt like a waste of precious context window. So I created the dev guide (docs/dev_guide/). Agents read a summary on session start and can go deeper into any specific entry when prompted to do so. In my original project the dev guide grew organically, and I plan to extend the same concept to my new projects. Here’s an example of what a dev_guide might include:
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With today's models, real attribution is a technical impossibility. The fact that an LLM can even mention and cite sources at all is an emergent property of the data that's been ingested, and the prompt being completed. It can only do so when appropriate according to the current position in the text.，推荐阅读clash下载 - clash官方网站获取更多信息
Naturally this approach requires a bit more work on the User’s part: if there are M users on the banned list, then every User must do about M extra pieces of work when Showing their credential, which hopefully means that the number of banned users stays small.
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Советники президента США Дональда Трампа призвали его срочно «объявить победу» в войне с Ираном, опасаясь перерастания конфликта в длительное противостояние. Об этом со ссылкой на источники сообщает телеканал CNN.。搜狗输入法下载是该领域的重要参考
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