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@alxfazio (2026-08-02) says studying harnesses like codex inside out is one of…

Brief

@alxfazio argues that deeply learning harnesses (e.g., codex) is highly valuable for improving LLM workflows. They claim codex and claude frequently fail to use harness commands like "codex exec" or "claude -p" correctly, and that knowing how a harness and its default tools actually work provides greater control and clearer prompts for steering agents.

Why it matters

@alxfazio (2026-08-02) says studying harnesses like codex inside out is one of the best investments you can make to get better at working with LLMs.

Key details

  • Models such as codex and claude often don't understand their own harnesses or how to drive tools like "codex exec" or "claude -p" correctly; the author has tried prompting them many times without success.
  • Understanding a harness and its installed tools gives much more control and a better vocabulary for steering agents—most models won't discover or use default-installed tools unless you explicitly guide them.
Source evidence

studying harnesses like codex inside out is low key one of the best investments you can make if you want to get better at working with llms

sadly models usually don't really understand their own harnesses or how to use them effectively. for example, codex and claude both have very little idea how to drive codex exec or claude -p correctly. i've tried getting them to do it many times

if you understand how the harness and its tools actually work, you gain a lot more control over them. you also develop a better vocabulary for driving them, which makes it much easier to steer the agent toward the outcome you actually want

most models don't even know about some of the tools that are installed by default unless you explicitly guide them to use those tools