[spIrr]

As a hobbyist, I find it difficult to figure out how to make Claude stick with some repeating things I want it to do after every major action, like re-evaluate the completeness of tests, update the documentation, etc. And CLAUDE.md/AGENTS.md definitely did NOT help there, sadly.

[victorbjorklund]

Hooks can be pretty useful for that. A hook when it is finished ”run tests suite and check coverage” ”check if your changes require updating the docs”

[Muromec]

Don't use the default harness, write your own instead.

[TeMPOraL]

Any way to do that AND use subscription instead of per-token pricing from SOTA providers?

[Muromec]

If the harness runs on your localhost, but the inference doesn't, it usually means it's calling some API. Whether you want to break TOS of your provider like that or simply buy tokens from our friends from UTC+8 timezone is an open question.

[Supermancho]

This is the way. Making your own agent to have a sticky memory context that is prepended to every execution is necessary to ensure each task is bounded by those precepts.

[Muromec]

The trick I'm doing -- the model is given a tool that runs a prompt in the current thread to consolidate it's working memory and identity (it has a memory tool bound to the agent persona). When the prompt ends, the parts of memory that are marked as identity are merged together into a new system prompt, then the context restarts with only system prompt and this tool call surviving. Then it just keeps going.

[drob518]

Doesn’t that blow your token cache hit rate and balloon your costs (essentially everything is billed at the input token rate, not the cached rate)?

[haus20xx]

If it moved a block like

<system_prompt>

<tools_etc>

<project_prompts>

<user_call>

<llm_response>

=>

<system_prompt>

<tools_etc>

//Removed project_prompts

<user_call>

<llm_response>

<user_call>

<project_prompts> //reinserted

<llm_response>

Cache would break but if you did instead

<system_prompt>

<tools_etc>

<project_prompts>

<user_call>

<llm_response>

<user_call>

<project_prompts> //Duplicated with new user messsage

<llm_response>

It wouldn't bust cache, it would just make your input prompts slightly larger. Technically inefficient as you're duplicating the same rules over and over but I imagine for a smallish checklist/principles that it is tremendously more efficient than a cache break every message

[cyanydeez]

What I'm currently doing in a large refactor, is I created a super-run script; the super run script is devided into super-dev (Setup dev), super-test (run all tests), super-build (build artificats), super-e2e (test all artifacts), super-deploy (deploy finished).

Each super's sub functions should _fail hard_, and each script should be highly detailed; of course I'm not doing it myself, but in small increments of directed work, it can build up the necessary harness.

What I get is a CI that just starts with "run super-run.sh" and that gives it context, then each sub script provides context depending on if it succeeds or fails. If it fails, the agent is provided what it needs.

It's basically, you have to design the products of the AI to give itself the context. Another technique I'm testing out is a parallel set of files like <subject-module>.js, <subject-module>.test.js, <subject-module>.md which get pulled up if the Agent is looking for a file.