Tools gave humans the edge over other animals.

unfortunately all the agent cli makers have decided that simply giving it access to bash is not enough. instead we need to jam every possible functionality we can imagine into a javascript “TUI”.

At it's heart it's prompt/context engineering. The model has a lot of knowledge baked into it, but how do you get it out (and make it actionable for a semi-autonomous agent)? ... you craft the context to guide generation and maintain state (still interacting with a stateless LLM), and provide (as part of context) skills/tools to "narrow" model output into tool calls to inspect and modify the code base.

I suspect that more could be done in terms of translating semi-naive user requests into the steps that a senior developer would take to enact them, maybe including the tools needed to do so.

It's interesting that the author believes that the best open source models may already be good enough to complete with the best closed source ones with an optimized agent and maybe a bit of fine tuning. I guess the bar isn't really being able to match the SOTA model, but being close to competent human level - it's a fixed bar, not a moving one. Adding more developer expertise by having the agent translate/augment the users request/intent into execution steps would certainly seem to have potential to lower the bar of what the model needs to be capable of one-shotting from the raw prompt.

That is why I am currently looking into building my own simple, heavily isolated coding agent. The bloat is already scary, but the bad decisions should make everyone shiver. Ten years ago people would rant endlessly about things with more then one edge, that requires a glimpse of responsibility to use. Now everyone seems to be either in panic or hype mode, ignoring all good advice just to stay somehow relevant in a chaotic timeline.

> It doesn't perform on par with Anthropic's models in my experience.

Why do you think that is the case? Is Anthropic's models just better or do they train the models to somehow work better with the harness?

I like it a lot, I find the chat driven workflow very tiring and a lot of information gets lost in translation until LLMs just refuse to be useful.

How does the human intervention work out? Do you use a mix of spec and audit editing to get into the ready to generate state? How high is the success/error rate if you generate from tasks to code, do LLMs forget/mess up things or does it feel better?

The spec driven approach is potentially better for writing things from scratch, do you have any plans for existing code?

This looks great, and I’ve bookmarked to give it a go.

Any reason you’ve opted for custom markdown formats with the @ syntax rather than using something like frontmatter?

Very conscious that this would prevent any markdown rendering in github etc.