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dvttoday at 8:06 PM3 repliesview on HN

I think this space is very untapped. Models are interesting, but I am absolutely obsessed with some things I've been researching/working on for the past few years:

Fractal tool discovery: tool taxonomy where an agent can "drill deeper" to find what specific tool it's looking for. Helps if/when polluting context with a zillion (mostly unnecessary) tools.

Leveraging splay trees: this is my favorite data structure and I think relatively unused in the context of agents/harnesses. A lot of times, recently-used workflows/tool-chains will be used again, so having those at the top of the search hierarchy is an awesome optimization.

Virtual containerized notebooks: models working in sandboxed (WASI) Python notebooks is incredible. Even local models (if given enough time) will usually converge on a good solution. Being able to mount tools/resources/fs is again, imo quite untapped. Some problems here are running native things (thing numpy/pandas) in containers is a nightmare (or impossible).

Anyway, happy to see other folks seriously doing stuff in this space. If anyone wants to collaborate on anything don't hesitate to reach out :) I'm also actively looking for a job or some contract gigs.

Fun times ahead.


Replies

spdustintoday at 10:06 PM

I've also explored s/Fractal tool discovery/Skill tree approaches, seems to work pretty well when you stick the equivalent of XREFs in the frontmatter.

wyretoday at 9:24 PM

Fractal tool discovery is a fascinating idea! Have you worked on implimenting this into any agent harnesses already to any success? My first impression is allowing the agent to fork itself, not unlike launching a subagent, then returning it's response back to the main agent.

tracerbulletxtoday at 8:28 PM

What makes it fractal?

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