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- Hacker News
- not a fan of README's where there's no clear way to run the program, like a getting started or how to build. i think this is the opposite of a docu-monster where AI documents everything, but clearly more documentation is better than lack of.by westoque
- Oh very nice, can you also compare it to https://maki.sh?
Would be interesting to compare to a harness optimizing for cost reduction too.
by tontinton - Crush [1] has had async tools for a long time, as has Claude Code.
They work well, except that the agent will often simply call wait() immediately after, and also do it with a longer timeout, causing crashes/hangs to really slow down the process; whereas immediate tool calls have a built-in 30s timeout in most harnesses.
I imagine this one will suffer from similar problems.
by ricardobeat - Funny, I solved this problem by having Claude write a pi extension
https://github.com/Pyrolistical/pi-notify
Now my pi agent setups its own trigger to notify itself when a background process is done
by pyrolistical - This is so interesting.
I'm unlocking savings (in both speed and costs) but using a different way of thinking by integrating Decision models:
In short, LLMs spend so many turns on discovery, triage, and safety checks for everyday tasks. A decision model cuts those turns by preparing the relevant context before the prompt and handling the simple judgments itself, so the LLM only does the real work.
Here is the full article I published yesterday with in-house benchmark: https://tawfeks.github.io/decisionkit/
What do you think, guys? I need feedback on this.
- Sounds like a trademark issue when Epic ships a wildly popular Unreal Engineby tapoxi
- The headline graph is kind of bizarre.
For some reason they're comparing their harness running on Astra xhigh to Codex with Astra max?
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Also worth noting that OpenAI just added support for async tool calling to their harness, which isn't 1:1 with this approach, but is slowly ramping up in being able to provide something similar.
A big part of why Codex uses so many tokens is that it basically hot loops on polling tasks it starts for... absolutely no good reason: https://www.reddit.com/r/codex/comments/1wdlp7q/weve_discove...
I fixed it on my fork of Codex too, also back in Jan/Feb – I keep this patch rebased, for anyone who wants it: https://github.com/tekacs/codex/commit/9ffcf8db9078eae43d411...
It results in token savings similar in scale to those displayed here by Unreal.
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My harness has used a slightly fancier version of the approach that Unreal is using since ~Feb, and... it definitely works excellently, but it's also assuredly smoother with Astra and other recent models that are more aware of async tool calling.
by tekacs - 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.
by dvt