51–60 of 145 posts
> Each agent is a TOML config with a focused job. Such as code reviewer, log analyzer, commit message writer. You can run them from the CLI, pipe data in, get results out. I'm a bit skeptical of this approach, at least for building general purpose coding agents. If the agents were humans, it would be absolutely insane to assign such fine-grained responsibilities to multiple people and ask them to collaborate.
Is there Gemini support?
Axe treats LLM agents like Unix programs—small, composable, version-controllable. Are we finally doing AI the Unix way?
I really like seeing the movement away from MCP across the various projects. Here the composition of the new with the old (the ol' unix composability) seems to um very nicely. OP, what have you used this on in practice, with success?
12MB for an "AI framework replacement"? That's either brilliant compression or someone's redefining "framework" to mean "toy model that works on my laptop." Show me the benchmarks on actual workloads, not the readme poetry.
This is not an LLM but a Binary to run LLMs as single purpose agents that can chain together.
The Unix-style framing resonates a lot. One thing I’ve noticed when experimenting with agent pipelines is that the “single-purpose agent” model tends to make both cost control and reasoning easier. Each agent only gets the context it actually needs, which keeps prompts small and behavior easier to predict. Where it gets interesting is when the pipeline starts producing artifacts instead of just text — reports, logs,…
> Curious if you’ve experimented with workflows where agents produce artifacts (files, reports, etc.) rather than just returning text. Yes! I run a ghost blog (a blog that does not use my name) and have axe produce artifacts. The flow is: I send the first agent a text file of my brain dump (normally spoken) which it then searched my note system for related notes, saves it to a file, then passes everything to agent 2…
Earlier quoted context omitted.
> Curious if you’ve experimented with workflows where agents produce artifacts (files, reports, etc.) rather than just returning text. Yes! I run a ghost blog (a blog that does not use my name) and have axe produce artifacts. The flow is: I send the first agent a text file of my brain dump (normally spoken) which it then searched my note system for related notes, saves it to a file, then passes everything to agent 2…
That’s a really nice pipeline. The “save to file between steps” pattern seems to appear very naturally once agents start doing multi-stage work. One thing I’ve noticed when experimenting with similar workflows is that once artifacts start accumulating (drafts, logs, intermediate reports, etc.), you start running into small infrastructure questions pretty quickly: – where intermediate artifacts live – how later agents…
> Each agent is a TOML config with a focused job. Such as code reviewer, log analyzer, commit message writer. You can run them from the CLI, pipe data in, get results out. I'm a bit skeptical of this approach, at least for building general purpose coding agents. If the agents were humans, it would be absolutely insane to assign such fine-grained responsibilities to multiple people and ask them to collaborate.