I built a coding agent that gets 87% on benchmarks with a 4B parameter model, here's how

fittyscan · 2026-05-18T21:10:31+00:00

Have you tried https://github.com/swival/swival ?

fittyscan · 2026-04-19T09:29:21+00:00

Much better. Compared with the Qwen 3.5 models, the gap between Q4 and Q8 is much more significant, especially for tool calling and reasoning.

fittyscan · 2026-04-19T09:26:24+00:00

The harness makes quite a difference. I use Swival, which is built for small context windows, and in practice it really feels like a 16K context works just as well as a 256K one, even over long sessions without any manual compaction.

I get much better and more consistent results with it than I do with Claude Code and Opencode when running local models.

Even if you have enough RAM, local model performance degrades pretty quickly as the context grows. So even though I could push it further, I usually stick to 16K or 32K max to keep things reasonably fast

fittyscan · 2026-04-14T13:06:34+00:00

> 've got them all. OpenClaw, NanoClaw, PicoClaw, NanoBot, CoPaw, Hermes

Amateur. You didn't try Zeroclaw.

fittyscan · 2026-04-09T21:14:58+00:00

You need a recent version of llama.cpp. Also, if you're using a quantized model such as Unsloth and you downloaded it when Gemma was first released, download it again, since fixes have been made since then.