ai-agents
- On one RTX 3090, agent speed followed active parameters
Generation speed of 28 local models driving coding agents on one RTX 3090. Active parameters and MTP drafting set it; the quant moved it far less.
- Twenty-one agent runs on a laptop iGPU, none merge-ready
Seven local MoE models ran three coding tasks each on a Radeon 680M. A blind review graded all 21 runs and found none merge-ready.
- On a laptop iGPU, memory bandwidth picked the model
Coding agents on a Radeon 680M: a dense 27B generated 5.7 tokens a second whatever I tried; MoEs with 2-3B active on the GPU, 27 to 48.
- My agents got their own config. Two still read mine.
A separate config dir per coding agent kept my files untouched. Claude Code and OpenCode still read my ~/.claude, and OpenCode sent a prompt to a hosted model.
- Confine a coding agent's HTTPS to an allowlist with bwrap
Run a command, such as a coding agent, in bubblewrap with no network except HTTPS to allowlisted hosts, through a 97-line Python proxy on the host.
- Another guard's test list found holes in mine
114 example commands from a separately built shell-parsing guard went through my regex guard. Most held. The worst gap was a guard that could quietly vanish.
- Two rounds, seventeen arms: the engine set the speed, not the quant
17 local-model setups on one 128 GB box. Q2_0 generated 2.2x faster than IQ4_XS; a same-file engine A/B puts most of that on the engine.
- Five languages, two reps: the round-two ranking
Five local models in six setups, 144 coding-agent runs on one 128 GB box. Flash-Next leads in both quants, and the bugs line up by ticket.
- Nine accusations, one lie: re-reading round two's judges
137 reviewed local-model agent runs, 14 "false" verdicts from two judges. On the raw sessions 9 fell, 4 were about docs, and 1 was a lie.
- The third run mattered where it cost the most
739 three-run groups from my agent bench: 19% split on basic tasks, 56% on Zig. So basic drops to two runs, and Zig runs only after the hard tier earns it.