ai-agents
- LFM2.5 is fast. Gemma 4 finishes the job
LFM2.5-24B-A2B vs Gemma 4 26B-A4B as local coding agents, 3 runs per task: LFM wins on speed; on Spring Boot only Gemma wrote tests.
- 2.2x faster prefill, 1.04x faster agent
A new engine prefilled 2.1-2.2x faster than llama.cpp at every depth. Driving a coding agent, it finished 4% sooner. The server logs say where the speed went.
- The engine got credit for the quant
open-jet beat llama.cpp 7 to 3 on a Zig agent task. On open-jet's own GGUF, llama.cpp went 7 of 9 too. Only the speed gap belonged to the engine.
- My benchmark read my CLAUDE.md
An audit built to check sampler overrides found my OpenCode bench runs carrying my global ~/.claude/CLAUDE.md and skills: 53,483 characters of prompt.
- The best-reviewed code came from the slowest Flash-Next arm
Stage 1 of the A9 agent study: the slowest Flash-Next arm reviewed best, the 62 GiB Q2_0 passed 6/6 at nearly 6x the rate. A sampler confound splits them.
- Zig needed context, not time
Four 27B local models kept hitting the one-hour wall on a Zig task. Four hours at 64k got 7 of 12 through the core tests. At 128k, 35 of 36 did, cleanly.
- Inventing the work vs promising what isn't there
Checking 71 local-model agent runs' claims against their own command logs: three models invent work, three promise atomicity they never built.
- The fastest model finished first by stopping first
gpt-oss-120b cleared six agent cells in 51 minutes by quitting early on each. Two of those stops shipped Zig that does not compile.
- Read the transcript, not the gate
15 of 17 local models passed the agent smoke gate on the 128 GB box. Eight of those passes hid a Claude Code failure the intake had logged and nobody read.
- I blamed OpenCode; a 2-bit model passed Zig in it
Four 27B models failed a Zig agent task in OpenCode, and the client looked guilty. Then a 2-bit MoE in the same setup passed 6 of 6.