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A worker in my local-model swarm ran pytest against a file that did not exist, twice, and finished the task green. The pipe had eaten the exit code. Measured on bash and zsh, on pytest and cargo — including why turning pipefail on just moves the lie to the other side.
MCP went stateless on 2026-07-28. I probed ten real servers: none implement it. Then I measured the thing that actually costs you — 145 tool definitions, 40,784 tokens, and the 30% that is one server repeating itself.
Metal will not give you the RAM on the box, and the number it does give is not the 75% everyone repeats. I measured the three ceilings on a 96 GB Mac Studio, then measured what modern hybrid-attention models actually spend against them — including a Gemma 4 cache that quietly holds three times its own sliding window.
Our swarm workers kept re-reading files they had already read. Context compaction was summarising the tool output that held the file. Pasting the file into the summary does not fix it; returning the last turns verbatim does. Measured three ways on the same 27B.
Multi-token prediction is merged in llama.cpp and still an open PR in mlx-lm. I measured both on an M3 Ultra with the same model. Every default MTP setting was slower than no MTP at all, and the runtime that deletes the MTP head outright is still the fastest thing on the box.
Ant Group's 124B/5.1B-active hybrid-linear MoE hit Hugging Face on 2 August. The memory arithmetic fits a 96 GB Mac with room to spare, and mlx-lm still refuses it. I counted exactly which tensors block it — 385 of 62,237 — then measured what that sparsity actually buys on the models that do run.
I quantised real Qwen3-Coder weights both ways on an M3 Ultra. MXFP8 reconstructs them about 10x worse than 8-bit affine at identical size — and then costs only 1% perplexity end to end. Both numbers are true, and the gap between them is the interesting part.
Three weeks after the swarm shipped its first honest builds, the work stopped being about making small local models smarter and became about making them stop lying to me. Here is what 602 commits bought, what the desktop looks like now, and the three defects I found in my own honesty machinery while writing this post.
We built a harness that makes local coding agents produce real software, grades it by running it, and ran the same model as GGUF and MLX. Here's the harness, its modes and archetypes — and which build wins.
