Machine learning engineer. I build AI tools, CLI applications, and civic tech, and write about the parts that were harder than they should have been.
I scanned 1.2 GB of my own Claude Code and Codex transcripts for credentials and found 373 across 105 files, including a live API key repeated 60 times and a plaintext password. The script is a single file with no dependencies. The write-up is mostly about the four bugs that got past a green test suite.
Five years of image search in one place. CLIP made text a query for pictures, compression got a proof and moved inside the graph, cross-modal queries turned out to break the assumption every index was built on, and storing a vector fell from memory prices to object-storage prices. A sourced synthesis, with no original benchmarks.
A measured MLX vs llama.cpp comparison on a 512GB Mac Studio M3 Ultra at a 234,158-token prompt. mlx-lm 0.31.3 decodes 1.49x faster than llama.cpp b10200 and ties on prefill, while llama.cpp uses 29% less system-wide memory and outranks every MLX serving stack measured on decode. The llama.cpp arm is a single unrepeated run, and this post is mostly about why that matters.
Sixteen model and engine configurations at a real 262,000-token prompt on a 512GB Mac Studio M3 Ultra. Nemotron-3.5-Lightning-30B-A3B wins at 60.45 tok/s decode with 8/8 needle recall, and it wins by degrading slowly: its decode falls 1.83x from 2k to 262k where a dense 14B falls 10.68x. Architecture beats parameter count, a 1M-native model fails at 1M, and all five recall failures I published turned out to be my own harness.
My LLM benchmarks kept collapsing 4x mid-session. macmon caught a firmware power limiter clamping the GPU to 338 MHz and holding it while the die cooled, fans never past 70%. Pinning them at max with fanpro: 2.57x sustained decode, and a 100k-context job in 259 s instead of 568+, byte-identical.
Last post I called local 1M context a mirage. Then Kimi-Linear-48B passed: 8/8 needle recall at a real 1,048,692-token prompt on a 512GB M3 Ultra, full ingestion proven. Native 1M is finally real. It just takes a five-hour prefill.
I ran a 754B model on a 512GB Mac Studio. It fits, it crawls, and its 1M context flag is a warning message and a silent cap. Where local models fail as coding tools, and the one job they are great at.
Easy-Switch moves the keyboard and abandons the mouse. One HID++ packet fixes that, once you get past the two permission gates macOS hides between you and your own devices.
Two switches I leave on: the advisor tool and /effort ultracode. An honest, log-backed account of where they help, where they are overkill, and where the model wisely skips them.
An opinionated Cloudflare-first template. Next.js 16, D1, Better Auth, Stripe. No monorepo. No edge-runtime religion. No status page.
Head-to-head on a Mac Studio M3. 26 prompts, 6 categories, and one surprising finding about thinking mode overhead.
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