robotnurseSeries
01Local StackDebugging a home LLM rig the hard way: skills that wouldn't load, config edits that silently did nothing, and the failure modes that ate whole days before the real cause surfaced.
› open series02QuantizationBits are not free. A practical tour of q4, q5, q8, and fp16 — what the numbers actually mean to your latency, your memory, and your output.
› open series03Inference Enginesllama.cpp, vLLM, and friends side by side. Same model, same machine, different results — measured, not marketed.
› open series04The Model ZooWhich open-weights models are worth the disk space right now, what they're good at, and what they hallucinate with confidence.
› open series05Hardware NotesReal numbers from real machines: memory bandwidth, thermal throttling, VRAM budgets. The spec sheet lies; the clock speed doesn't.
› open series06Running It DailyKeeping a local model honest over months, not minutes: updates, drift, tooling rot, and the mundane upkeep no blog post mentions.
› open series07Buyer's GuideThe decision side of local AI: what a setup actually costs, HIPAA and client-confidentiality constraints, three-year TCO, and sizing a box for a small office — measured, not guessed.
› open series08StandaloneLonger field notes that don't fit a series: a private LLM running in a small law firm, and 45 measured local-inference benchmark results collected in one place.
› open series