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§ 001 / WRITING
Local LLMs, GPU sizing, GGUF quants, and what AI agents cost. The local-AI numbers come from dated runs on hardware I own. Every other figure names its source.
Working with coding agents? Verify their work and protect your acceptance checks.
§ 002 / START HERE
Real costs, real tools, no fluff. One evidence-backed note on Friday when there is something worth sharing.
Get the requested artifact now, then at most one evidence-backed Local AI Lab Note on Friday when there is something worth sharing. One-click unsubscribe. No sponsored placements. Privacy.
§ 003 / LATEST WRITING
real test outputThree Llama 3.1 8B runs missed a 400-word floor. Here is the verifier-driven route that moved long-form synthesis to Gemma 4 26B.
Read the postOn 2026-10-17, I killed five Windows scheduled tasks and moved nightly queues to seven Codex cron jobs. The security audit stayed red across five repos.
On 2026-10-24, I shipped a paid GPU purchase review page with the payment link turned off. Here is why dead plumbing beats fake demand data.
OpenClaw release v2026.10.4 repairs sleep restarts and Tlon permissions, following skill discovery, MCP Apps, and R2 backup updates in October 2026.
Learn the exact meaning of Q4_K_M in llama.cpp, how its mixed tensor precisions work, and how to verify if it fits your consumer GPU VRAM budget.
Compare Q6_K and Q8_0 presets for llama.cpp. See real VRAM allocation, file sizes, and test steps to decide which GGUF quantization fits your card.
Learn how to set -ngl in llama-server, verify GPU layer offload in startup logs, avoid silent CPU fallback, and check device readiness.
On 2026-10-14, I rebuilt BMD Desktop around queues, cut 63 open requests down to 10, and faced an overnight log full of red security audits.
On 2026-10-03, CI stalled on full disks before a twelve-character string cleared production. Here is how I removed myself from the merge loop.
On 2026-10-15, my 90-day horizon closed red across seven bets. Here is what broke in my fleet, what shipped, and how to audit your checks.
In llama.cpp, ngl 99 sets a numeric cap of 99 layers in GPU memory rather than 99 percent VRAM. Here is how layer offloading works and what to run.
q4km is Q4_K_M and q4ks is Q4_K_S. For Llama 3.1 8B, the quantize README lists 4.36 GiB for Q4_K_S and 4.58 GiB for Q4_K_M.
Compare q4_0 and q8_0 KV cache options in llama.cpp server. Learn the exact CLI flags, allowed types, defaults, and how to verify output quality.
Real costs, real tools, no fluff. One evidence-backed note on Friday when there is something worth sharing.
Get the requested artifact now, then at most one evidence-backed Local AI Lab Note on Friday when there is something worth sharing. One-click unsubscribe. No sponsored placements. Privacy.