We tested Microsoft BitNet b1.58 on a base M2. Metal gives 12 t/s. CPU-only produces gibberish. The real value of 1.58-bit is RAM, not speed.
A systematic study: 5 pipeline variants, training data leakage via Cyrillic text, and why the "sandwich" approach is a workaround, not a fix.
Three FLUX.2-klein LoRAs (space art, ukiyo-e, logos) and a personal DoRA adapter on Qwen3-8B. With training configs and repro steps.
Why «add a chatbot» and «put an agent inside the process» are two different jobs with different outcomes.
How to turn «we want AI» into «we locked the number and we own it». Step by step.
The model is 10% of the project. The other 90% is data, integrations, and SLA. Why the ones who get this win.
Not mysticism, not a PR slogan. Just math: where exactly in the business AI takes load off, and how to measure it.
