Series
Multi-part deep dives into AI engineering projects, from concept to production.
A series that extends the NerveReflex research project. It tests AI that reads decisions and signals directly from internal representations such as hidden states, instead of generating text, with measured results on real tasks.
An evolutionary forge where a cheap frozen LLM writes candidate code and a deterministic verifier scores it — reaching results that rivaled large-scale RL, on one desktop for a few dollars. From Atari Pong's theoretical ceiling (21-0 in all 40 games) to a cache eviction policy that beat twenty years of classics on production traces.
Measuring outcome per dollar for LLM agents — KOI (= KPI ÷ cost) — with the open-source tool Nishiki. From the VOI/KOI concept to hands-on measurement on real tasks like Text-to-SQL.
A hands-on verification of whether OpenClaw — a much-discussed AI agent platform — is actually usable and secure in production. Covers cost traps, model selection, agent runaway, and multi-agent design, for anyone considering adopting AI agents.
Migrating a virtual try-on system from PASTA-GAN++ to Gemini and Vertex AI — replacing an entire GPU pipeline with API calls and resolving the body diversity problem that GANs could never solve.
Building an ML-powered meal planning system to solve the daily struggle of nutritious, varied cooking — from cosine similarity to LSTM to ChatGPT.