Practical insights on production ML, AI agents, and what actually works when shipping AI at scale. No sponsored content. No fluff.
Knowledge graphs give AI agents what vector search alone can't — structured relationships, multi-hop reasoning, and temporal memory. Here's what the architecture looks like.
Key takeaways from Google Cloud's Agent Advantage event — from Gemini 3 reasoning to why only 12% of agents reach production.
Four failure modes from 10+ years shipping ML in production — silent pipeline failures, real-time serving issues, undetected model drift, and misaligned integration. With the specific fix for each one.
One weekend. A model-agnostic AI marketing system built with Claude Code and OpenClaw. Social media strategy, SEO rewrites, and email campaigns — automated. The architecture, the decisions, and what it revealed about the AI gap.
The Linkby story — from zero to full production data platform with three people and AI-augmented engineering. How a one-pizza team can outship a conventional team three times its size.
Where agents actually deliver versus where they fail silently — and what the teams getting real value are doing differently. From someone who has deployed them at AWS and Linkby.
Three key factors that shape a solid foundation for an ML platform — cloud portability, simplicity of tooling, and notebook collaboration. Lessons from scaling teams to 80+ data scientists.
How to build a high-volume AI automation system that doesn't go bankrupt on API costs. The three-layer architecture of OpenClaw — interface, orchestration, and cost-routing.
80+ nodes, 100+ connections, 6 categories. A force-directed knowledge graph of the AI landscape — and three structural insights you can't see from reading the news.