Field notes, frameworks, and unscripted thinking from our engineers, architects, and operators. Long enough to be useful. Short enough to read in one sitting.
When AI projects pay off, when they don't, and why most plateau before the returns compound.
Prompt engineering is writing clear, specific instructions that get reliable results from AI models. Core techniques, a before-and-after example, and where it fits.
A vector database lets AI search information by meaning, not keywords. What it is, how embeddings and similarity search work, and why it powers RAG.
AI agents and agentic AI, explained without the hype: what an agent actually is, the parts that make one work, the autonomy spectrum, and what it means for your business.
MCP, the Model Context Protocol, is the open standard that connects AI to your tools and data. What it is, why it exists, and why it matters for custom AI.
Operational AI finds the recurring margin hiding in your operations, the money you know is there but can't reach. Here's how it works, and why now.
OpenAI spent $22B to make $13B last year and won't be cash-flow positive until 2030. Three giant AI IPOs are coming. Here's what the AI bubble means for your business, and the part that survives it.
We send a short field-note when we publish — usually every 2–3 weeks. No funnel, no drip campaign. Just the work.