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4 posts on ai.

Graph RAG vs vector RAG: when the graph earns its keep

Vector retrieval answers the question of what resembles this text, while a graph answers what is connected to it, and the two fail on different questions. The benchmarks say the difference is large on relational questions and close to nothing on lookups, which makes this an accounting decision rather than an architecture debate. What each one costs, the hybrid I actually run, and how to decide before you buy an index.

The questions similarity search can't answer

The assistant on this site can find anything that resembles your question. It cannot yet follow a chain of facts spread across three notes. Why that failure is structural, what a knowledge graph actually is, and how I plan to wire one into the studio's retrieval without tearing out what works.

AI slop, and the files that keep it out of my projects

Every AI project drifts toward the same fonts, the same gradients, the same sentences. Mine do not, because every agent I run inherits a small set of law files. Here is the whole playbook, structure and all.

The agency that never sleeps: running a business on AI loops

Loop engineering is taught for building software. The bigger idea is using the same loop to run the business: point an agent at a number, give it a scoreboard it can check itself, and let it improve your SEO, ads, and AI visibility for months while you sleep.

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