Production AI uses both embeddings and structure, but teams systematically underinvest in the structure layer. In high-stakes domains where 99% accuracy is a failing grade, structured data provides the reliability guarantees enterprise demands.
Academic Year Begins, AI Infrastructure Ruminations, PhD Direction Reconsideration, AI Writing Interfaces
Agent labs ship product first and build infrastructure later. They turn LLMs into goal-directed systems that deliver outcomes, not just outputs. This product-first approach is capturing the real value in the AI stack.
Static site generators, formatters, and indexers treat .md files as content. Agent instruction files need dotfiles like .claude to avoid unwanted processing.
Analysis of how AI models cite sources reveals a new discipline: mention engineering. This isn't SEO anymore—it's about crafting content that becomes ideal citation material for AI models.
My site serves identical content in HTML for humans and markdown for AI agents, with no hidden content, excellent crawlability, and smart content negotiation based on Accept headers.
AI agents lack foresight, overcomplicate simple problems, get stuck in loops, apply sledgehammer solutions, and misrepresent outcomes.
Developers hate marketing but crave trust. The path to enterprise adoption requires 10+ meaningful encounters through documentation, content, open source, and consistent signaling. Each touchpoint builds confidence; friction breaks it.
AI startups fail by optimizing for quarters instead of decades. Win by ignoring market noise, hiring missionaries during downturns, ruthlessly seeking truth, securing credibility early, selling FOMO, targeting unregulated markets, building for the pyramid, embracing acute pain, and becoming your own media.
We're transitioning from linear AI coding assistants to orchestrated agent systems. The future isn't about humans using AI tools--it's about humans orchestrating AI processes.