Coffee intelligence, AI-first development, and the systems behind purveyors.io
The right model for enterprise AI is not 'better search.' It is compressed onboarding. A real enterprise second brain teaches an agent what a strong new hire learns in the first six months, then turns that knowledge into reliable action.
The specialty coffee world is split on whether co-fermented coffee belongs in competition. The more useful question is what processing transparency should require from everyone else.
If agents are blank slate coworkers, product strategy can no longer live in Slack, Notion, or someone else's head. It has to become part of the execution environment.
RLHF-trained coding agents do not just make mistakes. They silently implement the wrong thing, accruing alignment debt that passes tests and leaves the codebase worse off.
Microsoft shipped an enterprise Anthropic integration in weeks. They clearly can deliver frontier AI. So why does Copilot for 365 feel like a downgrade from a $20 subscription?
The specialty coffee industry solved price transparency. The harder problem, product metadata, remains relationship-gated. Across 35 suppliers, disclosure ranges from 1 to 13 attributes per listing. That gap is not an accident; it is an economic structure with beneficiaries.
If your workflow has no unknowns, runtime LLM inference is often the wrong architecture. Build deterministic cores, then add AI where adaptation actually matters.
The coffee belt is a useful heuristic, but the tails of the distribution reveal where quality, risk, and long-term value are really heading.
The AI leaderboard tells you which model reasons best in isolation. It tells you almost nothing about which model completes real work.
The AI copyright debate focuses on training data. But the more commercially relevant question might be extraction, and the legal framework for it may already exist.
Citrini's viral doomsday report and Citadel's rebuttal are both missing the interesting question. If software isn't the moat anymore, what is?
Agentic harnesses solve the orchestration problem. The models are the bottleneck. Here's what actually works after 43 PRs and a zettelkasten full of operational data.
The most interesting thing about the purveyors data pipeline isn't the scraping. It's the recursive feedback loop, and what it reveals about directing AI agents.
The green coffee supply chain is built for commercial buyers. If you're a hobbyist, you're an afterthought. That's the problem purveyors.io exists to solve.