Eric Raymond's 1997 essay argued that decentralized, bazaar-style development would outcompete centralized cathedral models. Nearly three decades later, the bazaar won — but not the way Raymond imagined. The cathedral came back wearing bazaar clothes, and the efficiency gains from free infrastructure created more software, not less. What happened, and what it means.
Certain processors — the Z80, 68000, 6502, 8051 — have survived for four decades and counting while thousands of competitors vanished. This isn't nostalgia. It's a story about ISA simplicity, second-sourcing, toolchain inertia, and why the economics of "good enough" beat the engineering of "better" nearly every time.
Part one of a two-part series on designing a custom dual-Z80 RetroShield PCB. This piece covers the design process: choosing a shared-bus architecture, creating new schematic symbols, modifying the PCB layout programmatically, autorouting with Freerouting, and generating production Gerber files — all done over SSH without touching a GUI.
If AI follows a Jevons trajectory — and five pieces in this series argue that it does — the investment question isn't whether demand will expand, but where the expansion creates bottlenecks. This piece maps the Jevons framework onto concrete investment layers: energy, physical infrastructure, custom silicon, and the application tier. It also addresses the most common objection — that GPU diminishing returns cap the expansion — and explains why multiple overlapping cost curves make the case stronger, not weaker.
I used Claude Code with Opus 4.6 and the Claude Agent SDK to generate three technical handbooks — totaling over 2,400 pages and 232 chapters — from real project source code. The key was a framework that launches 10-12 AI agents in parallel, each reading actual codebases and writing LaTeX chapters simultaneously. This post describes how the system works, what goes right, what goes wrong, and what it means for the future of technical documentation.
Steve Yegge's "The AI Vampire" describes AI-driven burnout as an extraction problem — companies capture the productivity surplus while workers absorb the cognitive toll. But what he's actually describing is Jevons Paradox applied to human attention. AI makes cognitive output dramatically cheaper, demand for it expands exactly as the model predicts, and the expansion concentrates on the one input that can't scale: human judgment.
