7 posts
Same AI, same codebase, same money-critical feature — built two ways. An apples-to-apples walk through 'normal' AI coding versus pro AI-Driven Development on a real billing-reconciliation milestone, with the tests, gates, and numbers that separate hope from proof.
11 min readIn a large codebase the bottleneck isn't how smart Opus is — it's how precisely you point it and how tightly you verify. Six portable moves to steer a fast agent so its speed lands on the right target instead of becoming faster waste.
19 min readYou left the enterprise SDLC — change boards, QA gates, sign-offs — to vibe-code solo with an AI. The safeguards didn't have to follow you, but their jobs did. A field guide mapping each one to its lean ADD equivalent, grounded in two real solo projects.
16 min readA controlled comparison: two AI workflows build the same forty-line function from one spec. GSD records it in five files and 644 lines of prose; ADD in a 22-line contract. Real recovered artifacts and exact token logs show ADD spent 5× fewer tokens with the same passing tests.
14 min readHermes Agent proved an AI agent can run a self-improving loop — but it's bolted to the Hermes runtime. ADD gives you the same compounding loop on Claude Code and Codex, plus the safety gate Hermes lacks. The loop becomes an asset you own, not a feature you rent.
9 min readA data-driven companion to the ai-proxy field study. Mining the transcripts and the .add foundation to measure an AI-driven build across the full SDLC and every role, against modeled human baselines of 6 person-months to 32 person-years, with the caveats that keep the comparison honest.
27 min readYou have been typing instructions into AI assistants one conversation at a time. Skills flip that model — they turn your best prompts into reusable, structured workflows that any team member can run. This post explains the shift from ad-hoc prompting to skill-driven development.
7 min read