The systems behind the channel itself. Each build follows one framework: the thinking constraint that was blocking progress, the KPI that measures the improvement, and the AI system that solved it. Client & enterprise work lives on the Cases page.
Building each episode page took 2–4 hours of focused writing + assembly. I couldn't publish without being present and uninterrupted.
Jacob-time per episode build. AI reads the brief and builds the page unsupervised overnight.
Nightly engine: claude -p runs on a Task Scheduler cron. It reads a brief I drop at 20:30, asks clarifying questions via Telegram, then builds the full episode HTML and leaves a morning handoff note.
Before I could use a video’s claims in content I had to sit through the whole thing — a gating step on every piece of research.
Extract + full fact-check. Found income claims were 4× inflated vs. real market rates.
The /watch skill: yt-dlp downloads, ffmpeg extracts 80 frames, Whisper transcribes 565 segments, then three parallel research agents cross-verify the claims against external sources.
The site was built for human eyes. Agents scraped it badly and missed key content, blocking any workflow that needed site context.
npx tellingtechnology — any AI reads the entire site from a single call, in full context.
llms.txt manifest, JSON site index, an npm CLI package, and an MCP server — so any Claude instance can query the full site content without touching the HTML.
Long sessions degraded silently as context filled. I only noticed when output quality dropped — too late to act cleanly.
Silent context deaths. Two live token bars in the terminal show exactly when to /compact before quality drops.
statusline.js — a Node script that polls Claude Code’s internal session state and renders a real-time cockpit bar: model, effort level, context fuel, and session-token fuel.
Colleagues hit repetitive bottlenecks they didn’t know AI could remove. No one on the team could diagnose the problem, let alone prescribe a fix.
Colleagues now bring problems to me. Went from nobody knowing who to ask → the team’s default first call for anything AI-shaped.
No single artifact — the pattern. Applied constraint-first thinking inside one complex product company: audited real blockers, shipped targeted fixes, measured before/after. Results visible to the team compounded into trust.
“I’m not going to find this job on LinkedIn — because it doesn’t exist yet.”
I scope one constraint at a time, build the AI system to remove it, and measure the before/after.
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