Neuroscientist Lisa Genova explains why forgetting is normal — your brain filters out most of your day, loses context when you change rooms, and is terrible at remembering future intentions. I watched her TED talk and realized I had already encountered every one of these failures in AI agents I build for real organizations. Here is how the parallels work, what breaks in production, and what I built to fix it.

Andrej Karpathy tweeted about using LLMs to build personal knowledge bases — raw sources in, compiled wiki out, all in Obsidian. I implemented his entire workflow in one session using Claude Code skills. Four YouTube transcripts became 21 cross-linked wiki articles. The system now compiles new sources, health-checks its own consistency, and searches itself. It took an afternoon. It will compound forever.

I run an autonomous AI agent on a Mac Mini in my house. She handles 16 daily cron jobs — finances, email triage, outreach campaigns, device monitoring, morning briefings. The agent says “done.” But did it actually do anything? I built a 9-dimension eval rubric to find out. Along the way I discovered that my evals were broken, my agent was better than I thought, and the most important metric isn’t pass/fail — it’s whether a failure is your fault or the agent’s fault.