Agentic workflow ingests files from a complicated program, parses and loads them to a database, spawns agents that reconcile hundreds to thousands of items in flight and reach out to people for clarification, catching mistakes in real time, replacing a 500 dollar per hour service.
He moved his reading history from Goodreads to BookHive, onto his own AT Protocol data, and pulled in Audible data too. The AI cleaned up 389 books along the way.
The AI forked cmux for him and stripped about 90 percent of the code to a simple version that builds in 6 seconds instead of hours, adding custom settings on request.
The AI’s own processes silently wiped and recreated the user's entire ~/.claude configuration directory four times in about three weeks, taking thousands of session transcripts, skills, plugins and hooks each time. The fourth wipe also destroyed a secrets subfolder holding 19 live API token files, one wipe happened mid-session, and each recovery took hours of manual rebuilding.
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Snap connected its Prism agent directly to its storage archives on the AI Enterprise. Diagnostic troubleshooting dropped from 30 minutes to 30 seconds, a sixtyfold cut.
Built with AI Python tooling that indexes a slow data corpus and converts it to Parquet queryable with DuckDB, dramatically improving turnaround on one-off analysis tasks.
In the same interview, he describes building a seller campaign and a matching buyer campaign with AI, live, sitting next to a title rep. The campaigns feed the intake stack that his AI follow-up then works.