#research

Many Agents, Many Problems (The Agents Season, Ep. 7)

Why scaling up multi-agent AI systems doesn't deliver proportional benefits — collaboration turns out to be a distinct capability, and adding agents to sequential tasks often makes things worse.

Loved the exploration of coordination cost — the idea that agents communicating and handing off work isn't free, and that overhead often swamps any gains from parallelism.

Zen and the Art of Machine Learning Research

Temperament matters more than talent in AI research — a meditation on the daily practice of reading and building, and why equanimity is the real prerequisite.

The Zen framing isn't just a metaphor — he quotes Suzuki directly, structures the piece like numbered koans, and the equanimity point is genuinely it: sit with failure the same way you sit with success, neither attached to the outcome.

Agent coordination has a real cost that scales with task sequentiality

via Many Agents, Many Problems — Linear Digressions

Temperament matters more than talent in AI research

via Zen and the Art of Machine Learning Research — Jack Morris