AI

TLDR: Everyone is excited about multi-agent swarms. Few are talking about the actual bottleneck. This post is about that bottleneck: memory. We now have cheap inference, capable models, and production-ready orchestration frameworks. But when you scale to 10+ agents working in parallel across days of iteration, the coordination breaks down, not because the models are dumb, but because nobody has solved how agents store, share, and retrieve context at scale....

I was overly pessimistic about AI for a while. I harped on the harms, ethical shortcomings, the ways this tech could be misused or cause real damage. I still hold those concerns. I assure you they haven’t disappeared. But I had a profound experience this week that shifted something fundamental in how I’m thinking about this. I deployed my first multi-agent swarm I had a vague product idea. No detailed spec, just a rough concept....

What’s currently top of mind for me: AI is advancing insanely fast. Faster than any of us ever expected. The CEO of Anthropic predicted that by the end of 2025 over 90% of all software will be written by AI. Whether that number proves true or not, the trajectory is clear. I don’t think enough of us in university are thinking critically about the implications of this, as it will radically disrupt our lives in the next decade, regardless of what industry or profession you are in....