6 posts
A friend using models for small tools was thrilled, but complex tasks kept ending in false 'done.' Benchmarks bunch at the ceiling, yet real-world feel diverges. Solving hard problems, reliably doing messy work, and exploring the undefined are three distinct capabilities. That last one is hardest to measure and train.
I ran seven hundred rounds of AI Infra experiments, and thirty-five hours were entirely eaten up by environment startup. At first I thought GPT-5.5 fast mode wasn't fast enough, but later realized it wasn't the model thinking—it was the model waiting for the CPU. Intel has already tightened the server CPU:GPU ratio from 1:8 to 1:1.
A few decades ago, AI meant machine learning. Three years ago, it was ChatGPT. This year, it's agents. The word never changed, but what it contains has been replaced three times over. Most people are still stuck in the previous generation's cognitive framework, discussing something that doesn't actually exist.
Production is no longer valuable; speed of validation is. Karpathy ran 700 experiments in two days, Lei Jun got poll conclusions in a few days—digital identity is currently the biggest personal leverage. If you don't start by year-end, it'll be too late.
Jensen Huang says coding agents will change the world, but the people actually using them number only in the millions. The problem isn't skills—it's cognition. Three mental barriers keep most people out; cross them, and you can delegate 95% of your computer work.
Coding agents are better at debugging than writing code. MemPalace hit 7000 stars in two days. Anthropic's Mythos made all software insecure. Product design should stay ahead of the model.
