LLM

Thousands of AI interviews a day, dozens of teams calling LLM APIs, one sane bill. Here’s the system. Our CTO has one of the cooler awards you’ll see in an office: a plaque from OpenAI for crossing 10 billion tokens. I first saw it a year back, visiting our Santa Clara office. I love that plaque. It also mildly terrifies me. Ten billion tokens is not a milestone you hit by accident. It means AI is wired into everything we ship, and every one of those tokens was paid for. When you burn tokens at that scale, managing the budget well isn’t a finance chore. It’s the thing that decides whether you get to keep building. ...

Voice agents are easy to demo and hard to scale. The architecture we ended up with fires every possible response in parallel and lets a fast router pick the winner mid-flight. This post walks through everything we tried before it, and why this design is the one we kept. The problem nobody’s demo solves Every voice agent demo looks the same: clean audio, a cooperative user, a short scripted exchange. Then you ship it, and two things break immediately. ...

In 1990, Jeffrey Elman ran a small experiment with a tiny recurrent network. No billion parameters. No attention. No massive datasets. Just a simple question: If a model learns to predict the next word, can it discover structure on its own? The answer changed how we think about intelligence. The Experiment: Learning Grammar Without Rules Elman trained a simple recurrent network (now called an Elman network) on sentences generated from a small artificial grammar. ...