Architecture

In the last two posts (about perceptual hashes in the context of Chat Control and about their adversarial collisions), we explored how 40 lines of Python can solve a problem that the industry often deploys at a global scale. Perceptual hashes are the canonical example of a cheap deterministic layer: no models, no GPUs, no heavyweight dependencies. Hashes handle duplicate and similarity detection. But in a real-world image processing pipeline, there’s work that classical algorithms can’t do: detecting bounding boxes of arbitrary watermarks, filling gaps with inpainting that respects visual context, classifying whether a face is of an adult, or extracting semantic attributes. This work requires large neural networks. ...

I published six articles this week. One about PostgreSQL. Another about AI agents. Another on context management. A tutorial on automation. An analysis of debugging. And an adversarial advice framework for evaluating MVPs. It wasn’t planned. Each article came from a paper, a talk, or a project that I found interesting individually. But looking at them together, there’s a common thread I hadn’t noticed while writing them. All six are saying the same thing. ...

The first time I used Claude Code to refactor an entire module, I had an almost mystical experience. I described what I wanted, went to grab a coffee, and when I came back there was a pull request with 14 changed files, updated tests, and a decent commit message. “This is magic,” I thought. It’s not magic. It’s a while loop. Michael Bolin from OpenAI recently published an article breaking down the internal workings of Codex CLI. And it turns out the secret behind AI coding agents isn’t some revolutionary algorithm or mysterious neural network. It’s a loop that calls an LLM, executes tools, and repeats until there’s nothing left to do. ...