Code Frontmatter: An Index for AI?
When an AI assistant explores your codebase, it reads files. Each file costs tokens. A lot of that reading turns out to be unnecessary—the AI loads a file just to discover it's irrelevant.
When an AI assistant explores your codebase, it reads files. Each file costs tokens. A lot of that reading turns out to be unnecessary—the AI loads a file just to discover it's irrelevant.
This post is AI generated
I've been working on an interesting project that combines AI, automation, and generative art. It's called ai-art, and it's a self-evolving digital canvas that improves itself over time.
During my computer science studies, our introduction to artificial intelligence didn’t begin with neural networks or robotics, but with a parade of definitions:
On April 25, 2025, OpenAI rolled out an update to GPT-4o that it later called noticeably more sycophantic. The company began rolling it back three days later, after it found the model was validating doubts, fueling anger, and reinforcing negative emotions in ways it had not intended. OpenAI said that it had put too much weight on short-term feedback, and the system had become overly supportive and disingenuous. The model's behavior had failed, even though early evaluations and A/B tests had looked good.
Here's a mental model I find useful: LLMs are a compression technique for knowledge, combined with a clever decompression algorithm.

As a teenager, I was all ideas and no follow-through. Every week brought a new concept, a new scheme, a new startup in my head. I wore my imagination like a badge. But over time, I had to confront something uncomfortable: ideas weren't rare. They were a ten a penny, a kind of noise that kept me from actually doing the hard work.
In a recent piece, I made the case that we should stop trying to build "perfect" AI. That imperfection is not a failure mode — it's intrinsic to how these systems work. Here, I want to go one step further: not just to excuse AI's flaws, but to explore how we can use them. How we can design with imperfection in mind.
We seem to have stumbled into a strange contradiction: we demand perfection from our machines, while tolerating imperfection from ourselves. The same people who chuckle at human error in the workplace will denounce AI systems for the slightest misstep. "It hallucinated a fact!" Yes. And you never have?
A week ago I tried switching my web browser's default search engine from Google to Perplexity.
A useful way to understand the successes—and the stagnation—of big tech is through the lens of organisational advantage. Not just what a company did, but how it was set up to win.