AI & Software

Vector databases don't understand language. They store coordinates. What happens between those two facts explains a lot about where AI search succeeds and fails.

Prompt engineering gets dressed up as a new discipline, but it's really just debugging a system whose source code you can't read.

You write code in English-like syntax. The CPU speaks binary. Here's what happens in between, and why it matters more than you think.

When models train on AI-generated text, small errors compound into something worse than noise. This is a structural problem, not a data hygiene issue.

LLMs don't flag uncertainty, they just answer. That gap between confidence and accuracy is where real damage happens.

Fine-tuning promises to make a general model fit your specific needs. It frequently does the opposite. Here's why, and what to do instead.

Some bugs vanish the moment you try to observe them. This isn't magic — it's physics, timing, and a category of errors that your debugging tools can actively hide from you.

Spotify's recommendation problem reveals the core trick behind modern AI: words and concepts become vectors in geometric space, and similarity becomes distance.

A bug fix feels like progress. Sometimes it is. Sometimes it quietly breaks the three things that were working around the bug you just removed.

Spotify's recommendation engine didn't start with listening habits. It started with text. That choice reveals what embeddings actually are.

Engineers spend weeks optimizing inference. Meanwhile, the real latency culprit sits quietly in preprocessing, I/O, and orchestration code nobody's benchmarked.

Heisenbugs vanish the moment you try to observe them. Understanding why they exist is the first step to catching them.

When you craft a prompt carefully, you're not writing instructions. You're adjusting a model's behavior through its input layer. That distinction matters.

Your variable names mean nothing to the machine running your code. They mean everything to the person maintaining it at 2am.

Adding more information to your AI prompts seems like it should help. Often it makes things noticeably worse. Here's the mechanics of why.

Most AI output problems aren't model failures. They're prompt failures. Here's how to stop blaming the tool and fix the actual problem.

Most teams fine-tune a model expecting it to learn new information. That's not what happens. Here's what actually changes inside the model.

When a bug only appears in production, it's not bad luck. It's a signal that your test suite is modeling the wrong world.