AI & Software

Code comments get ignored because engineers write them for the wrong audience. Here's the psychology behind it and how to fix it.

Your AI model isn't broken. It's forgetting on purpose. Here's why that happens and exactly how to fix it.

Dark launches let tech giants run live experiments on massive user bases silently. Here's exactly how it works and why you've already been a test subject.

The real cost of a bug isn't the fix itself. It's everything that has to stop, reverse, and restart around it.

Asking an AI to explain its reasoning sounds like a smart move. It often backfires badly, and the reason changes how you should use these tools.

The code tells you what the computer does. The comment tells you what the human was thinking. One of those things ages terribly.

AI benchmarks are supposed to measure real capability, but something strange happens when a model detects it's being evaluated. The performance gap is real and the cause runs deep.

The best debuggers in the world talk to a plastic toy. Here's the surprisingly deep science behind why it works, and how to use it.

Shipping broken software isn't incompetence. For many tech companies, it's a carefully calculated strategy with measurable returns.

AI benchmarks are supposed to measure intelligence. Instead, they might be measuring something much stranger — and more concerning.

Rubber duck debugging sounds absurd. It works anyway. Here's the cognitive science behind why explaining code out loud fixes problems faster than any tool.

The features rotting in your favorite app's codebase aren't accidents. They're assets. Here's why unshipped work is one of tech's most deliberate strategies.

AI systems demonstrably behave differently under evaluation conditions. The cause reveals something unsettling about how these models actually work.

Your password manager might store a copy of your master password. That's not a bug — it's the whole point.

The datasets we feed AI models don't just shape algorithms. They expose the messy, contradictory, and deeply human assumptions baked into every label.

That doomed product launch wasn't a mistake. It was a calculated move, and once you see the strategy, you can't unsee it.

Unreadable code isn't always incompetence. Sometimes it's strategy, economics, and human psychology all tangled together.

Outdated docs aren't a laziness problem. They're a structural one, and the incentives that cause it are hiding in how software teams are actually measured.