AI & Software

Model collapse is real, measurable, and already happening in the wild. Here's what it looks like mechanically and why it matters.

AI models display confidence scores as if they're meaningful quality signals. They're not. Here's what's actually happening under the hood.

LLMs generate code by predicting plausible text, not by reasoning about execution. Understanding that gap changes how you use them.

AI coding tools produce fluent, well-structured code. They also produce bugs rooted in context they never had. Here's how to work with that limitation.

Every line of code you keep is a liability. The best engineers know that removing code is often the highest-value work they can do.

LLMs sound equally certain whether they're right or wrong. That's not a bug to be patched — it's a structural feature of how they work.

AI models don't hedge at the edges of their knowledge. They extrapolate smoothly and state the result as fact. Here's what's actually happening.

Bigger AI models aren't always better. Smaller, focused models are often faster, cheaper, and more accurate for real tasks.

More instructions feel like more control. They're often the opposite. Here's what actually happens when you pile rules into a system prompt.

You write prompts like instructions. The model reads them like a probability problem. That gap explains a lot of bad outputs.

AI writing tools are getting genuinely impressive. That's exactly why they're quietly degrading the cognitive skill they're supposed to support.

Softmax converts raw model scores into probabilities. But what it actually does to those scores in the process is stranger and more consequential than most explanations let on.

A race condition that vanished under a debugger taught one team something most engineers learn too late: observation changes what you're measuring.

Your LLM can technically read a novel. Whether it actually processes that novel is a different question entirely.

Most developers treat temperature as a creativity dial. It's actually reshaping token probabilities in ways that compound across every word the model generates.

Code-generating AI models predict text that looks like working code. That's not the same as knowing whether the code works.

A medical AI startup learned the hard way that high confidence scores don't predict accuracy. They predict familiarity. That distinction costs lives.

Writing code is a creative act with a blank canvas. Debugging production is forensic work with half the evidence missing and a clock running.