AI & Software

Code generation models predict plausible syntax, not working software. Understanding that distinction changes how you should use them.

That percentage your AI tool displays isn't a real probability. It's a number that looks trustworthy because numbers look trustworthy.

The AI industry is shipping powerful tools with the same blind spots that haunted web apps in the early 2000s. We should know better by now.

AI summarizers are fluent and fast, but they optimize for what's statistically central, not what's actually important. Here's what that costs you.

If a bug only shows up once and then vanishes, that's not luck. That's a system telling you something about its own hidden complexity.

Between your words and the model's attention lies a pipeline that silently rewrites your request. Understanding it changes how you work with AI.

Bigger AI models aren't always better. Smaller, focused models are faster, cheaper, and often more accurate at the tasks that actually matter.

Semantic search feels like magic. The mechanics underneath are concrete, learnable, and worth understanding if you're building anything with AI.

A fintech team shipped faster than ever with AI assistance. Then a senior engineer left and nobody could explain the codebase. A cautionary pattern worth taking seriously.

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.