Most people who code with AI do the same dance. You prompt the agent, wait, check the result, then prompt

I’ll admit something. For the longest time I treated AI prompts like a legal contract. A giant wall of “don’t

This shipped quietly last week. A trust layer system for LLM outputs, four states, MIT license on GitHub. The person

Empty isn’t always broken. Sometimes it’s the most deliberate thing you can send. A developer ran an experiment with 11

Quick summary: a psychology grad built a Bayesian reasoning prompt that forces ChatGPT to hold multiple competing solution paths open

Four months of documented research just dropped, and the model ranking at the end is going to start some arguments.

Here’s the premise: one tiny system prompt occasionally swaps the word “which” with “witch” so you can never fully trust

Most people blame the model when AI debugging goes sideways. Wrong diagnosis. The model isn’t the problem. The context is.