Your interaction style with AI reveals what you think you know. Someone asks an AI for five options and picks the first one. Someone else asks it to explore a direction and actually reads…
You’re not training the model. The model is training you. This is the dynamic nobody talks about. Every interaction with an AI system changes how you think. The system gives outputs. You interpret them….
The fear is wrong. AI won’t replace experts. It will expose non-experts. When a tool becomes powerful enough, mediocrity gets revealed. An expert with AI moves faster and deeper. A mediocre person with AI…
The centaur metaphor from Kasparov’s chess experiments was right for the wrong reasons. When computers beat the best human players, Kasparov realized something unexpected: a decent human plus a decent computer vastly outperformed either…
You don’t use a partner. You work with one. We’ve been thinking about AI wrong. The default metaphor is tools—calculators, search engines, productivity boosters. Tools serve. You command them, they obey. The relationship is…
If you’re a small team trying to align a language model, RLHF is probably overkill. DPO does the same job with less infrastructure, less compute, and fewer moving parts. Here’s why. The RLHF Pipeline…
RLHF works by aligning model outputs to human preferences. But preference alignment is surface-level optimization. What we need is architecture-level alignment — systems whose internal structure naturally produces aligned behavior without external reward signals….
Transformer architectures use selective attention: focus computation on relevant tokens, filter out noise. It works, but it’s limited. Selective attention is reactive. It responds to what’s in the input without active selection based on…
Error correction in neural systems requires two things: detecting when output diverges from intent, and adjusting for context. Machine learning models struggle with edge cases because they process literal signals. A human with genuine…
Language models suffer from a fundamental pathology: they over-commit to patterns learned during training, then apply those patterns regardless of context. This is the technical core of overfitting, sycophancy, mode collapse, and a dozen…