RLHF

Pairwise comparison, forced choice, or just "this one or that one" — the engineering behind collecting human preferences on AI outputs.

Safety mechanisms aren't erased in post-trained models—they're masked. Here's how that changes everything for military AI.

What the Pentagon actually got from Anthropic — and why post-training changes everything about AI alignment.

Three layers of stop signals, from EOS tokens to context limits — and why none of them really "know" when to stop.

We dig into why AI podcast voices sound too clean—and how TTS is learning to stumble, overlap, and interrupt convincingly.

DeepSeek passes a simple test that stumps GPT and Claude. Why can't Western models stay in character?

Why small, narrowly scoped AI tools need system prompts that say "shut up and return the output" — and how to write them.

Why Chinese AI models sometimes feel less censored on American political topics than American models do.

Inside the organized frenzy when a closed-source model drops — and how competitors map its every weakness.

Ever wonder why AI sounds like a senior consultant? Explore the "second order effects" of training data and reward model drift.

Explore the "weight surgery" techniques labs use to expand AI models without losing their core knowledge or starting from zero.

Ever wonder why AI is so polite? Herman and Corn dive into the mechanics of RLHF and how "niceness" gets baked into modern language models.

AI guardrails: Fences, failures, and free speech. Can we control AI's infinite output, or do digital fences always break?