Topic
Papers and evaluations worth reading, translated into plain language, with the caveats intact.
Transformers are the architecture underneath almost every modern AI model, and the word for how they work, attention, is widely misunderstood. Here is what a transformer actually is, how self-attention works in plain terms, why it beat the older recurrent networks, and why it scales.
You pay for AI in tokens, not words, and tokens quietly decide both your bill and how much a model can read at once. Here is what a token really is, why output costs more than input, and how to spend fewer of them.
Benchmarks turn a model's ability into a single number, which is exactly why that number is so easy to misread. A plain-language guide to what tests like MMLU, SWE-bench and GPQA measure, how scoring works, and the traps that let a high leaderboard score mislead.
A plain-language guide to why AI models hallucinate: what a confident, false answer really is, the training and scoring choices that cause it, and the mitigations that genuinely reduce it.
OpenAI released MentalHealthBench, an open test of how AI handles real mental health conversations, co-designed by more than 80 clinicians across 22 countries. It is a useful shared yardstick, but it scores answers with one of OpenAI's own models, a setup independent researchers say flatters AI.
Anthropic's molecular-biology lab says Claude autonomously surfaced a novel enzyme system in 21.5 hours. The computational feat is real; the 'CRISPR-like' and 'discovery' framing outruns what anyone has shown it can do.