Large language models and foundation models: releases, benchmarks, capabilities, pricing, and the labs behind them.
Most people get mediocre answers from AI because of vague prompts. A few simple techniques — being specific, giving examples, and asking for step-by-step reasoning — reliably improve results.
Our inaugural weekly roundup: why agents dominate the conversation, what the local-model ecosystem's maturity means for builders, and the regulatory deadlines coming into focus.
Perplexity answers questions with concise, cited summaries drawn from the live web — positioning itself between a search engine and a chatbot. Here is what it does and where it fits.
Before GPT dominated headlines, BERT showed that pretraining a Transformer to read text in both directions could transform language understanding. Here is what the landmark 2018 paper introduced.
The 2020 GPT-3 paper made a startling claim: make a language model big enough and it learns new tasks from a few examples in the prompt, with no retraining. Here is what it showed.
Every modern language model — GPT, Claude, Gemini, Llama — descends from one 2017 paper. Here is what 'Attention Is All You Need' actually proposed, in plain English, and why it changed everything.
LLMs power ChatGPT, Claude, and Gemini — but what are they, really? This beginner guide explains how they work, what they're good and bad at, and the vocabulary you need, with zero math.
The three major AI assistants are closer in quality than the marketing suggests — which means your choice hinges on ecosystem, workflow, and use case. A practical decision framework.