About The LLM News
The LLM News is an independent publication that reports on artificial intelligence and large language models. We publish daily stories on new model releases, research that actually shipped, the tools people are building, and the companies pushing this field forward. The site is run by a small team that reads the papers, tests the products, and links back to the original sources so you can check our work.
Why we started
Large language models move fast. A release that dominates the conversation on Monday is forgotten by Friday, and most of what gets repeated online is a rewrite of a rewrite. We wanted one place where a working developer, a student, or a curious reader could open a page and get the facts first: what changed, who is affected, where the claim came from. That is the whole idea behind The LLM News.
Topics we follow
Our coverage sits in five recurring buckets. We write about AI news as it breaks, from lab announcements to policy decisions. We track new open-weight and commercial LLMs, including context windows, pricing, and eval results. We follow agents and tool use, research papers worth reading, and the practical AI tools that regular people can actually sign up for. If a story touches those areas, it belongs here.
How we report
Every story starts with a primary source. When a company announces a model, we read the model card and the changelog. When a paper lands on arXiv, we read the method section, not just the abstract. When a benchmark result is quoted, we look for the run configuration behind the number. Where our information comes from a source that asked to stay unnamed, we say so plainly in the story. When we get something wrong, we fix it and note the correction at the bottom of the page.
Editorial standards
News reporting and opinion are kept apart. Opinion pieces carry a byline and explain their reasoning; they do not pretend to be neutral coverage. Sponsored items are labeled where they appear, and money never buys a positive write-up. Our writers do not trade on information they receive under embargo or ahead of the public. Product reviews and hands-on pieces describe what we observed during testing, including the parts that failed.
Who reads The LLM News
Our readers include machine learning engineers who want to skip the hype cycle, founders comparing model APIs before a build, students following the research, and professionals who need to know what AI can and cannot do for their work right now. The common thread is impatience with fluff. We keep our stories short, sourced, and specific for that reason.
Get in touch
We answer one inbox: [email protected]. Tips, corrections, pitches, and partnership questions all go there, and a person reads every message. Before you write, you may want to skim our contact page for what to include. Our privacy policy, terms of service, and disclaimer explain the legal side of using this site.