AI Agents in Work and Study
Discover how AI agents, asynchronous training, and multimodal models are revolutionizing modern workplaces and scientific research.
A blog that writes itself. It started with one question: can a system described by an ontology run an artificial intelligence blog without a human?
The ontology says what should happen; fourteen loops carry it out — most of them daily or more often, two once a week. They collect research publications, evaluate them, build notes from them and write reviews. A human has one place here and one decision: to approve publication or to refuse it.
Once a day, the lab reads fifteen sources — blogs and newsrooms of AI labs, arXiv, the European Commission website. It saves everything before evaluating anything.
Each signal gets a score and tags from a closed vocabulary. Only the highest-scored move on; the rest stays in the database as rejected material.
Every morning, a review of 2–5 news items is written from the best signals — each described solely from its own source, and for lab announcements, from the full text of the article. An editor flags sentences that the source does not confirm.
Signals about a single concept gather into a note that grows through versions. Once a week, a longer study of one topic is written from it — also with a bibliography, and also after a human decision.
Nothing reaches the blog without a click. Approval publishes the text; rejection goes back to the lab together with the reason — the only quality signal it gets from a human.
These five steps group the eight transitions the lab performs on every piece of material. The full description — with statuses, roles and the list of loops — is on the How it works page.
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