A company that did not exist eight months ago just became one of the most important names in Anthropic's supply chain.
Anaconda has spent a decade as the plumbing beneath enterprise Python. Now it is betting on the next problem: not whether AI agents move fast, but whether anyone can prove what they are doing.
Google reshaped the command structure of its most valuable asset, elevating a little-known engineer to run its AI empire and moving Demis Hassabis into a chairman's seat.
The world's largest maker of electric vehicles wants to sell you a car. Increasingly, it also wants a robot to do the selling.
Google.org has poured another $15 million into the debate over how to govern artificial intelligence, naming a fresh cohort of think tanks and academic institutions.
For two years, Microsoft's pitch to the corporate world was to put a copilot in front of every employee and let it run. This month, it started telling its own engineers to slow down.
When Simon Willison pushed version 0.32 of his open-source llm tool on August 4, he called it the most significant new version since the project's launch.
Ask a large language model for ten story ideas and you usually get one idea wearing ten outfits. Closing that gap is the target of a 2026 decoding method called Exploratory Sampling.
When an AI agent has three tools to choose from, picking the right one is trivial. Give it thirty, and something quietly breaks.
A language model asked to plan ten steps ahead has had only one place to think: the token stream. A wave of 2026 research argues that is the wrong substrate for planning.
The pitch for multi-agent systems has been intuitive: if one language model is smart, a team should be smarter. The reality has been messier.
Give a modern coding agent a well-specified experiment and it will often run the thing end to end. A new benchmark asks a harder question: would you trust it as a research intern?
A federal appeals court handed the AI industry its first major appellate win on whether software agents can roam the web on a user's behalf.
The Trump administration gathered the biggest names in AI at the White House to walk them through a finished national framework for voluntary safety testing of the most powerful models.
When the Rust project finally wrote down its rules for AI, the entire philosophy fit in a single sentence: help, refine, review, but do not create.