But it didn’t all go smoothly for Team Claude.At one point their robodog nearly barrelled into Team Claude-less (unintentionally, we should add). And their algorithm to detect the green beach ball went awry after their dog confused it for the green “grass” in the studio. ...

Team Claude got further and did so faster. They also wrote a lot more code. Having access to Claude meant they could try lots of approaches in parallel, but also made it easier to get distracted by side quests. https://t.co/ZXEHG7LZAY ...

New Anthropic research: Project Fetch.We asked two teams of Anthropic researchers to program a robot dog. Neither team had any robotics expertise—but we let only one team use Claude.How did they do? https://t.co/1pAZyLh3Fa ...

For the first time, Anthropic is building its own AI infrastructure.We’re constructing data centers in Texas and New York that will create thousands of American jobs.This is a $50 billion investment in America.https://t.co/hmJXNODQsG ...

We’re opening offices in Paris and Munich.EMEA has become our fastest-growing region, with a run-rate revenue that has grown more than ninefold in the past year. We’ll be hiring local teams to support this expansion.Read more here: https://t.co/JyDXiJil0k ...

Agent Efficiency - Anthropic Engineering blog provides tips on building more efficient agents [1] - The tips focus on handling more tools while using fewer tokens [1] Technology & Protocol - Introduction of Code execution with the Model Context Protocol (MCP) [1]

Even when new AI models bring clear improvements in capabilities, deprecating the older generations comes with downsides.An update on how we’re thinking about these costs, and some of the early steps we’re taking to mitigate them: https://t.co/VCTMW0d2e8 ...

We're announcing a partnership with Iceland's Ministry of Education and Children to bring Claude to teachers across the nation.It's one of the world's first comprehensive national AI education pilots: https://t.co/BC0Ziw27r8 ...

For more of Anthropic’s alignment research, see our Alignment Science blog: https://t.co/0UHuZdvWd9 ...

Reasoning Performance of LLMs - LLMs' reasoning performance significantly reduces when reasoning is obfuscated using simple ciphers [1] - LLMs struggle to reason accurately in ciphered text, except for simple ciphers [1] Ciphered Language Processing - LLMs can easily decode ciphered text to English [1] - Research involved testing 10 LLMs across 28 ciphers [1]