
Anthropic Launches Claude Corps, a $150M AI Fellowship
Anthropic commits $150 million to place 1,000 AI-trained fellows inside US nonprofits for a year at $85,000 each, no degree required.
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Anthropic commits $150 million to place 1,000 AI-trained fellows inside US nonprofits for a year at $85,000 each, no degree required.

A new impossibility theorem proves feedback-based training can't guarantee honest AI, while two papers cut agent memory costs 78% and multi-agent latency 7x.

A former xAI engineer filed a California whistleblower lawsuit claiming he was fired for warning that Grok could spread dangerous content - the day before SpaceX prices its historic $75B IPO.

Three new arXiv papers expose how context bloat tanks agent performance, agent memory bleeds private data, and misaligned behavior spreads through multi-agent systems.

Claude Fable 5 delivers the strongest coding and long-context results Anthropic has ever shipped publicly, but its safety classifiers block enough legitimate work to make that power conditional.

Claude Fable 5 is Anthropic's first publicly available Mythos-class model, with safety classifiers that fall back to Claude Opus 4.8 for high-risk requests across cybersecurity, biology, and chemistry.

Anthropic opens Mythos-class capabilities to the public with Claude Fable 5 at $10/$50 per million tokens, days after calling for a global AI pause.

PM Mark Carney's AI for All commits $2.3 billion to hit 250,000 new jobs and 60% business adoption by 2034 - but critics call it a wish list without hard delivery mechanisms.

Three papers: strategic attack timing exposes gaps in AI control evaluations, Perplexity's agents slash task time by 87%, and Lean4 formal proofs make agent workflows more reliable.

Anthropic published internal data showing Claude writes 80% of its own codebase - and called for a coordinated global AI pause - four days after filing a $965B IPO.

OpenAI's new Lockdown Mode cuts the network exits that prompt injection attacks use to steal data from ChatGPT - but won't stop malicious instructions from entering the model in the first place.

Three new arXiv papers expose how developers miss AI sabotage 94% of the time, why LLMs converge structurally in code evolution, and how ZK proofs could verify frontier AI training.