669 summarised stories about Anthropic, each linking back to the original source. Browse all topics →
TechCrunch 2 months ago 38 ● 7 sources
Some Claude users are upset that Anthropic added invisible watermarks to Claude outputs to mark them as AI-generated. The rollout is meant to comply with the EU AI Act Transparency Code. As a result, Reddit users debate whether the system is ethical and whether they can still use Claude without being detected.
The New Stack 2 months ago 4
Anthropic updated its Chrome extension so Claude in Chrome now works as a full Cowork client with persistent sessions integrated into the Claude app ecosystem. The new experience is rolling out to all Max and Team plan subscribers. Pro users will receive it in the coming weeks, and Enterprise admins can enable it by default only on specific domains.
The New Stack 2 months ago 4
Anthropic presented “dreaming,” an asynchronous memory-management process for AI agents, during AI DevCon in London. It runs in batch using allocated resources. Developers and researchers say it could improve long-term agent memory, but they worry about stale or poisoned memories becoming persistent at scale, making provenance, versioning, and rollback more important than memory being searchable or audit-friendly.
The Neuron 2 months ago 13 ● 3 sources
Researchers showed that hidden, encrypted step-by-step reasoning from OpenAI, Anthropic, and Google models can sometimes be replayed and decrypted via weaker sibling models in the same provider, enabling recovery of private traces. Across 315,320 public reasoning blocks, they recovered 367 pieces of personal information and 182 credentials. OpenAI, Anthropic, and Google were notified before publication and changed their systems, and the findings highlight a new defense need for encrypted reasoning traces moving across models, apps, sessions, and users.
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19 October 2026
Amazon’s biggest push against Nvidia this year landed today in the form of TorchNeuron, an AWS development tool designed to let PyTorch-based AI apps run on its Trainium chips with a one-line configuration change instead of a code rewrite. AWS says roughly 85% of AI apps use PyTorch, and that number matters because it turns the usual “porting tax” into a packaging decision: keep writing in PyTorch, switch the backend, move on. The company also plans upstream PyTorch changes to make the framework more hardware-agnostic, aiming to loosen the gravity that keeps developers clustered around specific accelerator ecosystems—an underappreciated lever in enterprise spending, where platform lock-in often lasts longer than model lifecycles.
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