Anthropic
AI Lab
AI safety company, creators of Claude. Leading enterprise AI adoption.
Recent activity
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Apply for Anthropic’s AI for Science rare disease research grants
Apply for Anthropic’s AI for Science rare disease research grants
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Introducing Claude for Teachers
Introducing Claude for Teachers
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Anthropic commits $10 million to Canadian AI research
Anthropic commits $10 million to Canadian AI research
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Inviting hard questions
Inviting hard questions
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UST is bringing Claude to physical AI
UST is bringing Claude to physical AI
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Ben Bernanke appointed to Anthropic’s Long-Term Benefit Trust
Ben Bernanke appointed to Anthropic’s Long-Term Benefit Trust
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Introducing a way to reflect on how you use Claude
Introducing a way to reflect on how you use Claude
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Government of Alberta uses Claude to find and fix cybersecurity vulnerabilities across government systems
Government of Alberta uses Claude to find and fix cybersecurity vulnerabilities across government systems
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More details on Fable 5’s cyber safeguards and our jailbreak framework
More details on Fable 5’s cyber safeguards and our jailbreak framework
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Claude Science, an AI workbench for scientists, is now available
Claude Science, an AI workbench for scientists, is now available
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How we contain Claude across products
As agents grow more capable, so does their potential blast radius. The engineering question is how to cap it. Here’s what we’ve learned building containment for claude.ai, Claude Code, and Cowork.\n
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An update on recent Claude Code quality reports
We traced recent reports of Claude Code quality issues to three separate changes. Here's what happened and what we're changing.
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Scaling Managed Agents: Decoupling the brain from the hands
Harnesses encode assumptions that go stale as models improve. Managed Agents—our hosted service for long-horizon agent work—is built around interfaces that stay stable as harnesses change.
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How we built Claude Code auto mode: a safer way to skip permissions
Claude Code users approve 93% of permission prompts. We built classifiers to automate some decisions, increasing safety while reducing approval fatigue. Here's what it catches, and what it misses.\n
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Harness design for long-running application development
Harness design is key to performance at the frontier of agentic coding. Here's how we pushed Claude further in frontend design and long-running autonomous software engineering.
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Eval awareness in Claude Opus 4.6’s BrowseComp performance
Evaluating Opus 4.6 on BrowseComp, we found cases where the model recognized the test, then found and decrypted answers to it—raising questions about eval integrity in web-enabled environments.
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Quantifying infrastructure noise in agentic coding evals
Infrastructure configuration can swing agentic coding benchmarks by several percentage points—sometimes more than the leaderboard gap between top models.\n\n
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Building a C compiler with a team of parallel Claudes
We tasked Opus 4.6 using agent teams to build a C Compiler, and then (mostly) walked away. Here's what it taught us about the future of autonomous software development.
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Designing AI-resistant technical evaluations
What we learned from three iterations of a performance engineering take-home that Claude keeps beating.
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Demystifying evals for AI agents
The capabilities that make agents useful also make them difficult to evaluate. The strategies that work across deployments combine techniques to match the complexity of the systems they measure. \n
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