Tag: Anthropic

Anthropic estimates that robots can perform 74% of US physical tasks in some settings, yet are cost-competitive for only 0.3% of work today. This analysis explains the tiers, cost assumptions, and implications for deployment decisions.

Anthropic proposes three measurements for AI-led R&D: automation, agent oversight, and safety compute; this article separates its internal self-report from methodology limits and cross-lab comparability.

An engineering reading of Anthropic's Claude-assisted optimization of more than 30 biomolecular and genomics models, from FlashPairformer and Big mode to stock/exact/fast contracts, cost curves, and evidence limits.

Anthropic's Managed Agents now expose session budgets, advisors, inference geography, repository skills, and inference hooks as runtime controls; this article maps their value and remaining boundaries.

Anthropic removed over 80% of Claude Code's system prompt for Claude Opus 5 and Claude Fable 5 without losing benchmark performance. Explore the paradigm shift from rigid instruction constraints to progressive disclosure, auto-memory, and rich reference harness engineering.

Anthropic has released Claude Tag, designed specifically for team collaboration. By tagging @Claude in Slack, AI becomes a virtual teammate that proactively participates in discussions, executes asynchronous tasks, and continuously learns. This article details its core features, usage, target audience, and billing model.

Anthropic releases a privacy-preserving analysis of 400,000 Claude Code interactions. The research reveals the true division of labor for AI coding agents: humans decide 'what to do', while AI decides 'how to do it'. More importantly, success depends not on 'coding ability', but on 'domain expertise'. This has profound implications for the future of knowledge work.

A reading map for long-running agent harnesses—setup, verification, handoff—and how to read related notes on this site.

Harness Engineering, in Martin Fowler's terms, means outer control loops around coding agents—feedforward guides plus feedback sensors—so trust is designed control, not a feeling.

A deep dive into Nicholas Carlini's experiment: nearly 2,000 sessions, about $20,000 in API costs, and a 100,000-line Rust compiler capable of compiling Linux 6.9—exploring task locking, test harnesses, GCC oracle, multi-role specialization, and capability boundaries.

A summary based on Anthropic's 'Labor market impacts of AI: A new measure and early evidence': introduces the 'observed exposure' metric, explains which occupations are most exposed to AI, its relationship with employment growth and unemployment rates, and implications for policy, businesses, and individual careers.