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Anthropic and Millennium are co-developing a digital risk analyst using Claude to identify risk insights and analyze exposure across various asset classes. This demonstrates a high-value application of LLMs in complex financial risk modeling.
Anthropic launches Inference Hooks for Claude Enterprise, allowing compliance teams to inspect and enforce policies on prompts and tool calls in real-time. This provides an inline DLP layer across Claude Code, Cowork, and chat surfaces.
Anthropic provides a comprehensive guide for IT admins to manage Claude spending. Key features include spend caps, usage analytics, and API optimizations like prompt caching.
Claude is integrating the latest MCP 2026-07-28 specification, moving toward a stateless core with standardized extensions. This rollout will enhance how Claude products interact with external data and tools.
Anthropic has drastically reduced Claude Code's system prompt for newer models. This guide shares key lessons in context engineering for building more efficient and capable AI agents.
Anthropic provides a comprehensive guide on selecting the optimal Claude model based on workload requirements. It covers the trade-off between cost per task and cost per token, and emphasizes the importance of building custom evaluations.
Claude's voice mode now runs across Opus, Sonnet, and Haiku models, enabling more natural multitasking and real-time problem solving. It now integrates with connected tools and supports a wider range of languages for developers and users.
Anthropic is expanding its Claude Certification Program with role-based paths. This initiative aims to standardize the deployment of AI across diverse team functions to ensure successful customer implementation.
Learn how to convert manual verification checks into reusable skills within Claude Code, allowing the agent to autonomously close its own feedback loops and verify its work.
Outtake demonstrates how to maintain stability and focus in multi-hour agent sessions to uncover complex attack network operations using Claude.
Anthropic shares its approach to securing an AI-native SDLC where AI authors 80% of merged code. The post outlines the security engineering patterns needed to maintain safety and quality in a high-automation environment.
Datadog developed a deterministic kernel system that allows Claude Code to write specifications and then execute application code reliably. This serves as a blueprint for creating higher-reliability AI coding workflows.
Rakuten leverages Claude Fable 5 to rapidly build and deploy agents, signaling a step change in model intelligence for long-running, complex tasks. Showcases the real-world application of next-gen models in enterprise agent automation.
Anthropic's Claude Fable 5 demonstrates significant breakthroughs in agentic coding, outperforming benchmarks used by Cursor. This marks a new frontier in the model's ability to handle complex, real-world programming tasks.
Anthropic's Deputy CISO outlines a strategic framework for managing the risks of agentic AI deployments. The guide focuses on bounding agent actions and maintaining auditability to balance innovation with security.
Anthropic shares a detailed guide on using Claude Code for large-scale codebase migrations, featuring a case study on Bun's million-line Zig-to-Rust port. This highlights the practical power of agentic coding tools for complex, systemic refactoring.
Introduction to Claude Fable 5 within the Claude Cowork environment, designed for managing long and complex autonomous workflows. The post provides strategies for maximizing the model's capacity for independent, multi-step work.
Case study on Base44 leveraging Claude Fable 5 for complex engineering tasks. Highlights the capability of next-gen models to handle high-stakes product development and frontier-level coding challenges.
Anthropic showcases how Claude Fable 5 is being used by Hebbia for high-accuracy financial diligence. The model achieves significant accuracy gains in complex domain-specific evaluations.
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Cognition integrated Claude Fable 5 into Devin, achieving a breakthrough where the model can run unattended for eight hours to deliver production-ready code. This marks a significant step in the reliability and autonomy of AI software engineers.