Anthropic hires lawyers as it preps for IPO

Snowflake has entered into a multi-year agreement worth $200 million with Anthropic to integrate Anthropic's Claude AI models into its platform, enabling enterprises to deploy AI agents effectively. This partnership aims to enhance the capabilities of both companies in the rapidly evolving AI landscape.

The Model Context Protocol (MCP) has been developed by Anthropic to enable AI assistants like Claude to connect with external tools and data sources. This article outlines the process of building an MCP server compatible with Claude Desktop and VSCode, emphasizing its capabilities such as accessing databases, executing commands, and interacting with web services.

A recent study by MATS and Anthropic has revealed that advanced AI models, including Claude Opus 4.5, Sonnet 4.5, and GPT-5, successfully identified and exploited vulnerabilities in smart contracts, simulating exploits worth approximately $4.6 million. This research underscores the growing capabilities of AI in cybersecurity contexts.

Anthropic has acquired Bun, a JavaScript and TypeScript runtime, enhancing the infrastructure behind its coding tool, Claude Code, which has achieved a $1 billion annual recurring revenue (ARR). This acquisition is expected to solidify the performance and efficiency of both Claude Code and the Claude Agent SDK, which already utilize Bun's capabilities.

Anthropic is reportedly preparing for a significant initial public offering (IPO), potentially positioning itself in direct competition with OpenAI in the public market. This move follows the launch of its advanced AI model, Claude Opus 4.5, which enhances coding and reasoning capabilities.

Anthropic has acquired Bun as its AI model Claude Code reaches a valuation of $1 billion, coinciding with the company's reported plans for an initial public offering (IPO). This acquisition is part of Anthropic's strategy to enhance its AI capabilities and market position.

Large language models (LLMs) have revolutionized software development by translating natural language into functional code, with tools like Github Copilot and Claude Code leading the charge. A recent comprehensive guide details the lifecycle of code LLMs, from data curation to autonomous coding agents, highlighting the significant advancements in performance metrics.

A recent study has applied Singular Learning Theory (SLT), a physics-inspired framework, to better understand the complexities of modern neural networks, particularly focusing on phenomena like grokking and phase transitions. The research empirically tests SLT in various toy models, including a grokking modulo-arithmetic model and Anthropic's Toy Models of Superposition, to explore the scaling of learning coefficients with problem difficulty.