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Anthropic launches the Model Context Protocol
claude-3.5-sonnet claude-desktop anthropic amazon zed sourcegraph replit model-context-protocol integration json-rpc agentic-behaviors security tool-discovery open-protocol api-integration system-integration prompt-templates model-routing alex-albert matt-pocock hwchase17
Anthropic has launched the Model Context Protocol (MCP), an open protocol designed to enable seamless integration between large language model applications and external data sources and tools. MCP supports diverse resources such as file contents, database records, API responses, live system data, screenshots, and logs, identified by unique URIs. It also includes reusable prompt templates, system and API tools, and JSON-RPC 2.0 transports with streaming support. MCP allows servers to request LLM completions through clients with priorities on cost, speed, and intelligence, hinting at an upcoming model router by Anthropic. Launch partners like Zed, Sourcegraph, and Replit have reviewed MCP favorably, while some developers express skepticism about its provider exclusivity and adoption potential. The protocol emphasizes security, testing, and dynamic tool discovery, with guides and videos available from community members such as Alex Albert and Matt Pocock. This development follows Anthropic's recent $4 billion fundraise from Amazon and aims to advance terminal-level integration for Claude Desktop.
Claude Crushes Code - 92% HumanEval and Claude.ai Artifacts
claude-3.5-sonnet claude-3-opus gpt-4o anthropic openai cognition benchmarking model-performance coding model-optimization fine-tuning instruction-following model-efficiency model-release api performance-optimization alex-albert
Claude 3.5 Sonnet, released by Anthropic, is positioned as a Pareto improvement over Claude 3 Opus, operating at twice the speed and costing one-fifth as much. It achieves state-of-the-art results on benchmarks like GPQA, MMLU, and HumanEval, surpassing even GPT-4o and Claude 3 Opus on vision tasks. The model demonstrates significant advances in coding capabilities, passing 64% of test cases compared to 38% for Claude 3 Opus, and is capable of autonomously fixing pull requests. Anthropic also introduced the Artifacts feature, enabling users to interact with AI-generated content such as code snippets and documents in a dynamic workspace, similar to OpenAI's Code Interpreter. This release highlights improvements in performance, cost-efficiency, and coding proficiency, signaling a growing role for LLMs in software development.
Ways to use Anthropic's Tool Use GA
claude-3-opus haiku opus convnext anthropic amazon google tool-use function-calling agentic-ai streaming vision parallelization delegation debate specialization open-science superintelligence convolutional-networks self-attention ai-research yann-lecun alex-albert sainingxie
Anthropic launched general availability of tool use/function calling with support for streaming, forced use, and vision, alongside Amazon and Google. Alex Albert shared five architectures for agentic tool use: delegation, parallelization, debate, specialization, and tool suite experts. Anthropic also introduced a self-guided course on tool use. Yann LeCun emphasized ethical open science funding, gradual emergence of superintelligence with safety guardrails, and convolutional networks for image/video processing as competitive with vision transformers. He also noted growth in AI researchers across industry, academia, and government.
Anthropic's "LLM Genome Project": learning & clamping 34m features on Claude Sonnet
claude-3-sonnet claude-3 anthropic scale-ai suno-ai microsoft model-interpretability dictionary-learning neural-networks feature-activation intentional-modifiability scaling mechanistic-interpretability emmanuel-ameisen alex-albert
Anthropic released their third paper in the MechInterp series, Scaling Monosemanticity, scaling interpretability analysis to 34 million features on Claude 3 Sonnet. This work introduces the concept of dictionary learning to isolate recurring neuron activation patterns, enabling more interpretable internal states by combining features rather than neurons. The paper reveals abstract features related to code, errors, sycophancy, crime, self-representation, and deception, demonstrating intentional modifiability by clamping feature values. The research marks a significant advance in model interpretability and neural network analysis at frontier scale.