This article provides a hands-on guide to Anthropic’s Model Context Protocol (MCP), an open protocol designed to standardize connections between AI systems and data sources. It covers how to set up and use MCP with Claude Desktop and Open WebUI, along with potential challenges and future developments.
Notte is an open-source browser using an agent, designed to improve speed, cost, and reliability in web agent tasks through a perception layer that structures webpages for LLM consumption. It offers a full stack framework with customizable browser infrastructure, web scripting, and scraping endpoints.
This article details an iterative process of using ChatGPT to explore the parallels between Marvin Minsky's "Society of Mind" and Anthropic's research on Large Language Models, specifically Claude Haiku. The user experimented with different prompts to refine the AI's output, navigating issues like model confusion (GPT-2 vs. Claude) and overly conversational tone. Ultimately, prompting the AI with direct source materials (Minsky’s books and Anthropic's paper) yielded the most insightful analysis, highlighting potential connections like the concept of "A and B brains" within both frameworks.
Claude Code is an agentic coding tool by Anthropic that operates in your terminal, understanding and modifying your codebase through natural language commands. It streamlines development workflows by executing commands, fixing bugs, and managing Git operations without requiring additional servers or complex setup.
Anthropic's new feature allows specifying a public URL for images/documents in their API, improving performance and usability. The article details implementation and successful testing with Claude 3.7 Sonnet.
This speculative article explores the idea that GPT-5 might already exist internally at OpenAI but is being withheld from public release due to cost and performance considerations. It draws parallels with Anthropic's handling of a similar situation with Claude Opus 3.5, suggesting that both companies might be using larger models internally to improve smaller models without incurring high public-facing costs. The author examines the potential motivations behind such decisions, including cost control, performance expectations, and strategic partnerships.
MCP is an open-source standard that enhances interaction between AI systems and various data sources, improving usability, response quality, and security.
"Contextual Retrieval tackles a fundamental issue in RAG: the loss of context when documents are split into smaller chunks for processing. By adding relevant contextual information to each chunk before it's embedded or indexed, the method preserves critical details that might otherwise be lost. In practical terms, this involves using Anthropic’s Claude model to generate chunk-specific context. For instance, a simple chunk stating, “The company’s revenue grew by 3% over the previous quarter,” becomes contextualized to include additional information such as the specific company and the relevant time period. This enhanced context ensures that retrieval systems can more accurately identify and utilize the correct information."
Last week, Anthropic announced a significant breakthrough in our understanding of how large language models work. The research focused on Claude 3 Sonnet, the mid-sized version of Anthropic’s latest frontier model. Anthropic showed that it could transform Claude's otherwise inscrutable numeric representation of words into a combination of ‘features’, many of which can be understood by human beings. The vectors Claude uses to represent words can be understood as the sum of ‘features’—vectors that represent a variety of abstract concepts from immunology to coding errors to the Golden Gate Bridge. This research could prove useful for Anthropic and the broader industry, potentially leading to new tools to detect model misbehavior or prevent it altogether.
An article discussing the concept of monosemanticity in LLMs (Language Learning Models) and how Anthropic is working on making them more controllable and safer through prompt and activation engineering.
