Enhancing Document Search with Visual RAG

📖 TUTORIALS & CASE STUDIES

Connect Cursor to MCP Servers in Minutes

Estimated read time: 15 min

This practical guide explores integrating Model Context Protocol (MCP) servers with Cursor IDE, enabling developers to extend their AI assistant's capabilities. Learn how to connect multiple MCP servers, leverage built-in authentication, and create custom implementations for enhanced development workflows using tools like GitHub, Notion, and Gmail.

Run LLMs in the Cloud with Docker and Azure

Estimated read time: 18 min

For developers wanting to experiment with AI models without straining their local machines, this step-by-step tutorial demonstrates how to set up Ollama and its web UI in Docker containers on Azure VMs. The guide covers Azure CLI automation, security configurations, and container deployment, enabling efficient testing of multiple models in the cloud.

Building AI Agents with CrewAI and CopilotKit

Estimated read time: 25 min

This detailed walkthrough demonstrates how to create, deploy, and integrate AI agents using CrewAI and CopilotKit. Developers will learn to build a restaurant finder application that showcases human-in-the-loop capabilities, real-time state management, and frontend UI integration, providing practical insights into modern agent development.

Evaluate AI Responses with Streamlit and Cosine Similarity

Estimated read time: 8 min

This hands-on guide shows how to create a Streamlit evaluation tool that automates AI response assessment using cosine similarity and GPT. The tool helps developers evaluate thousands of model outputs against reference responses, providing visualization and confidence scores for performance analysis.

Add Memory to Cursor IDE with Graphiti

Estimated read time: 8 min

This developer guide demonstrates how to upgrade Cursor IDE with persistent memory using Graphiti's Model Context Protocol. The integration enables AI coding assistants to retain project requirements, coding preferences, and specifications across sessions, making workflows more efficient and context-aware.

Enhancing Document Search with Visual RAG

Estimated read time: 15 min

This implementation guide demonstrates how to improve retrieval-augmented generation systems by combining visual document processing with intelligent agents. Using HuggingFace's smolagents framework, developers can build sophisticated search applications that leverage LLMs for multi-step reasoning, source verification, and improved query processing.

🧰 TOOLS

Qdrant's Vector Search for Code Context

Estimated read time: 12 min

Qdrant's MCP server enables developers to create semantic memory layers for LLM applications using vector search. This implementation supports code snippet storage, retrieval, and context management, integrating seamlessly with development tools like Cursor and Claude Desktop. The server leverages FastEmbed models for efficient vector embeddings.

Gemini 2.5: Google's Advanced Reasoning Model

Estimated read time: 4 min

Google has launched Gemini 2.5, their most advanced AI system featuring enhanced reasoning capabilities and a 1M token context window. The experimental 2.5 Pro version leads industry benchmarks and excels at complex coding tasks, including web app creation and code transformation. Developers can access it through Google AI Studio and Vertex AI.

Test MCP Applications with This Versatile CLI

Estimated read time: 15 min

MCP Tools provides a versatile command-line interface for developers working with Model Context Protocol servers. This toolkit enables testing, mocking, and proxying of AI model interactions, featuring interactive shells and multiple transport methods. Perfect for developers building RAG systems or applications requiring structured model interactions.

DeepSeek-V3-0324 Released

Estimated read time: 2 min

Deepseek has release a new milestone model; DeepSeek-V3-0324. It brings significant improvements in various benchmarks remaining toe to toe with models such as GPT 4.5 and Claude Sonnet 3.7.

Choosing the Right LLM Framework for Your Project

Estimated read time: 25 min

This detailed analysis compares three major frameworks: LlamaIndex for efficient data retrieval, LangChain for flexible application development, and Hugging Face's smolagent for code-based reasoning. Learn their strengths, trade-offs, and ideal use cases to make informed decisions for your next AI project.

📰 NEWS & EDITORIALS

The Hidden Mechanics of LLMs: How Claude Plans, Reasons, and Sometimes Fabricates

Estimated read time: 18 min

Anthropic's groundbreaking research reveals the internal workings of Claude, showing how LLMs plan ahead, process multiple languages through a shared concept space, perform parallel computations, and occasionally fabricate reasoning. These insights help developers understand model limitations and could lead to more transparent, reliable AI systems—critical knowledge for those building RAG frameworks or agent-based applications.

OpenAI Embraces Anthropic's MCP Standard

Estimated read time: 4 min

In a significant development for AI integration, OpenAI has announced support for Anthropic's Model Context Protocol. This open-source protocol simplifies LLM integration with external systems, reducing implementation time to under an hour. The support will roll out across ChatGPT Desktop, Responses API, and Agents SDK.

AI Functions as Team Member, Study Shows

Estimated read time: 12 min

A groundbreaking study from P&G reveals that AI can serve effectively as a teammate, not just a productivity tool. The research found that individuals using AI performed as well as traditional two-person teams, while AI-enabled teams produced the highest quality solutions and reported better emotional experiences. This has significant implications for how developers should approach integration.

Claude 3.7 Sonnet's 'Extended Thinking' Mode: A Developer's New Secret Weapon

Estimated read time: 10 min

Anthropic's latest Economic Index reveals software developers are among the top users of Claude 3.7 Sonnet's new "extended thinking" mode. The report shows increased usage for coding applications, with technical professionals representing the highest percentage of this advanced capability. The analysis also examines augmentation vs. automation patterns and includes a taxonomy of 630 usage categories, with datasets freely available for developer experimentation.