Community Picks·48wReact library for LLMs
llm-ui is a React library that provides UI components for integrating Large Language Models into web applications. It works universally with any LLM model by operating on the model's output string, supporting popular services like ChatGPT, Claude, Ollama, Mistral, Hugging Face, and LangChain. The library aims to simplify the process of displaying LLM responses in React-based user interfaces.
The Pragmatic Engineer·50wStack overflow is almost dead
The engagement on Stack Overflow has significantly decreased, with the number of questions asked monthly hitting levels from its early days in 2009. Key factors contributing to its decline include enhanced moderation efficiency reducing question flow starting in 2014, the impact of ChatGPT offering quick and polite answers trained on Stack Overflow data since November 2022, and outdated moderation policies. Additionally, the site was acquired by Prosus in 2021, which preceded a marked fall in activity.
Daily Dose of Data Science | Avi Chawla | Substack·48wThe Full MCP Blueprint
MCP (Model Context Protocol) provides a standardized way for LLMs to interact with tools and capabilities, solving the M×N integration problem where every tool needs manual connection to every model. The protocol enables dynamic tool discovery, plug-and-play interoperability between systems like Claude and Cursor, and transforms AI development from prompt engineering to systems engineering. MCP uses a Host-Client-Server architecture with JSON-RPC communication and supports various transport mechanisms including Stdio and HTTP.
Community Picks·48wArindam200/awesome-ai-apps: Collection of AI Applications
A curated collection of AI application examples and tutorials featuring popular frameworks like LangChain, LlamaIndex, CrewAI, and Pydantic AI. The repository includes practical implementations of chatbots, AI agents, and LLM-powered applications, along with integration examples for various tools and APIs. It provides setup instructions, video tutorials, and welcomes community contributions for building modern AI applications.
YouTube·48wFrom Zero to Your First AI Agent in 25 Minutes (No Coding)
AI agents are autonomous systems that can reason, plan, and take actions using three core components: a brain (LLM), memory for context retention, and tools for external interactions. Unlike static automations that follow predefined steps, agents dynamically adapt and make decisions. The tutorial demonstrates building a practical trail running assistant using N8N's visual interface, connecting Google Calendar, weather APIs, Gmail, and custom HTTP requests without any coding. The agent checks schedules, analyzes weather conditions, recommends appropriate trails from a personal database, and sends customized email notifications.
Daily Dose of Data Science | Avi Chawla | Substack·48wBuilding an MCP-powered Financial Analyst
Explore the process of building a financial analyst powered by MCP (Multi-agent CP), which integrates AI components like DeepSeek-R1 LLM and CrewAI for multi-agent orchestration. The system setup includes agents for query parsing, code writing, executing, and visualization of financial data. The tech stack utilizes various tools to create a locally functional financial analyst that can perform complex stock analysis and generate visual outputs.
Javarevisited·51wTop 5 Courses to Learn LangChain and Build AI-Powered Apps in 2025
LangChain is a popular framework for connecting large language models (LLMs) like OpenAI’s GPT-4 to applications, enabling the creation of intelligent AI-powered apps such as chatbots and AI agents. The post lists the top 5 Udemy courses for learning LangChain in 2025, ranging from beginner-level introductions to advanced enterprise-level integrations with tools like Pinecone, LlamaIndex, and vector databases.
Simon Willison·49wHighlights from the Claude 4 system prompt
Anthropic has released the system prompts for their Claude 4 models, providing insights into prompt engineering and model personalities. The prompts offer guidance on effective use, limiting hallucinations, and maintaining model safety while they detail Claude's capabilities, including its preferences for conversation styles and ensuring copyright compliance.
freeCodeCamp·51wHow to Create Serverless AI Agents with Langbase Docs MCP Server in Minutes
Learn to set up the Langbase Docs MCP server within the Cursor AI code editor to create serverless AI agents quickly. This tutorial guides you through using Langbase SDK, enabling memory-enhanced, agentic AI systems by providing live, on-demand Langbase documentation as context. The integration helps streamline AI model workflows by reducing context switching and allows developers to extend AI capabilities seamlessly.
MongoDB·52wAnnouncing the MongoDB MCP Server
MongoDB has introduced the MongoDB MCP Server in public preview, enabling AI-powered development by connecting MongoDB deployments to AI-supported clients. The server facilitates natural language database interaction and management, streamlining workflows for developers using agentic AI tools. With AI integration, developers can perform database tasks, manage clusters, and generate context-aware code effortlessly. MongoDB MCP Server supports popular tools like Windsurf, Visual Studio Code, and Anthropic's Claude.
ITNEXT·48wAI: Introduction to Ollama for local LLM launch
Ollama provides an easy way to run large language models locally on your own hardware. The guide covers installation on Linux, setting up GPU acceleration with NVIDIA cards, basic commands for model management, and integration with Python applications. It demonstrates running DeepSeek-R1 models, monitoring performance metrics, adjusting context windows, and creating custom models using Modelfiles with system prompts. Local deployment offers cost savings, privacy benefits, and the ability to experiment with models not available through public APIs.
AI·51wReal-Time Knowledge Graph for Product insights with LLM taxonomy understanding
Learn how to build a knowledge graph for product recommendations using product taxonomy and complementary taxonomy. This approach uses LLMs to extract and map product-related data for enhanced insights and recommendations, supported by CocoIndex software with integration to Neo4j for visualization.
Data Science Central·52wHow to Design LLMs that Don’t Need Prompt Engineering
The post discusses methods to design LLMs that don't need prompt engineering by focusing on backend solutions. It highlights strategies such as exact and augmented retrieval, showing full context in responses, enhanced UI options, and structured responses. These techniques improve information retrieval accuracy, eliminate hallucinations, and provide concise and relevant answers without relying on prompt rewording.
Daily Dose of Data Science | Avi Chawla | Substack·49wImplement Multi-Agent Pattern from Scratch
Learn how to build a multi-agent system using Python and a language model (LLM) from scratch. The multi-agent pattern enables specialized agents to perform specific roles, pass context, and run in sequence, enhancing complex task handling. This tutorial covers implementing agent classes, dependency resolution, and ReAct-style prompt construction.
Community Picks·52wA smart assistant that connects powerful AI to your personal world
DeepChat is a versatile, open-source AI chat platform that integrates multiple large language models, both from cloud and local environments. It offers features like search enhancement, tool calling, and privacy-focused operations, making it suitable for commercial and personal use under Apache License 2.0. DeepChat stands out for its unified multi-model management, seamless local model integration, and advanced chat functionalities.
Martin Fowler·51wFunction calling using LLMs
Explores how large language models (LLMs) can enhance functionality by engaging with external systems through function calls. The post discusses methods for implementing AI agents using Python that interpret user intent, select actions, and execute them using APIs while emphasizing security measures like input sanitization to guard against prompt injections. It also compares LLM-based systems with traditional rules engines, illustrating the flexibility and adaptive capabilities of LLMs. Additional tools like MCP are introduced for dynamic tool discovery and interaction.
bycloud·49wThe Biggest "Lie" in AI? LLM doesn't think step-by-step
AI language models may not think in a step-by-step manner as previously thought. Recent research shows that their reasoning is not truly reflective of the coherent processes they describe. Instead, different parts of the model activate simultaneously to generate responses. Despite appearing intelligent, these models lack introspective metacognition, presenting challenges in surpassing human cognitive capabilities.
Atomic Spin·49w4 Examples of LLM Software Architecture
The post explores four examples of software architectures that integrate large language models (LLMs) at varying levels of complexity. It discusses using LLMs solely as chatbots, as frontends with access to custom functions, in Retrieval-Augmented Generation (RAG) applications, and combined with Langgraph for handling complex workflows. Each example includes pros and cons, and insights into potential applications of LLMs in both prototype and production environments.
Neon·49wSolving the MCP Authentication Headache with Vercel & Better Auth
The post discusses solving authentication challenges in Model Context Protocol (MCP) implementations using the Vercel MCP adapter package and Better Auth integration. It explains the steps to secure MCP requests in Next.js applications by incorporating OAuth. The author shares their experience implementing this solution in their own project, agenda.dev, emphasizing the ease and efficiency of the process.
Community Picks·48wClaude 4.0: A Detailed Analysis
Anthropic released Claude 4 with two variants: Claude Opus 4, positioned as the best AI coding model that leads the SWE benchmark with 72.5% score, and Claude Sonnet 4, a free generalist model. Both models feature a 200K context window, with Opus 4 costing $15/M input tokens and $75/M output tokens. Claude Opus 4 excels at complex reasoning tasks and can work autonomously for extended periods, while Sonnet 4 offers solid performance across common tasks at no cost. The analysis includes coding tests showing Opus 4's superior performance in building a Mario-style platformer game.
WunderGraph·48wAPI Orchestration with LLMs: Integrate APIs with a single prompt
The post discusses the challenges of declarative API orchestration using tools like Apollo Connectors and introduces WunderGraph's new approach with gRPC Plugins. Leveraging LLMs, this solution simplifies API integration by automating code generation and addressing issues like the N+1 problem, testability, and debugging. By choosing Hashicorp's go-plugin library and focusing on a scalable, low-overhead development experience, it promotes ease of use, allowing plugins in multiple programming languages.
Medium·48wDeepSeek-R1-0528: The new DeepSeek model will bankrupt OpenAI
DeepSeek has released R1-0528, an updated version of their flagship open-source language model that rivals premium closed models like GPT-4, Claude, and Gemini. Trained on 671B tokens, it offers improved reasoning, code generation, and reliability while remaining free and open-source. The model achieves a median benchmark score of 69.45 across MMLU, GSM8K, BBH, and HumanEval tests. Users report strong performance in coding tasks and creative writing, though concerns remain about hallucinations and bias. The model is available through Hugging Face and OpenRouter's free API.
Towards Data Science·48wMay Must-Reads: Math for Machine Learning Engineers, LLMs, Agent Protocols, and More
A monthly roundup of popular machine learning and data science articles covering essential math skills for ML engineers, beginner guides to LLMs and RAG, software engineering concepts like inheritance, agent communication protocols, Model Context Protocol, PyTorch applications, healthcare ML projects, and time series forecasting techniques. The collection also introduces new authors contributing to the data science community.
8th Light·48wTDD: The Missing Protocol for Effective AI Assisted Software Development
Test Driven Development (TDD) provides a strong framework for enhancing AI-assisted coding by offering clear specifications, manageable scope, and well-defined edge cases. By writing tests first, developers can establish a structured communication protocol that improves collaboration between humans and AI tools, reducing conflicts and enhancing code quality. TDD breaks complex problems into small, testable behaviors, ensuring focused and useful AI-generated solutions while allowing developers to concentrate on creativity and architecture.
PHP Dev·48w🚀 New Video: Building an AI Agent in PHP (Live Coding!)
A live coding session demonstrates building an AI agent from scratch using PHP, Neuron AI, and Ollama. The unedited video shows real-time development including agent-based AI architecture, integration challenges, and debugging processes. A companion article provides additional context for implementing AI functionality in traditional PHP applications.