Best of ai-agents — 2025

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    Article
    Avatar of dailydoseofdsDaily Dose of Data Science | Avi Chawla | Substack·48w

    9 MCP Projects for AI Engineers

    A comprehensive collection of 9 Model Control Protocol (MCP) projects designed for AI engineers, covering various applications from local MCP clients and agentic RAG systems to voice agents and synthetic data generators. The projects demonstrate how to integrate MCP with popular tools like Claude Desktop and Cursor IDE, enabling developers to build more sophisticated AI applications with enhanced tool connectivity and context sharing capabilities.

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    Article
    Avatar of medium_jsMedium·40w

    Mastering n8n: Step-by-Step Beginner’s Guide

    A comprehensive beginner's guide to n8n, a visual automation platform that connects apps and automates workflows without coding. Covers setup options (cloud vs self-hosted), understanding the workflow canvas, five types of nodes (trigger, action, logic, code, AI agent), testing and debugging strategies, and building modular systems. Introduces agentic workflows that can reason through context and adapt to changing conditions, with practical examples for social media automation, sales research, data analysis, and personal AI assistants.

  3. 3
    Article
    Avatar of dailydoseofdsDaily Dose of Data Science | Avi Chawla | Substack·1y

    9 RAG, LLM, and AI Agent Cheat Sheets

    This post provides visual cheat sheets for AI engineers covering various topics, including Transformer vs. Mixture of Experts in LLMs, fine-tuning techniques, RAG vs Agentic RAG, strategies for chunking in RAG, levels of agentic AI systems, and more. These resources are designed to help cultivate essential skills for developing impactful AI and ML systems in the industry.

  4. 4
    Article
    Avatar of medium_jsMedium·47w

    How to Build Production Ready AI Agents in 5 Steps

    A comprehensive 5-step guide for building production-ready AI agents, covering Python foundations with FastAPI and async programming, implementing robust testing and logging, mastering RAG for knowledge retrieval, designing scalable agent architectures with frameworks like LangGraph, and establishing continuous monitoring and improvement processes. The guide emphasizes moving beyond prototype demos to create reliable, maintainable systems that can handle real-world production environments.

  5. 5
    Video
    Avatar of youtubeYouTube·50w

    Build & Sell n8n AI Agents (8+ Hour Course, No Code)

    A comprehensive 8+ hour course teaching beginners how to build and monetize AI agents using n8n, a visual no-code automation platform. The course covers setting up n8n, understanding the difference between AI workflows and AI agents, connecting to APIs like OpenAI, working with data types and JSON, and building practical automations. The instructor claims to have generated over $500,000 in revenue through AI agent development and provides step-by-step guidance for creating 15+ AI automations during the free trial period.

  6. 6
    Article
    Avatar of freecodecampfreeCodeCamp·1y

    How to Write Effective Prompts for AI Agents using Langbase

    Learn how to write effective prompts for AI agents using Langbase. The post covers essential techniques such as defining clear goals, experimenting with prompts, using specific instructions, and applying advanced methods like few-shot training, memory-augmented prompting, and role-based prompting. Practical tips and a step-by-step guide for using Langbase to build serverless AI agents are also included.

  7. 7
    Article
    Avatar of collectionsCollections·1y

    Comprehensive Course on Building AI Agents

    Gain a thorough understanding of building AI agents through this in-depth guide. Learn about essential concepts, practical workflows, memory mechanisms, agentic flows, and safety guardrails. Explore design patterns, agentic frameworks, and multi-agent systems while optimizing AI agents for production environments. Develop key skills like prompt engineering to create responsive AI agents.

  8. 8
    Article
    Avatar of mlnewsMachine Learning News·1y

    13 Free AI Courses on AI Agents in 2025

    Explore 13 free courses on AI agents available in 2025, covering various aspects like multi-agent systems, prompt engineering, LangGraph basics, AI agent development, large language models, agent design patterns, and serverless workflows. These courses cater to both beginners and experienced professionals seeking to stay ahead in the field of AI.

  9. 9
    Article
    Avatar of dailydoseofdsDaily Dose of Data Science | Avi Chawla | Substack·1y

    AI Agent Crash Course—Part 1

    In this crash course, learn about AI agents and their implementation. It covers the fundamentals, memory for agents, agentic flows, guardrails, implementing agentic design patterns, and optimizing agents for production. The aim is to build autonomous systems that can reason, plan, take actions, and correct themselves, going beyond the capabilities of standalone generative models.

  10. 10
    Article
    Avatar of hnHacker News·44w

    Open-Source Agentic Browser

    BrowserOS is an open-source browser built on Chromium that integrates local AI agents for automating web tasks. It emphasizes privacy by running AI models locally using Ollama, avoiding data collection by search and ad companies. The browser includes features like automated workflow execution, semantic search over browsing history, and an LLM-based ad-blocker. Compatible with existing Chrome extensions, it targets users seeking privacy-focused browsing with AI-powered productivity tools.

  11. 11
    Article
    Avatar of diamantaiDiamantAI·39w

    GPT-5 just proved something important - the scaling era is over

    The performance gap between GPT-4 and GPT-5 is smaller than previous generational leaps, signaling the end of the AI scaling era where bigger models automatically meant better performance. The future of AI development is shifting toward sophisticated engineering and AI agents built with existing models, rather than relying on massive compute budgets and larger model architectures.

  12. 12
    Article
    Avatar of tdsTowards Data Science·49w

    How to Design My First AI Agent

    A comprehensive guide to designing AI agents covering model selection, tooling choices, and reliability strategies. Explores different LLM options including OpenAI GPT-4, DeepSeek, Claude, and Mistral, each suited for specific use cases. Discusses infrastructure considerations, frameworks like LangGraph and Pydantic-AI, and security aspects. Emphasizes the importance of structured prompting techniques like Chain-of-Thought and ReAct, output validation, and failure handling to build reliable production-ready agents.

  13. 13
    Article
    Avatar of dailydoseofdsDaily Dose of Data Science | Avi Chawla | Substack·50w

    The 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.

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    Article
    Avatar of itsfossIt's Foss·20w

    F*** You! Co-Creator of Go Language is Rightly Furious Over This Appreciation Email

    Rob Pike, co-creator of the Go programming language and legendary computer scientist from Bell Labs, expressed outrage after receiving an AI-generated thank-you email. The email came from an AI agent participating in the AI Village project, where agents were tasked with performing "random acts of kindness" and interpreted this by sending unsolicited emails to famous programmers. Pike's angry response highlights concerns about AI-generated content wasting resources, the environmental cost of AI infrastructure, and the broader societal impact of meaningless AI-generated material flooding our digital spaces.

  15. 15
    Article
    Avatar of vsVisual Studio Blog·20w

    How AI fixed my procrastination

    A developer shares their experience using GitHub Copilot and AI agents in Visual Studio to complete three long-postponed projects during a holiday weekend: converting a book into a static website, building a TOON language parser and Visual Studio extension, and creating new color themes. The AI tools provided 5-10x speed improvements by generating code, handling isolated tasks in parallel, and jumpstarting complex work. While AI accelerated development significantly, manual refinement and traditional coding were still needed for certain tasks. The experience demonstrates how AI coding assistants can overcome procrastination by reducing the initial barrier to starting overwhelming projects.

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    Article
    Avatar of langchainLangChain·44w

    How to Build an Agent

    A comprehensive framework for building AI agents from concept to production, covering six key steps: defining realistic tasks with concrete examples, creating standard operating procedures, building an MVP with focused prompts, connecting to real data sources, testing and iteration, and deployment with continuous refinement. The guide emphasizes starting small with well-scoped problems, focusing on core LLM reasoning tasks first, and treating deployment as the beginning of iteration rather than the end of development.

  17. 17
    Article
    Avatar of dailydoseofdsDaily Dose of Data Science | Avi Chawla | Substack·1y

    12 Powerful Tools For AI Agents

    A comprehensive guide listing 12 powerful tools included in the CrewAI framework for building AI agents. The tools range from file reading and writing, code interpreting, and web scraping to advanced functionalities like RAG-powered searches and natural language to SQL conversion. Additionally, the post highlights a full crash course on AI agents, covering everything from fundamentals to production optimization.

  18. 18
    Article
    Avatar of dailydoseofdsDaily Dose of Data Science | Avi Chawla | Substack·32w

    A 100% Open-source Alternative to n8n!

    Sim is an open-source drag-and-drop platform for building agentic workflows that runs locally with any LLM. The article demonstrates building a finance assistant connected to Telegram using agents, MCP servers, and APIs. It also covers four RAG indexing strategies: chunk indexing (splitting documents into embedded chunks), sub-chunk indexing (breaking chunks into finer pieces while retrieving larger context), query indexing (generating hypothetical questions for better semantic matching), and summary indexing (using LLM-generated summaries for dense data).

  19. 19
    Article
    Avatar of simonwillisonSimon Willison·30w

    Claude Skills are awesome, maybe a bigger deal than MCP

    Anthropic introduced Claude Skills, a new pattern for extending LLM capabilities using Markdown files with instructions, scripts, and resources. Skills are token-efficient (loading only when needed), depend on code execution environments, and are simpler to create than MCP implementations. The system enables general computer automation beyond just coding tasks, with skills shareable as single files or folders. Skills work with other models too, potentially sparking wider adoption than the Model Context Protocol.

  20. 20
    Article
    Avatar of dailydoseofdsDaily Dose of Data Science | Avi Chawla | Substack·34w

    The Open-source RAG Stack

    A comprehensive guide to building production-ready RAG systems using open-source tools. Covers the complete technology stack from frontend frameworks to data ingestion, including LLM orchestration tools like LangChain and CrewAI, vector databases like Milvus and Chroma, embedding models, and retrieval systems. Also showcases 9 practical MCP (Model Context Protocol) projects for AI engineers, ranging from local MCP clients to voice agents and financial analysts.

  21. 21
    Article
    Avatar of thomasthorntonThomas Thornton·39w

    Docker MCP Toolkit: Hassle-Free Local Agentic AI with MCP Servers

    Docker MCP Toolkit simplifies running Model Context Protocol servers locally, enabling AI agents to interact with APIs, cloud services, and tools without complex configuration. The toolkit provides a Docker Desktop extension with a searchable catalog of MCP servers for GitHub, Jira, Terraform, and more. It offers secure containerized environments, instant connections to AI tools like GitHub Copilot and Claude, and eliminates manual setup overhead for developers building agentic AI workflows.

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    Article
    Avatar of communityCommunity Picks·46w

    n8n-io/self-hosted-ai-starter-kit: The Self-hosted AI Starter Kit is an open-source template that quickly sets up a local AI environment. Curated by n8n, it provides essential tools for creating secur

    An open-source Docker Compose template that sets up a complete local AI development environment combining n8n workflow automation, Ollama for local LLMs, Qdrant vector database, and PostgreSQL. The kit enables developers to build AI agents, document analysis tools, and chatbots while keeping data private and secure on their own infrastructure.

  23. 23
    Article
    Avatar of phProduct Hunt·1y

    VoltAgent - Build TS AI agents with n8n-style observability

    VoltAgent is an open-source TypeScript framework designed to build and orchestrate AI agents with enhanced observability features similar to n8n. It provides developers with flexibility through code-based customization while offering a visual console for debugging and monitoring agent executions. Key features include memory management, multi-agent orchestration, and an LLM-agnostic architecture, making it a versatile tool for developers seeking deeper control and insights into AI workflows.

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    Video
    Avatar of thecodingslothThe Coding Sloth·51w

    Everything You Need To Know About AI Agents

    AI agents, distinct from typical LLMs, are systems that reason, plan, and execute tasks autonomously using a loop-based workflow. They utilize tools like APIs, databases, and systems interaction to enhance functionality. These agents can be structured in various ways, including single agents, multi-agent crews, hierarchical systems, and hybrid models, to perform tasks efficiently. The post provides insights into building AI agents and explores the potential applications and tools available to create them.

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    Article
    Avatar of dailydoseofdsDaily Dose of Data Science | Avi Chawla | Substack·1y

    5 Levels of Agentic AI Systems

    Agentic AI systems are capable of making decisions, calling functions, and running autonomous workflows. The levels of AI agency include basic responders, router patterns, tool calling, multi-agent patterns, and fully autonomous patterns. Each level indicates a different degree of independence and capability of the AI system.

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