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🚨 THE EASIEST WAY TO ORCHESTRATE MULTIPLE AI AGENTS Someone built a clean, no-bloat framework for running teams of autonomous agents. Ships with: • **Crews**: Agents work together autonomously as a team • **Flows**: Precise step-by-step control over every action • Use both together for production-grade systems • 5.76x faster than LangGraph • Works with Claude Code, Cursor, Codex & Windsurf Built from scratch. Simple. Powerful. 👉 https://github.com/crewAIInc/crewAI Who’s running agent crews tod
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Knowing the difference helps you design better AI systems instead of just memorizing buzzwords. Here’s a quick cheat sheet: • RAG vs Agentic RAG – RAG retrieves information once before generation. Agentic RAG can reason, use tools, perform multiple retrieval steps, and refine its search based on intermediate results. • Vector Database vs Graph Database – Vector databases retrieve semantically similar information using embeddings. Graph databases model entities and explicit relationships, making
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Unexpected innovation is retraining other open source models. This is what DeepSeek has done with their distill models after R1. #developer #softwareengineer #programming #programmer #ai #llm #engineering #agent #agenticai #chatgpt #claude #gemini #deepseek #phi #llama #prompt #promptengineering #agents #openai #anthropic #opensource #boundryml #copilotkit #techtok #learnontiktok #coding #tech #aitools #aieducation #devcommunity #100daysofcode #baml #langchain #langgraph #llamaindex #chromadb #q
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Sri Lanka 🇱🇰 Transport Assistant #Langgraph Ai Agent Based 70% Code Base is done ✅ Use Chroma DB as DB #ai #ml #agent #langchain
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Most people try to learn AI engineering from random tutorials. But someone just open-sourced a complete AI engineering hub on GitHub. It’s called AI Engineering Hub, and it’s basically an entire curriculum disguised as a repository. Inside the repo you’ll find real projects like: • Agentic RAG pipelines from scratch • Multi-agent systems with CrewAI, AutoGen, and LangGraph • Voice AI agents with real-time streaming • MCP server implementations with real use cases • Fine-tuning DeepSeek models on
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A chain goes forward. A graph can go back. That's the whole difference. ⚡ Most people can't explain LangChain vs LangGraph vs LangSmith because they're described as three parts of one thing. They're not. They're three different kinds of thing. 🟢 LANGCHAIN — the framework Models, tools, retrievers, parsers. High-level abstractions for the common patterns. Reach for it to ship fast. The catch: a chain is a DAG. It only goes one direction. 🔵 LANGGRAPH — the runtime State, edges, loops, pauses. Du
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