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Analytics Vidhya
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@AnalyticsVidhya

Analytics Vidhya

@AnalyticsVidhya
Building Next-Generation AI Professionals 🚀 Unlocking knowledge, skills & opportunities through blogs, world-class courses, hackathons & a thriving community.
analyticsvidhya.com
Joined January 2014
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  • Pinned
    @AnalyticsVidhya
    Analytics Vidhya
    @AnalyticsVidhya
    20h
    "OpenCode is free" is true, and also where most people stop reading too early 💸 190K GitHub stars, MIT-licensed, works with any model, but the real story is the client-server split: swap models without touching your setup, run it headless, attach from another machine, even
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    OpenCode Explained: The Open-Source AI Coding Agent
    From analyticsvidhya.com
  • @AnalyticsVidhya
    Analytics Vidhya
    @AnalyticsVidhya
    Sep 3
    Qwen3-30B-A3B-Instruct-2507-gguf-q2ks-mixed-AutoRound isn't gibberish, it's a full spec sheet crammed into one line 🧩 Once you know it, every local model filename decodes itself: 🔢 30B = total parameters, A3B = only ~3B active per token (MoE magic) 🎓 Instruct vs Base =
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    A Complete Guide to Decoding LLM Model Names
    From analyticsvidhya.com
    1
  • @AnalyticsVidhya
    Analytics Vidhya
    @AnalyticsVidhya
    Sep 1
    Zero coding skills, zero excuses 🚫 7 free courses (certificates included) that take you from "what's n8n?" to building full multi-agent AI systems with n8n, Make, Zapier, Power Automate, and crewAI 🤖 Full list 👇
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    Top 7 Free AI Automation Courses with Certificates
    From analyticsvidhya.com
  • @AnalyticsVidhya
    Analytics Vidhya
    @AnalyticsVidhya
    Aug 31
    Standard RAG just retrieves and hopes for the best 🤷 Agentic RAG actually checks its own work before answering. This one builds a Corrective RAG system with LangGraph: a grader that scores retrieved docs, a rephraser that fixes bad queries, and a web search fallback for when
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    A Comprehensive Guide to Building Agentic RAG Systems with LangGraph
    From analyticsvidhya.com
  • @AnalyticsVidhya
    Analytics Vidhya
    @AnalyticsVidhya
    Aug 30
    Same model, wildly different output, all because of how you asked 🎯 that's the whole game behind prompt engineering. Zero-shot vs. few-shot vs. chain-of-thought, the 4 elements every good prompt needs, and the 3 mistakes (vague asks, info overload, weak constraints) quietly
    analyticsvidhya.com
    Prompt Engineering: Definition, Examples, Tips & More
    Explore prompt engineering, learn techniques like zero-shot and few-shot prompting, and find tips for crafting effective AI prompts.
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