#Llm Farm Github Projects

A curated collection of Awesome LLM apps built with RAG, AI Agents, Multi-agent Teams, MCP, Voice Agents, and more. Link : https://github.com/Shubhamsaboo/awesome-llm-apps Follow @thedataevangelist for more such content #aiagents #github #generativeai

👨🏻‍💻 Bro, wanna view any GitHub project LIVE without downloading it? 😏 Here’s the secret trick 🤫 Just open any GitHub repo → add “box” after github.com → and BOOM 💥 You can instantly preview project files and see it live ⚡ I’ve collected more hidden GitHub tricks like this in one handy document 📘 Comment “GitHub” 👇 and I’ll send it straight to your DM 🚀 Follow @coder.s for more mind-blowing developer hacks! 💻✨ #GitHub #CodingTips #WebDevelopment #Developers #CodeTricks #FrontendDeveloper #ProgrammerLife #TechReels #WebDev #GitHubProjects #OpenSource #JavaScript #MERNStack #CodeNewbie #FullStackDeveloper #TechContent #100DaysOfCode

Make any github repo llm friendly with this trick 🧙 #codewitharjit #coding

Chatbot for FAQs Fine-tune a pretrained LLM to answer domain-specific questions (e.g., product FAQs). Tech Stack: Python, HuggingFace Transformers, PyTorch, Datasets LegalDoc Assistant Fine-tune GPT/LLaMA on legal text to summarize contracts or answer legal queries. Tech Stack: HuggingFace, PyTorch, LangChain, PDF parsing libraries Code Completion Model Fine-tune CodeLlama or CodeT5 on a repo of code for auto-completion and suggestions. Tech Stack: HuggingFace, PyTorch, Tokenizers, GitHub API Emotion-Aware Chatbot Fine-tune an LLM to recognize emotions in messages and respond empathetically. Tech Stack: PyTorch, HuggingFace, GoEmotions Dataset, PEFT (LoRA/Adapters) Summarization Model Fine-tune BART or T5 to summarize articles, meeting notes, or emails. Tech Stack: HuggingFace, PyTorch Lightning, Datasets Customer Review Analyzer Fine-tune a small LLM on product reviews to generate insights, sentiment, or suggestions. Tech Stack: Transformers, PyTorch, Pandas, Sklearn Domain-Specific RAG Model Fine-tune an LLM to retrieve and answer questions from your company’s knowledge base. Tech Stack: LangChain, ChromaDB/FAISS, HuggingFace, PyTorch TinyGPT for Chat Fine-tune a small GPT model on your own chat logs for personal assistants. Tech Stack: PyTorch, HuggingFace, Tokenizers, WandB #datascience #machinelearning #womeninstem #learningtogether #progresseveryday #tech #consistency #ai #llm #largelanguagemodels

🚀 You’re Already Hired If You Build These Projects! Still wondering how to stand out from the crowd of resumes? 🔥 Build once → Impress recruiters forever. 1. AI Document Search (RAG Chatbot) What: Chat with PDFs using LLM + semantic search Stack: •Frontend: React, Tailwind •Backend: FastAPI •AI: OpenAI, LangChain •Vector DB: Pinecone / FAISS •Deploy: Vercel + Docker Code: 🔗 Github : github.com/mayooear/ai-pdf-chatbot-langchain 2. AI Code Review Bot What: GitHub bot that auto-reviews PRs using GPT Stack: •Backend: Python (FastAPI) •AI: GPT-4 / Claude •GitHub API + Actions •CI/CD: GitHub Actions Code: 🔗 Github : github.com/x86nick/openai-pr-reviewer 3. Custom AI Agent with Memory What: Voice/text assistant with long-term memory Stack: •Backend: Python (LangChain) •AI: OpenAI, Whisper •Memory: Redis / ChromaDB •Frontend: Streamlit / Next.js Code: 🔗 Github : github.com/langchain-ai/memory-agent ✨ Save this reel and tag a friend who's building the future! 👉 Follow @swatijha_123 for more high-impact project ideas & coding resources. Keywords: AI Resume Projects, ChatGPT with LangChain, GPT-4 Code Review, Real-World AI Applications, LLM-Based Projects, AI Coding Portfolio, FastAPI Projects, Open Source Internship Projects, Voice Assistant Python, Tech Resume Boosters Hashtags: #AIProjects #ResumeBoost #CollegeToCorporate #TechForStudents #CodersLife #OpenSourceContributions #PlacementReady #PythonProjects #AIInTech #EngineeringReels #JobSeekersIndia #TechContentCreators

Comment "AI" to get a list of portfolio projects to do If I were to get started to learn GenAI — here’s exactly how I’d go about it 👇 (No degree, no prior experience needed. Just consistency and curiosity.) Step 1: Learn Python (Weeks 1–4) → Do “Python for Everybody” or freeCodeCamp → Practice daily for 30 minutes → Push everything to GitHub — even your messy code → Start building your proof of work from Day 1 Step 2: Get ML Foundations (Weeks 5–8) → Take Andrew Ng’s ML course (still gold) → Pick 1 project on Kaggle (Titanic is great for starters) → Train a basic model → deploy it with Streamlit → Now you’ve built your first ML app 👏 Step 3: Deep Learning Phase (Months 3–4) → Learn through fast.ai — super hands-on → Fine-tune a small transformer (like BERT) on Hugging Face → Don’t aim for perfection, aim to finish one project well Step 4: Understand Transformers & LLMs (Months 5–6) → Rebuild a mini GPT using Karpathy’s nanoGPT → This will help you actually understand self-attention, tokens, and training loops → Watch explainer videos + read blog posts to reinforce the concepts Step 5: Learn the Real LLM Stack (Months 7–9) → LangChain for chaining prompts & calling tools → LangGraph for multi-agent workflows and memory → Weaviate or LanceDB for retrieval (RAG setups) → QLoRA for fine-tuning open models → vLLM for efficient inference Step 6: Build and Publish (Months 10–12) → Choose one simple use case (like a research assistant or AI chatbot) → Build it end-to-end using the stack you’ve learned → Make a demo video, write a short architecture breakdown → Share on GitHub, LinkedIn, and Twitter — this is your new resume [ai roadmap, llm learning path, genai career, how to learn ai, langchain tutorial, huggingface projects, python for ai, llm from scratch, build in public, ai agent builder, nanoGPT, deep learning 2025, vector dbs, fine tuning llms, ai portfolio project, ml roadmap, data science career guide, prompt engineering, ai learning journey]

Found a GitHub repository that covers everything — LLMs, RAG, and AI Agents, from beginner to advanced level projects. No theory overload, only real-world projects that actually help you build and understand AI systems. If you’re learning AI, this repo is a complete roadmap for 2025. 👇 Comment “github” and I’ll share the repo link directly in your DMs. #aiprojects #aiagents #llm #rag #llmprojects #githubrepo #opensourceai #learnai #aitools #ai2025 #machinelearning #aiengineering #developerlife #codingprojects #techreels #codingreels #buildinpublic #githubprojects #studentsintech #futureofai #techcommunity #codingjourney #pythondeveloper #reelitfeelit #viralreels #trendingtech #codewithme #aiinnovation #techcreators #learnbybuilding

Comment "GOOGLE" to get this New Free AI Tool from Google for understanding Github Codebases. Google just launched CodeWiki. It's a free AI tool. Paste a GitHub repo link. Boom. It turns the whole project into an interactive dashboard. No more digging through files. Think of it like a map for a messy city. Diagrams show how code parts connect. Like road signs pointing everywhere. Explanations break down the tough spots. Simple words. Clear steps. Videos pop up too. They explain code tricks. Perfect for quick lessons. The good news? A built-in chatbot sits right there. Ask it anything. "What's this function do?" It knows the full codebase. Answers fast. I recently tried it on a big open-source repo. Pasted the link. Asked about a weird module. Got a spot-on reply in seconds. Saved me an hour. How easy is setup? Head to Codewiki. Drop the repo URL. Done. Handles up to 1.4 million lines. Updates when code changes. Great for new team members. Or jumping into old projects. Why does this help? Onboarding speeds up. Questions answered on the spot. Like having a smart buddy next to you. Here's what you get: 1. Visual diagrams. Architecture. Class links. Workflows. 2. Code breakdowns. No guesswork. 3. Chat for stuck moments. 4. Auto-updates. Always fresh. Limited to open-source now. But it's free. Public preview rocks. Try a repo like TensorFlow. See the magic yourself. #codewiki #Googleai #githubrepo

Comment "Link" and we will DM you the link. Website that feels illegal to know (Part 107). Complete your tasks with all the latest LLM models. #aitools #powerfulwebsites #ai #aichat #aiwebsites #fixbug #writecode

Stop wasting time on random ML videos these GitHub repos will change your AI journey. {LLM machine learning AI engineer GitHub repos LLM course fine tuning RAG apps AI projects deep learning transformers open source ML roadmap AI student coding practice LLM tutorials production AI ML portfolio AI tools data science neural networks} #machinelearning #artificialintelligence #githubprojects #deeplearning #llm

This tool converts ANY GitHub repo into LLM-ready data! (open-source & takes just one step) Simply replace “hub” with “ingest” in a GitHub URL and receive a prompt-friendly text ingest for LLMs. Gitingest is 100% open-source and provides: Directory structure A brief summary of the project The entire content as LLM-ready text #analyticsvidhya #datascience #machinelearning #deeplearning #statistics #probability #python #neural #analyst #developers #openai #chatgpt #ai #artificialinteligence #AI #ML #programming #pythoncoding

TONL is a LLM-friendly serialization format that can cut your data size by up to 50–60% compared to JSON, which also means dramatically lower LLM token costs. #github #opensource