All-in-One RAG System! RAG-Anything is a unified framework with a multi-stage multimodal pipeline that extends traditional RAG architectures. It handles diverse content through intelligent orchestration and cross-modal understanding. 100% Open Source
@sumanth_077
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Neural Networks Zero to Hero: Karpathy’s Comprehensive Hands-On Course
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Neural Networks: Zero to Hero by Andrej Karpathy! This is the best hands-on course that takes you from the foundations of neural networks to advanced architectures. The code for all the lectures is available on Github
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Stanford releases free LLM and Transformer learning cheatsheets
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Stanford University released the best cheatsheets you'll ever find to learn LLMs & Transformers! These cheatsheets cover: • Self-attention, Flash Attention, LoRA, SFT
• Mixture of Experts, Distillation, Quantization
• RAG, Agents, LLM-as-a-judge 100% Free and Open Source -
AgenticSeek: Fully Local Autonomous AI Agent
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Fully local Manus AI! AgenticSeek is an autonomous agent that browses the web, writes code, and plans tasks, all on your device. It runs entirely on your hardware, ensuring complete privacy and zero cloud dependency. 100% Open Source
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Firecrawl Documentation: Web Scraping Tool for AI Applications
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Check out the Documentation here to get started: https://
docs.firecrawl.dev/introduction Link to the Github Repo: -
Web Scraping with Python /v2 Endpoint for LLM Data
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Scrape, crawl and search the web with just a few lines of Python code with the new /v2 endpoint. Below is the code snippet to get started and turn websites into LLM-ready data.
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Firecrawl v2 Enables 10x Faster Web Scraping
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Web scraping will never be the same! Firecrawl just released the new v2 endpoint with 10x faster scraping and semantic crawling. Firecrawl lets you input a URL, crawl it, and convert it into clean LLM-ready data.
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Google Releases Gemma-3-270M Lightweight Open Model
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Google just dropped a new light-weight 270M model! Gemma-3-270M is a lightweight, open-weight LLM that's perfect for task-specific fine-tuning with strong instruction-following. This notebook explains how to build Gemma-3-270M from scratch using PyTorch, step by step.
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Axolotl: YAML-based LLM fine-tuning and LoRA optimization framework
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4. Axolotl • Yaml-based setup for fine-tuning, LoRA/QLoRA, DPO, GRPO, and multimodal workflows
• Includes kernel optimizations for memory-efficient training GitHub repo: -
DeepSpeed: Distributed Fine-Tuning Framework for Large Language Models
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3. DeepSpeed • Built for large-scale distributed fine-tuning with ZeRO and FSDP
• Optimized for multi-GPU and multi-node training with advanced memory management
• Trusted in production environments for scalable LLM training GitHub repo: