A curated list of Artificial Intelligence Tools, Resources, References etc. for all of your needs

The ultimate curated collection of AI tools, frameworks, datasets, papers, courses and communities — everything you need to build, learn, and scale AI in one place.

From LLMs to computer vision, MLOps to AI ethics, this repo is your go-to hub for high-quality, up-to-date AI resources.

• Learning & Education
  • Free Courses
  • Books & Papers
  • YouTube & Blogs
• Frameworks & Libraries
  • Deep Learning
  • LLMs & Prompting
  • Computer Vision
  • Audio & Speech
  • Reinforcement Learning
• MLOps & Deployment
  • Experiment Tracking
  • Model Serving
  • Data Pipelines
  • Monitoring
• Datasets
  • Vision
  • NLP
  • Audio
  • Tabular & Time Series
• AI Tools & Apps
  • AI Coding Assistants
  • No-Code AI
  • AI APIs
  • Sales & Outreach
  • Design & Content
• Ethics, Safety & Governance
• Communities & Events
• Trending AI Tools (2025-2026)
• Hands-On Quick Start Guides
• Tool Comparisons
• Real-World Use Cases
• AI Learning Roadmap
• Expert Tips & Best Practices
• AI Interview Preparation
• Hidden Gems
• Contribute

• CS229: Machine Learning (Stanford)
• Deep Learning Specialization (Coursera)
• Fast.ai Practical Deep Learning
• Hugging Face NLP Course

• The Hundred-Page Machine Learning Book
• Attention Is All You Need (Transformer Paper)
• GPT-3 Paper

• 3Blue1Brown Neural Networks
• Andrej Karpathy
• Lil'Log (Lilian Weng)

• PyTorch – Dynamic & research-friendly
• TensorFlow – Production-scale
• JAX – High-performance ML

• LangChain – Build LLM apps
• LlamaIndex – RAG & data connectors
• Hugging Face Transformers

• OpenCV
• YOLOv8 (Ultralytics)
• Detectron2

• Whisper (OpenAI)
• Coqui TTS
• Torchaudio

• Stable Baselines3
• Ray RLlib

• Weights & Biases
• MLflow
• Comet ML

• BentoML
• Triton Inference Server
• vLLM

• Apache Airflow
• Dagster
• Prefect

• Evidently AI
• Arize AI

• COCO
• ImageNet
• Open Images

• SQuAD
• GLUE Benchmark
• The Pile

• LibriSpeech
• Common Voice

• UCI ML Repository
• Kaggle Datasets

• Cursor – AI-first code editor with Claude integration
• GitHub Copilot – AI pair programmer by GitHub & OpenAI
• Replit AI – Code, deploy, and collaborate with AI
• v0 by Vercel – Generate UI components from text prompts
• bolt.new – Full-stack web app development with AI
• Codeium – Free AI code completion and chat
• Tabnine – AI assistant for code completion
• Windsurf – Agentic IDE by Codeium
• Claude Code – Command-line AI coding agent

• Bubble + AI
• Make.com AI Agents
• Dify.ai

• OpenAI API
• Anthropic Claude
• Cohere

• ZeroGTM – Open-source AI SDR agent for automated outbound sales prospecting and outreach

• Midjourney
• Runway ML
• ElevenLabs

• AI Alliance
• Partnership on AI
• AI Safety Benchmarks

• r/MachineLearning
• Hugging Face Discord
• NeurIPS, ICML, ICLR

What's Hot Right Now — The latest AI tools making waves in the community

• OpenAI o1 – Next-gen reasoning model (Dec 2024)
• Google Gemini 2.0 – Multimodal powerhouse
• Anthropic Claude 3.5 Sonnet – Best coding assistant (200K context)
• Perplexity Pro – AI-powered search engine
• NotebookLM – Google's AI note-taking with audio insights
• Sora by OpenAI – Text-to-video generation (limited access)
• Pika Labs – Video generation from text/images
• HeyGen – AI avatar & video translation

• Zed AI – Lightning-fast multiplayer code editor with AI
• Pieces for Developers – AI-powered code snippets manager
• Phind – AI search engine for developers
• Continue.dev – Open-source Copilot alternative
• Cody by Sourcegraph – AI coding assistant with codebase context

• Magnific AI – AI image upscaler (insane quality)
• Leonardo.AI – Game asset generation
• Ideogram – Text rendering in images (finally!)
• CapCut AI – AI video editing for creators

# pip install langchain openai
from langchain.chat_models import ChatOpenAI
from langchain.schema import HumanMessage

llm = ChatOpenAI(model="gpt-4")
response = llm([HumanMessage(content="Explain AI in 3 sentences")])
print(response.content)

# pip install transformers pillow
from transformers import pipeline

classifier = pipeline("image-classification")
result = classifier("path/to/image.jpg")
print(result)

# pip install llama-index
from llama_index import VectorStoreIndex, SimpleDirectoryReader

documents = SimpleDirectoryReader('data').load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
response = query_engine.query("Your question here")
print(response)

Week 1: Foundations

• Day 1-2: Complete Fast.ai Lesson 1
• Day 3-4: Build a simple classifier
• Day 5-7: Fine-tune a pre-trained model

Week 2: Deep Learning

• Day 8-10: PyTorch basics
• Day 11-14: CNN for image recognition

Week 3: LLMs & NLP

• Day 15-17: Hugging Face Transformers
• Day 18-21: Build a RAG chatbot

Week 4: Production

• Day 22-25: Deploy with FastAPI
• Day 26-28: Add monitoring
• Day 29-30: Portfolio project showcase

1. Customer Support Automation

   • Stack: LangChain + GPT-4 + Pinecone
   • ROI: 60% reduction in support tickets
   • Example Repo

2. Code Review Assistant

   • Stack: Claude 3.5 + GitHub API
   • Impact: 40% faster PR reviews
   • Tutorial

3. Document Intelligence

   • Stack: Azure Document Intelligence + OpenAI
   • Use: Extract data from invoices, contracts
   • Demo

• AI Tutor: Personalized learning paths using RAG
• Essay Grader: Automated feedback with rubrics
• Language Learning: Conversation practice bot

• AI Game NPCs: Dynamic dialogue with memory
• Music Generator: Style transfer for audio
• Story Writer: Interactive fiction with AI

graph LR
    A[🌱 Beginner] --> B[📊 Python + Math]
    B --> C[🤖 ML Basics]
    C --> D[🧠 Deep Learning]
    D --> E[🔮 LLMs & Transformers]
    E --> F[🚀 Production & MLOps]
    F --> G[⭐ AI Expert]

• ✅ Python fundamentals
• ✅ NumPy, Pandas basics
• ✅ Linear algebra & calculus refresher
• ✅ Statistics fundamentals
• Project: Predict house prices with linear regression

• ✅ Scikit-learn
• ✅ Classification, regression, clustering
• ✅ Model evaluation & cross-validation
• ✅ Feature engineering
• Project: Image classifier (cats vs dogs)

• ✅ PyTorch or TensorFlow
• ✅ CNNs, RNNs, Transformers
• ✅ Transfer learning
• ✅ Hyperparameter tuning
• Project: Object detection with YOLOv8

• ✅ Hugging Face ecosystem
• ✅ Prompt engineering
• ✅ Fine-tuning LLMs
• ✅ RAG systems
• Project: Build a domain-specific chatbot

• ✅ Docker & Kubernetes
• ✅ FastAPI / Flask
• ✅ Model monitoring
• ✅ CI/CD for ML
• Project: Deploy scalable API

Total Time: 10-15 months to job-ready

1. Be Specific: ❌ "Write code" → ✅ "Write a Python function that validates email addresses using regex"
2. Use Examples: Few-shot learning dramatically improves output
3. Set Context: "You are a senior Python developer..."
4. Iterate: Start broad, then refine
5. Chain of Thought: Ask the model to "think step by step"

• Cache embeddings: Don't re-embed the same content
• Use smaller models: GPT-3.5 is 10x cheaper than GPT-4
• Streaming: Better UX and you can stop early
• Batch requests: Save 50% on API costs
• Monitor usage: Set up alerts for spend

• Never put API keys in code (use env variables)
• Implement rate limiting
• Sanitize user inputs
• Use prompt injection防护
• Audit AI outputs before production use

• Async everything: Use asyncio for I/O operations
• Vector DB indexing: HNSW > IVF for most use cases
• Quantization: Run LLMs 4x faster with minimal quality loss
• Caching: Redis for frequent queries

Technical:

1. Explain transformers architecture
2. Difference between RAG and fine-tuning?
3. How do you handle hallucinations?
4. Explain attention mechanism
5. What is RLHF?

System Design:

• Design a recommendation system
• Build a scalable chatbot architecture
• Create a real-time fraud detection system

• ML Interview Guide
• LeetCode ML Problems
• System Design for ML

• Marvin – Build AI-powered software
• Instructor – Structured outputs from LLMs
• DSPy – Programming foundation models
• Autogen – Multi-agent conversations
• LiteLLM – Universal LLM API
• Mem0 – Memory layer for AI apps
• Lamini – Enterprise LLM platform

We welcome contributions!
Found a missing gem? Want to add a new category?

1. Fork the repo
2. Add your resource in the correct section
3. Follow format: - [Name](link) – Short description.
4. Open a PR!

Developed with ❤️ by Dhanush Nehru