Your ultimate curated guide to modern Artificial Intelligence, Machine Learning, LLMs, Agents, Inference, Fine-Tuning, and Vector Retrieval.

Welcome to the AI Developer Resources Hub! This repository is a meticulously curated index of the most powerful tools, frameworks, platforms, and educational resources for AI developers, machine learning engineers, and researchers. Whether you're building production-grade agentic workflows, fine-tuning open-weight models, optimizing local inference, or tracking events in tech hubs like Washington DC & San Francisco, this hub is designed for you.

May my gradients always descend smoothly,
And my learning rates be ever-fitting.
May my context windows be vast and clear,
And my models free from hallucinations.
Deliver me from out-of-memory errors,
And keep my weights from exploding or vanishing.
Let the prompts be precise, the tools ever-responsive,
And the agents logical in all their steps.
In the name of the tensor, the GPU, and the open weights,
Amen. 🚀

• Agentic Patterns Resource — Comprehensive companion to the Packt book "Agentic Architectural Patterns for Building Multi-Agent Systems"
  • 16 chapter summaries with key concepts, diagrams, and code examples
  • Runnable Python implementations (single agent + multi-agent loan processing)
  • CrewAI & LangGraph framework examples
  • Pattern reference cards and decision frameworks
  • Curated papers, glossary, and resources
  • 📘 Get the Book (Packt) · 🎟️ RSVP Event

• Core Machine Learning - PyTorch, JAX, TensorFlow, and fundamental math libraries.
• LLM & Agent Frameworks - LangChain, LlamaIndex, DSPy, LangGraph, AutoGen, CrewAI, PydanticAI.
• VLM & Multimodal - Vision-Language Models, speech processing, image/video generation (Diffusers, Whisper).

• Inference & Model Serving - High-throughput serving engines (vLLM, Ollama, llama.cpp, Triton, SGLang).
• Vector Databases & Retrieval - Vector storage, embedding indexing, and hybrid search.
• Fine-Tuning & Optimization - PEFT, LoRA, RLHF, and training frameworks (Unsloth, Axolotl, DeepSpeed, FSDP).

• Evaluation & Observability - Model evaluation, LLM tracing, PromptOps, and monitoring.
• Tools & Local Environments - Local runtimes (LM Studio, Open WebUI) and AI-first IDEs (Cursor, Windsurf).
• Learning Resources - Hand-picked courses, podcasts, newsletters, and papers.
• Events & Communities - Major AI conferences and regional tech meetups in DC and SFO.

• Agentic Architectures: Moving beyond simple single-turn prompts to stateful, multi-agent systems that plan, use tools, and self-correct.
• Ultra-Fast Local Serving: Innovations like llama.cpp and Ollama allow running highly capable models (like Llama 3, Phi-4, Mistral) directly on developer workstations.
• Structured Outputs: Tool calling and JSON-schema constraints (supported natively by vLLM, SGLang, and PydanticAI) are making LLM integrations deterministic.
• VLM & Multimodal RAG: Leveraging vision-capable models (VLMs) to parse PDFs, diagrams, and videos without converting them to raw text first.

We welcome contributions from the AI developer community! Whether you want to add a new library, update setup guides, share a local meetup, or fix broken links, please read our Contributing Guidelines.

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This project is licensed under the MIT License - see the LICENSE file for details.

June 9, 2026 - Manually updated with the latest AI frameworks, model serving engines, and tools.