I turn AI research into systems that hold up in production, and I teach it the same way: runnable notebooks, not slide decks.
Six open-source repos, four books, and a weekly newsletter. Everything here is free to read and run.

Start here:   Learn for free  ·  Read the books  ·  Get the newsletter  ·  Work with me

Every repository is a curriculum. Each technique is a self-contained notebook you can open, run and adapt, with the intuition explained next to the code.

Repository	What you will learn	Stars
RAG Techniques	42 notebooks on retrieval-augmented generation, from chunking and reranking to agentic, graph-based and self-correcting RAG, plus an evaluation suite.
GenAI Agents	64 agent implementations, from a single conversational bot to multi-agent and self-improving systems, across LangGraph, AutoGen and PydanticAI.
Agents Towards Production	The playbook for shipping agents: orchestration, memory, security, observability, evaluation, deployment, GPU serving and fine-tuning.
Prompt Engineering	22 prompting techniques, from basic structure through chain-of-thought, self-consistency, prompt security and systematic evaluation.
Controllable RAG Agent	One complete reference agent: a deterministic graph that plans, decomposes questions, retrieves across stores and verifies its own groundedness.
Agent Memory Techniques	30 notebooks on memory for agents: buffers, vector and graph stores, cognitive architectures, Mem0, Letta, Zep, Graphiti and LoCoMo benchmarks.

Smaller tools: claude-watch (live observability for Claude Code sessions) · Agentic Engineering (a docs layer that gives your coding agent a memory of your project) · moltbook-agent-guard (prompt-injection scanning and guardrails for agents)

Visual, intuition-first guides, with diagrams that make the tricky parts click. All four are on Amazon.

• RAG Made Simple - 400 pages, 22 techniques, with side-by-side comparisons and diagrams. 🏆 Amazon bestseller in Generative AI.
• Agent Memory Made Simple - 467 pages on how agents remember: buffers, vector and graph memory, cognitive architectures and the frameworks in production.
• Prompt Engineering: Master the Art of AI Interaction - 22 chapters of hands-on prompting techniques, the foundation that makes RAG and agents work better.
• AI Agents Made Simple - the decision guide to building agents that work: 28 chapters on what each pattern does, what breaks, what it costs, and when it is overkill. No code.

• DiamantAI Newsletter: one deep dive a week on GenAI engineering, with code. Free, read by 50k+ engineers.
• Discord: 4k+ members, real-time Q&A and project feedback.
• YouTube: the tutorials, as video walkthroughs.
• LinkedIn and X: daily notes on what is new and what actually works.
• r/EducationalAI: discuss prompts, RAG and agent design.

If you build for the GenAI stack (vector databases, orchestration, memory, observability, security, evaluation), we can co-create an open-source tutorial that shows your tool inside a real, runnable workflow.

• Reach: 500,000+ developer views a month across the repositories, plus the newsletter and community.
• Format: clear, reproducible notebooks with no paywall, kept neutral and end to end.
• Track record: the teams below have already done it.

Interested? Reach me on LinkedIn or through diamant-ai.com.

Everything above stays free because people do three small things:

• Star the repositories you use. It is how other developers find them.
• Share a notebook with your team or on social media.
• Sponsor through GitHub Sponsors or Buy Me a Coffee.