AI researcher · Community builder – reaching 500 000+ developers every month
Turning cutting‑edge AI research into reliable, production‑ready systems
If you build technology that fits the generative AI agent or Retrieval‑Augmented Generation stack - vector databases, embeddings APIs, real time web search, orchestration layers, observability platforms, GPU hosting, security guardrails, and similar - let us co‑create an open source Jupyter tutorial that shows developers a neutral end to end workflow for your tool. The content stays free and vendor agnostic, you gain authentic adoption, and the community gets another fully runnable example.
- Reach - 500 000+ developer views per month
- Format - clear, reproducible notebooks with no paywalls or ads
- Goal - give developers runnable examples while partners gain authentic adoption
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Interested? Contact me on
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| Repository | What you learn | Live Stars |
|---|---|---|
| Agents Towards Production | Step‑by‑step notebook tutorials that cover ingestion, retrieval, memory, tool routing, guardrails, evaluation, observability, CI/CD, cost tracking, security, and cloud deployment for production‑ready AI agents. |  |
| Prompt Engineering | A 20‑chapter notebook series that moves from prompt basics to advanced techniques with hands‑on code examples and exercises drawn from the 170‑page Prompt Engineering handbook. |  |
| RAG Techniques | More than 30 tutorials demonstrating Retrieval‑Augmented Generation methods, evaluation metrics, and best practices to build accurate and efficient RAG pipelines. |  |
| GenAI Agents | Guides and reference implementations for building chatbots, autonomous agents, and multi‑agent workflows with memory and tool integrations. |  |
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Subscribe - 🧑💻 r/EducationalAI – discuss prompts, RAG pipelines, and agent designs
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Connect
- ⭐ Star the repositories you use – it boosts visibility
- ☕ Sponsor via GitHub Sponsors or Buy Me a Coffee
- 📢 Share tutorials with colleagues and on social media
Thank you for helping keep Generative AI education free for everyone 🙏