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lol, love this. But I think you could even go a step back and ask ‘define data’

17 skills and a 3 hour podcast turning into 7 clips without touching it thats wild

Commenting to see if the reply is AI.

‼️ If you're serious about learning AI, - STAR the repository to follow the projects. https://fandf.co/3QKK7Rg

Indeed a very useful repo. Good to see practical usecases on RAG, AI Agents and AI + Cloud. Just hit the STAR button. Will share my learnings soon through a post :)

Cfbr!

Thank you for the invaluable resource and unconditional support

The agent-reasoning and agentic_rag implementations in this repo are standout features. I'm currently deep in the trenches building an agentic RAG system for the AWS Well-Architected Framework, and moving past simple retrieval to 'Cognitive Architectures' (like ReAct or CoT) is where the real value is. It’s one thing to get an LLM to chat; it’s another to build a harness that ensures data integrity across 3,000+ chunks. Great share!

That's a very useful post. Thanks a lot for sharing this with us.

Thankyou Abhishek for sharing valuable insights 👏

Great reason Abhishek Veeramalla thanks

Your resources are also goldmine Abhishek Veeramalla

Thanks for sharing this amazing github repository on use cases of rag ai agents and cloud indeed this github repository will help me in learning agentops and llmsops In the future

Fantastic Abhishek. Learning by building real, production-ready AI agents is the best way to solidify concepts.

Mike Pappas the cost angle is what jumps out for me. One of the most common conversations I have with early-stage founders is about running lean, and AI infrastructure costs are increasingly part of that equation. If the efficiency claims hold up at scale, this is the kind of development that changes what's actually accessible to smaller teams. Worth watching closely.

Thank you Abhishek veeramalla for sharing the resources

Thank you Abhishek for sharing valuable resource

Production-ready Agentic RAG isn't a suggestion anymore—it's the price of admission for 2026. Oracle’s move to open-source these specific implementations (FastAPI + Redis + 23ai) signals a massive shift in how we handle low-latency intelligence at scale. If you're still stuck in theoretical notebooks, you're already behind. Stop scrolling and start deploying.

Hi Abhishek Veeramalla, Thank you very much for sharing the information.. Most of the friends are Saying future is the AI and AI is the future 🔮.

Indeed very useful repo.