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Thanks so much for not keeping this to yourself Abhishek Veeramalla. Thanks to Oracle for making it an open resource.

This is massive, I will really love to explore more deeper

Thank you very much for putting this out here

Its foss??

Thanks for sharing 🙌

Murali Doss I recommend. It’s one of the fastest ways to solve a problem and understand it better.

Thank you for this

Really helpful. Thanks for sharing the insights.

Can we get some projects on Scratch?

I just started my journey in AI automation engineering 🫠 and I think this resource will be useful. Thank you for sharing 😊 Abhishek Veeramalla

Spot on, Abhishek Veeramalla. Theory only takes you so far; true mastery happens when you get your hands dirty with systems solving real problems. The focus on multi-agent architectures and specialized financial Artificial Intelligence agents in this repository is brilliant. For anyone looking to understand enterprise-grade deployment and robust data workflows, this repository is an absolute must-have resource. Thank you for sharing this.

Everyone is forced to learn AI, no one wants to learn it! 😭 You need to update your words

Small doubt

https://www.linkedin.com/groups/19178002

Let’s connect 🤝 https://www.linkedin.com/in/mallikarjun-r-a85685367

Real understanding comes faster when concepts are tested through building, not just consuming theory.

This is very insightful!

Very helpful.

Thanks for sharing this 👌💯

One big question that stopped me while learning AI/LLMs: Till now, I understood the basics of AI architecture, learning algorithms, and semantic weights. But what really fascinates me is this: How do large LLMs discover and adjust the “right” weights to generate accurate answers for completely new questions they’ve never seen before? I understand the basics of weights and training logic, but this is the point where my curiosity became much deeper than my understanding. Would love to hear insights from people working deeply in LLM training/research.