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Thanks for sharing! I recently started one of the Columbia courses, will keep these as my reference for the assignments.

This is exactly what the AI space needed, Abhishek. Oracle just solved the biggest gap in AI education — the jump from tutorial to production. 🔥 After building AI-powered marketing workflows for real campaigns, I can tell you: theory doesn't move the needle. Production-ready apps with persistent memory, multi-agent orchestration, and actual deployment pipelines — that's what separates AI enthusiasts from AI practitioners. The agent-reasoning framework and finance-ai-agent-demo are gold. Anyone still learning from scattered tutorials needs to bookmark this immediately. Oracle just raised the bar. Appreciate you sharing this! 🚀 #AI #Oracle #MLOps #GenerativeAI #MultiAgentSystems #RAG #ArtificialIntelligence #DigitalTransformation #MachineLearning #AIOps #TechLeadership #OpenSource

Thank you for sharing this important resources

Thank you for sharing this

There is a reason people stop scrolling for something that feels real and lived in versus something that sounds polished but hollow because the difference is something you genuinely feel before you even finish reading Abhishek Veeramalla

What is the relation between the photo and the post?? Seems like spam

Esme apni photo lagane bali kya baat hai?

The gap between I finished the course and I can build this in production is where most developers get stuck. Resources like this that show complete working systems not just isolated code snippets are genuinely rare. As a Full Stack Dev this is going straight into my learning roadmap. Appreciate you and Oracle for making this accessible! 🙏

This is the kind of resource that shortens the learning curve massively. A lot of people say they want to learn AI… but they stay trapped in tutorial loops. Watching videos. Saving threads. Never actually building. What stood out to me here is the focus on production-ready systems.

👏🏻

Thanks for sharing the video Abhishek

Good luck👏🏻

Gobinda Bhattacharjee Advise please

Abhishek Veeramalla This is exactly the kind of practical AI learning I wish more people focused on 👏 There's a big difference between learning prompts and actually building something end-to-end. Coming from a Project Management background (not a developer), I recently used GenAI prompting to create a Project Management Web Tool that includes Gantt, Agile boards, RAID tracking, dashboards and AI-based project prediction. Made it freely available so people can explore both the live demo and the source code: 🚀 Demo: https://ppm-demo.netlify.app 💻 GitHub: https://github.com/daip85/offline-it-project-manager-workspace Would love to see more examples where domain experts use AI to solve real-world problems rather than just theory.

Bridging the gap between theory and building is always the hardest part. Seeing production-ready application references all in one hub is amazing. Definitely checking out the repository tonight!

#cfbr

Thank you for sharing! Abhishek Veeramalla

Abhishek Veeramalla The gap between "I understand AI" and "I can build with AI" is where most people get stuck for months. Having real codebases to pull apart changes that completely. You stop guessing how agents are supposed to work and start seeing the actual decisions behind them. The agentic RAG and agent reasoning implementations are what caught my eye. Those two alone cover problems most businesses I work with are actively trying to solve right now. Saving this one. Thanks for sharing it. Uchenna Richard

This is incredibly valuable 🔥 I like that the focus is on practical implementation and real-world AI systems, not just theory. Resources like this really help beginners connect learning with actual building. Thanks for sharing 🙌

Honestly, repositories like this save people a lot of unnecessary confusion. Sometimes the hardest part of learning tech is not even the learning itself… it’s knowing where to start from without getting overwhelmed. Having everything structured in one place makes the process feel more approachable.