I've spent the last year watching something interesting happen in our field... Everyone's talking about GenAI. Every tweet, every product launch, every investor memo. But here's what's really happening: When you zoom in on the AI systems actually running in production today, almost none of them are GenAI. They're still powered by traditional ML — logistic regression, XGBoost, neural nets. But that won't last. I predict those numbers will completely flip over the next decade. GenAI is going to eat everything. But honestly, most developers aren't ready for that shift. Not because they aren't good - but because GenAI is evolving faster than any tech wave we've ever seen. There's no real roadmap. No clean, proven path to go from "I can prompt ChatGPT" to "I build production-ready GenAI systems." I've felt this gap myself. Even with a strong background in AI, I had to dig for answers, hack things together, and experiment like crazy to keep up. And when I started hiring engineers at Datalumina, I realized there's no proper training program for this new wave of AI engineering. So I built one. It started as an internal roadmap. Then I shared it within our community. The response? "This is exactly what I needed!" So now, it's official: The GenAI Accelerator is open for enrollment. ✓ A 6-week cohort-based program ✓ Built for developers who want to level up fast ✓ Focused on real, production-grade systems — not playground projects ✓ Based on 10+ years of building AI systems ✓ First cohort starts May 5th You'll learn techniques that aren't shared on YouTube or Medium. These are the same approaches used by big tech companies, but their employees can't openly share them online due to confidentiality agreements. But I don't care about competition. The AI market is growing exponentially. There's more than enough opportunity for every skilled developer. At Datalumina, we can't even take on 1% of the project requests coming our way. The demand for AI engineers is so massive that I'm not worried about "giving away secrets" - I'm focused on helping more developers build the skills needed to meet this demand.

Hey all! It's been a while since you've heard from me! Q1 has been crazy busy for me working on all the projects going on behind the scenes at Datalumina. I've only been able to get out a few videos. But I have another one prepared for you which is now live! This is an exciting one and I couldn't simply ignore... It's about Anthropic's Model Context Protocol (MCP) When I first encountered MCP in November 2024, I was skeptical. Another framework in the already crowded AI ecosystem? Another tool to add to the ever-growing list of technologies to learn? But then something interesting happened. As I dug deeper into MCP, I realized it wasn't just another framework—it was a fundamental protocol that could standardize how AI systems interact with the world around them. The adoption rate has been nothing short of remarkable. Looking at GitHub star history, MCP is on track to overtake all other AI frameworks in the next few months. I've just released a complete crash course for Python developers that covers everything from setting up MCP servers to production deployment. Whether you're building AI agents, chatbots, or other LLM-powered applications, MCP can simplify your development process. I'd love to hear your thoughts on MCP and how you're planning to use it in your projects! Keep coding, Dave P.S. I'm excited to announce that enrollment for the next cohort of our GenAI Accelerator is now open! Starting May 25th, this 6-week program will transform you into a production-ready AI engineer with hands-on training in LLMs, RAG, and our battle-tested GenAI Launchpad framework. Spots are limited, so check it out here: ​GenAI Accelerator Details​

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Graph-Based AI Agents Transforming Code Localization for Scalable Software Maintenance. https://github.com/gersteinlab/LocAgent Some Key Takeaways from the Research on LocAgent include the following: - LocAgent transforms codebases into heterogeneous graphs for multi-level code reasoning.   - It achieved up to 92.7% file-level accuracy on SWE-Bench-Lite with Qwen2.5-32B.   - Reduced code localization cost by approximately 86% compared to proprietary models. Introduced Loc-Bench dataset with 660 examples: 282 bugs, 203 features, 31 security, 144 performance.  - Fine-tuned models (Qwen2.5-7B, Qwen2.5-32B) performed comparably to Claude-3.5.   - Tools like TraverseGraph and SearchEntity proved essential, with accuracy drops when disabled.   - Demonstrated real-world utility by improving GitHub issue resolution rates. - It offers a scalable, cost-efficient, and effective alternative to proprietary LLM solutions. @Dave Ebbelaar cud you please create a demo on this and teach us.