I asked chatGPT about Dave Ebbelaar and here is answer. Based on a thorough review of publicly available information, I’d like to share the following assessment regarding Dave Ebbelaar’s stated background in AI: 🔍 Verified Facts: - Education (2013–2019): Vrije Universiteit Amsterdam→ While the university does offer programs related to data science, no dedicated AI degree existed during this period. It’s likely that AI was covered as part of broader fields like business analytics or data-driven decision-making. - Internship at Ahold Delhaize (2019):→ Focused on marketing mix modeling for Albert Heijn. This is a typical marketing analytics project, with no direct connection to AI development. - Founder of Nouvelle Amsterdam (2021–2022):→ Ran a fashion e-commerce business, including content creation and digital advertising. Again, this aligns more with marketing and entrepreneurship than with AI engineering. - GitHub / public technical outputs:→ Most activity appears only from 2022 onward. There is no clear technical footprint showing AI system development from 2013 onward, as claimed. ✅ Assessment Summary: It seems unlikely that Dave Ebbelaar has 10+ years of deep technical AI experience.He appears to be a capable digital marketer or product creator who likely started working with AI in recent years — perhaps since 2021. The wording on his site appears exaggerated, intended to drive course sales, rather than supported by a documented technical legacy. Maybe Dave explain more..

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​

OpenAI just dropped a major update: the Responses API. Whenever OpenAI releases something like this, it changes the game for developers, forcing us to rethink how we build AI applications. In this week’s video, I break down exactly what this API does, what’s changing, and whether you should migrate your projects. Key updates: - It’s a superset of the Chat Completions API (meaning it does everything Chat Completions did—plus more). - New built-in tools: Web search, file search, and computer use. - Simplified API calls, but also more abstraction—which can be both good and bad. If you’re serious about staying ahead in AI development, you’ll want to watch this. Check out the full breakdown here

When building AI agents, you need them to understand your data—whether it’s PDFs, websites, or internal documents. Most tools for this are closed-source, requiring API keys and external platforms. But what if you could do it all in Python with an open-source library? In this week’s video, I show you how to build a fully open-source document extraction pipeline using Docling. You’ll learn how to: - Extract, parse, and chunk documents for AI processing. - Store and retrieve data efficiently with vector databases. - Build a working chat application that can answer questions based on your documents. Watch the video here.