After two years of building with GenAI, here’s what I wish I’d had from day one... This has been a long time in the making, and I’m excited to finally pull back the curtain on what we’ve been building behind the scenes: The GenAI Launchpad — officially launched on Product Hunt today! 🎉 For the past two years, my team and I at Datalumina have been deeply involved in the world of AI, building solutions with large language models (LLMs) for clients across industries. Each project taught us so much about what it takes to bring AI to life in practical, high-impact ways. But there was one recurring challenge... We spent way too much time setting up project structures, handling integrations, and putting out fires in the infrastructure — leaving less time for the real AI work, the work that brings ideas to life. Not only was setup eating into our time, but we also found that the agent frameworks on the market were just too optimistic. Real-world use cases are more complex and demand reliability and precision that many frameworks simply can’t deliver. So, we got to work! 👷🏼‍♂️ And after two years of trial and error, working with every system and structure you can imagine, we built our own solution. The GenAI Launchpad is the result of our journey — a project repository that streamlines everything from initial setup to deployment, ready to handle the demands of production at scale. And the time savings? ⏳ We’ve calculated that it saves us over 50 hours per project on average, so we can dive right into the creative work that actually advances AI. Today, we’re launching the GenAI Launchpad to share that time-saving power with you — our community of fellow AI enthusiasts and builders. This is more than just a repository; it’s a battle-tested, engineer-approved blueprint that I wish I’d had when we started. It’s here to help you skip the headaches, bypass the boilerplate, and focus on what matters: building innovative AI solutions for real-world problems. If you’ve ever spent weeks fighting project setup, only to finally reach the real work, then you’ll understand why I’m so excited to share this.

@Dave Ebbelaar I was going over the Agent Framework will Fail video, in the video your mentioned the data pipeline, is any of the code included in any of the classroom training.

When building RAG systems, embeddings + text search is better than embeddings on their own Combining these techniques together is called Hybrid Search. In this tutorial, I walk you through building a hybrid search pipeline using just PostgreSQL. On top of that, we'll also look at how we can further improve the accuracy using Reranking. Curious how a hybrid system works and how it can optimize your search accuracy for AI applications? Watch the video here P.S. I was supposed to record this from my new office, but, well… life had other plans. So instead, enjoy this kitchen table special — different setup, same Dave vibes. I think it turned out pretty alright!

So far 16 lessons... This repository provides tutorials and implementations for various Generative AI Agent techniques, from basic to advanced. It serves as a comprehensive guide for building intelligent, interactive AI systems. https://github.com/NirDiamant/GenAI_Agents

[Adding second video to my old post] Yes that's totally true, you learn best when you teach. Well, so... I started a YouTube channel to teach basic SQL 😅 And indeed learned some very basic things in the process. After trying to shoot and edit and etc etc, now I respect Dave (and other YouTubers) even more 🫡 Feedback is very very welcome!