Here is a collection of my hobbies, some tests, small projects, failed experiments and some successful ones, too!
And remember! “If things are not failing, you are not innovating enough.“ [Elon Musk]
Check out my GitHub repos for some small contributions to the FOSS world.
Let’s connect!
Have a question? Or need help with a similar problem? Find me on your favourite channel!
Start ML with about €200 and 200 hours: pick one course (fast.ai), use Mamba and PyTorch on Kaggle or Colab, build a safe but meaningful project, then win through data, honest evaluation, and steady iteration. Business sense multiplies impact.
Traditional project management fails in AI development due to high uncertainty, leading to wasted resources. Based on my 17 years in tech and research, I’ll show how Lean Startup principles can transform AI projects through rapid experimentation and validated learning, helping you deliver real business value faster.
Choosing a project management methodology when doing AI projects is like choosing a dog breed—there’s no perfect one, but you need to pick one that fits your needs. From the Waterfall’s rigidity to the chaotic YOLO runs, each approach has its quirks. Understanding these trade-offs is key to turning challenges into success.
This article presents a battle-tested framework that helps you identify winning AI projects before investing significant resources. By following these five criteria, you’ll be able to focus on technical execution with confidence, knowing you’re building something valuable.
The Day when:
– My Clouds Had Perfect Edges
– Life served me a humble pie right when I needed it most.