Digital Wire: Digital business, digital transformation, APIs, AI and payments

In the past few months, I've embarked on an experiment that has fundamentally changed how I approach software development. I've been "vibe coding" - essentially directing AI to build software for me without writing a single line of code myself. This journey has been eye-opening, and I'd like to share what I've learned and where I think this is all heading. My Vibe Coding Journey I started vibe coding with Claude and Anthropic's Sonnet 3.5 model, later upgrading to Sonnet 3.7, Claude Code, and other tools. My goal was straightforward but comprehensive: create a CRM system with all the features I needed: - Contact management (CRUD operations, relationships, email integration, notes) - Calendar management (scheduling meetings, avoiding conflicts) - Group management for organizing contacts - A campaign system with templates - A standalone application using the CRM's APIs for external contacts to book meetings directly The technical evolution of this pro...

Over the last decade, two seismic waves have taken shape in the digital realm. On one hand, we have the cryptocurrency movement, championed by Bitcoin and its ideological siblings, seeking to redefine money and power structures. On the other, artificial intelligence (AI)—and especially its recent breakout star, generative AI (GenAI)—is slipping into our everyday lives, reshaping how we work, create, and interact. As Bitcoin edges toward a mythical $100,000 valuation, and AI quietly proliferates, a fascinating contrast emerges: Are we witnessing the technological upheaval crypto once promised, or are we seeing existing institutions reinforced by the subtle genius of AI? Bitcoin’s Institutional Crown—and Limited Utility For years, Bitcoin has been heralded as digital gold, a hedge against uncertainty and the ultimate “people’s money.” If it hits $100K, it will symbolically confirm its acceptance by the very institutions it once aimed to circumvent. Big players—banks, asset managers, and ...

I did a little experiment. I asked ChatGPT 4o (in the cloud) and the Llama 3.1 70b model (locally via Ollama), the same question: "What verse changes did Schiller make for Beethoven's 9th symphony?". Both models got the right answer, in fairness GPT 4.o did it much faster and had a much more elaborate answer than Llama3.1 70b that took a good 2 minutes (running on my desktop PC!). But in the end, I liked the answer from Llama 3.1 better because it was concise, to the point, minimalistic (as you would expect from a 70b model) and in a way more helpful.  If you want to follow the the experiment in real-time, I've recorded it for just that purpose. This is also the reason why this is a blog post and not just an update, because I can upload more than one video file.  ChatGPT 4o: Llama 3.1 (70b) via Ollama (jump to 50 seconds, it takes a while to load the model):  What do you think?

If we listen to the media, business leaders, and the press, we should be getting behind the AI wagon because of its potential to automate many of the processes everyday companies struggle with. I don’t dismiss this notion entirely because I think it’s true if you have the ability to integrate this technology in a meaningful way. For example, the startup company " scrambl " (full disclosure, I’m a minority investor) is making use of gen-AI by "understanding" CVs (curriculum vitae) from applicants and identifying the skills to match them to open positions. This works great – I have seen this in action, and while there are some misses, most of that "normalization of skills" works. There are other promising examples, such as Q&A systems to understand the documentation of a complex environment. When combined with RAG ( retrieval augmented generation ), this has the potential to significantly reduce the time it takes to make complexities understandable. But ...

  If you believe Terushi Shimizu or rather, the way the press is formulating it , then camera phones will have better image quality in 2024 than your trusty DSLR or mirrorless digital camera. He backs this up with sensor technology advancements and computational photography. He has a point.     However, as a digital camera enthusiast myself, I must strongly disagree with this point of view. The message might be interpreted in such way that its meaning reflects a view that we are no longer bound by physics to get the best image quality.     The thing is this, the bigger your camera sensor, the more photons it can capture. However, this comes at the realization that big sensors require big lenses which in turn makes the camera big and heavy. I’m simplifying of course, but that’s physics. For camera makers it is therefore always a question of tradeoffs: do you want better image quality or do you want a smaller and lighter camera. Camera phones or cameras in smartph...