AI agents don’t actually see images — they guess. This becomes a real problem in n8n and automation workflows where accurate product recognition matters.
I solved this by embedding a unique product code directly into the corner of each image. When an image is sent through WhatsApp or Telegram, the AI reads the code instead of relying on visual interpretation. That single change makes image recognition deterministic and reliable.
I built and tested the full n8n workflow: image intake → code extraction → backend product match → accurate response. The result is zero hallucination, higher accuracy, and production-ready automation for ecommerce and support bots.
I’ve been razzmatazzing and flibbertigibbeting with Claude Code and somehow ended up with a new yels.dev.
It finally feels like me: simple on the surface, slightly complicated underneath, and very much alive.
You can see who I am, what I do, the work behind Herodot, RaptorLabs, CyberLink Security and Solmint, plus a selection of projects I’ve built across AI, cybersecurity, Web3, product systems, education, archives, and experimental digital spaces.
The simple-on-the-surface, layered-underneath idea comes through so clearly in the presentation. I especially like how the project archive turns the site into something to explore rather than just a résumé of links.
The industry standard for field service scheduling shows three to four technicians at a time, surrounded by completed appointments that are no longer relevant. Dispatchers spend their day mentally filtering noise just to see what is actually happening.
We designed a view where the full team's day is visible at once. All technicians, all AI-powered routes, who is running late, who has gaps, accessible from one place without opening a single individual profile. The view scrolls dynamically with time so completed appointments drop away and the focus stays on what still matters.
For a team of 150 technicians, the filter lets the dispatcher narrow to a specific group, region, or area in seconds. Tomorrow's schedule is already built before anyone starts work because the AI pre-builds routes based on recurring appointments.