A few days ago, a friend showed me how he uses Claude AI to generate reports about his online store. Some of the insights were straightforward, things you could easily get from a pivot table, like which color or size sells the most. Others were more involved, like figuring out how many orders come from coastal areas. Since he sells fishing gear, that kind of geographic insight could directly influence where to focus marketing efforts.
The real bottleneck in his workflow wasn’t the analysis, it was the setup. Every time, he had to log into the store’s back office, export an Excel file, and then upload it into Claude. It worked, but it was clunky and repetitive. So I offered to help automate it.
Many years ago, I was sending high altitude balloons to the sky with cameras attached. I wanted them to broadcast these images live to the ground. The solution I used was analog SSTV which ham with good antennas could receive. I also experimented with digital images over APRS. The idea was to decentralize the reception with numerous stations already operating and bridged to the internet.
In the past year there is a boom of local hams using Meshtastic, which is based on long range lora communication and packet relaying. I wondered if I could revisit this project using the new technology.
Over the past year, it’s been impossible to ignore the hype around AI coding tools. Headlines promised that AI tools would “redefine programming” and “let developers focus only on ideas, not code.” Friends and colleagues were raving about how AI was writing entire components for them in seconds. Naturally, I wanted to see for myself if these tools could really change the way I build apps.
My first test was building a mobile app using Flutter and Firebase. This should have been simple: with the built-in tools, you can spin up a working project with just two lines in the terminal. But when I asked AI to do it, things went downhill. Instead of giving me the straightforward setup, it spent hours trying to “engineer” a project structure. The result? Broken code, mismatched dependencies, and a project that wouldn’t even compile. I eventually gave up and went back to the manual two-line setup – which worked instantly.
That said, once the project was up and running, I found that for the mobile app things generally worked. AI could generate UI components, basic Firebase hooks, and navigation code with reasonable success. adding more and more screens and functionality was a breeze. It wasn’t perfect, but it saved some time, especially this is something I don’y usually do and I would have spent hours figuring out how thing should be done.
there were moments when it actually surprised me. While setting up authentication, it proposed a few field structures I hadn’t considered. They weren’t perfect, but they made me think differently about my data model. It felt less like “cheating” and more like brainstorming with a slightly eccentric colleague.
