Check out the latest article in My AI Diary: Teaching in the Age of Vibe Coding linkedin.com/pulse/teaching-… via @LinkedIn

96% of the data appears to be Shot Spotter where no race was identified.

Here's another vibe-coded webapp. This one is a live tracker of the NYC Subway trains. I have it set to refresh every 30 seconds using an API, which requires Python (or Node.js), but this is another example of something written entirely by Claude. This was 10 total prompts. A one-shot instructional prompt launched on my phone. Then 9 follow up prompts to correct various errors over the course of 30 minutes (while doing other things). I might try a few prompt iterations on this to better show the bread crumbs (maybe lines), and visualizing the current location vs. the next stop vs. future stops.

I still tell people the story, that you introduced me to Python back in 2002. I had heard of it, but you kept talking about it and so I gave it a try. Spent more time in R for the next 10 years, but then found my way back to Python. #MitchtheFortuneTeller We called Perl, "The Black Perl". Rick used Perl a number of times to solve some one-off things. It was like an old rusted, but trusted screwdriver that he pulled out of his toolbox.

Yesterday this application didn’t exist. Today it does. This was 100% vibe-coded. I didn’t write or edit a single line of code. Before you watch the video, here’s what you’re about to see. Nothing is sped up. The app is genuinely this fast. It pulls real-time traffic data for 30 U.S. cities using the HERE API and maps it on a Mapbox map. The "jam factor" is calculated live and displayed in the "jam bars" and directly on the map. Then I add five more cities. Each time, the data is instantly added, the bars automatically re-sort, and new points appear on the map. Across the top, BANs show the average jam factor, the most congested city, the least congested city, and the last update time. All updating dynamically. Then I pick the custom colors based on the jam factor, customize the color ramp scale, and even modify the bounding box (the area used to calculate traffic conditions) from rural to dense urban settings, or manually define the geography. How is this done? Knowing what to ask for and iterating with GenAI. I started building this app while I was grading assignments, and just checking back in on the progress! From zero to a real-time, interactive, API-powered dashboard. That’s where we are. Insights this morning: Indianapolis had a number of car accidents slowing down traffic, briefly popping it to the top of the list over NYC. But at the moment Miami, FL has the heaviest traffic.

I trust the data, it's the language in their report that seems a bit much. R went from 1% to 2.1%. Perl, not even in the top 10, went from .5% to 1.7%. Python dropped from 24% to 22%, so "gradually gaining ground at Python's expense, notably R and Perl" seems way overstated. These shifts look more like volatility, not "gaining ground", and their phrase “at Python’s expense” implies causality. And no mention in that sentence that C# went from 4% to nearly 7%, the biggest change. That seems more like "gaining ground". I doubt we'll see R or Perl reach even the 5-10% range. But good news, we can keep tracking and we'll know. So kudos to them for continuing to collect this data and report it.

And Wellington?