Show HN: NYCerebro, semantic search of NYC traffic cams (written by v0)
nycerebro.vercel.app
nycerebro.vercel.app
While the app is cool, the thing that blew my mind is that the entire app was coded by Vercel's v0 agent. In other words: I did not write a single line of code to create the app (though my teammate did write the backend scraper & DB filler by hand).
[1] Writeup: https://blog.roboflow.com/nycerebro/
[2] Repo (including the generated code + initial meaty prompts): https://github.com/yeldarby/nycerebro
[3] v0 session: https://v0.dev/chat/nyc-erebro-app-RwzRUEMGveH?b=b_6AuWalvG7...
I can't imagine someone non-technical building something like this with prompting. The success of the project was highly dependent on my direction of the model to do what I wanted it to do (even though I gave it leeway in exactly how to do it). It did feel a bit like managing another engineer to do something vs doing it myself.
I don't use agents like this in my day to day work yet (I experimented with OpenHands a couple of months ago but it was frustrating, expensive, and took just as long as doing the task myself). But I'm thinking I probably will be a year from now.
A few times when the model got stuck I copy/pasted some stuff into o1 and pasted its response back into v0 (felt kind of like "escalating" to a more senior engineer) and that helped it get unstuck. Future models will be even more capable than o1. I imagine there will likely need to be a UI for "bringing in the big guns" of a smarter model in the future even if the grunt-work is done by a fast+cheap base model.
There's probably also something to letting the model "speak its native tongue". I don't know next.js but letting the model work with patterns it's been trained on probably helped it be more effective (compared to having OpenHands work in my own codebase using a structure it's unfamiliar with).