I just released UrbanOS-PoC, a fully open-source system that uses real-time data to optimize public transport flow, without surveillance, black-box models, or cloud lock-in.
It runs on general-purpose hardware (Docker + Postgres + Python), uses MQTT for live data ingestion, and processes anonymized geospatial behavior to produce real-time routing and schedule predictions, without needing any personal identifiers.
I built this system solo over the last few years while raising two kids.
I kept it closed for a while, hoping it might help our situation, but since it doesn’t rely on buzzwords, the attention was limited. So now it’s free for anyone who wants to work on urban planning, public mobility, or smart cities, without compromising privacy.
No prompts
No user tracking
No centralized platform
Just logic, movement, and open AI for cities
You can simulate live data, inspect all modules, and spin it up locally on a laptop.
Whitepaper, API docs, and demo setup included.
I just released UrbanOS-PoC, a fully open-source system that uses real-time data to optimize public transport flow, without surveillance, black-box models, or cloud lock-in.
It runs on general-purpose hardware (Docker + Postgres + Python), uses MQTT for live data ingestion, and processes anonymized geospatial behavior to produce real-time routing and schedule predictions, without needing any personal identifiers.
I built this system solo over the last few years while raising two kids. I kept it closed for a while, hoping it might help our situation, but since it doesn’t rely on buzzwords, the attention was limited. So now it’s free for anyone who wants to work on urban planning, public mobility, or smart cities, without compromising privacy.
No prompts No user tracking No centralized platform Just logic, movement, and open AI for cities
You can simulate live data, inspect all modules, and spin it up locally on a laptop. Whitepaper, API docs, and demo setup included.
Repo: https://github.com/pablo-chacon/UrbanOS-POC Whitepaper: GitHub Wiki