Russian developer uses AI to build a crowdsourced map of gas stations that still have fuel

A Russian resident has built an online service on his own: an interactive map of gas stations that shows in real time where fuel is available, where gasoline has run out, which stations are clear and where lines have formed. The developer used neural networks to build it. The service runs directly in the browser and does not require a separate app.

How the gas station map works

The project's name, a "people's map," suggests the service most likely relies on data from drivers themselves. They mark whether a given station has fuel, whether the line is long or whether the station is closed altogether. This crowdsourced approach can refresh the picture much faster than the station chains or government agencies do. Neither has any mandate to publish up-to-the-minute data for every pump.

• The developer is a private individual, not a company
• The service runs directly in the browser, with no app to install
• The map marks stations with fuel, stations without fuel and stations with lines
• The developer used neural networks to build the service

Why the service caught on

Intermittent gasoline shortages at individual stations are not unusual across Russia's regions. Fuel demand rises unevenly during vacation season, and supply logistics don't always keep up. Under these conditions, drivers end up visiting several stations in search of a working pump, burning time and fuel on the hunt. A map that shows the current situation at each station removes some of that uncertainty and saves both time and gasoline.

The role of neural networks in development

The gas station map is a typical example of how modern AI tools lower the barrier to entry for software development. Just a few years ago, building a service like this, with mapping, a station database and a user interface, required a team of developers and months of work. Today, neural networks can generate a significant share of the code and help with backend logic and map integration. As a result, one person with an idea and basic skills can get a project to a working state alone.

How it differs from official services

Major mapping services such as Yandex Maps and 2GIS have spent years adding gas station data: prices, locations, reviews. But tracking real-time fuel availability and queue length specifically is not a priority for them. It is a narrow, local problem that flares up only occasionally and only in certain regions. Niche problems like this are exactly the kind that are easy to tackle on your own: there's no need to get a feature approved by a large product team, and you can quickly ship a minimum working version and keep tuning it based on feedback from early users.

What it means

Local services like this one, built by a single enthusiast with the help of AI, show that neural networks are shifting from a tool of big IT companies to a tool for individuals solving specific everyday problems, and doing it faster and cheaper than before.