Entertainment · Gaming · 1 hr ago
Free browser game recreates Toronto across a map nine times larger than GTA 5
COO is a free browser game that recreates Toronto as a large 3D city.
It was built using AI by Yegor Sak, co-founder of Windscribe, and the developer says it took seven days.
Its map covers about 630 square kilometres, nine times the size of GTA 5's map, using real streets, buildings, parks and landmarks.
Players control a government-operated robotic pigeon and can explore in three modes: Live, Operations and Free Roam.
Live reflects real-time weather, air traffic and time of day, while Operations gives players tasks and Free Roam lets them fly around freely.
The game lets players explore a detailed digital version of Toronto, including familiar places such as streets and coffee shops.
Players can try it for free on its official website, though the use of AI in making games remains a matter of debate.
COO is a free, browser-based 3D open-world game set in a recreation of Toronto.
Its map covers around 630 square kilometres, which the developer says is nine times the size of Grand Theft Auto 5’s map.
Players explore the city as a robotic pigeon in three modes: Live, Operations and Free Roam.
The game includes real-time weather, air traffic and time of day in Live mode, along with objectives in Operations mode.
According to Screen Rant, the game was developed in seven days using 10 parallel AI workstreams.
- Who
- COO was built using AI by Yegor Sak, co-founder of Windscribe.
- What
- A free, browser-based 3D open-world game recreating Toronto.
- When
- Not stated
- Where
- Toronto, Canada.
- Why
- Not stated
This story does not have two clearly opposing sides.
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- Map area
- Around 630 square kilometres
- Map size
- Nine times the size of Grand Theft Auto 5’s map
- Development time
- Seven days
- AI workstreams
- 10 parallel workstreams
- Game modes
- Live, Operations and Free Roam
What people are saying
real data: OpenStreetMap footprints for every building in Toronto, with heightsX from the City’s open 3D Massing dataset.
agents write procedural generators that dress each building in its district’s style.