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#Nianticspatial Reel by @aiintellects - Pokémon Go looked like a global game about catching virtual creatures. It was also quietly building a massive map of the real world. As players explo
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@aiintellects
Pokémon Go looked like a global game about catching virtual creatures. It was also quietly building a massive map of the real world. As players explored cities, parks, and landmarks, the app encouraged them to scan locations and point their cameras at real-world objects. Those images were collected and processed into 3D models of physical spaces, helping Niantic build a detailed spatial map used for augmented-reality positioning. Today that system powers Niantic’s Visual Positioning System (VPS), which allows devices, robots, and AR applications to locate themselves with centimeter-level accuracy by matching camera images to a database of real-world scenes. Behind the game was a larger ambition: creating a planet-scale 3D model of the world built partly from millions of scans contributed by players exploring their surroundings. #Al #PokemonGo #Data #AugmentedReality #TechExplained
#Nianticspatial Reel by @better.engineer - The millions of players who spent years scanning landmarks while playing Pokémon GO have unknowingly built the most advanced mapping technology for au
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@better.engineer
The millions of players who spent years scanning landmarks while playing Pokémon GO have unknowingly built the most advanced mapping technology for autonomous robots. Niantic Spatial recently partnered with Coco Robotics to use this data—a staggering 30 billion images—to help delivery robots navigate dense city streets where GPS often fails. This Visual Positioning System (VPS) allows robots to pinpoint their location with centimeter-level precision by comparing their live camera feeds to the 3D models created from player scans. While some see this as a privacy concern, others view it as a brilliant piece of incentive design that turned a mobile game into a massive, real-world engineering project. Because these robots rely on constant visual data, maintaining system uptime is critical. Engineers use observability tools like Better Stack to monitor these complex AI workflows in real-time, ensuring that "edge cases" like changes in weather or lighting don't ground the fleet. It turns out that getting Pikachu to run around a park and getting a robot to deliver a pizza are actually the exact same problem. #Robotics #PokemonGO #Niantic #AI #TechNews #BetterEngineer #BetterStack #Engineering #FutureTech #Logistics
#Nianticspatial Reel by @aice.services - Millions of people thought they were just catching Pokémon. 🎮 In reality, they were helping build one of the largest real-world AI datasets ever crea
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AI
@aice.services
Millions of people thought they were just catching Pokémon. 🎮 In reality, they were helping build one of the largest real-world AI datasets ever created. Niantic revealed that Pokémon Go collected 30+ billion images through AR scans and gameplay. Players captured: • Streets • Shops • Parks • Landmarks From every angle, lighting condition, and environment. This data is now being used to power visual navigation AI, including delivery robots. No traditional company could have built this dataset at this scale, this fast, or this cheaply. But there’s a catch. Most users had no idea their scans could be used to train commercial AI systems. This raises a bigger question: 👉 Are we unknowingly building the data layer of AI? The biggest AI advantage today isn’t just models. It’s who owns the data. Want more deep dives on AI, business, and hidden tech trends? Join the AICE newsletter — link in bio / first comment. 🚀 #AI #ArtificialIntelligence #PokemonGo #DataPrivacy #BigData #MachineLearning #TechNews #FutureOfWork #AICE
#Nianticspatial Reel by @the_l1r - This reel explains about how your childhood obsession just became the "brain" for future robots. 🤖✨ Did you know that every time you scanned a PokéS
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@the_l1r
This reel explains about how your childhood obsession just became the "brain" for future robots. 🤖✨ Did you know that every time you scanned a PokéStop, you weren't just catching a Pikachu? You were actually helping Niantic train a Large Geospatial Model. With over 30 billion images collected, they’ve built a visual GPS so precise it helps delivery bots navigate sidewalks within centimeters. If a gaming company can do this, imagine what’s happening with your other favorite apps. Is the "gamification" of our world just a massive data collection experiment? 🧐 Keywords: Pokémon GO, Niantic AI, Robotics training, Geospatial Model, AI Data, Augmented Reality, Future Tech, Robot Navigation, Data Privacy. Tags: #PokemonGO #Niantic #AI #Robotics #TechNews ArtificialIntelligence FutureTech DataPrivacy CyberSecurity TechTrends2026
#Nianticspatial Reel by @aiintellects - Pokémon Go looked like a global game about catching virtual creatures. It was also quietly building a massive map of the real world. As players explo
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AI
@aiintellects
Pokémon Go looked like a global game about catching virtual creatures. It was also quietly building a massive map of the real world. As players explored cities, parks, and landmarks, the app encouraged them to scan locations and point their cameras at real-world objects. Those images were collected and processed into 3D models of physical spaces, helping Niantic build a detailed spatial map used for augmented-reality positioning. Today that system powers Niantic’s Visual Positioning System (VPS), which allows devices, robots, and AR applications to locate themselves with centimeter-level accuracy by matching camera images to a database of real-world scenes. Behind the game was a larger ambition: creating a planet-scale 3D model of the world built partly from millions of scans contributed by players exploring their surroundings. #Al #PokemonGo #Data #AugmentedReality #TechExplained
#Nianticspatial Reel by @stable.ai2 - Pokémon GO players unknowingly helped build one of the largest AI datasets in the world. Over the past eight years, millions of players scanned stree
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@stable.ai2
Pokémon GO players unknowingly helped build one of the largest AI datasets in the world. Over the past eight years, millions of players scanned streets, parks, landmarks, and storefronts while simply playing the game and hunting Pokémon. According to Niantic, those scans have now produced a dataset of more than 30 billion real-world images, capturing environments from different angles, lighting conditions, weather, and times of day. The company is now using that data to train visual navigation AI, technology that helps delivery robots and other autonomous systems understand and move through real-world environments. What makes the dataset particularly valuable is how it was created. Instead of fleets of mapping vehicles, the data was collected organically by millions of players documenting the physical world while they played. Most people thought they were just catching Pokémon. But in reality, they were helping train the next generation of spatial AI. Some of the most valuable AI datasets in the world may not be built in labs or data centers - they’re being created by millions of people who don’t even realize they’re building them. Follow @stable.ai2 for daily insights that keep you ahead in AI, Business & Tech Credit: markgadala on X #ArtificialIntelligence #Stableai #PokemonGO
#Nianticspatial Reel by @tomorrow_dot_ai - Pokémon GO players unknowingly helped build one of the largest AI datasets in the world 🌍📱 Over the past 8 years, millions of players scanned street
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@tomorrow_dot_ai
Pokémon GO players unknowingly helped build one of the largest AI datasets in the world 🌍📱 Over the past 8 years, millions of players scanned streets, parks, landmarks, and storefronts — just by playing the game. According to Niantic, those scans have created a dataset of 30+ billion real-world images — captured across different angles, lighting, weather, and times of day. Now that data is being used to train visual navigation AI — the kind that powers delivery robots and autonomous systems to understand and move through real environments. What makes this powerful: It wasn’t built by fleets or labs. It was built organically by millions of people who thought they were just catching Pokémon. That’s the real shift. Some of the most valuable AI datasets aren’t being created in data centers — they’re being generated quietly, at scale, by everyday users. So the question is: How much of the future are we already building… without realizing it? 👀 🎥 Credit: markgadala on X 🔁 Follow @tomorrow_dot_ai for real AI insights shaping the world behind the scenes #ArtificialIntelligence #SpatialAI #PokemonGO #AI #technology2020
#Nianticspatial Reel by @100xengineers (verified account) - This augmented reality game from 2016 was secretly building a 30 billion image dataset to train future robot navigation. The name of this game was Po
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@100xengineers
This augmented reality game from 2016 was secretly building a 30 billion image dataset to train future robot navigation. The name of this game was Pokémon Go, and players have spent nearly a decade scanning streets, storefronts, and city corners to catch virtual creatures. But Niantic was quietly stitching all those scans into something massive, 30 billion real world images of urban environments. And now, that exact dataset is being used to navigate delivery robots through city streets. Here’s how it works. GPS is actually terrible in dense cities. Tall buildings reflect signals and throw off location by meters. So Niantic built something called a Visual Positioning System. Instead of satellites, the robot matches what its camera sees against Niantic’s prebuilt street level map and pinpoints itself with centimeter level accuracy. Coco Robotics, which runs about 1,000 delivery bots across cities like LA, Miami, and Chicago, is already using it. The crazy part? Niantic sold Pokémon Go itself, but kept the map. That data is now the entire business. I really feel sad for gamers like myself who thought they were hunting Pikachu, while they were actually training the robots of the future.
#Nianticspatial Reel by @thebhuvanyusharma - For 10 years, players of Pokémon Go weren't just catching Pokémon… They were unknowingly building a 30 BILLION image dataset powering real-world AI �
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@thebhuvanyusharma
For 10 years, players of Pokémon Go weren’t just catching Pokémon… They were unknowingly building a 30 BILLION image dataset powering real-world AI 🤯 From streets to landmarks, every scan helped train systems that now guide robots without GPS. The real twist? The game was free… you were the data. #AI #PokemonGo #DataEconomy #TechTruths #FutureTech
#Nianticspatial Reel by @10xaiengineer - Pokémon Go looked like a global game about catching virtual creatures. It was also quietly building a massive map of the real world. As players explor
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10
@10xaiengineer
Pokémon Go looked like a global game about catching virtual creatures. It was also quietly building a massive map of the real world. As players explored cities, parks, and landmarks, the app encouraged them to scan locations and point their cameras at real-world objects. Those images were collected and processed into 3D models of physical spaces, helping Niantic build a detailed spatial map used for augmented-reality positioning. Today that system powers Niantic's Visual Positioning System (VPS), which allows devices, robots, and AR applications to locate themselves with centimeter-level accuracy by matching camera images to a database of real-world scenes. Behind the game was a larger ambition: creating a planet-scale 3D model of the world built partly from millions of scans contributed by players exploring their surroundings. #Al #PokemonGo #Data #Augmented Reality #TechExplained
#Nianticspatial Reel by @ai.news.24 - Pokémon GO players unknowingly helped build one of the largest AI datasets in the world. Over the past eight years, millions of players scanned stre
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AI
@ai.news.24
Pokémon GO players unknowingly helped build one of the largest AI datasets in the world. Over the past eight years, millions of players scanned streets, parks, landmarks, and storefronts while simply playing the game and hunting Pokémon. According to Niantic, those scans have now produced a dataset of more than 30 billion real-world images, capturing environments from different angles, lighting conditions, weather, and times of day. The company is now using that data to train visual navigation AI, technology that helps delivery robots and other autonomous systems understand and move through real-world environments. What makes the dataset particularly valuable is how it was created. Instead of fleets of mapping vehicles, the data was collected organically by millions of players documenting the physical world while they played. Most people thought they were just catching Pokémon. But in reality, they were helping train the next generation of spatial AI. Some of the most valuable AI datasets in the world may not be built in labs or data centers - they’re being created by millions of people who don’t even realize they’re building them. Follow @infinitemindsai for daily insights that keep you ahead in AI, Business & Tech Credit: markgadala on X #ArtificialIntelligence #SpatialAI #PokemonGO
#Nianticspatial Reel by @atlasberry008 (verified account) - Pokémon Go wasn't just a game. It helped collect over 30B images of the real world, now being used to train robots like Coco's delivery bots to navig
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@atlasberry008
Pokémon Go wasn’t just a game. It helped collect over 30B images of the real world, now being used to train robots like Coco’s delivery bots to navigate cities. This is the same problem companies like Tesla, Waymo, OpenAI, and Figure are all trying to solve: real-world data for robotics. Pokémon Go, Niantic, Coco Robotics, Tesla, Waymo, OpenAI, Figure, robotics, computer vision, AI datasets #ai #robotics #computervision #startup #data

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