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@risingodegua • 8,885 subscribers

MTS (Fullstack Product) @SpAItial_AI

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Anya Rossi

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Fun weekend project: using AI at Meta Sam3 to segment objects inside a SpAItial AI scene. Pipeline: -> Generate a world on SpAItial AI app and export as .ply -> PlayCanvas viewer to render splat + a uniform spatial grid over splat centers -> Capture frames + camera pose, POST to a local SAM3 server -> Lift each 2D mask → 3D: frustum-cull splats via the grid, reproject with the pose -> Recolor / isolate the segmented Gaussians This is all running locally on my Mac. Static scene, segment once, reuse persisted mask. All open source (link in comment), you can clone and build on top of this 👇🏽

Fun weekend project: using AI at Meta Sam3 to segment objects inside a SpAItial AI scene. Pipeline: -> Generate a world on SpAItial AI app and export as .ply -> PlayCanvas viewer to render splat + a uniform spatial grid over splat centers -> Capture frames + camera pose, POST to a local SAM3 server -> Lift each 2D mask → 3D: frustum-cull splats via the grid, reproject with the pose -> Recolor / isolate the segmented Gaussians This is all running locally on my Mac. Static scene, segment once, reuse persisted mask. All open source (link in comment), you can clone and build on top of this 👇🏽

22,375 просмотров • 3 месяцев назад

I just one-shotted a 3D world with proper collision physics using the SpAItial AI API and PlayCanvas Here's how i did it: -> Install the spaitial-playcanvas-world skill: npx skills add spaitial-dev/spaitial-playcanvas-world -> Open Cursor or your preferred coding agent -> Paste the following prompt or use your own: "Using /spaitial-playcanvas-world skill and the spaitial api key provided build a PlayCanvas game from this world prompt: An empty monumental desert sci-fi palace interior with colossal stone arches, sand-dusted floors, bronze industrial machinery, filtered sunlight through high slit windows, carved geometric walls, long ceremonial corridors, immense scale, cinematic warm shadows, ancient-futurist architecture, quiet unoccupied environment. No people, no humans, no characters." This skill covers World and Collision mesh generation using the SpAItial api, and viewer + physics using Playcanvas.

I just one-shotted a 3D world with proper collision physics using the SpAItial AI API and PlayCanvas Here's how i did it: -> Install the spaitial-playcanvas-world skill: npx skills add spaitial-dev/spaitial-playcanvas-world -> Open Cursor or your preferred coding agent -> Paste the following prompt or use your own: "Using /spaitial-playcanvas-world skill and the spaitial api key provided build a PlayCanvas game from this world prompt: An empty monumental desert sci-fi palace interior with colossal stone arches, sand-dusted floors, bronze industrial machinery, filtered sunlight through high slit windows, carved geometric walls, long ceremonial corridors, immense scale, cinematic warm shadows, ancient-futurist architecture, quiet unoccupied environment. No people, no humans, no characters." This skill covers World and Collision mesh generation using the SpAItial api, and viewer + physics using Playcanvas.

22,838 просмотров • 3 месяцев назад

A fun way to try the new Claude Fable. I asked it to train a bot using RL to navigate 3D worlds created with SpAItial AI. This is all running in the browser including RL training! Link to demo and code in comments What you can do: -> Train a bot from scratch, watch it learn in minutes, then drop it into a world it has never seen and it keeps navigating obstacles. -> The worlds are real scenes generated from a text prompt or a photo via the SpAItial AI API (you get a splat + a collision mesh). -> Mesh is made into a 2D walkability + height grid that the RL environment runs on. -> The trick to generalization: the policy only ever sees egocentric inputs. A 16-ray lidar, the goal's body frame, and its speed. No global coordinates, no map. So it learns navigation, not a memorized world.

A fun way to try the new Claude Fable. I asked it to train a bot using RL to navigate 3D worlds created with SpAItial AI. This is all running in the browser including RL training! Link to demo and code in comments What you can do: -> Train a bot from scratch, watch it learn in minutes, then drop it into a world it has never seen and it keeps navigating obstacles. -> The worlds are real scenes generated from a text prompt or a photo via the SpAItial AI API (you get a splat + a collision mesh). -> Mesh is made into a 2D walkability + height grid that the RL environment runs on. -> The trick to generalization: the policy only ever sees egocentric inputs. A 16-ray lidar, the goal's body frame, and its speed. No global coordinates, no map. So it learns navigation, not a memorized world.

12,797 просмотров • 3 месяцев назад

Thrilled to announce @ByteMindApp is now on the App store. A couple of weeks ago, i was brainstorming ideas for the bolt.new hackathon, and got the idea of building a gamified, personalized learning app. What started as an idea has turned into a full product, and i'm so happy to see it go live. Please try it out and show some love! ❤️

Thrilled to announce @ByteMindApp is now on the App store. A couple of weeks ago, i was brainstorming ideas for the bolt.new hackathon, and got the idea of building a gamified, personalized learning app. What started as an idea has turned into a full product, and i'm so happy to see it go live. Please try it out and show some love! ❤️

23,746 просмотров • 1 год назад

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