👁 192
TripoSplat
Generate 3D Gaussian models from a single image
I asked a coding agent to build a beautiful website showcasing the monuments of Paris as 3D Gaussian splats. I never opened an image generator. I never touched a 3D reconstruction tool. The agent produced every asset (the images and the 3D splats) by calling two Hugging Face Spaces directly, then wired them into a cinematic viewer.
Here's the result, live as a static Space:
This post is about how that's possible now, and why I think it's a preview of how a lot of multimedia software gets built from here on.
Mitchell Hashimoto recently described a shift he calls the building block economy: the most effective path to software is no longer a polished monolith, but small, well-documented components that others (increasingly agents) can assemble. His key observation: AI is okay at building everything from scratch, but it is really good at gluing together proven pieces.
That thesis has mostly been told with code libraries. But the same forces are hitting multimedia AI. The hard part of using a state-of-the-art image model, a video model, a TTS model, or a 3D reconstruction model was never the model. It was the integration: SDKs, weights, GPUs, input formats, polling. If each model were instead a documented, callable block, an agent could glue them together the same way it globs together npm packages.
That's exactly what Hugging Face Spaces have quietly become.
agents.md
The Hub hosts thousands of state-of-the-art models (a huge share of them open-weights), and most are deployed as interactive Spaces. As of now, every Gradio Space also exposes a plain-text agents.md that tells an agent
curl https://huggingface.co/spaces/VAST-AI/TripoSplat/agents.md
returns everything needed in one shot: the schema URL, the call and poll templates, how to upload files, and the auth hint:
API schema: GET .../gradio_api/info
Call endpoint: POST .../gradio_api/call/v2/{endpoint} {"param_name": value, ...}
Poll result: GET .../gradio_api/call/{endpoint}/{event_id}
File inputs: POST .../gradio_api/upload -F "files=@file.ext"
Auth: Bearer $HF_TOKEN
No client library. No hardcoded integration. An agent reads that, and it can drive the Space end to end. Set an HF_TOKEN and you're going. You can find these instructions on any Gradio Space via its
The real unlock is chaining: the output of one Space becomes the input to the next. Prompt → image → 3D. That's the whole pipeline behind this gallery.
The agent chained two Spaces:
VAST-AI/TripoSplat
.ply
) from each single image. Image in, 3D out.Generated image
Reconstructed splat
The six source images the agent generated, all isolated on black, ready for single-image 3D reconstruction:
From there the agent did the "glue" work too. It noticed TripoSplat outputs are
Y-down and flipped them upright, auto-framed each monument, compressed the .ply
files to .ksplat
(~3× smaller, so they load fast), built a Three.js viewer with a scroll-to-switch and drag-to-rotate UI, and deployed the whole thing as a static Space. The only human inputs were taste-level: "make it zoomed out," "replace the obelisk with something better for splatting," "the transition lingers too long."
Several of those steps were the agent reacting to reality. A wide glass pyramid splats poorly. A thin obelisk is dull. A single-view reconstruction infers the back. That is exactly the "outsourced R&D, fast iteration" loop the building-block economy predicts, except the R&D was a conversation.
The real test of a building block is how cheaply you can reuse it. Once this pipeline existed, spinning up entirely new galleries cost about one sentence each. "Create a similar Space with splats for Japan," then the same for Egypt, and the agent did the rest: six monument images, six splats, compression, a viewer, and a deployed Space, per country.
<video autoplay loop muted playsinline width="100%" src="
"><video autoplay loop muted playsinline width="100%" src="
">Same two Spaces, same agents.md
, only the prompts changed. That is the building-block economy in one line: the marginal cost of a new multimedia app falls toward the cost of describing it.
agents.md
makes a Space
trivially reachable, so an agent will pick it over a model it has to set up by
hand. That is the same dynamic Hashimoto flags for open-source libraries.Point your own agent at a Space's agents.md
and let it cook:
curl https://huggingface.co/spaces/ideogram-ai/ideogram4/agents.md
curl https://huggingface.co/spaces/VAST-AI/TripoSplat/agents.md
Paste either link into your coding agent (Claude Code, etc.), set your
HF_TOKEN
, and ask it to build something. The full, reproducible pipeline for this
gallery, the scripts that hit those two agents.md
endpoints, lives in the Space repo.
The building blocks are sitting right there on the Hub. The agents already know how to glue.
I asked a coding agent to build a beautiful website showcasing the monuments of Paris as 3D Gaussian splats. I never opened an image generator. I never touched a 3D reconstruction tool. The agent produced every asset (the images and the 3D splats) by calling two Hugging Face Spaces directly, then wired them into a cinematic viewer.
Here's the result, live as a static Space:
This post is about how that's possible now, and why I think it's a preview of how a lot of multimedia software gets built from here on.
Mitchell Hashimoto recently described a shift he calls the building block economy: the most effective path to software is no longer a polished monolith, but small, well-documented components that others (increasingly agents) can assemble. His key observation: AI is okay at building everything from scratch, but it is really good at gluing together proven pieces.
That thesis has mostly been told with code libraries. But the same forces are hitting multimedia AI. The hard part of using a state-of-the-art image model, a video model, a TTS model, or a 3D reconstruction model was never the model. It was the integration: SDKs, weights, GPUs, input formats, polling. If each model were instead a documented, callable block, an agent could glue them together the same way it globs together npm packages.
That's exactly what Hugging Face Spaces have quietly become.
agents.md
The Hub hosts thousands of state-of-the-art models (a huge share of them open-weights), and most are deployed as interactive Spaces. As of now, every Gradio Space also exposes a plain-text agents.md that tells an agent
curl https://huggingface.co/spaces/VAST-AI/TripoSplat/agents.md
returns everything needed in one shot: the schema URL, the call and poll templates, how to upload files, and the auth hint:
API schema: GET .../gradio_api/info
Call endpoint: POST .../gradio_api/call/v2/{endpoint} {"param_name": value, ...}
Poll result: GET .../gradio_api/call/{endpoint}/{event_id}
File inputs: POST .../gradio_api/upload -F "files=@file.ext"
Auth: Bearer $HF_TOKEN
No client library. No hardcoded integration. An agent reads that, and it can drive the Space end to end. Set an HF_TOKEN and you're going.
The real unlock is chaining: the output of one Space becomes the input to the next. Prompt → image → 3D. That's the whole pipeline behind this gallery.
The agent chained two Spaces:
ideogram-ai/ideogram4
VAST-AI/TripoSplat
.ply
) from each single image. Image in, 3D out.Generated image
Reconstructed splat
The six source images the agent generated, all isolated on black, ready for single-image 3D reconstruction:
From there the agent did the "glue" work too. It noticed TripoSplat outputs are
Y-down and flipped them upright, auto-framed each monument, compressed the .ply
files to .ksplat
(~3× smaller, so they load fast), built a Three.js viewer with a scroll-to-switch and drag-to-rotate UI, and deployed the whole thing as a static Space. The only human inputs were taste-level: "make it zoomed out," "replace the obelisk with something better for splatting," "the transition lingers too long."
Several of those steps were the agent reacting to reality. A wide glass pyramid splats poorly. A thin obelisk is dull. A single-view reconstruction infers the back. That is exactly the "outsourced R&D, fast iteration" loop the building-block economy predicts, except the R&D was a conversation.
The real test of a building block is how cheaply you can reuse it. Once this pipeline existed, spinning up entirely new galleries cost about one sentence each. "Create a similar Space with splats for Japan," then the same for Egypt, and the agent did the rest: six monument images, six splats, compression, a viewer, and a deployed Space, per country.
Same two Spaces, same agents.md
, only the prompts changed. That is the building-block economy in one line: the marginal cost of a new multimedia app falls toward the cost of describing it.
agents.md
makes a Space
trivially reachable, so an agent will pick it over a model it has to set up by
hand. That is the same dynamic Hashimoto flags for open-source libraries.Point your own agent at a Space's agents.md
and let it cook:
curl https://huggingface.co/spaces/ideogram-ai/ideogram4/agents.md
curl https://huggingface.co/spaces/VAST-AI/TripoSplat/agents.md
Paste either link into your coding agent (Claude Code, etc.), set your
HF_TOKEN
, and ask it to build something. The full, reproducible pipeline for this
gallery, the scripts that hit those two agents.md
endpoints, lives in the Space repo.
The building blocks are sitting right there on the Hub. The agents already know how to glue.
Generate 3D Gaussian models from a single image
Ideogram 4 state of the art open weights
Explore interactive 3D models of Paris monuments
Explore 3D Egyptian monuments with interactive rotation
Explore 3D captures of Japan’s famous monuments