---
title: How an Agent Built a 3D Paris Gallery by Chaining Two Hugging Face Spaces
link: https://huggingface.co/blog/mishig/spaces-agents-md
source: huggingface-co
published: 2026-06-09T10:46:19Z
updated: 2026-06-09T10:46:19Z
first_seen: 2026-10-19T01:26:53.998049939Z
content: extracted
html: 2026-06-09-how-an-agent-built-a-3d-paris-gallery-by-chaining-two.html
remote_preview:
  url: https://cdn-uploads.huggingface.co/production/uploads/60a551a34ecc5d054c8ad93e/0tamlKpAvO3lZEklNmwHT.png
---

*An agent built a 3D Paris gallery from two Hugging Face Spaces.*

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:

👉 **[mishig/monuments-de-paris](https://huggingface.co/spaces/mishig/monuments-de-paris)**

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.

## The building-block economy comes for multimedia

Mitchell Hashimoto recently described a shift he calls the [building block economy](https://mitchellh.com/writing/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.

## Every Space is a building block, via `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`](https://huggingface.co/docs/hub/en/spaces-agents) that tells an agent *exactly* how to call it:

```bash
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`](https://huggingface.co/settings/tokens) and you're going. You can find these instructions on any Gradio Space via its **Agents** button:

![The Agents button on a Hugging Face Space](https://huggingface.co/spaces/mishig/monuments-de-paris/resolve/main/blog/assets/agents-triposplat.png)

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 worked example: Paris monuments → splats

The agent chained two Spaces:

1. **Image:** an image-generation Space turned each monument into a clean, dark-background "specimen" shot (and the Eiffel Tower into a little diorama on a plinth). Prompt in, image out.
2. **Splat:** [`VAST-AI/TripoSplat`](https://huggingface.co/spaces/VAST-AI/TripoSplat) reconstructed a 3D Gaussian splat (`.ply`) from each single image. Image in, 3D out.

Generated image

[![Generated Panthéon](https://huggingface.co/spaces/mishig/monuments-de-paris/resolve/main/blog/assets/gen-pantheon.jpg)](https://huggingface.co/spaces/mishig/monuments-de-paris/resolve/main/blog/assets/gen-pantheon.jpg)

Reconstructed splat

The six source images the agent generated, all isolated on black, ready for single-image 3D reconstruction:

[![Generated monument images](https://huggingface.co/spaces/mishig/monuments-de-paris/resolve/main/blog/assets/gen-opera.jpg)](https://huggingface.co/spaces/mishig/monuments-de-paris/resolve/main/blog/assets/gen-opera.jpg) [![Generated Arc de Triomphe](https://huggingface.co/spaces/mishig/monuments-de-paris/resolve/main/blog/assets/gen-arc.jpg)](https://huggingface.co/spaces/mishig/monuments-de-paris/resolve/main/blog/assets/gen-arc.jpg) [![Generated Sacré-Cœur](https://huggingface.co/spaces/mishig/monuments-de-paris/resolve/main/blog/assets/gen-sacre-coeur.jpg)](https://huggingface.co/spaces/mishig/monuments-de-paris/resolve/main/blog/assets/gen-sacre-coeur.jpg) [![Generated Eiffel diorama](https://huggingface.co/spaces/mishig/monuments-de-paris/resolve/main/blog/assets/gen-eiffel-diorama.jpg)](https://huggingface.co/spaces/mishig/monuments-de-paris/resolve/main/blog/assets/gen-eiffel-diorama.jpg)

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.

## Two prompts, a whole new gallery

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.

- 🏛️ [Monuments of Egypt](https://huggingface.co/spaces/mishig/monuments-of-egypt): the Great Pyramid, the Sphinx, Abu Simbel, the mask of Tutankhamun, Karnak, the Colossi of Memnon.

\<video autoplay loop muted playsinline width="100%" src="

">

- ⛩️ [Monuments of Japan](https://huggingface.co/spaces/mishig/monuments-of-japan): Tokyo Tower, Himeji Castle, Kinkaku-ji, Osaka Castle, the Great Buddha of Kamakura, the Itsukushima torii.

\<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.

## Why this matters

- **Models become composable.** A SOTA splat model and a SOTA image model, from different orgs, chained with zero integration code. The Hub's open-weights catalog turns into a library of callable multimedia primitives.
- **Agents prefer what's documented and reachable.** `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.
- **The barrier was integration, and it's largely gone.** "Turn a prompt into a rotating 3D monument" used to be a project. Here it was a step in a pipeline.

## Try it yourself

Point your own agent at a Space's `agents.md` and let it cook:

```bash
# image generation
curl https://huggingface.co/spaces/ideogram-ai/ideogram4/agents.md
# single-image to 3D gaussian splat
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](https://huggingface.co/spaces/mishig/monuments-de-paris/tree/main).

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:

👉 **[mishig/monuments-de-paris](https://huggingface.co/spaces/mishig/monuments-de-paris)**

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.

## The building-block economy comes for multimedia

Mitchell Hashimoto recently described a shift he calls the [building block economy](https://mitchellh.com/writing/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.

## Every Space is a building block, via `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`](https://huggingface.co/docs/hub/en/spaces-agents) that tells an agent *exactly* how to call it:

```bash
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`](https://huggingface.co/settings/tokens) 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 worked example: Paris monuments → splats

The agent chained two Spaces:

1. **Image:** [`ideogram-ai/ideogram4`](https://huggingface.co/spaces/ideogram-ai/ideogram4) turned each monument into a clean, dark-background "specimen" shot (and the Eiffel Tower into a little diorama on a plinth). Prompt in, image out.
2. **Splat:** [`VAST-AI/TripoSplat`](https://huggingface.co/spaces/VAST-AI/TripoSplat) reconstructed a 3D Gaussian splat (`.ply`) from each single image. Image in, 3D out.

Generated image

[![Generated Panthéon](https://huggingface.co/spaces/mishig/monuments-de-paris/resolve/main/blog/assets/gen-pantheon.jpg)](https://huggingface.co/spaces/mishig/monuments-de-paris/resolve/main/blog/assets/gen-pantheon.jpg)

Reconstructed splat

The six source images the agent generated, all isolated on black, ready for single-image 3D reconstruction:

[![Generated monument images](https://huggingface.co/spaces/mishig/monuments-de-paris/resolve/main/blog/assets/gen-opera.jpg)](https://huggingface.co/spaces/mishig/monuments-de-paris/resolve/main/blog/assets/gen-opera.jpg) [![Generated Arc de Triomphe](https://huggingface.co/spaces/mishig/monuments-de-paris/resolve/main/blog/assets/gen-arc.jpg)](https://huggingface.co/spaces/mishig/monuments-de-paris/resolve/main/blog/assets/gen-arc.jpg) [![Generated Sacré-Cœur](https://huggingface.co/spaces/mishig/monuments-de-paris/resolve/main/blog/assets/gen-sacre-coeur.jpg)](https://huggingface.co/spaces/mishig/monuments-de-paris/resolve/main/blog/assets/gen-sacre-coeur.jpg) [![Generated Eiffel diorama](https://huggingface.co/spaces/mishig/monuments-de-paris/resolve/main/blog/assets/gen-eiffel-diorama.jpg)](https://huggingface.co/spaces/mishig/monuments-de-paris/resolve/main/blog/assets/gen-eiffel-diorama.jpg)

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.

## Two prompts, a whole new gallery

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.

- 🏛️ [Monuments of Egypt](https://huggingface.co/spaces/mishig/monuments-of-egypt): the Great Pyramid, the Sphinx, Abu Simbel, the mask of Tutankhamun, Karnak, the Colossi of Memnon.

- ⛩️ [Monuments of Japan](https://huggingface.co/spaces/mishig/monuments-of-japan): Tokyo Tower, Himeji Castle, Kinkaku-ji, Osaka Castle, the Great Buddha of Kamakura, the Itsukushima torii.

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.

## Why this matters

- **Models become composable.** A SOTA splat model and a SOTA image model, from different orgs, chained with zero integration code. The Hub's open-weights catalog turns into a library of callable multimedia primitives.
- **Agents prefer what's documented and reachable.** `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.
- **The barrier was integration, and it's largely gone.** "Turn a prompt into a rotating 3D monument" used to be a project. Here it was a step in a pipeline.

## Try it yourself

Point your own agent at a Space's `agents.md` and let it cook:

```bash
# image generation
curl https://huggingface.co/spaces/ideogram-ai/ideogram4/agents.md
# single-image to 3D gaussian splat
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](https://huggingface.co/spaces/mishig/monuments-de-paris/tree/main).

The building blocks are sitting right there on the Hub. The agents already know how to glue.