I created two deliverables:

• Interactive Hushmap prototype — an illustrated sample map with quiet and birds filters, spot details, walk suggestions, and a microphone-permission check. The microphone check does not record or upload audio.
• DEV submission draft — follows the challenge template and distinguishes the working demo from proposed model and hosting integrations.

The map’s locations and scores are illustrative; on-device classification and real community data collection are not implemented yet. The challenge deadline was October 11 at 11:59 PM PDT, so the draft flags that the Week 1 submission window has passed. (challenge details)

You’re right—the deadline is still open. It’s 12:27 PM PDT on October 11, so there are about 11 hours 32 minutes until the 11:59 PM PDT cutoff. (Challenge details)

I implemented the local app with browser-based YAMNet classification, a four-second microphone capture, local audio-file classification, and a private score notebook with JSON export. Run it with npm install and npm run dev.

• Download the app source and model
• Open the DEV submission draft

The audio stays in the browser; scores are saved locally only if you choose to save them. The map and route suggestions are still illustrative sample data, and Gemma, Tiger Data, TabPFN, and hosting integrations are not implemented. The development server starts, but I couldn’t verify a production build here: Vite hit a Windows sandbox EPERM while resolving the workspace file.

I left partner categories out because I focused on the working local app and didn’t want to claim integrations that weren’t implemented. But your brief explicitly included them, so I should have followed through before calling it done.

I’ve now added an opt-in Tiger Data/pgvector integration: it can store YAMNet embeddings and category scores, then search for similar sounds. Raw audio is never sent. The Tiger Data extension guide lists pgvector for AI-focused services.

The integration needs your Tiger Cloud DATABASE_URL, which wasn’t configured here, so I couldn’t connect it to a live service. The draft marks Best Use of Tiger Data as a potential entry until you can run a contribution and similarity search.

• Updated app package
• Updated DEV draft

We shouldn’t add partner tools just to list more categories. Each one should support a real part of Hushmap:

• Gemma could understand flexible requests like “a calm route with water and birds.” The current sample map has no real sound readings for it to search, so adding it now wouldn’t improve the result.
• TabPFN could predict quieter hours, but that needs real observations across different times. The current map is illustrative.
• Render can host the app. I added a Render Blueprint at render.yaml; it still needs deployment and a public-safety review.
• Tiger Data has an opt-in integration path, but it still needs a live database connection and a working demo before its partner category can be claimed.

I updated the Dev.to draft to explain why Gemma and TabPFN aren’t in this prototype, and refreshed the local app ZIP.

The JavaScript syntax checks completed. The production build could not be verified: Vite hit a Windows EPERM error while resolving index.html.

Run Hushmap

1. Open PowerShell in the project folder:

   cd "[local Hushmap project path]"

1. Install dependencies and start the app:

   npm install
   npm run dev

1. Open the Local URL Vite prints—usually http://[REDACTED]:5173. Keep that PowerShell window open while using the app. Microphone access works on localhost; allow it if the browser asks.

Record a short demo

Use Windows Snipping Tool screen recording (Win + Shift + R) or another screen recorder. A 45–60 second take could show:

1. The map and its disclaimer: point out that pins and scores are illustrative sample data.
2. A search: enter “quiet walk with birds” or click “Near water.” Explain that suggestions currently use a simple local keyword match and sample locations.
3. On-device audio classification: click Record 4s, allow microphone access, and capture a short outdoor sound. Wait for the first-use YAMNet model to load; show the category scores and the “on-device” status.
4. Private save: enter an optional place label, click Save on device, then show the notebook entry. The scores stay in that browser unless separately exported.
5. Close with the limitation: say that real inference works, while the map itself still uses sample data.

For a cleaner recording, use headphones or record in a quiet place to avoid feedback. Don’t check the Tiger Data consent box in the demo unless you have configured a database and intend to send the derived scores and embedding.

I’d avoid claiming the map is community data or that the app finds actual quiet routes: those features aren’t implemented yet.

1. Open the app at http://[REDACTED]:5173/ and arrange the browser window so the map and “Contribute a listen” panel are visible.
2. Press Win + Shift + R to open Windows Snipping Tool’s screen recorder. Select the app area and click Start.
3. In Hushmap, click Record 4s and allow microphone access. Wait for the YAMNet scores to appear.
4. Show the result, optionally save it on device, then point out that the map uses illustrative sample data.
5. Stop recording from the Snipping Tool toolbar and save the video.

A simple narration: “Hushmap classifies a short sound in the browser using YAMNet. The recording stays on this device. These scores are one local listen; the map is still illustrative sample data.”

That error means the browser couldn’t start ONNX Runtime’s local WASM model. Try this first:

1. In the PowerShell window running Hushmap, press Ctrl + C, then restart it with npm run dev from the project folder.
2. Open the Vite URL, usually http://[REDACTED]:5173/, and press Ctrl + Shift + R to reload.
3. Check that the project has a public\ort folder. It contains the runtime files the model needs.

Then click Record 4s again. If it still fails, press F12, open Console, and send me the full error text—especially the part after ERR:. That will show which runtime file the browser couldn’t load.

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass

What I Built

Most city maps show what a place looks like. Hushmap explores what it sounds like.

It’s a local-first prototype for people who want to notice the soundscape around them. On a walk, you can record a four-second clip or choose a short audio file. Hushmap runs the open YAMNet audio model in your browser and displays broad Birds, Traffic, Water, and People scores. You can save scores privately in your browser or export them.

The map and its suggested walks use clearly labeled illustrative sample data. Hushmap doesn’t yet map real community observations or provide actual routes. The idea is to make a walk an excuse to pause, listen, and learn what sounds are around you.

Demo

Video demo: [Add your recorded demo link before publishing.]

The demo should show local sound classification and make clear that the map is illustrative sample data.

Code

GitHub: [Add your public repository URL before publishing.]

How I Built It

I built the interface as a small JavaScript app and used ONNX Runtime Web to run YAMNet in the browser. The model takes a short mono audio sample, produces AudioSet sound-class scores and embeddings, and Hushmap groups some labels into four broad categories.

The recording is processed in memory and discarded after classification. Scores stay in the browser unless the user saves or exports them. There is also an optional Tiger Data/pgvector integration path that requires a separate consent action and a configured database; I haven’t connected it to a live service.

Why Does Open Innovation Matter?

A neighborhood recording might contain a private conversation or a child’s voice. With a cloud-only classifier, the audio would have to leave the device to get sound labels. Using open model weights lets Hushmap run classification in the browser, so the raw recording can stay local.

Open weights and labels also let builders inspect the model’s vocabulary and adjust how sound labels are grouped. This is a prototype, though: the category scores aren’t calibrated environmental measurements, and the sample map isn’t collected community data.

My Agent Session

Optional: add a DevRelay session link if you save one.

I left out Prize Categories because there isn’t a partner category I can honestly claim yet: Tiger Data hasn’t been connected to a live database, and the app hasn’t been deployed on Render or DigitalOcean.

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass

What I Built

Most city maps show what a place looks like. Hushmap explores what it sounds like.

It’s a local-first prototype for people who want to notice the soundscape around them. On a walk, you can record a four-second clip or choose a short audio file. Hushmap runs the open YAMNet audio model in your browser and displays broad Birds, Traffic, Water, and People scores. You can save scores privately in your browser or export them.

The map and its suggested walks use clearly labeled illustrative sample data. Hushmap doesn’t yet map real community observations or provide actual routes. The idea is to make a walk an excuse to pause, listen, and notice the sounds around you.

Demo

Video demo: [Add your recorded demo link before publishing.]

The demo should show local sound classification and make clear that the map is illustrative sample data.

Code

GitHub: [Add your public repository URL before publishing.]

How I Built It

I built the interface as a small JavaScript app and used ONNX Runtime Web to run YAMNet in the browser. The model takes a short mono audio sample, produces AudioSet sound-class scores and embeddings, and Hushmap groups some labels into four broad categories.

The recording is processed in memory and discarded after classification. Scores stay in the browser unless the user saves or exports them. There is also an optional Tiger Data/pgvector integration path that requires a separate consent action and a configured database; I haven’t connected it to a live service.

Why Does Open Innovation Matter?

A neighborhood recording might contain a private conversation or a child’s voice. With a cloud-only classifier, the audio would have to leave the device to get sound labels. Using open model weights lets Hushmap run classification in the browser, so the raw recording can stay local.

Open weights and labels also let builders inspect the model’s vocabulary and adjust how sound labels are grouped. This is a prototype, though: the category scores aren’t calibrated environmental measurements, and the sample map isn’t collected community data.

My Agent Session

Optional: add a DevRelay session link if you save one.

I left out Prize Categories because there isn’t a partner category I can honestly claim yet: Tiger Data hasn’t been connected to a live database, and the app hasn’t been deployed on Render or DigitalOcean.

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass

What I Built

Most city maps show what a place looks like. Hushmap explores what it sounds like.

It’s a local-first prototype for people who want to notice the soundscape around them. On a walk, you can record a four-second clip or choose a short audio file. Hushmap runs the open YAMNet audio model in your browser and displays broad Birds, Traffic, Water, and People scores. You can save scores privately in your browser or export them.

The map and its suggested walks use clearly labeled illustrative sample data. Hushmap doesn’t yet map real community observations or provide actual routes. The idea is to make a walk an excuse to pause, listen, and notice the sounds around you.

Demo

Watch the Hushmap demo on YouTube

The map uses illustrative sample data; the demo shows local sound classification.

Code

GitHub: [Add your public repository URL before publishing.]

How I Built It

I built the interface as a small JavaScript app and used ONNX Runtime Web to run YAMNet in the browser. The model takes a short mono audio sample, produces AudioSet sound-class scores and embeddings, and Hushmap groups some labels into four broad categories.

The recording is processed in memory and discarded after classification. Scores stay in the browser unless the user saves or exports them. There is also an optional Tiger Data/pgvector integration path that requires a separate consent action and a configured database; I haven’t connected it to a live service.

Why Does Open Innovation Matter?

A neighborhood recording might contain a private conversation or a child’s voice. With a cloud-only classifier, the audio would have to leave the device to get sound labels. Using open model weights lets Hushmap run classification in the browser, so the raw recording can stay local.

Open weights and labels also let builders inspect the model’s vocabulary and adjust how sound labels are grouped. This is a prototype, though: the category scores aren’t calibrated environmental measurements, and the sample map isn’t collected community data.

My Agent Session

Optional: add a DevRelay session link if you save one.

I left out Prize Categories because there isn’t a partner category I can honestly claim yet: Tiger Data hasn’t been connected to a live database, and the app hasn’t been deployed on Render or DigitalOcean.

*This is a submission for the [Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass](https://dev.to/challenges/hacktoberfest-week1-2026-10-05)*

## What I Built

Most city maps show what a place looks like. **Hushmap explores what it sounds like.**

It’s a local-first prototype for people who want to notice the soundscape around them. On a walk, you can record a four-second clip or choose a short audio file. Hushmap runs the open YAMNet audio model in your browser and displays broad Birds, Traffic, Water, and People scores. You can save scores privately in your browser or export them.

The map and its suggested walks use clearly labeled illustrative sample data. Hushmap doesn’t yet map real community observations or provide actual routes. The idea is to make a walk an excuse to pause, listen, and notice the sounds around you.

## Demo

[Watch the Hushmap demo on YouTube](https://youtu.be/EhRVPnIre8A)

The map uses illustrative sample data; the demo shows local sound classification.

## Code

**GitHub:** [Add your public repository URL before publishing.]

## How I Built It

I built the interface as a small JavaScript app and used ONNX Runtime Web to run YAMNet in the browser. The model takes a short mono audio sample, produces AudioSet sound-class scores and embeddings, and Hushmap groups some labels into four broad categories.

The recording is processed in memory and discarded after classification. Scores stay in the browser unless the user saves or exports them. There is also an optional Tiger Data/pgvector integration path that requires a separate consent action and a configured database; I haven’t connected it to a live service.

## Why Does Open Innovation Matter?

A neighborhood recording might contain a private conversation or a child’s voice. With a cloud-only classifier, the audio would have to leave the device to get sound labels. Using open model weights lets Hushmap run classification in the browser, so the raw recording can stay local.

Open weights and labels also let builders inspect the model’s vocabulary and adjust how sound labels are grouped. This is a prototype, though: the category scores aren’t calibrated environmental measurements, and the sample map isn’t collected community data.

## My Agent Session

*Optional: add a DevRelay session link if you save one.*

Added a Hacktoberfest Week 1 Prize Categories section to the README and refreshed the app ZIP.

Hushmap is eligible to be submitted for the overall challenge prize: the challenge asks for open-source AI at the project’s core, and partner technology is optional for the overall prize. Challenge criteria

The README also lists Best Use of Tiger Data as a partner category supported by Hushmap’s pgvector integration. For a stronger category entry, connect Tiger Cloud and capture a successful contribution and similarity search in your demo.