Aura / build manual

Aura Build Manual

Source the pendant, flash the firmware, run the backend, and pair the app.

What Is Aura

Aura is an open-source AI wearable you wear around your neck. It listens, sees, and transcribes so you do not have to pause your day to take notes. Audio and images are captured and routed to a self-hosted or cloud backend. Summaries and transcripts are accessible from your phone. Built on the open Omi ecosystem. Parts cost approximately $50.

How It Works

01

The microphone captures audio continuously.

02

The camera captures images at set intervals.

03

Both are sent to the backend over Wi-Fi.

04

The backend transcribes audio via Deepgram or Whisper.

05

Images are analyzed via GPT-4o vision or moondream.

06

Everything is summarized and stored.

07

You see it all in the Omi app on your phone.

The ESP32-S3 handles capture and transmission. The backend handles all AI processing. Your phone is the interface.

Bill of Materials

Parts list with estimated costs.

Item

Where to buy

Approx cost

XIAO ESP32-S3 sense

Seeed studio / amazon

$15–24

150mah LiPo × 6

Amazon

$12

Wires

Amazon

$5

3D printed case

Print yourself or order online

$5–10

USB-C cable

Anywhere

Free

Total

Not applicable

~$50

Case STL files are in the Aura hardware folder on GitHub.

Caution

Use only LiPo cells with protection circuits. Never charge unattended. Follow the XIAO ESP32-S3 sense charging current limits (500 mA default).

Hardware Overview

The XIAO ESP32-S3 sense is the core. Camera and microphone are integrated on the board, so no extra modules are needed.

Component

Details

Microcontroller

XIAO ESP32-S3 sense

Camera

OV2640 (built into board)

Microphone

PDM (built into board)

Battery

6× 150mah LiPo cells

Enclosure

Custom 3D printed case

Connectivity

Wi-Fi 2.4 ghz + Bluetooth LE

Dimensions

50 × 68 × 18 mm

Weight

80 g

Battery life

4 h active / 45 min charge

Mount the board, route the battery wires, and snap the case shut. The pendant loop is built into the design.

Flashing Firmware

Clone the firmware repo and flash using PlatformIO:

git clone https://github.com/thesohamdatta/aura.git

git clone https://github.com/thesohamdatta/aura.git

git clone https://github.com/thesohamdatta/aura.git

cd aura/firmware

cd aura/firmware

cd aura/firmware

pio run --target upload

pio run --target upload

pio run --target upload

Connect the board via USB-C, select the correct port, and upload. Verify with the serial monitor:

pio device monitor --port COM3

pio device monitor --port COM3

You should see the boot log with Wi-Fi, camera, and ble initialization.

Backend Setup

The backend handles transcription, image analysis, and memory storage. Deploy with Docker:

git clone https://github.com/thesohamdatta/aura.git

git clone https://github.com/thesohamdatta/aura.git

git clone https://github.com/thesohamdatta/aura.git

cd aura/backend

cd aura/backend

cd aura/backend

cp .env.example .env

cp .env.example .env

cp .env.example .env

Edit .env with your API keys

docker compose up -d

docker compose up -d

docker compose up -d

Required services: Deepgram or Whisper for transcription, Groq or OpenAI for LLM inference, Pinecone for vector memory storage.

Companion App

The Omi app connects to Aura over Bluetooth LE. It shows your conversation history, memory timeline, and device controls. Download the app, create an account, and pair your device. The setup wizard will guide you through. Once paired, you can browse transcripts, search memories, and adjust capture intervals.

AI Providers

Aura supports multiple AI backends. Configure your providers in the .env file.

Service

Purpose

API key required

Deepgram nova-2

Speech-to-text

Yes

Groq lpu

Fast LLM inference

Yes

GPT-4o vision

Image analysis

Yes

Pinecone

Vector memory

Yes

Whisper (local)

Self-hosted transcription

No

Memory & RAG

01

Audio is transcribed, chunked, and embedded into Pinecone.

02

When you ask a question, the query is matched against stored vectors.

03

Matched context is fed to the LLM along with your query.

All memory is private and stored on your own Pinecone index.

Troubleshooting

Device not connecting to Wi-Fi

Verify SSID and password in the firmware config. Confirm 2.4 ghz band is enabled. The ESP32-S3 does not support 5 ghz.

Camera not capturing images

Ensure the camera ribbon cable is fully seated. Try re-flashing the firmware. The OV2640 may need a power cycle.

Audio transcription failing

Check your Deepgram API key in .env. Verify network connectivity from the backend to Deepgram’s API endpoint.

Battery not charging

The ESP32-S3 charges at 500 mA max. Use a quality USB-C cable and power supply. Check voltage with a multimeter.

FAQ

1

How much does it cost to build?

The bill of materials runs roughly $50 for all parts: the XIAO ESP32-S3 Sense, six LiPo cells, wires, and a printed case.

2

Do I need to buy a case?

The case is 3D printed. Print it yourself from the STL files in the hardware folder, or order the print from an online service.

3

Can I run the backend without cloud services?

Yes. Use the local Whisper model for transcription and any OpenAI-compatible server for inference. Pinecone is optional.

4

Is my data private?

You own your data and your device. Run the backend on your own hardware. Audio and images stay on your network unless you choose a cloud backend you deploy and control.

5

Is Aura open source?

Yes. Aura is MIT licensed. Firmware, backend, and app code are in the public Aura repository on GitHub.

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