Developers

Documentation

Everything you need to start building on the MindSpeak AI Baseboard.

Full guides are on the way.

Setup, firmware flashing and detailed pinouts will be published alongside the Dev Kit launch. Follow progress on GitHub in the meantime.

GitHub

Overview

The MindspeakAI Baseboard V1 is a complete voice-assistant peripheral system with a replaceable compute module. It works on its own with cloud AI, and becomes a fully local AI device when a compute module is attached.

For the full hardware story, including an exploded view of each layer, see the Dev Kit page.

Hardware at a glance

Controller
ESP32-S3-WROOM-1-N16R8 (16 MB flash, 8 MB PSRAM): Wi-Fi, BLE, OTA, buttons, LEDs, sensors
Audio DSP
XMOS XVF3800: AEC, beamforming, noise suppression, dereverberation, AGC, VAD, DoA
Microphones
4 × digital MEMS microphones (PDM)
Audio out
I²S DAC and Class-D amplifier, with an AEC reference back to the XVF3800
Power
USB-C PD with derived 5V and 3.3V rails
Sensors
Ambient light, temperature, humidity; expansion for mmWave, PIR and ToF

Compute module interface

The Mindspeak Compute Module (MCM) slot is an open, high-density board-to-board interface. It accepts adapters for Raspberry Pi CM5, NVIDIA Jetson, NPUs or custom AI accelerators.

High-speed
PCIe x1, USB 3.x, USB 2.0
Audio & control
I²S (TX/RX, MCLK, BCLK, LRCLK), UART, I²C, SPI, GPIO, RESET, WAKE
Media
MIPI CSI-2, MIPI DSI
Power
Dedicated 5V / 3.3V high-current rails

Running local models

With a compute module attached, quantized open models such as Llama 3, Mistral and Gemma can run on-device through Ollama, llama.cpp or vLLM. Wake word, far-field audio and inference stay on hardware you own, with no cloud API in the loop.

Debugging

The baseboard exposes ESP32 JTAG, an XMOS XSYS2/XTAG4 debug header and a UART console. A physical switch controls hardware microphone mute, independent of software.

Explore the Dev Kit