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Hardware, software and machine learning

I build hardware, software and ML that work together.

From the PCB to the firmware to the model to the app. Open source, shipped, and documented. Available for client projects.

596%
funded on Crowd Supply
318
GitHub stars across my public repos

Covered by

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Selected work

Open hardware you can buy, and developer tools you can run.

Spark Analyzer PCB featuring ESP32-C3 microcontroller, USB-C PD controller, OLED display, and power measurement circuitry with WiFi antenna

Spark Analyzer

£40

Open hardware

Open-source USB-C PD analyzer and programmable supply

Build details for Spark Analyzer

Open-source USB-C PD analyzer and programmable power supply built on the ESP32-C3. Set any voltage from 3.3V to 21V with 20mV precision, monitor current draw in real-time via WiFi or BLE, and log power consumption over time. Full transparency — inspect the schematic, modify the firmware, or manufacture your own.

Set the exact voltage, watch the live current draw, and log it, instead of poking at a charger and hoping.

  • See exactly what your USB-C charger and device are negotiating
  • Set any voltage from 3.3V to 21V with 20mV precision
  • Monitor current and power draw in real-time via WiFi or BLE
  • Open-source firmware—customize everything
GitHub (opens in new tab)
Tracer wearable sensor board with LSM6DSLTR 6-axis IMU, coin cell battery holder, compact circular PCB design with mounting holes

Tracer

£40

Open hardware

Ultra-low-power motion tracker designed for wearables

Build details for Tracer

Ultra-low power IMU tracker that runs 30+ days on a CR2450 coin cell. Combines a 6-axis IMU with deep sleep between motion events and adaptive reporting. Compact enough to embed in any wearable — step counters, gesture controllers, sports trackers. Open-source design, TinyML-ready firmware.

A month of motion tracking from a coin cell, small enough to actually build into a wearable.

  • 30+ days battery life from a CR2450 coin cell
  • 6-axis IMU with motion-triggered wake-up
  • Compact enough to embed in any wearable
  • Ready for TinyML deployment

From error to repair notes

  1. Production error
  2. Group with source context
  3. Open a GitHub issue
  4. Issue ready to work on

Patchly

Developer tool

Turn production errors into GitHub issues developers can act on, with source context and repair notes attached.

Build details for Patchly

Error tracking that reads your code. Patchly groups production errors, pulls the source from GitHub, and proposes a fix you can review.

Plant-Bot · £30 · Solar-powered plant care Explore the store

About

I'm Jason Too, a Cambridge-trained engineer who owns products end to end, from hardware and firmware to ML, apps and the web. I'm increasingly focused on using AI and agentic workflows to change how I build, test and ship.

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