★ Spotlight · AI / MLBuilding in public
Edge AI Lab — ML on the device
My next flagship: taking models from a Jupyter notebook down to resource-constrained hardware — quantisation, latency budgets and real sensor data. Combines both halves of my career.
Roadmap10%
- 1Pick a sensor problem & collect data← now
- 2Train a baseline model in Python
- 3Quantise & prune for the edge
- 4Deploy & benchmark on device
- 5Write it up on the blog
PythonPyTorchTinyMLQuantizationRaspberry Pi
▶ Try it · neural net playground
2 → 12 → 12 → 1click / drag to add points · shift = class Btap / drag to add points
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A real neural network training live in your browser — no libraries, just math. Add points and watch it learn.