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Just published a case study on Smart Foot — an end-to-end embedded ML system I built from an empty PCB to a trained model.
It reads foot pressure in real time from a 16-channel FSR array, runs the whole pipeline in bare-metal C on ARM Cortex-M, and classifies gait with a neural network running on-device — no cloud, under 50ms.
The hardest part wasn't the firmware. It was collecting my own dataset: 1,500+ gait samples from 50 subjects, evaluated on a subject-independent split. That's why the 91%+ accuracy actually holds up on people the model never saw.
Full write-up in the comments 👇
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The design direction balances emotional warmth with healthcare credibility. Soft pastel gradients, rounded components, translucent cards, warm abstract imagery, and deep navy typography create an experience that feels caring, intelligent, and easy to trust.
Amazing work 🙌
AI SaaS isn’t just about adding ChatGPT to a product.
The real challenge is turning AI into a useful, reliable product that solves a real business problem.
A strong AI SaaS needs:
→ A clear problem and target user
→ Simple, intuitive UX
→ Well-designed AI workflows
→ Reliable APIs and backend infrastructure
→ Proper data handling and security
→ Fast responses and scalable architecture
→ A clear path from MVP to a production-ready product
The goal isn’t to make something look “AI-powered.”
The goal is to build a product where AI genuinely makes the user’s job faster, easier, or better.
That’s what separates an AI demo from a real AI SaaS product.
Well done !