Developer builds local AI nutritionist app to avoid token costs and data privacy
A developer created a personal AI nutritionist app after concerns about token usage and data privacy. They initially built a menu-planning tool using Claude Opus 5.5 but wanted to avoid sending personal data like weight and age to external servers. The solution was to run the AI locally on a MacBook Air (M4, 16GB RAM) using LM Studio, choosing the Gemma 4 12B model (6.77GB) despite its resource…
Key points
- Developer avoids token costs and data privacy risks by running AI locally on MacBook Air (M4, 16GB RAM)
- Uses **Gemma 4 12B** (6.77GB) model via LM Studio, despite resource constraints and heat issues
- App limits AI to dish generation, while calculations and feedback loops stay on-device
The project is modular, allowing future swaps to cloud-based AI if needed. Development proceeds in five stages, with testing done using fictional data to avoid exposing real metrics. The developer notes local AI has limitations, such as less accurate nutritional guesswork, but the trade-off for privacy and cost savings justified the approach.
Model page: Claude Opus 5.5 →
The story of how I started building my own personal AI nutritionist after being told I was 'fat' (2)
note.com · 28 October 2026
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