Llama-3 in Your Pocket: Building a Privacy-First AI Health Journal with MLX Swift
A new tutorial explores the development of a privacy-first AI health journal using the Llama-3 model on iOS. By leveraging Edge AI and local model implementation, user data remains secure and private. The application allows for on-device semantic analysis, ensuring that sensitive health information does not leave the user's device.
- ▪The tutorial demonstrates how to deploy Llama-3-8B directly onto an iPhone using Apple's MLX framework.
- ▪Using Edge AI ensures zero latency, offline capability, and absolute privacy for health data.
- ▪The application is designed to analyze health logs for mood and physical symptoms without compromising user data.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 913145) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Beck_Moulton Posted on May 17 Llama-3 in Your Pocket: Building a Privacy-First AI Health Journal with MLX Swift #ios #machinelearning #swift #ai We live in an era where our most intimate thoughts and health metrics are often just one API call away from a third-party server. For developers building health-tech, this presents a massive hurdle: Privacy.
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