Private Mind represents a new era of AI—powerful, personal, and completely offline. Built around the belief that AI should live entirely on your device, Private Mind opens the door to a new kind of experience: fast, secure, and fully private.
Local, private AI that fits in your pocket
Private Mind represents a new era of AI—powerful, personal, and completely offline. Built around the belief that AI should live entirely on your device, Private Mind opens the door to a new kind of experience: fast, secure, and fully private.
The real game-changer would be adding local RAG capabilities to Private Mind - imagine being able to upload your PDFs, documents, and personal files to enhance the LLM's knowledge base, all while keeping everything completely private and on-device. Think about it: your personal research papers, company docs, project notes - all accessible to your AI assistant without ever touching the cloud. True knowledge augmentation that respects your privacy. And yes... this might just be what we're working
Hey Product Hunters ✌️ We're excited to launch Private Mind - a free, open-source app that demonstrates the incredible potential of local AI inference running entirely on your device. Why local AI matters? Your conversations stay completely private You get lightning-fast responses with no internet dependency You maintain full control over your data Private Mind showcases what's possible when powerful LLMs run directly on your hardware 🚀. Download the app on App Store or Google Play . If you're c
Love the idea of an entirely on-device AI. How does Private Mind handle incremental updates? I’m imagining I’m on a flight with no Wi-Fi and suddenly need to summarise a 50-page PDF—will the model still be snappy on a two-year-old MacBook Air, or should I expect noticeable lag?
No way—an AI that runs locally and keeps everything private? That’s exactly what I need for travel days when I don’t trust public WiFi. How do updates work?
so it's running LLM models on mobile? which models do you support? @norbert_klockiewicz
The scores reflect launch-period engagement. Historical data is preserved and doesn't change retroactively. The build date at the bottom shows when the index was last refreshed.
Check the similar products section on this page, or browse the category pages linked in the tags above. Each category page shows all products for a given year, sorted by engagement.
A measure of community engagement at launch. Higher means more people noticed and interacted with the product. It's a traction signal, not a quality rating.
Discussion threads divided by interest score. Above 0.30 is strong. Below 0.15 suggests the product got clicks but not conversation.