Putting the power and the data of the users in the hands of the users themselves! Well done. Getting it setup was easy. Wish the app recognized the keyboard and realized when it was displayed so the bottom menu and chat box weren't hidden under it.
This is genuinely exciting. The fact that you're getting 15-30 tok/s for text gen on phone hardware is wild — that's basically usable for real conversations.
Curious about a couple things: what GGUF model sizes are practical on a mid-range phone (say 8GB RAM)? And how's the battery impact during sustained inference — does it drain noticeably faster than, say, a video call?
The privacy angle is the real killer feature here IMO. There are so many use cases (journaling, health tracking, sensitive work notes) where people self-censor because they know it's going to a server somewhere. Removing that barrier entirely changes what people are willing to use AI for.
This sounds exactly like Claude wrote it. I've noticed Claude saying "genuinely" a lot lately, and the "real killer feature" segue just feels like Claude being asked to review something.
I haven't run it, but I looked through the repo. It looks very well thought out, the UI is nice. I appreciate the ethos behind the local/offline design. Cheers.
Really awesome idea though. I want this to work.
Thanks for spotting and reporting this.
So the lastest releases is at https://github.com/alichherawalla/off-grid-mobile/releases/l...
And the clone would be: git clone https://github.com/alichherawalla/off-grid-mobile.git
Curious about a couple things: what GGUF model sizes are practical on a mid-range phone (say 8GB RAM)? And how's the battery impact during sustained inference — does it drain noticeably faster than, say, a video call?
The privacy angle is the real killer feature here IMO. There are so many use cases (journaling, health tracking, sensitive work notes) where people self-censor because they know it's going to a server somewhere. Removing that barrier entirely changes what people are willing to use AI for.
I'd recommend going for any quantized 1B parameter model. So you can look at llama 3.2 1B, gemma3 1B, qwen3 VL 2B (if you'd like vision)
Appreciate the kind words!
That's using the word "real" very loosely.
Thanks for pointing this out
I found a guide for virtual box macOS which failed on intel then another for hyper-V but haven’t tried that one yet.
The dash in "off-grid" is missing.