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off-grid-ai
OGAM
TypeScript2026–2026
off-grid-ai / OGAM

OGAM

The Swiss Army Knife of Offline AI. Chat, see, speak, and generate images on your phone or Mac — GGUF LLMs, vision, Whisper speech-to-text, Stable Diffusion, tool calling, and local-network servers. Runs on your CPU, GPU, or NPU. No account, no API key, zero data leaves your device.

AI & Machine LearningWeb DevelopmentSystems & InfrastructureSecurity & PrivacyCreative & MediaMobile & DesktopNetworkingCommunication & Social
Explore on GitHubRead Hacker News discussions
8Distinct people
14Mentions
0Explicit recommenders
3.4KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

The Swiss Army Knife of Offline AI. Chat, see, speak, and generate images on your phone or Mac — GGUF LLMs, vision, Whisper speech-to-text, Stable Diffusion, tool calling, and local-network servers. Runs on your CPU, GPU, or NPU. No account, no API key, zero data leaves your device.

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:03:16 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by week14 dated mentions

Observed mentions: 2026-02-14 22:39:24 UTC to 2026-05-31 17:31:41 UTC. Weeks start Monday, UTC.

012342026-02-09 00:00:00 UTC to 2026-02-16 00:00:00 UTC (exclusive): 4 mentions; 1 submissions, 3 commentsFeb 92026-02-16 00:00:00 UTC to 2026-02-23 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-02-23 00:00:00 UTC to 2026-03-02 00:00:00 UTC (exclusive): 3 mentions; 2 submissions, 1 comments2026-03-02 00:00:00 UTC to 2026-03-09 00:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 comments2026-03-09 00:00:00 UTC to 2026-03-16 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-03-16 00:00:00 UTC to 2026-03-23 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-03-23 00:00:00 UTC to 2026-03-30 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2026-03-30 00:00:00 UTC to 2026-04-06 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsMar 302026-04-06 00:00:00 UTC to 2026-04-13 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-04-13 00:00:00 UTC to 2026-04-20 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-04-20 00:00:00 UTC to 2026-04-27 00:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 comments2026-04-27 00:00:00 UTC to 2026-05-04 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-05-04 00:00:00 UTC to 2026-05-11 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-05-11 00:00:00 UTC to 2026-05-18 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-05-18 00:00:00 UTC to 2026-05-25 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-05-25 00:00:00 UTC to 2026-06-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsMay 25

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Feb 92026-02-09 00:00:00 UTC2026-02-16 00:00:00 UTC413
Feb 162026-02-16 00:00:00 UTC2026-02-23 00:00:00 UTC000
Feb 232026-02-23 00:00:00 UTC2026-03-02 00:00:00 UTC321
Mar 22026-03-02 00:00:00 UTC2026-03-09 00:00:00 UTC211
Mar 92026-03-09 00:00:00 UTC2026-03-16 00:00:00 UTC000
Mar 162026-03-16 00:00:00 UTC2026-03-23 00:00:00 UTC000
Mar 232026-03-23 00:00:00 UTC2026-03-30 00:00:00 UTC202
Mar 302026-03-30 00:00:00 UTC2026-04-06 00:00:00 UTC000
Apr 62026-04-06 00:00:00 UTC2026-04-13 00:00:00 UTC000
Apr 132026-04-13 00:00:00 UTC2026-04-20 00:00:00 UTC000
Apr 202026-04-20 00:00:00 UTC2026-04-27 00:00:00 UTC211
Apr 272026-04-27 00:00:00 UTC2026-05-04 00:00:00 UTC000
May 42026-05-04 00:00:00 UTC2026-05-11 00:00:00 UTC000
May 112026-05-11 00:00:00 UTC2026-05-18 00:00:00 UTC000
May 182026-05-18 00:00:00 UTC2026-05-25 00:00:00 UTC000
May 252026-05-25 00:00:00 UTC2026-06-01 00:00:00 UTC101

What Hacker News says

14 sources
garethsprice·Apr 23, 2026
> I don’t think it’s crazy to believe that people will also be running local inference on their phones in the next 5 years. How about now? https://apfel.franzai.com/ (iOS/MacOS, runs the 3B param model already bundled for Siri) https://github.com/alichherawalla/off-grid-mobile-ai (Android, runs ~7B models on flagship phones at 15-30tok/s) Foundation model investment feels like the bubble around fiber optics circa 2001 - a great technology being pushed forward by a speculative mania as it seems like it'll be useful in some profitable way, but nobody's quite sure how.
In LLM pricing has never made sense
ali_chherawalla·Mar 3, 2026
ofcourse ofcourse, I've documented everything here: https://github.com/alichherawalla/off-grid-mobile-ai/blob/ma... llama.cpp compiled as a native Android library via the NDK, linked into React Native through a custom JSI bridge. GGUF models loaded straight into memory. On Snapdragon devices we use QNN (Qualcomm Neural Network) for hardware acceleration. OpenCL GPU fallback on everything else. CPU-only as a last resort. Image gen is Stable Diffusion running on the NPU where available. Vision uses SmolVLM and Qwen3-VL. Voice is on-device Whisper. The model browser filters by your device's RAM so you never download something your phone can't run. The whole thing is MIT licensed - happy to answer anything about the architecture.
In Show HN: Qwen 3.5 running on a $300 Android phone – on-device, open source
ali_chherawalla·Mar 3, 2026Submission

Show HN: Qwen 3.5 running on a $300 Android phone – on-device, open source

Qwen 3.5 Small dropped two days ago. I had it running on a mid-tier Android phone within hours. It's great seeing the on-device AI community light up around this release. Off Grid brings it to Android: phones with 6GB RAM in the $200-300 range, ~8 tok/sec on the 2B model. Fully offline. Text generation, vision AI, image gen, voice transcription, tool calling, document analysis — all on-device, nothing uploaded, ever. Works in airplane mode. 780+ GitHub stars. ~2,000 downloads across Android and iOS. Early days. GitHub: https://github.com/alichherawalla/off-grid-mobile-ai Play Store: https://play.google.com/store/apps/details?id=ai.offgridmobi... App Store: https://apps.apple.com/us/app/off-grid-local-ai/id6759299882
6 points · Open discussion
ali_chherawalla·Feb 24, 2026Submission

Show HN: Off Grid: On-device AI-web browsing, tools vision,image,voice–3x faster

Nine days ago I posted Off Grid here and you showed up - 124 points, 66 comments, bug reports I fixed same-day, and the kind of feedback that makes open source worth it. You told me what you wanted. Here's what I shipped: Your AI can now use tools — entirely offline. Web search, calculator, date/time, device info — with automatic tool loops. Your 3B parameter model doesn't just generate text anymore. It reasons, calls tools, and synthesizes results. On your phone. No API key. No server. No cloud function. So what? It means the gap between "local toy" and "useful assistant" just got dramatically smaller. You don't need GPT-4 to look something up and give you an answer. A quantized Qwen 3 / SMOLLM3 running on your Snapdragon can do it in no time. 3x faster with configurable KV cache. You can now choose between f16, q8_0, and q4_0 KV cache t
Read the rest
ypes. On q4_0, models that were doing 10 tok/s are hitting 30. The app even nudges you after your first generation: "Hey, you could be running faster." One tap. So what? The #1 complaint about on-device AI is "it's too slow to be useful." That argument just lost a lot of weight. 30tokens/second on a phone is faster than most people read. Live on both stores. No sideloading. No Xcode. Off Grid is now on the App Store and Google Play. Install it like any other app. Your parents could use this. So what? On-device AI just went from "cool weekend project for developers" to "thing normal people can actually try." That matters because privacy shouldn't require a CS degree. What hasn't changed: - MIT licensed. Fully open source. Every line - Zero data leaves your device. No analytics. No telemetry. No "anonymous usage data." - Text gen (15-30 tok/s), image gen (5-10s on NPU), vision AI, voice transcription, document analysis — all offline - Bring any GGUF model. Run Qwen 3, Llama 3.2, Gemma 3, Phi-4, whatever you want. I'm building this because I believe the phone in your pocket should be the most private computer you own — not the most surveilled. Every week the models get smaller and faster. The hardware is already there. The software just needs to catch up. https://github.com/alichherawalla/off-grid-mobile I'm in the comments. Tell me what to build next.
12 points · Open discussion
ali_chherawalla·Feb 24, 2026Submission

Off Grid: On-device AI-web browsing, tools, vision, image gen, voice – 3x faster

Nine days ago I posted Off Grid here and you showed up - 124 points, 66 comments, bug reports I fixed same-day, and the kind of feedback that makes open source worth it. You told me what you wanted. Here's what I shipped: Your AI can now use tools - entirely offline. Web search, calculator, date/time, device info - with automatic tool loops. Your 3B parameter model doesn't just generate text anymore. It reasons, calls tools, and synthesizes results. On your phone. No API key. No server. No cloud function. So what? It means the gap between "local toy" and "useful assistant" just got dramatically smaller. You don't need GPT-4 to look something up and give you an answer. A quantized Qwen 3 / SMOLLM3 running on your Snapdragon can do it in no time. 3x faster with configurable KV cache. You can now choose between f16, q8_0, and q4_0 KV cache
Read the rest
types. On q4_0, models that were doing 10 tok/s are hitting 30. The app even nudges you after your first generation: "Hey, you could be running faster." One tap. So what? The #1 complaint about on-device AI is "it's too slow to be useful." That argument just lost a lot of weight. 30tokens/second on a phone is faster than most people read. Live on both stores. No sideloading. No Xcode. Off Grid is now on the App Store and Google Play. Install it like any other app. Your parents could use this. So what? On-device AI just went from "cool weekend project for developers" to "thing normal people can actually try." That matters because privacy shouldn't require a CS degree. What hasn't changed: - MIT licensed. Fully open source. Every line: https://github.com/alichherawalla/off-grid-mobile- Zero data leaves your device. No analytics. No telemetry. No "anonymous usage data." - Text gen (15-30 tok/s), image gen (5-10s on NPU), vision AI, voice transcription, document analysis — all offline - Bring any GGUF model. Run Qwen 3, Llama 3.2, Gemma 3, Phi-4, whatever you want. I'm building this because I believe the phone in your pocket should be the most private computer you own — not the most surveilled. Every week the models get smaller and faster. The hardware is already there. The software just needs to catch up. If this resonates, a star on GitHub genuinely helps: https://github.com/alichherawalla/off-grid-mobile I'm in the comments. Tell me what to build next.
1 points · Open discussion
ali_chherawalla·Feb 14, 2026Submission

Show HN: Off Grid – Run AI text, image gen, vision offline on your phone

Your phone has a GPU more powerful than most 2018 laptops. Right now it sits idle while you pay monthly subscriptions to run AI on someone else's server, sending your conversations, your photos, your voice to companies whose privacy policy you've never read. Off Grid is an open-source app that puts that hardware to work. Text generation, image generation, vision AI, voice transcription — all running on your phone, all offline, nothing ever uploaded. That means you can use AI on a flight with no wifi. In a country with internet censorship. In a hospital where cloud services are a compliance nightmare. Or just because you'd rather not have your journal entries sitting in someone's training data. The tech: llama.cpp for text (15-30 tok/s, any GGUF model), Stable Diffusion for images (5-10s on Snapdragon NPU), Whisper for voice, SmolVLM/Qwen3
Read the rest
-VL for vision. Hardware-accelerated on both Android (QNN, OpenCL) and iOS (Core ML, ANE, Metal). MIT licensed. Android APK on GitHub Releases. Build from source for iOS.
124 points · Open discussion

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