It also has the largest gap between AI deployment and AI governance of any sector we looked at.

Three out of four US health systems are running AI in 2026. Less than one in five has governed it. That is not a laggard industry failing to adopt — it is an industry that adopted faster than it figured out what it was doing. The compliance exposure sitting inside most health systems right now is not hypothetical. It is running in production.

We mapped this across financial services, healthcare, energy, manufacturing, and logistics. Healthcare was the one that made us stop.

Full report: https://mobile.wednesday.is/the-enterprise-mobile-ai-report-...

Comments URL: https://news.ycombinator.com/item?id=47908677

Points: 1

# Comments: 1

We built RightSuite to compress that cycle. Describe your offer and target audience. The system runs it through a synthetic buyer population and tells you what's likely broken before you go live.

It won't replace talking to real customers. Nothing does. But it means you don't start at zero. Instead of spending month one discovering your price is wrong and month two learning your cold email is condescending, you walk in already knowing where the problems are. The simulation gets you roughly 70% of the way there. The last 30% is real-world iteration - but now that's weeks, not quarters.

Seven tools live: pricing, messaging, positioning, audience, channels, outreach, and ad creative. ROI calculators at rightsuite.co/roi so you can see what the iteration tax is actually costing you.

Comments URL: https://news.ycombinator.com/item?id=47626035

Points: 7

# Comments: 2

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.

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

Comments URL: https://news.ycombinator.com/item?id=47238519

Points: 6

# Comments: 10

Those changes are not live on the play store / app store but its available on GH. I'll make a release later today.

Let me know what you think!

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 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 - 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.

Comments URL: https://news.ycombinator.com/item?id=47142003

Points: 12

# Comments: 5

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 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.

Comments URL: https://news.ycombinator.com/item?id=47141803

Points: 1

# Comments: 1