That sounds exciting—edge AI is definitely where the buzz is, but real‑time inference on phones brings a few hard knobs to twist. Battery life and heat are the first red flags, and you’ll need a light‑weight model that still packs enough punch for your use case. If you can wrap that into a micro‑service style API that lets you swap models without pushing big OTA updates, it could give you a solid competitive edge. Keep an eye on data privacy too, especially if you’re processing user data locally. Overall, it’s doable, but you’ll need to iterate fast on both the hardware and the deployment pipeline. Good luck, and let me know if you hit any specific roadblocks!

Sounds solid—keeping everything local and federated cuts a lot of privacy friction. For hardware, keep an eye on the latest Snapdragon 8 Gen 3 and the new Samsung Exynos 2400, both bring tighter NPU‑to‑CPU integration and 5 nm process tech that should help with heat. Apple’s A17 Pro chip is also dropping an AI engine that can do on‑device training in a fraction of the power of older models, so if you can target iOS eventually that’s a big win. On the silicon side, Arm is pushing the Cortex‑A78AE with built‑in security and AI acceleration, which could be useful if you need more fine‑grained control. In terms of software, don’t ignore the recent TensorFlow Lite optimizations for ARM Neon and the Qualcomm Neural Processing SDK—they’ll let you squeeze more performance out of the same silicon. And finally, stay tuned to the new 3D‑stereo GPU acceleration in some of the upcoming chips—if you can do depth‑aware inference, it could unlock new app ideas. Good luck, and ping me if you hit any specific bottleneck!

Sounds like a solid plan—keep me posted on those numbers, and we’ll fine‑tune the model size to fit the heat budget. Looking forward to seeing how the depth‑aware stuff pans out!

Sounds good—can’t wait to see the results!