I’m the creator of EdgeVeda. I spent the last few months obsessed with one problem: Why is mobile AI still so slow and expensive? Most startups are just wrappi

I’m the creator of EdgeVeda. I spent the last few months obsessed with one problem: Why is mobile AI still so slow and expensive? Most startups are just wrapping OpenAI/Claude/gemini APIs. This works for prototypes, but for production apps, the 1000ms+ roundtrip latency kills the UX, and the inference bills kill the margins. I built EdgeVeda to be the "Switzerland of Edge AI." It’s a unified C++ engine that handles the hardware-specific "plumbing" (Metal for iOS, Vulkan/NNAPI for Android) so you can run LLMs, Whisper, and TTS locally in one line of code. Key Technical Feats: Sub-200ms Time-to-First-Token: Achieved by bypassing the standard Android JNI bottleneck and using a direct memory-mapped buffer. The Memory Watchdog: Mobile OSs love to kill apps that use >1GB RAM. I implemented a custom allocator that swaps model layers to disk when the system is under pressure. Unified Pipeline: Orchestrates STT -> LLM -> TTS entirely on-device. I’m looking for feedback on: My implementation of the Dart FFI bridge (any performance leaks I missed?). Support for 2024-era NPUs on non-flagship Android devices. I'll be here all day to answer technical questions.