Launching today
NobodyWho
Run AI models on any device
28 followers
Run AI models on any device
28 followers
NobodyWho is an inference engine for running LLMs fully on-device, built on llama.cpp. Open-source, free, no API keys, no cloud calls. We support Swift, Kotlin, Flutter, React Native, Python, and Godot. Includes type-safe tool calling with automatic grammar generation, multimodal input, Text-to-Speech & Speech-to-Text, GPU acceleration via Vulkan & Metal, and Hugging Face model downloads.
NobodyWho
Hey, I'm Pierre from NobodyWho 👋
We've spent the last months getting local inference to be production-ready across six platforms and frameworks, not just a cool demo that works on one device.
With NobodyWho you can:
- Get answers from any open-weight AI models: Gemma, Qwen, LFM...
- Analyse images and audio through multimodal input
- Transcribe speech to text with any Whisper models
- Generate natural-sounding speech with Supertonic, Pocket TTS and Kokoro models
- Tool calling with guaranteed schema-valid output, the grammar is built from your function signature so the model can't return malformed JSON
- Run long conversations without hitting a hard message-length wall, thanks to preemptive context shifting
Wanna try our work on your device? We've built a few demo apps: iOS, Android, Apple Watch & Vision Pro.
We've also built starter examples to get started in 5 minutes and a model selection page.
NobodyWho inference engine is open-source & free, please leave a star to support us on Github 👈
Happy to answer any questions :)
Cluing
NobodyWho
@ralic Thanks ! 😊
We are also planning a rust release soon, stay tuned ;)
Six platforms including Godot is a wild spread - most on-device inference projects stop at one and call it a day.
We went the other way on a consumer app I'm building: on-device only for the narrow stuff (Apple Vision for photo classification, SFSpeechRecognizer for voice, both tiny and purpose-built) and kept anything needing real context on the server. The deciding factor wasn't output quality - it was that the AI features need months of user history in the context window, and on a phone that's either impossible or unbearably slow.
So the honest question: at what tokens/sec and what context length does local actually replace a cloud call for you? Not for a demo - for a feature someone hits ten times a day without thinking about it. That's the number I keep failing to find in these projects.
And half-joking, half-not: if this takes off, the memory story gets interesting fast. We'll end up with phones shipping 32–64GB of RAM because a photo app wants a 12B-35B model resident. :)