Hey guys I made an open source engine for embedding local LLMs in your apps (works with flutter, react native , swift, and python ) the engine handles RAG, context management, voice and audio features https://github.com/iBz-04/quaynor
The main aim is to allow you to Embed local LLMs in your app: load GGUF checkpoints, chat on-device with native Tool use, Bindings available for Python, Flutter, React Native, and Swift and its open source: https://github.com/iBz-04/quaynor
Help me grow it ! 🤙🏼
I have been a contributor to the open source space for sometime, a huggingface employee used my local llm inference library and found it so easy to use and I ended up getting invited to speak at one of their events, idk how to feel about this 😂but in all we thank God
The library repo is https://github.com/iBz-04/quaynor, be sure to give it a star ⭐️
Ghana to the wiase 🇬🇭
1. Introduction
Quaynor is a lightweight inference layer that lets you embed open-source LLMs directly into your app. It supports GGUF checkpoints, runs on-device or GPU, and removes dependence on cloud APIs. Bindings are available for Python, Flutter, and React Native, so the same core logic can be reused across platforms.
2. Why it stands out (Key Features)
Fully offline inference: No API keys, no external calls. Models run locally or load from Hugging Face/URLs.
Cross-platform consistency: One chat-style API across Python, Flutter, and React Native.
Streaming + completions: Supports token streaming and full responses.
Tool calling: Grammar-based function execution integrated into native code (Python/mobile).
Optimized hardware usage: Vulkan (Android/Desktop) and Metal (Apple).
Advanced NLP stack: Embeddings and cross-encoder reranking where supported.
Multimodal support: Works with vision/audio models depending on capability.
Production-ready controls: Context window management and efficient inference handling.
3. Who’s using it (Traction)
Early-stage developer adoption via GitHub
Target users:
Indie developers building offline-first apps
Startups focused on privacy-first AI
Mobile engineers embedding on-device AI
Positioned for engineers who want control over inference instead of relying on APIs
4. Pricing Plans
Currently free and open-source
Free Plan
Full access to core inference engine
All bindings (Python, Flutter, React Native)
Local model execution
Streaming, tool calling, embeddings support
Future potential (if monetized)
Hosted model downloads / optimized builds
Enterprise support
Managed updates and performance tuning
5. Offers
No paid plans it’s open source → effectively 100% free access
Early adopters benefit from full capabilities without restrictions
6. Website
GitHub: https://github.com/iBz-04/quaynor
Docs: www.quaynor.site
Explore the repo, run a local model in minutes, and integrate offline AI into your app without relying on any external API.
I built an inference engine for embedding open source LLMs in your apps or running them on your pc, tools like olmama are already in this space but I built Quaynor for speed, in built tool usage and , utmost simplicity. If you’re building apps in react native or any language with python backend, you can use it
I named it after Nii Quaynor, and gave it an adinkra symbol as a logo, we have over 400 downloads over pypi, flutter and npm , link: https://github.com/iBz-04/quaynor
Give it a star ⭐️
Hey guys I made this for book lovers who commute and love free resources, welcome!
Hey guys I created the easiest way to use open weights models in apps with tool calling, vision and audio capabilities, there’s native support for frameworks like flutter and react native, but python bindings are also available, quaynor already hit 100 downloads on npm
And it’s open source: https://github.com/iBz-04/quaynor
I made an app for reading books offline, books are broken down into lessons and audio forms, and it’s free!
https://apps.apple.com/app/reademy-offline-microlearning/id6763537116
Please show some love 🤙🏼😎
Nii quaynor is the father of internet in Africa, I named my local ai inference engine after him, I feel more of our poriducts should commemorate the great Ghanaians and Africans, let me know your thoughts now! https://github.com/iBz-04/quaynor
It can be used in react native fluttter and python documentation here https://www.quaynor.site
computer-use agent’lara son iki yıldır baya kafayı takmış durumdayım.
Öyle normal bir ilgi de değil. Hani bir şey aklına takılır, sürekli geri dönersin ya, tam o hesap. Bir demo görüyorsun, kendin bir şeyler deniyorsun, duvara çarpıyorsun, baştan yapıyorsun, “acaba bu işin doğru yolu bu mu” diye sorguluyorsun ama yine de ertesi gün dönüp devam ediyorsun. Çünkü içinde gerçekten bir şey olduğuna inanıyorsun.
Beni en çok heyecanlandıran şey sadece “AI bir şey yapabiliyor” kısmı değil. Daha çok şu taraf ilgimi çekiyor: computer-use agent’ları sadece gösterişli demo olmaktan çıkarıp gerçekten işe yarayan, daha reliable, daha grounded sistemlere nasıl dönüştürürüz? Uzun zamandır cidden düşündüğüm konu bu.
O yüzden samimi feedback, eleştiri, fikir, ne varsa duymayı isterim.
Source code burada:
https://github.com/iBz-04/gloamy