Xybrid
run LLM and speech locally in your app (no back end, Rust)
Details
- External ID
- 47427332
- Source
- HN
- Company
- —
- Product
- Xybrid
- Website domain
- github.com
- Launched
- March 18, 2026
- Cohort
- —
- Upvotes
- 6
- Upvotes percentile
- 0.2853628536285363
- Tags
- —
- Fetched at
- Sept. 7, 2026, 9:26 p.m.
- Updated at
- Sept. 7, 2026, 9:26 p.m.
Description
Hi HN,We built Xybrid, a Rust library for running LLM + speech pipelines directly inside your app, no server, no daemon, just one binary.We started building it while working on a privacy-focused LLM app with Tauri and realized there wasn’t a straightforward way to embed models directly into shipped applications without relying on a separate server process.Xybrid links into your process like any other library. It supports GGUF / ONNX / CoreML and integrates with Flutter, Swift, Kotlin, Unity, and Tauri, letting you run pipelines like speech → LLM → speech in a single call.On recent phones, we’re seeing ~20 tok/s on Android and ~40 tok/s on iOS for small (~3B) quantized models (varies by device, backend, and thermals).The demo that shows it best: a Unity tavern scene where 6 NPCs generate real-time dialogue fully on-device — no API key, no internet, no per-request cost.Unity demo: https://youtu.be/vSPeTyeow6A Desktop demo (Tauri): https://youtu.be/o83YShqV7O4GitHub: https://github.com/xybrid-ai/xybridIt’s still early — there are rough edges, especially around model support and performance tuning. Happy to answer questions about the architecture, backends, or integrations (Flutter, Swift, Kotlin, Unity, Tauri).
Enrichment
- Theme
- voice dictation and control tools
- Vertical
- Horizontal
- Function
- Model & infra
- Audience
- Developer
- AI stance
- AI-native
- Project type
- Commercial product
- Normalized one-liner
- on-device llm and speech runtime
- Manually corrected
- False
Could you build this?
No Embedding local LLM and speech-to-text inference directly into a single cross-platform native Rust binary without external daemons requires specialized low-level systems and ML compilation expertise.
What it would actually take: The architecture relies on Rust bindings to C++ inference runtimes like llama.cpp and whisper.cpp (or pure Rust frameworks like Candle/Burn), managing memory-mapped GGUF models, hardware acceleration (Metal, CUDA, Vulkan), and native audio I/O via cpal. Developers must have deep expertise in systems programming, cross-compilation toolchains, audio buffer streaming, and low-level GPU acceleration.
Discussion
2 comments analyzed.
Competitors
Other products that read as similar to this one — 68 launches clear the similarity bar, closest 8 shown.
Attention rank: #47 of 69 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).
Launched 130 days after the earliest competitor.
- Voquill · hn · 2026-02-19 · 6 upvotes · similarity 0.38
- Llmpm · hn · 2026-03-09 · 6 upvotes · similarity 0.37
- TTSLab · hn · 2026-02-23 · 5 upvotes · similarity 0.37
- Private voice-to-text for macOS using Apple's SpeechAnalyzer · hn · 2026-01-04 · 9 upvotes · similarity 0.37
- Speechable · hn · 2026-01-03 · 5 upvotes · similarity 0.36
- Live Qwen3-Omni API (open-source speech-to-speech) · hn · 2025-12-02 · 5 upvotes · similarity 0.36
- Gerbil · hn · 2025-11-11 · 37 upvotes · similarity 0.36
- Yap · hn · 2026-07-27 · 104 upvotes · similarity 0.36
Other launches for this product
- No other launches for this product.
Same idea, different domain
Nobody's really built a model & infra tool for Fintech yet.