Show HN: QVAC SDK, a universal JavaScript SDK for building local AI applications

Hi folks, today we're launching QVAC SDK [0], a universal JavaScript/TypeScript SDK for building local AI applications across desktop and mobile.

The project is fully open source under the Apache 2.0 license. Our goal is to make it easier for developers to build useful local-first AI apps without having to stitch together a lot of different engines, runtimes, and platform-specific integrations. Under the hood, the SDK is built on top of QVAC Fabric [1], our cross-platform inference and fine-tuning engine.

QVAC SDK uses Bare [2], a lightweight cross-platform JavaScript runtime that is part of the Pear ecosystem [3]. It can be used as a worker pretty much anywhere, with built-in tooling for Node, Bun and React Native (Hermes).

A few things it supports today:

  - Local inference across desktop, mobile and servers
  - Support for LLMs, OCR, translation, transcription, 
    text-to-speech, and vision models
  - Peer-to-peer model distribution over the Holepunch stack [4],
    in a way that is similar to BitTorrent, where anyone can become a seeder
  - Plugin-based architecture, so new engines and model types can be added easily
  - Fully peer-to-peer delegated inference
We also put a lot of effort into documentation [5]. The docs are structured to be readable by both humans and AI coding tools, so in practice you can often get pretty far with your favorite coding assistant very quickly.

A few things we know still need work:

  - Bundle sizes are larger than we want right now because the current packaging of Bare add-ons is not as efficient as it should be yet
  - Plugin workflow can be simpler
  - Tree-shaking is already possible, but at the moment it still requires a CLI step, and we'd like to make that more automatic and better integrated into the build process
This launch is only the beginning. We want to help people build local AI at a much larger scale. Any feedback is truly appreciated! Full vision is available on the official website [6].

References:

[0] SDK: http://qvac.tether.io/dev/sdk

[1] QVAC Fabric: https://github.com/tetherto/qvac-fabric-llm.cpp

[2] Bare: https://bare.pears.com

[3] Pear Runtime: https://pears.com

[4] Holepunch: https://holepunch.to

[5] Docs: https://docs.qvac.tether.io

[6] Website: https://qvac.tether.io

22 points | by qvac 20 hours ago

3 comments

  • WillAdams 1 hour ago
    Do you really mean/want to say:

    >...and without permission on any device.

    I would be much more interested in a tool which only allows AI to run within the boundaries which I choose and only when I grant my permission.

    • elchiapp 1 hour ago
      That line means that you don't need to create an account and get an API key from a provider (i.e. "asking for permission") to run inference. The main advantage is precisely that local AI runs on your terms, including how data is handled, and provably so, unlike cloud APIs where there's still an element of trust with the operator.

      (Disclaimer: I work on QVAC)

      • sull 25 minutes ago
        thoughts on mesh-llm?
    • mafintosh 1 hour ago
      The modular philosophy of the full stack is to give you the building blocks for exactly this also :)
  • elchiapp 55 minutes ago
    Hey folks, I'm part of the QVAC team. Happy to answer any questions!
  • eddie-wang 1 hour ago
    [dead]