jax-js

AI

ml library

SHOW HN2026

84points
22comments
#807of 4,366 in 2026
Top 19%of 2026 by points

How HN took it

MixedNo clear lean

5 positive, 1 neutral or asking, 2 mixed, 1 critical, out of 9 comments about the project. Thread score +0.36.

Idea: 3 for, 0 againstPerformance: 1 for, 0 againstDocs: 1 for, 0 againstExecution: 0 for, 1 against

The thread, comment by comment

Each row is a top-level comment and its replies, in HN's order. Select a cell to open that comment on HN.

positive neutral or mixed critical about another commenter or something else the maker

What HN said

What people liked

  • Fills gap left by tensorflow.js for gradients in JavaScript

    From 1 comment
    • Congrats on the launch! This is a very exciting project because the only decent autodiff implementation in typescript was tensorflowjs, which has been completely abandonned by…
      fouronnes3 on HN
  • Supports WebGPU plus CPU, Wasm and WebGL backends

    From 1 comment
    • Very nice work. Like how it supports webgpu but also cpu/wasm/webgl. Would love to read more on the internals & design choices made like e.g. ref counting in README. P.S. And…
      forgotpwd16 on HN
  • Examples are useful

    From 1 comment
    • The examples are great. It would be really nice to have a sandbox with the full training code (e.g. MNIST) to play with.
      sbondaryev on HN

Objections

  • Firefox cannot run the demos without WebGPU

    From 2 comments
    • Could not run the demos on Firefox. On Chromium, the Great Expectations loads but then nothing happens.
      maelito on HN
    • According to page WebGPU supported (`dom.webgpu.enabled` flag) but is only enabled by default on Windows & macOS (i.e. not Linux).
      forgotpwd16 on HN
  • Ref and dispose API is low-level and error-prone

    From 1 comment
    • I have a project using tfjs and jax-js is very exciting alternative. However during porting I struggle a lot with `.ref` and `.dispose()` API. Coming from tfjs where you garbage…
      mlajtos on HN

Requests

  • Benchmark against other autodiff tools

    From 1 comment
    • Hey Eric, great to see you've now published this! I know we chatted about this briefly last year, but it would be awesome to see how the performance of jax-js compares against…
      sestep on HN
  • Sandbox with full training code such as MNIST

    From 1 comment
    • The examples are great. It would be really nice to have a sandbox with the full training code (e.g. MNIST) to play with.
      sbondaryev on HN
  • Write-up of internals and design choices

    From 1 comment
    • Very nice work. Like how it supports webgpu but also cpu/wasm/webgl. Would love to read more on the internals & design choices made like e.g. ref counting in README. P.S. And…
      forgotpwd16 on HN

Questions

  • Is it a side project or a long-term commitment?

    From 1 comment
    • Congrats on the launch! This is a very exciting project because the only decent autodiff implementation in typescript was tensorflowjs, which has been completely abandonned by…
      fouronnes3 on HN
  • Can the ref API be improved or could using help?

    From 1 comment
    • I have a project using tfjs and jax-js is very exciting alternative. However during porting I struggle a lot with `.ref` and `.dispose()` API. Coming from tfjs where you garbage…
      mlajtos on HN

Alternatives named

  • tensorflow.js

    From 2 comments
    • Congrats on the launch! This is a very exciting project because the only decent autodiff implementation in typescript was tensorflowjs, which has been completely abandonned by…
      fouronnes3 on HN
    • I have a project using tfjs and jax-js is very exciting alternative. However during porting I struggle a lot with `.ref` and `.dispose()` API. Coming from tfjs where you garbage…
      mlajtos on HN
  • ONNX Runtime Web

    From 2 comments
    • Congrats on the launch! This is a very exciting project because the only decent autodiff implementation in typescript was tensorflowjs, which has been completely abandonned by…
      fouronnes3 on HN
    • onnx on the web has the most models available and can use webgpu which is available everywhere. Huggingface’s transformers.js uses it. And I use that for https://workglow.dev…
      sroussey on HN
  • transformers.js

    From 1 comment
    • onnx on the web has the most models available and can use webgpu which is available everywhere. Huggingface’s transformers.js uses it. And I use that for https://workglow.dev…
      sroussey on HN
  • WebNN

    From 1 comment
    • What is the state of web ML? Anybody doing cool things already? How about https://www.w3.org/TR/webnn/ ?
      esafak on HN

Category, keywords, summaries and labels on this page were made by AI (Thread Reader v2) and not checked by hand. Each point links to the comments it came from.

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