STstabbles·Oct 20, 2020
A subset of Python for one, and debugging stays awkward with gdb.
Makes me think of this little pun: https://github.com/FluxML/Flux.jl/blob/master/src/utils.jl#L...
In The Accelerating Adoption of JuliaFluxML
Flux.jl
Julia2018–2025
FluxML / Flux.jl
Flux.jl
Relax! Flux is the ML library that doesn't make you tensor
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28Mentions
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4.8KGitHub stars
What to know before exploring
What it is
Relax! Flux is the ML library that doesn't make you tensor
GitHub
Where it is used
No documented product use was verified in the last source check.
What people are saying
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Also in these discussions
- fluxml/zygote.jl · linked in 3 of the same discussions
- enzymead/enzyme.jl · linked in 2 of the same discussions
Mentioned in the same Hacker News threads. That is not proof they are substitutes.
This week on Hacker News
Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 5:05:26 PM UTC.
No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.
Mentions over time
HN mentions by year28 dated mentions
Observed mentions: 2018-03-30 20:24:08 UTC to 2025-05-09 10:30:13 UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| 2018 | 2018-01-01 00:00:00 UTC | 2019-01-01 00:00:00 UTC | 3 | 0 | 3 |
| 2019 | 2019-01-01 00:00:00 UTC | 2020-01-01 00:00:00 UTC | 12 | 2 | 10 |
| 2020 | 2020-01-01 00:00:00 UTC | 2021-01-01 00:00:00 UTC | 5 | 1 | 4 |
| 2021 | 2021-01-01 00:00:00 UTC | 2022-01-01 00:00:00 UTC | 2 | 0 | 2 |
| 2022 | 2022-01-01 00:00:00 UTC | 2023-01-01 00:00:00 UTC | 2 | 0 | 2 |
| 2023 | 2023-01-01 00:00:00 UTC | 2024-01-01 00:00:00 UTC | 2 | 0 | 2 |
| 2024 | 2024-01-01 00:00:00 UTC | 2025-01-01 00:00:00 UTC | 1 | 0 | 1 |
| 2025 | 2025-01-01 00:00:00 UTC | 2026-01-01 00:00:00 UTC | 1 | 0 | 1 |
What Hacker News says
5 sourcesTechnically, RNNs, including LSTMs, are already turing complete, neural turing machines mostly decoupled memory from the hidden layers so it can grow the memory size without a square increase on the number of parameters, which helps with the unbounded part of turing machines. it also helped inspire many attention based models that came after. Also matlab isn't really related here, and automatic differentiation is different from symbolic differentiation, the direct comparison are those python libs like tensorflow, pytorch and jax.
Differentiable programming in the end is just a way of making something that you can already do better (just like you could create neural network long before theano/tensorflow/torch but it was not as streamlined). With a differentiable programming approach you can get something as dynamic as pytorch, with the per
Read the rest
formance optimizations and deploy capabilities of a tensorflow graph and with an easy way to plug any new operation and it's gradient by writing in the same host language (so no need to learn or restrict yourself the tensorflow/pytorch defined methods/DSL).
You don't even need to change the compiler or define a new language for it. Julia's Zygote [1] is just a 100% Julia library you can import, for which you can add at any point any custom gradient even if the library creators never added them, and them run either on CPU or GPU (for which you can also fully extend using just pure Julia [2]). And of course, you can also use a higher level framework like Flux [3] which is also high level Julia code.
I think the heart of differentiable programming is just another step in the evolution, from early (lua) torch-like libraries that gave you the high level blocks to compose, to autodiff libraries that gave you easy access to low level math operators to build the blocks to the point where you can easily create your own operators to create the high level blocks.
[1] https://github.com/FluxML/Zygote.jl
[2] https://github.com/JuliaGPU/CUDAnative.jl
[3] https://github.com/FluxML/Flux.jl
Flux.jl: Julia ML library that doesn't make you tensor
4 points · Open discussionI've started working with Flux [1] in Julia, and it's so elegant and such a great experience :). Just look at this definition of a U-net model for image segmentation: https://gist.github.com/haampie/bceb1d59fd9a44f092f913062e58.... Apart from that, you can write your own custom loss functions in pure Julia that run efficiently on the GPU, language level automatic differentiation, proper integration with other packages. If people are moving away from Tensorflow, then Flux could be a solid alternative as well.
[1] https://github.com/FluxML/Flux.jl
In Standardizing OpenAI’s deep learning framework on PyTorchDepends what you're trying to do, but Flux.jl is pretty nice: https://github.com/FluxML/Flux.jl . Failing that, the Julia Python FFI is very good, so it's possible to use PyTorch almost seamlessly in Julia (I previously used Tensorflow 1.x, and it was such a painful experience I'm not brave enough to touch 2.0).
In Haskell Problems for a New Decade