diffrax VS serenity

Optimistix has high-level APIs for minimisation, least-squares, root-finding, and fixed-point iteration and was written to take care of these kinds of subroutines in Diffrax.

Diffrax (https://github.com/patrick-kidger/diffrax).

Here is the GitHub: https://github.com/patrick-kidger/optimistix

The elevator pitch is Optimistix is really fast, especially to compile. It

Sure. So I've got some PyTorch benchmarks here. The main take-away so far has been that for a neural ODE, the backward pass takes about 50% longer in PyTorch, and the forward (inference) pass takes an incredible 100x longer.

FWIW this worked for me. :D My full-time job is now writing JAX libraries at Google. Equinox for neural networks, Diffrax for differential equation solvers, etc.

It's relatively early days yet, but JAX is in the process of developing its nascent scientific computing / scientific machine learning ecosystem. Mostly because of its strong autodifferentiation capabilities, excellent JIT compiler etc. (E.g. to show off one of my own projects, Diffrax is the library of diffeq solvers for JAX.)

Diffrax - solving ODEs with Jax and computing it's derivatives automatically functools - love partial and lru_cache fastprogress - simpler progress bar than tqdm

At least prior to this announcement: JAX was much faster than PyTorch for differentiable physics. (Better JIT compiler; reduced Python-level overhead.)

E.g for numerical ODE simulation, I've found that Diffrax (https://github.com/patrick-kidger/diffrax) is ~100 times faster than torchdiffeq on the forward pass. The backward pass is much closer, and for this Diffrax is about 1.5 times faster.

It remains to be seen how PyTorch 2.0 will compare, or course!

Right now my job is actually building out the scientific computing ecosystem in JAX, so feel free to ping me with any other questions.

https://github.com/patrick-kidger/diffrax

Which are neural network and differential equation libraries for JAX.

[Obligatory I-am-googler-my-opinions-do-not-represent- your-employer...]

Autodifferentiable programming!

Neural networks are the famous example of this, of course -- but this can be extended to all of scientific computing. ODE/SDE solvers, root-finding algorithms, LQP, molecular dynamics, ...

These days I'm doing all my work in JAX. (E.g. see Equinox or Diffrax: https://github.com/patrick-kidger/equinox, https://github.com/patrick-kidger/diffrax). A lot of modern work is now based around hybridising such techniques with neural networks.

I'd really encourage anyone interested to learn how JAX works under-the-hood as well. (Look up "autodidax") Lots of clever/novel ideas in its design.

SerenityOS replicates that look and feel. It is also implemented in a dialect of C++ that adheres to some of the good parts of C++98: https://serenityos.org

One example of a useful technique

https://serenityos.org/ apparently only makes source code available. There are no binary images of the OS to install

I think Andreas said this functions like a little test -- if you're not willing to build it from source, then you probably wouldn't be a good contributor anyway.

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Likewise, my shell project provides source tarballs only, right now - https://www.oilshell.org/release/0.21.0/

It is packaged in a number of places, which I appreciate. That means some other people are willing to do some work.

And they provide good feedback.

I would like it to be more widely available, but yeah I definitely see that you need to "gate" peanut gallery feedback a bit, because it takes up a lot of time.

Of course, it's a tricky balance, because you also want feedback from casual users, to make the project better.

Indeed, given the existence of `JS::NonnullGCPtr`, `JS::GcPtr` intentionally corresponds to a nullable pointer, so it seems dangerous to convert one to a reference without a null-check.

That said, a naive code search finds what *may* be more cases of this pattern:

https://github.com/search?q=repo%3ASerenityOS%2Fserenity+%2F...

Eg: https://github.com/SerenityOS/serenity/blob/a68b134e6dea5065... -> https://github.com/SerenityOS/serenity/blob/a68b134e6dea5065...

In some of those search results, it is fine because there is a preceding null-check, and obviously I know nothing about this code other than this naive search result, but perhaps it would be prudent to vet all of them.

It is a SerenityOS project. You can find the answer to that question in their primary project's FAQ[1].

1. https://github.com/SerenityOS/serenity/blob/master/Documenta...

https://github.com/SerenityOS/serenity

The best way to write proper exception free C++ is not to use the C++ Standard Library.

I initially confused it with Serenity OS prior to watching the video: https://github.com/SerenityOS/serenity

My contributions to SerenityOS[0] helped me get my current job. My team lead (who was also my interviewer) was interested in what I did since I listed some of it in my CV, and I showed him some PRs I made and explained what went into each of them. It was really exciting because I didn't have professional experience with low-level development, and basically got the job due to hobby programming.

[0]: https://github.com/SerenityOS/serenity/pulls?q=is%3Apr+autho...

Definitely not "literally impossible", just a great deal of work. https://github.com/SerenityOS/serenity/tree/master/Ladybird