bringing npm packages to Guix

bootstrap it with esbuild, then use the bootstrapped version to build sucrase again. (The bootstrapped sucrase seems to also work as expected!) The import script I wrote, alongside the sucrase package itself can be found here: - <https://gist.github.com/zamfofex/eac93bc0e00477a8b79f5ca4dc1a34ff>

Re: bringing npm packages to Guix

created for jQuery in the GitHub Gist I sent above. (And I can anecdotally verify that it works when tring it on a browser.) <https://gist.github.com/zamfofex/eac93bc0e00477a8b79f5ca4dc1a34ff#file-jquery-scm> I will admit that I cheated a bit, since I used the currently unreleased 4.0.0 v

GMP GCC C++ Hurd cross-build failure on core-updates

Hello, Guix! I have been trying to update the Hurd packages to their latest commits in the hopes that I can eventually get networking with netdde functioning. In , I got something that seemed to be working at first (at least it progressed farther than the stat

Re: GMP GCC C++ Hurd cross-build failure on core-updates

I realized it might also be useful to include GMP’s ‘config.log’ with ‘--keep-failed’, so I’ll send it as an attachment in this email. Thank you in advance once again!This file contains any messages produced by compilers while running configure, to aid debugging if configure makes a mistake. It

progress report on updating the Hurd packages

Hello, everyone! I just wanted to be able to report my progress with updating the Hurd packages. I have been able to get past my previous issues with building GMP, I think, but it seems that the glibc in Guix is still too outdated for the whole endeavor to succeed. I could either update the gl

Re: Guidelines for pre-trained ML model weight binaries (Was re: Where should we put machine learning model parameters?)

> To me, there is no doubt that neural networks are a threat to user > autonomy: hard to train by yourself without very expensive hardware, > next to impossible without proprietary software, plus you need that huge > amount of data available to begin with. > > As a project, we don’t have guideline

Re: Guidelines for pre-trained ML model weight binaries (Was re: Where should we put machine learning model parameters?)

> On 07/03/2023 6:39 AM -03 Simon Tournier wrote: > > Well, I do not see any difference between pre-trained weights and icons > or sound or good fitted-parameters (e.g., the package > python-scikit-learn has a lot ;-)). As I said elsewhere, I do not see > the difference between pre-trained neura

WebAssembly target for Guix (Showcase)

have shared it here: https://git.sr.ht/~zamfofex/guix-wasm The WebAssembly changes are applied on top of https://issues.guix.gnu.org/63088 (“Add Lc0”) because I had in my local Guix clone, and I didn’t bother to rebase. I’d love to know what y’all might think about it!

Further work on WebAssembly target for Guix

it is able to compile fmtlib and GMP. My hope was to compile daikichi, but it seems it uses various constructs that are not supported yet (e.g. ‘#include ’ is not supported). See my changes on https://git.sr.ht/~zamfofex/guix-wasm To try it out, run the following command. (Though note that it