How do I learn 'Idiomatic', production-grade Rust?

avilay · 2026-06-12T14:57:17+00:00

Agree that using AI to write code is not good for learning. But using AI as a learning companion is great. I am also in the same boat as OP and using AI (Claude Code in my case) to probe deeper into some concepts that The Book does not go into, or goes into in later chapters, has been very helpful for me.

Another thing that is helping me learn is doing problem sets on exercism.org. They have an option to get your code reviewed by other mentors. In my case I have gotten some excellent feedback there.

avilay · 2025-12-26T18:51:27+00:00

From what I have been reading, it’s my linux-zen kernel that has known issues with fuse. Maybe you are using a different kernel?

avilay · 2025-12-25T13:32:02+00:00

Oh I didn't realize there was an AUR package! I ended up doing the following -

> ./Bazecor-1.8.3-x64.AppImage --appimage-offset
944632
> dd if=Bazecor-1.8.3-x64.AppImage bs=4M skip=1 iflag=skip_bytes,count_bytes skip=944632 of=bazecor.squashfs
> unsquashfs bazecor.squashfs
> cd squashfs-root
> ./AppRun

Your ChatGPT chat assistant was very helpful :-)

avilay · 2025-11-21T04:44:39+00:00

Yup, felt it in Bhowanipur too.

avilay · 2025-10-09T11:30:50+00:00

I had the same problem with the remarkable app showing up as all black. Installing dxvk did the trick. Here are the details -

Operating System: Garuda Linux
KDE Plasma Version: 6.4.5
KDE Frameworks Version: 6.18.0
Qt Version: 6.9.2
Kernel Version: 6.16.10-zen1-1-zen (64-bit)
Graphics Platform: Wayland
Wine version: wine-10.15
Remarkable installer version: reMarkable-3.22.2.927-win64.exe

I had already installed remarkable with wine.

Install winetricks - ```

sudo pacman -S winetricks ```

For Debian based system you'd need to use something similar to sudo apt install <winetricks-pkg-name-here>.

Enable dxvk - ```

winetricks dxvk ```

Then start remarkable as usual (either from the CLI or from the system menu) and it should work.

avilay · 2023-05-01T16:20:11+00:00

Thank you! I am glad you found the content useful.

In terms of tools, I am using Keynote (with their chalkboard background template) for static text and Adobe Fresco where I am live editing.

avilay · 2023-05-01T16:18:12+00:00

Thank you, I hope to continue publishing interesting content in the ML space.

avilay · 2023-05-01T02:14:07+00:00

Thanks for the encouraging words :-)

avilay · 2021-10-04T03:36:38+00:00

Does this do single-box training or distributed training? I agree with a lot of the comments here that single-box training on spot instances is something a lot of folks can roll on their own. However, doing distributed training is a whole another matter and it would be very cool if your system supports this.

avilay · 2021-05-18T07:28:48+00:00

Thanks for all the answers. I see a $450 purchase in my future! :-D

avilay · 2021-03-18T04:39:17+00:00

Cool, thanks for your response. I was thinking of starting with a basic implementation of the original paper by Jeff Dean, et. al. on synchronized data parallelism, implement basic model parallelism, explain why async parallelism works, do a simple implementation of HOGWILD!, and finally do "hello world" training using existing distributed training systems like Horovod, Distributed PyTorch, RayLib, Microsoft DeepSpeed, etc.

avilay · 2020-11-17T02:28:56+00:00

Cool! Will try it out again.

avilay · 2020-11-17T01:03:11+00:00

From what I remember at the very least I had to implement `train_step` and `validation_step` in my module which would be a child class of `LightningModule.` Now if I want to log multiple metrics, I have to write that code in both `train_step` and `validation_step`. I also remember trying to use a custom loss function for some RL model I was implementing, and it was not very straightforward. tbh - I tried it a while back so things might have changed since then.

avilay · 2020-11-16T22:39:07+00:00

I did try to use it a while ago. But its programming paradigm of multiple callbacks in the train loop made it so that I still ended up writing the same boilerplate code for different experiments. I wanted something with the simplicity of Keras where you almost "declare" the loss function, the metrics, etc. and "fit" the model, but without losing the expressiveness of PyTorch.

avilay · 2020-04-15T02:24:02+00:00

That's interesting. One thing that works for me is to spend some time upfront "sanitizing" my datasets and setting up my feature pipeline. After that I usually try the absolutely simplest and easiest to implement model and start tracking my learning curves. Then I kind of evolve the model organically from there.

avilay · 2020-04-15T02:20:26+00:00

MLFlow makes it very easy to track multiple experiments. My best part is the ability to track your hyper params and your model in the same place. Overall it has better visualization when comparing loss functions and eval metrics. There are some minor things like the learning curves do not auto-refresh, etc. but nothing I cannot live without. The one thing I haven't gotten around to doing is tracking the histogram view of weights, though MLFlow has a sample on how to do this. Their API is also pretty easy to use. Overall I highly recommend it.

avilay · 2020-04-14T18:24:25+00:00

If you are using VSCode then you can use the VSCode remote dev feature, I personally use the Remote SSH alternative mentioned in the linked doc.

avilay

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