bitvoid

Yes. The article only shows Apple-owned apps: TextEdit and Calculator. And it shows the various border radiuses for different elements straight from XCode's UI builder.

Out of curiosity, which design decisions do you find questionable and what do you feel is half-baked with Pixi? It's been working well for us.

And 1967 was 58 years ago, which was when the first deep neural network was trained with stochastic gradient descent. Yet, DNNs didn't take off until the 2010s when the hardware became powerful enough and data became plenty enough to successfully train and utilize them such that they were practical.

> you cannot use them to solve real world problems

Doesn't waymo and other self-driving systems use reinforcement learning? I thought it was used in robotics as well (i.e., bipedal, quadrupedal movement).

I just want to give a hearty thank you for pixi. It's been an absolute godsend for us. I can't express how much of a headache it was to deal with conda environments with student coursework and research projects in ML, especially when they leave and another student builds upon their work. There was no telling if the environment.yml in a student's repo was actually up to date or not, and most often didn't include actual version constraints for dependencies. We also provide an HPC cluster for students, which brings along its own set of headaches.

Now, I just give students a pixi.toml and pixi.lock, and a few commands in the README to get them started. It'll even prevent students from running their projects, adding packages, or installing environments when working on our cluster unless they're on a node with GPUs. My inbox used to be flooded with questions from students asking why packages weren't installing or why their code was failing with errors about CUDA, and more often than not, it was because they didn't allocate any GPUs to their HPC job.

And, as an added bonus, it lets me install tools that I use often with the global install command without needing to inundate our HPC IT group with requests.

So, once again, thank you

I've been using Plex (connecting via Tailscale) with their Plexamp music player.

It's been working pretty well, but I might have to give this a try to compare. Although, it's not clear from the GitHub README or the Apple App Store listing if the mobile app allows you to download music for offline listening.

Backpropagation happens after some number of inferences. You need to infer to calculate a loss function to then backprop from.

> And calling teams low in the desired section is such an odd assessment.

I said it ranks low in "admired" (i.e., those who use it and want to continue using it). Less than 50% of those using it want to continue using it despite it being the most "popular" by a significant percentage. If everyone had their choice, Teams would drop heavily in popularity.

Regardless, it was just an example of how "popularity" doesn't mean anything because most people don't have a choice in their day jobs.

It seemed like "productive" was used in a relative sense. Would all those people be more productive on macOS or Linux? That's not clear.

Also, I thought the parent was replying to the following part, considering they said "I know plenty".

> I don't know a single software developer that prefers windows anymore.

This is the question from the survey:

> What is the primary operating system in which you work?

It doesn't ask anything about preference and I wager most people don't have a say in OS for their jobs. Same reason that Microsoft Teams is the most "popular" synchronous tool, yet so low in the "admired" section.

That's not to say you're wrong, just that the data you linked to does not "back it up".

> Nyxt differs fundamentally in its philosophy- rather than exposing a set of parameters for customization, Nyxt allows you to customize all functionality. Every single class, method, and function is overwritable and reconfigurable. You'll find that you are able to engineer Nyxt's behavior to suit almost any workflow.