Yes, but at this point most specialized hardware only really work for inference. Most players are training on NVIDIA GPUs, with the primary exception of Google who has their own TPUs, but even these have limitations compared to GPUs (certain kinds of memory accesses are intractably slow, making them unable to work well for methods like instant NGP).

GPUs are already quite good, especially with things like tensor cores.

Oh got it, thanks for the correction! In that case it shouldn’t be a blocker.

I think having an up to date kernel like Fedora does helps with peripheral usability while not updating packages so frequently as to run into crazy bugs. I guess that’s why some gaming distros base themselves on Fedora.

Does that automatically setup RPMFusion? That’s where most of the things I was talking about live. Last time I ran the installer was a few years ago (plus I use the KDE spin which maybe is a bit different) and I don’t remembet an option to enable RPMFusion, so maybe it’s changed.

I daily drive Fedora and if I had to guess, it’s because you need to manually enable non free software repos and features. If you don’t know what to look for, you can easily get frustrated by things like poor hardware acceleration in browsers (due to some codecs being nonfree and hence not available OOTB) and worse driver availability. IIRC you need to manually add the repos, you can’t just toggle something in settings.

Other distros tend to bundle these things (or give you a direct toggle).

For real though, Gojo soap seems to work the best for getting rid of grease and oil from machines. My guess is regular soaps don’t do a great job at carrying away the oil residue, but Gojo soap just sands down your top skin layer to remove it.

Yeah, you can certainly get it to reproduce some pieces (or fragments) of work exactly but definitely not everything. Even a frontier LLM’s weights are far too small to fully memorize most of their training data.

Most “50 MP” cameras are actually quad Bayer sensors (effectively worse resolution) and are usually binned 2x to approx 12 MP.

The lens on your phone likely isn’t sharp enough to capture 50 MP of detail on a small sensor anyway, so the megapixel number ends up being more of a gimmick than anything.

I agree with your thoughts. I hate what Bambu has done to the industry in terms of starting a patents arms race and encouraging other companies to reject open source, but I do love how they’ve pushed innovation and have made 3D printing easier for people just looking for a tool.

I hope the DIY printers like Voron, Ratrig, VzBot, and E3NG can continue the spirit of the RepRap movement.

I work in an area adjacent to autonomous vehicles, and the primary reason has to do with data availability and stability of terrain. In the woods you’re naturally going to have worse coverage of typical behaviors just because the set of observations is much wider (“anomalies” are more common). The terrain being less maintained also makes planning and perception much more critical. So in some sense, cities are ideal.

Some companies are specifically targeting offs road AVs, but as you can guess the primary use cases are going to be military.

Some apps only require ‘basic’ play integrity verification, but now check to see if they’re installed via the Play Store. They refuse to run if they’re installed via an alternative source.

This has been a problem for GrapheneOS, since some apps filter themselves out of the Play Store search if you don’t pass strong play integrity, despite the fact that they don’t require it. Luckily Graphene now had a bypass for this.

OBS can use NVENC, though IIRC it needs to be built with support enabled, which may not be the case for all distros’ package managers.

Yep, since this is using Gaussian Splatting you’ll need multiple camera views and an initial point cloud. You get both for free from video via COLMAP.

Yeah, in typical Google fashion they used to have two deep learning teams: Google Brain and DeepMind. Google Brain was Google’s in-house team, responsible for inventing the transformer. DeepMind focused more on RL agents than Google Brain, hence discoveries like AlphaZero and AlphaFold.

The general framework for evolutionary methods/genetic algorithms is indeed old but it’s extremely broad. What matters is how you actually mutate the algorithm being run given feedback. In this case, they’re using the same framework as genetic algorithms (iteratively building up solutions by repeatedly modifying an existing attempt after receiving feedback) but they use an LLM for two things:

1. Overall better sampling (the LLM has better heuristics for figuring out what to fix compared to handwritten techniques), meaning higher efficiency at finding a working solution.

2. “Open set” mutations: you don’t need to pre-define what changes can be made to the solution. The LLM can generate arbitrary mutations instead. In particular, AlphaEvolve can modify entire codebases as mutations, whereas prior work only modified single functions.

The “Related Work” (section 5) section of their whitepaper is probably what you’re looking for, see here.

40 series to 30 series was pretty tangible IMO (4090 gets something like 30-50% more perf in most tasks than 3090 Ti with the same TDP), in part thanks to the much higher L2 cache plus newer process node.

50 series was very poor though, probably because it’s the same process node.