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 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.

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.

Unfortunately proprietary professional software suites are still usually better than their FOSS counterparts. For instance Altium Designer vs KiCAD for ECAD, and Solidworks vs FreeCAD. That’s not to say the open source tools are bad. I use them myself all the time. But the proprietary tools usually are more robust (for instance, it is fairly easy to break models in FreeCAD if you aren’t careful) and have better workflows for creating really complex designs.

I’ll also add that Lightroom is still better than Darktable and RawTherapee for me. Both of the open source options are still good, but Lightroom has better denoising in my experience. It also is better at supporting new cameras and lenses compared to the open source options.

With time I’m sure the open source solutions will improve and catch up to the proprietary ones. KiCAD and FreeCAD are already good enough for my needs, but that may not have been true if I were working on very complex projects.