boristsr

No, alerts but not limits.

It's absolutely mind boggling to see claims of model distillation being theft, a class of attack, and all sorts of claims all the while Meta is in court for copyright violation, anthropic has had to settle a case with authors. With distillation "attacks" at least they paid API fees.

I think it depends on the depth of the summary, and the purpose. You can do quite an indepth analysis as part of educational material for example, which is one of the tests of fair use.

I think a key thing to remember when assessing your own liability is fair use is a defense, not an automatic guaranteed right for blanket uses.

Leaking spoilers of unpublished works can definitely cause market harm, and serves no wider good for the market the same way educational material would.

I wouldn't like to be on the receiving side of this lawsuit. At the very least it's going to be expensive to defend against.

Yes please!

I understand your thoughts. I've had similar motivation problems about blogging since the release of ChatGPT. Feels like you are writing for a machine rather than readers. Definitely seen a decline in readers since December 2023 on older articles that previously had steady traffic for years.

Also, I just purchased LazyVim For Ambitious Developers. I've used the online edition a number of times in recent months. Thanks for your work!

Excellent deep dive and explanation of the process of tracking down and fixing it. Thanks for sharing it, it was a fun read. Will definitely keep this in mind next time I fire up farcry for some nostalgia!

This reminds me very much of the fun of the "old internet", late 90's early 2000s geocities fun! Love it.

Nanite is a lot more than just a continuous lod system. The challenges they needed to solve were above and beyond that. Continuous lod systems have been used for literal decades in things like terrain. The challenges for continuous lod for general static meshes are around silhouette preservation, UV preservation and so on. One of nanites insights was that a lot of the issues around trying to solve automatic mesh decimation without major mesh deformation/poor results just disappear when you are dealing with triangles that are just a few pixels (as little as single pixel triangles) in size. The problem with small triangles is a problem called quad overdraw, where graphics cards rasterize triangles in blocks of 2x2 pixels, so you end up over drawing pixels many times over which is very wasteful. So the solutions they came up with in particular were:

- switch to software rasterization for small triangles. This required a good heuristic to choose between whether to follow the hardware or software path for rasterization. It also needed newer shader stages that are earlier in the geometry pipeline. These are hardware features that came with shader models 5&6.

- using deferred materials which drastically improves their ability to do batched rendering.

It's actually the result of decades of hardware, software and research advancements.

The 2 solutions posted in recent days seem heavily focused on just the continuous lod without the rest of the nanite system as a whole.

Also yes, there were also challenges around the sheer amount of memory for such dense meshes and their patches. The latest nvme streaming tech makes that a little easier, along with quantizing the vertices which can dramatically lower memory usage at the expense of some vertex position precision.

Nanite does a few things:

- continuous lod as this library does

- software rasterization for small single pixel triangles which reduces quad overdraw

- deferred materials (only material IDs and some geometry properties are written in the geometry pass to the gbuffers, which things like normal maps, base colour, roughness maps, etc being applied later with a single draw call per material)

- efficient instancing and batching of meshes and their mesh patches to allow arrows of objects to scale well as object count grows

- (edit, added later as I forgot) various streaming and compression techniques to efficiently stream/load at runtime and reduce runtime memory usage and bandwidth like vertex quantization etc.