Switching a Japanese dumbphone (Kyocera) was the best thing I ever did. Eliminating your smartphone (as inconvenient and life altering as it may be -- you'll need to figure out a path) is probably the single most effective thing you can do to get into the top 1-10%... of people with properly functioning cognition.
Insightful paper. Policy/lawmakers needs to take much more input from high-quality, publicly funded (aka unbiased) research and make informed decisions on restricting content type. The social media companies rn are akin to tobacco companies selling products/services to kids (and adults!) with zero meaningful restriction or warnings. There's a mountain of research showing cognitive performance impacts from content consumed through smartphone, especially fluffy, low quality "algorithmic feed" content.
BTW, I still need to use YouTube and this one extension has protected my YouTube experience from being TikTok-ified -- "ShortsBlocker - Remove Shorts from YouTube" [0]
When people do send me random Shorts, I use another browser (consciously) to watch that particular video and shut it back down. You can also pair that with "Block YouTube Feed - Homepage, Sidebar Videos" [1] for another layer of YouTube cruft removal.
Finally, I've also installed "Turn Off YouTube Comments & Live Chat" [2] which keeps me from scrolling down to comments and letting that 'color' my perception of the video -- has restored my own ability to judge the value of a video.
Okay, Gemini 3.0 Pro has officially surpassed Claude 4.5 (and GPT-5.1) as the top ranked model based on my private evals (multimodal reasoning w/ images/audio files and solving complex Caesar/transposition ciphers, etc.).
Claude 4.5 solved it as well (the Caesar/transposition ciphers), but Gemini 3.0 Pro's method and approach was a lot more elegant. Just my $0.02.
>Qwen 2.5's clocks, on the other hand, look like they never make it out of the womb.
More like fell headfirst into the ground.
I'm disappointed with Gemini 2.5 (not sure Pro or Flash) -- I've personally had _fantastic_ results with Gemini 2.5 Pro building PWA, especially since the May 2025 "coding update." [0]
As someone with barebones understanding of "world models," how does this differ from sophisticated game engines that generate three-dimensional worlds? Is it simply the adaptation of transformer architecture in generating the 3-D world v/s using a static/predictable script as in game engines (learned dynamics vs deterministic simulation mimicking 'generation')? Would love an explanation from SMEs.
Games are still mostly polygon based due to tooling (Even Unreal Nanite is a special variation of handling polygons), some engines have tried voxels (Teardown, Minecraft genererates polygons and would fall in the previous category as far as rendering goes) or even implict surface modes by composing SDF'y primitives (Dreams on Playstation and more recently unbound.io).
All of these have fairly "exact" representations, and generation techniques are also often fairly "exact" in trying to create worlds that won't break physics engines(big part) or rendering engines, often hand-crafted algorithms but nothing really that really stopped neural networks from being used on a higher level.
One important detail in most generation systems in games is that they are often built to be controllable to work with game-logic (think how Minecraft generates the world to include biomes,villages,etc) or more or less artist controllable.
3d scanning has often relied on point-clouds, but were heavy, full of holes,etc and have been infeasible for direct rendering for long so many methods were developed to make decent polygon meshes.
Nerf's and Gaussian splatting(GS) started appearing a few years back, these are more "approximate" and totally ignore polygon generation instead relying on quantization of the world into NN-matrix-"fields"(NERF) or fuzzy-point-clouds (GS), visually these have been impressive since they managed to capture "real" images well.
This system is built on GS since that probably meshed fairly well with neural network token and diffusion techniques for encoding inputs (images, texts).
They do mention mesh exports (there has been some research into polygon generation from GS).
If the system scales to huge worlds this could change game-dev, and there seems to be some aim with the control methods, but it'd probably require more control and world/asset management since you need predictability with existing things to produce in the long term (same as with code agents).
Your later point is what makes me think this doesn't have comprehensive legs, just niche usage.
A typical game has thousands of hand placed nodes in 3D space, that do things like place lights, trigger story beats, account for physics and collisions etc. That wouldn't change with Gaussian splats, but if you needed to edit the world then even with deterministic generation, the whole world might change, and all your gameplay nodes are now misplaced.
That doesn't matter for some games, but I think it does matter for most.
Oh I agree fully, this is probably more created by researchers and/or "AI-bros" with less experience as actual game developers (that they have actually added a way of placing objects is after all far more than most other tools has provided with their text-focus).
That said, all those collisions, triggers, lights, etc could be authored together with blockouts in Unity, Godot or some other editor capable of creating levels that integrates with the rest of the game authoring process.
If they create a way to keep the contexts of generation (or rebuild them from marker objects with prompts that are kept in the level editor and continiously re-imported) and allow for a sane way to re-generate and keep chunks then I feel that this could be fairly bad for world artists (Yes, they'd probably still be needed to adjust things to not look like total slop).
You could in theory combine point clouds and Nanite: cull sub-pixel points and generate geometry on the fly by filling the voids between remaining points with polygons. The main issue is bandwidth, GPUs are barely able to handle Nanite; and this would be at least an order of magnitude more complex to do at runtime.
Nanite is doing a lot of offline precomputation, storing some sort of intermediate models etc.
I agree, but I don't think this work is for realtime creation (like those Google models) but rather offline authoring. So the fixups can be done later.
What does the Gaussian approach do that resolves the issue with voxel engines? I recall if you wanted to start doing animation it becomes a mess of computational complexity.
GS does 2 things that makes it great for _rendering_ and _world approximation_, it's a view-dependent "fuzzy" thing, so rendering-wise you don't need to fill in blanks of reconstruction, they also encodes view dependent things like reflections (that should help an AI model infer beyond-view details).
The issue of real voxels (not MC style) is that they fill in fixed spaces that then can creates gaps once you start animating, you probably have the same issues with GS (but that's probably why they are doing exports).
Whenever I see these and play with models like this (and the demos on this page), the movement in the world always feel like a dolly zoom. Things in the distance tend to stay in the distance, even as the camera moves in that direction, and only the local area changes features.
That's the thing about this. Calling things "world models" is only done to confuse people, because "world" is such a loose word. In this scenario the meaning is "3d scene". When others use it, they may mean "screen space physics model". In the context of LLMs it means something like "reasoning about real-world processes outside of text".
Some of the best AI researchers and labs have been from the EU (DeepMind, Alan Turing Institute, Mistral, et al.). We in the US have mature capital markets and stupid easy access to capital, of course, but EU still punches well above its weight when it comes to deep, fundamental AI research.
Applying Occam's Razor -- "smart" people generally are a lot more aware of the ways of our world and its operating mechanics, and a lot of it is not pretty. Therefore, they're able to see behind the veil/'maya' of blissful ignorance, leading to "unhappiness."
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