I don't think it's a bad way to benchmark new models, I just find it concerning that the author implies that "pelican on a bicycle" has been exhausted.
At the risk of making overly broad, unfalsifiable claims I think multi-year exposure to AI content has dramatically raised our expectations for speed and volume but lowered them for quality.
We see a very janky pelican and declare the problem solved.
I don't think he's claiming it's been exhausted. It's just that things have progressed to a point where people are arguing over the finer points of which pelican looks better -- which is often a matter of taste, and an indication that we've hit the knee in benchmark where models are no longer failing in obviously awful ways.
I haven't really seen evidence that any ai can reliably draw a pelican riding a bicycle. Not if you look at the image long enough to take it in. Even the best ones have something wrong with them. Not a matter of taste but a matter of having both legs peddling on the viewer's side of the bicycle or having two beaks.
I'm actually beginning to wonder if some people who ignore these things have a different, somewhat lesser ability to percieve image details than I do.
I mean I guess its fine to go on to another test despite never actually passing the pelican bike test, but there's a sense that we have to use another test because AI is now good at pelicans on bikes, which is just not true.
When it started, it was clear what LLM would stand out, its style, etc.
Nowadays, the pelicans look similar, the difference is in details and sometimes hard to catch. Sure, the task is not completed perfectly, but that's not the point. It was supposed to be a benchmark to quickly benchmark a LLM against others.
AI has deeply changed the way I think, feel and act around a computer. In the same way that dialing into the internet changed things for me. Since using ChatGPT the first time until now I have never cared once to look at these pelicans on bikes people seem to get hung up about. It could never have been a thing and nothing would change. See the forest through the trees.
I think it's touching the limit of what one can reasonably expect any intelligent thing to produce with the only direction being "produce an svg of a pelican riding a bicycle".
When you aren't sure if an LLM can write an svg well, or that it will be able to form a pelican shape, or animate a bicycle, it's a good test. After that, it's all judgement: how detailed should the pelican be? pelicans are the wrong shape for a bicycle by default, so how much can I change its physiology to match using a bicycle before it isn't a pelican? Do I care about how well the client is able to render complex geometry?
It's not that there isn't room to do better, or that it doesn't tell you anything at all, but rather we've reached a point where what it tells us isn't very clear anymore.
Could you share any examples that come close to those limits? I haven’t seen any that don’t have obvious flaws in proportions, layering, composition, color palette, visual clutter, or stylistic consistency.
> touching the limit of what one can reasonably expect
If the expectation is that AI is going to replace "knowledge workers" then the limit would be a darn perfect drawing. We are nowhere close to that.
And Elon is already propagating the age of abundance where money won't exist anymore, right before calling the interviewing journalist dishonest and deservedly losing public trust. Smh my head.
Also, one of the advantages of the pelican test is that you can evaluate it all at once. There’s no reason the two-dimensional depiction can’t be made more challenging. Yes, at some point the pelicans might approach the subjectivity of a fine art painting, but we haven’t even seen a depiction that’s competent by the standards of a high school art class. That’s not to say the elementary school–level SVGs aren’t amazing - rather, I agree with your point.
Can someone explain what the pelican on a bicycle tests exactly? And why is it so important? I've never understood how it could translate to a useful task in real life.
On the most basic level, its asking the ai to generate valid svg code for a picture of a pelican riding a bicycle. Popularized (invented?) by simonw.
it cuts across several tasks that AI used to be very bad at, but now has improved quite a bit. Namely, spatial reasoning (because it has to manually place the points of the svg such that they make sense and form what it says it forms. This used to not work very well, with random shapes floating around that it would mark things like "eyebrows" but were nowhere near the "eyes", etc.
Before it was pelican on a bicycle there were people having it do horses/unicorns making the rounds - gpt4.0 or whatever would often make hideous abominations of legs and mouths
If your school mascot is a pelican and you need to make a flyer for the upcoming bike safety event, then this precise thing becomes useful. Most things that are useful in the real world seem not useful out of context.
Humans are drawing pelicans riding bicycles now. Just google it and you will find 5 or 10 of them in the first few results. Including a t-shirt design.
A lot of people are posting here about how bad the end product is, but that is kind of the point. Models have moved beyond generating images to a new kind of benchmark that better exposes understanding of the physical world, and we can use benchmarks like this to measure future progress. (Of course, it will have to be a qualitative/subjective measurement.)
I've fed a couple of those chicken-scratch sketches to nanobanana with prompt "Treat attached as a technical drawing of a bicycle. Produce photo-realistic image of the actual bike manufactured to that spec. Try to stay close to the input, where possible", and... wow, results are not great.
Humans aren't machines trained on the entire stolen corpus of human knowledge. We expect that a human will do poorly at arbitrary tasks they have no experience doing – especially drawing, which many (most?) humans aren't trained in at all. The same isn't true of AI, where its proponents, priests and proselytizers have spent time, energy and billions of dollars attempting to convince us it can do anything better than humans.
I don’t know what datasets are available to these LLMs, but I’d imagine if there was training on CAD code, text, and images, a prompt steered towards that probably could get it pretty good.
I am not a mechanical engineer, so even prompting well with ME lingo probably will take some effort.
Totally agree though, anyone with a vague understanding of how bikes works ignores the pelican because they know the bike is unrideable in the first place.
Yes, but I think the idea here is that most models produce very similar pelicans on bicycles, so a different test might be more useful in gauging the differences in models.
Agree. The pelican benchmark was interesting a year ago when most models struggled and a good pelican indicated an unusually capable model. Now it’s saturated and uninteresting.
A good new benchmark should have awful performance to start and there should be a lot of headroom for improvement. This benchmark is also intentionally difficult and requires the LLM to develop the animation through spatial reasoning and first principals rather than existing video generation pipelines. Similar to how SVG generation was out of distribution for most models a year ago.
Have you seen pelicans in Simon Willison’s tests? It is still not a pelican on bike I would like to publish :)
Imho we don’t need to make benchmarks that draw the whole 3D world. Pelican’s drawing is really nice in its simplicity and complexity at the same time.
It seems to be an obscene waste of compute time to generate useless 3D worlds that are just a bragging - 3D is really heavy discipline to make it right, see Mark Zuckerberg’s ceased attempt with 3D VR…
Multiply it by thousands times as a lot of people have found out threejs lib and prompt “generate 3D world and make no mistake” are new orange/black.
A general benchmark (even Simon mentioned that it was meant as fun at the beginning) should be quick and easy to run, since we can expect that more people will want to try it out. That’s why I’m more like “team Pelican on a Bike”… :) cheers
Very few things are universally hated. One can love something truly that is hated by most. But it doesn't change the fact that it's still hated by most. An objective and a subjective opinion can exist at the same time on this.
I worked with an LLM to build a ~3D animation of the Back to the Future delorean Time Machine as a way to spice up the hero on a docs page.
That took a fair amount of custom tuning and I had to create a tuning view to get some of the behaviors right.
But it was enough fun that I generalized it to take in ~any scene description from a film. It goes out and gets more detailed descriptions and film stills if available but also takes custom stills if you provide them.
My test scene was the Gauntlet scene from Apocalypto. It is low fidelity but does a pretty amazing sequence with somewhat believable physics of the javelins etc.
Here is the docs page with the vertical takeoff / 88 miles an hour time travel: https://contextify.sh/docs
I can share some of the Apocalypto bit if anyone is interested.
Ha, that's fun! I've built half your product for myself as a personal, native MacOS app, with a slightly different direction, but the same core idea. I might go do a little scavenging for ideas in your docs :-)
If you have time to try the product as it stands, I would definitely appreciate your feedback in particular, and would be interested in checking out what you've built so far for yourself.
The product you've built the animation for is only for macOS, which is a pity cause it has a way wider appeal than macOS market share is. You have low-hanging fruits right there, don't miss them.
Thank you for mentioning it and for putting your suggestion in the language you did.
That page with the time machine may not make it obvious, but the product does have a linux client!
It doesn't have the same app window and summarization of the macos version, but the transcript ingestion engine is efficient and the real value is in leveraging the database it builds using the packaged `/total-recall` or your own use of the api. (which is not yet documented but discoverable)
I am building the windows version now. I have been for the past four days. It uses a shared swift-core with the macOS and Linux versions which has been part of the reason it has taken "so long."
If anyone is on windows (or linux!) and would be willing to try it either of the clients my email is in my profile, I would be grateful.
Also I'm working up a short post with that apocolypto anim now.
iOS users spend dramatically more on e-commerce. I worked in e-commerce. Maybe it’s changed in the last 5 years. But that’s where the ops sentiment comes from.
Are the total number of Mac users who pay higher than the total number of Windows users who pay? I kind of doubt it. Windows market share is still much higher than MacOS.
It seems pretty clear that Anthropic models have been specifically trained to be good at generating three.js (JavaScript 3-D Graphics) code, so given current state of AI code generation in general, I don't find three.js models/animations as indicative of anything other than the model's ability to write three.js code.
When Fable was first released the day-1 demos of it on Twitter (presumably from people who were given early access, and/or Anthropic employees) were pretty much 100% three.js stuff. Yes, it looks nice, but it doesn't tell me any better than an Erdos proof whether the LLM will be able to run my vending machine.
Taking a single paragraph of literary text, which is abstract and ambiguous, and converting it into a 3D animation requires an enormous amount of implicit knowledge about spatial relationships, intuitive physics, everyday objects, and so forth. Not to mention the mathematics of 3D transformations and computer graphics more generally. Saying that it's indicative of no more than three.js coding ability is absurd.
Because the degree of realism in a video is determined by implicit knowledge of concepts like "in front of", "behind", "next to", "inside", "outside", "between", "occluded by", etc. as well as distances, angles, and the relative size of objects when viewed from a perspective.
I guess I just don't have an intuition for why that is different from what it does for non-"spatial" things. Like the fact that it works is still because it produced the code it did token by token. Whether its dealing with, e.g., "beside the rock" or "in the array", it's doing the same kind of inferential activity.
>Whether its dealing with, e.g., "beside the rock" or "in the array", it's doing the same kind of inferential activity.
Are you sure about that? Presumably the spatial concept of "inside" and the computer memory concept of "inside" have different contextual embeddings in an LLM, much in the same way that neural activation patterns vary in the human brain when we reason about different domains.
I don't find three.js models/animations as indicative of anything other than the model's ability to write three.js code.
This weekend I've been converting a game from three.js to ogl.js in order to see if I can optimise the time-to-interactive loading time. I took the three.js driven page weight from about 600KB (500KB being three.js) to about 50KB, and reduced the loading time from multiple seconds on a 4G mobile connection to around 0.5s.
This has mostly been a combination of Opus 5 and Sonnet 5 in Claude Code. It very clearly has a good grasp of WebGl, and of what impacts page loading times and rendering speed. It was able to drive Claude Code's integrated browser to measure the impact of changes, and as I spiked out a test of ogl.js it could test the differences changes made.
It's not the best game (https://tinyslots.ooer.com) but that's on me. As an exercise in building 3D in a browser, and in page speed optimization, with Claude models I am really impressed.
I don't think so. I've used Claude to generate 3D animations in python purely by making and manipulating raw meshes, and I've had great results. A more likely explanation is that 3D graphics is just pretty straightforward matrix algebra, and models (or Claude specifically) has that down cold.
Don’t worry. I’m sure they’re not training it to be good at things like writing database backends, financial services, logistics systems, user interfaces, or anything of economic value. As long as you’re not working on three.js specifically, I’m sure Anthropic isn’t making any progress you should be worried about.
Their "World Quester 2" tutorial shines as the holy grail of consistent and ergonomic user interface and game design. The menuing system is so magnificently structured and well organized, it bring tears to my eyes. Google Wave pales in comparison.
Sorry if it wasn’t clear, I was adding this info for context and for anyone who hopefully feels inspired to pick up Wave and make something from it given that it was all open sourced and all. And I figured that in the chain after your comment about it having been useful looking all along was a natural place to add this additional info and link.
This was the first big secret “you’re not allowed to know what this is or talk to anyone about it” project inside Google. They wanted to be left alone and especially to not have to integrate with the mail team. And everyone heard rumors of gigantic bonuses if they hit whatever milestones, which wasn’t a thing for other projects. All this made them isolated within the company, and when the project was an obvious flop, you didn’t see anyone rushing over to help them.
I used it to plan a group beach trip back when it came out. We had a single page shared with everyone going on the trip. The page had live shopping lists, maps, weather forecasts, and other snippets of useful information. Now in 2026 I still can't think of any single technology that provides the same utility. Although to be fair, this might be a case of rosy retrospection.
this is not a good benchmark for models, but it's great if you're optimizing for attention on twitter because video content and 3d animations perform best on social media.
a real benchmark is instead running evals on your own traces, and building a cost/quality/speed profile for models based on real workloads. but it doesn't get you a shiny video you can post on twitter.
I can forgive the modeling being godawful jank (windows floating in the air, disconnected from the house). But I expected it to have a better understanding of the text. Instead, we have Bilbo's "disappearance" interpreted as him magically transporting or cloaking, and similarly for his reappearance.
But this only seems wrong to you because you're familiar with the prior context. When there's only a single paragraph to work from, and it's a drily humorous text, why not employ comic literalism and lean into the perplexity with which his neighbors viewed him?
The fact that a ring appears at the end proves that the model has context beyond what was in the prompt. The model clearly knows the story that the prompt was extracted from.
It's been shown in tests that most humans can not from memory draw a functional bicycle given pen and paper.
Everyone knows roughly what a bicycle looks like - wheels, frame, seat, peddles, handlebars etc, but the details of the frame and exactly how the other parts connect to it throws people off. It seems people memorize the "concept" of a frame, but not the specifics.
Try it without cheating, then google a picture of a bicycle!
Even if you have memorized what a bicycle (and pelican) look like, writing code to draw one is not the sort of thing humans are good at any more than they are good at mentally calculating cube roots - better to use a computer for stuff like that.
I understand it as “write SVG, say when you are done and only then you are allowed to see the rendered result”. Multiple shots would be either “you get more than one try, we'll pick the best” or “you can see the rendered result and iterate” (or maybe that would be “model + harness with tools”?)
But the LLMs are also not one-shotting it, or are they? I assume they have some ways to verify it, e.g. to visualize it (convert to PNG, then feed as vision tokens back to the LLM), or other ways, maybe also pure text LLMs have some ways to verify the result at least somewhat? And with such feedback loop they can iterate.
Speaking of benchmarks has anyone given AIs Where’s Waldo pages and asked it to find Waldo?
I’ve been trying it on them all and can’t find one that does it consistently. The best will tell me they can’t. The worst confidently point out one of countless Waldo-likes.
It would be interesting to see the models work on a book it hasn't been trained on yet. I guess sadly that means any book released very recently.
Definitely impressive demo, I do wonder though if the countless artwork, films, images etc produced over many decades around Lord of the Rings somewhat influenced the outcome of this though.
Reading the title, I was thinking "Karpathy? I don't know this chess player."
(The Pelikan is a well known chess opening, and famous chess players often have book titles like "X's Y" where X is the player, and Y is the opening)
IMO, the area where AI is going to be most useful over the next couple years is in developing manufacturing processes top to bottom. Maybe a million token budget is too small, but something like "design me a sneaker and all the equipment to manufacture it autonomously".
They can't even run a vending machine. I don't want to do a shallow dismissal, but I think there's a gulf between my understanding and yours. I hope it's me so I learn something.
I think LLMs will be excellent glue of "find the right function/button and run/push it" but design without constraints and they just explode immediately
I've been getting into 3D printing recently and while the flagship Anthropic models can regurgitate community wisdom about the hobby they can't do spatial reasoning. They'll tell me that objects will fit on my print bed when they won't and vice-versa. They get confused about materials and nozzles. It takes several iterations to get them to do basic objects (in my case a slotted tray) in OpenSCAD. Useful, certainly, but very far from autonomous for end-to-end manufacturing workflows.
I think embodied AI or autonomous experimentation will need to make a lot more progress before that kind of thing is possible.
Asking AI to design real world objects doesn't work very well because all of its tests involve proxies and thus miss things that are glaringly obvious when the object is actually built.
> IMO, the area where AI is going to be most useful over the next couple years is in developing manufacturing processes top to bottom.
Why do you say this? I have some experience of manufacturing processes and am not seeing where AI would be useful other than to drive the robots which we can already do quite well without AI (see all the dark/lights-out factories that already exist).
Where do you see it being useful? An example would be nice.
Yes but the second order effect of this is that the cost of the tooling goes up since it is now the bottleneck, and therefore the shoemakers that survive do it off of technical complexity, branding, and regulatory capture.
> "design me a sneaker and all the equipment to manufacture it autonomously"
We already have sneaker designs and the equipment to manufacture them. Whatever it spits out is going to be, at best, a mediocre clone of something that already exists. What exactly is the point?
Sneaker manufacturing is a very manual process. Nike famously tried to automate it and failed (https://www.wsj.com/economy/trade/why-its-so-difficult-for-r...). Designing and constantly changing all the processes is more expensive than just having people do it by hand. If you can have AI design the process, then the equation switches.
You’d think tech enthusiasts would actually make an attempt to understand the tech they’re enthusiastic about. OpenAI’s mysterianist marketing has broken some people’s brains.
In the first paragraph (either of chapter 1 or the prologue), not at all. It's pulling everything from pre-training, so it is likely relying just as much on all the visual mediums like the Peter Jackson films, the animated The Hobbit (1977), and all kinds of random depictions of Tolkien's works.
Bilbo's house is actually described in The Hobbit and the exterior isn't really described at all in Fellowship. The prologue of Fellowship (Concerning Hobbits) mentions hobbits like round doors and windows and the fact some hobbit homes are underground, but the turf-dome design here is not mentioned. It actually mentions hobbit homes typically have bulging walls, so unless you've read the The Hobbit, you might not picture this entirely-underground style.
In The Hobbit, his home is described as a (nice) hole in "The Hill" with a perfectly round front door and round windows, which could imply the design here.
Right, yeah I asked because I assumed it copied the look from the movie. I think it'd be more interesting to do this for something that hadn't been adapted (yet). The first chapter of Neuromancer perhaps
Regarding the argument about LLMs having difficulties auditing their work:
I wonder whether we are entering the era of throwaway software. Just like cheap plastics and improved processes has enabled us to rapidly manufacture anything we want for a very low price, maybe LLMs give us the same for software. Produce it cheaply and if it breaks throws it away and reproduce it.
On the one hand yes, almost every task I work at now is one off one of scripts I throw away.
The question is - where does the software the spec or the “code”.
A really complex game will probably always be token heavy. At least for the next few years code is still not free.
But certain software is just iterative by design. If we mean we regenerate all the for loops of a game from scratch, sure but I think “code” Is really more spec then implementation, and we’ll want to continue building things through iteration.
And even on the for loop point -
Do you really want to spend millions of tokens rewriting a game every time you need to make balance changes?
I'm sure this will become the standard. And plastic is an excellent analogy. Maybe we can take it a step further and compare it to on-demand 3D printing.
Why would anyone still use off-the-shelf software when they can have a system that has access to all data, can transform it into any form, and can export it in any format?
After years of thinking that I needed to develop a decent movie management system for my own films or a columnar browser for large CSV files, Claude and Qwen each delivered exactly what I needed in just a day.
This. This is why the burst of posts on HN of “I made this useful tool/crate/application” were just so sad. The old model was getting what the kids now call aura by developing useful open source products: products where it’s far easier for someone to consume the product than write it themselves. We had people posting things as if that model still existed. Dude, you wrote it with an LLM! Posting them (here) is not only pointless, it’s advertising that the person who wrote it isn’t smart enough to understand that I have an LLM too.
Does this make sense with economics of software though? Throwaway products compete with more durable versions of the same product because there is a cost per unit produced that can be minimized by using cheaper materials or production processes that cut corners. With software there is no cost per unit. There might be a market for one-off software that serves a very specific purpose where throwaway software can compete with adapting more carefully engineered software to that purpose, but I am not convinced there is a lot value in this market.
> Just like cheap plastics and improved processes has enabled us to rapidly manufacture anything we want for a very low price
Have they? Most of the world production is tied down to expensive factories and machines. Yes, we have more products, but that the result of the global trade, which is a very complex system.
> Produce it cheaply and if it breaks throws it away and reproduce it.
I don't know why everyone would ever wants this. It's been parroted since forever, but the true usefulness of software is to be able to build it once and runs it indefinitely. If some edge case occurs, I fix it. Which is way cheaper than rebuilding the whole thing. The goal is to have something like OpenBSD's ed[0] or dmesg[1], which you only touch every few years or so
I'm pretty tired of the "Y made this game in Z tokens" all over the internet last week. They look impressive, and it's cool that it's even possible, but they're useless as games. None of them are any fun. They're like the most boring variant of basic controllers you can imagine. None have any cool mechanics. None have any tweaks made from hours and hours of testing. All have the same cel-shader.
I’ll never not be amazed that we can type some words and get those results back out.
However I do agree that the results are much worse when you try to use them. They look great in screenshots and video clips which makes them perfect for content farmers.
All of the LLM generated games I’ve played have been really bad to play, though. I even tried my hand at a simple game, thinking I could iterate on it with prompts to fix some rough edges. After the initial productivity burst every change turned into a slog of tokens with one thing changing and something else breaking it. I would try to use my remaining weekly token budget across Anthropic and OpenAI to refine it at the end of every week but after a couple weeks it felt like I wouldn’t be getting anywhere without scrapping it and going back to having the LLM build it one step at a time with my careful instruction.
Which, in retrospect, is the only way I can get usable output of an LLM for anything complicated, so it’s not surprising. It’s a fun reality check project though.
I think it’s interesting to watch that we all have to sort of fine tune our own expectations and build the mental model for how impressive this is.
On the one hand I think most of us are incredibly impressed because we know, that quick demo would have taken us months of work to build in the before times.
On the other hand the promise is a cure for cancer and the end of all work.
So when everyone is telling you “skill issue is why you can’t one shot WoW”. It’s hard to know how you’re supposed to feel about Karpathy advertising one shot custom virtual worlds but giving you slop. Incredibly impressive slop when compared to how long it would take to create it just 3 years ago, not so much compared to Elon saying - “by the end of this year, grok will create a version of the odyssey that competes with Nolan’s”
LLMs are bad at creative work and I don’t see them improving any time soon. Try asking an agent to write a story about raccoons. It will almost certainly involve either stealing food or raiding trash with a 50% chance of having a character named Pip. If a location is mentioned, it’ll be Elm Street.
I guess this is the average story and, similarly, the average game is boring and predictable
Worse in a very specific, bland, uninteresting way, too.
Early generative AI at least had the virtue of relentless, unsettling weirdness, in the same way that generative art from the late 90s and early 2000s did. A handful of people made creative use of that spooky weirdness.
Heh, tried it and got a story about raiding trash starring Pip. But yep, I'm well aware of this problem as well. /r/sillytavernAI is a sub dedicated to trying to coax LLMs into good creative writing but it's kinda impossible to do consistently.
I don't think this is bad. I want tools that deliver what I envision. The fact that given the same input, I get similar output is something I would rather consider a positive. The imagination should come from the human at the wheel.
While they are bad at creative work*, this reasoning isn't going to show it.
If you took the best, most creative, human writer in the world, and for thought experiment reasons they had amnesia (to mimic AI blank context windows) specifically while you asked them for a story idea 100 times in a row, my expectation is that this human would also give you the same idea at least 80 times out of that 100.
* still better than the mean human, but even the top 0.1% of humans aren't all professional authors.
Better than the mean human? The mean human creates far better stories while daydreaming. AI enthusiasts have such a distorted view of human capability, it’s bizarre.
The qualification "better" is doing some hefty lifting in your assumption.
What you think is better is not what I think is better. Imagination is not storytelling. You ask the "mean human" to write, it's going to be worse than an LLM in spelling and grammar, if you get anything at all.
Daydreaming is absolutely storytelling. The average person is able to craft elaborate stories on a whim. Writing is used, in part, for conveying stories, but writing is a separate skill. The good news is the average person can learn to write well, but LLMs, so far, have not demonstrated the ability to craft interesting stories and it’s pretty unlikely they’ll be able to learn, as this is an unverifiable domain.
The mean human gets a middling score in creative writing tasks at the end of their mandatory education, and most then leave school and forget what little they ever learned outside whatever their career path happened to be.
Most humans never do a creative writing course after school, and the longest fiction most people will write is their resume description of what their previous jobs involved, or perhaps their dating profile.
Don't mistake what you see published (or what your friends are like) for the average human: the average Hacker News comment easily above the writing grade (and creativity) of e.g. many of the one-shots stories I've seen attempted on some creative writing subreddits. "Mean" is not a high bar.
Seriously, what is the deal with Pip! I was experimenting with childrens' fiction more than a year ago and 9 times out of 10 you'd get a character named Pip unless you were specific in the prompt not to do so.
I've heard this called a "dancing bear" before. It's interesting because it's a bear dancing, not because the dancing is any good.
These conversations get frustrating since one group is saying the dancing is bad, a second group is saying it's good (for a bear), and a third group is saying we are a few years from a bear-only dancing industry.
LLMs currently have 0 imagination, I have strong doubts that will be solved any time soon even if they continue to become superhuman at everything else. They just have the creative instincts of a 50 year old accountant.
Unfortunately people are saying they are fun for the purposes of ragebait that goes viral, which is half the reason people intentionally post AI slop on social media.
And you could almost always tell a "game construction kit" game from a real one, couldn't you? Whatever was missing from those is always missing from these, and it seems just as rare that the game will reach much beyond its origins.
Whenever I think about AI games I find myself thinking about Tiny Wings, Flappy Bird and Angry Birds. Three simple, elegant games.
It is easy to see what makes Tiny Wings so completely loveable — it has a sculpted, adorable, perfect charm with a cleverly inverted game mechanic that has a calibrated level of exasperation and reward.
But why were Flappy Bird and Angry Birds, very basic games with very old game mechanics, so charming?
It seems equally impossible to imagine an AI coming up with a game with the quality of any of them, even with maximised creativity. But explaining why for Flappy Bird seems quite difficult, especially when you consider it uses some stolen visuals!
it's crazy because I've seen "implement flappy bird on an FPGA with VGA output" as a 2 week project to a human as something immediately understandable.
Maybe it's the smoothness of motion that makes these games understandable and LLMs seem to consistently fail at that. Ask them to do something snowboarding and they go really hard on the physics since it seems like they don't know what kind of approxmations feel good.
I've been hearing similar from a while back, but the blame wasn't being directed at AI, it was directed at Unreal's defaults now making random indies look like AAA in the screenshots.
(It's been a while since I was in the game industry, so IDK quite how accurate this is).
It’s called “demo porn” and it’s really obnoxious and borderline offensive to people that take the craft and art form of video games seriously.
If I see one more “one shot MMO” where you just walk around and do absolutey nothing or another menu slop idle battler or rogulike deck builder I’m going to go Postal in Minecraft.
Unfortunately the Phaser framework has decided to go all-in on this route, and it's really disappointing.
These one-shot products aren't games. They're barely even demos. I don't even know what to call them. For a mature framework like Phaser to sell-out like this and create a vibecoded platform for vibecoded games is shocking.
people are excited, let them be excited!
but more than the excitement is realizing the implications of what this means for building software, first it looks like a toy and then it doesn't. the OP is talking about how the games are not playable and fun. who cares? that's not the point.
What does it mean for building software? These games cannot become fun. There is no way to prompt it fun, and any change you try to prompt it to make will inevitably blow up something else. The code is a mess and impossible to build upon.
There is a tipping point between procedurally generating everything in SVG to maybe giving them tool access to something like 3dsmax (or having them build and then use a tool to do the thing vs doing the thing).
I’d love to see this benchmark using Blender. Asking a model to animate a scene in Three.js is a square peg/round hole; it doesn’t convey much when the model can’t get it to fit. With Blender the human expert ceiling for this task has been proven to be very high
This is just a very rough basic game skeleton. Companies believing AI is smart enough to spit out almost ready product will learn a hard way that it will will give them just a starting point that still needs a lot of work. This is a nature of all modern LLMs - they easily lose context: the bigger the context the more losses and distortions are, especially in the middle. So, giving AI the complete book does not mean it will follow everything in it - quite the contrary.
I think for a fair benchmark, the answer would be 0. You'd continue to run the benchmark with a 1M token budget until future models started to produce something of acceptable quality.
Anyway, the comment I was replied to originally claimed that $1M was spent on building this demo, and I was correcting the error. They seem to have edited their comment since.
Is anyone else getting "mongodb is webscale" vibes? (Except 16 years ago that was a lot smoother, because it used some sort of "render this conversation" engine...)
This has started to feel a bit like the beginning of railroads and then the steampunk fiction of "let's just build railroads to everywhere". We don't need it and there's no use for it.
As with painting, after a while there's nothing really new to paint, we genuinely need 0 new software. We need to fix our broken physical world, our social lives, our kids and what's left of our democracies.
This software crap is done, leave it to the nerds.
Agreed that software isn't going to fix our institutions. But we need 0 new software? AlphaFold won its creators a Nobel prize in chemistry and solved a research problem that feeds into every area of biology, medical research, and drug discovery. We still have an untold number of unsolved problems related to human health, food production, energy production, infrastructure, transportation, education... The list is practically infinite.
For childhood education sure, we don't really need high tech solutions there. But what about training the next generation of mathematicians, physicists, and engineers? Even decades ago, computer algebra systems and numerical solvers started to become indispensable, at least in many subfields. Now we're moving into the territory of automated proofs of mathematical conjectures. The state of the art is going to keep improving, and education is going to have to adapt to keep pace.
Beautifully put. Software is ok. It's not going to fix our broken world. If AI could at least find cure for some illnesses that would be great. Or decrease social injustice and help with global warming. But it's likely going to do the exact opposite.
I'd like to see the Silmarillion, specfically both Ainulindalë and the Fall of Numenor. At this point a visual model would probably produce something better than Amazon (but presumably not Jackson).
I really dislike this AI programming thing of “Mr LLM, go slam your face into the problem until there’s no problem left, then call me back”. (Not sure if it’s a recent trend or a fundamental nature.)
It always brings to my mind some words from Rich Hickey:
I think we’re in this world I’d like to call “guardrail programming”. It’s really sad: we’re like, “I can make change because I have tests!”. Who does that? Who drives their car around, banging against the guardrails, saying “whoah, I’m so glad I have these guardrails so I can make it to the show on time!”
I don’t think I really have a point to make here, other than it just feels like someone’s released a bunch of carnival bumper cars onto the highways.
To me, that's a totally normal way of using computers. Repeating tasks is what computers do best, AI or not.
Take chess engines of the "deep blue" era for instance. These engines are stupid, trying millions of moves that are obviously terrible, no human chess player would do that. And yet, this is what worked best, because computers are so good at repetition. Recent development using neural networks made chess engines smarter, but using repetition is still how the beat humans.
What you call "guardrail programming" in another context would genetic algorithms, a technique that has recognized applications. And if you look at videos of genetic algorithms learning to play racing games, it literally looks like a bunch of carnival bumper cars onto the highways, but done well, after some generations, it becomes competent driving, sometimes even record breaking.
It can be expensive, it is often the case for LLMs, so you may want to try to be a bit smarter at first to spare some resources, but to me, it is just using computers as intended.
> I don’t think I really have a point to make here, other than it just feels like someone’s released a bunch of carnival bumper cars onto the highways.
I like the analogy.
I guess this is why we got this before cars sold without steering wheels: literal guardrails on the literal roads are somewhat more expensive, especially for the people who keep bouncing off them on the way to their destination.
On consumer devices, AI communicates with us through speakers and screens. Screens are the richer medium, so most consumer AI innovation will happen there.
Computer graphics will have enormous applications because they are directly controllable by LLM-generated code. Video models are probabilistic and less suitable when precision matters. In education, for example, we need exact visuals. If an AI wants to plot y = sin(x), it should generate the precise graph through computer graphics rather than approximate it with a video model.
Coding, graphics, all seem to have very defined background data for an LLM to base their decisions on, test, and even when I correct it we're all on the same page. Seems like there's lots of room for AI productivity there.
What I find funny is that approximate to computer graphics are video games. When I ask AI about a decision available to me in a video game AI completely fails, OFTEN. I assume all the forums and changes made to a game over time might be quite confusing for AI. But I've also seen it completely make up characters and decisions and weapons and so on about some very clearly defined games and paths. It's an interesting dynamic.
This makes me think about using a game engine and a coding agent instead of current video generation AIs. It will probably cost much more, but it will have almost zero consistency problems. Is this line explored?
> I also like this kind of examples because no one in their right mind would ever spend the time to write something this custom
This is an odd take, given that Karpathy is certainly aware that the LotR films absolutely did create Bag End in digital format; that their creation was outstandingly high quality; and that Claude’s output here very obviously “leans heavily” on their prior art.
Do you guys notice that LLM can create fancy viz/animations by coding them instead of leveraging what we humans usually use (e.g Lottie, After Effects)?
I wonder if Flash is still popular... LLM can use that instead...?
Anthropic spokesman [0] Andrej Karpathy is here to tell you about token-wasting loops, and insists on the weird idea that they are "~free", when in fact, they are fuelled by expensively burning investor money.
[0] Seriously. Get used to mentally prefixing his and Boris Cherny's name like this, every time you see them quoted. These people are speaking while employed; there is no chance they are not aligned with the employers who will make them wealthy. The tech industry does like to pretend that for some reason AI people, uniquely, speak thoughts unbiased and for themselves or even for science or humanity.
you don't really need screenshots if you have an engine expressive enough for the scene generation while ensuring the visual appearance of the engine output itself is feasible.
that's why these things are actually pretty good at openscad/freecad/F360 mcps , the visual reality is enforced and guaranteed by rigor in the interpretation engine that is anchored to human physical reality.
I'm sad that Andrej Karpathy went from being one of the most reasonable, trusted, and credible voices in AI to peddling marketing slop for Anthropic.
8 months ago, he was (very reasonably) claiming that reliable agents are at least a decade away, but this now goes against the interest of his employer, so the narrative has been changed.
He makes a very good point here about llms or lrms not being good at taking video as inputs, and i think he's signaling his intent to help change that. He's pointing at an open problem. This isn't a carefully structured blog post, it's just a tweet.
> 8 months ago, he was (very reasonably) claiming that reliable agents are at least a decade away, but this now goes against the interest of his employer, so the narrative has been changed.
Or maybe over these 8 months agents improved a lot? You know, few years ago many AI experts predicted that things we are routinely doing now with AI are decades away. I mean how can you look at this post and not be impressed? It's insane what AI is currently capable of.
What narrative has changed ? I just see an experiment with a very perfectible result and some reflections about what capacity are currently missing to have better results.
I love how nobody cares about copyright anymore. Not even an after thought. Might be okay if you're an employee of anthropic/openai but for us mere mortals I'm not sure I would share something this blatant. In US it's $150k+ per violation and you've given them all the proof (even a confession)
Yeah, please check in with the folks protesting data center builds in their town causing their electricity prices to skyrocket and tap water to turn into a scarce resource.
And now, instead of actually doing the above, please go ahead and downvote me, because how dare he question those LLM games.
After watching this video I am absolutely positive that the issue is not a lack of stamina in humans. It is that humans have the capacity to realize that this is a bad idea long before they complete it.
> I gave it the first paragraph of the Lord of the Rings, a 1M token budget (~$10) and asked for three js render of it.
I think it's interesting that the "Bag's End" interpretation in the video clearly looks like the one from the movies, but generated here as a three.js 3D asset.
It makes sense that the movies (or shots/frames from them) were in the training data, and I can also easily imagine an association in concept space between the textual description of Bag's End and the frames from the movie.
But how on earth does the model then go on and convert the latent representation of those images into coordinates for a 3D mesh, without ever even restoring the image? In what kind of representation are the images from the movies stored that it can do that?
I know this is a plug but I thought of it, too! I hope this is the next benchmark they saturate.
I like where Karpathy is going; I had the same thoughts about LLM generated slop scenery. I just want some variety of scenery for the goblins to get massacred in in whatever fantasy slop game I play.
I'd like to see tests of things the current AIs are bad at, like drive a car. Or maybe take instruction to complete some novel activity to test how well they can learn.
Benchmarks like the pelican thing are about correlation with “how good the model is”. Better models produce better pelicans.
It’s a useful benchmark (aside from being “cute”) because of its simplicity, both in how many output tokens it takes (though I understand some models think a lot now to do it) and how easily one can subjectively judge. It’s this efficient as a benchmark of performance.
Making a long video takes way more tokens, and presumably is a lot tougher to easily compare. swillison has a presentation that’s pelicans from 2023-present (roughly) showing the progression. Imagine “lord of the rings videos from 2026-2029” or whatever, it would take a long time to watch and be harder to judge, and probably just end up being a comparison of screenshots anyway.
TLDR I feel like the post misunderstands the role of the pelican thing though if find it very hard to believe he really doesn’t understand, so maybe I’m missing something.
It would be better to ask model to render segmented 3d, with placeholders, like magenta is water, blue is sky, green is grass, purple is Frodo's face, etc, then pass the result through img2img model to properly "render" it.
As a former gamedev watching non-gamedev AI talk about games is so amusing. They really truly do not understand anything about games or consumer entertainment.
There’s a reason AI slop games have literally zero engagement. Last summer that stupid flying game blew up. Maybe a million people “played” the game. Where play means they clicked a link and checked it out not because of what the game was but solely because of how it was made.
In terms of concurrent players that game wouldn’t have cracked the Top 5,000 on Steam.
My metric for AI games is “number of players who spent more than 15 minutes playing”. I’m not aware of any vibeslop that has achieved 1 such player.
Now obviously LLMs are transformative for game dev. But “hyper custom worlds you can drop into” shows an extreme ignorance of what players want imho.
> I also like this kind of examples because no one in their right mind would ever spend the time to write something this custom but LLMs have all the stamina and patience in the world, so it's an example where we go from "no one would ever do this" to "sure, why not, it's ~free".
Except it's not ~free, it cost ~$10. And no one in their right mind would ever exchange $10 for that crap output except in this brief moment that we're in because it's fun and surprising to see what will happen. The actual result is as close to useless as it's possible to be - it's not interesting in its own right, it's not aesthetically pleasing, nor funny, nor informative. It's just slop.
This gets me thinking (although there is not much to divine from three letters). Maybe it's a Jennifer Lawrence GIF-inspired "Yeah, right" then? Downplaying the significance?
LLMs should be tested in the same way people should be tested for a job interview (but often aren’t) - with tasks RELEVANT to usage.
So you don’t just randomly pick some random thing to make the LLM randomly do (like many job interviewers do).
You start with clear statements about real world usage scenarios. THEN you come up with tests that give insight to how well the LLM/hob seeker gets the job done.
Please, stop coming up with random tests like it’s Microsoft in 1990 and you’re asking job seekers how the would move Mount Fuji, as a way of assessing their programming skills.
No stupid irrelevant pelicans on bicycles and no stupid renderings of Lord Of The Rings. Unless those are relevant use cases.
Any test that anyone comes up with must clearly state the context and how the outcome is measured.
But this demonstrates we're a couple orders of magnitude away from generating 1:1 hyper-personalized entertainment and media for individuals, rather than the masses.
I don't really get the desire for hyper-personalized entertainment. People are very good at pointing out things they dislike, but not very good at coming up with how to fix them (common wisdom in game design).
On top of that, a decent chunk of the joy of entertainment is the social aspect.
I very much doubt that. We now have nearly-perfect AI image generation and it hasn't really changed the nature of human expression. I don't see my friends getting wildly creative. I mostly see it used for spammy blogs, spammy books, and cringeworthy corporate marketing - basically, a negative signal, rather than "ooh, AI image, I'm in for a treat". Is your experience different? If not, what changes with moving images?
Ultimately, most people don't have ideas for the kinds of personalized entertainment they want, and they don't want to be in charge of content production (even if you have an LLM do most of the work). I don't doubt that there are niches for it, especially stuff like porn, and I'm sure that pros (game studios, film studios) will leverage AI more and more, but I suspect that most of us will just want to sit on the couch, watch Spiderman XVIII, and then be able to talk about that shared Spiderman XVIII experience with all our friends.
All small business marketing material now looks exactly the same.
LLMs produce an extremely small range of artistic expression, and its kind of ironic, that while it certainly looks functionally good. It immediately becomes noise because everything looks identical.
So companies are going to have to hire creatives again, in order to stand out, and be "creative"
I'm mostly seeing the impact of GenAI images in everydays life for example in the small posters small associations or individuals usually attach on streets to promote some small local event. We went from just text created with PowerPoint and maybe some stock image just a Google search away to now images depicting the topic closely. But yeah, it's not like a revolution.
And this personal media thing, yeah maybe for terminally online persons that are REALLY into a sub-genre but otherwise, it's too much effort, I agree. Until we get machines that can read our (subconscious) mind, that will not exist.
I don't want to say this is indicative, but my local library used AI-generated event poster/images for the past year. But recently they switched back to word/clipart event posters. When I was checking out, I query the librarian about it, and apparently they saw a 40-60% drop in attendance. On one hand, I found that hard to believe, but on the other hand, simple word/clipart event posters do have a certain inviting charm about them.
Not even close. Current AIs have very poor spatial awareness, they can generate some kind of scene but they can't tell you where objects are in the scene, nor can they move objects into different places. They're very useful but it's difficult to be very creative with them because they can't update an image to make it more aligned with your vision for what should be in the image.
Ultimately, most people don't have ideas for the kinds of personalized entertainment they want
1:1 AI entertainment probably won't be a cold start experience.
Much like the "Choose Your Own Adventure" books of the 80's, consumers choose a baseline template, customize the characters, and interact at various points within the plot.
No, we don't. It's quite good, but it's nowhere near perfect. For example, you can see: https://genai-showdown.specr.net/ or others that highlight how far from perfection we still are.
> and it hasn't really changed the nature of human expression.
It sure has changed our discourse. Look at hackernews, where people just can't help themselves to engage in ragebait and flamewar comment threads about llm generated accusations. We've got half the people convinced that looking at ai content is like consuming food.
I'd venture a guess that it's too early to determine how this technology will change expression. A good analog would be photography, which caused a similar meltdown in the arts at the time.
If that becomes a thing, that will be fun 2 weeks, then become a gimmick only a small niche will be using. What’s the point in having a hyper personalized entertainment? People want to experience the games, movies, tv shows, books others created and are also experiencing
Amazon recently kiboshed a new Stargate television series. The fan community was quite heartbroken because the Amazon had involved some of the writers from the original television series and apparently one of the reasons that they cancelled the show before it made it to production was that the felt that the proposed story would appeal too much to the fans and not new people.
The fans were real heart broken about this but I think you're right on where this is going. We're not going to be seeing the dominance of centrally produced content like this for much longer, like sure, I think there will be big blockbusters will stick around, but I think the day is coming where media becomes a choose your own adventure sort of scenario.
It'll be interesting to see where this scales to. there will definitely be some amazing solo projects but we'll also see the like 4 player co-op version of productions and then the larger mine-craft server 'Minas Tirith' scale ambitious projects that involve a few dozen people. And of course passive consumers will remain a thing, or people who just provide some suggestions or nudges for what they'd like to see others make.
But I don't think it'll be dominated by big companies like Disney, Netflix, Amazon or Paramount.
I'm sure a lot of them will suck but it'll be neat to see the inevitable Seinfield - Star Trek Voyager cross over episodes.
Elaine and B'Elanna Torres feud after a transporter accident leaves the crew stranded the delta quadrant. Jerry attempts to date 7of9 but is rebuffed as she finds Kramer's quirky bluntness more relatable. George panics after someone compares him to Neelix.
I don't think it's a bad way to benchmark new models, I just find it concerning that the author implies that "pelican on a bicycle" has been exhausted. At the risk of making overly broad, unfalsifiable claims I think multi-year exposure to AI content has dramatically raised our expectations for speed and volume but lowered them for quality. We see a very janky pelican and declare the problem solved.
I don't think he's claiming it's been exhausted. It's just that things have progressed to a point where people are arguing over the finer points of which pelican looks better -- which is often a matter of taste, and an indication that we've hit the knee in benchmark where models are no longer failing in obviously awful ways.
I haven't really seen evidence that any ai can reliably draw a pelican riding a bicycle. Not if you look at the image long enough to take it in. Even the best ones have something wrong with them. Not a matter of taste but a matter of having both legs peddling on the viewer's side of the bicycle or having two beaks.
I'm actually beginning to wonder if some people who ignore these things have a different, somewhat lesser ability to percieve image details than I do.
I mean I guess its fine to go on to another test despite never actually passing the pelican bike test, but there's a sense that we have to use another test because AI is now good at pelicans on bikes, which is just not true.
> I haven't really seen evidence that any ai can reliably draw a pelican riding a bicycle
Please remember, we've started from there :
https://simonwillison.net/2024/Oct/25/pelicans-on-a-bicycle/
When it started, it was clear what LLM would stand out, its style, etc. Nowadays, the pelicans look similar, the difference is in details and sometimes hard to catch. Sure, the task is not completed perfectly, but that's not the point. It was supposed to be a benchmark to quickly benchmark a LLM against others.
AI has deeply changed the way I think, feel and act around a computer. In the same way that dialing into the internet changed things for me. Since using ChatGPT the first time until now I have never cared once to look at these pelicans on bikes people seem to get hung up about. It could never have been a thing and nothing would change. See the forest through the trees.
I think it's touching the limit of what one can reasonably expect any intelligent thing to produce with the only direction being "produce an svg of a pelican riding a bicycle".
When you aren't sure if an LLM can write an svg well, or that it will be able to form a pelican shape, or animate a bicycle, it's a good test. After that, it's all judgement: how detailed should the pelican be? pelicans are the wrong shape for a bicycle by default, so how much can I change its physiology to match using a bicycle before it isn't a pelican? Do I care about how well the client is able to render complex geometry?
It's not that there isn't room to do better, or that it doesn't tell you anything at all, but rather we've reached a point where what it tells us isn't very clear anymore.
Could you share any examples that come close to those limits? I haven’t seen any that don’t have obvious flaws in proportions, layering, composition, color palette, visual clutter, or stylistic consistency.
> touching the limit of what one can reasonably expect
If the expectation is that AI is going to replace "knowledge workers" then the limit would be a darn perfect drawing. We are nowhere close to that.
And Elon is already propagating the age of abundance where money won't exist anymore, right before calling the interviewing journalist dishonest and deservedly losing public trust. Smh my head.
Also, one of the advantages of the pelican test is that you can evaluate it all at once. There’s no reason the two-dimensional depiction can’t be made more challenging. Yes, at some point the pelicans might approach the subjectivity of a fine art painting, but we haven’t even seen a depiction that’s competent by the standards of a high school art class. That’s not to say the elementary school–level SVGs aren’t amazing - rather, I agree with your point.
Agreed. It's far from solved. Modern LLMs still generate pelican bike SVGs with obvious errors:
* some omitted the bottom of the diamond which connects from the pedals to the rear wheel
* some added an extra connection from the pedals to the front wheel, making it impossible to steer
* none could align the head tube with the fork
* none added a correct offset to the fork
* none could generate the chain properly in a way that attaches to the two sprockets correctly
I mean just look at these:
* Grok 4.5: https://s3.eu-west-1.amazonaws.com/images.dylancastillo.co/p...
* GPT 5.6 Terra: https://s3.eu-west-1.amazonaws.com/images.dylancastillo.co/p...
* Sonnet 5: https://s3.eu-west-1.amazonaws.com/images.dylancastillo.co/p...
from https://dylancastillo.co/posts/pelicanmaxxing.html
Can someone explain what the pelican on a bicycle tests exactly? And why is it so important? I've never understood how it could translate to a useful task in real life.
On the most basic level, its asking the ai to generate valid svg code for a picture of a pelican riding a bicycle. Popularized (invented?) by simonw.
it cuts across several tasks that AI used to be very bad at, but now has improved quite a bit. Namely, spatial reasoning (because it has to manually place the points of the svg such that they make sense and form what it says it forms. This used to not work very well, with random shapes floating around that it would mark things like "eyebrows" but were nowhere near the "eyes", etc.
Before it was pelican on a bicycle there were people having it do horses/unicorns making the rounds - gpt4.0 or whatever would often make hideous abominations of legs and mouths
If your school mascot is a pelican and you need to make a flyer for the upcoming bike safety event, then this precise thing becomes useful. Most things that are useful in the real world seem not useful out of context.
Sure, but then you would just use an image generation or multimodal model to generate that image. I don't think you'd want a weird looking svg.
Exactly. I would want a good looking svg. That's the evaluation.
Yeah, pelican on a bicycle tells you how well the model can extrapolate outside of existing data, rather than just interpolate between it.
Humans are drawing pelicans riding bicycles now. Just google it and you will find 5 or 10 of them in the first few results. Including a t-shirt design.
So it's a pretty much pointless test now.
Yet no LLM can actually do it.
It's quite surprising actually.
100%. Why waste the tokens to render Lord of the Rings when the pelican test still clearly benchmarks so well.
A lot of people are posting here about how bad the end product is, but that is kind of the point. Models have moved beyond generating images to a new kind of benchmark that better exposes understanding of the physical world, and we can use benchmarks like this to measure future progress. (Of course, it will have to be a qualitative/subjective measurement.)
Aren't they still bad at understanding how bicycle frame works? Especially the steering part?
Maybe that makes them human..
https://www.booooooom.com/2016/05/09/bicycles-built-based-on...
I've fed a couple of those chicken-scratch sketches to nanobanana with prompt "Treat attached as a technical drawing of a bicycle. Produce photo-realistic image of the actual bike manufactured to that spec. Try to stay close to the input, where possible", and... wow, results are not great.
That was very fun, thanks for the link
No. But it reveals something about how this technology works, its statistical properties, from which you can infer the limitations.
Humans aren't machines trained on the entire stolen corpus of human knowledge. We expect that a human will do poorly at arbitrary tasks they have no experience doing – especially drawing, which many (most?) humans aren't trained in at all. The same isn't true of AI, where its proponents, priests and proselytizers have spent time, energy and billions of dollars attempting to convince us it can do anything better than humans.
Pelican enjoyers didn't like this one lol
I don’t know what datasets are available to these LLMs, but I’d imagine if there was training on CAD code, text, and images, a prompt steered towards that probably could get it pretty good.
I am not a mechanical engineer, so even prompting well with ME lingo probably will take some effort.
Shhh...you'll alert the models :)
Totally agree though, anyone with a vague understanding of how bikes works ignores the pelican because they know the bike is unrideable in the first place.
Yes, but I think the idea here is that most models produce very similar pelicans on bicycles, so a different test might be more useful in gauging the differences in models.
Agree. The pelican benchmark was interesting a year ago when most models struggled and a good pelican indicated an unusually capable model. Now it’s saturated and uninteresting.
A good new benchmark should have awful performance to start and there should be a lot of headroom for improvement. This benchmark is also intentionally difficult and requires the LLM to develop the animation through spatial reasoning and first principals rather than existing video generation pipelines. Similar to how SVG generation was out of distribution for most models a year ago.
I think it's interesting to see them visibly struggling to improve. Claude pelicans aren't much better today than they where 18 months.
Have you seen pelicans in Simon Willison’s tests? It is still not a pelican on bike I would like to publish :)
Imho we don’t need to make benchmarks that draw the whole 3D world. Pelican’s drawing is really nice in its simplicity and complexity at the same time.
It seems to be an obscene waste of compute time to generate useless 3D worlds that are just a bragging - 3D is really heavy discipline to make it right, see Mark Zuckerberg’s ceased attempt with 3D VR…
Multiply it by thousands times as a lot of people have found out threejs lib and prompt “generate 3D world and make no mistake” are new orange/black.
Do you expect the SVG to emulate a hand-drawn picture, become more realistic, or just a more detailed illustration?
As for the often quoted issues with the bike's frame or problem with the steering column, I can't really tell, I am no bike expert.
I can instead judge how poor of a job it is doing with a LOTR rendition in Three.js, so that seems like a better benchmark.
A general benchmark (even Simon mentioned that it was meant as fun at the beginning) should be quick and easy to run, since we can expect that more people will want to try it out. That’s why I’m more like “team Pelican on a Bike”… :) cheers
But it's another benchmark on how good models are at generating intensely average, unwanted things with unthinking design. Just scaled up.
Bad? It has a charming style. I would watch the whole book if it was made like this.
yeah, definitely, in the same way that we all regularly go and look back fondly at our chatgpt ghiblified family photos
Very few things are universally hated. One can love something truly that is hated by most. But it doesn't change the fact that it's still hated by most. An objective and a subjective opinion can exist at the same time on this.
I worked with an LLM to build a ~3D animation of the Back to the Future delorean Time Machine as a way to spice up the hero on a docs page.
That took a fair amount of custom tuning and I had to create a tuning view to get some of the behaviors right.
But it was enough fun that I generalized it to take in ~any scene description from a film. It goes out and gets more detailed descriptions and film stills if available but also takes custom stills if you provide them.
My test scene was the Gauntlet scene from Apocalypto. It is low fidelity but does a pretty amazing sequence with somewhat believable physics of the javelins etc.
Here is the docs page with the vertical takeoff / 88 miles an hour time travel: https://contextify.sh/docs
I can share some of the Apocalypto bit if anyone is interested.
Ha, that's fun! I've built half your product for myself as a personal, native MacOS app, with a slightly different direction, but the same core idea. I might go do a little scavenging for ideas in your docs :-)
Please feel free to do so! I have a source available copy of the self-hosted sync server up on github here: https://github.com/PeterPym/contextify-cloud-self-hosted
If you have time to try the product as it stands, I would definitely appreciate your feedback in particular, and would be interested in checking out what you've built so far for yourself.
I will publish the API to the database as well.
The product you've built the animation for is only for macOS, which is a pity cause it has a way wider appeal than macOS market share is. You have low-hanging fruits right there, don't miss them.
Thank you for mentioning it and for putting your suggestion in the language you did.
That page with the time machine may not make it obvious, but the product does have a linux client!
It doesn't have the same app window and summarization of the macos version, but the transcript ingestion engine is efficient and the real value is in leveraging the database it builds using the packaged `/total-recall` or your own use of the api. (which is not yet documented but discoverable)
I am building the windows version now. I have been for the past four days. It uses a shared swift-core with the macOS and Linux versions which has been part of the reason it has taken "so long."
If anyone is on windows (or linux!) and would be willing to try it either of the clients my email is in my profile, I would be grateful.
Also I'm working up a short post with that apocolypto anim now.
macOS covers 90% of people who would ever pay him.
This is a bizarre claim to make in this case, AI tech is used on all platforms universally.
iOS users spend dramatically more on e-commerce. I worked in e-commerce. Maybe it’s changed in the last 5 years. But that’s where the ops sentiment comes from.
Literally just google “mac users more likely to pay” and you will find many such instances
Are Mac users more likely to pay? Likely yes.
Are the total number of Mac users who pay higher than the total number of Windows users who pay? I kind of doubt it. Windows market share is still much higher than MacOS.
Used, yes. That alone won't pay the bill.
I am interested, I'd love to see! I'm waiting for the day my dad's self published books become self produced movies!
It seems pretty clear that Anthropic models have been specifically trained to be good at generating three.js (JavaScript 3-D Graphics) code, so given current state of AI code generation in general, I don't find three.js models/animations as indicative of anything other than the model's ability to write three.js code.
When Fable was first released the day-1 demos of it on Twitter (presumably from people who were given early access, and/or Anthropic employees) were pretty much 100% three.js stuff. Yes, it looks nice, but it doesn't tell me any better than an Erdos proof whether the LLM will be able to run my vending machine.
Taking a single paragraph of literary text, which is abstract and ambiguous, and converting it into a 3D animation requires an enormous amount of implicit knowledge about spatial relationships, intuitive physics, everyday objects, and so forth. Not to mention the mathematics of 3D transformations and computer graphics more generally. Saying that it's indicative of no more than three.js coding ability is absurd.
Why does it require knowledge about spatial relationships?
Because the degree of realism in a video is determined by implicit knowledge of concepts like "in front of", "behind", "next to", "inside", "outside", "between", "occluded by", etc. as well as distances, angles, and the relative size of objects when viewed from a perspective.
I guess I just don't have an intuition for why that is different from what it does for non-"spatial" things. Like the fact that it works is still because it produced the code it did token by token. Whether its dealing with, e.g., "beside the rock" or "in the array", it's doing the same kind of inferential activity.
>Whether its dealing with, e.g., "beside the rock" or "in the array", it's doing the same kind of inferential activity.
Are you sure about that? Presumably the spatial concept of "inside" and the computer memory concept of "inside" have different contextual embeddings in an LLM, much in the same way that neural activation patterns vary in the human brain when we reason about different domains.
You are correct. It’s important not to let hype peddlers imply AI possesses consciousness.
I don't find three.js models/animations as indicative of anything other than the model's ability to write three.js code.
This weekend I've been converting a game from three.js to ogl.js in order to see if I can optimise the time-to-interactive loading time. I took the three.js driven page weight from about 600KB (500KB being three.js) to about 50KB, and reduced the loading time from multiple seconds on a 4G mobile connection to around 0.5s.
This has mostly been a combination of Opus 5 and Sonnet 5 in Claude Code. It very clearly has a good grasp of WebGl, and of what impacts page loading times and rendering speed. It was able to drive Claude Code's integrated browser to measure the impact of changes, and as I spiked out a test of ogl.js it could test the differences changes made.
It's not the best game (https://tinyslots.ooer.com) but that's on me. As an exercise in building 3D in a browser, and in page speed optimization, with Claude models I am really impressed.
I don't think so. I've used Claude to generate 3D animations in python purely by making and manipulating raw meshes, and I've had great results. A more likely explanation is that 3D graphics is just pretty straightforward matrix algebra, and models (or Claude specifically) has that down cold.
Don’t worry. I’m sure they’re not training it to be good at things like writing database backends, financial services, logistics systems, user interfaces, or anything of economic value. As long as you’re not working on three.js specifically, I’m sure Anthropic isn’t making any progress you should be worried about.
Do you feel that rendering a 3-D picture of Bilbo Baggins' house is a good indicator of generating economic value (i.e "AGI" in OpenAI parlance)?
I'd rather have them battle on the topic "Who builds a better Google Wave for LLM chats" to explore the space of how AI studios could be.
"Getting Started with Google Wave": https://www.youtube.com/watch?v=eKUAqNGVwX0
Also see Game Helpin' Squad's "A Pissed Off Tutorial For Google Wave":
https://www.youtube.com/watch?v=4Z4RKRLaSug
Their "World Quester 2" tutorial shines as the holy grail of consistent and ergonomic user interface and game design. The menuing system is so magnificently structured and well organized, it bring tears to my eyes. Google Wave pales in comparison.
https://www.youtube.com/watch?v=0Gy9hJauXns
It’s a fun idea to re-animate dead google products by feeding product videos to an AI
i remember this failing, but this product looks pretty useful in 2026
It looked pretty useful in 2009. Failing to push it was baffling then too.
It was open sourced as Apache Wave when Google shut it down.
Years later Apache moved it to read only because of low community activity.
The archived git repo on GitHub remains available to clone and revive as a fork.
https://github.com/apache/incubator-retired-wave
This doesn't explain the lack of marketing. Apache isn't exactly known for pushing tech. Google never tried to improve on gmail.
Sorry if it wasn’t clear, I was adding this info for context and for anyone who hopefully feels inspired to pick up Wave and make something from it given that it was all open sourced and all. And I figured that in the chain after your comment about it having been useful looking all along was a natural place to add this additional info and link.
This was the first big secret “you’re not allowed to know what this is or talk to anyone about it” project inside Google. They wanted to be left alone and especially to not have to integrate with the mail team. And everyone heard rumors of gigantic bonuses if they hit whatever milestones, which wasn’t a thing for other projects. All this made them isolated within the company, and when the project was an obvious flop, you didn’t see anyone rushing over to help them.
I used it to plan a group beach trip back when it came out. We had a single page shared with everyone going on the trip. The page had live shopping lists, maps, weather forecasts, and other snippets of useful information. Now in 2026 I still can't think of any single technology that provides the same utility. Although to be fair, this might be a case of rosy retrospection.
Notion pages are pretty good for shared trip planning docs. Although maybe without so many live updating widgets.
notion isn't too far off
wave failed for weird google organizational reasons far more than anything inherent to the product or tech
It was awesome when released. I used it a lot, the multiplayer experience was awesome, and the mix of document-forum-wiki is still something I miss
It was too far ahead of its time.
this is not a good benchmark for models, but it's great if you're optimizing for attention on twitter because video content and 3d animations perform best on social media.
a real benchmark is instead running evals on your own traces, and building a cost/quality/speed profile for models based on real workloads. but it doesn't get you a shiny video you can post on twitter.
I can forgive the modeling being godawful jank (windows floating in the air, disconnected from the house). But I expected it to have a better understanding of the text. Instead, we have Bilbo's "disappearance" interpreted as him magically transporting or cloaking, and similarly for his reappearance.
But this only seems wrong to you because you're familiar with the prior context. When there's only a single paragraph to work from, and it's a drily humorous text, why not employ comic literalism and lean into the perplexity with which his neighbors viewed him?
The LLM has access to the full text and every translation of it ever produced, both in its corpus and on the searchable web.
The fact that a ring appears at the end proves that the model has context beyond what was in the prompt. The model clearly knows the story that the prompt was extracted from.
This is a rationalization. The simpler explanation is that the model failed to properly depict the passage. As the AI booster crowd would say, cope.
which to be fair, happens just a few paragraphs later :) I first watched without sound and thought "oh that's the birthday speech disappearance"
I always thought of the Pelican more of like a gimmicky quick test. There are people who took it as a serious benchmark for overall model performance?
"Draw a pelican on a bicycle" is not a serious benchmark.
"Draw an animation of this long ass scene from a movie, and only call me when everything works e2e" can be.
I feel like we are going to look back on this era of llm usage like we do at the period of time when we thought radiation was magic.
Consuming radium and using uranium glass, that’s what we’re doing.
I’d like to see a human one shot a pelican on a bicycle in raw svg.
It's been shown in tests that most humans can not from memory draw a functional bicycle given pen and paper.
Everyone knows roughly what a bicycle looks like - wheels, frame, seat, peddles, handlebars etc, but the details of the frame and exactly how the other parts connect to it throws people off. It seems people memorize the "concept" of a frame, but not the specifics.
Try it without cheating, then google a picture of a bicycle!
Even if you have memorized what a bicycle (and pelican) look like, writing code to draw one is not the sort of thing humans are good at any more than they are good at mentally calculating cube roots - better to use a computer for stuff like that.
What do you mean by "oneshot"? The term applied to genai makes sense, but it doesn't make a whole lot of sense to apply the term to human art.
I understand it as “write SVG, say when you are done and only then you are allowed to see the rendered result”. Multiple shots would be either “you get more than one try, we'll pick the best” or “you can see the rendered result and iterate” (or maybe that would be “model + harness with tools”?)
What doesn’t make sense? They aren’t drawing it by hand, they are still using SVG. Give them one chance before rendering it.
But the LLMs are also not one-shotting it, or are they? I assume they have some ways to verify it, e.g. to visualize it (convert to PNG, then feed as vision tokens back to the LLM), or other ways, maybe also pure text LLMs have some ways to verify the result at least somewhat? And with such feedback loop they can iterate.
Speaking of benchmarks has anyone given AIs Where’s Waldo pages and asked it to find Waldo?
I’ve been trying it on them all and can’t find one that does it consistently. The best will tell me they can’t. The worst confidently point out one of countless Waldo-likes.
It would be interesting to see the models work on a book it hasn't been trained on yet. I guess sadly that means any book released very recently.
Definitely impressive demo, I do wonder though if the countless artwork, films, images etc produced over many decades around Lord of the Rings somewhat influenced the outcome of this though.
Yeah that seems like an enormous problem with this example.
Reading the title, I was thinking "Karpathy? I don't know this chess player." (The Pelikan is a well known chess opening, and famous chess players often have book titles like "X's Y" where X is the player, and Y is the opening)
IMO, the area where AI is going to be most useful over the next couple years is in developing manufacturing processes top to bottom. Maybe a million token budget is too small, but something like "design me a sneaker and all the equipment to manufacture it autonomously".
They can't even run a vending machine. I don't want to do a shallow dismissal, but I think there's a gulf between my understanding and yours. I hope it's me so I learn something.
I think LLMs will be excellent glue of "find the right function/button and run/push it" but design without constraints and they just explode immediately
I've been getting into 3D printing recently and while the flagship Anthropic models can regurgitate community wisdom about the hobby they can't do spatial reasoning. They'll tell me that objects will fit on my print bed when they won't and vice-versa. They get confused about materials and nozzles. It takes several iterations to get them to do basic objects (in my case a slotted tray) in OpenSCAD. Useful, certainly, but very far from autonomous for end-to-end manufacturing workflows.
Physical reality is not software or math, LLMs can't design "equipment to manufacture autonomously".
You think the bottleneck in creating a sneaker factory is not having an LLM to tell you what machines to buy?
I think embodied AI or autonomous experimentation will need to make a lot more progress before that kind of thing is possible.
Asking AI to design real world objects doesn't work very well because all of its tests involve proxies and thus miss things that are glaringly obvious when the object is actually built.
> IMO, the area where AI is going to be most useful over the next couple years is in developing manufacturing processes top to bottom.
Why do you say this? I have some experience of manufacturing processes and am not seeing where AI would be useful other than to drive the robots which we can already do quite well without AI (see all the dark/lights-out factories that already exist).
Where do you see it being useful? An example would be nice.
Is anyone genuinely excited about these kind of scenarios?
Yes but the second order effect of this is that the cost of the tooling goes up since it is now the bottleneck, and therefore the shoemakers that survive do it off of technical complexity, branding, and regulatory capture.
> "design me a sneaker and all the equipment to manufacture it autonomously"
We already have sneaker designs and the equipment to manufacture them. Whatever it spits out is going to be, at best, a mediocre clone of something that already exists. What exactly is the point?
Sneaker manufacturing is a very manual process. Nike famously tried to automate it and failed (https://www.wsj.com/economy/trade/why-its-so-difficult-for-r...). Designing and constantly changing all the processes is more expensive than just having people do it by hand. If you can have AI design the process, then the equation switches.
Gotta love when a techbro just says some complete nonsense like this with total confidence
You’d think tech enthusiasts would actually make an attempt to understand the tech they’re enthusiastic about. OpenAI’s mysterianist marketing has broken some people’s brains.
Grok build me a spaceship to mars, make no mistakes
How much are the hobbit houses described in the book? The ones here look exactly like the movie
In the first paragraph (either of chapter 1 or the prologue), not at all. It's pulling everything from pre-training, so it is likely relying just as much on all the visual mediums like the Peter Jackson films, the animated The Hobbit (1977), and all kinds of random depictions of Tolkien's works.
Bilbo's house is actually described in The Hobbit and the exterior isn't really described at all in Fellowship. The prologue of Fellowship (Concerning Hobbits) mentions hobbits like round doors and windows and the fact some hobbit homes are underground, but the turf-dome design here is not mentioned. It actually mentions hobbit homes typically have bulging walls, so unless you've read the The Hobbit, you might not picture this entirely-underground style.
In The Hobbit, his home is described as a (nice) hole in "The Hill" with a perfectly round front door and round windows, which could imply the design here.
Right, yeah I asked because I assumed it copied the look from the movie. I think it'd be more interesting to do this for something that hadn't been adapted (yet). The first chapter of Neuromancer perhaps
Regarding the argument about LLMs having difficulties auditing their work:
I wonder whether we are entering the era of throwaway software. Just like cheap plastics and improved processes has enabled us to rapidly manufacture anything we want for a very low price, maybe LLMs give us the same for software. Produce it cheaply and if it breaks throws it away and reproduce it.
To a certain extent to what end though,
On the one hand yes, almost every task I work at now is one off one of scripts I throw away.
The question is - where does the software the spec or the “code”.
A really complex game will probably always be token heavy. At least for the next few years code is still not free.
But certain software is just iterative by design. If we mean we regenerate all the for loops of a game from scratch, sure but I think “code” Is really more spec then implementation, and we’ll want to continue building things through iteration.
And even on the for loop point - Do you really want to spend millions of tokens rewriting a game every time you need to make balance changes?
I'm sure this will become the standard. And plastic is an excellent analogy. Maybe we can take it a step further and compare it to on-demand 3D printing.
Why would anyone still use off-the-shelf software when they can have a system that has access to all data, can transform it into any form, and can export it in any format?
After years of thinking that I needed to develop a decent movie management system for my own films or a columnar browser for large CSV files, Claude and Qwen each delivered exactly what I needed in just a day.
This. This is why the burst of posts on HN of “I made this useful tool/crate/application” were just so sad. The old model was getting what the kids now call aura by developing useful open source products: products where it’s far easier for someone to consume the product than write it themselves. We had people posting things as if that model still existed. Dude, you wrote it with an LLM! Posting them (here) is not only pointless, it’s advertising that the person who wrote it isn’t smart enough to understand that I have an LLM too.
but are you willing to spend the time or tokens to build it again for yourself ?
Yes. It's fantastic for one-off tasks.
Does this make sense with economics of software though? Throwaway products compete with more durable versions of the same product because there is a cost per unit produced that can be minimized by using cheaper materials or production processes that cut corners. With software there is no cost per unit. There might be a market for one-off software that serves a very specific purpose where throwaway software can compete with adapting more carefully engineered software to that purpose, but I am not convinced there is a lot value in this market.
> Just like cheap plastics and improved processes has enabled us to rapidly manufacture anything we want for a very low price
Have they? Most of the world production is tied down to expensive factories and machines. Yes, we have more products, but that the result of the global trade, which is a very complex system.
> Produce it cheaply and if it breaks throws it away and reproduce it.
I don't know why everyone would ever wants this. It's been parroted since forever, but the true usefulness of software is to be able to build it once and runs it indefinitely. If some edge case occurs, I fix it. Which is way cheaper than rebuilding the whole thing. The goal is to have something like OpenBSD's ed[0] or dmesg[1], which you only touch every few years or so
[0] https://github.com/openbsd/src/commits/master/bin/ed
[1] https://github.com/openbsd/src/commits/master/sbin/dmesg/dme...
I'm pretty tired of the "Y made this game in Z tokens" all over the internet last week. They look impressive, and it's cool that it's even possible, but they're useless as games. None of them are any fun. They're like the most boring variant of basic controllers you can imagine. None have any cool mechanics. None have any tweaks made from hours and hours of testing. All have the same cel-shader.
I’ll never not be amazed that we can type some words and get those results back out.
However I do agree that the results are much worse when you try to use them. They look great in screenshots and video clips which makes them perfect for content farmers.
All of the LLM generated games I’ve played have been really bad to play, though. I even tried my hand at a simple game, thinking I could iterate on it with prompts to fix some rough edges. After the initial productivity burst every change turned into a slog of tokens with one thing changing and something else breaking it. I would try to use my remaining weekly token budget across Anthropic and OpenAI to refine it at the end of every week but after a couple weeks it felt like I wouldn’t be getting anywhere without scrapping it and going back to having the LLM build it one step at a time with my careful instruction.
Which, in retrospect, is the only way I can get usable output of an LLM for anything complicated, so it’s not surprising. It’s a fun reality check project though.
I think it’s interesting to watch that we all have to sort of fine tune our own expectations and build the mental model for how impressive this is.
On the one hand I think most of us are incredibly impressed because we know, that quick demo would have taken us months of work to build in the before times.
On the other hand the promise is a cure for cancer and the end of all work.
So when everyone is telling you “skill issue is why you can’t one shot WoW”. It’s hard to know how you’re supposed to feel about Karpathy advertising one shot custom virtual worlds but giving you slop. Incredibly impressive slop when compared to how long it would take to create it just 3 years ago, not so much compared to Elon saying - “by the end of this year, grok will create a version of the odyssey that competes with Nolan’s”
LLMs are bad at creative work and I don’t see them improving any time soon. Try asking an agent to write a story about raccoons. It will almost certainly involve either stealing food or raiding trash with a 50% chance of having a character named Pip. If a location is mentioned, it’ll be Elm Street.
I guess this is the average story and, similarly, the average game is boring and predictable
Ironically, they're getting worse at creative work because all the RL is collapsing their distributions.
Worse in a very specific, bland, uninteresting way, too.
Early generative AI at least had the virtue of relentless, unsettling weirdness, in the same way that generative art from the late 90s and early 2000s did. A handful of people made creative use of that spooky weirdness.
Now it turns out "Airspace" art.
Heh, tried it and got a story about raiding trash starring Pip. But yep, I'm well aware of this problem as well. /r/sillytavernAI is a sub dedicated to trying to coax LLMs into good creative writing but it's kinda impossible to do consistently.
I don't think this is bad. I want tools that deliver what I envision. The fact that given the same input, I get similar output is something I would rather consider a positive. The imagination should come from the human at the wheel.
While they are bad at creative work*, this reasoning isn't going to show it.
If you took the best, most creative, human writer in the world, and for thought experiment reasons they had amnesia (to mimic AI blank context windows) specifically while you asked them for a story idea 100 times in a row, my expectation is that this human would also give you the same idea at least 80 times out of that 100.
* still better than the mean human, but even the top 0.1% of humans aren't all professional authors.
Better than the mean human? The mean human creates far better stories while daydreaming. AI enthusiasts have such a distorted view of human capability, it’s bizarre.
The qualification "better" is doing some hefty lifting in your assumption.
What you think is better is not what I think is better. Imagination is not storytelling. You ask the "mean human" to write, it's going to be worse than an LLM in spelling and grammar, if you get anything at all.
Daydreaming is absolutely storytelling. The average person is able to craft elaborate stories on a whim. Writing is used, in part, for conveying stories, but writing is a separate skill. The good news is the average person can learn to write well, but LLMs, so far, have not demonstrated the ability to craft interesting stories and it’s pretty unlikely they’ll be able to learn, as this is an unverifiable domain.
The mean human gets a middling score in creative writing tasks at the end of their mandatory education, and most then leave school and forget what little they ever learned outside whatever their career path happened to be.
Most humans never do a creative writing course after school, and the longest fiction most people will write is their resume description of what their previous jobs involved, or perhaps their dating profile.
Don't mistake what you see published (or what your friends are like) for the average human: the average Hacker News comment easily above the writing grade (and creativity) of e.g. many of the one-shots stories I've seen attempted on some creative writing subreddits. "Mean" is not a high bar.
This is so absurdly condescending and simultaneously incorrect that it astounds me you’re able to function as part of a society.
Seriously, what is the deal with Pip! I was experimenting with childrens' fiction more than a year ago and 9 times out of 10 you'd get a character named Pip unless you were specific in the prompt not to do so.
I recently saw a study on this actually. https://arxiv.org/abs/2605.26492
I saw that, tested it in Qwen. Elias Thorne, on the first run, as a clockmaker too.
I think the trick is to use LLMs like scalpels instead of hammers. That said, you can place a scalpel in the hands of a deterministic (or not) robot.
Elias Thorne!
I've heard this called a "dancing bear" before. It's interesting because it's a bear dancing, not because the dancing is any good.
These conversations get frustrating since one group is saying the dancing is bad, a second group is saying it's good (for a bear), and a third group is saying we are a few years from a bear-only dancing industry.
LLMs currently have 0 imagination, I have strong doubts that will be solved any time soon even if they continue to become superhuman at everything else. They just have the creative instincts of a 50 year old accountant.
> They just have the creative instincts of a 50 year old accountant
That's oddly specific and it'd really hurt my cousins feelings :)
Especially since the whole AI IPO concept seems at least somewhat predicated on creative accounting! ;-)
We're all just impressed that it's even possible. No one is saying these are fun.
I guess the next question is if they can be made fun without too much additional work with a human guiding the AI.
How well encoded is "good game feel" in the weights of an LLM. I am guessing not very well.
> the next question is if they can be made fun without too much additional work
They can't. If you think about how these things are trained it's blatantly obvious fun is an impossible metric to optimize them for
Training impossibilities aside, how would you even make an optimization loop for "fun"?
Brain-computer interface, maybe. Hook a million play-testers up to the output and have it iterate.
..feels like there was a black mirror episode about that though
Unfortunately people are saying they are fun for the purposes of ragebait that goes viral, which is half the reason people intentionally post AI slop on social media.
Right, but the new phenomena is that we've lost a signal.
Used to be if a game looked that good it probably had time spent on the game part too.
And you could almost always tell a "game construction kit" game from a real one, couldn't you? Whatever was missing from those is always missing from these, and it seems just as rare that the game will reach much beyond its origins.
Whenever I think about AI games I find myself thinking about Tiny Wings, Flappy Bird and Angry Birds. Three simple, elegant games.
It is easy to see what makes Tiny Wings so completely loveable — it has a sculpted, adorable, perfect charm with a cleverly inverted game mechanic that has a calibrated level of exasperation and reward.
But why were Flappy Bird and Angry Birds, very basic games with very old game mechanics, so charming?
It seems equally impossible to imagine an AI coming up with a game with the quality of any of them, even with maximised creativity. But explaining why for Flappy Bird seems quite difficult, especially when you consider it uses some stolen visuals!
it's crazy because I've seen "implement flappy bird on an FPGA with VGA output" as a 2 week project to a human as something immediately understandable.
Maybe it's the smoothness of motion that makes these games understandable and LLMs seem to consistently fail at that. Ask them to do something snowboarding and they go really hard on the physics since it seems like they don't know what kind of approxmations feel good.
I've been hearing similar from a while back, but the blame wasn't being directed at AI, it was directed at Unreal's defaults now making random indies look like AAA in the screenshots.
(It's been a while since I was in the game industry, so IDK quite how accurate this is).
It’s called “demo porn” and it’s really obnoxious and borderline offensive to people that take the craft and art form of video games seriously.
If I see one more “one shot MMO” where you just walk around and do absolutey nothing or another menu slop idle battler or rogulike deck builder I’m going to go Postal in Minecraft.
It's the flashy game/movie trailer equivalent for LLMs. Completely unrelated to the real experience, but good for marketing.
Unfortunately the Phaser framework has decided to go all-in on this route, and it's really disappointing.
These one-shot products aren't games. They're barely even demos. I don't even know what to call them. For a mature framework like Phaser to sell-out like this and create a vibecoded platform for vibecoded games is shocking.
It's blockchain for gaming all over again, putting the cart before the horse.
you're tired because you lack imagination.
It’s the people spamming these shitty games that lack imagination.
people are excited, let them be excited! but more than the excitement is realizing the implications of what this means for building software, first it looks like a toy and then it doesn't. the OP is talking about how the games are not playable and fun. who cares? that's not the point.
What does it mean for building software? These games cannot become fun. There is no way to prompt it fun, and any change you try to prompt it to make will inevitably blow up something else. The code is a mess and impossible to build upon.
There is a tipping point between procedurally generating everything in SVG to maybe giving them tool access to something like 3dsmax (or having them build and then use a tool to do the thing vs doing the thing).
I’d love to see this benchmark using Blender. Asking a model to animate a scene in Three.js is a square peg/round hole; it doesn’t convey much when the model can’t get it to fit. With Blender the human expert ceiling for this task has been proven to be very high
This is just a very rough basic game skeleton. Companies believing AI is smart enough to spit out almost ready product will learn a hard way that it will will give them just a starting point that still needs a lot of work. This is a nature of all modern LLMs - they easily lose context: the bigger the context the more losses and distortions are, especially in the middle. So, giving AI the complete book does not mean it will follow everything in it - quite the contrary.
No, it cost 1M tokens, or about $10
How many more millions of tokens do you need to spend to get something of acceptable quality?
I think for a fair benchmark, the answer would be 0. You'd continue to run the benchmark with a 1M token budget until future models started to produce something of acceptable quality.
Anyway, the comment I was replied to originally claimed that $1M was spent on building this demo, and I was correcting the error. They seem to have edited their comment since.
I suspect LotR is a singularly unrepresentative choice here considering how much info exists about it.
Is anyone else getting "mongodb is webscale" vibes? (Except 16 years ago that was a lot smoother, because it used some sort of "render this conversation" engine...)
We must not be using the same opus 5, because if I tried to generate this it would refuse based on copyright grounds.
Karpathy works for Anthropic so he obviously has full access to everything, and doesn’t have to pay for token burn either.
I saw that "~free" and laughed.
In my experience SVGs are still too hard for LLMs.
I gave Fable a jpeg and asked to draw an SVG, using a loop that renders the SVG into an image so Fable can inspect it.
Results looked like drawing of a 5 year old.
This has started to feel a bit like the beginning of railroads and then the steampunk fiction of "let's just build railroads to everywhere". We don't need it and there's no use for it.
As with painting, after a while there's nothing really new to paint, we genuinely need 0 new software. We need to fix our broken physical world, our social lives, our kids and what's left of our democracies.
This software crap is done, leave it to the nerds.
Agreed that software isn't going to fix our institutions. But we need 0 new software? AlphaFold won its creators a Nobel prize in chemistry and solved a research problem that feeds into every area of biology, medical research, and drug discovery. We still have an untold number of unsolved problems related to human health, food production, energy production, infrastructure, transportation, education... The list is practically infinite.
Agree for stuff like alphafold, for the likes of education, imho, it's been solved for at least a couple of centuries.
Textbook and blackboard > ipad.
For childhood education sure, we don't really need high tech solutions there. But what about training the next generation of mathematicians, physicists, and engineers? Even decades ago, computer algebra systems and numerical solvers started to become indispensable, at least in many subfields. Now we're moving into the territory of automated proofs of mathematical conjectures. The state of the art is going to keep improving, and education is going to have to adapt to keep pace.
Beautifully put. Software is ok. It's not going to fix our broken world. If AI could at least find cure for some illnesses that would be great. Or decrease social injustice and help with global warming. But it's likely going to do the exact opposite.
I'd like to see the Silmarillion, specfically both Ainulindalë and the Fall of Numenor. At this point a visual model would probably produce something better than Amazon (but presumably not Jackson).
I really dislike this AI programming thing of “Mr LLM, go slam your face into the problem until there’s no problem left, then call me back”. (Not sure if it’s a recent trend or a fundamental nature.)
It always brings to my mind some words from Rich Hickey:
I don’t think I really have a point to make here, other than it just feels like someone’s released a bunch of carnival bumper cars onto the highways.To me, that's a totally normal way of using computers. Repeating tasks is what computers do best, AI or not.
Take chess engines of the "deep blue" era for instance. These engines are stupid, trying millions of moves that are obviously terrible, no human chess player would do that. And yet, this is what worked best, because computers are so good at repetition. Recent development using neural networks made chess engines smarter, but using repetition is still how the beat humans.
What you call "guardrail programming" in another context would genetic algorithms, a technique that has recognized applications. And if you look at videos of genetic algorithms learning to play racing games, it literally looks like a bunch of carnival bumper cars onto the highways, but done well, after some generations, it becomes competent driving, sometimes even record breaking.
It can be expensive, it is often the case for LLMs, so you may want to try to be a bit smarter at first to spare some resources, but to me, it is just using computers as intended.
> I really dislike this AI programming thing of “Mr LLM, go slam your face into the problem until there’s no problem left, then call me back”.
Not difficult to see why the employees of AI firms are thrilled with it though, eh?
> I don’t think I really have a point to make here, other than it just feels like someone’s released a bunch of carnival bumper cars onto the highways.
I like the analogy.
I guess this is why we got this before cars sold without steering wheels: literal guardrails on the literal roads are somewhat more expensive, especially for the people who keep bouncing off them on the way to their destination.
Also, where the guardrails are absent: oh look, felonies. https://www.google.com/search?q=ai+hacks+company&tbm=nws
Like hitting pinball bumpers, except the high score in the end is the bill you pay the token provider.
One difference for human to understand the video is that we only care the changes on a picture compare to LLM
On consumer devices, AI communicates with us through speakers and screens. Screens are the richer medium, so most consumer AI innovation will happen there.
Computer graphics will have enormous applications because they are directly controllable by LLM-generated code. Video models are probabilistic and less suitable when precision matters. In education, for example, we need exact visuals. If an AI wants to plot y = sin(x), it should generate the precise graph through computer graphics rather than approximate it with a video model.
Coding, graphics, all seem to have very defined background data for an LLM to base their decisions on, test, and even when I correct it we're all on the same page. Seems like there's lots of room for AI productivity there.
What I find funny is that approximate to computer graphics are video games. When I ask AI about a decision available to me in a video game AI completely fails, OFTEN. I assume all the forums and changes made to a game over time might be quite confusing for AI. But I've also seen it completely make up characters and decisions and weapons and so on about some very clearly defined games and paths. It's an interesting dynamic.
I think I could tolerate 50 Shades of grey rendered in this style.
Still images seem like a better quick test because we can see them at a glance. Maybe ask it to make a comic?
The tilde thing remains uniquely obnoxious in a field that seems want to mangle language for fun, so that's innovative I guess
This makes me think about using a game engine and a coding agent instead of current video generation AIs. It will probably cost much more, but it will have almost zero consistency problems. Is this line explored?
> I also like this kind of examples because no one in their right mind would ever spend the time to write something this custom
This is an odd take, given that Karpathy is certainly aware that the LotR films absolutely did create Bag End in digital format; that their creation was outstandingly high quality; and that Claude’s output here very obviously “leans heavily” on their prior art.
Do you guys notice that LLM can create fancy viz/animations by coding them instead of leveraging what we humans usually use (e.g Lottie, After Effects)?
I wonder if Flash is still popular... LLM can use that instead...?
Anthropic spokesman [0] Andrej Karpathy is here to tell you about token-wasting loops, and insists on the weird idea that they are "~free", when in fact, they are fuelled by expensively burning investor money.
[0] Seriously. Get used to mentally prefixing his and Boris Cherny's name like this, every time you see them quoted. These people are speaking while employed; there is no chance they are not aligned with the employers who will make them wealthy. The tech industry does like to pretend that for some reason AI people, uniquely, speak thoughts unbiased and for themselves or even for science or humanity.
you don't really need screenshots if you have an engine expressive enough for the scene generation while ensuring the visual appearance of the engine output itself is feasible.
that's why these things are actually pretty good at openscad/freecad/F360 mcps , the visual reality is enforced and guaranteed by rigor in the interpretation engine that is anchored to human physical reality.
I'm sad that Andrej Karpathy went from being one of the most reasonable, trusted, and credible voices in AI to peddling marketing slop for Anthropic.
8 months ago, he was (very reasonably) claiming that reliable agents are at least a decade away, but this now goes against the interest of his employer, so the narrative has been changed.
He makes a very good point here about llms or lrms not being good at taking video as inputs, and i think he's signaling his intent to help change that. He's pointing at an open problem. This isn't a carefully structured blog post, it's just a tweet.
> 8 months ago, he was (very reasonably) claiming that reliable agents are at least a decade away, but this now goes against the interest of his employer, so the narrative has been changed.
Or maybe over these 8 months agents improved a lot? You know, few years ago many AI experts predicted that things we are routinely doing now with AI are decades away. I mean how can you look at this post and not be impressed? It's insane what AI is currently capable of.
What narrative has changed ? I just see an experiment with a very perfectible result and some reflections about what capacity are currently missing to have better results.
I can't believe the video demo is $10
I love how nobody cares about copyright anymore. Not even an after thought. Might be okay if you're an employee of anthropic/openai but for us mere mortals I'm not sure I would share something this blatant. In US it's $150k+ per violation and you've given them all the proof (even a confession)
> sure, why not, it's ~free
Yeah, please check in with the folks protesting data center builds in their town causing their electricity prices to skyrocket and tap water to turn into a scarce resource.
And now, instead of actually doing the above, please go ahead and downvote me, because how dare he question those LLM games.
> because how dare he question those LLM games. I thought you are the one who's on Greta's side, you ar e the one who should be saying "how dare you?"
it shows the possibility that SVG replace PNG/JPG even video.
> I also like this kind of examples because no one in their right mind would ever spend the time to write something this custom
There are people in their right mind who would do that and their are already examples of people who did similar things.
But maybe not in the future if people would confuse all the effort with AI
After watching this video I am absolutely positive that the issue is not a lack of stamina in humans. It is that humans have the capacity to realize that this is a bad idea long before they complete it.
It may make sense to switch this to USD/Omniverse.
AI is now somewhere between "Money for Nothing" (https://www.youtube.com/watch?v=wTP2RUD_cL0) and "Knick Knack" (https://www.youtube.com/watch?v=9uhM_SUhdaw) in capabilities.
> I gave it the first paragraph of the Lord of the Rings, a 1M token budget (~$10) and asked for three js render of it.
I think it's interesting that the "Bag's End" interpretation in the video clearly looks like the one from the movies, but generated here as a three.js 3D asset.
It makes sense that the movies (or shots/frames from them) were in the training data, and I can also easily imagine an association in concept space between the textual description of Bag's End and the frames from the movie.
But how on earth does the model then go on and convert the latent representation of those images into coordinates for a 3D mesh, without ever even restoring the image? In what kind of representation are the images from the movies stored that it can do that?
If you like this check: https://news.ycombinator.com/item?id=47400868 it can be used to generate animations (not games) as well.
I know this is a plug but I thought of it, too! I hope this is the next benchmark they saturate.
I like where Karpathy is going; I had the same thoughts about LLM generated slop scenery. I just want some variety of scenery for the goblins to get massacred in in whatever fantasy slop game I play.
I think the pelican test is better.
I'd like to see tests of things the current AIs are bad at, like drive a car. Or maybe take instruction to complete some novel activity to test how well they can learn.
Would you say that the generated video is good?
Better than I would have expected? Sure. Impressive that it got that far? Definitely. But is the end result good?
Benchmarks like the pelican thing are about correlation with “how good the model is”. Better models produce better pelicans.
It’s a useful benchmark (aside from being “cute”) because of its simplicity, both in how many output tokens it takes (though I understand some models think a lot now to do it) and how easily one can subjectively judge. It’s this efficient as a benchmark of performance.
Making a long video takes way more tokens, and presumably is a lot tougher to easily compare. swillison has a presentation that’s pelicans from 2023-present (roughly) showing the progression. Imagine “lord of the rings videos from 2026-2029” or whatever, it would take a long time to watch and be harder to judge, and probably just end up being a comparison of screenshots anyway.
TLDR I feel like the post misunderstands the role of the pelican thing though if find it very hard to believe he really doesn’t understand, so maybe I’m missing something.
perfect benchmark to burn more tokens - convenient
It would be better to ask model to render segmented 3d, with placeholders, like magenta is water, blue is sky, green is grass, purple is Frodo's face, etc, then pass the result through img2img model to properly "render" it.
As a former gamedev watching non-gamedev AI talk about games is so amusing. They really truly do not understand anything about games or consumer entertainment.
There’s a reason AI slop games have literally zero engagement. Last summer that stupid flying game blew up. Maybe a million people “played” the game. Where play means they clicked a link and checked it out not because of what the game was but solely because of how it was made.
In terms of concurrent players that game wouldn’t have cracked the Top 5,000 on Steam.
My metric for AI games is “number of players who spent more than 15 minutes playing”. I’m not aware of any vibeslop that has achieved 1 such player.
Now obviously LLMs are transformative for game dev. But “hyper custom worlds you can drop into” shows an extreme ignorance of what players want imho.
I wish there was a timeline where I never ever had to see the pelican SVG test ever again.
This is awful
> I also like this kind of examples because no one in their right mind would ever spend the time to write something this custom but LLMs have all the stamina and patience in the world, so it's an example where we go from "no one would ever do this" to "sure, why not, it's ~free".
Except it's not ~free, it cost ~$10. And no one in their right mind would ever exchange $10 for that crap output except in this brief moment that we're in because it's fun and surprising to see what will happen. The actual result is as close to useless as it's possible to be - it's not interesting in its own right, it's not aesthetically pleasing, nor funny, nor informative. It's just slop.
Can we please link directly to XCancel and leave the fascist garbage site behind?
Don't miss Elon Musk's reply:
> @elonmusk 13h
> Yah
> 158 replies, 74 reposts, 1400 likes
Thanks Elon, you goofy fuck
https://xcancel.com/elonmusk/status/2083761408932458568
Can someone please translate that expression? What would that mean?
Affirmation?
Well, in that case - if it is just a "hear, hear" - I do not see why the utterance from that actor would be of note.
Maybe nozzlegear wanted to suggest some importance on Musk remaining a bet-ter on the general tech, regardless of the competition?
This gets me thinking (although there is not much to divine from three letters). Maybe it's a Jennifer Lawrence GIF-inspired "Yeah, right" then? Downplaying the significance?
To some online sources, it is used for both.
> "Yah": slang spelling of the word "yeah" (which of course can also be used ironically)
> Merriam-Webster: "Yah": used to express disgust, contempt, defiance, or derision; probably imitative of the sound of retching
Concerning
Tech bros continue wasting money to make the absolute worst art
> wasting money
Testing, assessing, tasting...
We also do it when we build other things - this is just a different scale.
Do you think this was supposed to be art? Wow.
"Check the current situation and make a new Iran Lego (tm) truth bomb video."
This is equally bad as a pelican test.
LLMs should be tested in the same way people should be tested for a job interview (but often aren’t) - with tasks RELEVANT to usage.
So you don’t just randomly pick some random thing to make the LLM randomly do (like many job interviewers do).
You start with clear statements about real world usage scenarios. THEN you come up with tests that give insight to how well the LLM/hob seeker gets the job done.
Please, stop coming up with random tests like it’s Microsoft in 1990 and you’re asking job seekers how the would move Mount Fuji, as a way of assessing their programming skills.
No stupid irrelevant pelicans on bicycles and no stupid renderings of Lord Of The Rings. Unless those are relevant use cases.
Any test that anyone comes up with must clearly state the context and how the outcome is measured.
Screenshotting for later
It's difficult to think in exponentials.
But this demonstrates we're a couple orders of magnitude away from generating 1:1 hyper-personalized entertainment and media for individuals, rather than the masses.
I don't really get the desire for hyper-personalized entertainment. People are very good at pointing out things they dislike, but not very good at coming up with how to fix them (common wisdom in game design).
On top of that, a decent chunk of the joy of entertainment is the social aspect.
I very much doubt that. We now have nearly-perfect AI image generation and it hasn't really changed the nature of human expression. I don't see my friends getting wildly creative. I mostly see it used for spammy blogs, spammy books, and cringeworthy corporate marketing - basically, a negative signal, rather than "ooh, AI image, I'm in for a treat". Is your experience different? If not, what changes with moving images?
Ultimately, most people don't have ideas for the kinds of personalized entertainment they want, and they don't want to be in charge of content production (even if you have an LLM do most of the work). I don't doubt that there are niches for it, especially stuff like porn, and I'm sure that pros (game studios, film studios) will leverage AI more and more, but I suspect that most of us will just want to sit on the couch, watch Spiderman XVIII, and then be able to talk about that shared Spiderman XVIII experience with all our friends.
All small business marketing material now looks exactly the same.
LLMs produce an extremely small range of artistic expression, and its kind of ironic, that while it certainly looks functionally good. It immediately becomes noise because everything looks identical.
So companies are going to have to hire creatives again, in order to stand out, and be "creative"
I'm mostly seeing the impact of GenAI images in everydays life for example in the small posters small associations or individuals usually attach on streets to promote some small local event. We went from just text created with PowerPoint and maybe some stock image just a Google search away to now images depicting the topic closely. But yeah, it's not like a revolution.
And this personal media thing, yeah maybe for terminally online persons that are REALLY into a sub-genre but otherwise, it's too much effort, I agree. Until we get machines that can read our (subconscious) mind, that will not exist.
I yearn for the old days of bad photoshop/wordart/powerpoint flyers.
They were visually bad but honest and sometimes soulful or playful.
The AI slop that's everywhere now looks superficially more professional but it's very busy, samey, unnatural and it really turns me off.
I don't want to say this is indicative, but my local library used AI-generated event poster/images for the past year. But recently they switched back to word/clipart event posters. When I was checking out, I query the librarian about it, and apparently they saw a 40-60% drop in attendance. On one hand, I found that hard to believe, but on the other hand, simple word/clipart event posters do have a certain inviting charm about them.
> We now have nearly-perfect AI image generation
Not even close. Current AIs have very poor spatial awareness, they can generate some kind of scene but they can't tell you where objects are in the scene, nor can they move objects into different places. They're very useful but it's difficult to be very creative with them because they can't update an image to make it more aligned with your vision for what should be in the image.
Ultimately, most people don't have ideas for the kinds of personalized entertainment they want
1:1 AI entertainment probably won't be a cold start experience.
Much like the "Choose Your Own Adventure" books of the 80's, consumers choose a baseline template, customize the characters, and interact at various points within the plot.
> We now have nearly-perfect AI image generation
No, we don't. It's quite good, but it's nowhere near perfect. For example, you can see: https://genai-showdown.specr.net/ or others that highlight how far from perfection we still are.
> and it hasn't really changed the nature of human expression.
It sure has changed our discourse. Look at hackernews, where people just can't help themselves to engage in ragebait and flamewar comment threads about llm generated accusations. We've got half the people convinced that looking at ai content is like consuming food.
I'd venture a guess that it's too early to determine how this technology will change expression. A good analog would be photography, which caused a similar meltdown in the arts at the time.
> I don't see my friends getting wildly creative.
Maybe you don't have creative (enough) friends.
If that becomes a thing, that will be fun 2 weeks, then become a gimmick only a small niche will be using. What’s the point in having a hyper personalized entertainment? People want to experience the games, movies, tv shows, books others created and are also experiencing
Judging by the Seedance 2.5 demos today, I’d say it’s not that many orders of magnitude away now.
I kind of like a sense of community even if it means losing out on personalisation.
Amazon recently kiboshed a new Stargate television series. The fan community was quite heartbroken because the Amazon had involved some of the writers from the original television series and apparently one of the reasons that they cancelled the show before it made it to production was that the felt that the proposed story would appeal too much to the fans and not new people.
The fans were real heart broken about this but I think you're right on where this is going. We're not going to be seeing the dominance of centrally produced content like this for much longer, like sure, I think there will be big blockbusters will stick around, but I think the day is coming where media becomes a choose your own adventure sort of scenario.
It'll be interesting to see where this scales to. there will definitely be some amazing solo projects but we'll also see the like 4 player co-op version of productions and then the larger mine-craft server 'Minas Tirith' scale ambitious projects that involve a few dozen people. And of course passive consumers will remain a thing, or people who just provide some suggestions or nudges for what they'd like to see others make.
But I don't think it'll be dominated by big companies like Disney, Netflix, Amazon or Paramount.
I'm sure a lot of them will suck but it'll be neat to see the inevitable Seinfield - Star Trek Voyager cross over episodes.
Elaine and B'Elanna Torres feud after a transporter accident leaves the crew stranded the delta quadrant. Jerry attempts to date 7of9 but is rebuffed as she finds Kramer's quirky bluntness more relatable. George panics after someone compares him to Neelix.
I still believe my bench is superior to Pelican or Karpathy’s.
Everyone knows how a MacBook looks. Any missing or incorrect detail will be obvious. However, Pelican or this world can be anything.
https://playcode.io/blog/macbook-svg-benchmark
Anyway, only Opus 5 Max and Fable 5 XHigh + Max make a good-looking Mac.