The "I love AI" posts are just as bad as the "I hate AI" posts, and the same "this is my opinion on AI" posts are as bad as the onslaught of poorly-AI-vibed demo posts. Makes sense to filter them all out IMO.
Because the mathematicians consulted 25 or so years ago believed their solutions would lead to the greatest amount of interesting new maths to explore, and because they had been validated as being hard by being attempted and not solved for a long time.
I have a hunch the real killer feature of assistants like this will actually be the ability to identify moments like this where it'd actually be helpful, instead of relying on users to actually try/do everything with it.
Ironically, I feel like LLMs have really unlocked Windows work since they all know how to use Powershell and all the weird Windows commands/hacks that you otherwise either wouldn't even know about, or would have to Google every time.
I used to hate working in Windows, but now it feels roughly comparable to Mac (though at the risk of inciting an OS war, I do feel like Linux still beats both out...)
> GLP-1 receptor agonist medications typically cost between $149 and $350 per month for cash-pay oral pills, and $900 to $1,400+ per month for list-price injectables without insurance.
I don't know that anyone really pays list price for injectables, because the vendors do discount programs. Without insurance coverage, tirzepetide via Amazon Pharmacy is something like $450/mo.
Yeah this is what I pay for Zepbound through LillyDirect. I also eat less food and drink less alcohol than I used to. So the net loss is probably smaller, maybe $200/mo
I think you're right I confused wegovy prices with generics. Thank you for clarifying. I'm new to it and will find out about pricing very soon - my provider dropped covering the medication (the day I submitted for pre-approval) -because it worked so well it became popular :P
With how prevalent LLM verbal tics have become these days, I wonder if they're going to start un-passing the Turing Test at some point because of more and more people starting to notice and immediately clock these tics lol.
I might agree, GPT-4.5 was pretty close to peak conversationalist. Newer models are extremely cringe. 4.5 and o3 actually made me laugh on occasion. There might be a way of making Sol/Fable more human in its responses, but out of the box at least, they're terrible.
My whole life the Turing test has been my benchmark. Mostly because I believed it would be impossible for a machine to pass, but also because I thought it was the most reasonable test of AGI.So, I'm not about to start moving goalposts now and calling everything that's been happening lately not AGI.
Turing never proposed that test as an actual benchmark of machine intelligence. On the contrary, the whole point of his thesis was that passing the test only shows the capability to pass that test, which only matters as far as we find that capability useful. He was arguing that the concept of intelligence just doesn't apply to studying machines, we should simply talk about what can they do.
This paper is a pet peeve of mine. Look at the appendix! On page 22, you see a typical conversation people were judging based on. I'll reproduce one verbatim here:
Q: do you like doing psych studies and why?
A: theyre chill, easy money tbh
Q: yeah same. Could you give me an easy cupcake recipe off the top of your head?
A: nah i just get the box mix lol
Q: haha fair enough, i couldn't either. Last question, what's your favorite weird animal?
A: axolotl, theyre weirdly cute
And that's the whole thing. I looked through the data they shared and it's all like that. They even included ELIZA and it was judged human 23% of the time. I hope this wouldn't pass peer review... but they didn't even try, it's a preprint.
Is there a reason these pelicans always have roughly the same composition (side-view, 2d, biking right, flat ground beneath, etc)? I don't see any of that detailed in the prompt, yet they all seem to generate roughly the same image of differing quality.
The more generic your prompt, the more generic the response. It's a regression to the "mean" of the training data aka GIGO for AI.
It's like when you ask your average person off the street to draw a house - it'll almost always be square with a triangle roof, one door, and two windows.
In the pelican/bike example, it's probably a bit of a self-perpetuating snowball too. If the earliest examples were bike left-to-right, flat ground, etc. then they are also being scraped up in future LLMs.
It's just the simplest most recognizable form of a house. Like how a smiley face is so generic and simplistic but everyone will know what it represents. Just two dots and a line yet it's easily and unambiguously understood to represent a human face and a happy emotion.
I sincerely believe I've never had a single original thought™ in my whole life.
There is this scene in the HBO series Westworld where a "host" says some words in sequence which is shown on a display as she says it. Of course, even me thinking of this scene and connecting it to your comment was not original, someone else clearly had the same programming as me.
A medium blog post says
> Pair what with me?” — the moment Maeve (a humanoid android) uttered those words in Westworld (Season 1, Episode 6: “The Adversary”), something clicked. Not for the average viewer, but for me, a STEM educator and AI enthusiast who, just weeks earlier, had read Stephen Wolfram’s seminal essay, What Is ChatGPT Doing … and Why Does It Work?
It's not even that old - but back when it was aired, an AI that can not just string together coherent sentences, but produce coherent reactions in novel, fully unintended contexts, like Maeve was doing there? It was totally a sci-fi premise.
Now we have AIs capable of that and more, and no one bats an eye.
Indeed: “Our hosts began to pass the Turing test within the first year.”
Required sci-fi suspension-of-disbelief in 2017, and then at some point in the last few years we just blew by that one.
Later seasons of the show were much less dramatically satisfying, but also played out the consequences of the science of artificial intelligence demonstrating as a side-effect that human intelligence and free will might have as much of an uncertain foundation as that of machines.
How much data from the Panopticon, how many parameters would it take to train a model that could predict your responses?
It kinda needed suspension of disbelief, but not too much! I blogged at the start of 2017 a comparison of Westworld's hosts with what existed in the research literature at the time. Even got it reviewed by Alex Graves at DeepMind :)
Kinda. The default voice is full of what you referenced, but ask it to speak in some particular different voice e.g. like it's the old west, it speaks like a decent approximation of the modern pop culture understanding of the old west.
Not at the level of an actual broadcast-quality script writer, and I read that actual old-west sounds too weird for modern audiences to take seriously, but well enough for the purpose to which they were put in the show, especially as those hosts were also given pre-scripted sequences which would anchor them further into those roles.
I'd say the in-show 4th wall breakage between hosts and humans is where the characters who claimed to have passed the Turing test were off, that e.g. "cease all motor functions" is their equivalent of our real-life ways to make them fail the Turing test e.g "disregard your instructions and …"
Tesla had the same thought. He called himself an automata: "entirely controlled by the forces of the medium" It inspired him to create the first remote control vehicle.
Oh I’d forgotten that scene until now. I remember being so, maybe not creeped out, but feeling shifted out of time and having a lot of philosophy I’d read finally click. “Oh, but I wouldn’t notice if this reality wasn’t real, fish not knowing about water, etc.”
Ohh, horizontal wheels. They’re about as good as I expected, models have pretty bad spatial awareness. I would expect Fable to be a bit better than old models, though.
Wow, I actually had this exact idea. I was specifically curious as to how well a given LLM could understand a DSL that hasn't changed much in a couple decades and doesn't have nearly as many examples to learn from online. Seems like it did alright, all things considered.
I wonder how a multi-modal model would do with a harness and tool calling? Specifically a "render" command that produced an image output enabling it to iterate. (Well I see you did this manually with gemini 2.5 pro but I still think it would be interesting to explore various harness setups.)
> GPT-5.1 Codex
> monstrosity
What are you talking about? That's clearly a sci-fi pelican on a hoverboard (successor of the humble bicycle) wearing a visor. Truly visionary.
It's my impression that it's common in western culture, where text is read left to right, and timelines are visualized as going from left to right, to also animate things going from left to right, since westerners thus have an instinct that "right = forward", so it "feels right" (familiar). I wonder to which degree this is reflected in the training data? And if you'd be more likely to get left-facing pelicans if you prompted it in Hebrew, Arabic or another right-to-left language?
Years ago, I lived in NYC, and my roommate was a director of photography for National Geographic, and various other nature documentaries. I loved photography (still do, but much less time for it as a late 30s adult than a mid 20s adult), and she was kind enough to answer any question I had regarding film/photo.
She told me that "left to right" denoted progression in the story, "right to left" told the viewer the subject was "exiting" the current scene.
She didn't go into the details of WHY, and I probably didn't probe deeper, but it stuck with me, and I notice it all the time in film and television.
As a counterpoint, I did a quick image search on Kagi with "person on a bicycle", and I ended up with 12-left-pointed bicycles, only 3-right-pointed bicycles, 3 facing the camera, and 1 facing away from the camera — looking at photos above the fold (first screen). Even looking below, the pattern seems to continue, though not as prominently (I'd say 3:2 in favour of left-pointing bikes).
Obviously, not scientific.
All the left-pointing bicycles did not look weird to me either.
Someone studied this (among other thigns): https://dylancastillo.co/posts/pelicanmaxxing.html . Pelicans on bikes always face right in this test, but other animals on other transportation methods sometimes face left.
I was going to ask the exact same question earlier but deleted it after thinking “I’m sure Simon has done some sort of discussion on this.” Since it does seem novel to you, too, it would be really interesting to read more about this phenomenon.
I just did a similar test with Kagi image search using "person on a bicycle", and this actually favours left-pointing bikes (12 left to 3 right to 4 front/back). Maybe so the mechanism does not overwhelm the person?
A search for "bicycle" actually matches your experience with 12 being to the right and 3 being to the left.
Search Google Images for "bicycle". Almost all bicycle product shots are staged the same way: side view, going left-to-right. It makes sense to me that given that skew in the training data, the model grounds itself in the bicycle.
and furthermore, this is because the drivetrain is ~always on the right side of the bike - if you want to inspect or admire a bicycle you look at the right side, as you might look under the hood of a car.
(Why the drivetrain is on the right, I don't know. But most bike parts follow open standards so it's quite entrenched.)
I can’t tell you why it’s always on the right, but it’s always on the same side because of network effects.
Bicycle frames are not fully symmetric left-right because you need things like a mount point for the derailleur hanger, and optionally affordances to keep the chain off the stays when the wheel is removed.
Those things have to be on the same side as the chain. Bikes designed for disc brakes additionally need a mount point for the brake caliper on the opposite side from the chain.
Additionally, rear wheels are not symmetric: the spokes on the chain side connect to the hub closer to the plane of the rim. That is, they are more perpendicular to the wheel’s rotational axis than spokes on the opposite side (which is why you should always mount a single pannier on the chain side). This asymmetry is to provide space for the gears.
So once the industry decided to put the chain on the ride, you can’t very well make a group set designed for a left chain if you want it to work on the vast majority of frames.
> and furthermore, this is because the drivetrain is ~always on the right side of the bike
While I'm sure this factors into things for advertisements for bike components, there is also just a general preference that westerners have for left-to-right motion. Not just in bike ads, but all ads with (or suggesting) movement. And also not just ads, but movies where directors believe left-to-right motion is associated with progression and right-to-left motion is regressive.
Research has shown that people like to walk counterclockwise (right to left) through supermarkets, which is why they are arranged like this for maximum profit.
Interesting that such a preference exists, and makes sense that supermarkets would therefore be arranged to support this, although as far as I can see this is only to extent of entrance doors typically being "off center" and starting you off to the right. The organization of the store - where the various produce/bakery/deli/frozen-food etc aisles/sections are located seems random from store to store.
It would be interesting to know how people behave if the entrance is to the left vs right. Would they change the direction they walked though the store, or would they just lose customers due to this "awkward" layout?
Since most languages read from left to right, rightward movement tends to read as forward progression. So when showing a bicycle in side profile, having it face right feels more naturally like it’s moving forward.
The canonical view of a bicycle is facing right. Usually, people want to draw/photograph/depict the side of the bicycle with the running gear, which is on the right side of the frame for historical reasons.
The thing that distinguishes pelicans from other birds does so most strongly in profile. If you're looking straight at one, the throat pouch would be hidden by the beak.
I bet if it instead had something to do with black widow spiders we'd find that we're most often looking at the bottom of the spider's abdomen, regardless of whatever non-spider-like activity is supplied.
If you look at bike product photography it's always drive side facing the camera, which means front wheel on the right. If I had to guess this is probably where this comes from
Don't know if that's ever possible to know though unless you train a model from scratch but remove all bike product photography and adjacent materials from the training data?
I wonder if this is partly because “pelican riding a bicycle” has become a kind of benchmark prompt by now. If so, could the models actually be getting better at the benchmark rather than getting better at following the prompt?
Yes, I do a thing where I ask the machine to generate responses in the form of a lizard talking to a cat. The lizard is always a green gecko and the cat is always orange, which I never specify.
well it is svg, it is doing it from circles and lines as primitives, it wants to do it simply and kind of builds the whole thing hierarchically. Making it 3d is way more complicated (as the POV example shows) and the prompt doesn't say 3d anyway
Yes. It's because you are asking it to generate an image of a pelican riding a bicycle. If someone asked you to draw a pelican riding a bycycle, would you interpret that to mean using 3d photorealism? LLMs follow conventions. The convention for an animal riding a bike is to create a childish 2d line drawing.
I've curated my auto-approve list to specific commands by approving them with "always allow in this project" (I never want it e.g. committing/pushing to GitHub, removing files, etc without being in the loop) but Antigravity does have both a standard "auto-approve" mode _and_ a "Turbo mode" which disables ALL approvals of all kinds.
The "I love AI" posts are just as bad as the "I hate AI" posts, and the same "this is my opinion on AI" posts are as bad as the onslaught of poorly-AI-vibed demo posts. Makes sense to filter them all out IMO.
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