Arguably the "social coding" angle is the reason why Tangled is the only actual alternative to GitHub. Otherwise there's actually plenty of other alternatives to GitHub, but none of them have GitHub's punchcard, which I'm ashamed to admit is the primary draw for me.
I'd like to know if most people use GitHub because of the social network part, or other reasons. I, for one, hate the social network part.
One thing with GitHub for open source projects is that the CI is free (and included macOS runners). The other is that many people have a GitHub account and would refuse to contribute to a project on another forge if it means that they must create a new account.
The federated design of Tangled solves the latter problem, so there's that.
I actually bought one of these and love it. My road bike is indistinguishable from any other normal road bike with the exception of it being much easier to get around now.
From experience I can say that it seems like a lot more common sense to simply replace the front wheel of your bike with a wheel containing an electric motor, rather than attaching whatever Asus is selling to your bike.
One theory I've been entertaining is that whenever GPT-3.5 came out a lot of people were talking about the "bitter lesson" and how scale was all we really needed to get to AGI. No need for any fancy tricks, just release a larger model trained on more data, by the time we released a hypothetical "GPT-5 sized" model we'd have AGI.
Anyway, the actual theory is that Google and Meta have fallen behind because they've been playing by this playbook of focusing on scale and training data, whereas OpenAI and Anthropic have done so well because they are likely doing much more interesting things to improve their models over time. It makes sense when you realize that one of Google's key strengths, besides talent, is that they have an incredible amount of data they can use for training due to being both the world's leading search engine as well as having all that video data from YouTube. Scaling the training data makes more sense to them than it does to Anthropic and OpenAI, who are both relatively data-disadvantaged.
You can kind of see this when you look at the Gemini 3 scorecard when it came out (https://blog.google/products-and-platforms/products/gemini/g...) and notice that while it wasn't as good as Claude And GPT at coding, it scored higher on a bunch of other non-coding benchmarks, and I think the reason why is simply because of Google's data advantage.
If true, I feel even more vindicated for believing that the "scale is all we need" narrative was bullshit.
Other than attention optimizations and other minor changes, the top Chinese models (which are way better than gemini) have basically the same architecture as GPT2. Of course RL is key for agentic workloads, but I'd say it's correct that progress has been mostly scaling models,adding more data and cleaning it better.
Google has a ton of smart researchers trying all sorts of stuff. They didn’t really go all in on any one thing. They hedge their bets.
But they suck at harnesses, developer tooling etc. their internal environment is very complex and an intern on a MacBook with codex can probably move faster than a seasoned deepmind engineer
> Anyway, the actual theory is that Google and Meta have fallen behind because they've been playing by this playbook of focusing on scale and training data, whereas OpenAI and Anthropic have done so well because they are likely doing much more interesting things to improve their models over time.
I always got the impression that the only way a relatively average software developer could make a “successful” SaaS is if they built something weird and niche that appeals to an audience of <1000 paying customers. In that sense, it doesn’t really matter if your competition is better at SEO since your competition never even thought to build something like this, never cared, and not to mention that the market for this thing is so small that SEO is arguably a wasted skill. You’ll need to acquire these people by finding them directly or through word of mouth.
This blog post seems to fundamentally misunderstand the nature of solo-developer SaaS, but then again arguably mostly software developers also fundamentally misunderstand it.
Finding out that this is over 10 years old has made me profoundly sad. Despite the age of LLMs arguably unlocking massive amounts of productivity and agency for developers and non-developers alike, it feels as though we are living in a dark age of creativity on the web, maybe even a dark age for computer culture in general.
I'm keenly aware, I have a pretty extensive collection of Hacker News bookmarks. It's hard to articulate why I think these are different, but I think the best way to put it is that cachemonet feels a lot more avant garde, and perhaps also a reflection of a very particular form of "web culture" that has no clear successors.
People are experimenting with what you can do on the web, but the experiments aren't very "aesthetically inspiring". For that reason I'm kind of lukewarm on neal.fun.
EDIT: so I think a better way to describe it is that when artists experiment with technology, you get something like cachemonet. When developers experiment with technology, you get a web experiment that challenges conventional notions of what you can do with the web, but with varying degrees of creativity. I think terra.layoutit.com is best appreciated by other web devs who can appreciate the sheer amount of work required to figure out how to render a terrain map in CSS, but otherwise it's basically just a tool to generate terrain height maps, and not a particularly good one. Generating terrain maps in CSS is not a feature, but a handicap.
I believe the demoscene is still ongoing, especially in France. Would love to understand why that is (French tech: parallel early teletype internet, high demoscene, more open approach to UFOs (GEIPAN) - French don't seem obviously "that different" to Americans, but there's obviously something different going on).
To me demoscene is kind of synonymous with the Amiga, so I would argue that peak demoscene lines up with the rise and fall of the Amiga brand.
So I think that's maybe the other differentiator between web experiment and art, because demoscene has a very distinct but difficult to describe cultural element that makes me identify it as art.
I posit that periods of relatively high creativity [ in art science music literature ] coincide with periods of relatively low inequality.
ie. if everyone is working so hard to pay rent / college, nobody has time to work on side projects in the garage, or go deep into books, or dedicate spare time to a craft or do down a science research rabbit hole.
Im not sure LLMs will free up much time for people in the middle of the economy - they might produce more but get paid the same.
I'm not sure if that's true. The Renaissance was peak creativity, but also high inequality - from peasants to the Medicis. Chinese and Japanese art seemed to flourish during wealthy imperial times, but decline during war, where the blender of chaos made people much more equal. Chinese art surged back in the last two decades in new modern forms.
Basquiat thrived during peak 1980s New York, and had a rags to riches trajectory, I think. Art is not generally something people get to "as a hobby" when they have time among normal life. The artist mindset is different: you need to do it. It's survival. Not about money. You have to express and create. You probably don't choose like other people.
The true creatives find a way with what they have. This is not to denigrate people who take up painting or photography as a hobby and often produce high quality stuff. It is to distinguish separate experiences. It's also to highlight that "great creativity" comes from a psychic imperative and visceral drive on part of the people who do it.
Ironically the physics are kind of my biggest criticism. They call these "world models", but I think it's more accurate to call them "video game models" because they employ "video game physics" rather than real world physics, among other things
It's getting better staggeringly fast, just a year ago I wouldn't expect it to be at even video game physics level so quickly.
If there is a possibility where it continue to improve at a similar rate with llms. A way to simulate fluid dynamics or structural dynamics with reasonable accuracy and speed can unlock much faster pace of innovation in the physical world. (And validated with rigorous scientific methods)
Numerical simulation is a well explored field, we know how to do all sorts of things, the issues lie rather in the tooling and robustness of it all put together (from geometry to numerical results) than in conceptual barriers. Finite Differences have existed since the 1700's! What hadn't for the longest time, is the computational power to crunch billions of operations per simulation.
A nice thing about numerical simulation from first principles, is it innately supports arbitrary speed/precision, that's in fact the backbone of the mathematical analysis for why it works.
In some cases, as is the case for CFD, we're actually mathematically screwed because you just have to resolve the small scales to get the macro dynamics. So the standard remains a kind of hack, which is to introduce additional equations (turbulence models) that steer the dynamics in place of the small (unresolved) scales. We know how to do better though (DNS), but it costs an arm and a leg (like years to milenia on a super computer).
I’m sure there’s some boring neuro-chemical explanation for this, and I won’t doubt or deny the neuro-chemical explanation, but the fact that there’s a mushroom that consistently brings about hallucinations of tiny people is so bizarre that I kind of want to indulge in equally bizarre explanations. Maybe it’s not a hallucination and this mushroom simply allows us to see the tiny people all around us. Maybe mushrooms are intelligent and are intentionally making us hallucinate tiny people.
It’s a little bit crazy, I know, but it’s odd to me that evolutionary forces would produce a mushroom that makes you have some specific hallucinations, rather than simply make things swirl together or simply produce intense feelings of euphoria or dread. I mean, marijuana just gets you high and that’s that.
Perhaps human-like creatures are so common in drug hallucinations because we're human, social animals, creatures who are maximally interested in other humans. If you gave drugs to dogs then perhaps they'd see human-like things mixed with dog-like things. I assume crocodiles, solitary animals, would see nothing besides wounded fish or maybe sexy female crocodiles.
Something I find weird about AI image generation models is that even though they no longer produce weird "artifacts" that give away that the fact that it was AI generated, you can still recognize that it's AI due to stylistic choices.
Not all examples they gave were like this. The example they gave of the word "Typography" would have fooled me as human-made. The infographics stood out though. I would have immediately noticed that the String of Turtles infographic was AI generated because of the stylistic choices. Same for the guide on how to make chai. I would be "suspicious" of the example they gave of the weather forecast but wouldn't immediately flag at as AI generated.
Similar note, earlier I was able to tell if something was AI generated right off the bat by noticing that it had a "Deviant Art" quality to it. My immediate guess is that certain sources of training data are over-represented.
We are just very sharp when it comes to seeing small differences in images.
I'm reminded of when the air force decided to create a pilot seat that worked for everyone. They took the average body dimensions of all their recruits and designed a seat to fit the average. It turned out, the seat fit none of their recruits. [1]
I think AI image generation is a lot like this. When you train on all images, you get to this weird sort of average space. AI images look like that, and we recognize it immediately. You can prompt or fine tune image models to get away from this, though -- the features are there it's a matter of getting them out. Lots of people trying stuff like this: https://www.reddit.com/r/StableDiffusion/comments/1euqwhr/re..., the results are nearly impossible to distinguish from real images.
What determines which “average” AI models latch onto? At a pixel level, the average of every image is a grayish rectangle; that's obviously not what we mean and AI does not produce that. At a slightly higher level, the average of every image is the average of every subject every photographed or drawn (human, tree, house, plate of food, ...) in concept space; but AI still doesn't generate a human with branches or a house with spaghetti on it. At a still higher level there are things we recognize as sensible scenes, e.g., barista pouring a cup of coffee, anime scene of a guy fighting a robot, watercolor of a boat on a lake, which AI still does not (by default) average into, say, an equal parts watercolor/anime/photorealistic image of a barista fighting a robot on a boat while pouring a cup of coffee.
But it is undeniable that AI images do have an “average” feel to them. What causes this? What is the space over which AI is taking an average to produce its output? One possible answer is that a finite model size means that the model can only explore image space with a limited resolution, and as models get bigger/better they can average over a smaller and smaller portion of this space, but it is always limited.
But that raises the question of why models don't just naturally land on a point in image space. Is this just a limitation of training, which punishes big failures more strongly than it rewards perfection? Or is there something else at play here that's preventing models from landing directly on a “real” image?
> At a pixel level, the average of every image is a grayish rectangle; that's obviously not what we mean and AI does not produce that.
That isn't correct since images in the real world aren't uniformly distributed from [0, 255] color-wise. Take, for example, the famous ImageNet normalization magic numbers:
If it were actually uniformly distributed, the mean for each channel would be 0.5 and the standard deviation would be 0.289. Also due to z-normalization, the "image" most image models see is not how humans typically see images.
The model "averages" in the latent space. That is in the space of packed image representations. I put "averages" into scare quotes, because I think it might be due to legal reasons. The model training might be organized in such a way as to push its default style away from styles of prominent artists. I might be wrong though.
Isn't the space you're talking about the input images that are close to the textual prompt?
These models are trained on image+text pairs. So if you prompt something like "an apple" you get a conceptual average of all images containing apples. Depending on your dataset, it's likely going to be a photograph of an apple in the center.
See the third diagram in https://www.mdpi.com/1424-8220/24/18/6049 . There are elements of noise, of input embeddings in the form of images, or in the form of text.
It's a bit odd to say, but another big clue identifying something as AI-generated is that it simply looks "too good" for what it is being used for. If I see a little info graphic demonstrating something relatively mundane, and it has nice 3D rendered characters or graphical elements, at this point it's basically guaranteed to be AI, because you just sort of intuitively know when something would've justified the human labor necessary to produce that.
Funny enough that had crossed my mind with the woodchuck example, because at a glance I can't see any weird artifacts, but I felt confident I could tell it was AI generated immediately if I saw it in the wild, and I couldn't really explain why. My immediate guess was "well, who the hell would actually bother to make something like this?"
I think it's because they're all trained on the same data (everything they could possibly scrape from the open web). The models tend to learn some kind of distribution of what is most likely for a given prompt. It tends to produce things that are very average looking, very "likely", but as a result also predictable and unoriginal.
If you want something that looks original, you have to come up with a more original prompt. Or we have to find a way to train these models to sample things that are less likely from their distribution? Find a way to mathematically describe what it means to be original.
An more original prompt wont fix things. Modern base models want to eliminate everything that puts their creators at risk, which is anything that is clearly made by someone else, more or less accurately reproducible. If you avoid decent representation of any artist style, or anything/anyone that is likely to go to court, you wont get the chance of an creative synthesis either.
If you want a chance for real creativity, flexibility and you have a decent gpu go local. Check out comfyui, download models and play around. The mainstream services have zero knobs to play around with, local is infinite.
It still has some artifacts more often than not, they are a lot subtler in nature but they still come out, whether it's texture, proportion, lighting, or perspective. Now some things are easier to fix on second pass edits, some are not. I guess it's why they consider image editing to be the next challenge.
We can also pick up hints on discordant production value. This is quite noticeable on websites such as Amazon/Alibaba/Etsy/Ebay/etc where there's a lot of scam listings that use AI images for cheap or basic items.
So even though the image shown doesn't present obvious flaws, the fact that the image is high quality is the tell-tale sign of being AI generated.
This also isn't something that can be easily fixed - even if we produce convincing low production value imagery using AI, then the scam listing doesn't achieve its goal because it looks like junky crap.
The problem is how they are fine tuned with human feedbacks that are not opinionated, so they produce some "average taste" that is very recognizable. Early models didn't have this issue, it's a paradox... Lower quality / broken images but often more interesting. Krea & Black Forest did a blog post about that some time ago.
Oh yeah, funny enough even though I’m a bit of an AI art hater I actually thought very early Midjourney looked good because of all had an impressionistic, dreamy quality.
I wonder if we'll get to the point where we train different personalities into an image model that we can bring out in the prompt and these personalities have distinct art/picture styles they produce.
I don't think it's solely an data issue. Flux models for example are quite stylized, very notable with photorealism. But I think it was an deliberate choice to to have outputs that are absent of likeness and distinct style. I think it's an side effect that it washes away fine details and creates outputs feel artificial. The problem is that closed models can't be fixed easily, while models like flux or even older architectures can add back details and style with fine tuning and LoRas.
Maybe the AI feeling is illusion because you already know it's AI-generated, just confirmation bias. Like wine tastes better after knowing it's expensive. In real world AI-generated images have passed Turing test. Only by double blind test do you can be really sure.
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