Certainly true; by "genetically gifted" I meant more that her face or body was granted some essential goodness that 99.99% of women lack.
That's just trivially true, isn't it? Among women who were already pre-selected to have similar faces, ages and BMI's to movie starts most of them can be made extremely attractive, with the help of right makeup, clothes, context and so on.
I would think so, but people seem to disagree!
I didn't select their photos because they were successful actors, I selected them because they're the celebrities most commonly cited as beautiful on the internet, and because they either appear at the top of popular surveys for most attractive women, or are the most viewed women on deepfake websites.
Of course, for any category of sex symbol you put up in a post like this - instagram models, regular models, onlyfans models, actors, singers - you're gonna get the response "Ah, but those aren't the prettiest women!" And, fair enough, but I suspect that if you or romeo left an example of a particular woman you find more attractive than Ana de Armas, you'd find that actually a large proportion of observers disagree with you. My thesis is not that you can't find a woman that you find significantly prettier than her, but that it's very hard to find a woman who broadly and significantly more appealing.
Also, I feel like what you and Romeo are saying is not actually incompatible with the broader point? It's a little like if I said that height was normally distributed, and as evidence I pointed out that Kareem Abdul-Jabbar was only 1.5 feet taller than the average human, and someone went "But the tallest person in human history, Robert Wadlow, was 8 foot 11 inches!" If "women more attractive than Ana" are so rare that they don't rise to the top of acting, music, porn, or modeling, and they're not generally the ones mating with the highest status men, then of what use is their attractiveness?
An enormous, unconscionable amount of information shared on Twitter/"TPOT" is like this. Plausible sounding anecdotes that get stretched and pixelated through legions of cross-platform and intra-platform quote-tweeting.
A good friend of mine works for a company called Outflank, where they basically develop "legal" malware for red teamers to use during sanctioned tests of organizations' security. He does not have a standard ML background, but for work he recently created an RL environment for training LLMs to generate shellcode that bypasses EDR/antivirus, which they use to great success. He wrote a blog post about it here and gave a related talk at DEFCON this month.
Normies probably underestimate the significance of being able to bypass EDR very quickly and cheaply. This is very critical security control in a large organization where you expect some proportion of your workforce to download malware at regular intervals. Training small models to do these kinds of tasks is possible on a shoestring budget well within the means of black hat organizations, and I expect them to start doing this sort of thing regularly within the next ~year or so.
I actually find that they do appear in the New York Times and other newspapers a lot.
He'd have to be insane or incredibly psychopathic
Unfortunately I think this is a misunderstanding of what a psychopath is.
Okay, but... why. Why do you think that. Is there a reason you think that, which other people could inspect your reasoning on, which is more viewable than unenumerated "complaints"? Again, I believe the complaints exist. How many, order of magnitude? Were they all from unique complainants?
I hate to be indelicate, but are you insane? It's a goddamn web forum, not the ICC. The mods got complaints about a users' behavior and they banned him. They can't run a focus group to see how everybody feels about the situation first.
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Disclaimer: I'm an AI YC founder from a recent batch (S24). I have no access to internal metrics about YC's portfolio, but I keep in touch with some startup founders from my batch, and we trade insights and metrics.
Anecdotally, my sense is that YC has done a particularly poor job of evaluating AI startups. Mostly this comes as a result of not taking AGI seriously, and YC's general philosophy of picking companies with impressive founders rather than evaluating ideas directly. YC has essentially bet as if AGI is a platform like smartphones and model quality is not going to improve, which at least for the last two years has been very wrong.
There are basically two ways to add value as an "AI wrapper" company. You can either:
Companies in the #1 bucket are the quintessential YC startup of the last few years, something like "An AI that can do your accounting". The way most of them tackle the problem is by breaking it down into pieces and doing context engineering. Investors love these startups because their potential TAM is huge ("all of accounting!"), they sound like they provide the possibility of deep technical moats, and the utility of the imagined solution is really obvious.
But if you look at the fastest growing companies of the AI era - Cursor, Lovable, Bolt, Windsurf - the majority of their utility comes from providing an interface for something the LLMs can do on their own. In some cases (Cursor, for instance) these companies started by offering custom flows for tasks and then deprecated them in favor of simple AI agents later.
This is because it's hard to get performance that's way better than what you get natively from the API. In some cases the scaffolding is important, but typically the models are either so good they can just OODA all by themselves, or else they're not good enough to get a startup to product market fit even with scaffolding. And because these models are improving all the time, even if your wrapper is really effective now, often the models improve quickly enough that all of the hard work you've done is moot.
As we get closer to AGI I expect these dynamics to start reversing the growth even some companies that have done well so far. For example, YC has funded like 5 PR review companies, including Greptile. In 2023, it was really easy to improve base model performance as a PR review company - just ingest the diff, search for related bits to what's being changed, and pull those bits into context. But now it's 2025, and, because PR review is a short-horizon task, it's been one of the first things to fall. I don't really know what these companies are going to do in the future other than hope their customers don't notice that the service they're getting is not that much better than what Claude Code can do for them directly.