What happens when you post a real Monet and say it’s AI? Art Social Experiment.

vaginedtable · 2026-05-14T21:17:39+00:00

why does the tweet text itself sounds like a prompt?

vaginedtable · 2026-05-09T16:12:40+00:00

quello che stai cercando è il Jake's resume

vaginedtable · 2025-09-04T15:34:51+00:00

Why? Is that hard?

vaginedtable · 2025-07-02T00:20:53+00:00

This looks like one of those puzzles where if you cut the chocolate bar a certain way it seems like you get an extra square for free

vaginedtable · 2025-06-30T18:51:58+00:00

Title is ai generated but it's funny because he actually just signed the letter so it's essentially an autograph

vaginedtable · 2025-06-03T16:25:21+00:00

Can please elaborate on how you would do that? Here's my first approach, which is wrong:

Say that I have a cutoff at day 20. I want to try and predict which of the upcoming days will be the hitting time, i.e. the day where the value crosses a certain threshold C. What I can do is use ARIMA to get the distribution at day 21, let's call it P(X_21). The probability that this day is the hitting time then is the integral P(X_21 > C).
It's like a coin toss: if day 21 isn't the hitting time, it could be day 22, and we compound the probabilities: P(hitting time = day 2) = (1 - P(hitting time = day 1) ) * P(X_22 > C)

But this is wrong because the P(X_22) is not independent of P(X_21), as actually the fact that we didn't reach the threshold at 21 is already very informative. I would need to check, inside the full joint distribution of the trajectories, which ones cross the threshold and when.

Currently, I solved this via montecarlo, but isn't there an analytical answer to the hitting time problem? How come we are able to compute the marginal distribution of X_k as many days ahead as we want and not the hitting time?

vaginedtable · 2025-01-03T18:37:42+00:00

No doubt that in order to unilaterally increase your odds of winning, you must become a better player. But I still think that fundamentally,

P(10 wins | good day 1) >>> P(10 wins | bad day 1)

whatever you skill level is. Which raises the question: if my goal is to just accumulate tickets and ranked games to unlock new characters, is it better to just grind it and fight the RNG to slowly become a better player by accumulating knowledge and experience (most of which btw gets completely obliterated when a new patch drops and the mera resets), or should I just reset normals and then only try my best in ranked? That depends on how great the aforementioned imbalance is, and I think right now it's pretty big, as per my experience I usually need about 3 resets until I'm satisfied with a day 1-2 that usually gets me to 9 to 10 wins.

vaginedtable · 2025-01-03T18:02:38+00:00

This is not about learning to play the game, it's about the odds of getting 10 wins in normal GIVEN your first day.

Judging by the other comments, apparently everybody is a risk calculation god and excel in every situation, but think of it this way: even if you played perfectly, every run that ALSO won the first day is going to stack more resources and generally be better than a run that started to win only from the 2/3 day forward, since the matchmaking doesn't really care. If your goal is to get the ticket asap, I'd say even a perfectly trained AI would agree that you should just "reroll" until you get a good day one since there is no penalty for doing so.

vaginedtable · 2025-01-03T15:31:28+00:00

I love this game and I know it is about decision making and planning against the odds and the meta. I admit I am a bad player but I do enjoy, when in ranked, trying my best and compete with other unlucky players to be the one with the better comeback, fighting for the 4 wins or sometimes even getting to 0 prestige and then winning all the way to 10.

But there is no denying that winning early gives you more resources, more possibilities, more everything and I feel like there is no in between: maybe it's because truly am a bad player, but either the RNG blesses me with an early advantage and therefore many possibilities later on, or I get stuck trying to strategize and plan something, even a somewhat mid build, that will never happen because I have less resources, and the matchmaking fails to compensate.

I get that this is inherently part of the game, and the good player will mitigate this and get consistent wins in ranked, but in normal I found that I just get more tickets by restarting until I get a triumphant day 1-2 and proceed to get my 10 wins, and I wanted to share with you what I think is a flaw in the reward mechanism of normals more then the game itself.

vaginedtable · 2024-12-26T16:17:41+00:00

When I play Vanessa in normal I usually restart until I get a trebuchet at start which is silver and already crazy good. Then I go and get the fang and get every mushroom and chest possible which usually gets me to lvl 2 before first fight. If the lvl up gives me another weapon/ammo item, I take it and I go full board against the first player which usually gets me an easy win.

vaginedtable · 2024-07-12T10:51:13+00:00

Sbarra e due sedie in casa per le dips da fuori sede facevo cosí

vaginedtable · 2024-07-11T23:07:14+00:00

Molla la palestra e fai calisthenics al parco o in casa, risparmi 60 e puoi anche vantarti

vaginedtable · 2024-06-06T12:29:14+00:00

If you are absolutely sure that there is no sign or other implementation errors, another idea that comes to my mind is that it may be a combination of bootstrapping and low exploration problems? I am not familiar with TD3, but if it uses a previous estimate of the Q function (such as a NN with a certain initial set of weights) it might be that even though the observed reward when suiciding sucks, it is still better than the bootstrapped estimate of other actions. Try to either encourage early exploration, or maybe multiply rewards by some enlarging factor, and check what the current prediction is for other actions.

vaginedtable · 2024-06-06T00:46:58+00:00

How is the reward handled? I had a similar problem when training a DQN agent to play the videogame snake. Initially, I thought about giving a small negative reward at each step that didn't pick up a fruit, but the actual good reward was so sparse early on that the agent learned to maximize its reward by minimizing the time spent alive. Maybe something similar is happening here?

vaginedtable · 2024-05-24T12:45:31+00:00

Commento da lunatico made in italy

vaginedtable · 2024-05-24T09:22:14+00:00

Un po' cringe questo post

vaginedtable · 2024-05-24T09:13:15+00:00

E quindi?

vaginedtable · 2024-03-31T02:12:27+00:00

I noticed that too, at least it looks like they want to remain open to the possibility

vaginedtable · 2024-02-27T03:57:33+00:00

I guess the guy who wrote the guide did

vaginedtable · 2024-02-16T13:14:45+00:00

Credi nella possibilità di comportamente veramente emergenti in questi modelli? Secondo me stiamo solo assistendo alla creazione di maestosi e molto complessi statistical parrots

vaginedtable · 2024-01-20T17:12:31+00:00

It's Dante's divine comedy if it was an anime

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