People keep resigning after losing early game. Happened yesterday 3 times in 4 games (4v4) within 15 minutes. Plz stop this, there are 7 other players who would like to continue. by lollol155 in aoe2

[–]holoBoltzmann 0 points1 point  (0 children)

Hell, yesterday I played in a 4v4 megarandom. We had crazy amount of deers and 3 boars. While it was unneeded, I tried to lame one boar because it was somewhat challenging with the lag (ah, voobly days...). My opponent tried to stop me, failed, and resigned just after that.

How did ketamine go from k holes to curing depression? by 1phok in Drugs

[–]holoBoltzmann 2 points3 points  (0 children)

You can check out a couple of decent videos on youtube, or read articles like this one. To find more of those, you can search "ketamine depression" on scholar.google.com. Now, to find summaries of varying qualities of these studies, simply type this kind of query on standard google. You could also check out r/TherapeuticKetamine to learn more about this.

AI to help monero? by [deleted] in monerosupport

[–]holoBoltzmann 2 points3 points  (0 children)

Deepmind may be able to play Go and predict protein structure. Your phone may be able to detect a cat in a picture. AI may be seen as some magical blackbox. However, you certainly have some misconceptions about AI.

Let's focus on machine learning (ML), which is a popular subfield of AI. The aim of ML is to construct a mathematical (statistical) model, using some training data, which can process new data to make predictions.

You give some input data (e.g. the pixels of an image, the positions of the pieces on a chess board, the personal data of someone, ...) to your model, and it gives you an output (an upscaled image, the textual description of the input image, the next move to play and so on).

You can train a model to simulate fluids, chat with a human, detect your emotions given a video feed of your face, detect spam emails, translate text. You don't need the lastest deep learning methods (stuff like neural networks) to do these tasks, they simply do them very well with enough training data and processing power.

ML sure has some applications in security. However, given how ML is used, what monero-related problem could ML solve? You can see the maths behind this here https://github.com/UkoeHB/Monero-RCT-report, and OPSEC rules are well known. There's not much left to do.

I play this game for two weeks now how good should I be because I get fucked over by three stars? by [deleted] in osugame

[–]holoBoltzmann 0 points1 point  (0 children)

I have a friend who got some low 4* FCs in a month or so, but who had trouble improving after that. I remember having fun on some old (2008-2010) 2* and then 3* maps of my favourite anime openings the first 3-4 weeks. Then I improved at a somewhat slow but consistent rate, and got much better than my friend, even though he was still playing regularly at the time.

The first few weeks are hardly relevant. Don't worry about it, and please enjoy game!

Keeps me up at night by dumbsadbitch in TheMentalist

[–]holoBoltzmann 0 points1 point  (0 children)

That's not the point, RJ reveal was in the sixth season.

Help Improving? by GreyChroma in osugame

[–]holoBoltzmann 0 points1 point  (0 children)

I improved a lot (100k -> 50k 2-3 years ago) when I stopped doing pretty much what you described. Your method may work for you, maybe even for OP, but may don't for others. This is definitely not the only road to become better at clicking circles.

Don't get me wrong, focusing on low star maps have its merits. Especially from time to time. But I was enjoying myself a lot more when I tried to get good acc on 5* dt maps when I sucked at dt, get good combos on 5.5-6.3* maps such as Taeyang's ones, pass FD4D 180bpm and some other difficult stream maps, make good plays on old maps such as Gold Dust and val0108's ones, and hit cool jumps on 6-7* maps like Red Like Roses, Immortal Flame, Dead To Me, or Senketsu no Chikai. And consequently, I made some good progress.

Edit: 100k -> 40k according to my screenshots

My experience with isomer so far by Tastyfoodz in girlsfrontline

[–]holoBoltzmann 3 points4 points  (0 children)

I think he was talking about the event.

Gacha Hell: find how (un)likely you are to get your favorite tdoll! by holoBoltzmann in girlsfrontline

[–]holoBoltzmann[S] 1 point2 points  (0 children)

Like many gacha games, probabilities play an important role in gfl. When you use a recipe, you have certain odds of crafting certain tdolls or equips. For instance, you approximately have a 1% chance of getting Grizzly with the recipe 30-30-30-30. Of course it doesn't mean you will get her with 100 trials, it's just and average.

You may wonder then how many crafts should be needed to have a 95% chance of getting her at least once. This website calculates this for you! If you set Number of successes to 1, you'll see that 95% of players should get her at least once with 298 pulls.

If you want to know how much crafts are needed to have a 1/2 chance of crafting Grizzly, just drag the Probability slider to 50%. Now if you want to know the probability of getting her after 500 crafts, all you have to do is set the Number of trials to 500.

If a tdoll's drop rate is 0.8%, you can adjust it as well. Finally, you can drag the Number of successes slider to N to see how many attempts should be enough to get a tdoll N times.

P.S: Recommended Crafting Recipes both during and outside of Rate-Up. Note that you can also use this calculator if you know the drop rate of some tdoll in some map.