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Boobs vs Butt as Random guy Reveals His Preference during @asherclan Street Reaction Interviews... 📺 via YouTube #asherclantv ⭐️model Shay Coxx | YOUR NEW FAVORITE HOT MOM for MilfCandy
15,635 görüntüleme • 10 ay önce •via X (Twitter)
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NEW VIDEO Just Dropped! Turns out this same Chinese Principal from a China secondary school wasn’t just raging at security guards in Singapore… he was also leading vulgar “games” on the tour bus with students during the study tour! 🇸🇬 Li Cheuk Hing (the man caught screaming “Shut up!”, “Fuck your mother!”, and sticking his tongue out at female guards) apparently loves turning lowbrow, crude antics into “fun” for the group. Honest take: Keeping him as a school principal is a massive waste of his talents. A guy with this unique skill for low-class vulgarity should switch to insurance sales immediately. Western friends — picture those sleazy corporate team-building sessions where insurance managers force cringy, dirty jokes and games on everyone, then act like they’re hilarious motivators. This principal would be perfect as an Upline “inspirer.” He’d fit right in, run the same trashy games with his downlines, and probably crush MDRT rankings. Let the Chinese students go free. Time for him to quit education and “shine” in the insurance world instead! Disgracing Chinese people abroad again 😡 What kind of role model behaves like this on a school trip? #Singapore #China #Viral #SchoolPrincipal #BadEducation
The Great Translation Movement 大翻译运动
28,150 görüntüleme • 2 ay önce
![I think I can finally report some success training a quite accurate IDM capable of recovering keystrokes from Minecraft gameplay, even in quite PvP-heavy situations. At this point the model does not only know what keys are pressed to the extent reasonably discernible, it also knows how fast it is moving in 3D space at all times, even when knockback is mixing with the self-move impulse. Now, recovering keystrokes from normal external capture footage is just about impossible. E.g. W/A/S/D does exactly nothing during partial tick frames and jumping mid-air is also equally useless, so asking the model to recover key down states is inherently unreasoanble. Mouse deltas are also completely arbitrary units, as game mouse sensitivity introduces an arbitrary scale factor into the equation. The only good option is to think carefully about your model-environment contract, and only record "logical actions", not raw keystrokes. So here's a few unfortunate lessons I had to learn in roughly this order. - Choose good units. (bad: mouse deltas, good: delta radians [yes, you will need game-internal state]) - Capture from inside the main game loop and read the game fbo to get consistent frame-action pairing. Doing post-mortem pairing is hopeless. - Carefully define when you think keystrokes actually have an effect. (jump only works on ground, when flying or in water etc.) More subtle: The key may already be down, but no tick has happened yet to actually use the value. Hence: ignore Seperate gamestate into "fast and slow-moving" components. E.g. movement is likely tick based, camera rotation is very likely updated every frame in essentially every game ever. - Think about your frame-action correspondance contract (How old is the frame in relation to the inputs you capture? Will double or tripple buffering affect you?) Think about the game loop timeline, where you are sampling, how old the data you are reading is, and where the ticks are happening around you. Language models used to simply not have a model-environment contract, but even now with the model "living" in a designated harness, the contract still boils down to formatting, and tool implementation intrinsics. While also important, it is still quite a bit more obvious because the violations are in some way shape or form reflected as text you can actually see. - ffmpeg dropping frames cummulatively screws the model the further you get into the sequence because your targets are now shifted. If you can't encode the video in real-time, too bad. - Sodium has a frames in flight system different from vanilla Minecraft, which will also offset your targets from your frames. (there goes that data...) - Models are succeptible to latency. If there is too big of a delay between action and on-screen reflection, your performance degrades. At this point I realize ~100hours of gameplay is essentially no longer usable as a dataset. You can train on this data, but all you'll get is a mushy mess. However, some good news: - Making the model predict physics gamestate scalars helps the model generalize. For instantaneous events like jump, it's unreasonable to ask the model emit a short burst of jump=true at exactly the right time, however if you also predict your current y-velocity, the model has supervision signal for the "latent" from which that onground jump becomes apparent. Recovering x/z motion is also somewhat easier than unmixing it into plausible keystrokes for inertia-heavy player controller logic. - Regressing physics gamestate scalars also seems to make your dataset "bigger". While pure keystroke classification will overfit quickly, predicting exact physics gamestate scalars forces the model to generalize more and you can tolerate far more epochs before validation loss starts to stall out. This is the only reason why it was bearable to dump 100h+ of dataset hours and replace it with ~3 hours of gameplay after the 4th revision of the file format (yeah...) and somehow still have better performance. Now, you might be asking, "isn't this brittle?" and the answer is yesn't. Frame-action correspondance matters for training, but not so much during inference. So as long as you are sampling in roughly the same interval as your training data, you aren't violating any hard contract per-se. Somewhere around the frames ticks are happening, and during training you capture various tick-capture offset relations per random chance, so nothing is too obviously wrong here. HOWEVER, you will get screwed by gui scale, shaders, resource packs, "shit that recording is 1920x1040 because somebody doesn't know fullscreen exists" and other unfortunate edge cases of reality. But I suppose this is the role of dataset size. If all those "contract violations" that a youtube video has compared to the training data are addressed, I think this is a way to turn Youtube into a labeled dataset. I could never shake the feeling that VPT is a sound idea in practice, while never having been properly executed, and I think one reason why it hasn't is because that label boostrapping part is just a pain in the butt to get right. Now, what the player is doing is of course not the only label you can extract from video, but it has to be one of the targets predicted during pretraining to "align" the pretraining objective. Some notes on the video here, the colored dots on the analog visualizer are the ground truth, while the gray dot is the model prediction. Green means correct prediction, red means incorrect prediction at that frame. Model P(key) reports how wrong the prediction is from green (0.0) to red (1.0). You will also notice that during periods of rapid slow down, left and right actions become close to irrecoverable, because there is just that little motion. And some jump actions are not predicted correctly because I got the detection condition for jump events wrong... (duh) LMB/RMB for other than sustained events (like item-consume and block break) also seem to be hopelessly irrecoverable for now. Swing was supposed to do the same thing as motion y did for jump, but its too well behaved as an increasing counter. Maybe partial-tick interpolated values work better (v5 file format then... ugh..)](https://image.24vids.com/tw-2042731804780826849/amplify_video_thumb/2042721691844120576/img/yugYQfuXXQmJpdVp.jpg)
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Funny how women get so pissed when guys go full agenda mode on a date. She handled it like a cunt with no class after the guy’s blunder but she was also mirroring where the guy was coming from in terms of energy: If the man is going to be as overtly transactional in his delivery he could have gone for an escort and he could have saved himself time and money. But no he did not and he was indecisive. She does not want to make the decision. “Are you going home with me or no?” And if you wanna bring a girl back home: Don’t give her a choice, lead the way. This is an example of how “nice” he was. Key points - Don’t take her out to a place you can barely afford. Paying for the date is the cost of doing business. - Don’t be fake nice just to try to get the coochie, she will smell it - he revealed himself to be as such by his reaction. She won’t give it up to you because you paid a meal and was nice. - If you invite her for dinner, you are playing on harder mode, because you frame yourself default as the provider, and you will deal with more BS (even non-dinner dates you will deal with more nonsense vs meeting her on a night out or on holidays - her environment will dictate her default rules of engagement). - Don’t respond logically, troll her with what she gives you at this stage when it is completely fucked: instead of “that is how it is supposed to be” - she does not give a fuck about your rules - reply with “Cool, I will message Jessica” / instead of “I am not going to cry home” reply to her verbal jabs with “quite the challenge considering your crying for paying the full bill, but thank you that meal was really nice” Relationships are transactional, but it is not because it is that you have to be so overt about, it only creates resentment for both men and women. This is the male example of agenda mode, that women do often trying to hijack dates into interviews, and amusingly enough, it is hilarious to see how pissed off they are about it.
French OG | Your Dating and Relationship Guide
285,267 görüntüleme • 7 ay önce