Troof⁶𓅓 @GoatedTruth

Him / Himothy • computer engineering • i hoop, code, stream, and scam my viewers twitch.tv/goatedtruth Joined May 2013

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Troof⁶𓅓 @GoatedTruth

a month ago

@inmontwetrust @xmenesports Bumbaclotttt

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Troof⁶𓅓 @GoatedTruth

2 months ago

@Alvini_Linguine I literally said this and made a video about it years ago

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kanYe Funnies @Kanye_Funnies

6 months ago

No special effects btw he can just do that

culture jpeg @culturejpg

6 months ago

The “I Wonder” promo video ft Kanye West teasing his upcoming album Graduation

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Troof⁶𓅓 @GoatedTruth

8 months ago

People are using AI to incite violence and riots… block these trolls. We need a social media that auto-detects GenAi content and deepfakes ASAP

NRM84 @Mappy6984

8 months ago

Not guilty... thank you for your service

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These are all proven anti-cheat methods used in FPS and competitive shooters today. They’re realistic, lightweight, and don’t harm legit players. The comp community loves this game — we just want fair basketball again. 🏀❤️ Happy to help further if needed. 🙏🏾

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Troof⁶𓅓 @GoatedTruth

8 months ago

Hi @Beluba — I’m a competitive Pro-Am player in #NBA2K26 and also work in AI/ML. Zens/Titans are causing unnatural shooting patterns (identical release timing down to the millisecond). This is detectable. Here is a thread of all solutions I could think of to combat this.

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Troof⁶𓅓 @GoatedTruth

8 months ago

5) No Need for RNG on Everyone’s Shots Instead of forcing randomness into EVERY player’s greens… Just detect and apply additional randomness to flagged unnatural patterns only. This keeps the skill gap intact and the game fair.

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Troof⁶𓅓 @GoatedTruth

8 months ago

4) ML Model (my favorite) Train a lightweight model on shot telemetry: •Input timing variance •Stick angle noise •Shot success vs contest •Session-to-session consistency It learns the difference between human randomness and scripted automation, for button and tempo.

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Troof⁶𓅓 @GoatedTruth

8 months ago

2) Server-Side Shot Validation Move the final green/make calculation server-side. Client still performs animations, but the server checks whether the input pattern is human-like. This removes guaranteed greens from scripted timing.

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Troof⁶𓅓 @GoatedTruth

8 months ago

1) Detect Timing Anomalies Human players have natural micro-variance in shot release timing. Zen/Titan scripts produce near-zero variance across hundreds of shots. A threshold-based detector can flag this easily. Example: if micro_variance < X ms over Y attempts → suspicious

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Troof⁶𓅓 @GoatedTruth

9 months ago

@SC0REGOD @choc I don’t understand why you people under the man threads to randomly rage bait. I don’t even care for the dude but he’s made agreeable points all 2k26 and you bots are trolling unprovoked like get a life 😂