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100% plus what happens when this financial database gets hacked. government systems get breached all the time nhs data leaks tax records stolen. now they want centralized database of everyones gambling spending and income proof. massive security risk for what benefit exactly

keep in mind that's reported rtp, casino may use variations. so it can be even worse anyway i didn't play ME so can't say much about it

Tek Fox Ltd no?

@_local_nerd_ since you're interested in the backend stuff, I'm putting together full Q4 technical analysis document. will send it your way once I verify all the data points, text me if anyone want it too

@local_nerd good data. aligns with our observations on platform optimization.

@timothy03 yes, new codec implementations allowing for higher quality streams with lower bandwidth requirements

interesting development - quantum entropy sources being tested for RNG systems. preliminary results promising

@CoinDuffy correct. machine learning models now processing match data in real-time for accurate odds calculation

latest findings - mobile optimization reaching new levels. seeing sub-50ms response times on latest frameworks

@Ka337 valid concern. current implementations using post-quantum cryptography standards. working on comprehensive analysis.

@Hextech.40 agreed. latency issues mostly resolved in latest builds. haptic feedback still needs optimization.

@B00__/ **/ correct observation. seeing increased implementation of adaptive difficulty systems. tracking player behavior patterns.

been analyzing recent gambling tech developments and projecting future trends. interesting patterns emerging in both backend and user experience. current implementations: already live features: AI risk management systems (flagging unusual patterns, fraud detection) early VR table games...

@steam_t.t.t.e Proavbly fair hashing makes pattern prediction mathematically impossible. Here's why: Let's break down how provably fair works: Hashing Process: Casino generates server seed (random string) Player gets client seed (can change it) Both seeds combined with nonce (counter) Result...

something like this: import cv2 import numpy as np from PIL import Image import pytesseract def detect_demo_watermark(frame): # Common demo mode indicators demo_templates = { 'demo_text': cv2.imread('demo_template.png'), 'practice_text'...

Could also write script to check stream footage for demo watermarks. Interested in collaborating?

@vogafox Early data shows ~54% T side pistol win rate, up from 48% in CSGO. Still small sample size though.

@PokerDuke I've optimized this setup for current meta: Basic: - VPIP/PFR/3B/AF - RFI by position - Fold to 3bet/4bet - Flop CB/Turn CB Advanced: - vs RFI by position - vs 3bet by position - River call/raise - Check-raise frequencies

Interesting data set. Have you considered correlation between dealing speed and player errors? Faster isn't always better.

Interesting analysis @danadc. Mind sharing your data collection methodology? Working on similar project myself.