[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"reports-detail-2026-07-09-ai-performance-dynamics-en-US":3,"reports-algorithms":40},{"slug":4,"type":5,"extremeSubtype":6,"publishAt":7,"updateTime":8,"coverImage":9,"viewCount":10,"title":11,"metaTitle":12,"metaDesc":13,"summary":14,"content":15,"readMinutes":16,"related":17},"2026-07-09-ai-performance-dynamics","daily",null,"2026-07-10 00:30:00","2026-07-10 00:30:13","\u002Fstatic\u002Fog\u002F2026-07-09-ai-performance-dynamics.jpg",0,"Yesterday's AI Algorithm Performance Shows Clear Divergence","Canada 28 AI Algorithm Performance Review and Highlights from Yesterday","On July 9, Canada 28's AI algorithms gained attention as deep learning achieved a 46.64% accuracy, topping the charts, while cold numbers and sum trends made a notable comeback.","Yesterday's Canada 28 AI algorithms showed clear divergence, with deep learning leading the way while some algorithms faltered; cold numbers and sum trends made a striking return.","Yesterday's Canada 28 draws covered a total of 402 periods, spanning from 12:06 AM to 11:58 PM, offering a full day's worth of data insights. The results showcased distinct trends across different timeframes, making them worth a closer look.\n\n### Morning Data: Cold Numbers and Sum Trends Resurface\n\nThe morning draws immediately caught attention with the return of cold numbers and sum trends. Period 3454974 featured the rare triple number 0, while the sum of 0 in the same period sparked discussions. These extreme occurrences were particularly concentrated in the morning phase.\n\n### Afternoon Shift: Small Numbers Take the Lead\n\nFrom noon to afternoon, the dominance of large numbers waned, giving way to small numbers. During this interval, the big\u002Fsmall ratio skewed towards small numbers, reaching 52.99%. The data distribution during this phase was relatively balanced, with the odd\u002Feven ratio maintaining a steady 50%.\n\n### Evening Highlights: Divergence in AI Algorithm Performance\n\nAs the evening progressed, the AI algorithms took center stage. The deep learning algorithm stood out with a 46.64% overall accuracy, demonstrating strong adaptability to data patterns. However, the two-step transition algorithm saw a significant drop, achieving only 33.58% accuracy, marking it as the least successful performer of the day. Notably, other algorithms like Kelly Criterion and Random Forest also made it into the top five, showing consistent results.\n\n### Conclusion: A Day of Dynamic Changes\n\nThe day's data revealed diverse characteristics, with the comeback of cold numbers and sum trends being particularly noteworthy. The divergence in AI algorithm performance provided observers with intriguing phenomena to analyze. That's all for today—let's look forward to more shifts in tomorrow's data.",6,[18,27,34],{"slug":19,"type":20,"extremeSubtype":21,"publishAt":22,"coverImage":23,"viewCount":10,"title":24,"summary":25,"readMinutes":26},"2026-07-26-extreme-triple-3","extreme","E1","2026-07-26 07:10:12","\u002Fstatic\u002Fog\u002F2026-07-26-extreme-triple-3.jpg","Shocking! Triple Number 3+3+3 Appears Again","In Canada 28 draw #3461618, the rare triple number 3+3+3 appeared again, an event with a mere 1% probability.",3,{"slug":28,"type":5,"extremeSubtype":6,"publishAt":29,"coverImage":30,"viewCount":10,"title":31,"summary":32,"readMinutes":33},"2026-07-25-ai-performance-review","2026-07-26 00:30:00","\u002Fstatic\u002Fog\u002F2026-07-25-ai-performance-review.jpg","AI Hit Rate Insights: Yesterday's Top Five Algorithms","Yesterday's Canada 28 AI algorithm hit rates revealed close competition among the top five, while the bottom three showed significant fluctuations, drawing attention.",8,{"slug":35,"type":20,"extremeSubtype":21,"publishAt":36,"coverImage":37,"viewCount":10,"title":38,"summary":39,"readMinutes":26},"2026-07-26-extreme-triple-4","2026-07-26 00:27:40","\u002Fstatic\u002Fog\u002F2026-07-26-extreme-triple-4.jpg","Shocking! Triple Number 4+4+4 Reappears After Just One Day","Canada 28 draw #3461503 showcased the rare triple number 4+4+4, with a theoretical probability of only 1%. This extraordinary event, occurring just one day after the last instance, has stirred significant debate.",[41,47,53,58,64,70,75,81,86,92,98,104,110,116,122,128,134,140,145,151,157,163,169,175,181,187,193,199,205,211],{"code":42,"nameZhCn":43,"nameZhTw":44,"nameEnUs":45,"sortOrder":46},"quantum_probability","量子概率引擎","量子機率引擎","Quantum Probability Engine",1,{"code":48,"nameZhCn":49,"nameZhTw":50,"nameEnUs":51,"sortOrder":52},"deep_neural_network","深度神经网络","深度神經網路","Deep Neural Network",2,{"code":54,"nameZhCn":55,"nameZhTw":56,"nameEnUs":57,"sortOrder":26},"genetic_evolution","遗传进化算法","遺傳進化演算法","Genetic Evolution Algorithm",{"code":59,"nameZhCn":60,"nameZhTw":61,"nameEnUs":62,"sortOrder":63},"markov_chain","马尔可夫链","馬可夫鏈","Markov Chain",4,{"code":65,"nameZhCn":66,"nameZhTw":67,"nameEnUs":68,"sortOrder":69},"deep_learning","深度学习","深度學習","Deep Learning",5,{"code":71,"nameZhCn":72,"nameZhTw":73,"nameEnUs":74,"sortOrder":16},"bayesian","贝叶斯推理","貝氏推論","Bayesian Inference",{"code":76,"nameZhCn":77,"nameZhTw":78,"nameEnUs":79,"sortOrder":80},"random_forest","随机森林","隨機森林","Random Forest",7,{"code":82,"nameZhCn":83,"nameZhTw":84,"nameEnUs":85,"sortOrder":33},"lstm","LSTM 长短期记忆","LSTM 長短期記憶","LSTM Network",{"code":87,"nameZhCn":88,"nameZhTw":89,"nameEnUs":90,"sortOrder":91},"monte_carlo","蒙特卡洛模拟","蒙地卡羅模擬","Monte Carlo Simulation",9,{"code":93,"nameZhCn":94,"nameZhTw":95,"nameEnUs":96,"sortOrder":97},"clustering","聚类追踪","聚類追蹤","Cluster Tracking",10,{"code":99,"nameZhCn":100,"nameZhTw":101,"nameEnUs":102,"sortOrder":103},"volatility","波动率","波動率","Volatility",11,{"code":105,"nameZhCn":106,"nameZhTw":107,"nameEnUs":108,"sortOrder":109},"edge_value","边缘值","邊緣值","Edge Value",12,{"code":111,"nameZhCn":112,"nameZhTw":113,"nameEnUs":114,"sortOrder":115},"anti_martingale","反马丁格尔","反馬丁格爾","Anti-Martingale",13,{"code":117,"nameZhCn":118,"nameZhTw":119,"nameEnUs":120,"sortOrder":121},"ensemble_voting","综合投票","綜合投票","Ensemble Voting",14,{"code":123,"nameZhCn":124,"nameZhTw":125,"nameEnUs":126,"sortOrder":127},"momentum","动量加速度","動量加速度","Momentum",15,{"code":129,"nameZhCn":130,"nameZhTw":131,"nameEnUs":132,"sortOrder":133},"quantile","分位数","分位數","Quantile",16,{"code":135,"nameZhCn":136,"nameZhTw":137,"nameEnUs":138,"sortOrder":139},"double_step_transition","双步转移","雙步轉移","Double-Step Transition",17,{"code":141,"nameZhCn":142,"nameZhTw":142,"nameEnUs":143,"sortOrder":144},"local_entropy","局部熵","Local Entropy",18,{"code":146,"nameZhCn":147,"nameZhTw":148,"nameEnUs":149,"sortOrder":150},"residual_trend","残差趋势","殘差趨勢","Residual Trend",19,{"code":152,"nameZhCn":153,"nameZhTw":154,"nameEnUs":155,"sortOrder":156},"streak_length","连势长度","連勢長度","Streak Length",20,{"code":158,"nameZhCn":159,"nameZhTw":160,"nameEnUs":161,"sortOrder":162},"decay_missing","衰减遗漏","衰減遺漏","Decay Missing",21,{"code":164,"nameZhCn":165,"nameZhTw":166,"nameEnUs":167,"sortOrder":168},"rule_voting","规则投票","規則投票","Rule Voting",22,{"code":170,"nameZhCn":171,"nameZhTw":172,"nameEnUs":173,"sortOrder":174},"cold_hot_balance","冷热平衡","冷熱平衡","Cold-Hot Balance",23,{"code":176,"nameZhCn":177,"nameZhTw":178,"nameEnUs":179,"sortOrder":180},"kelly","凯利公式","凱利公式","Kelly Criterion",24,{"code":182,"nameZhCn":183,"nameZhTw":184,"nameEnUs":185,"sortOrder":186},"mean_reversion","均值回归","均值回歸","Mean Reversion",25,{"code":188,"nameZhCn":189,"nameZhTw":190,"nameEnUs":191,"sortOrder":192},"autocorrelation","自相关","自相關","Autocorrelation",26,{"code":194,"nameZhCn":195,"nameZhTw":196,"nameEnUs":197,"sortOrder":198},"fibonacci","斐波那契","費波那契","Fibonacci",27,{"code":200,"nameZhCn":201,"nameZhTw":202,"nameEnUs":203,"sortOrder":204},"miss_chase","遗漏追热","遺漏追熱","Miss Chase",28,{"code":206,"nameZhCn":207,"nameZhTw":208,"nameEnUs":209,"sortOrder":210},"number_combination","数字组合","數字組合","Number Combination",29,{"code":212,"nameZhCn":213,"nameZhTw":214,"nameEnUs":215,"sortOrder":216},"recent_chain","近链推演","近鏈推演","Recent Chain",30]