🚨🚔👮Woop-Woop! That's The Sound of Da Police! 🚔🚨 Legendary New York rapper KRS-One dropped "Sound of da Police" on December 6, 1993 — 32 years ago. Three decades later and it still hits the same. A track about excess police brutality and the link between the "overseer" and the officer. #GenZ# all around the world needs to hear the ORIGINAL anthem. Play this at the protests. Loud. Proof that some anthems don't age, because the problems havent!! #KRSOne# #GenZ# #CJP# #JantarMantar# #CJPProtest# #NEET# #ChaloSansad#

Plato: God ever geometrizes! Jacobi: God ever arithmetizes! Kronecker: God created the natural numbers, all else is the work of man! When Henry Briggs (1561–1630) died, his epitaph claimed that ‘his soul still astronomizes and his body geometrizes’.

Cantor was often misunderstood, both by his contemporaries and by later historians. Leopold Kronecker strongly opposed Cantor's work and called him a scientific fraud, a renegade, and a “corrupter of youth.” In contrast, Bertrand Russell considered Cantor one of the greatest thinkers of the nineteenth century. David Hilbert praised Cantor's work and believed it had opened new possibilities in mathematics. Henri Poincaré and others disagreed. They considered set theory and Cantor's theory of transfinite numbers a serious problem in mathematics that would eventually need to be corrected. Georg Cantor (1845–1918), the founder of transfinite set theory, was one of the most influential and controversial mathematicians in history. In the late nineteenth century, his work on continuity and infinity led him to develop new ideas that differed greatly from the traditional understanding of infinity in mathematics.

A video world model for robot manipulation that actually verifies whether the generated video faithfully follows the prescribed actions has arrived. DreamX-Phi 1.0: Action-Conditioned Video World Model for Robotic Manipulation Built on Wan2.2-TI2V-5B, this model predicts future frames with high fidelity from an initial observation, language instruction, and bimanual action trajectory. It ranked 1st among 31 teams on WorldArena 2.0 Track 1 (EWMScore-P: 60.65). 🔷 Highlight 1: PRoPE — Injecting SE(3) Directly into Attention Instead of compressing actions into generic tokens, the model injects end-effector positions, rotation matrices, and gripper states as SE(3) transformations directly into the attention mechanism. Attention heads are partitioned per arm, and token-wise transforms are applied via Kronecker products to eliminate dependence on global coordinate frames. This yields a remarkable controllability score of 98.55. 🔶 Highlight 2: Depth Branch + Object-Centric Supervision Beyond Visual Plausibility A two-pronged approach tackles the fundamental problem that RGB loss alone cannot constrain surface ordering or object extent. A lightweight depth branch (the final M blocks replicated with one-way cross-attention) enforces geometric consistency, while SAM3 masks combined with Gram matrix constraints from a frozen V-JEPA teacher preserve temporal coherence of manipulated objects — ensuring contact-local errors remain influential despite large static backgrounds. 🟣 Highlight 3: DMD Distillation Compresses Multi-Step into Few-Step Distribution-Matching Distillation (DMD) combining KL divergence and a non-saturating GAN loss drastically reduces inference steps. Trained on over 6,000 hours of diverse data spanning Ego4D, AgiBot World 2026, and RoboTwin 2.0, the model balances broad visual priors with precise action grounding. Robot world models have taken a decisive step from "looks realistic" to "moves correctly." #Robotics# #WorldModel#

World Currencies vs. the U.S. Dollar One-Year Change As of June 30, 2026: 🇨🇴 Colombian peso: +19.2% 🇮🇱 Israeli shekel: +13.2% 🇭🇺 Hungarian forint: +8.9% 🇿🇦 South African rand: +8.1% 🇲🇽 Mexican peso: +7.7% 🇨🇳 Chinese yuan: +5.6% 🇦🇺 Australian dollar: +5.2% 🇧🇷 Brazilian real: +5.2% 🇲🇾 Malaysian ringgit: +3.1% 🇳🇴 Norwegian krone: +1.8% 🇨🇱 Chilean peso: +1.0% 🇪🇬 Egyptian pound: +0.8% 🇭🇰 Hong Kong dollar: +0.1% 🇦🇪 UAE dirham: 0.0% 🇶🇦 Qatari riyal: 0.0% 🇸🇦 Saudi riyal: -0.2% 🇷🇺 Russian ruble: -0.4% 🇨🇿 Czech koruna: -1.1% 🇸🇬 Singapore dollar: -1.7% 🇨🇭 Swiss franc: -1.8% 🇹🇭 Thai baht: -2.3% 🇸🇪 Swedish krona: -2.4% 🇪🇺 Euro: -3.0% 🇩🇰 Danish krone: -3.1% 🇬🇧 Pound sterling: -3.4% 🇨🇦 Canadian dollar: -4.1% 🇵🇱 Polish złoty: -4.2% 🇳🇿 New Zealand dollar: -6.8% 🇹🇼 Taiwan dollar: -8.2% 🇵🇭 Philippine peso: -8.2% 🇮🇩 Indonesian rupiah: -9.3% 🇮🇳 Indian rupee: -9.4% 🇯🇵 Japanese yen: -11.3% 🇰🇷 South Korean won: -12.6% 🇹🇷 Turkish lira: -14.6% 🇦🇷 Argentine peso: -18.9% Source: Deutsche Bank, Bloomberg Finance LP.

Top 100 Assets in the World 💰 1. 🏠 Real Estate - $448.08 Trillion 2. 🛢️ Oil - $144.66 Trillion 3. 💵 Government Bonds - $117.03 Trillion 4. 🇨🇳 Chinese Yuan - $53.06 Trillion 5. 🪙 Gold - $30.34 Trillion 6. 🇺🇸 United States Dollar - $23.16 Trillion 7. 🇪🇺 Euro - $19.15 Trillion 8. 🧱 Copper - $19.04 Trillion 9. 🔥 Natural Gas - $9.43 Trillion 10. 🇯🇵 Japanese Yen - $8.14 Trillion 11. 🇺🇸 NVIDIA - $5.25 Trillion 12. 🇺🇸 Apple - $4.67 Trillion 13. 🇬🇧 British Pound - $4.41 Trillion 14. 🇺🇸 Alphabet - $4.24 Trillion 15. 🇺🇸 Microsoft - $3.81 Trillion 16. 🥈 Silver - $3.80 Trillion 17. 🇰🇷 South Korean Won - $3.04 Trillion 18. 🇺🇸 Amazon - $2.87 Trillion 19. 🇭🇰 Hong Kong Dollar - $2.76 Trillion 20. 🇦🇺 Australian Dollar - $2.49 Trillion 21. 🇹🇼 New Taiwan Dollar - $2.20 Trillion 22. 🇨🇦 Canadian Dollar - $2.05 Trillion 23. 🇹🇼 Taiwan Semiconductor Manufacturing Co. - $1.96 Trillion 24. 🇺🇸 SpaceX - $1.87 Trillion 25. 🇺🇸 Broadcom - $1.75 Trillion 26. 🇸🇦 Saudi Aramco - $1.68 Trillion 27. 🇷🇺 Russian Ruble - $1.61 Trillion 28. ₿ Bitcoin - $1.58 Trillion 29. 🇧🇷 Brazilian Real - $1.48 Trillion 30. 🇺🇸 Meta Platforms - $1.47 Trillion 31. 🇨🇭 Swiss Franc - $1.39 Trillion 32. 🇺🇸 Tesla - $1.38 Trillion 33. 🇰🇷 Samsung Electronics - $1.22 Trillion 34. 🇺🇸 Berkshire Hathaway - $1.08 Trillion 35. 🇺🇸 Micron Technology - $1.06 Trillion 36. 🇺🇸 Eli Lilly - $1.05 Trillion 37. 🇲🇽 Mexican Peso - $993.78 Billion 38. 🇺🇸 JPMorgan Chase - $950.62 Billion 39. 🇮🇳 Indian Rupee - $861.32 Billion 40. 🇰🇷 SK hynix - $847.31 Billion 41. 🇺🇸 Walmart - $820.40 Billion 42. 🇸🇦 Saudi Riyal - $818.45 Billion 43. 🇦🇪 UAE Dirham - $783.00 Billion 44. 🇵🇱 Polish Złoty - $781.73 Billion 45. 🇺🇸 Advanced Micro Devices - $760.05 Billion 46. 🇹🇭 Thai Baht - $742.83 Billion 47. 🇺🇸 Visa - $712.47 Billion 48. 🇸🇬 Singapore Dollar - $702.11 Billion 49. 🇻🇳 Vietnamese Đồng - $684.23 Billion 50. 🇳🇱 ASML Holding N.V. - $668.84 Billion 51. 🇲🇾 Malaysian Ringgit - $656.19 Billion 52. 🇺🇸 Johnson & Johnson - $645.95 Billion 53. 🇺🇸 ExxonMobil - $644.38 Billion 54. ⚪ Platinum - $596.14 Billion 55. 🇹🇷 Turkish Lira - $586.10 Billion 56. 🇮🇩 Indonesian Rupiah - $585.34 Billion 57. 🇸🇪 Swedish Krona - $545.39 Billion 58. 🇨🇳 Tencent - $524.76 Billion 59. 🇺🇸 Mastercard - $521.49 Billion 60. 🇮🇱 Israeli New Shekel - $510.61 Billion 61. 🇺🇸 Intel - $472.95 Billion 62. 🇺🇸 AbbVie - $451.46 Billion 63. 🇺🇸 Palantir Technologies - $447.67 Billion 64. 🇨🇳 Industrial and Commercial Bank of China - $436.06 Billion 65. 🇺🇸 Bank of America - $435.79 Billion 66. 🇺🇸 Oracle - $434.52 Billion 67. 🇺🇸 Cisco Systems - $433.28 Billion 68. 🇺🇸 Costco Wholesale - $419.38 Billion 69. 🇨🇳 China Construction Bank - $415.34 Billion 70. 🇺🇸 Chevron - $396.87 Billion 71. 🇳🇴 Norwegian Krone - $390.12 Billion 72. 🇺🇸 The Coca-Cola Company - $305.77 Billion 73. 🇺🇸 Lam Research - $377.77 Billion 74. 🇺🇸 Caterpillar - $367.65 Billion 75. 🇺🇸 Applied Materials - $366.38 Billion 76. 🇺🇸 Merck - $366.00 Billion 77. 🇨🇭 Roche - $362.92 Billion 78. 🇬🇧 HSBC - $356.71 Billion 79. 🇺🇸 General Electric - $356.63 Billion 80. 🇺🇸 GE Aerospace - $355.15 Billion 81. 🇺🇸 UnitedHealth - $352.71 Billion 82. 🇨🇿 Czech Koruna - $318.17 Billion 83. 🇺🇸 Netflix - $340.78 Billion 84. 🇺🇸 Morgan Stanley - $334.71 Billion 85. 🇺🇸 Procter & Gamble - $334.81 Billion 86. 🇺🇸 The Home Depot - $329.13 Billion 87. 🇵🇭 Philippine Peso - $325.51 Billion 88. 🇨🇴 Colombian Peso - $319.17 Billion 89. 🇨🇳 PetroChina - $310.47 Billion 90. 🇿🇦 South African Rand - $308.34 Billion 91. 🇺🇸 Palo Alto Networks - $307.98 Billion 92. 🇺🇸 Goldman Sachs - $301.57 Billion 93. 🇩🇰 Danish Krone - $300.31 Billion 94. 🇨🇭 Novartis - $300.25 Billion 95. 🇪🇬 Egyptian Pound - $299.84 Billion 96. 🇺🇸 Philip Morris - $299.08 Billion 97. ♦️ Ethereum - $297.52 Billion 98. 🇨🇳 Alibaba - $297.51 Billion 99. 🇨🇳 Bank of China - $296.65 Billion 100. 🇺🇸 Dell Technologies - $291.50 Billion Note: Commodities and Currencies are estimates. Source: AssetMarketCap