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Perfect way of doing Surya Namaskar Yoga…! 🔥 Start today 12 steps daily Lifetime of vitality.

This is a traditional Mongolian practice known as “camel coaxing” - That even a song has the power to heal✨ In the Gobi Desert, mother camels sometimes reject their calves, especially if the calf is weak or unusual. Mongolian nomads use a special type of singing, sometimes accompanied by the morin khuur, to “coax” the mother camel. As she listens, the mother gradually softens, becomes emotional, often shedding tears, and slowly accepts her calf 💖 This practice is not just a ritual; it reflects the Mongolian philosophy that humans and animals are connected through patience, understanding, and respect, rather than force.

Former Greek Finance Minister Yanis Varoufakis joined Mehdi Hasan for an insightful discussion, highlighting the dark history behind the United States' foundation. He stated, “How was the United States built? It was built by effectively carrying out a massive genocide of Native Americans.” Their conversation covered this long history of genocide, spanning from Europe to the US, among other important topics.

Our culture is rooted in science, and dhoti is a part of it.... So learn how to wear a proper dhoti.

The Cosmos doesn't just move planets; it composes symphonies. Watch Sun • Mercury • Earth • Jupiter weave this breathtaking geometric masterpiece through space Living proof of the sacred harmony and divine order governing our Universe.

OpenClaw is the single most important software release ever It is critical you use it to its max potential In this video I cover EVERY aspect of OpenClaw you need to know From set up to use cases to local models. EVERYTHING This is the only OpenClaw video you'll ever need:

Richard Feynman Explains Why GENIUS RAMANUJAN Got Math Answers In His Dreams. When I see equations, I see colors. The letter X is dark brown. The letter N is violet. I always thought that made me strange. Different. Then I learned about a man from India who died before I was even two years old. His name was Srinivasa Ramanujan. This man received mathematical formulas in his dreams. A goddess would write them on a screen of flowing blood. He would wake up and copy them down. No calculation. No derivation. Just... answers. Appearing in his sleep. And they were correct. Results that had defeated the greatest mathematicians in Europe he saw them while sleeping.

Fantastically well-researched maps on not just modern India but also ANCIENT India & beyond. Finally we can see the world of Panini, Chanakya, & the Golden Age of the Indosphere.

Chronological history of approximation of Pi

NYC psychiatrist Aruna Khilanani just tore apart racist narratives in one go 🔥 “White people are psychopathic. They steal countries. They stole yoga and vegetarianism. They steal everything and call it discovery. They say they discovered America. They actually got lost along the way. They were trying to go to India.” What a woman, absolutely spitting facts and how!

🚨 BREAKING: Researchers at UW Allen School and Stanford just ran the largest study ever on AI creative diversity. 70+ AI models were given the same open-ended questions. They all gave the same answers. They asked over 70 different LLMs the exact same open-ended questions. "Write a poem about time." "Suggest startup ideas." "Give me life advice." Questions where there is no single right answer. Questions where 10 different humans would give you 10 completely different responses. Instead, 70+ models from every major AI company converged on almost identical outputs. Different architectures. Different training data. Different companies. Same ideas. Same structures. Same metaphors. They named this phenomenon the "Artificial Hivemind." And the paper won the NeurIPS 2025 Best Paper Award, which is the highest recognition in AI research, handed to a small number of papers out of thousands of submissions. This is not a blog post or a hot take. This is award-winning, peer-reviewed science confirming something massive is broken. The team built a dataset called Infinity-Chat with 26,000 real-world, open-ended queries and over 31,000 human preference annotations. Not toy benchmarks. Not math problems. Real questions people actually ask chatbots every single day, organized into 6 categories and 17 subcategories covering creative writing, brainstorming, speculative scenarios, and more. They ran all of these across 70+ open and closed-source models and measured the diversity of what came back. Two findings hit hard. First, intra-model repetition. Ask the same model the same open-ended question five times and you get almost the same answer five times. The "creativity" you think you're getting is the same output wearing a slightly different outfit. You ask ChatGPT, Claude, or Gemini to write you a poem about time and you keep getting the same river metaphor, the same hourglass imagery, the same reflection on mortality. Over and over. The model isn't thinking. It's defaulting to whatever scored highest during alignment training. Second, and this is the one that should really alarm you, inter-model homogeneity. Ask GPT, Claude, Gemini, DeepSeek, Qwen, Llama, and dozens of other models the same creative question, and they all converge on strikingly similar responses. These are models built by completely different companies with different architectures and different training pipelines. They should be producing wildly different outputs. They're not. 70+ models all thinking inside the same invisible box, producing the same safe, consensus-approved content that blends together into one indistinguishable voice. So why is this happening? The researchers point directly at RLHF and current alignment techniques. The process we use to make AI "helpful and harmless" is also making it generic and boring. When every model gets trained to optimize for human preference scores, and those preference datasets converge on a narrow definition of what "good" looks like, every model learns to produce the same safe, agreeable output. The weird answers get penalized. The original takes get shaved off. The genuinely creative responses get killed during training because they didn't match what the average annotator rated highly. And it gets even worse. The study found that reward models and LLM-as-judge systems are actively miscalibrated when evaluating diverse outputs. When a response is genuinely different from the mainstream but still high quality, these automated systems rate it LOWER. The very tools we built to evaluate AI quality are punishing originality and rewarding sameness. Think about what this means if you use AI for brainstorming, content creation, business strategy, or literally any task where you need multiple perspectives. You're getting the illusion of diversity, not the real thing. You ask for 10 startup ideas and you get 10 variations of the same 3 ideas the model learned were "safe" during training. You ask for creative writing and you get the same therapeutic, perfectly balanced, utterly forgettable tone that every other model gives. The researchers flagged direct implications for AI in science, medicine, education, and decision support, all domains where diverse reasoning is not a nice-to-have but a requirement. Correlated errors across models means if one AI gets something wrong, they might ALL get it wrong the same way. Shared blind spots at massive scale. And the long-term risk is even scarier. If billions of people interact with AI systems that all think identically, and those interactions shape how people write, brainstorm, and make decisions every day, we risk a slow, invisible homogenization of human thought itself. Not because AI replaced creativity. Because it quietly narrowed what we were exposed to until we all started thinking the same way too. Here's what you can actually do about it right now: → Stop accepting first-draft AI output as creative or diverse. If you need 10 ideas, generate 30 and throw away the obvious ones → Use temperature and sampling parameters aggressively to push models out of their comfort zone → Cross-reference multiple models AND multiple prompting strategies, because same model with different prompts often beats different models with the same prompt → Add constraints that force novelty like "give me ideas that a traditional investor would hate" instead of "give me creative ideas" → Use structured prompting techniques like Verbalized Sampling to force the model to explore low-probability outputs instead of defaulting to consensus → Layer your own taste and judgment on top of everything AI gives you. The model gets you raw material. Your weirdness and experience make it original This paper puts hard data behind something a lot of us have been feeling for a while. AI is getting more capable and more homogeneous at the same time. The models are smarter, but they're all smart in the exact same way. The Artificial Hivemind is not a bug in one model. It's a systemic feature of how the entire industry builds, aligns, and evaluates language models right now. The fix requires rethinking alignment itself, moving toward what the researchers call "pluralistic alignment" where models get rewarded for producing diverse distributions of valid answers instead of collapsing to a single consensus mode. Until that happens, your best defense is awareness and better prompting.

This is the best Visual Explanation of how LLMs actually work

Sanskrit has >2000 verb roots (dhātus). But do you really need to learn them all? I had Claude analyze 270 Sanskrit texts, and it found that with just the 192 most common dhātus, you can understand ~90% of verbs in literature. Below are those 192 dhātus, ordered by frequency:

This is hugely important. Water is the big oil of this century. 👇

The irony is Fibonacci himself never claimed this as his invention. The mathematical foundations were already described centuries earlier by Pingala, Virahanka & Acharya Hemachandra. But West has a long tradition of repackaging older knowledge under its own names. The sad part is we Indians barely pushed back & happily studied that rewritten version of history.

If you read one article on the US-Israel war on Iran and its many risk factors - from binary to systemic, bounded to unbounded duration - read this one. 🙏

🚨 Someone just open sourced a full Perplexity AI clone. And it might actually be better. It's called Perplexica. A privacy-first AI search engine that runs entirely on your machine. Same cited sources. Same deep research. Zero data leaving your computer. You're paying Perplexity $20/month. This is free. Forever. No accounts. No tracking. No ads. No data collection. Just answers. Here's what this thing does: → Searches the entire web using SearxNG (a meta-search engine that hits Google, Bing, DuckDuckGo, and more at once) → Reads the top results, understands them, and gives you a cited answer with sources → 6 specialized focus modes: Academic papers, YouTube, Reddit, Wolfram Alpha, writing, and general web → Upload PDFs, text files, and images. Ask questions about them → Search specific domains when you know where to look → Image and video search built in → Full search history saved locally → Works with Ollama (100% local), OpenAI, Claude, Gemini, Groq, or any OpenAI-compatible API Here's the wildest part: One command to install. That's it. docker run -d -p 3000:3000 perplexica Open your browser. Go to localhost:3000. You now have your own private Perplexity. It even has a "Discover" feed that surfaces interesting articles throughout the day. Like a private, ad-free Google News powered by AI. You can set it as your default search engine in Chrome or Firefox. Replace Google entirely. Every search you've ever made on Perplexity? They have it. Every search on Perplexica? Only you have it. 27.7K GitHub stars. 2.9K forks. 744 commits. 44 contributors. 31 releases. Actively maintained. 100% Open Source. MIT License.

📢 Open-sourcing the Sarvam 30B and 105B models! Trained from scratch with all data, model research and inference optimisation done in-house, these models punch above their weight in most global benchmarks plus excel in Indian languages. Get the weights at Hugging Face and AIKosh. Thanks to the good folks at SGLang for day 0 support, vLLM support coming soon. Links, benchmark scores, examples, and more in our blog - sarvam.ai/blogs/sarvam-3…

World's Top 100 Biggest Economies in 2026 1. 🇨🇳 China - $43.49 Trillion 2. 🇺🇸 United States - $31.82 Trillion 3. 🇮🇳 India - $19.14 Trillion 4. 🇷🇺 Russia - $7.34 Trillion 5. 🇯🇵 Japan - $6.92 Trillion 6. 🇩🇪 Germany - $6.32 Trillion 7. 🇮🇩 Indonesia - $5.36 Trillion 8. 🇧🇷 Brazil - $5.16 Trillion 9. 🇫🇷 France - $4.66 Trillion 10. 🇬🇧 United Kingdom - $4.59 Trillion 11. 🇹🇷 Turkey - $3.98 Trillion 12. 🇮🇹 Italy - $3.82 Trillion 13. 🇲🇽 Mexico - $3.55 Trillion 14. 🇰🇷 South Korea - $3.49 Trillion 15. 🇪🇸 Spain - $2.94 Trillion 16. 🇸🇦 Saudi Arabia - $2.85 Trillion 17. 🇨🇦 Canada - $2.81 Trillion 18. 🇪🇬 Egypt - $2.53 Trillion 19. 🇳🇬 Nigeria - $2.39 Trillion 20. 🇵🇱 Poland - $2.12 Trillion 21. 🇹🇼 Taiwan - $2.07 Trillion 22. 🇦🇺 Australia - $2.06 Trillion 23. 🇻🇳 Vietnam - $1.94 Trillion 24. 🇮🇷 Iran - $1.93 Trillion 25. 🇹🇭 Thailand - $1.92 Trillion 26. 🇧🇩 Bangladesh - $1.90 Trillion 27. 🇵🇰 Pakistan - $1.76 Trillion 28. 🇵🇭 Philippines - $1.59 Trillion 29. 🇦🇷 Argentina - $1.58 Trillion 30. 🇲🇾 Malaysia - $1.56 Trillion 31. 🇳🇱 Netherlands - $1.56 Trillion 32. 🇨🇴 Colombia - $1.24 Trillion 33. 🇿🇦 South Africa - $1.06 Trillion 34. 🇦🇪 United Arab Emirates - $1.00 Trillion 35. 🇸🇬 Singapore - $988.8 Billion 36. 🇰🇿 Kazakhstan - $973.4 Billion 37. 🇷🇴 Romania - $949.3 Billion 38. 🇧🇪 Belgium - $925.7 Billion 39. 🇩🇿 Algeria - $915.8 Billion 40. 🇨🇭 Switzerland - $909.1 Billion 41. 🇮🇪 Ireland - $836.7 Billion 42. 🇸🇪 Sweden - $809.5 Billion 43. 🇨🇱 Chile - $740.4 Billion 44. 🇮🇶 Iraq - $739.1 Billion 45. 🇺🇦 Ukraine - $730.8 Billion 46. 🇦🇹 Austria - $705.0 Billion 47. 🇵🇪 Peru - $682.8 Billion 48. 🇨🇿 Czech Republic - $677.7 Billion 49. 🇳🇴 Norway - $621.1 Billion 50. 🇭🇰 Hong Kong - $618.1 Billion 51. 🇮🇱 Israel - $600.5 Billion 52. 🇵🇹 Portugal - $556.4 Billion 53. 🇪🇹 Ethiopia - $530.8 Billion 54. 🇩🇰 Denmark - $529.3 Billion 55. 🇺🇿 Uzbekistan - $511.0 Billion 56. 🇬🇷 Greece - $485.1 Billion 57. 🇭🇺 Hungary - $478.5 Billion 58. 🇲🇦 Morocco - $457.5 Billion 59. 🇰🇪 Kenya - $430.3 Billion 60. 🇦🇴 Angola - $417.2 Billion 61. 🇶🇦 Qatar - $410.6 Billion 62. 🇫🇮 Finland - $384.9 Billion 63. 🇩🇴 Dominican Republic - $353.7 Billion 64. 🇧🇾 Belarus - $319.5 Billion 65. 🇹🇿 Tanzania - $317.9 Billion 66. 🇪🇨 Ecuador - $315.9 Billion 67. 🇬🇭 Ghana - $314.6 Billion 68. 🇳🇿 New Zealand - $309.1 Billion 69. 🇬🇹 Guatemala - $297.1 Billion 70. 🇨🇮 Côte d'Ivoire - $289.1 Billion 71. 🇲🇲 Myanmar - $286.4 Billion 72. 🇰🇼 Kuwait - $285.9 Billion 73. 🇦🇿 Azerbaijan - $282.2 Billion 74. 🇧🇬 Bulgaria - $279.2 Billion 75. 🇸🇰 Slovak Republic - $266.9 Billion 76. 🇴🇲 Oman - $245.9 Billion 77. 🇻🇪 Venezuela - $231.4 Billion 78. 🇷🇸 Serbia - $225.6 Billion 79. 🇨🇩 Dem. Rep. of the Congo - $225.5 Billion 80. 🇵🇦 Panama - $211.0 Billion 81. 🇭🇷 Croatia - $207.4 Billion 82. 🇺🇬 Uganda - $205.3 Billion 83. 🇳🇵 Nepal - $194.9 Billion 84. 🇹🇳 Tunisia - $193.6 Billion 85. 🇨🇲 Cameroon - $183.3 Billion 86. 🇨🇷 Costa Rica - $178.0 Billion 87. 🇱🇹 Lithuania - $173.1 Billion 88. 🇵🇷 Puerto Rico - $166.3 Billion 89. 🇰🇭 Cambodia - $160.0 Billion 90. 🇹🇲 Turkmenistan - $159.0 Billion 91. 🇵🇾 Paraguay - $145.1 Billion 92. 🇿🇼 Zimbabwe - $144.9 Billion 93. 🇯🇴 Jordan - $138.0 Billion 94. 🇸🇩 Sudan - $135.9 Billion 95. 🇺🇾 Uruguay - $135.1 Billion 96. 🇱🇾 Libya - $132.8 Billion 97. 🇸🇮 Slovenia - $128.1 Billion 98. 🇬🇪 Georgia - $123.0 Billion 99. 🇧🇭 Bahrain - $118.1 Billion 100. 🇱🇺 Luxembourg - $108.6 Billion Note: GDP Figures Based on PPP (Purchasing Power Parity) Source: IMF via Voronoi by Visual Capitalist