Data Visualization

💇‍♂️ Men’s Haircut Price: 🇳🇴 Norway: $64.50 🇯🇵 Japan: $56.00 🇩🇰 Denmark: $48.21 🇸🇪 Sweden: $46.13 🇦🇺 Australia: $46.00 🇺🇸 USA: $44.00 🇨🇭 Switzerland: $42.96 🇫🇷 France: $37.05 🇰🇷 South Korea: $36.94 🏴󠁧󠁢󠁥󠁮󠁧󠁿 England: $35.74 🇩🇪 Germany: $35.39 🇦🇹 Austria: $35.06 🇫🇮 Finland: $31.03 🇮🇪 Ireland: $26.90 🇨🇿 Czechia: $25.69 🇨🇦 Canada: $24.28 🇵🇱 Poland: $23.54 🇵🇹 Portugal: $23.40 🇮🇹 Italy: $23.21 🇪🇸 Spain: $21.68 🇸🇦 Saudi Arabia: $20.34 🇿🇦 South Africa: $18.27 🇷🇺 Russia: $16.66 🇨🇱 Chile: $16.41 🇬🇷 Greece: $14.79 🇧🇾 Belarus: $14.52 🇭🇺 Hungary: $13.92 🇰🇿 Kazakhstan: $11.71 🇺🇦 Ukraine: $11.10 🇨🇴 Colombia: $10.65 🇲🇽 Mexico: $9.66 🇻🇳 Vietnam: $8.98 🇧🇷 Brazil: $8.67 🇩🇿 Algeria: $5.96 🇮🇳 India: $5.29 🇵🇰 Pakistan: $4.44 🇦🇷 Argentina: $3.15 🇳🇬 Nigeria: $1.83 🇿🇲 Zambia: $1.65 Note: in capital cities According to NetCredit

📃 Most Viewed Wikipedia Pages in 2025 (July 1, 2024 – June 30, 2025)

People die every hour 🪦 (selected countries): 🇨🇳 China: 1,221 🇮🇳 India: 1,069 🇺🇸 United States: 332 🇳🇬 Nigeria: 313 🇮🇩 Indonesia: 238 🇷🇺 Russia: 198 🇵🇰 Pakistan: 181 🇯🇵 Japan: 180 🇧🇷 Brazil: 167 🇩🇪 Germany: 108 🇧🇩 Bangladesh: 105 🇨🇩 DR Congo: 104 🇲🇽 Mexico: 99 🇪🇹 Ethiopia: 88 🇻🇳 Vietnam: 78 🇪🇬 Egypt: 78 🇵🇭 Philippines: 75 🇿🇦 South Africa: 74 🇮🇹 Italy: 72 🇺🇦 Ukraine: 71 🇬🇧 United Kingdom: 70 🇫🇷 France: 70 🇹🇭 Thailand: 63 🇲🇲 Myanmar: 55 🇮🇷 Iran: 51 🇹🇷 Turkey: 51 🇪🇸 Spain: 50 🇵🇱 Poland: 47 🇰🇪 Kenya: 46 🇹🇿 Tanzania: 43

🌏 The Best Countries for Food in the World 1.🇹🇭 Thailand – 98.33 2.🇮🇹 Italy – 96.92 3.🇯🇵 Japan – 96.77 4.🇻🇳 Vietnam – 96.67 5.🇪🇸 Spain – 95.91 6.🇳🇿 New Zealand – 95.79 7.🇱🇰 Sri Lanka – 95.56 8.🇬🇷 Greece – 95.42 9.🇿🇦 South Africa – 94.76 10.🇵🇪 Peru – 94.55 11.🇲🇻 Maldives – 94.55 12.🇨🇴 Colombia – 94.29 13.🇲🇦 Morocco – 94.00 14.🇫🇷 France – 94.00 15.🇹🇷 Turkey – 93.88 🖇️ Source: Condé Nast Traveller, 2025 Readers’ Choice Awards

Where Do Employees Work Longer Than 9 to 5

Countries with the highest fertility rates on the planet: 1. 🇹🇩 Chad - 5.9 births per woman 2. 🇸🇴 Somalia - 5.9 3. 🇨🇩 DRC - 5.9 4. 🇨🇫 Central African Republic - 5.8 5. 🇳🇪 Niger - 5.8 6. 🇲🇱 Mali - 5.4 7. 🇦🇴 Angola - 5.0 8. 🇧🇮 Burundi - 4.7 9. 🇦🇫 Afghanistan - 4.7 10. 🇲🇿 Mozambique - 4.6 11. 🇲🇷 Mauritania - 4.6 12. 🇹🇿 Tanzania - 4.5 13. 🇧🇯 Benin - 4.4 14. 🇾🇪 Yemen - 4.4 15. 🇳🇬 Nigeria - 4.3 16. 🇸🇩 Sudan - 4.2 17. 🇨🇲 Cameroon - 4.2 18. 🇨🇮 Côte d’Ivoire - 4.2 19. 🇹🇬 Togo - 4.1 20. 🇺🇬 Uganda - 4.1 According to UN World Population Prospects 2024

Sometimes reality outpaces expectations in the most unexpected ways. While global AI development seems increasingly fragmented, Sber just released Europe's largest open-source AI collection—full weights, code, and commercial rights included. ✅ No API paywalls. ✅ No usage restrictions. ✅ Just four complete model families ready to run in your private infrastructure, fine-tuned on your data, serving your specific needs. What makes this release remarkable isn't merely the technical prowess, but the quiet confidence behind sharing it openly when others are building walls. Find out more in the article from the developers. GigaChat Ultra Preview: 702B-parameter MoE model (36B active per token) with 128K context window. Trained from scratch, it outperforms DeepSeek V3.1 on specialized benchmarks while maintaining faster inference than previous flagships. Enterprise-ready with offline fine-tuning for secure environments. GitHub | HuggingFace GigaChat Lightning offers the opposite balance: compact yet powerful MoE architecture running on your laptop. It competes with Qwen3-4B in quality, matches the speed of Qwen3-1.7B, yet is significantly smarter and larger in parameter count. Lightning holds its own against the best open-source models in its class, outperforms comparable models on different tasks, and delivers ultra-fast inference—making it ideal for scenarios where Ultra would be overkill and speed is critical. Plus, it features stable expert routing and a welcome bonus: 256K context support. GitHub | Hugging Face Kandinsky 5.0 brings a significant step forward in open generative models. The flagship Video Pro matches Veo 3 in visual quality and outperforms Wan 2.2-A14B, while Video Lite and Image Lite offer fast, lightweight alternatives for real-time use cases. The suite is powered by K-VAE 1.0, a high-efficiency open-source visual encoder that enables strong compression and serves as a solid base for training generative models. This stack balances performance, scalability, and practicality—whether you're building video pipelines or experimenting with multimodal generation. GitHub | Hugging Face | Technical report Audio gets its upgrade too: GigaAM-v3 delivers speech recognition model with 50% lower WER than Whisper-large-v3, trained on 700k hours of audio with punctuation/normalization for spontaneous speech. GitHub | HuggingFace Every model can be deployed on-premises, fine-tuned on your data, and used commercially. It's not just about catching up – it's about building sovereign AI infrastructure that belongs to everyone who needs it.

World's Top Economies and 10 Years of Growth

Sometimes reality outpaces expectations in the most unexpected ways. While global AI development seems increasingly fragmented, Sber just released Europe's largest open-source AI collection—full weights, code, and commercial rights included. ✅ No API paywalls. ✅ No usage restrictions. ✅ Just four complete model families ready to run in your private infrastructure, fine-tuned on your data, serving your specific needs. What makes this release remarkable isn't merely the technical prowess, but the quiet confidence behind sharing it openly when others are building walls. Find out more in the article from the developers. GigaChat Ultra Preview: 702B-parameter MoE model (36B active per token) with 128K context window. Trained from scratch, it outperforms DeepSeek V3.1 on specialized benchmarks while maintaining faster inference than previous flagships. Enterprise-ready with offline fine-tuning for secure environments. GitHub | HuggingFace GigaChat Lightning offers the opposite balance: compact yet powerful MoE architecture running on your laptop. It competes with Qwen3-4B in quality, matches the speed of Qwen3-1.7B, yet is significantly smarter and larger in parameter count. Lightning holds its own against the best open-source models in its class, outperforms comparable models on different tasks, and delivers ultra-fast inference—making it ideal for scenarios where Ultra would be overkill and speed is critical. Plus, it features stable expert routing and a welcome bonus: 256K context support. GitHub | Hugging Face Kandinsky 5.0 brings a significant step forward in open generative models. The flagship Video Pro matches Veo 3 in visual quality and outperforms Wan 2.2-A14B, while Video Lite and Image Lite offer fast, lightweight alternatives for real-time use cases. The suite is powered by K-VAE 1.0, a high-efficiency open-source visual encoder that enables strong compression and serves as a solid base for training generative models. This stack balances performance, scalability, and practicality—whether you're building video pipelines or experimenting with multimodal generation. GitHub | Hugging Face | Technical report Audio gets its upgrade too: GigaAM-v3 delivers speech recognition model with 50% lower WER than Whisper-large-v3, trained on 700k hours of audio with punctuation/normalization for spontaneous speech. GitHub | HuggingFace Every model can be deployed on-premises, fine-tuned on your data, and used commercially. It's not just about catching up – it's about building sovereign AI infrastructure that belongs to everyone who needs it.

OnlyFans is possibly the most revenue-efficient company in the world.

🏀 NBA History – All-Time Points Leaders 1. 🇺🇸 LeBron James — 42,184 2. 🇺🇸 Kareem Abdul-Jabbar — 38,387 3. 🇺🇸 Karl Malone — 36,928 4. 🇺🇸 Kobe Bryant — 33,643 5. 🇺🇸 Michael Jordan — 32,292 6. 🇩🇪 Dirk Nowitzki — 31,560 7. 🇺🇸 Wilt Chamberlain — 31,419 8. 🇺🇸 Kevin Durant — 30,847 9. 🇺🇸 Shaquille O'Neal — 28,596 10. 🇺🇸 Carmelo Anthony — 28,289 11. 🇺🇸 James Harden — 27,962 12. 🇺🇸 Moses Malone — 27,409 13. 🇺🇸 Elvin Hayes — 27,313 14. 🇳🇬 Hakeem Olajuwon — 26,946 15. 🇺🇸 Oscar Robertson — 26,710 16. 🇺🇸 Dominique Wilkins — 26,668 17. 🇺🇸 Tim Duncan — 26,496 18. 🇺🇸 Paul Pierce — 26,397 19. 🇺🇸 John Havlicek — 26,395 20. 🇺🇸 Russell Westbrook — 26,392 21. 🇺🇸 Kevin Garnett — 26,071 22. 🇺🇸 Vince Carter — 25,728 23. 🇺🇸 Stephen Curry — 25,706 24. 🇺🇸 Alex English — 25,613 25. 🇺🇸 DeMar DeRozan — 25,528 📖 Source: NBA

🚨 Gold reaches new all-time high of $4,385.

The Cost of 1 GB Mobile Data Worldwide. 1. 🇨🇭 Switzerland – $7.29 2. 🇺🇸 United States – $6.00 3. 🇳🇿 New Zealand – $5.89 4. 🇨🇦 Canada – $5.37 5. 🇰🇷 South Korea – $5.01 6. 🇦🇪 UAE – $4.60 7. 🇳🇴 Norway – $4.07 8. 🇯🇵 Japan – $3.48 9. 🇨🇿 Czechia – $3.12 10. 🇬🇷 Greece – $2.79 11. 🌍 Worldwide – $2.59 12. 🇸🇪 Sweden – $2.33 13. 🇧🇪 Belgium – $2.23 14. 🇩🇪 Germany – $2.14 15. 🇲🇽 Mexico – $1.80 16. 🇿🇦 South Africa – $1.77 17. 🇵🇹 Portugal – $1.71 18. 🇭🇺 Hungary – $1.61 19. 🇳🇱 Netherlands – $1.61 20. 🇸🇦 Saudi Arabia – $1.49 21. 🇭🇷 Croatia – $1.19 22. 🇦🇷 Argentina – $0.98 23. 🇭🇰 Hong Kong – $0.92 24. 🇹🇼 Taiwan – $0.82 25. 🇩🇰 Denmark – $0.69 26. 🇪🇬 Egypt – $0.66 27. 🇨🇱 Chile – $0.63 28. 🇲🇦 Morocco – $0.63 29. 🇸🇬 Singapore – $0.63 30. 🇬🇧 United Kingdom – $0.62 31. 🇰🇪 Kenya – $0.59 32. 🇵🇭 Philippines – $0.59 33. 🇷🇴 Romania – $0.54 34. 🇦🇹 Austria – $0.48 35. 🇪🇸 Spain – $0.48 36. 🇦🇺 Australia – $0.42 37. 🇹🇷 Turkey – $0.41 38. 🇹🇭 Thailand – $0.41 39. 🇬🇭 Ghana – $0.40 40. 🇧🇷 Brazil – $0.38 41. 🇳🇬 Nigeria – $0.38 42. 🇨🇳 China – $0.37 43. 🇵🇱 Poland – $0.28 44. 🇻🇳 Vietnam – $0.28 45. 🇲🇾 Malaysia – $0.28 46. 🇮🇩 Indonesia – $0.28 47. 🇷🇺 Russia – $0.25 48. 🇨🇴 Colombia – $0.20 49. 🇫🇷 France – $0.16 50. 🇮🇳 India – $0.09 51. 🇮🇹 Italy – $0.09 52. 🇮🇱 Israel – $0.02 Source: Cable. co. uk

A world map of flamingos 🦩

Largest employers in the world: 1. 🇮🇳 Ministry of Defence - 2.99M 2. 🇺🇸 Department of Defense - 2.93M 3. 🇨🇳 People's Liberation Army - 2.55M 4. 🇺🇸 Walmart - 2.30M 5. 🇺🇸 Amazon - 1.61M 6. 🇨🇳 PetroChina - 1.45M 7. 🇬🇧 National Health Service (NHS) - 1.38M 8. 🇹🇼 Foxconn - 1.29M 9. 🇮🇳 Indian Railways - 1.21M 10. 🇮🇳 Tata Group - 1.02M Source: World Population Review

Gender Balance in The World's Largest Countries

Most Dangerous Vs Safest U.S. States

Ranking of countries with the highest testosterone: 1. 🇺🇿 Uzbekistan: 773 ng/dL 2. 🇭🇷 Croatia: 752 3. 🇨🇲 Cameroon: 731 4. 🇦🇿 Azerbaijan: 694 5. 🇲🇳 Mongolia: 693 6. 🇪🇹 Ethiopia: 671 7. 🇨🇩 D.R. Congo: 668 8. 🇳🇬 Nigeria: 625 9. 🇸🇦 Saudi Arabia: 599 10. 🇸🇩 Sudan: 588 11. 🇺🇬 Uganda: 586 12. 🇳🇱 Netherlands: 571 13. 🇷🇸 Serbia: 570 14. 🇬🇭 Ghana: 569 15. 🇩🇰 Denmark: 566 Source: World Population Review