A landscaper sent me this. He’s building his own software with Claude, because his current software isn’t built for him, it’s built for the masses. The trades are building!

.@SecMullinDHS on the threat landscape today: It's still terrorists — but now, terrorists have joined with the cartels, and Democrats let a lot of them in with open borders. We're reversing the damage, but Democrats want to stop that progress.

2026 Chinese model landscape in one list ▪️ Qwen3.8-27B, Qwen3.8-Max ▪️ GLM-5.3 ▪️ Kimi K3 ▪️ DeepSeek-V4 ▪️ ERNIE 5.1 ▪️ Baichuan-M3-235B, Baichuan-Omni-1.5 ▪️ Yi 1.5 ▪️ MiniCPM-o 4.5, MiniCPM-V 4.5, MiniCPM-V 4.6 They’re getting interesting in very different ways. Here we explained what makes each one stand out:

A Chinese landscape painting takes shape above a hutong in Beijing’s Qianmen. Inspired by the iconic Song Dynasty masterpiece A Thousand Li of Rivers and Mountains, flowing sheer fabric creates a dreamlike scene. Can you feel the Chinese aesthetic?

🔥 Wildfires and landscape fires area burnt YTD as of August 1, 2026 (hectares): 1. 🇨🇩 DR of Congo: 13,611,126 2. 🇦🇺 Australia: 11,801,650 3. 🇦🇴 Angola: 7,473,034 4. 🇸🇸 South Sudan: 6,955,022 5. 🇨🇫 Central African Republic: 6,017,881 6. 🇲🇲 Myanmar: 4,325,702 7. 🇮🇳 India: 4,029,704 8. 🇷🇺 Russia: 3,335,029 9. 🇬🇳 Guinea: 3,250,992 10. 🇿🇲 Zambia: 3,213,687 11. 🇻🇪 Venezuela: 2,977,466 12. 🇳🇬 Nigeria: 2,807,200 13. 🇹🇭 Thailand: 2,669,550 14. 🇱🇦 Laos: 2,641,321 15. 🇺🇸 United States: 2,371,207 16. 🇨🇳 China: 2,265,057 17. 🇨🇦 Canada: 2,031,754 18. 🇧🇷 Brazil: 2,030,209 19. 🇪🇹 Ethiopia: 1,851,131 20. 🇬🇭 Ghana: 1,763,395 21. 🇰🇭 Cambodia: 1,525,312 22. 🇲🇱 Mali: 1,490,698 23. 🇨🇲 Cameroon: 1,435,373 24. 🇹🇩 Chad: 1,309,155 25. 🇲🇽 Mexico: 1,264,823 Source: Global Wildfire Information System (GWIS) via Our World in Data

building an ai landscape knowledge map that improves after every mts interview goal is to help me understand a guest's perspective across many angles (technical, product, policy, economic, geopolitical, social, etc.) here's how it works: 1. obsidian vault to organize landscape into: > domains > cross-cutting themes > debates > evidence/sources 2. before an interview, I map the guest's work onto the landscape and identify the knowledge gaps they can fill 3. then i craft interview questions around those gaps 4. after the interview, I update the knowledge graph with my new learnings for example, did this today for my interview with @hamandcheese's on his sorcerer’s apprentice essay. now, my knowledge map has a better understanding on the alignment debate and agent swarms. even though i started my career as a software engineer and have an okay high level technical understanding, hosting for mts taught me that there's still so much more to learn hoping this helps me identify blind spots so i can better teach myself what i dont already know looking for feedback on this. please share if you have ideas on how it can be improved!!