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!!

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