To round out your week, check out this review of the intersection of deep learning and neurodegenerative disease with a focus on variant effect prediction. Written by the lab's first high school student (🤯) @_alexlan. The future of science is bright! frontiersin.org/articles/10.33…

The concept of an AI OS is appealing, but I believe the AI ecosystem will be a lot more decentralized in nature. I might have research in Perplexity, docs in Notion, transcripts in Granola—each platform has their unique advantages. AI should enable them to interact

The play right now is to go deep in a vertical and build AI Agents with the context of the critical workflows, domain expertise, specialized instructions, and data of that industry. And build anticipating what’s possible with AI models in a year from now, not just today.

becomes even more challenging when llms exhibit jagged intelligence—even the best coding benchmarks can't guarantee performance on your specific use case. what's the best way to approach evals for much narrower tasks when building custom evals doesn't scale?

gonna need something to parse the signal from the noise

In the past, many vertical software companies were started by domain experts who added technical capacity later. Now it’s the inverse. AI-native startups are led by technical founders who hire for context early. AI delivers differentiation. Context drives defensibility.

great approach that's friendly to both your average user and developers who want to tinker more with voice agents—more startups should be thinking abt this!

maintaining streaming endpoints across models and tool call patterns is such a pain — if AI SDK handles this well it's going to be a very sticky product