How to cultivate a high-signal AI feed

Basic tips on how to assess inbound ML content and cultivate your news feed.
This is AI generated audio with Python and 11Labs. Music generated by Meta's MusicGen.
Source code: https://github.com/natolambert/interconnects-tools
Original post: https://www.interconnects.ai/p/making-a-ml-feed

00:00 How I assess all these AI releases
01:22 1. Model access and demos are king of credibility
02:31 2. Focus your feed on depth or breadth
03:09 3. Examples of using the model normally show its usable, shockingly
04:10 4. Leaderboards as the single leading claim is often anti-signal
05:00 5. Basic deep learning conceptual checks will often save you
06:13 6. If it's not even remotely reproducible or verifiable, it's not science
07:10 7. Don't over-index on Twitter
08:32 8. Data sharing, licenses, communication clarity, and small things add up
08:58 9. Research papers, technical reports, blog posts, and Tweets all serve different purposes
09:49 10. Socialize your information and build relationships

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