About this episode
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
This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.interconnects.ai/subscribe
Get every episode summarized
Each time Interconnects publishes, we email you a written briefing from the transcript — the topics, who appeared, and any specific claims, with the ad reads skipped.
Email me new episodesFree for 3 shows. No card needed.
Hosts & guests
No transcript yet
This episode has not been transcribed. Request it and it moves to the front of the queue.
More episodes
More from Interconnects
I wrote an AI textbook — how long until AI can do it better?
Interconnects
Open models recap: more on Kimi K3, Qwen 3.8, Xi's WAIC speech, distillation, th...
Interconnects
Kimi K3: The open-weights escalation
Interconnects
GLM-5.2 is the step change for open agents
Interconnects