

Why you should write your own LLM benchmarks — with Nicholas Carlini, Google DeepMind
Today's guest, Nicholas Carlini, a research scientist at DeepMind, argues that we should be focusing more on what AI can do for us individually, rather than trying to have an answer for everyone. "How I Use AI" - A Pragmatic Approach Carlini's blog post "How I Use AI" went viral for good reason. Instead of giving a…
We score an episode from what we can actually measure — its reach and engagement, what listeners say about it, and what it covers. We don't have those signals for this one yet, so it doesn't get a number.
Buzzmeter says it's buzzing across platforms — the Hive Vote says whether the people who actually listened liked it.
Rate this episode
Add your vote to the Hive — listeners rate every episode after they finish.
- No reviews yet — be the first.
More from Latent Space: The AI Engineer Podcast
See all episodes →
🔬Scaling Past Informal AI - Carina Hong, Axiom Math

🔬Bio-security is an AI Arms Race - Eric Nguyen (CEO, Radical Numerics)

Runway’s WorldPrompt and the Engineering of Real-Time Worlds
Similar episodes from other shows
More like Latent Space: The AI Engineer Podcast →
Why AI Needs Better Benchmarks

The coming AI security crisis (and what to do about it) | Sander Schulhoff

AI incidents, audits, and the limits of benchmarks

Really Big Test-Time Compute in AI Changes Benchmarks, Safety and Research with OpenAI Research Scientist Noam Brown
One great episode in your inbox, daily — free.
One email a day, unsubscribe anytime. No spam, ever.