
First-hand insight from Claude Code's Head of Product on how an AI-native team compresses shipping cycles and builds against future model capabilities, plus a concrete prompting tactic (asking the model to introspect on its own mistakes) that engineers can apply immediately.
“We want to remove every single barrier to shipping things.”
Cat Wu
“So an extreme example is, if Cloud Code failed but Anthropic succeeded, I would be extremely happy.”
Cat Wu
“As code becomes much cheaper to write, the thing that becomes more valuable is deciding what to write.”
Cat Wu
“And if an automation doesn't work 100% of the time, it's not really an automation.”
Cat Wu
“I think the hard thing is figuring out for the current model, how do you elicit the maximum capability?”
Cat Wu
podcastThe 100-person AI lab that became Anthropic and Google's secret weapon | Edwin Chen (Surge AI)
podcastWhy humans are AI’s biggest bottleneck (and what’s coming in 2026) | Alexander Embiricos (OpenAI Codex Product Lead)
podcastThe coming AI security crisis (and what to do about it) | Sander Schulhoff
podcastWhy most AI products fail: Lessons from 50+ AI deployments at OpenAI, Google, and Amazon
podcastAnthropic’s first technical PM on token maxing, the jagged edge, and living in the future | Dianne PennLenny Rachitsky
podcastWhat happens after coding is solved? | Fiona Fung (Manager of the Claude Code and Cowork Teams)Lenny Rachitsky
podcastAI’s third era: the rise of persistent AI coworkers | Tara Seshan (OpenAI’s product lead)Lenny RachitskyChecking sign-in…
Loading comments…