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LF

Lex Fridman Cast

02/03/25

@ Lex Fridman

The art of model development involves knowing when a model is undercooked and how to gauge its readiness for various tasks.

Video

LF

DeepSeek, China, OpenAI, NVIDIA, xAI, TSMC, Stargate, and AI Megaclusters | Lex Fridman Podcast #459

@ Lex Fridman

DeepSeek, China, OpenAI, NVIDIA, xAI, TSMC, Stargate, and AI Megaclusters | Lex Fridman Podcast #459

02/03/25

Related Takeaways

O

OpenAI Cast

04/11/25

@ OpenAI

The process of developing a model involves collaboration between machine learning and systems teams, from inception to the training run, often facing unresolved issues at launch.

ZW

Zack Witten

09/06/24

@ Anthropic

Prompt engineering is about getting the model to perform tasks effectively by clear communication and understanding the model's psychology.

A

Anthropic Cast

09/06/24

@ Anthropic

The skill of prompting is about introspection, understanding what you want, and making yourself legible to the model.

LQ

Lin Qiao

05/19/25

@ Sequoia Capital

Many app developers currently rely on off-the-shelf models, but infusing product knowledge into these models through prompt engineering is a new and necessary skill.

TB

Trenton Bricken

05/23/25

@ Dwarkesh Patel

The distinction between how models claim to perform tasks and how they actually do them can reveal significant insights into their capabilities.

O

OpenAI Cast

04/11/25

@ OpenAI

Training large models is challenging because new issues arise that weren't anticipated at the project's inception, requiring ongoing adjustments and problem-solving.

A

Anthropic Cast

09/06/24

@ Anthropic

A good prompt engineer should be able to step back from their own knowledge and communicate the necessary information clearly to the model.

DP

Dwarkesh Patel Cast

05/23/25

@ Dwarkesh Patel

We often underutilize models, expecting them to solve tasks quickly without giving them enough time to iterate and improve.

CS

Carola Schönlieb

05/10/18

@ Y Combinator

Understanding the properties of handcrafted models can help inform the development of neural networks, particularly in terms of stability and performance.