Large language models are capable of capturing user intent in a common-sense way, and translating it into code. This new approach to software empowers many users who don't have the time or knowledge to write code themselves, allowing them to instead focus on the task at hand. Making sure that the translation is accurate is a challenge, requiring all-new tooling and techniques.
 
Rahul Sonwalkar, founder and CEO of Julius - the AI data scientist, joins Anton to discuss how they use large language models to write code, integrate LLM tool use, detect and mitigate errors, and how to quickly get started and rapidly iterate on an AI product. They also discuss how thinking about products in terms of 'GPT wrappers' kills creativity, and what kinds of experiments AI developers should be trying today.

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