The goal is to make the time to result as short as possible, transforming how engineers approach machine learning experiments.

Deliberate deoptimization involves choosing in advance what to neglect, allowing focus on what truly matters.

An iterative approach is essential in working with AI; continuously debugging and refining prompts can lead to more reliable outcomes.

Constraints in search can drive creativity, as they force us to innovate and find better solutions that enhance user experience without compromising on quality or speed.

At Y Combinator, we advise against starting with a solution in search of a problem. Instead, focus on identifying real problems that need solving, which will lead to more efficient growth.

The challenge of reducing 'slop' in model outputs involves teaching models to produce more elegant and effective solutions, especially in coding.

Scientific progress is often hindered not by a lack of new ideas, but by the slow and expensive process of testing those ideas in the lab. My focus is on how AI can replace or minimize the need for these lab experiments.

Improving latency can be costly, so we explore alternatives like pruning the number of parameters served at runtime.

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