When developing new technology, expect that it won't work on the first try or even the tenth; it's crucial to iterate quickly and avoid anything that slows down your progress.

A strong growth strategy involves running numerous experiments and being willing to adapt quickly based on data.

The ongoing competition in AI development underscores the need for rapid iteration and experimentation, as companies strive to find the right combination of hyperparameters for their models.

It's important to quickly build and test ideas. If something doesn't work, it's better to kill it and start again rather than justifying its existence with additional features that may not address user needs.

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

The better the AI models become, the more opportunities there are for startups to leverage these advancements. Startups need to be proactive in building their products in anticipation of new model releases rather than reacting to them.

If your product's success depends solely on the models getting smarter, you're likely building the wrong thing. Instead, create something that improves as the models advance.

Solutions may change over time due to evolving data and model quality, necessitating ongoing iteration and adaptation.

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.