When creating benchmarks for AI models, it's crucial to consider whether to prioritize distinguishing high-quality outputs or providing a continuous improvement path.

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.

Our focus is on optimizing for reasoning and behavior of models rather than just their scores on traditional benchmarks like MMLU.

The 'prompt to leaderboard' approach teaches us that we can evaluate models by learning how they perform in various scenarios, rather than relying solely on static benchmarks.

Developers should focus on the holistic behavior of AI applications at scale, rather than just performance checks on small datasets.

Quality should be prioritized over speed and scale; maintaining high standards is essential for long-term success.

The scaling of models has worked consistently, and it's incredible that making these models bigger leads to predictable improvements in their performance.

The current landscape shows that large model labs are focused on building powerful foundation models that require less fine-tuning, which may lead to a decline in the relevance of fine-tuning in the near future.

If you're building a product that's right on the edge of the capabilities of the models, keep going because you're doing something right. Give it another couple of months, and the models will be great, making your product really sing.