By allowing models to think longer, large language models can leverage more compute on the fly, enhancing their intelligence for complex problems.

The new technology of large language models is creating unprecedented opportunities for startups, enabling tasks that previously required extensive research teams to be accomplished more easily.

Large language models should be referred to as large event models because they are not only effective in language but also applicable in pharmaceuticals and various other fields.

When developing a new large language model product, it's essential to iterate on prompts to find what works best for your specific use case.

Humanloop enables large language models to have enhanced capabilities, allowing you to build differentiated applications and products on top of these models, with the range of use cases limited more by imagination than by technology.

Developers are increasingly finding that a significant portion of their code is being generated by large language models, which raises questions about the future impact of these models on society.

Using large language models as judges, combined with human review, allows for scaling without overwhelming human subject matter experts.

Customization of large language models is crucial for differentiation, as everyone has access to the same base models, but tailoring them to specific contexts enhances user experience.

Large language models are becoming significantly larger and smarter, with AI labs adopting a strategy of scaling more parameters, data, and compute to improve model performance.