Why Longer and Longer Context Capabilities?

Currently, artificial intelligence models have two primary methods of acquiring knowledge: through training and in-context learning during inference. The traditional approach has been training, as contexts have typically been limited in size. However, the emergence ultra-long context capabilities could ultimately revolutionizing the way we interact with code on a fundamental level.

Ultra-long context capabilities, like Magic's Long Term Memory model as an example, are specifically designed to engage in reasoning based on up to 100 million tokens of context provided to them during the inference process.

Consider the significant improvements that could be made in code synthesis if models had access to all aspects of your codebase, documentation, and libraries within their contextual understanding, even those that are not publicly available on the internet.

This advancement in AI technology opens up a world of possibilities for enhancing software development practices, streamlining processes, and ultimately revolutionizing the way we interact with code on a fundamental level.