Large Language Models (LLMs) function like a new operating system, being developed by labs and distributed like utilities, reminiscent of computing in the 1960s.

The introduction of large language models (LLMs) provides a language interface for robots, allowing for easier task descriptions and enabling robots to perform generic tasks more effectively.

The advancements in large language models (LLMs) have enabled a language interface for robots, allowing them to perform a wider range of tasks more efficiently, such as laundry folding and packing egg crates.

The programming paradigm is evolving; now, prompts for LLMs are written in English, making software development more accessible.

The emergence of partial autonomy apps, like Cursor, illustrates how LLMs can enhance traditional software interfaces by managing context and orchestrating multiple calls. Software is fundamentally changing again, as we enter the era of 'Software 3.0,' where natural language becomes the new programming interface.

I believe we are nearing the upper limits of large language models, as the future will likely involve many smaller, specialized models rather than a few large ones. We're at the upper end of large language models, and they're rapidly becoming a commodity as they mature.

English is becoming the new assembly language for large language models, meaning that effective communication with AI requires structuring language correctly, even if syntax is less critical than before.

Large language models (LLMs) are primarily designed to regurgitate information based on statistical patterns in the text they've been trained on, which limits their ability to invent new concepts or solutions.

With the rise of large language models (LLMs), distinguishing between good and bad dev tool ideas has become challenging; ideas that seemed bad yesterday may now be viable due to advancements in technology.