Comment by ellis0n

I think it was 2007 when the vision of software, developers and resources was still limited. We didn't even have IoT and the first iPhone was seen as a novelty. Now it's clear that this is not the case. I founded a company in 2006 and started focusing on code review because I was interested in code and wanted to be the best. Since the advent of GitHub, I have reviewed thousands of repositories across many languages, worked on architecture, tried to create better code, developed several operating systems, hundreds of projects and dedicated 12 years to programming languages. In the end, I realized that the world is ruled by chaos and the simplest LLM AI is best suited for the task of code functionality.

For example, consider this analogy: when we make a car, we have one wheel and a multitude of parameters that change over time. The wheel is a derivative of these parameters. In car manufacturing, time is virtually unlimited for improving the quality of the wheel. But when we develop software, we always face time constraints and an enormous set of parameters. Thousands of car engineers spend decades improving a single wheel with a limited number of parameters, while a single programmer (or a couple of programmers) have only a short amount of time to release software with a randomly variable number of parameters (a service depends on Auth0 and its parameters change). As a result the programmer cannot be certain that all parameters have been correctly handled. This is why the programming process is similar to learning, and this is where LLMs are the best.