> Nienders concluded that this was due to the difference in the information available. Sophy had information about the track curvature of the upcoming 6 seconds

> Nienders concluded that this was due to the difference in the information available. Sophy had information about the track curvature of the upcoming 6 seconds of track, based on the current speed. TMRL, however, only had distance measurements from the LIDAR. While the TMRL program could plan for the next turn, it could not plan two turns ahead, and this fundamentally limited the program to mere safe driving, avoiding walls and crashes, but never optimizing. I think that point is an important one. ML algorithms work better when they are given better context. Especially in programming, it is clear the models are trained on code, rather than repositories. They know about files and repositories, but i always get the impression that they are totally clueless about whole programs. What could be done better in code, is provide in training more data about where each function is located in the project, some other files where similar functions are defined or called and so on. In general before each code is fed into the training, to do a little bit of data mining in the project like the tree-hugger project [1] enables. Tree-hugger however is a little bit older code, and tree-sitter has advanced a lot the last 4 years. In my opinion 5x to 10x in code, is within reach, with no need to increase GPU compute or electricity. [1] https://github.com/autosoft-dev/tree-hugger https://github.com/autosoft-dev/tree-hugger