Most of the narrative is about how AI is writing all/most code, but I’d wager that the fraction of human reviewed code is approaching zero far faster than anyone is realizing or willing to admit.
Most of the narrative is about how AI is writing all/most code, but I’d wager that the fraction of human reviewed code is approaching zero far faster than anyone is realizing or willing to admit.
Very true. Last year I at least glanced at every line of AI generated code. Now if some AI makes a 10k line program for some one-off tasks, I run the program, glance only over the output, and move on.
Which one-off tasks need 10k lines of code?
Would depend on what AI and prompt you use ultimately. Ask it to add tests (functional, E2E and unit, maybe invent a new type too), packaging, modular code and/or whatever, and you get to 10K relatively quickly with some of the more verbose LLMs out there.
Personally it's probably the biggest struggle, trying to rein in the "spray and pray" approach LLMs typically like to take, and reducing the "patch on top of patch" syndrome too.
Calculate the engine power of a 2015 VW polo when travelling 70 mph on a flat road behind a box truck. Draw a chart of drag Vs follow distance. How significant is humidity on the result?
Java programs