I enjoyed "old fashioned" hackathons, which you had 24h to build or play with some technology or API. It has lost its charm (for me)since it moved to be startup-ish like events that you need to pitch a product instead.
I think it will be considered a "blast from the past" at some point, due to the AI era we are getting into.
Some companies and OSS groups have a policy to inform if a patch's source code was AI generated, even if it was just parts of it. Committs messages are the "obvious place" to put it.
Honest question: Does anyone know about any quantitative study or analysis on productivity gains using code assistants? Asking for numbers comparing between the "pre AI era" and now.
Also, I have the impression that LLMs bring some gains or benefits for individuals but not relevant enough at the organization level.
I believe it is very hard to quantify „productivity“. I’m sure that for suitable definitions you can find gains from coding assistants. Personally I get more code written and more features implemented. Yet I’m very wary of coding assistants because I believe they deal a fatal blow to my ability to understand the system. All LLM generated code is (at best!) code that was written by an intern which I just helped with the design and reviewed (unless productivity expectations cut down my review time and I get LLM assistance for reviews too). My grasp on the inner working of that code is much more tenuous than had I written it myself. I will never become an expert by just reviewing code and prompting.
For a while this is not a problem: I can work with my current mental model. But every generated PR erodes my expertise a little bit. Eventually my mental model won’t fit anymore.
So how much of that model maintenance should I count into my productivity metric? Does that even matter or will the next model be able to reason well enough that my mental model doesn’t matter?
I think it will be considered a "blast from the past" at some point, due to the AI era we are getting into.