alt Hacker NewsI think software was indeed 9/10 accidental activities before AI. Probably still mostly accidental activities with the current LLM.
The essence: query all the users within a certain area and do it as fast as possible
The accident: spending an hour to survey spatial tree library, another hour debating whether to make our own, one more hour reading the algorithm, a few hours to code it, a few days to test and debug it
Many people seem to believe implementing the algorithm is "the essence" of software development so they think the essence is the majority. I strongly disagree. Knowing and writing the specific algorithm is purely accidental in my opinion.
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It that's the essence, then of course 9/10 is accident. I think that's not software engineering though.
The essence: I need to make this software meet all the current requirements while making it easy to modify in the future.
The accident: ?
Said another way: everyone agrees that LLMs make it very easy to build throw away code and prototypes. I could build these kind of things when I was 15, when I still was on a 56k internet connection and I only knew a bit of C and html. But that's not what software engineers (even junior software engineers) need to do.
Isn't the solution to that standardizing on good-enough implementations of common data structures, algorithms, patterns, etc? Then those shared implementations can be audited, iteratively improved, critiqued, etc. For most cases, actual application code should probably be a small core of businesses logic gluing together a robust set of collectively developed libraries.
What the LLM-driven approach does is basically the same thing, but with a lossy compression of the software commons. Surely having a standard geospatial library is vastly preferable to each and every application generating its own implementation?