[raincole]

I 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.

[idle_zealot]

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?

[raincole]

I mean, of course libraries are great. But the process to create a standardized, widely accepted library/framework usually involves with another kind of accidental complexity: the "designed by committee" complexity. Every user, and every future user will have different ideas about how it should work and what options it should support. People need to communicate their opinions to the maintainers, and sometimes it can even get political.

At the end, the 80% features and options will bloat the API and documentation, creating another layer of accidental activity: every user will need to rummage through the doc and something source code to find the 20% they need. Figuring how to do what you want with ImageMagick or FFmpeg always involved with a lot of reading time before LLM. (These libraries are so huge that I think most people only use more like 2% instead of 20% of them.)

Anyway, I don't claim AI would eliminate all the accidental activities and the current LLM surely can't. But I do think there are an enormous amount of them in software development.

[etamponi]

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.