Comment by viccis
8 days ago
The one thing AI is good at is building greenfield projects from scratch using established tools. If want you want to accomplish can be done by a moderately capable coder with some time reading the documentation for the various frameworks involved, then I view AI as fairly similar to the scaffolding that happened with Ruby on Rails back in the day when I typed "rails new myproject".
So LLMs are awesome if I want to say "create a dashboard in Next.js and whatever visualization library you think is appropriate that will hit these endpoints [dumping some API specs in there] and display the results to a non-technical user", along with some other context here and there, and get a working first pass to hack on.
When they are not awesome is if I am working on adding a map visualization to that dashboard a year or two later, and then I need to talk to the team that handles some of the API endpoints to discuss how to feed me the map data. Then I need to figure out how to handle large map pin datasets. Oh, and the map shows regions of activity that were clustered with DBSCAN, so I need to know that Alpha shape will provide a generalization of a convex hull that will allow me to perfectly visualize the cluster regions from DBSCAN's epsilon parameter with the corresponding choice of alpha parameter. Etc, etc, etc.
I very rarely write code for greenfield projects these days, sadly. I can see how startup founders are head over heels over this stuff because that's what their founding engineers are doing, and LLMs let them get it cranking very very fast. You just have to hope that they are prudent enough to review and tweak what's written so that you're not saddled with tech debt. And when inevitable tech debt needs paying (or working around) later, you have to hope that said founders aren't forcing their engineers to keep using LLMs for decisions that could cut across many different teams and systems.
I get what point you're trying to make, and agree, but you've picked a bad example.
That boilerplate heavy, skill-less, frontend stuff like configuring a map control with something like react-leaflet seems to be precisely what AI is good at.
Yeah it will make a map and plot some stuff on it. It might do well at handling 20 millions pins on the map gracefully even. I doubt it's gonna know to use alpha shapes to complement DBSCAN quite so gracefully.
edit: Just spot checked it and it thinks it's a good idea to use convex hulls.
I got the feeling for your cross-team use case is that tech leaders have a dream of each team exposing their own tuned MCP agent and your agents will talk to each other.
That idea reminds me of "DevOps is to automate fail". Perhaps: "agent collaboration is to automate chaos"