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viccis 3 days ago

I agree. For me it's a modern version of that good ol "rails new" scaffolding with Ruby on Rails that got you started with a project structure. It makes sense because LLMs are particularly good at tasks that require little more knowledge than just a near perfect knowledge of the documentation of the tooling involved, and creating a well organized scaffold for a greenfield project falls squarely in that area.

For legacy systems, especially ones in which a lot of the things they do are because of requirements from external services (whether that's tech debt or just normal growing complexity in a large connected system), it's less useful.

And for tooling that moves fast and breaks things (looking at you, Databricks), it's basically worthless. People have already brought attention to the fact that it will only be as current as its training data was, and so if a bunch of terminology, features, and syntax have changed since then (ahem, Databricks), you would have to do some kind of prompt engineering with up to date docs for it to have any hope of succeeding.

pvorb 3 days ago | parent [-]

I'm wondering what exact issue you are referring to with Databricks? I can't remember a time I had to change a line I wrote during the past 2.5 years I've been using it. Or are you talking about non-breaking changes?

viccis 2 days ago | parent [-]

They have changed a lot of their DLT (not even called that anymore lol, it's Lakeflow Pipelines now I think) syntax. I tried asking ChatGPT to convert a very simple Python one to Spark SQL, and it gave me a bunch of outdated SQL syntax.

Aside from that, if you use their Python connector package, it's a shit show to put it mildly. For example, 15.4 works with serverless but tells you (via deprecation warning) it doesn't and that you need to use 15.1 (which lacks a lot of variant stuff). So then you decide screw it I'm gonna just update to 16, except that serverless (which works on 15.4) doesn't work on 16.0 or 17.0.