zlacker

>>meetpa+(OP)
So, are people using these tools without the org they work for knowing? The amount of hoops I would have to jump through to get either of the smaller companies I have worked for since the AI boom to let me use a tool like this would make it absolutely not worth the effort.

I'm assuming large companies are mandating it, but ultimately the work that these LLMs seem poised for would benefit smaller companies most and I don't think they can really afford using them? Are people here paying for a personal subscription and then linking it to their work machines?

>>crat3r+n5
Not every coding task is something you want to check into your repo. I have mostly used Gemini to generate random crud. For example I had a huge JSON representation of a graph, and I wanted the graph modified in a given way, and I wanted it printed out on my terminal in color. None of which I was remotely interested in writing, so I let a robot do it and it was fine.

>>jeffbe+U5
Fair, but I am seeing so much talk about how it is completing actual SDE tickets. Maybe not this model specifically, but to be honest I don't care about generating dummy data, I care about the claims that these newer models are on par with junior engineers.

Junior engineers will complete a task to update an API, or fix a bug on the front-end, within a couple days with lets say 80 percent certainty they hit the mark (maybe an inflated metric). How are people comparing the output of these models to that of a junior engineer if they generally just say "Here is some of my code, what's wrong with it?". That certainly isn't taking a real ticket and completing it in any capacity.

I am obviously very skeptical but mostly I want to try one of these models myself but in reality I think that my higher-ups would think that they introduce both risk AND the potential for major slacking off haha.

>>crat3r+O6
I don’t know about tickets but my org definitely happily pays for Gemini Advanced and encourages it’s use and would be considered a small org.

The latest SOTA models are definitely at the point where they can absolutely improve workflows and not get in your way too much.

I treat it a lot like an intern, “Here’s an api doc and spec, write me the boilerplate and a general idea about implementation”

Then I go in, review, rip put crud and add what I need.

It almost always gets architecture wrong, don’t expect that from it. However small functions and such is great.

When it comes to refactoring ask it for suggestions, eat the meat leave the bones.