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AI usage patterns in software teams

linear.app

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Re: AI usage patterns in software teams

#61

Post by anon7000 »

sebiandev wrote: post #34

this seems inappropriate. I think its a bad paradigm that just because you use a platform's service, they get intimate details about your usage. And for them to be so bold about publishing the statistics they've stolen from their customers data? Gives me a reason to never recommend my org use this platform.

I’d rather it was published for free in a blog post than sold without my knowledge. As a linear user. This shit doesn’t matter, everyone in the industry know these kinds of stats are being tracked, I’m happy for a company to be transparent about it.

Re: AI usage patterns in software teams

#62

Post by anon7000 »

nik282000 wrote: post #37
tankaiji wrote: post #6

Earlier quoted context omitted.

And then more hours cleaning it up and re-prompting.

I program as a hobby, personal projects because I can. I recently set up a local llm to see what the fuss is about and other than the few ringer solutions my experience is as you described. 2min promping, 5min waiting, 3hrs debugging or just doing it myself. I am very likely doing it wrong, and it does speed up some aspects, but I wouldn't say I trust llm code any more than my own. Until it runs and throws an error,…

A normal agentic loop will have the agent using a type system and basic tests to do some basic validation of changes. A good agentic loop would give the agent a very easy way to verify if it’s on the right track. I think agents are better than many humans at writing error free code (runtime errors, not bugs. The code could still be buggy or incorrect.)

Re: AI usage patterns in software teams

#63

Post by jmtulloss »

This is pretty interesting but I wish it would have been refined in two ways:

- The prose before the data appears to be AI generated. Not a big deal but it makes the reader work harder to figure out what's actually being said.

- Linear didn't control for their platforms AI changes over the past year. The platform has become much more AI integrated, so a lot of numbers will move. I'm not sure how you do it but this is only useful signal with a control.

Re: AI usage patterns in software teams

#64

Post by deimos_28 »

slopinthebag wrote: post #11
danggggg wrote: post #9

Earlier quoted context omitted.

[dead]

It’s worthwhile to me under specific conditions, mainly that the codebase has existing patterns and abstractions that the LLM utilizes, and they’re actually good. It’s not a common case but I’m lucky that one of the codebases I’m in is one I built myself mostly by hand, and LLMs are really effective in it at the moment. My main concern is making sure the generated code maintains the high standard, but it’s definitely…

I’ve reached a similar conclusion, but there’s a part that worries me: the expertise that allows us to judge AI’s output was itself built by doing the work we’re now delegating. So there’s a risk that our judgement will decay over time. I’ve been thinking about the problem as choosing where we can afford to “borrow” comprehension, versus where we need to keep exercising it, and how to “claim back” the critical comprehension we lost.

Re: AI usage patterns in software teams

#66

Post by [deleted] »

viccis wrote: post #50
danggggg wrote: post #10

Earlier quoted context omitted.

[dead]

In my experience? It used to be that one person had one to three codebases they knew intensely at my company. If you needed a bug in codebase X fixed, person Y was the one to do it and if they aren't available, person Z can do it, just not as quickly. Now every person on my team has to handle tickets for every single codebase. There are about two dozen different large codebases involved here. It's a ludicrous antipat…

[deleted]

Re: AI usage patterns in software teams

#68

Post by hypfer »

anon7000 wrote: post #62
nik282000 wrote: post #37

Earlier quoted context omitted.

I program as a hobby, personal projects because I can. I recently set up a local llm to see what the fuss is about and other than the few ringer solutions my experience is as you described. 2min promping, 5min waiting, 3hrs debugging or just doing it myself. I am very likely doing it wrong, and it does speed up some aspects, but I wouldn't say I trust llm code any more than my own. Until it runs and throws an error,…

A normal agentic loop will have the agent using a type system and basic tests to do some basic validation of changes. A good agentic loop would give the agent a very easy way to verify if it’s on the right track. I think agents are better than many humans at writing error free code (runtime errors, not bugs. The code could still be buggy or incorrect.)

Fundamentally though, an agent cannot produce great code, because great code requires intent, which is the opposite of the statistical mean.

You will get a solution that works with a proper workflow, but you won't get one that scales or would be truly maintainable. Which is also what you get with random midwit drive-by contributors, but faster. I'll give it that.

Re: AI usage patterns in software teams

#69

Post by hypfer »

nozzlegear wrote: post #36
jdw64 wrote: post #15

Earlier quoted context omitted.

Writing code directly takes longer to warm up. Usually, I'd keep tens of thousands of lines in my head. In the past, I spent a lot of time designing error propagation and execution contexts. (Talented people might figure it out right away, but unfortunately I don't have that kind of talent.) So I'd have to think about things like Result and how far to propagate errors—and worry about whether my approach would conflic…

> But these days, AI just generates code following the existing patterns of the codebase Is this sarcasm?

It does so better the more "standard" the "existing patterns" are. :^)
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