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AI usage patterns in software teams

linear.app

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Re: AI usage patterns in software teams

#51

Post by conradludgate »

danggggg wrote: post #10
tankaiji wrote: post #6

Earlier quoted context omitted.

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

[dead]

What I've observed is that by prompting for longer I get to keep my brain focused on the architectural ideas (networking, protocols, data structures, etc) rather than worrying about the most performant/elegant implementations.

I always enjoyed writing code and I am very good at writing very performant and elegant code, but it would sometimes come at the detriment of focusing on the code and not the design.

Re: AI usage patterns in software teams

#52

Post by Fordec »

gkamal wrote: post #22

This looks like measuring what is easy to do, rather than what really matters. PR open counts, issues created , ceos/founders spending more time on linear don't automatically lead to better outcomes (in my experience they are often negatively correlated:-) )

Yeah all of these along with raw token usage are metrics that were being used around December to February by people who just didn't have anything to go off yet.

Skill/Hook usage rates, budget spend, auto-approval rate, focus area heatmaps, MTTR, MTTD are all there now

Re: AI usage patterns in software teams

#53

Post by onion2k »

gkamal wrote: post #22

This looks like measuring what is easy to do, rather than what really matters. PR open counts, issues created , ceos/founders spending more time on linear don't automatically lead to better outcomes (in my experience they are often negatively correlated:-) )

This looks like measuring what is easy to do, rather than what really matters.

Even that's hard. There aren't enough signals to attribute changes directly to AI, so these apps seem to correlate the signal that the user was interacting with AI to the changes they made e.g "Bob used AI at that time, and they opened a PR at a similar time, so Bob probably used AI to make that PR."

Until the tooling for gathering data on AI usage improves the data will be fairly interesting because correlations often point to something related, but won't be a source of truth.

Re: AI usage patterns in software teams

#54

Post by onion2k »

hexasquid wrote: post #46
gkamal wrote: post #22

This looks like measuring what is easy to do, rather than what really matters. PR open counts, issues created , ceos/founders spending more time on linear don't automatically lead to better outcomes (in my experience they are often negatively correlated:-) )

I'd be interested to overlay, I don't know, customer satisfaction or anything that can show the follow-on effect of all this output. Linear won't have that information. My guess is some will jump up (where the team has managed to make themselves move effective and responsive) and many will plummet (doesn't need explaining). Then there might be something to look at.

I'd be interested to overlay, I don't know, customer satisfaction or anything that can show the follow-on effect of all this output. Linear won't have that information.

Very few businesses can accurately attribute customer value to the work they do, especially once they're passed start-up scale. A mature company makes lots of small changes and they're rarely measurable.

Re: AI usage patterns in software teams

#55

Post by whateveracct »

clintonb wrote: post #45
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.

The data is aggregated, and you cannot possibly identify a single user from what's been published. I see no issue here.

lol isn't this what google said and yet..the NSA cometh

Re: AI usage patterns in software teams

#57

Post by jdm2212 »

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,…

Try using Fable and report back. Local LLM is to Fable as Little Tike car is to a Porsche.

Re: AI usage patterns in software teams

#58

Post by denkmoon »

monkpit wrote: post #49
what wrote: post #31

Earlier quoted context omitted.

Write a script to make random prompts and use tokens. Not like they look at what you’re actually prompting.

No, don’t be silly, they get ai to spy on you en masse instead. Nobody has to look at anything anymore for it to be actionable.

Absolutely, have warned my juniors of this. Doesn't stop malicious compliance though, I can just prompt and burn tokens with source material from my assigned tickets for no reason perfectly fine.

I look at my colleagues' screens and they're prompting shit like 'restart this program' and 'is [service] running correctly'. I have below average prompt frequency because I know crazy shit like #!/bin/bash and ps aux. It's so goddamn insane.

And to top it all off, my token count is above the average, it's just the prompt count that is low. Got questioned about it earlier this week.

Re: AI usage patterns in software teams

#59

Post by giraffe_lady »

clintonb wrote: post #45
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.

The data is aggregated, and you cannot possibly identify a single user from what's been published. I see no issue here.

I think their point is that there's business value in the usage data and linear is using that value in a way that benefits them but not the customers they got it from.

It reminds me of matt levine's reframing of insider trading where it's not about fairness it's about theft. You're supposed to get secret insights and use them to get an edge. What you can't do is get an edge for yourself with secret insights that your employer got.

So it roughly comes down to "that data is valuable and rightfully belongs to the originating company." Which then makes this a contract diligence type situation.

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