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Eight Myths on Software Engineering and GenAI

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Re: Eight Myths on Software Engineering and GenAI

#261

Post by geoduck14 »

TheOtherHobbes wrote: post #206
geoduck14 wrote: post #10

Earlier quoted context omitted.

I used it to write SQL and make dashboards. Back in the day, I would spend a lot of time doing that, then I changed roles. I dipped my toe in it recently and used AI exclusively. I would send a prompt, see the output, decide if that is what I wanted or not. I kept my brain in "what-if mode" and I let the LLM handle the technical specs.

'What-if' mode is a game changer. And underrated. The cost of prototyping has crashed. So you can A/B/C... various ideas, test them in the real world or by personal preference, to taste, and converge on designs that work for you. This used to very hard and expensive. Now it's so cheap it makes the idea -> test -> refine loop much tighter and faster.

>The cost of prototyping has crashed. So you can A/B/C... various ideas)

Oh yes! I would think "I wonder how the data would look if I did X" and feel the pain of complicated SQL... but then the LLM would just do it and I got to see if I liked or not! It was a delight

Re: Eight Myths on Software Engineering and GenAI

#262

Post by deterministic »

a_bonobo wrote: post #16

>On my visits to the Bay Area, I would ask AI researchers or interns why they are doing their current research or projects, when in a year or three agentic LLMs could probably do them; This is such a weird point to make that doesn't become correct just because everyone makes it, all the time. Why clean the ocean if some magic future tech will clean them? Why save the world now if some benevolent AI is 'just around th…

I completely agree. There's no guarantee that researchers won't still be needed despite AI.

Re: Eight Myths on Software Engineering and GenAI

#264

Post by xnickb »

01100011 wrote: post #68
simonw wrote: post #4

> We already know developers don’t actually spend most of their time writing code, with studies at Microsoft and elsewhere showing it’s closer to 14 percent. Anyone else finding they're spending more time writing code (or at least driving agents to write code) now? 14% used to feel about right for me - I'd spend the rest of the time researching approaches and libraries, planning things out in issues, or sometimes jus…

You read my mind. I suspect it is a transient spike while I blow through the backlog and I'll run out of things that AI can vastly accelerate in, say, 6 months. But right now? I'm spending 80-90% of my time blasting through implementing features, finding bugs, fixing old bugs, writing and improving my tools... Code was never the majority of my job. But right now the bar to doing things is so low and the sheer joy of…

And when you are done, what will happen with all that code you wrote?

Re: Eight Myths on Software Engineering and GenAI

#265

Post by sensanaty »

anon-torment wrote: post #201
01100011 wrote: post #72

Earlier quoted context omitted.

Also AI is now drafting design docs, generating PLC work products, entering it all in Jira, characterizing and root causing bugs... It's speeding up the 86% of my job that isn't coding. The article is a bit myopic and frankly contradicts itself.

I pity the poor souls who need to read that.

Nobody reads it, silly. At most they'll just run it through their own AI and copy-paste the output if a response is needed.

It's like all those AI meeting tools. Full transcripts of entire meetings and no human ever takes a 2nd look at them for literally any reason, ever. My favorite is the meeting summaries that nobody reads, and then 2 weeks later a miracle happens and it does get read, only for the AI to summarize nothing useful, or what happens even more often, for the summary to just be blatantly wrong and it implies the opposite of what was said during the meeting!

Re: Eight Myths on Software Engineering and GenAI

#266

Post by xmcqdpt2 »

swat535 wrote: post #241
germandiago wrote: post #127

Earlier quoted context omitted.

> This is such a weird point to make I think it is a great point to make, because if everyone really believed that AIs will do everything without human intervention in a handful of years, as the marketing repeats again and again (AGI, singularity, etc.) and have been saying for years... why then get bothered? Because we DO know LLMs have their hallucinations, limitations, perform tasks not previously seen way worse t…

> you can feed more and more and more I have a meta thought.. Hypothetically what happens once there is no more data to be fed to the system? Are we expecting AI to invent its own data and reach full cognition? Currently we are feeding it the data that humans created but if we stop (i.e "why bother?") thinking that AI will do it all?

This is a well-known problem that has been an issue for years now. You can't use models to generate data for models because it leads to "model collapse" where it amplifies quirks in the generated data until it's all quirks. Here is a random university press release about it (grain of salt etc)

https://www.utoronto.ca/news/training-ai-machine-generated-t...

In practice you can do it a bit (generated data from a better / different model is fine, some generated data might be useful if there is non generated data etc.)

Re: Eight Myths on Software Engineering and GenAI

#267

Post by ilmaz10x »

xmcqdpt2 wrote: post #266
swat535 wrote: post #241

Earlier quoted context omitted.

> you can feed more and more and more I have a meta thought.. Hypothetically what happens once there is no more data to be fed to the system? Are we expecting AI to invent its own data and reach full cognition? Currently we are feeding it the data that humans created but if we stop (i.e "why bother?") thinking that AI will do it all?

This is a well-known problem that has been an issue for years now. You can't use models to generate data for models because it leads to "model collapse" where it amplifies quirks in the generated data until it's all quirks. Here is a random university press release about it (grain of salt etc) https://www.utoronto.ca/news/training-ai-machine-generated-t... In practice you can do it a bit (generated data from a better…

[dead]

Re: Eight Myths on Software Engineering and GenAI

#268

Post by gwern »

antonvs wrote: post #91
a_bonobo wrote: post #16

>On my visits to the Bay Area, I would ask AI researchers or interns why they are doing their current research or projects, when in a year or three agentic LLMs could probably do them; This is such a weird point to make that doesn't become correct just because everyone makes it, all the time. Why clean the ocean if some magic future tech will clean them? Why save the world now if some benevolent AI is 'just around th…

It reminds me of Richard Hamming’s notorious question. I like the summary at https://bestjelly.substack.com/p/hamming-questions (which starts out with a quote from another site): > > Mathematician Richard Hamming used to ask scientists in other fields "What are the most important problems in your field?" partly so he could troll them by asking "Why aren't you working on them?" and partly because getting asked this qu…

https://gwern.net/doc/science/1986-hamming#the-importance-of... https://theonion.com/study-average-person-s-life-plan-can-on...

Re: Eight Myths on Software Engineering and GenAI

#269

Post by mountainriver »

dgellow wrote: post #143
mountainriver wrote: post #20

Earlier quoted context omitted.

It does because you can now just steamroll features out the door and make everyone look bad that’s sitting around in meetings all day

Are you aware literally everyone else also has access to AI stuff? You’re not special for using an agent. You’re part of the crowd

Depends on where you work... where I happen to work people sit in Jira meetings all day and in the time they talk about it my team implements it.

So yes "literally everyone has access to it" (what a revelation) but you still need to be smart in how you use it

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