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Eight Myths on Software Engineering and GenAI

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Re: Eight Myths on Software Engineering and GenAI

#131

Post by slopinthebag »

simonw wrote: post #105
geraneum wrote: post #90

Earlier quoted context omitted.

Does the code get reviewed? How do you deal with increased amount of code that may need to be looked at?

I review the code that matters - anything security adjacent or that's an API that will be used by other code in the future. I don't review code that either works or doesn't - most HTML and CSS layout code for example. There I test it on desktop and mobile and commit it if it works. Ditto for stuff that's simple. A JSON endpoint that runs a SQL query and returns some JSON? If it works and a glance at the tests looks O…

I think it also depends on what you're building. Some solo project or basic html thing? Sure no need to review every line. It's a bit different when you're working on foundational libraries that a business relies on, anything touching a production database, etc.

Re: Eight Myths on Software Engineering and GenAI

#132

Post by nextlevelwizard »

germandiago wrote: post #127
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…

> 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…

Why spend money and time making the new flagship model when a future flagship model can make you the flagship model?

Re: Eight Myths on Software Engineering and GenAI

#134

Post by sevenzero »

ChicagoDave wrote: post #130
sevenzero wrote: post #115

Earlier quoted context omitted.

Also people tend to forget that LLMs still just work on compressed data... Where are the MAJOR breakthroughs? Where is all the "crazy" AI output going? Software seemed to degrade in quality a lot in the recent years. All "improvements" LLMs go through are simply improvements on how to burn more tokens out of my pockets given that Claude now want an actual browser extension to "visually" confirm small changes every ti…

There’s a small group of established architects talking about harness engineering, but I’m not sure anyone is actually listening to them. And those same architects are quietly extracting real productivity from GenAI. And even this write up skips that info by waving, “Some people…”

Idk, maybe some people get crazy productivity out of LLMs. To me, going deep into the AI bubble, reading about terms I've never seen before just feels like some crypto bro bubble with people being too deep into the sauce to notice that these things are not the wonder machines they believe so hard in...

Re: Eight Myths on Software Engineering and GenAI

#135

Post by simonw »

otabdeveloper4 wrote: post #123
simonw wrote: post #103

Earlier quoted context omitted.

I'm planning an article at the moment in defense of lines of code. Saying "lines of code are a bad measurement" is too easy! It's about time someone presented an opposing argument. In my specific case, lines of code for my published open source projects is a metric that I trust, because I have high standards for those. I have plenty of other projects where I'll accept poor quality, unreviewed code (almost all of http…

> Lines of code are akshually a good metric now, because that's the only metric LLMs can optimize and I really, really, really love LLMs, they're the bee's knees. Really now?

No.

Re: Eight Myths on Software Engineering and GenAI

#136

Post by germandiago »

nextlevelwizard wrote: post #132
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…

Why spend money and time making the new flagship model when a future flagship model can make you the flagship model?

Then why not stop researching and doing the definitve model that will solve every problem? Why some people are not doing it?

Bc they are aware of the marketing and limitations. If they did believe it, then they would switch area of research.

Re: Eight Myths on Software Engineering and GenAI

#137

Post by skydhash »

simonw wrote: post #103
the_af wrote: post #77

Earlier quoted context omitted.

Isn't this falling into the "lines of code" trap TFA mentions? More code written is not a good measure of productivity. It could be garbage, or redundant code, or simply not addressing the real or more pressing needs, it could be building the wrong thing, etc. Like TFA mentions, it's been known for decades LoC is a misleading metric for productivity. It's one of the lessons of software engineering.

I'm planning an article at the moment in defense of lines of code. Saying "lines of code are a bad measurement" is too easy! It's about time someone presented an opposing argument. In my specific case, lines of code for my published open source projects is a metric that I trust, because I have high standards for those. I have plenty of other projects where I'll accept poor quality, unreviewed code (almost all of http…

> Saying "lines of code are a bad measurement" is too easy! It's about time someone presented an opposing argument.

Maybe because there is none.

One of the main quality of good codebase is simplicity. Which is about how easy for someone else to understand the code. It’s hard to define what simplicity looks like, so the best bet is to avoid the other side, making the code too complex.

And you can make the code complex by shortening variable name, doing code golfing with quicks of the platforms, so smaller LoC. You can also go the other way and increase the LoC by adding unneeded abstractions, repeating slices of code,… There’s a window where the LoC is perfect to attain simplicity, but that amount is an effect of striving for simplicity, not a cause of it. And it’s variable for every problem.

So you got something where the correct value is a different for each case. And trying to manipulate it artificially often results in complex code. And you want to say that is a good metric for productivity?

And in the cases of your projects, there are a lot more info could share that are interesting, like the amount of issues (reported or found by you) that are tied to implementation bugs (coding, libraries API breakage,…) or design issues (requirements conflicts,…), documentation improvement,… Anything that is tied to the actual usefulness of the projects, and not fumbling around with code.

Re: Eight Myths on Software Engineering and GenAI

#139

Post by dgellow »

ChicagoDave wrote: post #130
sevenzero wrote: post #115

Earlier quoted context omitted.

Also people tend to forget that LLMs still just work on compressed data... Where are the MAJOR breakthroughs? Where is all the "crazy" AI output going? Software seemed to degrade in quality a lot in the recent years. All "improvements" LLMs go through are simply improvements on how to burn more tokens out of my pockets given that Claude now want an actual browser extension to "visually" confirm small changes every ti…

There’s a small group of established architects talking about harness engineering, but I’m not sure anyone is actually listening to them. And those same architects are quietly extracting real productivity from GenAI. And even this write up skips that info by waving, “Some people…”

Mind sharing some names or something else we can learn about?

Re: Eight Myths on Software Engineering and GenAI

#140

Post by aryehof »

paul7986 wrote: post #36
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…

By trade I'm a UX Researcher/Designer who designs in code (HTML/CSS) and have done so since 2009. Recently I vibe coded an entire python app with a database and each time I didnt know what to do I would just feed screenshots to Gemini or Codex for guidance (i think i could share my screen with Codex and it can guide me via a voice conversation). I know I could follow up and build a companion iPhone and Android app us…

The industry has vast (and increasing) oversupply of “programmers” versus diminishing demand. Add to this, the adoption of AI.

> Overall, I'd like to understand those who have a positive outlook on design and software engineering as a career.

I think until the market better achieves some equilibrium, there is no way general software programming (sorry “engineering”) should be considered as a career. That said, there will always be opportunities in particular markets or specialties.

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