351–360 of 1001 posts
Sometimes I wonder why we would want LLMs spit out human readable code. Wouldn’t be a better future where LLMs generate highly efficient machine code and eventually we read the “source map” for debugging? Wasn’t source code just for humans?
To my experience, AIs can generate perfectly good code relatively easy things, the kind you might as well copy&paste from stackoverflow, and they'll very confidently generate subtly wrong code for anything that's non-trivial for an experienced programmer to write. How do people deal with this? I simply don't understand the value proposition. Does Google now have 25% subtly wrong code? Or do they have 25% trivial code…
> the kind you might as well copy&paste from stackoverflow This bothers me. I completely understand the conversational aspect - "what approach might work for this?", "how could we reduce the crud in this function?" - it worked a lot for me last year when I tried learning C. But the vast majority of AI use that I see is...not that. It's just glorified, very expensive search. We are willing to burn far, far more fuel t…
In other words, it's a skill issue. LLMs can only make this worse. Hiring unskilled programmers and giving them a machine for generating garbage isn't the way. Instead, train them, and reject low quality work.
People talk about how AI is bad at generating non-trivial code, but why are people using it to generate non-trivial code? 25% of coding is just the most basic boilerplate. I think of AI not as a thinking machine but as a 1000 WPM boilerplate typer. If it is halucinating, you're trying to make it do stuff that is too complex.
Thats my main problem: for trivial things it works but isnt much better than conventional tools, for hard things it just produces incorrect code such that writing it from scratch barely makes a difference
To my experience, AIs can generate perfectly good code relatively easy things, the kind you might as well copy&paste from stackoverflow, and they'll very confidently generate subtly wrong code for anything that's non-trivial for an experienced programmer to write. How do people deal with this? I simply don't understand the value proposition. Does Google now have 25% subtly wrong code? Or do they have 25% trivial code…
For most of my career, Software Engineering was a misnomer. The field was too young, and the tools used changed too quickly, for an appreciable amount of the work to be systematic and boring enough to consider it an Engineering discipline.
I think we're now at the point where Software Engineering is... actually Engineering. Particularly in the case of large established companies that take software seriously, like Google (as opposed to e.g. a bank).
Call it "trivial" and "boring" all you want, but at some point a road is just a road, and a train track is just a train track, and if it's not "trivial and boring" then you've probably fucked up pretty badly.
Earlier quoted context omitted.
That's basically a non-answer. Measuring "productivity" is a well known hard problem, and managers haven't really figured it out...
It's not a non-answer. Good managers need to figure out what metrics make sense for the team they are managing, and that will change depending on the company and team. It might be new features, bug fixes, new product launch milestones, customer satisfaction, ad revenue, or any of a hundred other things.
Hi, I lead the teams responsible for our internal developer tools, including AI features. We work very closely with Google DeepMind to adapt Gemini models for Google-scale coding and other Software Engineering usecases. Google has a unique, massive monorepo which poses a lot of fun challenges when it comes to deploying AI capabilities at scale. 1. We take a lot of care to make sure the AI recommendations are safe and…
Earlier quoted context omitted.
> the kind you might as well copy&paste from stackoverflow This bothers me. I completely understand the conversational aspect - "what approach might work for this?", "how could we reduce the crud in this function?" - it worked a lot for me last year when I tried learning C. But the vast majority of AI use that I see is...not that. It's just glorified, very expensive search. We are willing to burn far, far more fuel t…
A human can't be trusted to not make memory safety bugs. At the same time we can trust AI with logic bugs.