661–670 of 1001 posts
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Writing a prime-number factorization function is hardly "leetcode".
Empirical testing (for example: https://news.ycombinator.com/item?id=33293522 ) has established that the people on Hacker News tend to be junior in their skills. Understanding this fact can help you understand why certain opinions and reactions are more likely here. Surprisingly, the more skilled individuals tend to be found on Reddit (same testing performed there).
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That is what we pay managers -to figure out- for. They should find out which and how by knowing the team, familiarity with domain knowledge, understanding company dynamics, understanding customer, understanding market dynamics.
That sounds like a micro manager. I would imagine good engineers can figure out something for themselves.
This is quite interesting to know. I will be curious to see if it has any impact positive or negative over a couple of years. Will the code be more secure since the AI does not make the mistakes humans do? Or will the code, not well enough understood by the employees, exposes exploits that would not be there? Will it change average up time?
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We are sorely lacking a "Make Computer Science a Science" movement, the tech lead's blurb is par for the course, talking about "SWE productivity" with no reference to scientific inquiry and a foundational understanding of safety, correctness, verification, validation of these new LLM technologies.
Did you know that Google is a for-profit business and not a university? Did you know that most places where people work on software are the same?
Did you know? How WEIRD.
How about you not harass other commenters with such arrogantly ignorant sarcastic questions?? Or is that part of corporate "for-profit" culture too????
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We are sorely lacking a "Make Computer Science a Science" movement, the tech lead's blurb is par for the course, talking about "SWE productivity" with no reference to scientific inquiry and a foundational understanding of safety, correctness, verification, validation of these new LLM technologies.
Did you know that Google is a for-profit business and not a university? Did you know that most places where people work on software are the same?
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Even last year's gpt4 could make a whole iphone app from scratch for someone that doesn't know how to code. You can find videos online. I think you are applying the ostrich method which is understandable. We need to adapt.
Complexity increase over time. I can create new features in minutes for my new selfhosted projects, equivalent work on my entreprise work takes days...
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I'm working on a CRM with a flexible data model, and ChatGPT has written most of the code. I don't use the IDE integrations because I find them too "low level" - I work with GPT more in a sort of "pair programming" session: I give it high level, focused tasks with bits of low level detail if necessary; I paste code back and forth; and I let it develop new features or do refactorings. This workflow is not perfect but…
I share your sentiment, I've written three apps where I've used language models extensively (a different one for each: ChatGPT, Mixtral and Llama-70B) and while I agree that they where immensely helpful in terms of velocity, there are a bunch of caveats: - it only works well when you write code from scratch, context length is too short to be really helpful for working on existing codebase. - the output code is pretty…
This is kind of true, my approach is I spend a fairly large amount of time copy-pasting code from relevant modules back and forth into ChatGPT so it has enough context to make the correct changes. Most changes I need to make don't need more than 2-3 modules though.
> the output code is pretty much always broken in some way, and you need to be accustomed to doing code reviews to use them effectively.
I think this really depends on what you're building. Making a CRM is a very well trodden path so I think that helps? But even when it came to asking ChatGPT to design and implement a flexible data model it did a very good job. Most of the code it's written has worked well. I'd say maybe 60-70% of the code it writes I don't have to touch at all.
The slow typing is definitely a hindrance! Sometimes when it's a big change I lose focus and alt-tab away, like I used to do when building large C++ codebases or waiting for big test suites to run. So that aspect saps productivity. Conversely though I don't want to use a faster model that might give me inferior results.
I cannot imagine this to be true, cause imo current LLM's coding abilities are very limited. It definitely makes me more productive to use it as a tool, but I use it mainly for boilerplate and short examples (where I had to read some library documentation before). Whenever the problem requires thinking, it horribly fails because it cannot reason (yet). So unless this is also true for google devs, I cannot see that 25…
My guess is that they counted each line of code made by an engineer using AI coding tools. Besides, even google employees write a lot of boilerplate, especially android IIRC, not to mention simple but essential code, so AI can prevent carpal tunnel for the junior devs working on that.
I work for Google, and I just got done with my work day. I was just writing I guess what you'd call "AI generated code." But the code completion engine is basically just good at finishing the lines I'm writing. If I'm writing "function getAc..." it's smart enough to complete to "function getActionHandler()", and maybe suggest the correct arguments and a decent jsdoc comment. So basically, it's a helpful productivity…
Do people find these AI auto complete things helpful? I was trying the XCode one and it kept suggesting API calls that don't exist. I spent more time fixing its errors than I would have spent typing the correct API call.
I work for Google, and I just got done with my work day. I was just writing I guess what you'd call "AI generated code." But the code completion engine is basically just good at finishing the lines I'm writing. If I'm writing "function getAc..." it's smart enough to complete to "function getActionHandler()", and maybe suggest the correct arguments and a decent jsdoc comment. So basically, it's a helpful productivity…
I also work at google (until last Friday). Agree with what you said. My thoughts are 1. This quote is clearly meant to exaggerate reality, and they are likely including things like fully automated CL/PR's which have been around for a decade as "AI generated". 2. I stated before that if a team of 10 is equally as productive as a team of 8 utilizing things like copilot, it's fair to say "AI replaced 2 engineers", in my…
That's only worth doing if you're trying to cut costs though. If the company has unmet ambitions there's no reason to shrink the headcount from 10 to 8 and have the same amount of output when you can keep 10 people and have the output of 12 by leveraging AI.