41–50 of 75 posts
Rant mode on. For the second time of the week this morning, I spent 45 min reviewing a merge request where the guy has no idea what he did, didn’t test, and let the llm hallucinate a very bad solution to a simple problem. He just had to read the previous commit, which introduced the bug, and think about it for 1min. We are creating young people that have a very limited attention span, have no incentive to think about…
I'm a coding tutor and the most frustrating part of my job is when my students use LLM generated code. They have no clue what the code does (or even what libraries they're using) and just care about the pretty output. Whenever I try asking them questions about the code one of them responded verbatim "I dunno" and continued prompting ChatGPT (I ditched that student afterward). Something like Warp where the expectation is to not even interact with the terminal is equally bad as far as I'm concerned since students won't have any incentive to understand what's under the hood of their GUIs.
To be clear, I don't mind people using LLMs to code (I use them to code my SaaS project) but what I do mind is them not even trying to understand wtf is on their screen. This new breed of vibe coders are going to be close to useless in real world programming jobs which when combined with the push targeted at kids that "coding is the future" is going to result in a bunch of below mediocre devs both flooding the market and struggling to find employment.
Every study I've read says nobody is seeing productivity gains from AI use. Here's an AI vendor saying the opposite. Funny.
There's a few explanations for this, and it's not necessarily contradictory. 1. AI doesn't improve productivity and people just have cognitive biases. (logical, but I also don't think it's true from what I know...) 2. AI does improve productivity, but only if you find your own workflow and what tasks it's good for, and many companies try to shoehorn it into things which just don't work for it. 3. AI does improve prod…
This options is insidious in that not only people initially asked about the effect are initially oblivious, it is very beneficial for them to deny the outcome altogether. Individual integrity may or may not overcome this.
My (merged) PR rate is up about 3x since i started using claude code over the course of a few months. I correspondingly feel more productive and that i have a good grasp of what it can and cannot do. I definitely see some people use it wrong. I also see it fail on some tasks id expect it to succeed at, such as abstracting a singleton in an ios app i am tinkering with, that suggests its not merely operator error but also that its skill is uneven dep on task, ecosystem, and language.
I am curious for those that use it regularly, have you measured your actual commit rates? Thats ofc still not the same as measuring long term valuable output, but were still a ways off from being able to determime that imho.
> This indicates that AI outputs are perceived as useful and valuable by many of this year’s survey respondents, despite a lack of complete trust in them. Or the respondents have hard times admitting AI can replace them :-) I'm a bit cynical but sometimes when I use Claude, it is downright frightening how good it is sometimes. Having coded for a lot of year, I'm sometimes a bit scared that my craft can, somtimes, be…
Anyone got a pulse on what the art community thinks?
Earlier quoted context omitted.
I've just started immediately rejecting AI pull requests. I don't have time for that. There's going to be a massive opportunity for agencies that are skilled enough to come in and fix all of this nonsense when companies realize what they've invested in.
Almost worse is AI bug reports. I've gotten a few of them on GitHub projects, where someone clearly pasted an error message into ChatGPT and asked it to write a bug report... and they're incoherent .
Every study I've read says nobody is seeing productivity gains from AI use. Here's an AI vendor saying the opposite. Funny.
There's a few explanations for this, and it's not necessarily contradictory. 1. AI doesn't improve productivity and people just have cognitive biases. (logical, but I also don't think it's true from what I know...) 2. AI does improve productivity, but only if you find your own workflow and what tasks it's good for, and many companies try to shoehorn it into things which just don't work for it. 3. AI does improve prod…
> This indicates that AI outputs are perceived as useful and valuable by many of this year’s survey respondents, despite a lack of complete trust in them. Or the respondents have hard times admitting AI can replace them :-) I'm a bit cynical but sometimes when I use Claude, it is downright frightening how good it is sometimes. Having coded for a lot of year, I'm sometimes a bit scared that my craft can, somtimes, be…
When I see the fabulous images generated by AI, I can't help but wonder how artists feel. Anyone got a pulse on what the art community thinks?
Rant mode on. For the second time of the week this morning, I spent 45 min reviewing a merge request where the guy has no idea what he did, didn’t test, and let the llm hallucinate a very bad solution to a simple problem. He just had to read the previous commit, which introduced the bug, and think about it for 1min. We are creating young people that have a very limited attention span, have no incentive to think about…
Why did you I spent 45 min reviewing instead of outright rejecting it? (Honest question.)
Earlier quoted context omitted.
There's a few explanations for this, and it's not necessarily contradictory. 1. AI doesn't improve productivity and people just have cognitive biases. (logical, but I also don't think it's true from what I know...) 2. AI does improve productivity, but only if you find your own workflow and what tasks it's good for, and many companies try to shoehorn it into things which just don't work for it. 3. AI does improve prod…
I think it's 5. I was very impressed when I first started using AI tools. Felt like I could get so much more done. A couple of embarrassing production incidents later, I no longer feel that way. I always tell myself that I will check the AI's output carefully, but then end up making mistakes that wouldn't have happened if I wrote the code myself.
* Checking it closely myself, which sometimes takes just as long as it would have taken me to implement it in the first-place, with just about as much cognitive load since I now have to understand something I didn't write
* OR automating the checking by pouring on more AI, and that takes just as long or longer than it would have taken me to check it closely myself. Especially in cases where suddenly 1/3 of automated tests are failing and it either needs to find the underlying system it broke or iterate through all the tests and fix them.
Doing this iteratively has made the overall process for an app I'm trying to implement 100% using LLMs to take at least 3x longer than I would have built it myself. That said, it's unclear I would have kept building this app without using these tools. The process has kept me in the game - so there's definitely some value there that offsets the longer implementation time.