Comment by jazzyjackson
3 days ago
I’ll explain it: these tools are non-deterministic and people have different experiences with them. For a few people every interaction is totally fumbled and they think the cheerleaders of gen AI must be lying, for others the chatbot hits one home run after another and lets them add microcontrollers to their CAN bus. When these people’s good luck runs out and they start getting mixed results like the average user, they assert the service must have been down graded
I'll add to that: you are more likely to have a good experience if it has a lot of relevant data that it was trained on. You are also more likely to have a good experience if errors don't cause major issues.
So one-shotting a game of Snake should be great (tons of training data, errors are easily caught because it's a small program). Similar with building a lot of web UI front end, or one-shotting a personal project. On the other hand, I haven't been convinced that it's good enough to maintain large codebases or assist with niche topics that are not very well documented.
> if it has a lot of relevant data that it was trained on
This became evident to me the moment I tried to have these models work on some PowerShell tasks for me. Even Opus today struggles with PowerShell.
Since anything in PS is probably some internal sysadmin tool, there's not much public code out there outside of Microsoft's documentation. Plus the Verb-Noun naming scheme makes it really easy to just hallucinate cmdlets (which it does, often). Its easier to have the LLM just do things in python using M365 Graph API than any of the provided PowerShell cmdlets.
OTOH, I've been using Claude for a lot of Swift & Swift UI work lately and it has no problems there, and I'd imagine there's even less publicly available training data for that so to be honest I'm not entirely sure why it fails so badly at powershell.
I have deepseek or grok write bash-likes in pwsh often enough to wonder what sort of things you're doing in pwsh...
I use it to wrap ping.exe with colors and fewer columns, for example. yt-dlp wrapper to fetch 480p bestaudio with English subtitles, no playlist, works on a surprising number of video sites.
It does make cmdlets up, you're right, there.
> On the other hand, I haven't been convinced that it's good enough to maintain large codebases or assist with niche topics that are not very well documented.
Same is true of humans. So far my experience is that addressing the issue with the help of AI is faster than not (ie comprehending the system and creating the documentation).
I don't understand the comments of the kind of "same is true with human".
This feels a bit like whataboutism.
It also feels like people don't listen to each others.
For example, reading the previous comment, it feels like the thing that reduce the enthusiasm was that at first GenAI looks like it was "reading, understanding and using its own knowledge to answer the problem", but as soon as it is a ore niche or a more complex situation, GenAI looks like it "does not understand the code, just does the equivalent of a StackOverflow search and try to apply the solutions that it found there, and this is why it felt like it understood the code before".
It does not at all means that GenAI is not terribly useful. And even better than humans in some situations.
But it feels that answering "same with humans" is missing this point: that's the opposite, humans usually try to understand the code and are bad at covering a very large range of very well documented subjects. That's the "uncanny valley" they talk about: they assumed GenAI performance on a subject X is due to a "human-like" approach, and it feels very strange when this impression falls apart.
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I still don’t get it I can dictate a prompt and sometimes I do it so quickly the text looks like a drunken parrot dictated it and it still always gets exactly what I’m asking for. I’m just going to attribute malice to the naysayers.
Some people are really bad at specifying what they want to ask for. Or they already start prompting with the attitude that it can't possibly work so they don't even really try, or stop at the first failure to point and say how bad it is.
People are really, really bad at specifying what they actually want. I've worked in IT for my whole career, starting in help desk (now an IT manager). My days in the service desk was enough proof that people have no idea what they actually want, or at least, they really struggle to articulate it into words.
It's the famous "email broken, fix pls" but in the form of an LLM prompt.
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Or maybe people see how complex the code is and all the failure points, and don’t feel it’s ethical to use the output. In most of the comments, the most relevant point is that the poster is not an expert in the domain they got helped. While they can observe the result, they don’t have a causal model of the situation.