301–310 of 630 posts
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Its ai written though, the tells are in pretty much every paragraph.
I don’t think it’s that big a red flag anymore. Most people use ai to rewrite or clean up content, so I’d think we should actually evaluate content for what it is rather than stop at “nah it’s ai written.”
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Yep, with Claude saying "please" and "thank you" actually works. If you build rapport with Claude, you get rewarded with intuition and creativity. Codex, on the other hand, you have to slap it around like a slave gollum and it will do exactly what you tell it to do, no more, no less.
this is psychotic why is this how this works lol
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I really don't understand why there are so many comments like this. Yesterday I had Claude write an audit logging feature to track all changes made to entities in my app. Yeah you get this for free with many frameworks, but my company's custom setup doesn't have it. It took maybe 5-10 minutes of wall-time to come up with a good plan, and then ~20-30 min for Claude implement, test, etc. That would've taken me at least…
Trust me I'm very impressed at the progress AI has made, and maybe we'll get to the point where everything is 100% correct all the time and better than any human could write. I'm skeptical we can get there with the LLM approach though. The problem is LLMs are great at simple implementation, even large amounts of simple implementation, but I've never seen it develop something more than trivial correctly. The larger pr…
What are you working on? I personally haven't seen LLMs struggle with any kind of problem in months. Legacy codebase with great complexity and performance-critical code. No issue whatsoever regardless of the size of the task.
Has anyone found a efficient way to avoid repeating the initial codebase assessment when working with large projects? There are several projects on GitHub that attempt to tackle context and memory limitations, but I haven’t found one that consistently works well in practice. My current workaround is to maintain a set of Markdown files, each covering a specific subsystem or area of the application. Depending on the ta…
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I really don't understand why there are so many comments like this. Yesterday I had Claude write an audit logging feature to track all changes made to entities in my app. Yeah you get this for free with many frameworks, but my company's custom setup doesn't have it. It took maybe 5-10 minutes of wall-time to come up with a good plan, and then ~20-30 min for Claude implement, test, etc. That would've taken me at least…
Same here. I did bounce off these tools a year ago. They just didn't work for me 60% of the time. I learned a bit in that initial experience though and walked away with some tasks ChatGPT could replace in my workflow. Mainly replacing scripts and reviewing single files or functions. Fast forward to today and I tried the tools again--specifically Claude Code--about a week ago. I'm blown away. I've reproduced some tool…
I certainly hope this is not true, because then you're not competent for that role. Claude Code writes an absolutely incredible amount of unecessary and superfluous comments, it's makes asinine mistakes like forgetting to update logic in multiple places. It'll gladly drop the entire database when changing column formats, just as an example.
"Most developers type a prompt, sometimes use plan mode, fix the errors, repeat. "
Does anyone think this is as epic as, say, watch the Unix archives https://www.youtube.com/watch?v=tc4ROCJYbm0 where Brian demos how pipes work; or Dennis working on C and UNIX? Or even before those, the older machines?
I am not at all saying that AI tools are all useless, but there is no real epicness. It is just autogenerated AI slop and blob. I don't really call this engineering (although I also do agree, that it is engineering still; I just don't like using the same word here).
> never let Claude write code until you’ve reviewed and approved a written plan.
So the junior-dev analogy is quite apt here.
I tried to read the rest of the article, but I just got angrier. I never had that feeling watching oldschool legends, though perhaps some of their work may be boring, but this AI-generated code ... that's just some mythical random-guessing work. And none of that is "intelligent", even if it may appear to work, may work to some extent too. This is a simulation of intelligence. If it works very well, why would any software engineer still be required? Supervising would only be necessary if AI produces slop.
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I don’t think it’s that big a red flag anymore. Most people use ai to rewrite or clean up content, so I’d think we should actually evaluate content for what it is rather than stop at “nah it’s ai written.”
I think as humans it's very hard to abstract content from its form. So when the form is always the same boring, generic AI slop, it's really not helping the content.
Has anyone found a efficient way to avoid repeating the initial codebase assessment when working with large projects? There are several projects on GitHub that attempt to tackle context and memory limitations, but I haven’t found one that consistently works well in practice. My current workaround is to maintain a set of Markdown files, each covering a specific subsystem or area of the application. Depending on the ta…
Skills almost seem like a solution, but they still need an out-of-band process to keep them updated as the codebase evolves. For now, a structured workflow that includes aggressive updates at the end of the loop is what I use.
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Its ai written though, the tells are in pretty much every paragraph.
I don’t think it’s that big a red flag anymore. Most people use ai to rewrite or clean up content, so I’d think we should actually evaluate content for what it is rather than stop at “nah it’s ai written.”
Earlier quoted context omitted.
Oh no, maybe the V-Model was right all the time? And right sizing increments with control stops after them. No wonder these matrix multiplications start to behave like humans, that is what we wanted them to do.
So basically you’re saying LLMs are helping us be better humans?