The premise might possibly be true, but as an actually seasoned Python developer, I've taken a look at one file: https://github.com/dx-tooling/platform-problem-monitoring-co...

All of it smells of a (lousy) junior software engineer: from configuring root logger at the top, module level (which relies on module import caching not to be reapplied), over not using a stdlib config file parser and building one themselves, to a raciness in load_json where it's checked for file existence with an if and then carrying on as if the file is certainly there...

In a nutshell, if the rest of it is like this, it simply sucks.

As a long time hobby coder, like 25 years and I think I’m pretty good(?), this whole LLM /vibecoding thing has zapped my creativity the past year or so. I like the craft of making things. I used tools I enjoy working with and learn new ones all the time (never got on the JS/react train). Sometimes I have an entrepreneur bug and want to create a marketable solution, but I often just like to build. Im also the kind of guy that has a shop he built, builds his own patio deck, home remodeling, Tinker with robotics, etc. Kind of just like to be a maker following my own creative pursuit.

All said, it’s hard on me knowing it’s possible to use llm to spit out a crappy but functional version of whatever I’ve dreamt up with out satisfaction of building it. Yet, it also seems to now be demotivating to spend the time crafting it when I know I could use llm to do a majority of it. So, I’m in a mental quagmire, this past year has been the first year since at least 2000 that I haven’t built anything significant in scale. It’s indirectly ruining the fun for me for some reason. Kind of just venting but curious if anyone else feels this way too?

Increasingly I’m realizing that in most cases there is a SIGNIFICANT difference between how useful AI is on greenfield projects vs how useful it is on brownfield projects. For the former: pretty good! For the brownfield, it’s often worse than useless.

Aah.. I have been repeating this to my team for several days now. Thanks for putting this together.

I start every piece of work, green or brown, with a markdown file that often contains my plan, task breakdown, data models (including key fields), API / function details, and sample responses.

For the tool part, though, I took a slightly different approach. I decided to use Rust primarily for all my projects, as the compile-time checks are a great way to ensure the correctness of the generated code. I have noticed many more errors are detected in AI-generated Rust code than in any other language. I am happy about it because these are errors that I would have missed in other languages.

That prompt looks horrifying.

I am not going to spend half an hour coming up with that prompt, tweaking it, and then spend many hours (on the optimistic side) to track down all the hallucinated code and hidden bugs. Have been there once, never going to do that again.

I'd rather do it myself to have a piece of mind.