I built my private version of Infinity for Android. The steps were 90% automated. They were in a Reddit thread like this one (I just pasted the first thing I found now), although I don't know if the build process still works. https://www.reddit.com/r/Infinity_For_Reddit/comments/1izm7c... -- you have to side-load the app, which is a little tricky because you have to load it onto your phone. I think put it on a private Google Drive.
Maybe similar solutions exist for iOS, but maybe the side-loading is not as easy?
Anyway, on Windows/Chrome I use uBlock Origin and never see any ads in Reddit online. There was a lot of drama about reddit selling out in 2023 (I was sad to see Infinity die), but there are tech solutions to avoid its enshittification.
I recently experimented with Gemini on Colab for building a discrete simulation in Python—initially started with ChatGPT, then moved platforms due to free-tier limits. Gemini was responsive in analyzing graph outputs and made quick progress with rapid prototyping. However, when I shifted focus to refactoring and improving code structure, e.g., extracting classes and encapsulating behavior, it defaulted to a weird hybrid class/functional approach, often placing logic outside domain objects rather than applying polymorphism. Even after I explicitly mentioned principles like "Tell, don’t ask," I had to insist before it adjusted its design choices accordingly. I asked why those principles are NOT there by default, and it said basically most coders don't use them and it seeks direct solutions.
While Gemini performed well in tweaking visualizations (it even understood the output of matplotlib) and responding to direct prompts, it struggled with debugging and multi-step refactorings, occasionally failing with generic error messages. My takeaway is that these tools are incredibly productive for greenfield coding with minimal constraints, but when it comes to making code reusable or architecturally sound, they still require significant human guidance. The AI doesn’t prioritize long-term code quality unless you actively steer it in that direction.
I sometimes imagine, even before LLMs, how much human-written code still matters, like the DNA that has survived evolution. It's got to be a super low percentage.
The trend with Autocomplete Industrialization (AI) is just speeding up the creation of shanty towns of code, as opposed to architecturally robust foundations. The survivability of code is dropping because of the Copilotz. But perhaps this rapidity of creating crude solutions will increase the chances of something truly significant?
p.p.s there are too many times "it's" should be "its" in that blog post to have been AI-generated. That's a different kind of irony IMO, especially since that's just tricky English syntax (the thing AI is supposed to be good at). Maybe the author used AI to come up with the snarky metaphors. I asked ChatGPT for a sarcastic meaning for AI that starts with Autocomplete :)
It's anecdotal, but several nasty bugs involved code committed (according to git blame) between 11pm and 6am. Sometimes I hardly recognized the code as mine.
As a Mac developer (system 6, 7) in the late 80s, I always wrote in C. But Mac devs couldn't escape Pascal, because the many Mac OS functions (in the ROM) were from Pascal.
You had to differentiate between C and Pascal strings, and you'd better well have declared that Mac OS call-back function in C with the 'pascal' keyword, lest you be shocked when your software crashes and you debug it in MacsBug to find the order of arguments is reversed on the stack! Doh!
In the early 90s I coded lots in Modula 2 (for what it's Wirth) on Solaris, but never got much chance to do Pascal directly.
Maybe similar solutions exist for iOS, but maybe the side-loading is not as easy?
Anyway, on Windows/Chrome I use uBlock Origin and never see any ads in Reddit online. There was a lot of drama about reddit selling out in 2023 (I was sad to see Infinity die), but there are tech solutions to avoid its enshittification.