It’s what’s called in software engineering as “casual software” as a differentiator of “business software” and “critical software”. Not all types needs a high bar of quality, and most of the software engineering thought practices are tailored for business applications that will be made available to multiple users.
As you said, building a script that only you use personally or a very simple thing that just accomplishes one task and it’s easy to test require almost no engineering, and an LLM can often build those with very little downsides.
The way I see it, this is only possible because you can trust the lower layers work reliably and predictably enough that you can move up.
If your operating system was regenerated every day slightly differently and with certain things working and others not, you’d quickly revert to the lower predictable abstraction.
I guess the thing here (which they admit in the post) is that they’re just porting it to Vite, which is the real champ of the story. The LLM basically worked as a translator instead of rebuilding the whole thing from scratch.
So maybe the project is sort of maintainable, as long as people maintain Vite.
Actually, most VPN providers explicitly label the virtual locations as such, I think the famous ones at least do it (ex: Proton and NordVPN even explain them in their respective docs).
It depends on whether the VPN is lying to you. Proton, for example, makes them quite explicit in the software and even lists them for you here: https://protonvpn.com/support/how-smart-routing-works and seems like NordVPN also has a page explaining that.
I have this impression that LLMs are so complicated and entangled (in comparison to previous machine learning models) that they’re just too difficult to tune all around.
What I mean is, it seems they try to tune them to a few certain things, that will make them worse on a thousand other things they’re not paying attention to.
Anything that is very specific has the same problem, because LLMs can’t have the same representation of all topics in the training. It doesn’t have to be too niche, just specific enough for it to start to fabricate it.
One of these days I had a doubt about something related to how pointers work in Swift and I tried discussing with ChatGPT (don’t remember exactly what, but it was purely intellectual curiosity). It gave me a lot of explanations that seemed correct, but being skeptical and started pushing it for ways to confirm what it was saying and eventually realized it was all bullshit.
This kind of thing makes me basically wary of using LLMs for anything that isn’t brainstorming, because anything that requires knowing information that isn’t easily/plentifully found online will likely be incorrect or have sprinkles of incorrect all over the explanations.
It doesn’t really solve it as a slight shift in the prompt can have totally unpredictable results anyway. And if your prompt is always exactly the same, you’d just cache it and bypass the LLM anyway.
What would really be useful is a very similar prompt should always give a very very similar result.
Exactly, it’s really weird to see all this people claiming these wonderful things about LLMs. Maybe it’s really just different levels of amazement, but I understand how LLMs work, I actually use ChatGPT quite a bit for certain things (searching, asking some stuff I know it can find online, discuss ideas or questions I have etc.).
But all the times I tried using LLMs to help me coding, the best it performs is when I give it a sample code (more or less isolated) and ask it for a certain modification that I want.
More often than not, it does make seemingly random mistakes and I have to be looking at the details to see if there’s something I didn’t catch, so the smallest scope there better.
If I ask for something more complex or more broad, it’s almost certain it will make many things completely wrong.
At some point, it’s such a hard work to detail exactly what you want with all context that it’s better to just do it yourself, cause you’re writing a wall of text to have a one time thing.
But anyway, I guess I remain waiting. Waiting until FreeBSD catches up with Linux, because it should be easy, right? The code is there in the Linux kernel, just tell an agent to port it to FreeBSD.
I’m waiting for the explosion of open source software that aren’t bloated and that can run optimized, because I guess agents should be able to optimize code? I’m waiting for my operating system to get better over time instead of worse.
Instead I noticed the last move from WhatsApp was to kill the desktop app to keep a single web wrapper. I guess maintaining different codebases didn’t get cheaper with the rise of LLMs? Who knows. Now Windows releases updates that break localhost. Ever since the rise of LLMs I haven’t seen software release features any faster, or any
Cambrian explosion of open source software copying old commercial leaders.
You’re over complicating something that is very simple. The stock market reflects people’s sentiments: greed, excitement, FOMO, despair…
A bubble doesn’t need a grand catalyst to collapse. It only needs prices to slip below the level where investors collectively decide the downside risk outweighs the upside hope. Once that threshold is crossed, selling accelerates, confidence unravels, and the fall feeds on itself.
Brazil had many times a judge punished WhatsApp by blocking it in Brazil, and all the times that happened, Telegram gained hundreds of thousands of new users.
As you said, building a script that only you use personally or a very simple thing that just accomplishes one task and it’s easy to test require almost no engineering, and an LLM can often build those with very little downsides.