The current wave of AI companies did this to themselves. Had things moved more slowly and actively worked with all the affected industries, I suspect people would be far less interested in seeing the technology fail.
The goal was to raise as much money as possible as fast as possible before the curtain is pulled back to reveal the Wizard's empire of lies.
Agreed. KDE apps are slowly getting feature parity between crossplatform builds, Kate's nearly there but Dolphin is still missing some features on macOS.
Hope there's a day I can just use Dolphin on any system
If a real person gave them this advice, like a doctor or pharmacist, there would be standing for a lawsuit, might even be criminal.
Looking past "drugs bad mkay", the same ChatGPT that gave this advice is just as capable of giving the same, or worse, advice to someone wondering if they can take an allergy medication like Benedryl with their MAOI antidepressant.
If I had a hammer robot that I told to go hammer some nails in a birdhouse and it goes "Sure, I'm on it!" then it nails a cat to the wall and says "Here's you new complete birdhouse, it's perfect in everyway and will make everyone jealous", then yes, that is a tooling issue.
All modern PCs ship with Pluton coprocessors. The end-to-end remote attestation hardware infrastructure is all already there, waiting for someone to flip a switch and turn it on.
When it first shipped out, Secure Boot was used to lock other OSes out on early devices, it was after pushback that it was implemented such that it allowed you to enroll your own keys.
That said, there are countless mobile devices with locked bootloaders and and boot integrity attestation that will never run anything other than OEM OSes. That's equivalent to a locked Secure Boot + UKI-like system on PCs and it's already here.
All of the "solo green field projects" I let LLMs mostly write, despite supplying the scaffolding, structure and specific implementation details as code, prompts or context, I can't tell you much about 6+ months later, except for the parts I did write.
It's like I never wrote them, because I didn't. I've got the gist of them, but it's the same way I get the gist of something like Numpy: I know how it works theoretically, but certainly not specifically enough to jump in and write some working Fortran that fixes bugs or adds features.
I now have a bunch of stalled projects I'm not very familiar with. I no longer do solo green field projects that way.
It's important that developers have an accurate mental model of how things work, are structured and why.
LLMs promote a decoupling of mental models and the actual codebase.
As much as some may want to believe, just reviewing what the LLM outputs is not equivalent to thinking about implementation details, motivations, exactly how and why things are, and how and why they work the way they do, and then writing it yourself. The process itself is what instills that knowledge in you.
It helps if you're doing mundane things and want to help people who need to mix their sensitive traffic with it.
More people "legitimately" using Tor makes it less likely to have its exit nodes outright blocked, as well, and assuming all traffic from them is malicious.
This is the case. The advent of libraries like Rich and others certainly helped, along with the trend of Rust TUIs for system programming/lack of good GUI options.
They're appealing to entities that have surveillance and voyeuristic fetishes, showing that you can ubiquitously invade privacy in real-time, even in spaces society considers sensitive, is a feature worth demonstrating if you want to get contracts from psychopaths.