It discovered the tablet was running a unisoc t606, found a CVE from a couple years ago, and unlocked the bootloader for me. I was the meat puppet holding the "volume up" button and plugging in the usb cable a bunch of times. Like most of my experiences with this stuff, it was pretty eerie.
Next step for me is to attempt mainline linux, there seems to be some postmarketOS devs playing with it. We've probed most of the tablet's hardware except the exact display.
What sort of token spend did this take?
The key is to have downstream sources and be very very conservative with the AI, slowly build step by step.
You also have to know C and have a spider sense of what's acceptable or not.
Another key is to ask for approval before editing any source with a patch of what it intends to do. This way you can judge what it wants to do and ask for a double check of the patch. Go quality over quantity.
This isn't web frontend with Tailwind, you have to be very strict and somewhat knowledgeable. Nobody can use AI to write kernel code without some good low level and engineering knowledge.
As for PostmarketOS, I've built my own tooling scripts around it to make it easier to build patches, debug hex variables, switch between downstream/mainline and rebuilding everything with a single command. (Unrelased yet though).
I find their tooling okay for a release for end-users but a bit clunky for debugging.
My address is my username @ism.rocks
Alternatively, if you released the article on your blog, I could just follow the RSS feed.
I completely agree, this is not the place to let AI blindly edit kernel code. The useful approach is to use it conservatively: understand the error, compare against downstream sources, propose a small patch, review it, test it, and then move one step further.
I’d be happy to work together on an article or guidance document, where to start, how to approach debugging, what to never let AI touch blindly, and how to build confidence step by step. That could help others avoid a lot of mistakes and maybe give a second chance to other devices.
I prefer spending my time doings I actually want to do. Let the machine do the boring things.
You can be dedicated to Biomedical Medical science and your whole world may revolve around it. You may be the smartest person in any given room, although sometimes it might not be worth learning something else given your time constraints or energy constraints.
If said Biochemist needed to write a simple Python script, why would he bother learning Python, setting up the .env and debugging when an AI could do it and he could go back to doing whatever he was doing?