

Uptime Kuma, only really care if it’s up or not. Got about 30 services monitored, sending notifications to Signal.


Uptime Kuma, only really care if it’s up or not. Got about 30 services monitored, sending notifications to Signal.


Thats not a difference. Other clean room projects, for example ReactOS, have source projects that are incompatibly licenced. Its the entire point of clean room reversing, to bypass copyright and licencing.
The training data is on the dirty room side, it doesnt get passed to the clean side.
The clean room concept is violated if raw source code is in the model generated, but to my knowledge its not. There is no way to pull out a chunk of the model, and say “This is a direct copy of main.c from xyz project”. At best, you can ask the LLM to guess what it should be, and it’ll regurgitate something statistically close. I just tried asking my local qwen3.8, and did produce a close summary of the nginx main file, but it wasn’t even close to a 1 to 1 copy.
The clean room would also be violated if you consider the training, model and inference as a singular entity, but that would be tough to argue when the inference can happen on one server, the resulting model isn’t functional in any way, its a big box of data, and the inference can happen on any GPU anywhere else in the world.


Clean room reversing is just laundering code, always has been.
Rewriting code into English/GGUF/Klingon/whatever, and then getting someone else to rewrite it back into code is just laundering it. Its been legally accepted for a while, and many projects we take for granted today started that way.
Whether this case is “clean” enough is up to the courts ultimately, but its laundering either way.


It is a good idea. Firmware updates can improve stability and performance. Just because it failed this time doesnt mean that all the other times it silently worked were also bad ideas.


Just sue them already and leave us alone. You are spamming this story everywhere, and most of the time its the wrong place. Go away.
The vast majority of web browsers run JS, any browser that doesn’t run the JS, and therefore doesn’t hit the fingerprinting endpoint, is defacto fingerprinted.


They usually also have emails and other IDs, might have to wait a while for them to add it.


https://haveibeenpwned.com/ - you may need to wait a bit for them to add the latest leaked datasets.
Change passwords never hurts.
Noscript has 100k users on chrome, 240k on Firefox, according to their respective stores. I don’t know if thats total downloads or active installations though.
Ublock origin lite is 19M on chrome.
There are ~6B global internet users. So definitely more than 1:100k, but still very unique.
So they are very different scales.
Adblock, incognito tabs, VPN. Probably other good alternatives as well.
If they can’t sell you ads, fingerprinting you doesnt gain them much.
And if you can reset/perturb your fingerprint enough via incognito/vpn, they can’t track you for dynamic pricing shenanigans.
Blocking JS is a fingerprint, and a pretty unique one. If you care about your privacy, forget about fingerprinting and instead mitigate its impacts instead.


Yeah, there would be a lot of potential for leaks, but if they are one-time-codes that wouldn’t be too bad.


I’ve had the same though re. shipping addresses, but I don’t think it scales well. You’d need every postie to have an online device that allows them to link QR code/uuid code to the real address, and delivery just gets a lot harder.
In Australia, AusPost allows you to generate a “virtual” PO box, which is a lot easier to deal with, but you have to go to the post office to pickup.


If the merchant is hoarding the data, they want to do it. If they want to do it, why would they use your framework?
Also, merchants need your name and address to ship the product to you…


Detecting motion is relatively easy, but actual positioning would require knowledge of where the WiFi router is placed within the home, and where the other static WiFi devices are. So position detection is realistically about as much as they could do without calibration.
The xfinity one is more about smart home stuff, and is opt-in (and it’s a fair point to distrust that).
The reason I don’t think they are tracking motion/position within your home is that there just isnt any useful way to turn that into a targeted ad, and therefore has no viable revenue path. At best, they could count how many times you go to the bathroom, maybe target a metamucil ad, but its so niche (and likely extremely noisy data) that its just not useful.
They know when your at home already, as your phone will connect to your network, and your network traffic (VPN/encrypted or not) will spike. They don’t need WiFi positioning to determine that.
I’m not saying people should just trust things are okay, its more that there are much higher priorities to tackle. WiFi positioning is way down the list of realistic concerns.


Realistically, your router isnt tracking your movement in your house. It may be possible, but there just isnt any value to doing that.
https://cameroncros.github.io/wifi-condom.html
That’s a little out of date, but might be useful if you want a WiFi router to protect yourself in a hotel. Pointless at home though.


KYC is not just for funsies, they are legal requirements for a lot of businesses. If they don’t legally have to store information, they can just choose not to. It doesnt require a framework, Lemmy manages just fine without storing personal data.


Thats happened for ages for me, and its because the headunit needs to connect directly to your phone, which many VPN apps break intentionally.
At the cost of a little disk space, you can install KDE,gnome,i3,etc, and they can try each.
Is this user far away, and will they need support?
Except that they are separated. The clean side (model + implementors) doesnt have the original source code. The dirty side is the training room, which does have original source.
The model that you download from huggingface is basically the notebook passed between the two rooms, and as far as im aware, the model doesnt have the actual training data in it, just enough statistics to “hallucinate” something close.