I'm working on a news analysis project where I'll have to identify articles that speak about the same event. For now, I think it'll be based on date + "asking an LLM", but you probably came up with something better. How do you approach this problem?
There was a report posted on HN months ago showing the proportion of fraudulent vs legitimate activities on Tor vs non-Tor across a pool of websites. Tor users were (slightly, IIRC) more likely to be malicious, but they represented a drop in the bucket against all malicious users. Maybe some sites that do block Tor might be seeing different proportions, I'd wager most of them do it because Tor == bad for most people.
The paragraph starting with "it would be a shame..." has their entire point laid out in simple, unambiguous terms. There's no mention of human nature or calls to not worry.
A workaround I saw a few months ago on HN: for both Organic Maps and OSMand, you can use Acastus to search for something and then open the result in the maps app (+ share the broken query with Organic Maps, as requested by the founder).
I'm trying to find a good project to work on to start learning QGIS, and what you described sounds interesting. Would you happen to have a write-up of this?
Dang, you're a model for my own communication style, a source of indirect advice for a project that I'm building (one day I'll be ready to reach out to you for direct advice), and someone I see as an example of a good human. Thank you!
As I said, it's way out of my wheelhouse, but I'm planning on spending some time in the next months on trying to merge a decrypted backup of old iOS Signal chat history into the decrypted backup of current Android chat history and trying to restore from that (re-encrypted) backup (there is no native iOS to Android transfer on Signal yet), so I'm starting to look into any learning material that will allow me to not fail within the first 2 minutes of trying :).
Re keychain, you're probably aware of it, but https://github.com/ptoomey3/Keychain-Dumper/ is very thorough in extracting keychain data (including data that one would expect to no longer be in there).
For those who are going through the thread finding new tools: pandas-profiling[0] is a library for automatic EDA (which bamboolib[1], mentioned elsewhere, also does).
Probably little to no effect, but I'd hope it's slightly more effective than just tweeting a hashtag (which is arguable simpler than my "simplest" suggestion) because it will actually pierce the techie anti-privacy bubble and be heard by/bother the .
To be honest, I'd wager that most of the benefit in doing something as little as sending copy-pasted emails is that it increases the probability of the protester doing a more meaningful action in the future (by generating some cognitive dissonance, by reinforcing a positive internal narrative, by weakening the illusion of powerlessness, etc.).
For those who won't read the article: it provides a few ways to protest [0] (contact Commissioners, request media to cover this issue, get active on social media) and offers a list of arguments [1] (such policies don't work against CSAM but make it worse, while better/more targeted methods are being ignored).
If anyone has experience calling to protest, could they share it here? Otherwise, the simplest course of action might be to copy-paste the arguments given in [1] into an email addressed to the parties mentioned in the parent comment.
Would you be able to list some of the negative developments you expect from GitLab going forward? For example, I know they are reducing support for Free Tier on their managed instance (https://news.ycombinator.com/item?id=30791162). Is that the kind of thing you're talking about?
Great resource, a surprisingly clear and detailed introduction to the various attacks faced by websites!
The relevant part:
> "we concluded that approximately 1 in 380 http requests coming out of Tor is verified to be malicious, while only 1 in 11,500 http requests coming out of a non-Tor ip were verified to be malicious. In essence, an http request from a Tor ip is 30 times more likely to be a malicious attack than one that comes from a non-Tor ip."