• 6 Posts
  • 22 Comments
Joined 4 years ago
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Cake day: June 30th, 2020

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  • Once I had to use the internet without and ad blocker ( shiver ). It was horrible. I still have nightmares.

    Joking aside. I couldn’t believe how crammed full and chaotic sites were without an ad blocker. I have no evidence to support this other than my experience but I think , for me , ad blockers are good for my mental health. Being constantly exposed to all those messages trying to exploit insecurities can’t be good for people.

    Anyways ad blockers are the best.


  • This seems very obvious to me , not that it isn’t worth highlighting. Particularly in a world with open models and weights , which we should desperately want. The don’t worry water marks will be a thing just seems like an attempt have some response that dampers concerns. I don’t imagine most people working in the AI space actually think this would work. I could be wrong.


  • Yeah I 100% understand and to a large extent agree with this. I think money should be involved , creators should get paid. I don’t think peertube has become “the answer” yet and there is some combination of market level event and technology/feature set that needs to be in place to create enough moment for people to move off YouTube. It will happen eventually ( I think ) but what exist today isn’t enough of a pull to overcome the momentum YouTube has but that doesn’t mean that “we” should give up.










  • I’ve used StandardNotes for years. They are great, very privacy friendly and lots of good features. I’ve also used Obsidian like others have mentioned but I didn’t use 95% of the features on either standard notes or Obsidian – now days I just use a general markdown files and store them in a git repo – low complexity and I like the simplicity of it. 100% recommend.





  • No worries. Honestly , this is a pretty hard question to answer. I’ve always been NB, but for a long time I didn’t have the ‘words’ or ‘desire’ to acknowledge it. For me it is very much a combination of ‘feeling’ and ‘philosophy’ – It started as an acknowledgment that I was pretending a lot , mostly to try and fit into gendered social circles. For me I wasn’t pretending to be male or female , I was pretending to be either. Once I acknowledge that I was pretending to feel and like or not feel and not like specific things to fit into social structures I started to be more honest with myself and actually be open to who I am. It was and continues to be a Journey. Very similar to you I realized that I “can’t be bothered with [socially] constructed masculinity or the version of masculinity my family expected from me and started distancing myself.” At first I distanced toward Fem but that didn’t work for me either – it does for a lot of people , many of which are probably here and I am absolutely in love with the fact that it does work for them and inspired to continue figuring out my own truth. Eventually , I was really tired of not ‘knowing’ where I fit and just decided to not give a F*** and set everything aside and embrace my non-definable-self. This worked for me and was a literal weight off my shoulders. It just feels right for me. I call this NB. Others do it differently.

    I feel bad going off topic to NB stuff in this thread/community meant to celebrate and unite TransFem ( I love my chosen family and community ) but also didn’t want to not respond to you given all of this stuff is hard and we all need support. Happy to answer more questions in Direct Messages if you want. Hope this is helpful : )








  • Hey, I am a machine learning engineer that works with people data. Generally you measure bias in the training data, the validation sets, and the outcomes ( in an ongoing fashion - AIF 360 is a common library and approach ). There are lots of ways to measure bias and or fairness. Just checking if a feature was used isn’t considered “enough” by any standards or practitioner. There are also ways to detect and mitigate some of the proxy relationships you’re pointing to. That being said, I am 100% skeptical that any hiring algorithm isn’t going to be extremely bias. A lot of big companies have tried and quit because despite using all the right steps the models were still bias https://www.reuters.com/article/us-amazon-com-jobs-automation-insight-idUSKCN1MK08G. Also many of the metrics used to report fairness have some deep flaws ( disprate impact ).

    All that being said the current state is that there are no requirements for reporting so vendors don’t do the minimum 90% of the time because if they did it would cost a lot more and get in the way of the “AI will solve all your problems with no effort” narrative they want to put forward so I am happy to see any regulation coming into place even if it won’t be perfect.