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The guy speaking at 3:35 reminds me of a recent blog post by a certain tech celebrity, where he was recalling his recent visit to London and was unhappy to find less white people that he remembered from his previous visit. History repeats itself.
Mind providing a link to the blog post?
Awesome video. So much great content is so easily accessible today. The challenge is discovery! Grateful HN is a quality “feed” - way better than all the algorithmic feeds.. If something as curated as HN existed & appealed to the masses - even if it was ad funded! - we could live in a different world.
The US always has failings, but this message is something we can be proud of.
Earlier quoted context omitted.
the people who need to watch this aren't likely on HN or critically thinking about any of this.
The people who need to watch this are precisely the ones on HN, because we have outsized money and power. Keep in mind it was the tech elite that helped elect Trump. Some of them are here and will see this. Lets see how long until this post is flagged...
This is important for everyone here to watch. A divided house does not stand, and if you haven't noticed, it's getting more divided every day. Don't be a sucker, don't let them divide us. For perspective, we now have masked agents roaming the streets kidnapping people in broad daylight. In the United States. Think about how fast this came. EDIT: Why not have a conversation instead of downvoting. What did I say is wro…
Your second paragraph is implying that the half of Americans who voted for Trump are "bad Americans". That seems to be sowing the division that your first paragraph warns against (even if it is a reason to dislike Trump).
I don't think either democrats or republicans can claim the moral high ground about sowing division.
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I think this is a nuclear bad not only because I think it excuses bad behavior but also because I think it’s just intellectually lazy.
If I’m misinterpreting you please let me know because I hope I’m mistaken.
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How do I demonstrate that I arrived at a conclusion reasonably, with data?
How do I calibrate my probabilities, instead of a binary "safe or unsafe"?
How do I keep from overanalyzing appearance and making incorrect perceptions?
I think the primary sign of danger in your example is being in a dark alley.
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