I am finding myself getting sucked into the decisiveness and click baiting nowadays. Want to implement some of these counter measures. Here are my thoughts:
1. DDG or searxng for search
YouTube only via the search engine (not YouTube's recommendations - worse than tiktok)
2. Reuters for news (no CNN, CNBC, MSNBC, fox, Google, Yahoo)
3. No social media (don't do this much anymore already)
4. Turn off personalization for Google (I use this the most)
This should limit a large percent of external influence. Any other thoughts/suggestions?
I understand your point about “boring”. But, I wonder if I will form my own interests if given more balanced options.
> This should limit a large percent of external influence. Any other thoughts/suggestions?
Not really, beyond just trying to give all the information you do receive both the "benefit of the doubt" and a fair bit of skepticism, measuring the message itself, the sender, the state of mind you are when you receive it, etc. Over time you'll probably not only better be able to filter out junk messages, but learn about your own needs and preferences too.
> I understand your point about “boring”. But, I wonder if I will form my own interests if given more balanced options.
That's the hope!
Though HN is a bubble too... don't spend too much time here, lol. (easier said than done... I need to get out more)
Not sure why. Criticizing the republican (especially Trump) government will be hugely profitable - just under 50% of the US population (175M people) is looking for such criticism, and there is no one else to do it. Every day there will be many opportunities to discuss/analyze/criticize/mock/fact-check/challenge.
They should be upsizing, not downsizing. My $0.02.
Hehe. You are right. There are many leftist media outlets.
But my point was not to be partisan; just to note that there will be lots of news to cover. Trump is a controversial figure. Every news outlet will profit. Now is not the time to downsize.
The quality of their news coverage has plummeted since then, and I get the impression it failed to convert the Trump voters they were trying to attract.
For one thing, Trump made fun of them when they were the first to call some state for him on election night.
My question was tongue-in-cheek. I feel the Nobel committee, like every other organization, is also trying to stay relevant and topical. Hard to do this by giving out prizes to old scientists from obscure fields, no matter how important their findings. Hence, a congratulatory prize for the first black president of the USA, a consolation prize for another for losing to Bush, now prizes for computer scientists at the center of the AI fad... An influencer is the logical endpoint for this trend. Other examples of such slipping-down-the-slope: "Time's person of the year" is always a person in the limelight, Oscars favor blockbusters rather than movies with great acting, ditto with music awards, companies that rebranded to the .com fad or the iDevice fad...
Social media influencers aren't going away. They will evolve over time and become more ingrained into our society. It wouldn't surprise me if an economist with a large following got a Nobel. Using social media will just become part of many people's careers. It's part of the future.
This implementation does not because the query has to be a word. One way to extend it to phrases is to average the vectors of each word in the phrase. Another way is to have a word2vec model that embeds phrases. (The large Google News model I think does have phrases, with spaces converted to underscore). But going from words to phrases opens up a whole new can of worms - how large a phrase to consider from the input stream etc. Plus, I don't think averaging words in the phrase is the same as learning the embedding for the phrase. Sentence embedding models are necessary for that, but they are far too slow for this use case as pointed out by others.
To summarize, this is a simple implementation that works for the simplest use case - semantic matching of words.
