Matsuoka Shuzo [松岡修造 ] - あきらめかけているあなた (NEVER GIVE UP!!) [English] on HackerTube: recommended by 2 Hacker News commenters across 2 separate months, 2018–2025.
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A short English-subtitled clip of Shuzo Matsuoka, a retired Japanese tennis player turned motivational speaker, delivering a never-give-up pep talk. It is a well-known Japanese internet meme.
HN commenters post it as a quick dose of encouragement, or as a joke reply.
“this brief inspirational vid may help with the hard slog needed to finish”
5 Hacker News comments link this video. Each one links back to its thread.
For a different phase .. if you find your way beyond the current(?) association between the work and an understandable repulsive toxicity experience .. this brief inspirational vid may help with the hard slog needed to finish: ▶ this video <3, stranger
in “I've procrastinated working on my thesis for more than a year” · View on HN
The consequence of all this: Websites, apps and advertisers providing consumers with everything from news, games and consumer goods make less money selling ads and have to fork over more money to exchanges and other intermediaries. According to industry studies, ad intermediaries take 30 to 50 percent of every trade. In other words, when your local gym buys $1,000 worth of advertising on a local news site, the exchanges and other industry players can take $500 of that, leaving websites and apps with just half. Who pays the price? Again, the consumer, through things like more expensive gym memberships and news subscriptions. Thank you so so so much, Ms. Srinivasan! You are very courageous for fighting a Goliath! Keep fighting the good fight! And in case you need more cheering up, here's something else: ▶ this video
in “Google Is Dominating This Hidden Market with No Rules” · View on HN
I'm very adamant that if you really want to be an AI researcher, it starts with the mathematics. Multi-variable calculus, linear algebra,discrete math, probability and statistics are key. The classic books and courses others have suggested are excellent starting points. I'll also say something here that isn't established, so I may take some heat. I believe there are two main paths in AI that will eventually converge to general AI: symbolic and sub-symbolic reasoning. If you go the path of symbolic reasoning, studying functional programming, theory of computation,type theory, natural language processing/compilers are in your future. If you go the path of sub-symbolic reasoning, you will be closer to optimization methods, neural networks, etc. It really depends on what you want to do. Ex: Computer vision is all about sub-symbolic reasoning, while natural language processing is heavily about symbolic reasoning. Of course advanced applications mix both! If you want to go after general AI, you gotta figure out how to tackle both forms of reasoning. In the end, if you are serious about being an AI researcher, you will have to be a great computer scientist and mathematician. This is why it seems so difficult to get into. "Do I focus on working on messing with libraries and algorithms or the mathematical theory? And if I do both, how!?" It's not easy, but just start and keep going. Others have provided great advice. One of my greatest joys in life has been coming to understand the ma
in “Ask HN: How to Seriously Start with Machine Learning and AI” · View on HN
Never give up! ▶ this video I can't tell if your "give up" is a reference to the "never gonna give you up" line from the rick roll video. The internet is a web of labyrinthine references, and I don't know what means what, if anything anymore.
in “Konva.js - Declarative 2D Canvas for React, Vue, and Svelte” · View on HN
I was fully expecting [1] but then I just listened for a while and honestly, I get it. I can't explain, but yeah. [1] ▶ this video
in “I have made the decision to disband Hindenburg Research” · View on HN