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OK let’s try this: ———————- Pretend you’re 5 and the algorithm is a smart robot that picks which toys (posts) to show you in your toy box (For You feed). The robot doesn’t just count how many times people already played with a toy (likes, reports, etc.). Instead, it looks at you and guesses: • “How likely is this kid to like this toy?” • “How likely is this kid to report this toy as yucky?” • “How long might this kid play with it?” Those guesses are little numbers between 0 and 1 (probabilities). The “weights” are like special multipliers the robot puts on those guesses: • A like-guess might get multiplied by a small number (like 0.5). • A report-guess might get multiplied by a big negative number (like –234). Then the robot adds all those multiplied guesses together to give the toy a final “yay or nay” score and sorts the toys by that score. Why the numbers look so big and why people get confused Reports almost never happen, so the robot’s “will you report this?” guess is usually a tiny number (like 0.0001). To make that tiny guess still matter a little bit in the final score, they give it a big multiplier. People sometimes look at the big number and think “1 report = 468 likes!” 
That’s wrong. It’s not counting real reports or real likes. It’s only multiplying the guesses the robot made about you. If the robot thinks you’re the kind of person who almost never reports anything, even a big weight on “report” barely moves the score. The whole thing is personalized to how you usually behave. The people who wrote the code even put big comments in the GitHub repo so no one (and no other robots) gets confused about this again. That’s the whole idea in kid words: the weights turn the robot’s personal guesses about you into a ranking, not a simple “count the hearts and flags.”

3x WNBA champion. 2016 WNBA Finals MVP. 2x Kia WNBA MVP. 10x All-WNBA. 7x WNBA All-Star. 2020 Kia WNBA Defensive Player of the Year. 2008 Kia WNBA Rookie of the Year. Candace Parker gets her @Hoophall orange jacket 🙌

2002 WNBA scoring champion. 1999 Kia WNBA Rookie of the Year. 6x WNBA All-Star. 3x All-WNBA. 2x WNBA rebounding champion. Chamique Holdsclaw accepts her @Hoophall orange jacket 👏

2019 WNBA Champion. 2015 WNBA scoring champion. 2013 Kia Rookie of the Year. 2x Kia WNBA MVP. 7x WNBA All-Star. 5x All-WNBA. Elena Delle Donne is presented her @Hoophall orange jacket 🤩

知识分两种：增强性和突破性的。 增强性知识让你在已有认知越走越深，算法推给你的全是这类。它不挑战、冒犯你，只让你觉得“我果然是对的”。久而久之，你在信息茧房转圈。 真有价值的是突破性知识，和现有认知相悖，你甚至本能地回避。一旦吸收，重构思维框架，撬动之前无法企及的问题，找到ROI极高。