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#知乎问答# 问：想买美股怎么买啊？ 答：如果你不是小打小闹，是正儿八经想投资。我说个邪修，但是非常稳妥的方法。 1、申请一个学费最便宜的新加坡野鸡大学，学费大概（7到8w人民币）没有啥排名那种，点击就送offer 2、开学后大概一周内，拿到学生签证stp。去办新加坡银行卡，DBS，UOB，OCBC，汇丰，渣打，量大管饱，这些银行学生签证过期都能用。去办券商账户，老虎，长桥等等，量大管饱，随便开。一般注册新用户还可以白嫖几百新币。如果你有加密货币投资需求，开Coinbase ，跟新加坡银行卡资金互转都是秒到。 3、从中国的银行汇款到新加坡，理由是交学费和生活费，不会卡，随便汇。 4、开学后2周到一个月，跟学校申请退学，学费应该能退回绝大多数，有的学校交费晚，甚至你还没交学费，估计留位费不会退你了。 5、注销学生签证stp，回国。有些银行或者券商会让你更新证件为护照，更新就行。 此时，你就拥有了，大量海外银行账户和券商账户，想怎么炒股，就怎么炒股。（作者：Mark；来源：知乎）

Conviction is happening now! 🔥 Can’t make it to the venue? Join our livestream online! Exciting giveaways will be running throughout the event. Invite your friends to join the livestream and experience the passion and energy of the OnCoin community together! Livestream link: If you’re attending in person, be sure to visit the OnCoin Booth #20# to claim exclusive merchandise and gifts, and experience the #Conviction# atmosphere firsthand. #Giveaway# #Conviction#

有分析者发一张图，号称 ai 前沿公司的 arr 最近已经超过 windows + office 的年收入，这是一种缺乏商业常识的比较。 1. Windows + office 是垄断性定价，没有竞争。ai 公司 token 则面对持续变化的，激烈的价格竞争。 2. Windows + office 边际成本接近于零，研发成本也很低，利润巨大。ai 公司的模型要持续更新训练，推理成本巨大，需要足够算力支持服务，所有竞争者目前都在亏钱。 等到尘埃落定后，大家会发现大部分 ai 公司的 arr (年度经常性收入）的真相是：即非年度，也非经常，更不是收入。

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 🙌