Parse

Parse’s hosted services will be fully retired on January 28, 2017. We’re proud that we’ve been able to help so many of you build great mobile apps. Read more on this announcement and what this means for your app here. Thank you for using Parse.

A surprising announcement.

Source: Parse

Diving into the big world of API Guidelines — Erica Sadun

A ridiculous amount of mailing list effort has gone into discussion of SE-0023 API Design Guidelines. You’ll find the original guidelines here at swift.org, and I highly recommend you pop over and read them. For the most part, I really like the guidelines. I also think some bits about naming and labels are too prescriptive.

Source: Diving into the big world of API Guidelines — Erica Sadun

读书笔记「微信思维」 | 萧宸宇

而在张小龙最近在微信公开课里面演讲中,他明确的表示,希望在微信里面用户用完即走。我保守估计微信在手机上的使用时长绝对能排进中国移动互联网用户99%人里面的前三。但是微信的最高决策者却在思考怎么让用户用完即走。但全世界都在争留存,争打开率,争使用时长的时候。居然龙头老大是在做相反的事情,是以用户利益为导向的事情。

Source: 读书笔记「微信思维」 | 萧宸宇

Farewell, Marvin Minsky (1927–2016)—Stephen Wolfram Blog

That afternoon we were driving through Pasadena, California—and with no apparent concern to the actual process of driving, Feynman’s visitor was energetically pointing out all sorts of things an AI would have to figure if it was to be able to do the driving. I was a bit relieved when we arrived at our destination, but soon the visitor was on to another topic, talking about how brains work, and then saying that as soon as he’d finished his next book he’d be happy to let someone open up his brain and put electrodes inside, if they had a good plan to figure out how it worked.

Feynman often had eccentric visitors, but I was really wondering who this one was. It took a couple more encounters, but then I got to know that eccentric visitor as Marvin Minsky, pioneer of computation and AI—and was pleased to count him as a friend for more than three decades.

A true pioneer in AI.

Source: Farewell, Marvin Minsky (1927–2016)—Stephen Wolfram Blog

Research Blog: AlphaGo: Mastering the ancient game of Go with Machine Learning

We first trained the policy network on 30 million moves from games played by human experts, until it could predict the human move 57% of the time (the previous record before AlphaGo was 44%). But our goal is to beat the best human players, not just mimic them. To do this, AlphaGo learned to discover new strategies for itself, by playing thousands of games between its neural networks, and gradually improving them using a trial-and-error process known as reinforcement learning. This approach led to much better policy networks, so strong in fact that the raw neural network (immediately, without any tree search at all) can defeat state-of-the-art Go programs that build enormous search trees.

A breakthrough for machine learning.

Source: Research Blog: AlphaGo: Mastering the ancient game of Go with Machine Learning

#138: Using CocoaPods in Xcode Playgrounds ???????? – Little Bites of Cocoa – Tips and techniques for iOS and Mac development – Weekday mornings at 9:42 AM

We’re continuing our look at Xcode Playgrounds today with CocoaPods. Let’s see what it takes to import a CocoaPod into a Playground.

Source: #138: Using CocoaPods in Xcode Playgrounds ???????? – Little Bites of Cocoa – Tips and techniques for iOS and Mac development – Weekday mornings at 9:42 AM