Facebook 推出了 Hermes,為了 React Native 而生的 JS Engine

Facebook 提供了一個對 React Native 最佳化的 JS engine:「Hermes: An open source JavaScript engine optimized for mobile apps, starting with React Native」。

裡面有提到兩個比較重要的的部份是 No JIT 與 Garbage collector strategy,針對行動裝置的特性而設計:避免 JIT 產生的 overhead,以及降低記憶體使用量。

官方給的改善主要也都是偏這兩塊:

不過沒有提到 CPU usage 會上升多少,只是帶過去:

Notably, our primary metrics are relatively insensitive to the engine’s CPU usage when executing JavaScript code.

對於 Facebook 也許是可以接受的數量,但對於其他人就沒概念了... 要入坑的人自己衡量這部份的風險 XD

Slack 改善桌面應用程式的效能與記憶體用量

Slack 桌面版改版的消息,在「Slack’s new desktop app loads 33 percent faster and uses less RAM」與「Slack speeds up its web and desktop client」這邊都有提到這兩個數字,不過看了官方的「When a rewrite isn’t: rebuilding Slack on the desktop」這篇,好像沒提到這兩個數字... 但看引用的圖片似乎是官方的評估數字,不知道是從哪邊得到的。

這是一個堅持繼續使用 Electron 的前提下改善效能的過程。如果過個幾年他們決定寫 native application 也不意外就是了,要一直壓榨效能,最後大概都會走到這邊... 當然也有可能靠 Google 一直改善 V8 engine 的效能撐很久 (畢竟 Google 是真狂砸人改善),現在大家都在賭可以改善多少 XD

這一波最主要的記憶體用量改善是來自於現在使用的 workspace 當然要有完整資料,而其他 workspace 的頁面就只保留狀態 (透過 Redux):

從記憶體用量可以看出來:

也可以理解因為這樣就不需要在啟動時馬上處理所有 workspace 的資料,所以啟動時間也就下降了不少,但這邊的 trade-off 是切換時的速度就會變慢 (需要重新 render),不過大概是考慮到常見情境下的切換次數而決定這樣做,應該還算 ok...

Twitter 對 2x 與 3x 的圖片的研究...

所以發現很多時候用 2x 的圖片就夠了?:「Capping image fidelity on ultra-high resolution devices」。

會這樣討論主要是發現螢幕特性:

The most modern screens are OLED. These screens boast some really great features like pure blacks, and are marketed as 3x scale. However, nearly no "3x scale" OLED actually has perfect 3x3 pixels per dot on their screen.

因為螢幕不是真的到 3x 的要求,丟 2x 的圖片出去就好,省頻寬又省下載時間:

This means that most OLED screens that say they are 3x resolution, are actually 3x in the green color, but only 1.5x in the red and blue colors. Showing a 3x resolution image in the app vs a 2x resolution image will be visually the same, though the 3x image takes significantly more data. Even true 3x resolution screens are wasteful as the human eye cannot see that level of detail without something like a magnifying glass.

省下 38% 的資料量,32% 的時間:

There's no difference that the human eye can see, but will save 38% on data and 32% on latency on the capped image load for this particular example which is reflective of most images that load on Twitter.

這也另外帶出了其他的想法,如果沒有太多時間研究的話,可以考慮先提供 2x 的就好,不需要特地做 3x 的版本...

幫你的 iPhone 電話簿找到對應的頭像

前幾天看到的:「Announcing Vignette」,透過 social network 的資料,把本來電話簿裡面的 icon 更新:

透過 app store 的搜尋找不太到,我一開始用了「Vignette」搜不到,但用「Vignette Update」就可以。或者你可以透過他提供的連結直接開 app store:「Vignette – Update Contact Pics」。

這是一個 IAP 類的付費服務,搜尋是免費的,但如果要把資料更新回通訊錄,需要付 USD$4.99 (一次性),台灣帳號是付 TWD$170,應該是因為最近的稅務調整:

Vignette allows you to scan your contacts and see what it can find for free. If you wish to actually save these updates to your contact list, you must pay for a one-time in-app purchase. That purchase costs $4.99, is not a subscription, and is the only in-app purchase.

搜尋的範圍包括了 GravatarTwitterFacebookInstagram

Email is used for Gravatar
Twitter
Facebook
A custom network called Instagram

另外作者有提到這個 app 不傳資料到伺服器上,都是在自己的裝置上連到上面提到的 social network 尋找:

Privacy is paramount
All the processing is done on-device; this isn’t the sort of app where your contacts are uploaded en masse to some server, and out of your control.

所以速度不會太快,但對隱私比較好...

GrabFood 用定位資料修正餐廳的資訊

Grab 的「How we harnessed the wisdom of crowds to improve restaurant location accuracy」這篇是他們的 data team 整理出來,如何使用既有的資料快速的修正餐廳資訊。裡面提到的方法不需要用到 machine learning,光是一些簡單的統計算法就可以快速修正現有的架構。

這些資訊其實是透過司機用的 driver app 蒐集來的,在 driver app 上有大量的資訊傳回伺服器 (像是定時回報的 GPS 位置,以及取餐狀態),而這些司機因為地緣關係,腦袋裡的資訊比地圖會準不少:

One of the biggest advantages we have is the huge driver-partner fleet we have on the ground in cities across Southeast Asia. They know the roads and cities like the back of their hand, and they are resourceful. As a result, they are often able to find the restaurants and complete orders even if the location was registered incorrectly.

所以透過這些資訊他們就可以反過來改善地圖資料,像是透過司機按下「取餐」的按鈕的地點與待的時間,就可以估算餐聽可能的位置,然後拿這個資訊比對地圖上的資料,就很容易發現搬家但是地圖上沒更新的情況:

Fraction of the orders where the pick-up location was not “at” the restaurant: This fraction indicates the number of orders with a pick-up location not near the registered restaurant location (with near being defined both spatially and temporally as above). A higher value indicates a higher likelihood of the restaurant not being in the registered location subject to order volume

Median distance between registered and estimated locations: This factor is used to rank restaurants by a notion of “importance”. A restaurant which is just outside the fixed radius from above can be addressed after another restaurant which is a kilometer away.

另外也有不少其他的改善 (像是必須在離餐聽某個距離內才能點「取餐」,這個「距離」會因為餐聽可能在室內商場而需要的調整),整個成果就會反應在訂單的取消率大幅下降:

整體看起來是系統產生清單後讓人工後續處理 (像是打電話去店家問?),但這個方式所提供的清單準確度應該很高 (因為司機不會沒事跟自己時間過不去,跑到奇怪地方按下取餐),用這些資料跑簡單的演算法就能夠快速修正不少問題...

Cloudflare 打算再推出 VPN 服務

去年在四月一日推出 1.1.1.1 服務的 Cloudflare 打算更進一步保護連線內容,提供 Wrap 服務 (就是 VPN):「Introducing Warp: Fixing Mobile Internet Performance and Security」。

不過這樣在 privacy 上的保護就變弱了,因為 Cloudflare 手上就拿到更多流量資訊可以交叉比對... 大概會申請起來放著在外面用,而不會平常就開著。

申請是透過 app 申請,Android 的在「1.1.1.1: Faster & Safer Internet」這邊,而 iOS 的在「1.1.1.1: Faster Internet」這邊。

目前申請後需看到排隊的編號,像是這樣:

WireGuard 上 macOS 了...

在「WireGuard for macOS」這邊看到 WireGuard 進到 Apple 家的 Mac App Store 了。

除了是透過 app store 下載外,另外的重點在於整合了 NetworkExtension API

This is built from the same code base as our existing iOS app and makes use of Apple's Network Extension API to provide native integration into the operating system's networking stack.

這樣可以確保相容性,而且可以用內建的 VPN 機制管理。另外也給了一些 screenshot 可以看,可以看出來就是走 Mac 上的管理方式:

用關鍵字搜尋 Apple 的 App Store 對不同地區的上架情況

The Intercept 提到了 Apple Censorship 這個站,可以丟關鍵字進去看 app 在不同地區的上架情況:「New Site Exposes How Apple Censors Apps in China」。

預設有美國 (United States) 與中國大陸 (China (mainland)) 當作比較,也可以自己增加地區。除了拿來看 censorship 外 (像是搜尋 vpn 或是 tor),也可以看其他的 app 上架的情況...

整個站其實頗簡單的,是個簡單的 PHP 站台,可以參考 GitHub 上的 greatfire/applecensorship 專案。

Facebook 花錢向使用者購買他們的行為記錄

這則從 Nuzzel 上看到的,國外討論得很凶:「Facebook pays teens to install VPN that spies on them」。

Facebook 付錢給使用者,要他們安裝 VPN (以及 Root CA,看起來是為了聽 HTTPS 內容),然後從上面蒐集資料,這本身就不是什麼好聽的行為了,但更嚴重的問題在於包括了未成年人:

Since 2016, Facebook has been paying users ages 13 to 35 up to $20 per month plus referral fees to sell their privacy by installing the iOS or Android “Facebook Research” app. Facebook even asked users to screenshot their Amazon order history page. The program is administered through beta testing services Applause, BetaBound and uTest to cloak Facebook’s involvement, and is referred to in some documentation as “Project Atlas” — a fitting name for Facebook’s effort to map new trends and rivals around the globe.

這個計畫在 iOS 平台下架了,但 Android 平台看起來還是會繼續:

[Update 11:20pm PT: Facebook now tells TechCrunch it will shut down the iOS version of its Research app in the wake of our report. The rest of this article has been updated to reflect this development.]

Facebook’s Research program will continue to run on Android. We’re still awaiting comment from Apple on whether Facebook officially violated its policy and if it asked Facebook to stop the program. As was the case with Facebook removing Onavo Protect from the App Store last year, Facebook may have been privately told by Apple to voluntarily remove it.

未成年人部份應該會是重點,拉板凳出來看...