Infoman

Member since 2017-07-15T03:50:57Z. Last seen 2025-03-18T19:45:31Z.

2753 blog posts. 128 comments.

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Posts by Infoman

• 
  

  Infoman

  4月12日，周四。Facebook教主朱克伯格（Mark Zuckerberg）先後出席美國國會參眾兩院聽證會，首輪接受參議員質詢，全世界當笑片嚟睇，難為朱仔要扮嚴肅，以免態度輕浮罪加一等。裝作老成持重唔難，忍笑先戥佢辛苦。

  老畢有睇首輪答問直播，次輪卻只看媒體報道，朱克伯格在眾議院算唔算過關，感覺不像第一輪那麼「真實」。無論如何，朱仔已闖過國會山莊木人巷，重回Facebook大本營，不妨跟大家分享一下個人對這場大龍鳳的看法。

  代表股東提問？

  不必報上年齡，睇樣已知向朱克伯格提問的參議員，好多都做得佢爺爺嫲嫲。84歲的猶他州參議員Orrin Hatch質問朱仔，Facebook不向用戶收費，如何經營下去？網上所以瘋傳聽證會這一幕，只因問題本身已暴露了Hatch對社交媒體一無所知，搞到朱仔呆了數秒才「回魂」，冷面笑匠般應之曰：「Senator, we run ads.」朱仔唔可以笑，但網上網下已當正佢係星爺。

  老畢當然也有份笑，可是轉念一想，84歲老人家不懂社交媒體營運模式，何奇之有？與其笑這位元老級政客「科技盲」，不如問一問：若非事先知道這是參議院聽證會，你會否以為周二開的乃Facebook AGM，席間一位年老股東質問做得他孫子的CEO，公司不收費如何能賺錢？姑勿論Hatch的問題白癡不白癡，從出發點着眼，提問者關心的是Facebook不收費憑什麼經營下去，怎看也是站在股東利益要求朱克伯格解畫。

  聽證會有預先設定的agenda，幾十名參議員聚首一堂所為何事，跟年紀、懂不懂科技無關，何以Hatch好像關心Facebook不收費便難以替股東創造價值，多於大會白紙黑字寫明的濫用數據侵犯私隱？

  爺爺錫住小朱

  另一個有趣的地方是，參院聽證會以司法及商務委員會聯席名義召開，意味具深厚法律背景的議員約佔半數。律師不可能凡事皆懂，但處理的案件卻經常涉及不同範疇專業，除了搜證不容馬虎外，對己避重就輕、對敵咄咄逼人，是一種把勝算推向自己的重要技巧，而不斷要求對方正面回答Yes或No，往往有助達到這種效果。

  從當天直播發現，不懂科技但懂法律的參議員不少，惟由始至終不見有人以嚴峻語氣逼朱克伯格清楚回答Yes或No。法律界出身的議員如此「錫住」小朱， Facebook股價怎會不一路問一路升？

  眾議院在下並未現場觀戰，但從各方報道得知，有議員用此招試圖逼小朱埋牆角，他想帶發問者遊花園，對方便打斷其話頭，要求朱仔直接回答Yes或No。

  其中一位議員問朱克伯格：「你是否願意為了保護用戶私隱而改變Facebook的商業模式？」老畢心想，即使是一個普通記者，只要稍為了解社交媒體，都懂得問這條「尖銳」問題。不難想像，以朱仔上國會山莊前演練之充足，此類涉及Facbook生意與用戶私隱之間重大矛盾的問題，要應付該不難。然而，朱克伯格非但沒有直接答Yes或No，且以「不明白閣下問什麼」（I'm not sure what that means）作答。小朱不敢觸碰這個話題，足證答Yes又死答No又死，惟有扮唔明博大霧。

  用戶現在要回頭已很難，馬斯克（Elon Musk）大大聲話刪除Tesla的fb專頁，蘋果老總庫克（Tim Cook）又同朱仔隔空對罵，但科技公司其實坐埋同一條船，加強監管唔會只針對Facebook。

  Tesla傳破產，Model 3又趕唔上進度，馬斯克要頭痕的問題多籮籮，居然仲有時間身體力行delete Facebook，得閒得滯乎？「鐵甲奇俠」點會死錯人，趁朱仔畀人插緊轉移視線罷了。

  科網巨頭的問題不在用戶怕私隱外洩，集體改變已經改變唔到嘅生活習慣，而係眾巨頭已成眾矢之的，自我監管行不通，就由外部力量加以限制，一旦觸及數據的蒐集、使用、分享和儲存，FANG經營成本會增加，業務增長會受壓；早已當正四家公司可以控制全世界的投資者，還會不會給予它們「世界級」的估值？

  2018/04/13
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  Infoman

  房屋委員會「出售居者有其屋計劃單位2018」(下稱新居屋)3個全新屋苑共4431個單位，昨天截止申請，房委會數據顯示，截至昨天下午7時累計收到約16.6萬份申請，較推出單位超額逾36倍。

  同日截止接受申請的白居二2500個名額，則累計收到約3.9萬份申請，超額約14.6倍。

  發言人表示，由於大部分綠表申請須經屋邨辦事處核實資格，然後才送交居屋銷售小組登記；加上在截止前經郵遞送交的申請表仍未完全送抵，故此上述數字仍未反映最終的申請數目。

  新居屋預計將於6月進行攪珠，屆時公布最終的申請數字。

  今期新居屋分布於九龍東啟德啟朗苑、長沙灣凱樂苑和東涌裕泰苑，實用面積278至631方呎，以市價七折定價，售價159萬至630萬元。

  2018/04/12
• 
  

  Infoman

  4 ways to monetise your app

  Many developers view monetization and design as two separate entities. But based on our experience at Tappx, they should have monetization deeply rooted at the starting point when building an app.

  But there are many factors to consider when picking a strategy. So, we’ve listed down a few strategies and their unique qualities to help you evaluate which will be the most compatible with your app/business.

  In-app purchase (IAP) For the in-app purchase model, you need to find the proper balance between what content or functionality will be included in the base download vs what can be unlocked with payment. If too little content is provided, users will look elsewhere for similar products. On the flip side, users won’t have a reason to upgrade if you give too much free content.

  For game developers, consider offering both fixed-price and in-game currency options for unlocking content. This allows non-paying users to experience unlocking “unlockable” content while allowing paying users to freely spend via both methods. Alternatively, you can employ video ads to reward users with “free” currency, thus obtaining ad partners and user allegiance.

  How to implement it No matter what your app is, you’ll need to deploy and scale a large variety of premium options to offer your audiences at different price tiers. You can do this by defining a pricing strategy.

  Be sure to communicate the value of your premium offerings at the right time by discovering the maximum engagement moments for your users and then impacting them with paid offers. Take advantage of urgency and offer discounts as well. You can communicate to users via in-app messages or through push notifications.

  You can also offer users a “taster” experience of what it feels like to be a user with premium capabilities. If you use ads, you may use them to offer paid offerings to your users.

  In-app advertising In-app advertising consists of distinct categories: video ads, display ads, and native ads. By implementing ads, you can focus on coding and improving your game. On the other hand, this allows users to download apps for free without restrictions on content or functionality other than the ads themselves.

  When choosing how to monetize your app via advertising, you should choose an ad partner that will help you find the right advertising strategy and ad format for your app.

  How to implement it Putting ads in your app is easy, but getting good revenue from them is challenging.

  You can start by selecting an ad partner. Then, select the ad format you’ll implement. Depending on these, your revenues can fluctuate a lot. Try to find a balance between the frequency your ads will show and the session length. Keep the user at the center of the experience.

  Take into account that eCPM (effective cost per thousand impressions) is not the main metric to keep an eye on. This is because the metric depends on other factors, some of which you cannot control. Keep monitoring ARPDAU (average revenue per daily active user) data, check your metrics regularly, and segment your user base. Based on these, set up an ad strategy accordingly, and don’t forget to do extensive AB testing.

  Subscription-based Although subscriptions only accounted for 4 percent of mobile app revenue in 2017, they can be highly lucrative for the right kind of app (e.g. Netflix, Amazon, Spotify). Another type of subscription lets users access the full functionality of an app with a recurring charge.

  Giving users a free trial to your subscription-based app is a common method to allow them to fully experience the benefits of your app before committing to the service. Subscriptions often have cheaper monthly pricing schemes than fixed-cost apps, thus making for a more affordable alternative.

  Subscription success relies on the quality of the service, so it can be a good way to gauge your audience’s response to features or content. Developers should try to release a steady stream of new content, along with regular app updates to keep audiences engaged and subscriptions renewed.

  (Note: Paid users will be more discerning than those who download free apps on a whim and will demand greater levels of customer service and support.)

  How to implement it If you’re looking to integrate this model, it’s important to know what the premium features are going to be and the reasons why users need to pay for them. It’s a risk to offer features for free. So, one of the first things you need to do before implementing a subscription model is to ascertain what is valuable for your users when they use your app and then execute a trial plan for them to experience what it’s like to be a premium user.

  The objective is to make users feel that everything will be the same for them in your app after the trial. Also, take into consideration that users are different and have varying needs. So, it’s recommended that you develop a subscription plan based on different price tiers and features, according to your different segmented audiences.

  Direct price The direct price model (aka pay-per-install model) can be divided into two categories: paid and “paidmium” apps. The former refers to apps that have a one-time download cost while the latter refers to paid apps that also offer in-app purchases. For this model to work successfully, your app needs to have original content that cannot be found in free or copycat apps.

  Due to the heavy saturation of the app market, this model has dropped in revenue and popularity. Competition with apps that allow users to test products before investing in it has caused fewer developers to go for this model.

  How to implement it Ensure first that your value proposition is unique and that it deserves to be premium. Study what the competition is doing and what prices they are offering. Then, create your pricing structure. Take into account the elasticity/seasonality in the year to maximize revenues and downloads.

  Don’t think of pricing as fixed at one level; it isn’t. It can be changed according to the evolution of your products or according to the feedback of your users. Understand the market demands and never forget that the price should be sustainable for your business in the long term.

  ---

  Decide which monetization model to use from the very beginning. Then, test and improve along the way. Study your users, observe how they interact with your product, and try new improvements. Ultimately, the user relationship with your app will dictate your product’s success.

  Stay a step ahead with the upcoming Tech in Asia Premium.

  Committed to producing deeply reported content on tech and startups in Asia, we will be moving into a paid subscription model in the near future. This will allow us to continue investing in creating content of value for you.

  Secure an early bird rate today when you pre‑register.

  LEARN MORE Editing by Jaclyn Teng

  (And yes, we’re serious about ethics and transparency. More information here.)

  Community Writer Kinga Kruzmanowska Publisher Development Director at Tappx. VIEW ALL COMMENTS

  2018/04/12
• 
  

  Infoman

  港產配對補習導師服務平台Snapask宣布，完成1500萬美元（約1.17億港元）的A輪融資，由泰國正大集團家族成員謝展領投是次融資。

  據Snapask表示，是次融資所得資金將用作全力發展機器學習（Machine Learning）和招募數據科學人才，公司現正着手開發全新「學習助理」（Learning Planner），計劃透過統計模型分析出學生的弱點與長處，再利用機器學習做到資料分群，並基於學生所提供的學習歷程建立學習模型。

  今次並非Snapask首次成功融資，去年5月公司宣布完成Pre-A輪融資，獲注入500萬美元（約3900萬港元），該次融資已獲風險投資公司Kejora Ventures、微光創投（由騰訊集團前首席技術官張志東及前高級執行副總裁吳宵光創辦），以及美圖（01357）董事長蔡文勝等注資。

  Snapask成立初期，主要透過手機應用程式Snapask提供一對一功課問答服務，發展至現時已與50間香港及台灣中學合作推數據為本學習，透過與學校合作收集前線教學經驗及意見，並與日校老師分享學生課後學習數據，讓老師可以更全面掌握學生課後學習情況。

  騰訊領投社交電商拼多多

  另一方面，內地媒體報道，內地社交電商「拼多多」近日完成新一輪30億美元（約234億港元）的融資，估值接近150億美元（約1170億港元），由騰訊（00700）再次擔當領投角色，紅杉亦參投。早在2016年7月，騰訊已在「拼多多」的1.1億美元B輪融資參與投資。

  據了解，成立兩年多的「拼多多」，去年商品交易總額（GMV）已超過1000億元人民幣。有分析指出，騰訊再度加注，或反映零售領域對其戰略意義愈來愈重要。

  2018/04/12
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  Infoman

  Ask HN: What is the best AI powered product demo you have experienced?

  48 ussumant 4 hrs 32

  news.ycombinator.com/item?id=16818219

  Looking for something where the Value of the Product is shown in real time like Grammarly's Text correction. fulafel 2 hrs The latest US presidential election.

  pesenti 43 mins http://densepose.org/ Which can extract the surface of the human body in real time.

  ericjang 3 hrs Tacotron 2 text-to-audio samples:

  https://google.github.io/tacotron/publications/tacotron2/ind...

  Blog post: https://research.googleblog.com/2017/12/tacotron-2-generatin... Paper: https://arxiv.org/abs/1712.05884

  philprx 2 hrs GitHub GIT or it doesn't exist ;)

  jcmeyrignac 2 hrs https://www.deepl.com/translator

  alex_duf 25 mins I just tried the french to english. It's better than google translate but not that good.

  arunbahl 3 hrs WordLens blew me away when it launched in 2010 (now part of Google Translate). https://en.wikipedia.org/wiki/Word_Lens

  zawerf 3 mins I thought augmented reality with mobile camera was going to take off after seeing that demo. But 8 years later and all we have are snapchat filters (and pokemon go for a brief few weeks).

  Hopefully ARKit/ARCore will make it easier to create the next the-future-is-now level of awe-inspiration app.

  WorkLifeBalance 29 mins It should have been a killer-app for google glass.

  Had google glass not stored video and processed everything in real-time it could have avoided the "glasshole" stigma.

  People would have likely mocked it for not being able to capture video, something they could have added in later in "response to demand".

  The world doesn't need more omni-present recording devices, but solid AR would be a net benefit.

  akerro 27 mins DuckDuckGo and Google Search probably.

  MiniCreo 3 hrs This site uses AI to enhance your low-res photos: https://letsenhance.io/

  rtcoms 2 hrs Google photos

  Face and object recognition helped me quite a lot in finding old photos.

  vbsteven 2 hrs Spotify daily mixes

  tontonius 1 hr would you care to elaborate on what parts of Discover that are powered by AI?

  In all honesty, I think the lines are severely blurred at this point.

  blixt 1 hr AI has been a moving goalpost since its conception, all the way from

  • "it can play tic tac toe";

  • to "it can beat people at chess, Jeopardy, Go, DotA";

  • to "it can see, understand and physically move around our world";

  • to "it solves problems the smartest humans can't begin to fathom"

  Spotify's machine learning algorithms are definitely beyond tic tac toe as they can learn from millions of listeners and look at a single listening history to figure out great music suggestions. I would put that under the AI umbrella as it's something people were doing before computers did it.

  GrumpyNl 1 hr Is that AI from spotify or just smartly compairing lists?

  blixt 1 hr What is "smartly comparing lists"? To describe further (as I'm an ex-Spotify employee I have some outdated insight into how it works):

  Spotify has historically used machine learning to tweak a predictive engine that can convert a track or artist into an N-dimensional value and then use the distance to other tracks/artists in this N-dimensional space.

  Is that AI? Maybe. How does the brain work? Maybe when you see a dog it's converted into an N-dimensional space where cats are pretty close, at least much closer than turtles. So if that is human intelligence, is Spotify's recommendation engine not artificial intelligence?

  denzil_correa 1 hr Microsoft's Skype Real Time Language Translation (2014)

  https://www.youtube.com/watch?v=RuAp92wW9bg

  purplezooey 1 hr Eliza

  louismerlin 3 hrs https://experiments.withgoogle.com/ai/teachable-machine

  The teachable machine from Google is a great little experiment.

  neel8986 3 hrs Google search, translation?

  cm2012 2 hrs Facebook's newsfeed is basically a superimpressive AI that learns from what people click and maximizes ad revenue from that.

  Spearchucker 1 hr That same AI drove me away. I subscribe to 3 hobby-related groups and because virtually all of my family and friends no longer post anything on Facebook, I only ever see content related to that hobby.

  majewsky 1 hr If that description is honest, why do you blame the algorithm for selecting hobby-related content from a set of inputs that only contains hobby-related content? That's like buying a football magazine, then complaining that it's only football, football, football in there.

  kotapi 3 hrs Cozmo https://www.youtube.com/watch?v=DHY5kpGTsDE

  oceanman888 1 hr I personally think the impressive part of cozmo comes from interaction and design, rather than AI.

  robax 3 hrs Ambient.ai has a very demonstrative landing page. https://ambient.ai

  jawrainey 2 hrs There's an object detection library (YOLO1) freely available that achieves similar bounding boxes/matches to ambient's homepage demo; Joseph Redmon also gave a TedX2 about it that describes some of the technical details.

  nstj 1 hr I just saw the YOLO stuff in action at AWS Summit and it was epic. Super fast and really really awesome.

  taneq 2 hrs Is there meant to be something on the second 'page' (ugh I hate this single-page-permascroll-with-fixed-header layout that they all do these days) other than the 'let there be more light' box? It's just a grey background so I suspect something didn't load there. Otherwise I just see a generic video showing object tagging on the first 'page' and the pretty-but-pointless graph thing on the third 'page'.

  Firefox 59.0.2 with PrivacyBadger and uBlock Origin (although I turned both off and still nothing loaded).

  jackbrown77 3 hrs Both siri and google assistant are very good AI's, but there is lot to explore in the industry of AI.

  arvigeus 2 hrs Not Hotdog app

  MiniCreo 3 hrs Silly Siri

  2018/04/12
• 
  

  Infoman

  美股周二（10日）續向上，三大指數均漲逾1%。儘管如此，屬長期「大淡友」的對沖基金經理赫斯曼（John Hussman）最近發表評論文章再度提出警告，認為股市近期出現如此劇烈震盪，進一步加深他對後市悲觀的看法。赫斯曼估計，標指將至少回落60%以上；而未來10年或更長時間，股市的回報將會是零，甚至是負數。

  本欄上月底〈美股震幅超去年 重拾升軌無咁易〉一文曾指出，股市波幅擴大是不可忽視的因素。進入第二季度不久，波動情況依然頻密。截至上個交易日止，標普500指數已合共出現28次逾1%的變幅（15次升、13次跌），而對上一次如此波動的市況是2016年。回看去年，只有8個交易日出現逾1%的收市變幅；然而，今年僅第一季度已出現23次，接近去年全年的3倍。另外兩個主要指數的情況亦近似，道指及納指今年以來分別有29個和30個交易日出現逾1%變幅，是2009年以來最多。

  更甚的是，標指去年未曾出現逾2%的變幅，今年卻已出現了8次，當中只有一次是上升，其餘皆是下挫，2月5日的跌幅更超過4%。而向來相對波動的納指，在去年只有3次2%以上的變幅（一升兩跌），但今年迄今已出現了10次（3升7跌，其中兩次下跌幅度逾3%）。

  此外，美國芝加哥期權交易所波動指數（CBOE Volatility Index, VIX）今年也幾乎翻了一番；近期徘徊於20%這個長期平均水平附近，呈橫行待變格局。VIX理論上是反映期權交易商預期標指未來30天的波幅，同時也有「恐慌指數」之稱，今年初以來合共出現6次飆升逾20%，其中2月初更曾暴漲逾倍。在風聲鶴唳的市況下，難免令投資者異常不安。

  赫斯曼認為，以潛在的貿易戰甚至美國聯儲局對利率前景取態轉變等新聞用作衡量市場風險，在某程度上是無關重要。

  他指出，聯儲局的量化寬鬆政策（QE）是抬高股市估值到不可持續水平的最大力量；惟隨着接近零成本的廉價資金逐漸消失，一旦下跌漩渦形成，將會令更多投資者尾隨。赫斯曼認為，投資看股票估值，投機看市場心理，目前上述兩個因素都不利後市發展。

  無疑，美股經歷2月至3月多次急挫，多少也會令投資者感到恐慌，但實際上仍有一些令人信服的理由堅持繼續持有股票。Facebook行政總裁朱克伯格上日在美國參議院作證，力求避免國會立法監管公司營運，股價隨之反彈；普遍科技股走勢似乎有穩定下來的跡象。再者，根據FactSet數據顯示，美股第一季度企業盈利增長應該高於17%，為2011年第一季度以來最高；而去年底預測僅為增長11%。這意味着儘管過去3個月的宏觀環境存有很大的不確定性，但企業盈利前景看起來仍然不俗。

  港股方面，恒生指數收報30897點，升168點（0.55%），主板成交金額1329.77億元。從技術走勢來看【圖】，恒指重返由2017年向上伸延的上升通道底線之上；如能企穩，料有機會進一步回穩甚至造好。

  2018/04/12
• 
  

  Infoman

  周一美股表現先高後低，美國總統特朗普於周日向中國示好，以及美國財長努欽同日說中美爆發貿易戰的可能性不大，緩和了投資者緊張情緒，令美股周一愈升愈有，道指一度飆440點。然而，收市前聯邦調查局（FBI）搜查特朗普私人律師科恩（Michael Cohen）辦公室的消息傳出後，美股三大指數隨即拾級向下，更以貼近全日最低位收市，道指最終僅升46點（0.19%）；標普500指數由最多漲1.88%縮窄至0.33%；納指由上升2.31%減至僅漲0.51%。

  過去一個多月來，政治消息充斥市場，不論中美貿易戰還是「通俄門」事件，一直左右美股及港股的走勢。當政治因素逐漸淡化後，市場焦點將重回企業基本因素。

  昨天提到美股市場焦點，將在本周稍後時間轉移至企業季度業績上，一眾大型銀行如摩根大通、花旗及富國等本周陸續派發首季成績表。市場研究機構Bespoke整合分析師對標普1500指數成分股的盈利預測，把獲分析師上調盈利預估的企業數目減去被下調盈利預估的企業數目，再除以相關企業總數，所得「淨盈利修訂比率」用來推測美股整體業績期的表現。

  按過去4星期美股分析師的預估，標普1500指數的淨盈利修訂比率由上季的+27.2%跌至-1.3%，意味目前被下調盈利預估的企業數目多於獲上調的企業。

  不過，值得注意的是，淨盈利修訂比率出現負數未必是壞事。如【圖】所示，從2009年至2017年間，統計標普500指數在業績期的表現，逾八成時間都與淨盈利修訂比率的方向背馳，即該比率出現負數時，標普500指數於業績期內多數報升，反之亦然。例如對上一季淨盈利修訂比率急升至+27.2%，標指就錄得0.4%跌幅。此外，於這36個季度中，只有7個季度出現例外（圖中紅點）。淨盈利修訂比率出現負數時，某程度反映分析員下調對企業盈利的預測水平，這意味企業實質業績表現較容易beat贏分析員預測（即好過市場預期），因而對股價產生刺激作用。

  總括來說，當市場焦點重新聚焦企業基本因素後，美股盈利增長將主導後市，尤其目前市場對企業盈利增長期望較低，美股較容易「意外地」造好。

  雖然周一美股升幅幾近全數蒸發，卻無礙恒生指數連續3個交易日上揚。恒指昨早低開24點後，早段一度倒跌66點，見30163點全日低，其後反覆走高，中午收市前成功突破100天線（約30515點）。港股午後走勢續強，A股高收後，恒指最多飆577點，高見30806點，逼近20天線（約30824點），最終收報30728點，大漲499點（1.65%）。主板成交額增至1383億元，上升股份佔59%。

  技術走勢上，恒指周初重新進入長方形形態後，料再度進入橫行整固的局面。現水平已突破100天線，下一個較大阻力將會是50天線，約31081點，這亦相當於2017年上升通道的底線。若能升穿此底線，後市將會較為樂觀，這期間宜觀察多項大市指標走勢，尤其強勢股買盤有否配合增強，作為指數反彈持久力的參考。

  2018/04/11
• 
  

  Infoman

  周一美股表現先高後低，美國總統特朗普於周日向中國示好，以及美國財長努欽同日說中美爆發貿易戰的可能性不大，緩和了投資者緊張情緒，令美股周一愈升愈有，道指一度飆440點。然而，收市前聯邦調查局（FBI）搜查特朗普私人律師科恩（Michael Cohen）辦公室的消息傳出後，美股三大指數隨即拾級向下，更以貼近全日最低位收市，道指最終僅升46點（0.19%）；標普500指數由最多漲1.88%縮窄至0.33%；納指由上升2.31%減至僅漲0.51%。

  過去一個多月來，政治消息充斥市場，不論中美貿易戰還是「通俄門」事件，一直左右美股及港股的走勢。當政治因素逐漸淡化後，市場焦點將重回企業基本因素。

  昨天提到美股市場焦點，將在本周稍後時間轉移至企業季度業績上，一眾大型銀行如摩根大通、花旗及富國等本周陸續派發首季成績表。市場研究機構Bespoke整合分析師對標普1500指數成分股的盈利預測，把獲分析師上調盈利預估的企業數目減去被下調盈利預估的企業數目，再除以相關企業總數，所得「淨盈利修訂比率」用來推測美股整體業績期的表現。

  按過去4星期美股分析師的預估，標普1500指數的淨盈利修訂比率由上季的+27.2%跌至-1.3%，意味目前被下調盈利預估的企業數目多於獲上調的企業。

  不過，值得注意的是，淨盈利修訂比率出現負數未必是壞事。如【圖】所示，從2009年至2017年間，統計標普500指數在業績期的表現，逾八成時間都與淨盈利修訂比率的方向背馳，即該比率出現負數時，標普500指數於業績期內多數報升，反之亦然。例如對上一季淨盈利修訂比率急升至+27.2%，標指就錄得0.4%跌幅。此外，於這36個季度中，只有7個季度出現例外（圖中紅點）。淨盈利修訂比率出現負數時，某程度反映分析員下調對企業盈利的預測水平，這意味企業實質業績表現較容易beat贏分析員預測（即好過市場預期），因而對股價產生刺激作用。

  總括來說，當市場焦點重新聚焦企業基本因素後，美股盈利增長將主導後市，尤其目前市場對企業盈利增長期望較低，美股較容易「意外地」造好。

  雖然周一美股升幅幾近全數蒸發，卻無礙恒生指數連續3個交易日上揚。恒指昨早低開24點後，早段一度倒跌66點，見30163點全日低，其後反覆走高，中午收市前成功突破100天線（約30515點）。港股午後走勢續強，A股高收後，恒指最多飆577點，高見30806點，逼近20天線（約30824點），最終收報30728點，大漲499點（1.65%）。主板成交額增至1383億元，上升股份佔59%。

  技術走勢上，恒指周初重新進入長方形形態後，料再度進入橫行整固的局面。現水平已突破100天線，下一個較大阻力將會是50天線，約31081點，這亦相當於2017年上升通道的底線。若能升穿此底線，後市將會較為樂觀，這期間宜觀察多項大市指標走勢，尤其強勢股買盤有否配合增強，作為指數反彈持久力的參考。

  2018/04/11
• 
  

  Infoman

  Lessons on Bubbles from Bitcoin

  46 kgwgk 8 hrs 13

  https://www.bloomberg.com/view/articles/2018-04-10/bitcoin-was-prone-to-bubbles-until-bears-could-bet-against-it

  news.ycombinator.com/item?id=16805125

  Unable to load the content https://www.bloomberg.com/view/articles/2018-04-10/bitcoin-was-prone-to-bubbles-until-bears-could-bet-against-it

  2018/04/11
• 
  

  Infoman

  Why I’m Using a 30 Year Old Development Method in 2018

  https://news.ycombinator.com/item?id=16787247

  Making games is hard.

  And by far the hardest part of making games is pre-production. That statement may be confusing. We’ve all heard about very difficult production periods for games, and we’ve also observed light, easy, fun pre-production periods. So why am I claiming that pre production is harder? Because one of the things that can poison production is that pre-production is happening during it. As hard as pre-production is, it’s even harder (and way more expensive) to do while you’re also in production. Allow me to explain: In an ideal world, you’d never go into production on a commercial game that’s going to fail. If you were making a game with the intent of turning a profit, and you knew that game would not do so, you wouldn’t go into production. In spite of the obviousness of that statement, many projects, including projects of which I have been a part, do not see their look, mechanics, sound, and level design come together until the game is almost finished. The tail end of production is a terrible time to find out whether or not a game works. Frustrating or underwhelming mechanics will need to be thrown out or reworked, which will have a hugely expensive cascading effect on the technology, art, and sound. This synthesis of all the parts (aka first playable) is also the first time you find out whether or not your team is up to the original task. This is less than ideal if they’ve already been working on it for years. “But game development is inherently chaotic,” the strawman in my head complains. Well he’s right. It is. Innovation requires time, talent, discarded work and endless iteration. How do we accommodate that chaos and allow for innovation without risking the whole farm? Remember when I said pre-production is harder than production? The reason so many projects have such nightmarish development is that the pre-production was never truly finished. They may have put together some concept art, a few documents, maybe even a white box prototype. But doing this is a far distant thing from completing pre-production, at least according to Mark Cerny. In Cerny Method, by the end of pre-production, you should have one level of your game that is indistinguishable from a completed professional product. And there can be no cut corners. A demo is not a first playable. An alpha is not a first playable. A whitebox is absolutely not a first playable. The art, animation, mechanics and sound must all be working together, they must be great, and together they must represent something which will outsell competition 2 years from now. But Why?

  So why bother making a first playable if it’s so much work? Because according to Cerny, “It’s the only way to give yourself a proper chance of making a good game.” Cerny Method isn’t just about keeping chaos out of production; it’s about keeping conservatism and arbitrary schedules out of pre-production. Games require innovation and taking chances, and you won’t want to do that once the production train is rolling. By first deliberately setting aside time for chaotic, rapid iteration, you empower yourself and your team to come up with interesting new ideas. You are free to change things on a whim and try ideas that have never been done before. Nothing is too dangerous, too weird, too unfaithful to the genre, because you’re not counting on taking any of the things you make into production in their current form. In fact you should proceed as though you’re going to throw it all out. Then, as the game’s concept starts to congeal, you take your best ideas and give them the most polished presentation you possibly can. Once you have endured the flames of pre-production, you know so many things about your project that most developers don’t get to learn until years into production. You know what works, what doesn’t, what’s fun, and what makes players hate you. You know how the art, animation, and sound mesh with and support the mechanics. You know all the things your avatar can do, and therefore what the levels, obstacles and/or enemies are like. And perhaps best of all, because you’ve been iterating on so many levels, you know how long it takes to make them which means you can actually make an accurate schedule and budget. You are now free to build your game, armed with the knowledge of exactly how to do it, how long it will take, and what it will cost. But there’s one giant caveat emptor: Caesarthumbsdown.gif

  The linchpin of Cerny Method is this: not every game will survive pre-production. If you have made 5 level prototypes, and you don’t have that publishable first playable, that lightning in a bottle that will outsell the competition 2 years from now, you have kill the project. “There’s no point in following this process if you’re not going to old the output of pre-production to a very high standard.” It sounds harsh, but remember the entire point of Cerny Method is to ensure that you don’t find yourself in the situations I described earlier, where you’re in full-blown production mode on a doomed game that will never work. Skipping pre-production is like launching a rocket and then trying to decide where it should land and figure out its trajectory and how much fuel it needs, all while it’s already in the air. When I look back on my failed projects, there’s absolutely no question that I’d much rather have cancelled after 6 months of pre-production than 3 years of production. Easy Street

  But if you do reach the end of pre-production, you have that publishable quality first playable, your game is incredibly great, then congratulations! From here the work is easy; all you have to do is make the game. As Cerny puts it, The publishable first playable proves that you understand precisely what your game is and what it is not, and that you know enough to produce the game. It delineates pre-production, where you know nothing, from production, where you know everything. I’m being flippant of course, there’s nothing easy about making a game. But imagine being able to make levels knowing with 100% certainty that your character’s jump distance isn’t going to change. Imagine being able to look at the overarching design of your game as a single-page spreadsheet. Imagine not needing to constantly re-write documentation, throw out production-quality assets, and have design arguments while a release deadline is looming. I don’t think everybody needs to use Cerny Method. It’s outdated for a reason, plenty of teams are perfectly capable of using Agile or another iterative method. But I’m spearheading a game of my own for the very first time, and I know I’m not the only developer doing so A studio is far more likely to be able to dust itself off after a failure than an indie mortgaging their home. If you’re thinking of putting your own money into a game, for god’s sake do pre-production. Don’t wait until 90% of the production budget’s gone to test a concept Limitations

  There are limitations to following Cerny Method to the letter. One of them is that once you transition from pre-production to production, your mindset has to switch from boundless creative openness to staunch conservatism. And you can see this play out in Cerny games. The later levels of Crash 2 don’t have much going on that isn’t present in some form in the early levels. The difficulty increases, and the obstacles are re-combined in new ways but the fundamentals remain more or less the same. Crash offsets this with what Cerny calls a “special mechanic”. For Crash 1, the special mechanic was riding a wild boar. But the special mechanic cannot be an after thought in Cerny Method; it must be iterated and tested during pre-production just like the base mechanics. Crash 3 is the most varied in the series, giving Crash a new ability after each boss. Must of the uncertainty is mitigated by making these powers variations on existing abilities. The only truly new ability is a bazooka. It is also the most cumbersome mechanic in the game, and does not show the same refinement as the other mechanics. The bazooka is an example of what can happen when you aren’t conservative enough during production. I dearly love games like Portal 2, Rayman Legends, Psychonauts, and Antichamber, where each new area seems to offer more innovation than the last. Games like these probably couldn’t be made in Cerny Method because it’s not possible to separate their production and pre-production in that way. Games like these require constant (CONSTANT) iteration and playtesting of near-production-quality levels. Otherwise every new level will feel like an untested prototype. Ubisoft and Valve have the resources to do this, and I do not. Antichamber’s development doesn’t bear comparison because it took so long to make and because the art is so minimal (brilliant, but minimal.) I’m making a game with a tiny team and very limited resources. I don’t want it to take ten years and I don’t want to reach the end of development only to learn that I was doomed a month in. That’s why I’m using Cerny Method.

  2018/04/09
• 
  

  Infoman

  About rust

  Rust is a language designed for writing highly reliable and fast software in a simple way. It can be used from high-level code down to hardware-specific code, and from big irons to tiny devices. Rust is a systems programming language focused on three goals: safety, speed, and concurrency. It maintains these goals without needing a garbage collector, making it a useful language for a number of use cases other languages aren’t good at: embedding in other languages, programs with specific space and time requirements, and writing low-level code, like device drivers and operating systems.

  It improves on current languages targeting this space by having a number of compile-time safety checks that produce no runtime overhead, while eliminating all data races. Rust also aims to achieve ‘zero-cost abstractions’ even though some of these abstractions feel like those of a high-level language. Even then, Rust still allows precise control like a low-level language would.

  Try Rust online in the Rust Playground

  The Rust Playground is an online application that allows you to experiment with Rust by editing and running Rust code from the comfort of your browser; no need to install Rust locally! Several crates (Rust's reusable libraries) are available in the playground to showcase some of Rust's ecosystem.

  Version

  After a long period of iteration and experimentation, Rust 1.0 shipped on May 15th, 2015. This release is the official beginning of the Rust language's commitment to stability, and as such it offers a firm foundation for building applications and libraries. From this point forward, breaking changes are largely out of scope (some minor caveats apply, such as compiler bugs).

  Rust employs three release channels: stable, beta, and nightly. Every six weeks the beta channel is released as a stable version and the current nightly channel becomes the beta candidate.

  What does it do?

  Rust supports a mixture of programming styles: imperative procedural, concurrent actor, object-oriented and functional. It supports generic programming and meta-programming, in both static and dynamic styles. Rust is also capable of creating bindings for C foreign function interfaces.

  The language design pursues the following goals:

  Compile-time error detection and prevention. Clarity and precision of expression. Run-time efficiency. High-level zero-cost abstractions. Safe concurrency. Standard Library

  You can view what the standard library has to offer, and search through it, here. The scope of the standard library will continue to grow in future versions of the language.

  Getting Started

  To get started with the language:

  Start with the book. Read the official frequently asked questions and the questions tagged rust-faq. Open up the standard library documentation. Read Rust by Example. When you are ready for the deepest and darkest arts of Rust, dive into The Rustonomicon. You can find a lot more learning resources (including non-English ones) in the rust-learning repository.

  Getting Help

  Not all questions are suitable for Stack Overflow, and discussions certainly are not. If you wish to ask for a recommendation for an IDE, a library, or wish to discuss about the best way to organize a bit of code, then you may consider alternative venues.

  More open-ended questions and discussions are welcome on:

  the Rust-Beginners IRC channel, the Rust subreddit, the Rust users forum. There is a list of Rust IRC channels on the community page, and there are even language-specific channels if English is not your forte.

  Additionally, the AreWeXYet collection of websites provide helpful summaries and lists of thematic resources:

  AreWeIDEYet AreWeWebYet AreWeGameYet AreWeLearningYet Producing a Minimal, Complete, Verifiable example for Rust code

  Questions on Stack Overflow that seek debugging help ("why isn't this code working?") must include the shortest code necessary to reproduce it in the question itself. Without it, the question is off-topic and is likely to be closed. Here are some common points to follow to reduce your code to create a good MCVE:

  Try to run your code in a different environment. On the Rust Playground In a separate Cargo project Remove unused crates modules types enum variants / members struct members function arguments / return values Combine multiple modules into a single file by rewriting mod foo; as mod foo { / contents of foo.rs / } Replace complete or partial function bodies with the macro unimplemented!(). Remember that some steps might "unlock" other steps; try to apply each step in turn until no steps can be followed! Ensure that your error continues to occur after each change to avoid going down the wrong path.

  There's also some information that you should provide about your code:

  It will be assumed that you are using the current stable version of Rust. If you are not using this version of Rust, state your version. This can be found by running rustc --version. If you are using crates, state the versions of every crate that you are using. This can be found in your Cargo.lock file. Books

  Programming Rust Fast, Safe Systems Development Rust Essentials Rust Cookbook Beginning Rust - From Novice to Professional Code Editors & IDEs:

  Atom (Rust language support) Eclipse (plugin) Emacs (plugin) GNU Emacs (Rust-mode) InteliJ IDEA (plugin) Sublime Text (Sublime Text package for Rust) Vim (Rust.vim) Visual Studio Code (Rust extension, Rust extension with Language Server Support) Visual Studio (Visual Studio extension for Rust) Geany IDE (Rust Language support out-of-the-box) Gnome Builder (Rust Language support out-of-the-box) LightTable (plugin) See: Text editor features overview: (Syntax highlighting, Snippets, Code Completion, Linting, Go-to Definition, Syntax Checking).

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  2018/04/09
• 
  

  Infoman

  Amazon Elastic Compute Cloud running Linux/UNIX $2.52

  $0.0152 per On Demand Linux t2.micro Instance Hour166 Hrs$2.52

  EBS $3.54

  $0.05 per GB-Month of snapshot data stored - Asia Pacific (Tokyo)7.511 GB-Mo$0.38

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  2018/04/08
• 
  

  Infoman

  Berkeley offers its fastest-growing course – data science – online for free

  387 seycombi 13 hrs 89

  http://news.berkeley.edu/2018/03/29/berkeley-offers-its-fastest-growing-course-data-science-online-for-free/

  news.ycombinator.com/item?id=16768021

  By Public Affairs, UC Berkeley | MARCH 29, 2018 Click to share on Twitter (Opens in new window)4K+Click to share on Facebook (Opens in new window)4K+Click to share on Google+ (Opens in new window)Click to share on Reddit (Opens in new window)Click to email this to a friend (Opens in new window)Click to print (Opens in new window) The fastest-growing course in UC Berkeley’s history — Foundations of Data Science — is being offered free online this spring for the first time through the campus’s online education hub, edX.

  Data science is becoming important to more and more people because the world is increasingly data-driven — and not just science and tech but the humanities, business and government.

  David Wagner, Ani Adhikari, John DeNero Data 8X instructors David Wagner, Ani Adhikari and John DeNero

  “You’ll learn to program when studying data science — but not for the primary purpose of building apps or games,” says Berkeley computer science Professor John DeNero. “Instead, we use programming to understand the world around us.”

  The course — Data 8X (Foundations of Data Science) — covers everything from testing hypotheses, applying statistical inferences, visualizing distributions and drawing conclusions, all while coding in Python and using real-world data sets. One lesson might take economic data from different countries over the years to track global economic growth. The next might use a data set of cell samples to create a classification algorithm that can diagnose breast cancer. (Learn more from a video on the Berkeley data science website.)

  The online program is based on the Foundations of Data Science course that Berkeley launched on campus in 2015 and now has more than 1,000 students enrolling every semester.

  The intent is “helping students from any major or any background to address questions that interest them, using the powerful tools of data science,” Provost Paul Alivisatos told a congressional subcommittee on research and technology in July 2017. “From linguistics to chemistry, and from history to economics, students from over 60 majors have responded, and this is the fastest-growing program in the history of Berkeley…”

  The Foundations of Data Science edX Professional Certificate program is a sequence of three five-week courses taught by three winners of Berkeley’s top teaching honor, the Distinguished Teaching Award: DeNero, statistics professor Ani Adhikari and computer science professor David Wagner.

  “It’s exciting to be part of an initiative that brings data science to all, regardless of their background,” says Adhikari. “After all, everyone has to be able to reason based on data and to gain insight from data. And data science itself will benefit from diverse perspectives about what’s important. Our goal is to bring data science to the widest possible audience.”

  Anyone in the world can enroll for free; learners who want to earn the certificate will need to pay.

  The first of the three parts starts at 9 a.m. (PDT) on Monday, April 2. Enrollment will remain open after the course begins.

  2018/04/06
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  Infoman

  View Watchlist Amazon.com Inc. (AMZN) is closing in on music streaming leader Spotify (SPOT) and Apple (AAPL), now in second place, revealing for the first time that it has tens of millions of paid subscribers, a number that is growing at a fast rate.

  Steve Boom, vice president of Amazon Music told Billboard that Amazon Music Unlimited subscriptions have more than doubled during the course of the last six months and that it has tens of millions of customers. Boom said the growth is being driven by an uptick in the number of Amazon Prime customers and the growth of smart speakers like its line of Echo devices powered by its voice-activated digital assistant Alexa. He said that while lots of the news coverage predicts streaming music via smart speakers will become the norm in the future, it's actually happening now. “We wouldn't have grown to this scale if it hadn't been happening already," Boom told Billboard. (See more: Amazon Prime Price Hike Will Bring in $300M More Annually: Cowen.)

  The executive’s comments about Amazon’s streaming music service come just as leading music streaming provider Spotify launched its IPO this week. But Spotify doesn’t have a voice-activated smart speaker line, which is where Boom thinks Amazon can drive more subscribers to its music service. He said the smart speakers have given the Seattle, Washington e-commerce giant an untapped group of music streamers, which is where most of the new paid subscriptions are coming from. The executive noted that it's both older listeners who tend to be slower to embrace new technologies as well as country music listeners, a demographic that hasn’t been quick to move to streaming music.

  Amazon Music claims country songs are streamed 2.5 times more frequently on its platform than on others. Boom told Billboard that the goal is to expand the streaming market, not to go after the same demographics that other streaming services are targeting. "The technology itself is so simple that we don't just rely on those people who I would say are early tech adopters, which has been where a lot of growth in music streaming has been because it's been wrapped up inside of a smartphone. Not everybody wants to listen to music on a smartphone, it turns out," he said.

  Read more: Amazon Music Subscriptions Doubled in Six Months | Investopedia https://www.investopedia.com/news/amazon-music-subscriptions-doubled-six-months/#ixzz5BqiZW4Tr Follow us: Investopedia on Facebook

  2018/04/06
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  Infoman

  Show HN: Travel spontaneously on a budget

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