Infoman

Member since 2017-07-15T03:50:57Z. Last seen 2025-04-09T16:00:01Z.

2754 blog posts. 128 comments.

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Posts by Infoman

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  Infoman

  不少朋友對全球愈來愈兩極化深感不安，而這趨勢的深化，社交媒體的「迴音室效應」自然大有責任。社交媒體Facebook創辦人朱克伯格也坦承自己公司的演算式，容易被不同組織利用，所以正努力調節公式，希望撥亂反正云云。

  結果可能雙輸

  Facebook聲稱要令「生活時報」（Timeline）顯示更多朋友資訊，調低公眾專頁的內容，令它還原為生活化社交平台，但結果卻很可能雙輸：世人、特別是新一代愈來愈少使用Facebook，而同時繼續用Facebook的用戶，又會不自覺的進一步墮入兩極思維。

  這是因為當演算式只顯示有限的公眾專頁資訊，只會更貼近每個用戶的意識形態度身訂造內容；從前一些相對中立的知識型內容，還可能出現在對公共事務有興趣的用戶的第二、第三選項，現在卻成為新演算式的犧牲對象。

  換句話說，對公共事務有興趣的人，只會看見更偏頗的單一資訊，他們和其他群組的距離，也是愈來愈遠，對自身的意識形態卻更為堅定。至於其他對公共事務本來略感興趣的正常人，在新演算式下，現在卻只會看見完全的衣食住行資訊，令社會除了出現極右、極左的對立，同也時出現「關心政治」和「討厭政治」之間的二元對立。

  與此同時，個人用戶發布自己資訊的動力愈來愈低，講求圖像傳訊、「顏值」的會到Instagram，深度文章到了Medium，一句短句更新的會用Twitter，純粹即興「抽水」的會用閱後即焚的Snapchat，結果Facebook雖然依然有全球最多用戶，氣氛卻是暮氣沉沉。只是關心政治、已經進入不同小圈子的小眾已經不能自拔，也就繼續在自己的遊戲空間內存活下去。

  大數據影響力

  這樣的虛擬秩序，自然容易被各種各樣的高人利用，而「高人」可以是國家機器、商業巨子，也可以是恐怖組織、宗教團體，反正到了最後，都是資訊科技專才的壟斷。

  各種針對性廣告在社交媒體出現，有了大數據的幫助，客觀效果就是進一步加強了一個人的偏見或愛憎：本來對特朗普略有好感的人，經過社交媒體洗禮，逐漸變成死忠；本來只是稍為喜歡名車的人，耳濡目染下，逐漸覺得擁有保時捷是「常態」，積習難返。

  當社交媒體的attention span（注意力長度）已經以秒來計算，任何深度內容都要化作簡單圖像吸眼球，複雜內容固然難以出現，人的腦海也逐漸習慣以套版形象固化不同概念，成為「接觸大量資訊的愚民」。

  面對這樣的「虛擬實體化」、「複雜簡單化」時代，講求知識、品味的真正沉默大多數，必須有意識的繞過社交媒體演算式，既然避免不經不覺間自我激化，也要避免徹徹底底的消費化。

  避免這些趨勢的唯一途徑，唯有建構另一種「知識型消費習慣」，令它成為目標受眾的首選，而不是次選、三選，否則永遠被演算式、大數據牽着鼻子走，後果可以不堪設想。

  (編者按：沈旭暉著作《平行時空2 – 解構本土主義崛起的世界》現已發售)

  歡迎訂購：實體書、電子書

  2018/11/12
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  Infoman

  that institutions are now more willing to sell on strength than buy the dips.

  2018/11/12
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  Infoman

  社交產品最近回暖，備受追捧的社交App有強調精神交流的「Soul」、興趣社區的「Flow」、個人主頁的「Soda」、實時視頻聊天的「Tiki」等等，內容全為「00後」的社交語言。在美國，主打00後視頻社交的「Monkey」超越Snapchat與Facebook，吸引蘋果CEO庫克（Tim Cook）的關注。

  智能手機普及前後，八十後與九十後的代溝較為明顯，00後至九五後俱為移動互聯網原住民，10年以來，青少年的社交圈、工具型產品如作業幫、小猿搜題仍屹立不倒，微信、QQ、貼吧、淘寶仍聚合年輕一代。

  代溝愈來愈大

  泛娛樂產品強調代際差異，聚焦於青春期特殊的心理需求，比如喜愛幻想、內心較孤獨、渴望聆聽、自我表達極端等。八十後選擇QQ交友、論壇灌水；九十後選擇貼吧、二次元、cosplay；八十後熟悉的豆瓣通過興趣小組提供「文青」社交環境，不相熟的人群亦可通過共同話題交流，至於00後，預先設定場景對他們很重要。美國網紅產品Monkey就為用戶設定短暫的交流時間，視頻時間一到，用戶可選擇繼續交談或者直接切掉。

  很多《00後社交報告》把這一代人歸納出如下特質，如「00後愛追星」、「偏愛直播短視頻」、「二次元、遊戲」等，若換成九十後、九五後亦同樣成立；當00後步入大學、走進職場，有更龐大的熟人社會關係網絡，遲早就流向微信這些「老一輩」的社交工具。瘋狂追捧00後社交的背後，其實隱藏着狂熱的資本和商機；決定了最有市場價值的年輕人永遠不缺商業吹捧，尤其是以流量為生的「注意力經濟」時代。儘管絕大多數00後連經濟獨立都未達到，卻不妨礙媒體與資本家積極鼓吹「00後社交」的商業價值，成為被市場爭奪的頭號對象。

  九十後、九五後剛闖入社會之時，就受到媒體的集體追捧，打開網絡，所有人都在說年輕人的故事；00後在新技術與新事物的接受程度確有優勢，尤其是新事物的替代速度愈來愈快，幾天不上網就看不懂了；成年人對新事物的無力感，自然讓年輕一代顯得與別不同。

  號稱「中國版Monkey」的Tiki，在「00後使用最多的App排行榜」中排名很前，用戶在Tiki視頻時有60秒倒數計時不斷提醒，若不快速打動對方就會被切掉；美顏、送禮物、動態效果及貼圖是經常用來吸引對方的手段；另外，00後與其他世代之間的文化差距變得愈來愈大。

  專屬話語體系

  九十後喜愛採用「火星語」，但僅對漢字萌化，旁人靠猜亦懂得大概意思；但00後的「社交黑話」破解就難得多──比如說，擴列cqy、ky別來、日常養火、隨時nss與nbcs等等，此為理解00後最大的挑戰之一。這種現象所帶來的煩惱不僅於內地出現，就連美國人也無法避免。

  00後是在全新技術體系與教育理念下成長的一代，有一整套他們世代的話語體系，表面化的模仿及迎合並不會讓他們覺得你很酷，反而會引來他們群起嘲弄，說得殘酷一點，就只有00後始可賺到更年輕一代的金錢。

  撰文：聶飛

  2018/11/10
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  Infoman

  Cloud Computing Without Containers

  336 zackbloom 11 hrs 126

  https://blog.cloudflare.com/cloud-computing-without-containers/?hH

  news.ycombinator.com/item?id=18415708

  Cloudflare has a cloud computing platform called Workers. Unlike essentially every other cloud computing platform I know of, it doesn’t use containers or virtual machines. We believe that is the future of Serverless and cloud computing in general, and I’ll try to convince you why.

  Isolates

  Two years ago we had a problem. We were limited in how many features and options we could build in-house, we needed a way for customers to be able to build for themselves. We set out to find a way to let people write code on our servers deployed around the world (we had a little over a hundred data centers then, 155 as of this writing). Our system needed to run untrusted code securely, with low overhead. We sit in front of ten million sites and process millions and millions of requests per second, it also had to run very very quickly.

  The Lua we had used previously didn’t run in a sandbox; customers couldn’t write their own code without our supervision. Traditional virtualization and container technologies like Kubernetes would have been exceptionally expensive for everyone involved. Running thousands of Kubernetes pods in a single location would be resource intensive, doing it in 155 locations would be even worse. Scaling them would be easier than without a management system, but far from trivial.

  What we ended up settling on was a technology built by the Google Chrome team to power the Javascript engine in that browser, V8: Isolates.

  Isolates are lightweight contexts that group variables with the code allowed to mutate them. Most importantly, a single process can run hundreds or thousands of Isolates, seamlessly switching between them. They make it possible to run untrusted code from many different customers within a single operating system process. They’re designed to start very quickly (several had to start in your web browser just for you to load this web page), and to not allow one Isolate to access the memory of another.

  We pay the overhead of a Javascript runtime once, and then are able to run essentially limitless scripts with almost no individual overhead. Any given Isolate can start around a hundred times faster than I can get a Node process to start on my machine. Even more importantly, they consume an order of magnitude less memory than that process.

  They have all the lovely function-as-a-service ergonomics of getting to just write code and not worry how it runs or scales. Simultaneously, they don’t use a virtual machine or a container, which means you are actually running closer to the metal than any other form of cloud computing I’m aware of. I believe it’s possible with this model to get close to the economics of running code on bare metal, but in an entirely Serverless environment.

  This is not meant to be an ad for Workers, but I do want to show you a chart to reflect just how stark the difference is, to showcase why I believe this is not iterative improvement but an actual paradigm shift:

  This data reflects actual requests (including network latency) made from a data center near where all the functions were deployed, performing a CPU intensive workload. Source Cold Starts

  Not everyone fully understands how a traditional Serverless platform like Lambda works. It spins up a containerized process for your code. It isn’t running your code in any environment more lightweight than running Node on your own machines. What it does do is auto-scale those processes (somewhat clumsily). That auto-scaling creates cold-starts.

  A cold-start is what happens when a new copy of your code has to be started on a machine. In the Lambda world this amounts to spinning up a new containerized process which can take between 500 milliseconds and 10 seconds. Any requests you get will be left hanging for as much as ten seconds, a terrible user experience. As a Lambda can only process a single request at a time, a new Lambda must be cold-started every time you get an additional concurrent request. This means that laggy request can happen over and over. If your Lambda doesn’t get a request soon enough, it will be shut down and it all starts again. Whenever you deploy new code it all happens again as every Lambda has to be redeployed. This has been correctly cited as a reason Serverless is not all it’s cracked up to be.

  Because Workers don’t have to start a process, Isolates start in 5 milliseconds, a duration which is imperceptible. Isolates similarly scale and deploy just as quickly, entirely eliminating this issue with existing Serverless technologies.

  Context Switching

  One key feature of an operating system is it gives you the ability to run many processes at once. It transparently switches between the various processes which would like to run code at any given time. To accomplish that it does what’s called a ‘context switch’: moving all of the memory required for one process out, and the memory required for the next in.

  That context switch can take as much as 100 microseconds. When multiplied by all the Node, Python or Go processes running on your average Lambda server this creates a heavy overhead which means not all of the CPUs power can actually be devoted to running the customer’s code; it’s spent switching between them.

  An Isolate-based system runs all of the code in a single process and uses its own mechanisms to ensure safe memory access. This means there are no expensive context switches, the machine spends virtually all of its time running your code.

  Memory

  The Node or Python runtimes were meant to be ran by individual people on their own servers. They were never intended to be ran in a multi-tenant environment with thousands of other people’s code and strict memory requirements. A basic Node Lambda running no real code consumes 35 MB of memory. When you can share the runtime between all of the Isolates as we do, that drops to around 3 MB.

  Memory is often the highest cost of running a customer’s code (even higher than the CPU), lowering it by an order of magnitude dramatically changes the economics.

  Fundamentally V8 was designed to be multi-tenant. It was designed to run the code from the many tabs in your browser in isolated environments within a single process. Node and similar runtimes were not, and it shows in the multi-tenant systems which are built atop it.

  Security

  Running multiple customers' code within the same process obviously requires paying careful attention to security. It wouldn’t have been productive or efficient for Cloudflare to build that isolation layer ourselves. It takes an astronomical amount of testing, fuzzing, penetration testing, and bounties required to build a truly secure system of that complexity.

  The only reason this was possible at all is the open-source nature of V8, and it’s standing as perhaps the most well security tested piece of software on earth. We also have a few layers of security built on our end, including various protections against timing attacks, but V8 is the real wonder that makes this compute model possible.

  Billing

  This is not meant to be a referendum on AWS billing, but it’s worth a quick mention as the economics are interesting. Lambdas are billed based on how long they run for. That billing is rounded up to the nearest 100 milliseconds, meaning people are overpaying for an average of 50 milliseconds every execution. Worse, they bill you for the entire time the Lambda is running, even if it’s just waiting for an external request to complete. As external requests can take hundreds or thousands of ms, you can end up paying ridiculous amounts at scale.

  Isolates have such a small memory footprint that we, at least, can afford to only bill you while your code is actually executing.

  In our case, due to the lower overhead, Workers end up being around 3x cheaper per CPU-cycle. A Worker offering 50 milliseconds of CPU is $0.50 per million requests, the equivalent Lambda is $1.84 per million. I believe lowering costs by 3x is a strong enough motivator that it alone will motivate companies to make the switch to Isolate-based providers.

  The Network is the Computer

  Amazon has a product called Lambda@Edge which is deployed to their CDN data centers. I’m sure they’re doing their best with the technology they have to make it compelling, but it’s three times more expensive than traditional Lambda, and it takes 30 minutes to deploy a code change. I don’t think there’s a compelling reason to use it.

  Conversely, as I mentioned, with Isolates we are able to deploy every source file to 154 data centers at better economics than Amazon can do it to one. It might actually be cheaper to run 154 Isolates than a single container, or perhaps Amazon is charging what the market will bear and it’s much higher than their costs. I don’t know Amazon’s economics, I do know we’re very comfortable with ours.

  Long-ago it became clear that to have a truly reliable system it must be deployed to more than one place on earth. A Lambda runs in a single availability zone, in a single region, in a single data center.

  Disadvantages

  No technology is magical, every transition comes with disadvantages. An Isolate-based system can’t run arbitrary compiled code. Process-level isolation allows your Lambda to spin up any binary it might need. In an Isolate universe you have to either write your code in Javascript (we use a lot of TypeScript), or a language which targets WebAssembly like Go or Rust.

  If you can’t recompile your processes, you can’t run them in an Isolate. This might mean Isolate-based Serverless is only for newer, more modern, applications in the immediate future. It also might mean legacy applications get only their most latency-sensitive components moved into an Isolate initially. The community may also find new and better ways to transpile existing applications into WebAssembly, rendering the issue moot.

  Your Help

  I would love for you to try Workers and let us and the community know about your experience. There is still a lot for us to build, we could use your feedback.

  We also need engineers and product managers who think this is interesting and want to take it in new directions. If you’re in San Francisco, Austin, or London, please reach out.

  2018/11/10
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  Infoman

  最近接受一個有關老人醫療服務的訪問，提到Apple Watch的ECG心電圖技術，獲得美國食物及藥品管理局（FDA）認可。我個人認為這功能很好，起碼能幫助老人檢測身體，做好防預。

  一年多前，我參加一個論壇的討論。當中幾位大醫生大教授都認為電子穿戴裝置得到的數據，不可以作任何醫療參考用途。他們強調，醫生、護士和那些數以十萬計的醫療器材，是不能輕易被取代。當時，我有點不忿，表示擁有的數據是否值得參考，與量度數據時的環境或質量關係較大，並非因數據從什麼裝置得來。

  記得有一次陪岳母到醫院檢查，她因為太累和緊張，醫生要替她量度3次血壓，數據才不偏高。即使醫生這麼專業，但數據的準確性都可能受現場環境影響，所以不能一概而論，抹殺穿戴式裝置的數據就不值得參考。

  智能手錶感測心電圖，或許未達專業級的醫療檢測儀器水平，但亦不可輕視其作用和貢獻。試想，Apple Watch全球銷售量超過2億，假若佩戴者在每天食飯、睡覺的正常生活狀態下，量度超過50次，所得的資料對個人而言，已經是「大數據」了，比起只作一次身體年檢，可能更具參考價值。假如我家老人日日量度心跳平均值是80，突然跳升至110，與平時情況不一樣，當然趕緊送他求診。

  現在，每天去公立醫院排隊看醫生的人甚多，醫生總不能花太多時間替每個病人好好看診。現在香港60歲以上的有130萬人，而未來10年將有100萬人退休，意味人口老化問題加劇，政府未來單單增加兩所醫院，又怎足夠應付呢？如果檢測心跳、血壓或者血糖指數等技術普及，價錢愈來愈平，人人都可以了解自己身體基本狀況和知悉變化，預知自己生病，及早向區內醫生求診，其實就是間接減低對整個社會公共醫療服務的需求。

  不同的行業都習慣舊有的做法，如果沒有競爭，服務質素會愈來愈差。數年前的士濫收車資及拒載情況嚴重，當Uber出現後，單憑一個App，可隨時預知車資及選擇車的型號，提供比一般的士更優質和更專業的服務，整個的士行業都大地震，紛紛應用更多科技及改善服務。醫生的專業一向備受公認，毋庸置疑。這與人「生死攸關」的行業，在科技日新月異的數碼年代，假如有隨時被「轉化」的心理準備而精益求精，比一般人更能擁抱新科技新事物，我深信對任何人都是一件大好事。

  黃岳永 香港科技大學副教授兼高級顧問（創業）

  2018/11/09
• 
  

  Infoman

  Chinese startup Makeblock is bringing robots to classrooms

  33 johnshades 12 hrs 5

  https://www.fastcompany.com/90242046/chinese-startup-makeblock-robots-are-invading-classrooms news.ycombinator.com/item?id=18387277 By Joshua Bateman5 minute Read

  Jianjun “Jasen” Wang wasn’t exposed to technology as a kid. He was an “ordinary, poor child,” the 33-year-old founder and CEO of Makeblock, says at his company’s Shenzhen office. He’s wearing jeans, glasses, and a company T-shirt that reads, “Instruct your Dreams.” “My parents didn’t have the spare money for me to learn something like [robotics].”

  Two decades later, Wang’s robots are making their way into kids’ hands across the world, and have made his startup one of the pioneers in a new trend in educational tech. Launched in 2012, Makeblock produces do-it-yourself robotics kits for kids that combine mechanical, electronic, and software components—think LEGO-like pieces that can be assembled into bots and controlled with a few lines of code. The idea began when Wang was in college, studying aerodynamics and tinkering with robotics on the side. He noticed a largely unaddressed market: those interested in robots but with a limited understanding of the technology. “All the products we developed are to help people create in the physical world,” he says.

  Wang initially founded Makeblock at Shenzhen-based HAX, a venture capital firm and hardware accelerator. After graduating from HAX, Wang kept the company in Shenzhen, a young city that has a strong manufacturing base and thousands of high-tech enterprises. He turned to Kickstarter to raise early funds, which also helped strengthen brand awareness and customer loyalty overseas.

  The company is now a success outside of China and Asia, still a rarity among Chinese high-tech startups. Makeblock says its robots are now used by more than 6 million users across 140 countries, including in more than 25,000 schools globally. In fact, 70% of Makeblock’s revenues last year came from overseas sales, mainly through educational channels in the U.S., Europe, and Japan. At an event in Mexico in April, the company even helped set a Guinness World Record for the world’s largest robotics lesson: 971 K-12 students used Makeblock kits to learn about technology and programming.

  [Animation: courtesy of Makeblock] Business is good, too. The company’s robot kits, which range in price from $79 to several hundred dollars, helped bring in $30 million in revenue last year, a 63% increase. In August, the startup closed a $44 million series C funding round, with a $367 million valuation. Makeblock’s product platforms include drivable and flyable robotics, as well as a building block system. Targeted to 6- to 12-year-olds, these kits include motors, wires, wheels, sensors, LED lights, metal body frames, and fasteners. Components are interchangeable across platforms, enabling users to build customized robots.

  Related: Shenzhen is building a mile-long superhighway for trees

  To operate the robots via laptop or mobile phone, children drag and drop embedded graphics or write their own code, depending on expertise and interest levels. Makeblock’s Neuron Explorer Kit is also compatible with Apple’s Swift Playgrounds, an app to help children learn the Swift programming language.

  Although Makeblock users learn basic artificial intelligence and programming skills, the process is meant to be fun. Users have designed robots that can feed pets, water plants, fetch goods, play instruments, and serve food.

  “The key point is not to teach them how to code. We provide them with a tool to realize their ideas. So, programming is one way for them to tell their ideas to the machines,” Wang says.

  Wang says the technological barrier to create has historically been too high for novices. “It’s very hard for them. If you provide a platform, that can make it easy,” he says.

  ‘There’s big money at stake’

  Airblock [Photo: courtesy of Makeblock] Makeblock’s robots have arrived at an opportune moment. Technology and STEAM (science, technology, engineering, the arts and mathematics) education, broadly, are increasingly central to school curriculums, says Douglas Levin, founder and president of consultancy EdTech Strategies. Lessons are presented on interactive whiteboards, homework done on tablets, tests conducted on computers, and grades recorded in the cloud. “There’s lots of experimentation,” Levin says. “People are excited about new technologies.” For example, low-cost virtual reality is gaining traction. And at an educational conference this summer, Levin saw numerous vendors marketing robotics and electronics kits.

  Makeblock operates in a cross-section of growing markets, competing against companies like Danish giant LEGO Mindstorms, New York-based littleBits, as well as startups like ArcBotics, Flybrix, Vex Robotics, and Wonder Workshop. By 2020, the global market for educational robots is expected to reach more than $6 billion, according to London-based research firm Technavio. “There’s big money at stake,” Levin says.

  MakeX competition [Photo: courtesy of Makeblock] Although Europe is the biggest market, the domestic Chinese market is expanding. According to DBS, Chinese education and training spending will reach ¥3 trillion ($438 billion) by 2025. Nine-tenths of that spending will come from China’s 300 million K-12 students. This contrasts with the U.S. Levin, who helped the Clinton Administration craft the first national education technology plan for K-12 public schools, said national and state governments encourage technological innovation, but that funding declined since the early 2000s. “Money is coming from repurposed dollars at the local level,” he says.

  U.S. resources differ by district, and underfunded rural and urban areas are acutely impacted. “It’s absolutely uneven in that regard,” Levin says.

  Codey Rocky [Photo: courtesy of Makeblock] Globally, the gender divide is also an issue. Makeblock users are predominantly boys, and the company is researching various product images and color schemes to increase female involvement. “Our products are not just only designed for boys. My daughter likes all these things,” says Makeblock director Lunitta Lu. Beyond demographic discrepancies, Levin said there are privacy worries with educational hardware products in general, particularly as manufacturers increasingly glean consumer data. The last U.S. privacy laws governing education and data collection were passed in 1973, and Levin said schools are not equipped to manage these issues.

  For Makeblock, macro trends may also be complicated, especially now that the U.S. and China are locked in a trade war. But Wang is focused on internal impediments. “I think the biggest challenge is on the people side and managing ourselves,” Wang says. “Management is challenged because the team is growing very fast.” (The company outsources the manufacturing of the hardware itself to nearby factories.)

  To bolster employee engagement, the startup holds internal maker-thons. Staffers across departments join small teams that have two days to build new products using Makeblock components. With the new funding, the company intends to bolster international sales and marketing as well as research and development. Roughly half of the company’s 500 employees are already in R&D, working to refine robot kits to make them more advanced—and easier for greenhorns. “Building a robot is still hard for many normal people,” Wang says.

  Joshua Bateman is based in Greater China. He can be reached @joshdbateman.

  2018/11/06
• 
  

  Infoman

  Learning to Read in Your 30s Profoundly Transforms the Brain

  214 dnetesn 10 hrs 111

  http://maxplanck.nautil.us/article/338/learning-to-read-in-your-30s-profoundly-transforms-the-brain

  news.ycombinator.com/item?id=18385536

  Reading is such a new ability in human evolutionary history that the existence of a “reading area” could not be specified in our genes. A kind of recycling process has to take place in the brain while learning to read: Areas evolved for the recognition of complex objects, such as faces, become engaged in translating letters into language. Some regions of our visual system thereby turn into interfaces between the visual and language systems.

  “Until now it was assumed that these changes are limited to the outer layer of the brain, the cortex, which is known to adapt quickly to new challenges,” says project leader Falk Huettig from the Max Planck Institute for Psycholinguistics. The Max Planck researchers together with Indian scientists from the Centre of Bio-Medical Research (CBMR) Lucknow and the University of Hyderabad have now discovered what changes occur in the adult brain when completely illiterate people learn to read and write. In contrast to previous assumptions, the learning process leads to a reorganization that extends to deep brain structures in the thalamus and the brainstem. The relatively young phenomenon of human writing, therefore, changes brain regions that are very old in evolutionary terms and already core parts of mice and other mammalian brains.

  brain_breaker When becoming literate neuroplasticity conquers a network that is deeply rooted in the brain. This reorganization makes us more and more efficient in visually navigating through letter strings.

  © Max Planck Institute for Human Cognitive Brain Sciences

  “We observed that the so-called colliculi superiores, a part of the brainstem, and the pulvinar, located in the thalamus, adapt the timing of their activity patterns to those of the visual cortex,” says Michael Skeide, scientific researcher at the Max Planck Institute for Human Cognitive and Brain Sciences (MPI CBS) in Leipzig and first author of the study, which has been published in the renowned magazine Science Advances. “These deep structures in the thalamus and brainstem help our visual cortex to filter important information from the flood of visual input even before we consciously perceive it.” Interestingly, it seems that the more the signal timings between the two brain regions are aligned, the better the reading capabilities. “We, therefore, believe that these brain systems increasingly fine-tune their communication as learners become more and more proficient in reading,” the neuroscientist explains further. “This could explain why experienced readers navigate more efficiently through a text.”

  Large-scale study with illiterates in India

  The interdisciplinary research team obtained these findings in India, a country with an illiteracy rate of about 39 percent. Poverty still limits access to education in some parts of India, especially for women. Therefore, in this study, nearly all participants were women in their 30s. At the beginning of the training, the majority of them could not decipher a single written word of their mother tongue Hindi. Hindi, one of the official languages of India, is based on Devanagari, a scripture with complex characters describing whole syllables or words rather than single letters.

  Participants reached a level comparable to a first-grader after only six months of reading training. “This growth of knowledge is remarkable,” says project leader Huettig. “While it is quite difficult for us to learn a new language, it appears to be much easier for us to learn to read. The adult brain proves to be astonishingly flexible.” In principle, this study could also have taken place in Europe. Yet illiteracy is regarded as such a taboo in the West that it would have been immensely difficult to find volunteers to take part. Nevertheless, even in India where the ability to read and write is strongly connected to social class, the project was a tremendous challenge. The scientists recruited volunteers from the same social class in two villages in Northern India to make sure that social factors could not influence the findings. Brain scans were performed in the city of Lucknow, a three hours taxi ride away from participants’ homes.

  A new view on dyslexia

  The impressive learning achievements of the volunteers do not only provide hope for adult illiterates, they also shed new light on the possible cause of reading disorders such as dyslexia. One possible cause for the basic deficits observed in people with dyslexia has previously been attributed to dysfunctions of the thalamus. “Since we found out that only a few months of reading training can modify the thalamus fundamentally, we have to scrutinize this hypothesis,” neuroscientist Skeide explains. It could also be that affected people show different brain activity in the thalamus just because their visual system is less well trained than that of experienced readers. This means that these abnormalities can only be considered an innate cause of dyslexia if they show up prior to schooling. “That’s why only studies that assess children before they start to learn to read and follow them up for several years can bring clarity about the origins of reading disorders,” Huettig adds.

  Lead image: In two villages in Northern India, illiterate women—nearly all in their 30s—learned how to read and write their mother tongue Hindi for six months. Credit: © Max Planck Institute for Psycholinguistics

  This article was originally published by Max Planck Neuroscience on May 24, 2017. The relevant study can be retrieved here.

  2018/11/06
• 
  

  Infoman

  The End Is Near for Mobile Apps

  13 miguelrochefort 2 hrs 7

  https://medium.com/s/story/mobile-apps-will-disappear-soon-4b4e54f46eb8

  news.ycombinator.com/item?id=18380293

  In the next three to seven years, I expect most mobile apps to disappear. With them, we’ll witness the loss of billions in venture capital that we’ve poured into the mobile startup sector. It will all be burned to ashes, with nothing left but stray lines of code.

  The vision was that, one day, every legal entity would have its own mobile app. The logic came to me six years ago. At the time, I was thinking about investing in a startup that created a mobile app to create apps for users without needing them to code (the concept is similar to website builders like Wix except for a native mobile app). You would simply enter some basic information and the app would generate an app for you on its cloud server. You could then download and install this app on your phone and ask others to do the same by sharing a link or QR code. The idea was to make it easy for individuals and small businesses to create their own personal apps for social or marketing purposes.

  The vision was that, one day, every legal entity (human beings and companies) would have its own mobile app. These apps would be dotted all over the internet, like physical properties on a map. Unfortunately, it didn’t happen. I doubt it ever will for a few straightforward reasons that are already affecting app usage.

  If you don’t believe me, count the number of apps you have on your phone. Chances are, if you take away the manufacturer’s pre-installed apps that you can’t delete, you have, at most, 100 apps. Those you use frequently probably number less than 30. Too many apps slow down your phone. They take up memory space, run background processes, and constantly check for push notifications even when not in use.

  In any case, would you really install a few hundred of your friends’ apps or the apps of all your favorite restaurants, grocers, and laundromats?

  When smartphones first appeared, major corporations rushed to make apps. Then they realized it was a real headache to maintain them. Every time you update information on your website or promote a product, you have to do the same on your app. And every time a handset manufacturer updates its operating system, you have to debug your app to make sure it keeps working — plus there are the pains of managing bugs on different brands, models, and screen sizes. If you’ve ever been involved in mobile app development, you know what I’m talking about.

  The truth is, unless you are a major retailer or content publisher that needs to sell or deliver to customers frequently, all you really need is a mobile-friendly website. If information is all people want, they’re going to Google it in a browser.

  Would you install so much different software on your laptop or PC? Given the first two points, this third is a logical evolution and is already happening in some parts of the world. It’s what the industry calls “building an ecosystem.” The strategy involves binding users’ daily behaviors and spending into their mobile apps.

  A good example is how restaurants and cafes are integrating into food delivery apps instead of maintaining their own online order and delivery systems. In turn, these food delivery apps are consolidating with mobile wallet or ride-share apps to provide synergy and convenience to users. Consider Go-Jek, the biggest motorcycle ride-share app in Indonesia. To many people, it’s an all-in-one mobile wallet, ride-hailing, food delivery, and lifestyle services app.

  Go-Jek took its inspiration from China’s WeChat, the biggest instant messaging app in that country, which has integrated just about every lifestyle service you can think of into their mobile wallet section. Every serious Chinese business has a WeChat official account, where users can access both static information as well as interactive services and transactions. WeChat has also introduced a “mini-program” section, an API-based ecosystem that basically allows third-party developers to create “child apps” within the main WeChat app so users never have to leave WeChat itself. Third-party developers’ apps connect seamlessly to the payment and social functions of a user’s existing WeChat account.

  WeChat mobile wallet, official accounts and mini-programs While WeChat approached integration from the beginning as an instant messaging app, other players in China are attempting to compete from other directions — Alipay from its base in e-commerce and Baidu from search engines. The Western world is lagging in this game of becoming a dominant app. It remains to be seen who will become the giant “app of apps” in other countries.

  Any industry consolidates as it matures. This is especially true in the world of native apps, where economies of scale, large user bases, frequent traffic, and so on are required for monetization. No matter how wonderful a new app idea may be, it costs more and more in advertising and promotion before it reaches critical mass to effectively monetize, much less break even.

  Apps were called “applications” in the first place because that’s what Apple called locally installed software in their computers to differentiate their system from Windows (which called them programs). Would you install as many different pieces of software on your laptop or PC as you do on your phone?

  As far as installing anything on a hard drive goes, think about the trend toward cloud-based services and the software as a service (SaaS) models that are delivered over browsers instead of installing software.

  Maintaining a mobile app requires time, effort, and money, especially when operating systems like iOS and Android update frequently. Have you noticed that every time you update the software on your phone, something goes wrong in one of your apps? Native apps are no longer as necessary as they once were. Consolidation is coming, and the era of “there’s an app for that” is coming to a close.

  Since this story was published, it has been picked up on Flipboard, Twitter and LinkedIn, spawning new discussions on other platforms. Here’s an interesting one from LinkedIn. I welcome all to join in the discourse in the name of crowdsourced wisdom!

  2018/11/05
• 
  

  Infoman

  Google's interview system: it's not about solving the problem

  144 mariedm 23 hrs 151

  https://threader.app/thread/1058433116002381824

  news.ycombinator.com/item?id=18374938

  Google's interview system is not great but there are various reasons why it feels less than it is. Most candidates don't know what the goal of our interviews are. First rule, it is not about finding an optimal solution or any solution at all.

  You are interviewed for multiple skills simultaneously. Cognitive skills, communication, leadership are a few to name. If the point is not finding a solution, then what is it? Let me explain.

  Your interviewers try to understand what it feels like to work with you on a daily basis. An interview question is just a tool in achieving that, it is not there to specifically measure your skills on a topic but a tool to understand the depth of your thinking.

  Before the interview starts, ask them what they want to get out of this interview. Good interviewers should already have a plan and a set of expectations. Ask them what you should do. Don't start coding yet. Ask them you should produce. Discussion, diagrams, pseudo code, code?

  Then, start cracking the question. List whatever questions you think it is important to solve this question, ask your edge cases. Get to a point where you are discussing about pros/cons of the solutions. These steps are critical. Don't just start coding. Have a consensus first.

  Your interviewer will try to give you hints. Don't ignore them because you are too confident about your solution. This is not a smartness contest. Also, don't panic if a hint doesn't make sense. Ask what interview's perspective is that they gave that hint.

  Talk, talk, talk as you are doing your thing. Talk about even the most obvious steps. Ask about testing, talk about testing cases if testing discussion is expected. Discuss what you'd fix to iterate your solution if you realized you are missing something.

  Don't get freaked out if the interviewer jumps to a different question. Sometimes, they think they got enough of what they have been looking for from a question and will do something else. Don't try to overdo a solution if interviewer think it is good enough.

  If you are not highly senior, you won't get asked system design or open ended questions about industry best practices. This style of interviews are also about being able to discuss pros/cons.

  Don't freak out just because your interviewer is taking too many notes. They need to provide evidence to the hiring committee and they want to remember as many signals as possible. Make sure you are clearly communicating your new ideas if you are iterating on the initial ones.

  At any interview regardless it is at Google or not, don't see your interviewer as someone who has the full authority but some who you are already working with. Try to have a feel how productive it will feel in real life. Remember, interviews shouldn't be a monologue.

  You can follow JBD. ____

  Tip: mention @threader_app on a Twitter thread with the keyword “compile” to get a link to it.

  Enjoy Threader? Tell @jack.

  Download Threader on iOS.

  2018/11/05
• 
  

  Infoman

  What is the revenue generation model for DuckDuckGo?

  67 lettergram 2 hrs 23

  https://www.quora.com/What-is-the-revenue-generation-model-for-DuckDuckGo/answer/Gabriel-Weinberg?share=1

  news.ycombinator.com/item?id=18320413

  DuckDuckGo has been a profitable company since 2014 without storing or sharing any personal information on people using our search engine. As we like to say, what you search on DuckDuckGo is private, even from us! We’re proud to have a business model for a web-based business that’s profitable without making your personal information the product. I’m happy to tell you all about how we make it work (and how other companies can, too).

  Though first, if you’re not familiar with DuckDuckGo, we are an Internet privacy company that empowers you to seamlessly take control of your personal information online, without any tradeoffs. We operate a search engine alternative to Google at http://duckduckgo.com, and offer additional apps and extensions to protect you from Google, Facebook and other trackers, no matter where you go on the Internet.

  The Big Myth

  It’s actually a big myth that search engines need to track your personal search history to make money or deliver quality search results. Almost all of the money search engines make (including Google) is based on the keywords you type in, without knowing anything about you, including your search history or the seemingly endless amounts of additional data points they have collected about registered and non-registered users alike.

  In fact, search advertisers buy search ads by bidding on keywords, not people. It makes intuitive sense, too. If you search for ‘car’, you are more likely to respond to a car ad than something you searched for last week.

  This keyword-based advertising is our primary business model. When you search on DuckDuckGo, we can show you an ad based on the keywords you type in. That’s it. And it works. Our privacy policy, in a nutshell, is to not collect or share any personal information at all. Every time you search on DuckDuckGo it is as if you were there for the first time – anonymous.

  For example, if you type in ‘dishwasher’ you will get a dishwasher ad.

  Google also makes most of their money via this same type of keyword-based advertising that doesn’t require any search-history tracking.

  So why do they track it all then? Because Google is not really a search company; they are an advertising company. On Google, your searches are tracked, mined, and packaged up into a data profile for advertisers to follow you around the Internet through intrusive and annoying ever-present banner ads, using Google’s massive ad networks, embedded across millions of sites and apps.

  This is why if you search for something on Google, you may start seeing ads for it everywhere. Using the internet doesn’t have to feel like you’re being watched, listened to, and monitored.

  Google, Facebook, and The Creepy Line

  Eric Schmidt, former Google CEO and Chairman, famously said “Google’s policy on a lot of these things is to get right up to the creepy line, but not cross it.” But for most people, that line was crossed by Google, Facebook, and others long ago.

  Alarmingly, Google now deploys hidden trackers on 76% of websites across the web to monitor your behavior and Facebook has hidden trackers on about 25% of websites, according to the Princeton Web Transparency & Accountability Project. It is likely that Google and/or Facebook are watching you on most sites you visit, in addition to tracking you when using their products.

  As a result, these two companies have amassed huge data profiles on individuals, which can include interests, past purchases, search, browsing and location history, and much more. This personal data is stored indefinitely and used for invasive targeted advertising that can follow you around the Internet.

  This advertising system is designed to enable hyper-targeting, which has many unintended consequences that have dominated the headlines in recent years, such as the ability for bad actors to use the system to influence elections, to exclude groups in a way that facilitates discrimination, and to expose your personal data to companies you’ve never even heard of.

  The operative question is, though, is all of this tracking necessary to make substantial profits? Is this the only way to run a profitable digital consumer focused service company? Not in my opinion. The fact is, these companies would still be wildly profitable if, for example, they dropped all of these hidden trackers across the web and limited the amount of data they keep to only what is most necessary.

  Yes, this additional tracking probably helps them compete with each other and adds some incremental revenue, but I believe the vast majority of their revenue would still exist if the tracking dial was turned way down, and they backed far away from the creepy line.

  The reason is simple: Google and Facebook are the undisputed champions of audience and reach across the internet, something advertisers will always pay for. Their business models don’t need to be this invasive.

  It is a choice to squeeze every last ounce of profit at the expense of privacy, democracy and society. A choice they don’t have to make. Without all this tracking, I’m confident they would still be among the most profitable companies in the world, and we’d all be better off.

  Anonymous Affiliates

  As mentioned, DuckDuckGo is profitable based mostly on keyword-based search ads, though we have always been on the search for other ways to anonymously make money so that we can reduce the dependence on advertising. The only other way we’ve found so far, which currently accounts for a much smaller portion of our revenue, is non-tracking affiliate partnerships with Amazon and eBay.

  When you visit those sites through DuckDuckGo, including when using !bangs, and subsequently make a purchase, we receive a small commission. This mechanism operates anonymously and there is no personally identifiable information exchanged between us and Amazon or eBay. These partnerships also don't affect the ranking of search results. The reason we can do this in an anonymous way with Amazon and eBay, though not with other retailers, is because Amazon and eBay run their own affiliate networks.

  What Other Companies Can Do

  At the beginning of this answer, I noted that other companies using an advertising business model could follow a similar path to DuckDuckGo. Here are a few actionable things companies can do to remain profitable without tracking the maximum amount of information possible on consumers:

  Favor interest-based advertising instead of hyper-targeted advertising. For us, that is basing ads just on the keywords people type in. For others, that could mean basing ads on the content on the page and not on the individual viewing the page. Sell advertising directly based on such interests, avoiding going through the hyper-targeted advertising systems of Google and Facebook. Consider using an anonymous affiliate system like DuckDuckGo does as described above. This can help you get away from as much advertising on your pages. Our vision is to set a new standard of trust online. The Internet shouldn't feel so creepy and getting the privacy you deserve online should be as simple as closing the blinds.

  2018/10/28
• 
  

  Infoman

  外國月亮是否特別圓？達利奧（Ray Dalio）認為世上所有的周期都是信貸周期，現在市況就似完全跟着他新書中的劇本走。美國最厲害的武器不是軍火，也不是跨國企業，而是聯儲局。

  【表1】是聯儲局資產負債表和美國貨幣供應（M2）。金融海嘯前，聯儲局十分淡定，每年增表2%至3%，經濟便十分繁榮。

  直到金融海嘯出現，聯儲局便行霹靂手段，1年增加資產負債規模超過1倍，可惜銀行不買賬，M2只增加10%。聯儲局在2010年、2011年、2013年及2014年再度擴表，M2和經濟終於有反應，回到中單位數增長。到了2015年之後的3年，聯儲局便不再擴表，靜待資金流動加速，結果經濟增長真的加速。

  資產價格要看鮑威爾心情

  聯儲局看到經濟終於重回正軌，於是今年開始縮表。科網股是過去10年美股牛市的領頭羊，但現在也開始支持不住。

  貨幣真的是決定一切資產價格的關鍵因素。現在聯儲局資產負債表規模約4.2萬億（美元．下同），若未來每月縮表500億元，便等於每月減少貨幣1%。唯一的緩解方法，是貨幣快速流動，但投資市場表現欠佳，貨幣流動大概不容易加速。

  另一個值得注意的，是人民銀行的資產負債表規模。人民幣以美元作錨，美元多發，人民幣也多發。但人行有一個厲害處，那便是銀行都聽話。不但人行的資產負債表擴大，M2也同步擴大【表2】。2015年起聯儲局不再擴表，人民銀行也部署縮表。

  近日美股和香港物業價格的調整，說明中美央行同步收水下，資產價格不容易有好表現。什麼時候才會見底回升？

  人行跟隨聯儲局（因為美元是國際貨幣嘛），一切要看鮑威爾的心情。鮑威爾到底在想什麼？

  達里奧認為，現在世界的危機，在於貨幣周期走到尾聲，而社會又因貧富懸殊（頂部10%人的財富，堪比底部的60%）、新移民等因素而不穩定。他認為，緩解方法，就是聯儲局政策以60%低下階層為對象。

  既然QE有助有錢人，那麼鮑威爾便可能會想再QT一下，拉近窮富懸殊。社會問題不是沒有飽飯吃，而是買不起樓，資產價格太高。

  美國重行QE機會極小

  另外一個可能左右鮑威爾想法的是美股表現。現在美股已從高位回落10%，如繼續以這速度回落，聯儲局可能很快就受不了。人們可以接受資產價格調整，但沒有人能接受資產價格崩塌。聯儲局可能明年中便會掉轉減息，甚至停止QT。

  下一周期買什麼好？只有企業才會創造價值。現在美國科技股下跌，不是公司本身出了問題，而是市場流動性在收緊。科網依舊最具創新能力。

  樓宇本身不會創造價值，只會因銀紙增加而升值（又或者人流改變而被賦予價值）。美國經濟並沒有如10年前般水深火熱（當年是整個金融體系差點崩潰），重行QE機會極小。下一牛市最快明年首季開始（股市快過實體經濟3到6個月）。

  hcl.hkej@gmail.com

  (編者按：郝承林著作《致富新世代2──科網君臨天下》現已發售)

  歡迎訂購：實體書、電子書

  2018/10/26
• 
  

  Infoman

  OliverJones 9 hours ago [-]

  My spouse is a human lawyer. She's working now for a software company; she did her part taking it public a few years ago. She knows our trade pretty well. The difference between a lawyer on the one hand, and a "jailhouse lawyer" (an engineer who does legalish work) on the other, are these: The lawyer knows how to get things done to support the business. It's rare for a real in-house lawyer to tell the business, "we can't do that." More often, it's "here's how we do that." The lawyer knows how to write a form contract (sales, lease, nondisclosure, etc) so other lawyers will read it and say "ok, that's fair" and sign it. The lawyer knows how to read an incoming form contract to see if it's fair. The lawyer has real relationships with her counterparts at customer and vendor companies. When a vendor tries to change terms, she calls her counterpart and asks, "what's this about? My business has problems with this. How can we get to yes?" The lawyer knows that if a dispute gets to a judge, the remedy will be money. With very few exceptions there's no way for a judge to repair a business deal that's gone sideways. So the lawyer says, "anything but court." At some level, the law is like computer code. But the computer code has been around for many centuries and it has been patched over and over and over again as needed to solve real-world problems: in the US and English system that's called "case law.". There's an adage that "hard cases make bad case law." Lawyers know the ins and outs of this stuff. In my experience as an entrepreneur, when I've played lawyer I've generally overdone things to my detriment. The US is definitely litigation-happy. That makes things tough.

  2018/10/23
• 
  

  Infoman

  We Are All Lawyers Now – The Rise of the Legalish

  124 artificiallawya 15 hrs 149

  https://www.artificiallawyer.com/2018/10/23/we-are-all-lawyers-now-the-rise-of-the-legalish/

  news.ycombinator.com/item?id=18281504

  By Max Cole, Co-Founder of legal workflow automation pioneer, Autto.

  Are we right in thinking that the rapidly growing legal tech sector belongs in law firms and in-house legal departments staffed by lawyers? Or are we missing the point?

  We seem to be defining our customers by who they are, rather than by what they do.

  This was my conclusion after attending a recent legal tech get together where one of the themes seemed to be how hard it is to get a conservative legal community to adopt new technology. We have all had this conversation.

  This experience got me thinking: ‘Aren’t we all in the legal field to some extent?’

  Even if we don’t work as lawyers, we all deal with legal documents and processes. Outside of work we complete voter declarations, vehicle paperwork, and planning applications.

  Running a startup, regulations and legal requirements are everywhere: employment contracts, pensions, data privacy, insurance, health and safety, and so on.

  We think nothing of this, as the law touches most of us frequently as part of business life. We are all lawyers now, you might say.

  So, where is the majority of legal work to be found?

  The answer is probably not in the offices of the one hundred and forty-two thousand solicitors registered in the UK. It’s not in the big law firms that dominate our view of the legal industry. It’s elsewhere, in the industries and services that have one shared element: that the nature of their business has a constant legal underpinning.

  These are the regulated industries like finance, pensions, insurance, or those with heavy contract management demands. These are industries whose work requires constant contact with the law even if the work doesn’t involve legal advice and is not done by lawyers.

  It’s work that we at Autto have come to call legalish. It has become key to the way we look at the market for legal technology.

  The Importance of The Legalish

  This insight has produced obvious benefits. First and most obviously the addressable market is much bigger if it includes businesses doing legalish work – by our reckoning about three to four times larger.

  Second, we have found that those who are responsible for managing legalish work are in general much more open to tech adoption, and this is the case whether or not they are lawyers themselves by training. In general they are less conservative as customers and more focused on efficiency as a virtue in itself. They see the benefits more readily. I stress the generalization here – we are coming across more and more innovators within the traditional law firm model.

  One of the benefits of automation – our field – is that it helps ensure precision and consistency. This is one reason why it naturally suits legalish work where those doing the work may have little or no legal training.

  ‘We are all lawyers now,’ you might say. Nevertheless, the crossover between the needs of legalish and what we perceive as the legal field is remarkable. If there was any doubt about this then it’s worth considering that the biggest companies doing legalish work tend to have the largest in-house legal teams to support their operations.

  Almost everyone I talk to about my lawtech business assumes that we are selling to traditional law firms. And this is where we started, in fact.

  But the understanding that legal is: (a) everywhere, and (b) not done by lawyers, has given us a new perspective. It shapes our marketing efforts and our product design.

  So, I encourage the lawtech community to embrace the concept of the legalish – or whatever you choose to call it: a different way of thinking that includes a far wider range of industries, with new opportunities for both startups and users.

  —

  P.S. for anyone wondering who Autto are and what they do, please check out this short video below.

  2018/10/23
• 
  

  Infoman

  As an electrical engineer with 15+ years in global enterprise software sales, a track record of 100MIO$+ sales in the past ten years and 2 failed software startups as an experience i can share following:

  • A company only has excistence with paying customers.
  • The global economy excists because of supply and DEMAND. So start preaching the benefits of your products or services, because there is a very high likelyhood there is potential client in the 70 Trillion dollar economy.
  • Sales has evolved to being the trusted advisor and filling the blind spots for your clients with facts, value and business cases.
  • Don't sell a drill, sell the hole. Clients are not looking for the specs of the drill, but the size of a hole.
  • Before you start building your MVP, sell the value proposition. If you are not able to sell and validate your value proposition, you will struggle selling your MVP.
  • Selling can't always be expressed in metrics. It's like sex. You have to do it, to get better in it. So stop talking about how to do it, just do it...
  • You think you can't do sales? Have you ever applied for a job, sold yourself to your current girlfriend, husband or wife, challenge a friend with fact or a value creation discussion? You have done sales...
  • The conversion rate of off-line sales is higher then on-line sales (for B2B complex sales cycles). So start engaging with your clients face 2 face.
  • There is an absolute relation between your activities and your (revenue) results. P.s. If you are in 1MIO$+(global) B2B (SaaS) enterprise software sales: Complex sales for global matrix organisations with multi-level stakeholders can't be achieved trough reading a book.

  2018/10/23
• 
  

  Infoman

  Founders need to get radically better at sales

  76 stanleyhtml 7 hrs 18

  https://buzzways.at/collection/745/

  news.ycombinator.com/item?id=18283667

  For technical founders it is irrationally, obscenely hard to reverse years of programming (ba dum bum) that sales is a value-destroying activity. Sales is CLEARLY a value-creating activity, contingent on you have a value-creating product. The world will not drop what they are doing to adopt your work. This is particularly true in B2B, where simply building a better mousetrap won't overcome the activation energy required to get people with additional non-mice problems to prioritize changing mousetraps today. This is very non-obvious for founders because founders are not often people who want to be sold to. We often come from a background where trying out tools is a bit of a fun hobby. We like looking at all the options, making charts, and ripping out partially complete tests. "This week I unsuccessfully trialed four software options for automating that thing that has been killing us. Our actual production process remains the same as last week. Don't worry; this was a great use of time." is not a thing you want to write in a progress report to manager. The process of sales pushes the burden for understanding the market and distilling it from the customer to the sales person. That is a very valuable thing; it is why almost all businesses buy almost all of their big-ticket purchases (including e.g. employees) from sales. (A candidate is absolutely doing a sales job when attempting to get hired by a company. This is one of the things that both sides of that market are frequently in denial about, but I digress.) Simply getting oneself to be comfortable with sales (or at least present as being comfortable for a phone call or two) unlocks a crazy amount of success for founders, because founders have a few advantages over every other sales rep. One is that they present as being almost irrationally obsessed with the market. Founders tend to live and breath the product for a few years, and it often shows, mostly in positive ways. Unlike most sales reps, founders can credibly promise tight feedback loops for product.

  Stealing a line from @asmartbear which has paid for at least a year or two of my kids' education: "If you go with BigCo, you can call them at 3 AM. Someone will listen to you politely, explain they have no solution, and open a ticket. If you call at 3 PM, same answer."

  "You can't get a useless answer from me at 3 AM in the morning. But when I'm up, your business really is important to me. I am the engineering team. I will push fixes so fast you will be amazed. I will get this right because it actually matters to my business that I do." Founders have to sell. Many, many technical founders of my acquaintance want to offload this to someone ASAP. I've never seen this work: you need to have a deep understanding of your market and customers to arm that first non-founder salesperson. It is gained by doing.

  (And even if you don't do sales day-to-day anymore, you still have to sell candidates, investors, and partners on the desirability of using your company. Though most B2B SaaS CEOs that I know still very much have an account in their CRM and talk to "opportunities" frequently.) The transition from founder-driven sales to founder-assisted sales generally starts with hiring a "maverick" sort of salesperson; someone who is comfortable extracting what you know about the market, iterating rapidly on scripts, and doing "things that don't scale" to win. This may often be a necessary step, but it generally doesn't last for growing companies, partly because there is a crushing market undersupply of very effective mavericks. These folks are capable of writing their own ticket and then, by construction, getting folks to buy it.

  So eventually one tends to hire a VP of Sales who has done this before. They'll immediately start hiring reps under them, and start systematizing what you've learned about sales into scripts and playbooks. You'll see this maturity model start to creep into all parts of the funnel, too, which will probably actually be written down for the first time somewhere around this stage. Sophisticated, mature processes for marketing to pass leads over to sales for qualification.

  Sophisticated, maturing processes for sales to actually physically close deals. ("What, you mean we don't just edit a few things in Word and then ask them to print, sign, scan, and send back?") Defined, scheduled startup calls, onboarding, and handover to AMs / CS aftewards. (Sales, like many professions, benefits from specialization of labor. One that happens early is splitting the team into "account executives" (AEs) focused on getting new accounts and "account managers" (AMs), focused on keeping existing customers happy and expansion revenue. (Some companies even split their AM teams into dedicated subteams for doing true expansion, for cross-selling products, for winning renewals, and for the generic "I want the direct contact information for someone at your company because our business must be important enough" job) If you're at all interested in these topics, the people to read a heck out of are Steli and @jasonlk (who writes https://t.co/7FpEMvXdLZ ). Steli is so effective at sales he has closed deals he wasn't even a party to. My favorite anecdote about this:

  At a previous company I had invited myself out to lunch with a software CEO, with intent to get them to sign up, but was not really sure where we were at end of lunch."Hey apropos of nothing: do you know Steli?""Oh yeah he's great." "He is. Steli wouldn't let me leave lunch...

  ... until you explicitly tell me you're going to use our product.""He wouldn't, would he.""He wouldn't.""OK then; we will." "Great! Email me and we'll figure out logistics."

  That is called "asking for the sale" and, while that is a very unconventional way to ask for the sale, a ridiculous portion of all energy expended in the art of sales is to get conversations to the point where someone has to actually say yes or no. Relatedly: in the highly likely event that you get an answer which is not a yes or no, effective salespeople follow up until the sun goes nova waiting for either a yes or no. Travis NorthcuttTravis Northcutt: I am stealing this and putting it on my sales page Gordon CampbellGordon Campbell: read this thread mate, its a cracker! Catherine 🍀 Redhead ExtraordinaireCatherine 🍀 Redhead Extraordinaire: I just looked at a startup website today where they aren't asking for the sale in some way on every single page. Amy Hoy ✨Amy Hoy ✨: i covered the “how to think this way before creating the product” angle in my microconf talk that you contributed to 😎 https://t.co/LJtDmz0dGC K OBrienK OBrien: To sum it up: Founders need to sell Gary BGary B: unroll this please Rob MayoffRob Mayoff: Dear Thread Reader App,Kindly unroll this thread for me at your earliest convenience.Very Truly Yours, Rob

  Dave PaolaDave Paola: this is awesome! Confused by the "AMA" title instead of "how to price a product, sell it to customers, and build a sales team" Thanks for putting this together!

  Jeff AtwoodJeff Atwood: I have to say the line “Mother effer, you got me to open the email” triggers all my worst stereotypes of salespeople. Is that kind of aggressive language necessary so near the start of the article? — Patrick McKenzie (@patio11) October 18, 2018 Founders have to sell.

  Many, many technical founders of my acquaintance want to offload this to someone ASAP. I've never seen this work: you need to have a deep understanding of your market and customers to arm that first non-founder salesperson. It is gained by doing.

  — Patrick McKenzie (@patio11) October 18, 2018 (And even if you don't do sales day-to-day anymore, you still have to sell candidates, investors, and partners on the desirability of using your company. Though most B2B SaaS CEOs that I know still very much have an account in their CRM and talk to "opportunities" frequently.)

  — Patrick McKenzie (@patio11) October 18, 2018 What do Swift generics and recursive data types have to do with algebra? \U0001f914

  Today's episode goes deep, but in the end our exploration leads us to construct a precise type that can be useful in everyday programming!https://t.co/8wxOjGEZLi pic.twitter.com/TyhGcbjnzq

  — Point-Free (@pointfreeco) June 11, 2018 The transition from founder-driven sales to founder-assisted sales generally starts with hiring a "maverick" sort of salesperson; someone who is comfortable extracting what you know about the market, iterating rapidly on scripts, and doing "things that don't scale" to win.

  — Patrick McKenzie (@patio11) October 18, 2018 It's been hard to have a good feeder in the US but the @usmlr @Usmis already helping that. We've picked up some great homegrown talent from the inaugural season and there's more to come!

  — Robin Parnell (@RobinParnell4) October 18, 2018 Stealing a line from @asmartbear which has paid for at least a year or two of my kids' education:

  "If you go with BigCo, you can call them at 3 AM. Someone will listen to you politely, explain they have no solution, and open a ticket. If you call at 3 PM, same answer."

  — Patrick McKenzie (@patio11) October 18, 2018

  2018/10/23