Profitable remote job site built with single PHP file (4.5K lines of code) - vanilla-almond https://twitter.com/levelsio/status/938707166508154880 <comment><AUTHOR> Business value isn't measured in lines of code though, especially with the proliferation of libraries that can perform most functions when wired up together correctly. You could probably make the core functionality of twitter in a similar number of lines.

</comment><comment><USER2> Currently the top two tweets in the thread, both from @levelsio:

> [http://RemoteOK.io](http://RemoteOK.io) is a single PHP file called > "index.php" generating $2,342.04 in a day.

or then again:

> Record sales yesterday of $2,342.04 on > [https://RemoteOK.io](https://RemoteOK.io) for no apparent reason (maybe > companies are spending their EOY HR budgets?). Normal sales is like $299

</comment><comment><USER3> So is this considered good engineering or bad engineering

<comment><USER4> Probably good marketing, since it seems to make money and generate buzz, but in my mind, also probably bad engineering.

It's not a library, no one is supposed to drop it into their codebase like a C header, so keeping everything in a single file gives no objective advantage over using multiple files, even with a similar sized codebase.</comment></comment>


Sex, religion and a towering treatise on anatomy - <AUTHOR> https://www.nature.com/articles/d41586-018-05941-0 <comment><USER2> wow, what a sensationalist headline</comment>


The Brilliant "Baloney Slicer" That Started the Digital Age - <AUTHOR> http://nautil.us/blog/the-brilliant-baloney-slicer-that-started-the-digital-age

<comment><USER2> They have one at the computer history museum, rather large. It is also very long to spin up or spin down. Not surprisingly it has a crap ton of angular momentum when it is running.

Always fun to realize that the top engineers of their day, if they were using the "best system money could buy" had a 5MB hard drive to work with.

</comment><comment><USER3> I remember the days when anything over a Megabyte was something you lifted off the floor, if you could, placed gingerly atop the bundle, took the fly-lid off, then closed the door, pushed "MOUNT" and stood back.

I think those days still exist, but instead of a trained Wang or Tandem monkey, we have the spoolbots: [http://www.sciencephoto.com/image/344314/530wm/T2500330-Comp...](http://www.sciencephoto.com/image/344314/530wm/T2500330-Computer_tape_storage_robot- SPL.jpg)

I've always wanted to build my own hard-drive - or at the least, mass storage device, with my own hands. I guess those days will come around again in the Maker movement, the NSA backlash, and all that ..</comment>


In the "web vs. apps" debate for online publishers, apps are on a run - <AUTHOR> http://www.niemanlab.org/2015/02/in-the-web-vs-apps-debate-for-online-publishers-apps-are-on-a-run/?utm_source=Daily+Lab+email+list&utm_campaign=68832a6498-dailylabemail3&utm_medium=email&utm_term=0_d68264fd5e-68832a6498-395827456 <comment><USER2> From the post: "Smartphone owners "use an average of 24 apps per month but spend more than 80 percent of their [in app] time on just five apps.""

So you want to be one of those 5 apps, maybe being into the other 19 is acceptable. There isn't space for everybody.

I'd like to know how many web sites smartphone owners look at every day. My unsupported by data gut feeling is that there is more space there: bookmarks are not more manageable than apps but web sites can be discovered and accessed from other sites and even linked from within apps (think FB). I won't recommend going app only. Example: Techcrunch has both and I don't think they would ditch the site. Anybody from TC here?

I dare to say that apps are for frequent readers, probably not all of them.

<comment><USER3> The notion the entire Internet will narrow down to 10 or 20 non-gaming apps, is obviously absurd. There's clearly vastly more room on the Web. It's going to stay that way.

Take a simple example. Quora, Answers.com, Yahoo Answers, eHow, Wikipedia, Stack Exchange, WikiHow, How To Geek, WebMD, Instructables - and about 87 other sites people turn to via Google for information or answers.

There's zero chance a person is going to use all of those in the form of apps. However over the course of time, I've used every single one of them via Google search results (some a lot more than others). On the Web, those services have a lot of real value; were they just apps, probably at least half of them would not exist.

</comment></comment><comment><USER4> Gawker and Buzzfeed are building some new apps--so what? We have no idea if they will succeed. That hardly counts as "on a run."

<comment><USER5> I don't use a single native app for news. Not even aggregators like Flipboard who wrap it up with fancy animations. For me and my friends it's web all the way.

Not sure about others though, I could be an outlier but I'd rather assume not.

</comment></comment><comment><USER6> I get the "apps for utilities" , but "apps for content"? I just cannot see people bothering to download an app for every website they visit and personally i never have . Data for this?

<comment><USER7> If it's content that I access regularly (BBC News/Guardian/Twitter/Facebook), then I'll have the app. For other content consumption, I'll use the web.

What I don't understand is why, for organisations whose primary purpose in life is content, it's even much of a dilemma. Putting together a simple web page or a simple app to serve up identical content different ways should really not be a huge amount of work.

If you want your app or site to really stand out from a usability perspective - like Flipboard - then I can understand it. But Flipboard's product is primarily its UI, not its content. For the Beeb and The Guardian, all I want is a reasonably functional app, and I wouldn't be surprised if you could find a free framework for 90% of what you need on Github for that.

</comment></comment><comment><USER8> My 20 most-used apps:

7 by Google - Chrome, Gmail, Hangouts, Maps, Calendar, Translate

4 by Facebook - WhatsApp, Facebook, Messenger, Instagram

4 by start-ups - Netflix, Dropbox, Skyscanner, Meetup

3 utilities - Camera, Jota text editor, Avia

3 Netherlands essentials - 9292, nu.nl, NPO

In case of Facebook, nu.nl (News site) and Translate, I actually prefer the mobile site, because their apps are too restrictive and suck at text. I think that's typical for text content apps.

Interestingly, it appears most apps that I use regularly started out as websites that became an app when they were successful, stable products.

<comment><USER9> Netflix and Dropbox are startups? I know less about Skyscanner and Meetup, but I have a hard time calling Netflix a startup anymore.

</comment></comment><comment><USER10> Haven't the script already won over the app? Instead of downloading and installing each app, you import / require the modules needed.

There's one huge advantage with apps though, that they are easier to make money from.

</comment><comment><USER11> My hate for the overuse of "app", isn't a good number of them just web wrappers?

<comment><USER12> Well, they are downloadable from their respective app stores. Therefor they are apps.</comment></comment>


NASA wants more humans to walk on the moon – and stay there - <AUTHOR> https://www.usatoday.com/story/tech/nation-now/2018/07/18/nasa-return-moon/795647002/ <comment><USER2> Can we please start with the entire Republican party?</comment>


Marvin Camras - <AUTHOR> https://en.wikipedia.org/wiki/Marvin_Camras <comment><USER2> > In May 1962 Camras wrote a predictive paper titled "Magnetic recording and > reproduction - 2012 A.D.". In his paper Camras predicted the existence of > mass-produced portable media players he described as memory packs the size > of a package of playing cards holding up to 1020 bits of information. Such > devices would not have any mechanically moving parts and would store both > sound and movies. He also predicted music and movie downloads, online > shopping, access to online encyclopedias and newspapers and the widespread > use of online banking transactions.

That's pretty neat. Apparently there's also a "Children's Engineering School" named after him in Chicago.

<comment><USER3> That is 10^20 bits or 12.5 exabytes. Still bit far (6 orders of magnitude) from the current state of the art. Also it is electrostatic, not magnetic _solid state_ storage, leading the density race. The whole article this was mentioned is quite a good read: (paywall) [https://ieeexplore.ieee.org/document/4066743](https://ieeexplore.ieee.org/document/4066743)

</comment></comment><comment><USER4> Here's a link to his short paper, which is probably the reason this was posted.

[http://sci-hub.tw/10.1109/JRPROC.1962.288062](http://sci- hub.tw/10.1109/JRPROC.1962.288062)</comment>


The 2020 SaaS startup essential toolkit - <AUTHOR> https://www.sleuth.io/post/the-2020-saas-startup-100-essential-toolkit <comment><USER2> Your site is not opening. Please check.

<comment><AUTHOR> Checked from a number of browsers and locations, seems fine, are you still seeing issues?

<comment><USER3> +1, works well for me</comment></comment></comment>


Ask HN: When a customer contacts support, the battle is already lost. T/F? - <AUTHOR>

Back then, I always felt that whenever a customer contacted us I could show off what good customer service we had -- how quick, effective, or friendly we were. Customer support emails were treated as reasons to be happy, since we got to learn from them and impress them at the same time!
But I was wrong. Now, I always feel that the moment a customer contacts us, there is already some growing, irreversible dissatisfaction. Quick, effective customer support is merely the best apology that we can offer, a weak compromise. The best customer support is customer support that is not needed.
This is completely anecdotal, but whenever we have customers switching away from us we try to look back and see that, despite how quickly or effectively we helped, the fact that said customers had to contact us on anything (especially when it's called a "turn-key" solution) means that they already were unhappy. No amount of quality customer support was going to remedy those situations. Mitigate perhaps, but not remedy.
Anyone else feels the same way? <comment><USER2> Without knowing anything about your product, I have to disagree -- especially for "turn-key" solutions.

The turn-key solutions that I've had the pleasure (sic) of delivering depend in no small part on post-implementation support (disclaimer: I manage telecom infrastructure deployments). This seems to be the case (and yes, I'm generalizing here) for complex software solutions that have either many components, or are sold as a single complex point product.

Quick, effective customer support in the form of P1 issue resolution would be the second highest praise one aims to receive from a customer. Your point about the "best customer support is customer support that is not needed" is the highest praise of all (especially in an infrastructure play).

Quality customer support undoubtedly helps with customer satisfaction and often is one of the underlying factors to sales' ability to up(sell|grade) existing customers.

Your unhappy customers may be a marketing or sales issue; not necessarily a customer support problem.

As such, without additional information, my answer is "F", qualified with "you may have framed this question incorrectly."

Customer support can be considered a last line of defense to help resolve deeper issues, such as fundamental dissatisfaction with the product. The feedback that you hopefully take back from disgruntled customers should be fed back to your sales and marketing organizations to understand if the problems are indicative of a larger trend (i.e. product/solution not meeting needs of key customers), or if the lost customers were one-offs with specific circumstances unlikely to 'spread' to other customers.

EDIT: Added last paragraph.</comment>


First Test of Seismic Invisibility Cloak - <AUTHOR> http://www.technologyreview.com/view/510716/first-test-of-seismic-invisibility-cloak/ <comment><USER2> Not sure when folks will start using meta-materials to shield their border crossing tunnels from ground penetrating radar but you know its coming.</comment>


Ask HN: Getting into Machine Learning? - <AUTHOR> Recently I took a Coursera course by Andrew Ng on Machine Learning, and I have to say, I absolutely love this subject. Somehow it just clicks in my brain. I vectorized backpropagation early on, even though in the course Andrew tells you not to try at first, and the speed improvement was a marvel to behold.
So, to sum up, I really love the subject and want to learn a lot more! But I also need to eat ;) and I learn way better while doing. My background is in law and finance, though I do have coding skills in Swift, JS, PHP and Python, but nothing significant. Any recommendations for getting into the ML field without official CS degree or something similar? I think I'll really love it, and would be good at it.
Thanks for any ideas! <comment><USER2> Some general suggestions:

\- At least occasionally read something that isn't on a screen (in a book).

\- Do some experimentation on your own. Too many pretenders in this field parrot whatever they heard elsewhere. It's hard to argue with actual results.

\- Don't mindlessly chase whatever's trendy: that's an endless treadmill and "the crowd" wastes a lot of time.

\- Once you understand a few machine learning algorithms, you probably have enough. A bigger toolbox is better, but I recommend paying attention to things other than the machine learning activity itself, such as application of machine learning to the problem, data collection, data preparation and model validation.

<comment><AUTHOR> Thanks for the advice! Yes, reading in any field is paramount. Not sure about the screen limitation, since most of my library is on my laptop as is, but I get the point. :) And I know what you mean about actually experimenting with the tools for yourself and not chasing the latest thing. I am not getting into ML for the money, I would keep doing what I am doing now if that was the goal. I really do find it interesting and fascinating. I always loved math, but never really found an interesting application for it until stumbling on this subject while doing research into Micron, of all things.

Anyway, thanks again!</comment></comment>


Protect yourself against today's (and future) PDF zero-days - <AUTHOR> http://secbrowsing.blogspot.com/2010/09/protect-yourself-against-todays-pdf.html <comment><USER2> Some sound advice for tech users, not so useful for corporates and the non- tech savvy.

I always recommend that friends and family use something like sumatra or if they have to, foxit. A large amount of PDF vulnerabilities exist in functionality most people never use. I like the chrome sandbox option but haven't tried it out to see if it's effective.

Most companies can't upload PDFs to Google Docs or use Chrome, and have to use Adobe Reader (as an internally supported but usually unmanaged app). For that reason we normally recommend turning off Javascript, but disabling the plugin in the browser is new to me. Nice.

</comment><comment><USER3> I didn't even know Chrome had its own PDF viewer. Thanks for sharing.</comment>


The Dark Side of Entrepreneurship - <AUTHOR> http://boss.blogs.nytimes.com/2009/07/08/the-dark-side-of-entrepreneurship/ <comment><USER2> Some decent points in there. But also the most misunderstood and misrepresented business statistic of all time:

> Some 70 percent of businesses fail within seven years, according to the > Small Business Administration.

Wrong. Very wrong. 70% of businesses _are not going concerns_ within seven years. Doesn't mean they "failed". They might have been acquired, or closed, or a different project might have taken the entrepreneur's focus. If a guy starts two companies as small side projects, and one takes off, he closes the other. That'd be reported as a "50% failure rate" for people who can't get their head around the statistics. But they didn't fail - they served the entrepreneur during that part of his life, he learned lessons, hopefully made some money, and then moved on to bigger and better things. Starting a business is hard, but it's not this impossibly bleak predicament that people make it out to be.

<comment><USER3> Another thought occurs:

What percentage of jobs "fail within seven years"? I wouldn't be surprised if it's a comparable percentage. Yet, somehow, no one ever says "Are you SURE you want to be looking for a job? X% of jobs are terminated within seven years, you know".

<comment><USER4> Yes, _but_ : you rarely put up your house as collateral on starting a job.

<comment><USER5> Yes, but: you often make financial commitments on starting a job.

</comment></comment></comment></comment><comment><USER6> This shows two important rules of running your own business very clearly: You need to understand every aspect of what's going on in your company. And don't hire friends and relatives.

Had the subject of this story kept at least some basic track of his financial position, I am sure he would have been able to flag the problem much earlier and probably avoid the disaster. Something basic like how much money comes in, goes out, what's owed to you and by you.

<comment><USER7> Another important rule: don't borrow money.

<comment><USER8> And if you do, do it under the corporation's name so you aren't personally liable for the debts if it fails.

<comment><USER9> I believe it is nearly impossible for a small business to borrow money without a personal guarantee or collateral.

<comment><USER10> And that's why you plow every spare penny into the retirement account.

[http://en.wikipedia.org/wiki/Individual_Retirement_Account#B...](http://en.wikipedia.org/wiki/Individual_Retirement_Account#Bankruptcy_status)

</comment></comment></comment></comment></comment><comment><USER11> More background [http://www.cepro.com/article/baumeister_goes_out_of_business...](http://www.cepro.com/article/baumeister_goes_out_of_business_after_19_years/)

His average contract was about 100K, he lost control of his cash flow in the worst recession in at least three decades, perhaps six. He must have done a lot of things correctly to run a well respected business for 20 years, I don't understand the strongly negative tone of the article.

His conclusion "He is no longer a business owner, but I suspect he is doing what he should be doing" implies that someone who ran a business for 20 years with 40 employees wasn't cut out for business.

<comment><USER12> He focuses on the negative because most businesses fail, and as entrepreneurs are eternal optimists, many don't fully consider the downside of starting a business. Right now MANY small businesses and startups are failing. Its important to understand the potential negative consequences of starting a business. Most people, and especially around here, are in denial about this basic fact: by the numbers, your startup or small business will most likely fail. Thats ok. Your job is to do everything you can to beat those odds.

Also, most people don't have the luxury of 'capital light.' When small businesses fail, families usually suffer. That is part of the game, and its worth talking about once in a while, in between articles about guys that hit it big.

<comment><USER13> Very true. We constantly hear the "I woke up rich the very next day!" type stories, and it skews the perception of what running a business is like.

I have a lot of sympathy for old-school businesses that need a lot of operating capital. Putting your house in danger to support your business is a choice I hope I never have to make. In my case, that would essentially be the same as putting my marriage on the line.

<comment><USER12> Interesting quote I heard last week, from Stephen Fleming, successful VC and now Vice Provost of Georgia Tech. It was something like, "I've never seen a married founder bootstrap a company to a large exit and had the marriage survive."

<comment><USER11> I think it depends upon what value of large. One counter-example: Greg Gianforte bootstrapped RightNow technologies to 100M in revenue, still runs the company, and is still married. Two others would be Bill Hewlett and David Packard.

<comment><USER12> Its not that there aren't outliers. Its that bootstrapped divorce is very common - maybe more common than not.

</comment></comment></comment></comment></comment></comment><comment><USER14> Quick summary: Guy starts a successful business but outsources the accounting to his wife's friend's brother. The accountant books unfinished business as money in the bank. Economy sours and the truth comes to light. Guy goes out of business.

So the "dark side of entrepreneurship" is losing your business because you outsource a critical component and don't bother to double check the results?

I often wonder if the high 90% startup failure statistic is a result of negligence and lack of common sense. I hope to put my money where mouth is and find out on my own in the near future. Can anyone on the other side of the fence chime in on this?

<comment><USER15> Negligence is not always obvious.

Most entrepreneurs with a technical background should take a class or two in managerial accounting before they start. They should work hard at understanding it and developing financial models for their business. And they should pay as close attention to their business as they do their product.

Perusing HN you see how few articles there are about managing the receivables cash flow cycle, negotiating with vendors, forecasting financial performance and cash flow, dealing with the legal aspect, working with banks, documentation, managing salespeople or negotiating sales relationships, etc. But there are many companies with decent products that fail, and many are caused by one of those problems.

<comment><USER16> A course in accounting isn't necessary. The first few chapters of a "Principles" text and PAYING ATTENTION is all that is needed if you're not doing the actual accounting yourself. EDIT: "Accounting the Easy Way" is a good book for anyone who just wants/needs to understand it rather than doing it.

</comment></comment></comment><comment><USER17> We need more articles like this on HN. Pursuing a career in entrepreneurship & startups is a long, hard, and unglamorous road. If you're prepared for that at the beginning it won't make things much easier but it might give you the resolve to not give up.

I've see too many founders (myself included) quit this lifestyle because they weren't an instant success.

Beware the "trough of sorrow."

</comment><comment><USER18> We all know entrepreneurship entails huge risks. However, that's no reason to not forge ahead. Most people can't seem to understand that entrepreneurship is not about money, fame or power. Those are all secondary things. _Entrepreneurship is about an idea and its development_. It's about developing a thought, a concept only you and maybe the 2 other people starting the venture with you understand. It takes balls and passion. It takes courage. But more importantly, it takes unrelenting love. Love for the idea behind the venture, love for the concept of creating something valuable out of just abstractions, and, ultimately, love for the better world you're helping shape with the economic impact of your plan.

I'm not downplaying the importance of following sound economic principles (e.g. do NOT put your house as a collateral for a loan). Nonetheless, if you're going to be deterred by the economy, your wife, this article, or a friend, then you clearly don't have, and probably never had, what it takes.

<comment><USER19> Of course, sometimes "what it takes" involves a degree of ruthlessness which allows you to put not only your finances, but you marriage and your guardianship of your children at stake. Different individuals have different scales on which they decide whether that kind of ruthlessness is worth the reward, even if they did have the capacity for it.

<comment><USER18> I agree. Tolerance for risk is another big aspect of all this. I'm not married. I don't have kids. So my real only concern is myself right now. Others aren't as lucky.

<comment><USER19> Hah, I think a large number of people would disagree with you that not being married and not having kids is lucky.

<comment><USER18> In the context of starting your first startup while you're still young and single: yes, you'd be lucky. Life in general: that's actually unfortunate. =)

</comment></comment></comment></comment></comment><comment><USER20> The end of the article is overdoing it:

> "Was it worth it? It comes down to deciding what horrifies you more: the > possibility of waking up one day and realizing you never took a shot at your > dream or the possibility of losing your house."

For _him_ , it came down to that. Okay, granted. But this:

> "That's what entrepreneurship is really about."

Is it? I'm starting a web-based service business, and my fixed costs are well under $100 per month ongoing, and under $500 to start up. I'm financing it by working 3 months per year doing blue-collar work, and spend 9 months per year doing volunteer work in the third world.

I'm right and the New York Times is wrong.

#

<comment><USER21> Obviously a one person web based start up that doesn't pay wages is going to be a lot less risky than a 40 employee business that is buy supplies in advance for each job.

<comment><USER20> I don't understand the downvotes here.

I mean, obviously, you're correct.

But the NYT just said that entrepreneurship means caring more about your dreams than a house for your family.

Really? That's the decision people have to make to start a business? If you don't do that, you aren't an entrepreneur?

It's a great cautionary tale, but as you yourself point out, not all kinds of business have that risk. And as others point out, his problems were avoidable.

So, the NYT is wrong. Right?

</comment></comment></comment><comment><USER22> No liability for the negligent accountants?

<comment><USER23> Likely the financial (and social family) cost of "gross negligence" litigation would well exceed any measurable benefit. If the owner has a strong case, a private settlement for 30-50% damages is the most likely outcome.

Even in the unlikely event the accounting firm broke/abused their fiduciary duties AND a circuit/general court tort verdict was in this owner's favor, it would take years for the funds to work their way through the system.

</comment></comment><comment><USER24> i think the key is the last sentence:

"Was it worth it? It comes down to deciding what horrifies you more: the possibility of waking up one day and realizing you never took a shot at your dream or the possibility of losing your house."

<comment><USER25> absolutely. it amazes me how many people there are (that i meet) who fall under the umbrella of fearing for themselves rather than proactively pursuing their passions and gaining invaluable experiences in the world. better to live at home and minimize risk so you dont die.

fuck that.

<comment><USER19> I think it changes things a bit when there are two kids with you in said house. Living at home and minimizing risk so your two kids don't die suddenly doesn't seem so amazing.

</comment></comment></comment><comment> brown9-2 First of all I have to admit that that I'm pretty clueless when it comes to startups, entrepeneurship, running a business (of any size), etc. Zero experience or first-hand knowledge.

I have two questions that are likely pretty basic after reading this article:

1\. If the company was successful enough to be around for 20 years and have 40 employees, isn't that "successful enough" to be able to incorporate the company or form a LLC so that all debt is taken out in company name, and that the owner was on the line for debt personally?

2\. Is there no recourse for hiring accountants (especially a firm) that completely failed at it's job to properly book revenue?

<comment><USER26> And instead of just shutting it down cold-turkey why not try to see if he could borrow money from the employees to keep the business afloat. I believe the article writer, his friend pushed him too fast into closing the business down especially when he had customers already and a robust work force that he could have paired down.

<comment> brown9-2 I don't know if that would have been a viable solution - sounds like the owner might just have ended up losing the employee's money too in the end.

</comment></comment></comment><comment><USER27> This is a very informative story. I'd like to give it a summarization so that the rest of us would not go into the same trouble.

1\. Keep a very keen eye on your customers and selling. Mr. Baumeister sold high-end products. With economic downturn, fewer people spend money on luxueries. He should predict that when economic was going down.

2\. Pay constant attention to cash flow and balance statement.

3\. As a startup, don't outsource critical component. If you have to, always double check it. Seems the incompetent accounting firm played a significant role in failure of his business.

</comment><comment><USER28> There is a second moral to this story: the person you marry will significantly determine your success or failure as an entrepreneur. In my opinion, a nagging wife who is quick to judge and blame you is one of the worst threats to your startup/business.</comment>


An SQL in Shell, written by Bruce Momjian of PostgreSQL core. - <AUTHOR> http://blog.andrewvc.com/before-postgresql-bruce-momjian-wrote-an-sql

<comment><USER2> <http://cm.bell-labs.com/cm/cs/awkbook/> has a 1-page relational query program called qawk. Awk-based syntax, not SQL.

</comment><comment><USER3> There are actually several shell script relational databases. If I remember correctly one of them was even a successful commercial venture.</comment>


Abandon Your Big Idea. But Don't Give Up Your Big Ambition. - <AUTHOR> http://calnewport.com/blog/2011/12/08/abandon-your-big-idea-but-dont-give-up-your-big-ambition/ <comment><USER2> Remember, entrepreneurs: this is how academics think. And this is the attitude that makes begging for money via grants a lot more appealing than entrepreneurship. It's "give me money to do this because I'm an expert in it" instead of "make something people want and they will give you money."

<comment><USER3> Huh? So entrepreneurs are "better" than academics? That's just silly and pompous.

Remember, 90% of everything is crap. That goes for both businesses and research.

<comment><USER2> I have no idea how you got that conclusion from my comment. My point was that the academic attitude about expertise is pompous. I've worked with several academics and research labs trying to turn their research into startups, and you would not believe the attitudes prevalent in academia. The most common example of this I've seen is when professors bring up a potential competitor and state that the competitor does not have as much expertise (no founders with PhDs in the subject matter) as the professor, so the professor's new company should succeed wildly.

If the attitude present in this article was paid any mind by innovators, we would not have some things we truly love. My favorite example is ZFS. Built by prodigious hackers, not distributed filesystem experts by any means. I can't find the article anymore, but I remember reading that they felt their lack of expertise allowed them to approach the problem from a new angle, ignoring common wisdom.

My message to young entrepreneurs is this: Innovation doesn't work by experts expertly shaping the future incrementally. Innovation is messy. It works by lots of people - some experts, some not - trying to solve a problem. Almost everyone will fail, but you can't succeed if you don't try.

</comment></comment></comment><comment><USER4> I agree with this, and see this trap in lots of entrepreneurs. They want to disrupt healthcare, or teleconferencing without any domain expertise in it. They think having a keen sense of UI, superb technical skills, with some SEO mixed in will get the job done, but without the deep domain knowledge, you're just rolling the dice. I think that's what the author is getting at.

Example: The person who just wants to innovate for the heck out of it will create an app that allows people to learn foreign languages through Skype. The person who's actually studied foreign languages and accelerated learning will create an app that concentrates on input/listening first.

<comment><USER5> On the other hand, I wonder about the philosophy from David Sacks. But I guess he's also admitting that when you reach a certain point, you need people who know how to get inside from experience in order to really hit it big. But when starting up, he seems to say avoid getting those people involved.

 _One of the lessons we learned is that professional managers hired from the industries you're trying to disrupt tend not to be very beneficial. This whole idea that Mark Zuckerberg should stay CEO of Facebook, as opposed to being replaced by a professional manager, that was one of the things we learned at PayPal early on. If you look at the PayPal companies, they did not try to replace the initial leadership with supposedly more qualified people. Don't hire people from the industries you're trying to disrupt. They have too much ideological baggage about the way things have to work.

One of the ways PayPal was so successful is we did not know all the Visa and MasterCard rules we were supposedly violating. There were other companies who didn't pursue what we were pursuing because they thought it'd be a violation of the rules. In fact, was it a violation of the rules? At most it was a grey area because Visa and MasterCard rules weren't written for the Internet....

eBay certainly felt constrained -- this may have been a rationalization after the fact for losing to a company they so clearly should have beaten -- but they always said they felt too constrained with the risks and liability they could take on. PayPal was able to take on these massive liabilities because if the company didn't work it was going to go out of business anyway so there was nothing to lose.

BI: How do you apply those lessons here at Yammer? Particularly in hiring -- do you look for people who have never worked at big software companies?

DS: Yes. There's a strong strand of consumer DNA in our company. Out of the first 15 people who started, none of them worked at an enterprise software company. I don't think we hired anyone who had worked at an enterprise software company until last year. We came at it from a blank slate. What we've learned is there are certain enterprise functions we've had to backfill. There's been this steady process of starting with a consumer mindset and then saying, "OK, we need an enterprise sales team. We need some enterprise marketing to back them up."

BI: Do you find you need a big enterprise sales force to compete with the big guys?

DS: In the large accounts, yes._

[http://www.businessinsider.com/david-sacks- qa-2011-11#ixzz1g...](http://www.businessinsider.com/david-sacks- qa-2011-11#ixzz1g2Aa0Cyn)

</comment></comment><comment><USER6> Question from the Article: She admitted that she was having trouble with this ambition because no one at her school did behavioral neuroscience research. "But I really want to get involved in that area," she emphasized. "How do I find someone to work with me? I'm stuck."

Answer Given: "I told her to abandon the idea."

Correct Answer: Find another school.

<comment><USER7> Are you kidding me? She's an undergraduate. Presumably she chose the best school she could get into. It's not graduate studies.

<comment><USER8> > Presumably she chose the best school she could get into.

There is no "best school". There's a "best school for what you're trying to do". If you change what you're trying to do, the "best" school may change as well.

</comment></comment></comment><comment><USER9> Important to note also, this blog article is aimed at students, who don't have significant domain experience in _any_ field most likely.

Therefore, his advice is sound _for this audience_ as giving up big ideas is necessary when they're so nebulous and you don't have anything you could apply it to. An entrepreneur, by definition, has a skill in 'making something people want' and getting them to give you money and can apply that skill to a field, regardless of if they have much experience in that field.

<comment><USER10> While agree that this must be filtered for his audience of students, I have to take issue with his tone that seems to boil down to what most of academia does: very unambitious incremental research. Sure - this schlep is needed, but I think his cadre our too biased in this direction. The system dislikes risky research, and that's a shame.

Also, a glance at his books looks exactly like the grade-grubbing BS that leads to kids becoming valedictorian and then failing to do anything of significance after. These zombie achievers miss the point of education, and I feel like this guy is only encouraging them.

<comment><USER11> The article merely says that to make progress in a field, your best bet is to become an expert in it first.

<comment><USER12> I think he's saying, to make progress in a field, find a place where there are experts and work with them.

</comment></comment></comment></comment><comment><AUTHOR> Not sure I agree with his thesis in the context of entrepreneurship... sometimes, the inexperienced person comes up with a surprising (and surprisingly scaleable and profitable) take on a problem precisely _because_ of lack of experience. However, I don't know if that's a statistical anomaly.

<comment><USER9> I'd lean towards the idea he's half correct - small amounts of progress are relatively guaranteed to be made by someone with experience and lots of background in the field.

However, for someone to revolutionize or fork a given field, perhaps you do often need the inexperienced person with a different perspective...

</comment></comment><comment><USER13> If you think you're interested in problem, start trying to solve it. If you really get into it, along the way you'll become a domain expert.

At least for me, that's how I learn. Who cares if you end up solving a different problem than the one you started with.

</comment><comment><USER14> I think his advice is "good" in the sense of safe. If it is followed, it is very likely that the student ends up in a good place professionally. But, I think he ignores the fact that to become really good at something is not pure grit, but something has to feed your motivation. I'm just as sceptical about "passion" as Cal is, but at least one might need to discover some kind of drive towards achievement. For me it seems to be a particular kind of curiosity to "understand" something difficult, and use it to build stuff. It's not exactly passion, but still one needs the drive, not just a direction that when diligently pursued usually gives good results.

</comment><comment><USER15> Every member of each generation grows up and implicitly faces a decision: am I going to follow the path of others towards success? or am I going to redefine success? The former is for iBankers, lawyers, doctors, carrer academics and social capitalist. The latter is for innovators.

</comment><comment><USER16> Ok. A thesis that sounds reasonable, except how do we test this belief and what measuring stick?

In other words, how do we know that experience depth lead to being a successful research scientist. What does a successful research career mean? Discover something new and significant? Getting a teaching position? Citation? Nobel prize in a science field?</comment>


How to Pick Your Next Gig: Evaluating Startups - <AUTHOR> http://www.samvitjain.com/blog/evaluating-startups/ <comment><USER2> Using Zuckerberg/Jobs/Bezos/Gates as archetypal examples of founders is a terrible idea as they are _extreme_ \-- and I mean EXTREME -- outliers.

I also don't get how Jennifer Lawrence fits in any of this. As a counter- example, Chris Pratt, an equally-successful actor, was discovered by a stroke of luck when he was waiting tables in Hawaii.

Listen, I get it. It's cute to come up with pseudo-mathematical models that justify one opinion over another and that might help us make decision-making easier (Startup A vs. Startup B), but allow me to make a controversial point: if you're thinking about working for a startup, _don 't_. Starups traditionally underpay (due to offering equity), often have brogrammer cultures (and no real HR departments), will fire/lay you off at the drop of a hat, and working late hours with little recognition will be expected. The only exception to this rule is if you're one of the first ten-ish employees -- and, obviously, if you're a founder.

For obvious reasons, cap tables _highly_ favor early investors and owners. If you have a high-risk/high-reward type A personality, start a company! If you just want to have a career for a few dozen years and then more-or-less comfortably retire, join Google, or Facebook, or whatever. It makes absolutely no sense to join a startup if you're not even getting one point of equity.

Further, vesting will absolutely screw employees -- with either inflated share costs (if you leave the company) or with unfair lockout schedules (if you ever IPO). Not only that, but equity is utterly opaque: you literally have no idea how much it's _actually_ worth. It's important to understand that startups are a gamble for founders and for investors -- which is exactly why I _love_ startups: it's a perfect mix of skill and luck. With that said, in my opinion, for mid-to-late stage employees, the opportunity cost no longer makes sense. Unless you need a job to pay rent -- I've been there :)

<comment><USER3> I remember a CTO who idolized Jobs. And to make his company successful like Apple he proceeded to act like an asshole, apparently out of all the qualities and personality traits he deemed that to be the most important one. If someone accused him of that behavior he always justified it with "That's what Steve Jobs and look at Apple!". Everyone rolled their eyes but didn't say anything. They just quit and the projects were failed. The CTO was baffled and blamed others not realizing he was the problem.

<comment><USER4> Unfortunately everyone who worked at a handful startups has probably worked with someone like that.

</comment></comment></comment><comment><USER5> Perhaps more perspective is needed before composing such blog post (undergrad, had single intern). Seems the norm now is once you've walked 100m, stop and write about your amazing journey.

My selfish considerations for a startup:

      Can I remote
      Does it matter

If you're working on something that matters, and you need help, and you're accidentally reading this... please drop me a line.

<comment><USER6> You may have just been trying to make a point, but in case you were actually hoping to get contacted via this comment, it would help if you had your contact information in your profile. :)

</comment></comment><comment><USER7> My advice to junior software developers has always been that the things that matter are:

1: "will I get the chance to work on technology that I really want to work on?"

2: "will I get mentoring from great senior developers?"

I advise them to spend the first five years of their career honing their skills in the craft of software development, I advise them to pay no attention to money - I say "it will come later if you have become a great software engineer".

Interestingly, I feel alot more is to be learned from working at a disorganised and chaotic company, at least in the early stages of your career. When you later see things done right then you'll truly understand why things are done in the way they are, because you know how it can be done badly. Which is to say, it doesn't matter if the company you work at is well run or not.

It always makes me feel that a junior developer is off track if their primary selection criteria is anything but the opportunity to learn how to become a great developer.

I guess maybe its different in Silicon Valley.

<comment><USER8> How can you learn anything in a chaotic and disorganized environment? Either nobody has time to teach you anything, because everything is chaotic and disorganized and they need to fix it, or they are the reason everything is messed and you don't want to learn from that.

I feel that you would be much better off learning the right way to do things first, then going somewhere that is chaotic and seeing why best practices are in fact best. Much harder to unlearn the wrong way than just learn the right way in the first place if you ask me.

</comment></comment><comment><USER9> Never ever compromise salary for equity.

The equity is what you get in exchange for the risk that the company will not be successful; and for all the extra hours you'll probably be working.

The salary is what you get because that is how much your position pays in the market.

Things to compromise salary for:

\- You need a job badly. You're already planning your own exit.

\- If you were to get a higher salary somewhere else, you'd still prefer to work here.

\- You don't need the money.

<comment><USER10> If you go by this, no one would bootstrap. By definition you give up salary for 100% equity.

Dont sacrifice salary for not enough equity is a better rule.

<comment><USER11> Arguably that falls into the last category. If you can work for nothing but equity, then somehow or other you don't need the money (right now).

</comment></comment></comment><comment><USER12> Part II contains this Dustin Moskovitz quote: _A lot of graduating students think I just want to work on the hardest problems. If you are one of these people, I predict that you 're going to change your perspective over time. I think that's kind of like a student mentality, of challenging yourself, and proving that you're capable of it. But as you get older, other things start to become important, like personal fulfillment, what are you going to be proud of, what are you going to want to tell your kids about, or your grandkids about, one day._

I guess I never grew up, then. I still want to work on the hardest problems. That's what I would want to tell my kids about :-)

</comment><comment><USER13> This is a great hard analysis of evaluating opportunities but I am struck that there's not even a crack at evaluating the human interaction element of personal fit. Working for shitty people sucks, even if it leads to a boatload of money. You wind up spending more time with people you work with than your family, so try to price that accordingly.

<comment><USER2> There is in part 2: [http://www.samvitjain.com/blog/evaluating- startups-2/](http://www.samvitjain.com/blog/evaluating-startups-2/)

</comment></comment><comment><USER14> Must be nice to get to pick. My whole career I've only managed to have two choices on the hook once.

<comment><USER15> I agree with you. I always wonder who these engineers are who have tons of offers. I always wonder how come everyone's competing for talent, yet landing a job is so damn difficult.

<comment><USER16> This. Anytime I see a blog post with somebody humblebragging about simultaneous offers, I mentally call BS.

<comment><USER17> The people are out there.

I could add my own story as a data point, but I doubt people want to hear another one if they're in such denial.

<comment><USER18> I would like to know your story. And also I would like to know what are the resources you followed/used to become a great front end developer. Any suggestions or tips will be highly appreciated.

</comment></comment></comment></comment></comment><comment><USER19> Let's first of all recognize that if you could pick likely winners (especially with limited financial and business info) you would be an all-star VC and not an employee. It isn't something you should even think about because you will never have enough info to judge accurately.

What you CAN do is figure out what stage the business is in. Are they pursuing series A funding? Series B? Looking to be acquired? Minting money?

Are they growing staff and offices as fast as possible? That could be good or bad. Companies that grow suddenly end suddenly, too.

How much runway does the company have? You can probably get away with asking this. What you probably want to hear is "we are self-sufficient and growing", what you will probably hear is "we have a vaguely long runway". What you don't want to hear is "don't worry about it" or "I have no idea". Healthy businesses have an idea. Startups should want people who are invested in growing the whole business, not just employees and they should like that you ask (otherwise, you are accepting risk for no day-to-day benefit. Stock options aren't the real benefit. Working a job where you can more fully contribute is the real perk).

Last, do you like and understand the product? If not, is the team awesome and has much to teach you? If not, don't join, no matter how shiny they make the future sound.

</comment><comment><USER20> Essentially picking a good startup to work for is as hard as making a good investment. Except as an investor you can hedge between 100 startups hoping that one will give you a stellar return, but as an employee you are forced to put all your eggs into one basket.

</comment><comment><USER21> It might be wise to also consider the community and values of a company before you commit to it. For most people, and particularly for people at startups or in demanding technical roles, you'll spend more time with your co-workers than almost anyone else. You'll be relying on them in crises, supporting them when they fall, and looking to them for a lot more than a check. In some ways a job is like an investment, but in others it's like a marriage.

Years ago, I picked a small startup (with a great CEO, a great team, a great product - all the things mentioned in the article), but when I think about the best part of the decision to join them, it was how the whole company came together to support me when I suffered a personal tragedy. I'm happy about that startup's success, but I'm even happier that I was able to make (and keep) the friends I made there.

</comment><comment><USER22> Did I miss something? It lists 6 super successful founders with no startup experience - and then recommends to look for founders with past experience at a Rocketship or Serial Entrepreneurs as the best bet for how to evaluate startups.</comment>


Which Simplex Method Do You Like? - <AUTHOR> http://bob4er.blogspot.com/2015/01/which-simplex-method-do-you-like.html <comment><USER2> When I learned the simplex methond in my Engineering classes I had to perform it by hand for exams (only a couple steps or to get an updated matrix). The tableau method was easy to write out and learn. Yet for solution computation in the practical world it is very computationally expensive. I would solve in matrix form as the author suggests since linear algebra has come a long way since then.

Another note is that the simplex method is just one aspect of solving optimization problems. Most computation solvers now use a series of heuristics to get a "close enough" solution than perform either a simplex or gradient descent approach to get a final solution. For those that want to know more about this research I highly recommend the field of Operations Research. [https://www.or-exchange.org/](https://www.or-exchange.org/) is a good Q&A site for detail.

</comment><comment><USER3> Interesting. I don't remember the exact steps of the Simplex method (and to be fair I remember only one matrix, but yeah, if you want the Dual you solve it again)

I suspect there are still several methods that are solved in an "old way" and not optimized with most modern knowledge (in algorithmic and computing efficiency - like optimizing for cache)

</comment><comment><USER4> I am revising for my exam in two weeks and have been practising the tabulaue method for a month now ! Not sure if I should read this and and get confused since I want to make sure to get good grades.

<comment><USER5> Maybe wait until after your exam :)

The tableau method is in some sense the "bubble sort" of simplex algorithms. It's simple, it works and it's easy to explain, but it's not something anyone would use in practice.

That said, if you're not going to actually be implementing these things (and few people are), it's more important early on to get practice reducing problems to linear and integer programs than to know exactly how they're being solved.

<comment><USER4> Makes sense !

The tableau method when it was first introduced didn't seem like conventional linear algebra.

I found it odd though that we learnt machine learning topics BEFORE we learnt optimization theory. You would think that optimization theory would be taught before anything in Machine Learning. I found many topics in machine learning to be really opaque and it seemed it was just "try this model" without having a deep understanding of the mathematics or the reasoning behind them.

I think its because of all the media hype surrounding ML and my peers thinking its "cool".

Linear, Integer, and non-linear and dynamic programming really does fill that gap between conventional linear algebra and many of the topics in ML. I wish optimization theory was taught and emphasized more widely since its such an amazing topic.

<comment><USER5> A lot of people are suspicious of integer programming because it's NP-hard, and there can a bit of wizardry involved in formulating a problem well. It doesn't help that the mainstream solvers are black-boxes packed to the brim with their own peculiar heuristics.

I definitely agree that it's undersold, though.

Nonlinear programming I'm a bit more suspicious of, but mostly because I have a lot less experience with it.

Dynamic programming, on the other hand, I don't think should be taught in the same way at all -- to me it's something in the algorithmic toolbox, slightly less vague than "divide and conquer", mostly just a way of thinking and approaching problems. It's incredibly useful once you "get it", but I don't trust anyone who tries to systematize it by trying to reduce it all down to Markov decision processes and the Bellman equation. Maybe I can get behind someone saying "Dynamic programming is upside-down memoization," though.

In some sense all of these should "come before" machine learning because they're often used in particular machine learning algorithms. On the other hand, most ML pedagogy is at a higher level than all that, so maybe it's not needed so much.

<comment><USER6> Convex optimization is very robust and the algorithms are easy. In fact it is far easier to implement a good interior point method for linear programming than implementing a good simplex method. Sadly in most schools they only let students solve tons of linear programming problems by hand by the tableau method. It's incredibly boring and frustrating and students don't learn anything.

<comment><USER7> Interior point methods become ugly if the feasible region is not full- dimensional. Then you have to find the affine hull, find a base of the affine hull, project down etc. For all these steps you have to pay attention to the numerics etc. Not nice. So I think (working in mathematical optimization, but not on interior point methods) implementing a good interior point method is only considered easy because you usually don't care about these ugly parts. The simplex algorithm is less problematic since the only ugly situation that (you could imagine) occur here is that the polyhedron has a non-trivial lineality space (thus has no vertices). But this can't happen since at the beginning the simplex algorithm converts the polyhedron into A x = b, x >= 0^n form and this class of polyedra is pointed as you can prove easily.

Even in the nice case interior point methods are only nice as long as you are interested in the optimum value. As soon as you also want properties from polyhedral combinatorics for your solution (say: a feasible base), interior point methods become really ugly, especially if the face where the optimum value is attained is not just a vertex.

Disclaimer: I'm working in mathematical optimization, but not on interior point methods.

<comment><USER6> What do you mean by a not full dimensional feasible region? Interior point methods can handle equality constraints just fine, so do you mean two inequality constraints that conspire to be an equality? I think any method that uses inexact arithmetic will have trouble with that.

The very nice feature of interior point methods is that they also work for non-linear optimization, which has far broader applicability. That's why in my opinion courses that teach simplex but not interior point are outdated. With interior point you simply get far more usefulness in fewer lectures, so if you have to make a choice, make it interior point.

<comment><USER7> > so do you mean two inequality constraints that conspire to be an equality?

Exactly. This can be solved by the described method (but there are probably better ones).

> The very nice feature of interior point methods is that they also work for > non-linear optimization, which has far broader applicability.

I would not argue with broader applicability, if adding a constraint after finding an optimal solution requires a complete new fresh start (the dual simplex algorithm doesn't have this problem). This is very important for cutting plane algorithms for solving (mixed-)integer linear programs.

<comment><USER6> Interior point methods can benefit greatly from a hot restart too. You simply initialize it with the previous optimal point, and you start from there.

For integer linear programming, sure, you should probably use simplex for solving the relaxation. I'm talking about continuous problems in engineering, statistics, machine learning, etcetera. Very few of those problems are linear, and you don't care at all about the corner case you described. That is a completely different world than combinatorial optimization. In continuous optimization if you have two or more inequality constraints that make the space lower dimensional that simply means that your problem statement is wrong, because the parameters are not exact, and with a small variation in the parameters your space will become empty.

</comment></comment></comment></comment></comment></comment></comment></comment></comment><comment><USER8> I was taught the variant with two linear systems at each step. I don't remember the exact details, but the general concept of a "dual to the problem" made an impression on me.</comment>


Overcast 2.0 - <AUTHOR> http://www.marco.org/2015/10/09/overcast2 <comment><USER2> While I appreciate the gesture, I'm always wary of people releasing their commercial-grade software for free. Organizations with lots of money (Marco, Google, Apple) can subsidize their flagship products and drive the price of the market way, way down. This means that developers without Marco's wealth will have a much harder time getting their podcast apps out there. Great for the users, but terrible for developers and the ecosystem.

At least Marco's app has a donation button built-in.

</comment><comment><USER3> The big news: app is free, supports streaming, all features are now free (no longer pay-to-unlock).

The only revenue is through a voluntary "patron" subscription, which offers no additional benefits presently.</comment>


Ask HN: Strong practical knowledge, weak in theory. Where to start? - <AUTHOR>

I've been developing for around 10 years now without any formal training. I'm strong in problem solving and analysis, but feel like I'm not a well-rounded developer without a CS background, or at least that I look worse on paper for it.<p>Maybe someone can give me an overview of what they'd expect a fresh CS grad to know. Or better yet, a collection of CS topics that you've actually applied in your career. <comment><USER2> Go to the CS department websites of universities that you respect. My two favorites are MIT ([http://ocw.mit.edu/courses/#electrical-engineering-and- compu...](http://ocw.mit.edu/courses/#electrical-engineering-and-computer- science)) and Stanford (<http://www-cs.stanford.edu/courses>). Pick a bunch of courses that seem interesting to you. Download their syllabi, find the section that says "Textbook", and go buy it off Amazon. Read it. If you want, you may also want to download the lecture notes and a bunch of homework assignments and give them a try as well.

You're in a better position than someone who's strong in theory and weak in practice. There's a relatively straightforward roadmap to improving your theory skills - it'll take some time, your undivided attention, and intellectual engagement with the work, but all the steps are laid out for you in the courses and textbooks. There are often no such guidelines for improving practical skills, because most of the knowledge is locked up in the heads of practitioners, and they're only aware that they know it when it comes in handy.

</comment><comment><USER3> I seem to have the opposite problem. Spent the last 10 years getting degrees and certifications, constantly reading, learning new technologies, techniques, theories, attending conferences, workshops and tutorials, yet during the work day converting the same old boring enterprise specification documents into the same old boring enterprise code.

I can suggest ways to leverage my knowledge, and convert it into practical experience, but unless the decision makers aka managers are familiar with it (i.e. whatever we have been doing up till now) it gets dismissed as fringe.

That is why I am becoming a manager, so I can finally turn that knowledge into practical experience in order to create great products.

</comment><comment><USER4> You do look worse on paper. The only way to fix this is to get a degree.

However, you don't need to worry about this. I haven't used anything from uni that I learned, without having learned it by myself with a bit of elbow grease. I had already known SQL, javascript, etc before dealing with them at college.

Universities are quickly becoming obsolete. If I were looking to hire someone, and if one applicant had a very impressive Github repo, and another had a 4.0 from an ivy-clad school, I would choose the former.

<comment><AUTHOR> Thanks. I've had the option to return to school for a while now and couldn't find the long-term motivation to do it for the reasons you cited. I think, especially in software, that there are many more useful criteria for judging a candidate, and degrees don't represent too much more than your ability to operate in the real world (which can be supplanted with experience), and an understanding of foundational theory without necessarily being able to apply it </sweeping generalization>. I'm not too worried about being out of work for lack of degree. My primary concern is being stuck on a rung in my career by not taking the time to learn it. I have an idea for what my gaps are but wanted to get some feedback here in case I'm missing out on something.</comment></comment>


Tell HN: Twitter over capacity. - <AUTHOR>

"Merry Christmas" must be trending. Just got this message at Twitter.
Twitter is over capacity. Please wait a moment and try again. <comment><USER2> Too many instagram pics of trees and presents</comment>


ASCII/Unicode Delimited Text – Not CSV or TAB delimited - <AUTHOR> http://ronaldduncan.wordpress.com/2009/10/31/text-file-formats-ascii-delimited-text-not-csv-or-tab-delimited-text/

<comment><USER2> The article doesn't mention how to type these characters.

CSV, TAB delimited , YAML and even basic XML have the advantage of being human readable AND human-creatable.</comment>


The Big Bang project aims to create a typed language with the feel of scripting - <AUTHOR> https://pl.cs.jhu.edu/projects/big-bang/ <comment><USER2> I feel like this is the direction Julia is going in. It's in this interesting space between statically and dynamically typed languages - technically it's dynamically typed, but you get a lot of the safety and performance guarantees afforded by static type systems through Julia's aggressive type inference [1]. As the type system gets richer, it should only get better on this front.

If you haven't checked out Julia yet, it's a beautiful language. There's a reason why Graydon (the guy who made the very early versions of Rust) likens it to a Goldilocks language [2].

[1] [https://stackoverflow.com/questions/28078089/is-julia- dynami...](https://stackoverflow.com/questions/28078089/is-julia-dynamically- typed)

[2] [https://graydon2.dreamwidth.org/189377.html](https://graydon2.dreamwidth.org/189377.html)

<comment><USER3> The difference between Julia and Big Bang is that Big Bang will check your types at compile time. You'll know before any of your code executes whether or not it will yield any type errors.

Edit: (Disclaimer, I work on big bang)

<comment><USER4> How do you handle, say deserialization of objects over a network (to give the canonical non-trivial example)?

<comment><USER5> I don't know about Julia or Big Bang, but in Haskell your values conforming to types for serialization / deserialization would typical be checked once when parsing, and throw an error (or use a Maybe type) if anything is off.

<comment><USER4> Yes, that's the standard approach, but I was wondering how that meshed with not having to declare types (maybe the answer is just, that's the one place where you have to - or that's disallowed), but I was curious.

<comment><USER5> In Haskell, if you don't do anything weird, you don't have to declare types either in the norm case: it can all be inferred.

</comment></comment></comment></comment></comment></comment><comment><USER6> Someone beat you to it [https://donsbot.wordpress.com/2010/08/17/practical- haskell/](https://donsbot.wordpress.com/2010/08/17/practical-haskell/)

<comment><USER3> Haskell has an equational type system, and doesn't support subtyping in the same way that big bang does.

(Disclaimer I'm working on big bang, so I'm a bit biased)

</comment></comment><comment><USER7> Chapel Programming Language? By which I mean, Chapel seems like a great project to through support behind to add these feature sets. It is already very capable, had a good amount of resources, a roadmap, and seems that it would lend itself to this.

</comment><comment><USER8> Doesn't Go basically accomplish that via near instant compile time?

<comment><USER9> Not quite, polymorphism is a bit iffy in Go (there is interface, and it's doable but not great). Type inference is extremely limited (this is also due to a weak type system, with comparison point being ML-family languages for examples) . Global type inference is off the picture without generic/ polymorphic typing.

I don't think Go achieves the look and feel of working with Python and the like.

<comment><USER10> The go philosophy is that inheritance "does not scale" and they deliberately went another route: [https://golang.org/doc/faq#inheritance](https://golang.org/doc/faq#inheritance)

<comment><USER11> Errm - where did the parent say anything about inheritance?

<comment><USER12> Isn't the feel of working with Python all about inheritance?

<comment><USER13> Duck typing is far more important.

</comment></comment></comment></comment></comment></comment><comment><USER14> Use Nim

<comment><USER15> Doesn't Nim require types (e.g. for function arguments)?

<comment><USER16> In Nim, you can use the type `auto` for proc parameters & return types: [http://nim-lang.org/docs/manual.html#types-auto-type](http://nim- lang.org/docs/manual.html#types-auto-type)

When you use the `auto` type, the compiler will infer the type automatically from the context of the proc invocation or from the proc body. The inferred types can be different for the different parameters. So you can have a proc declaration that looks like:

      proc inferTypes(a, b: auto)  # `a` & `b` can accept different argument types

Within a proc, you can also create bindings & variables without needing to specify a type:

      let iBinding = 1
      var fVariable = 1.2

The following typeless code will compile & run without any complaints or problems:

      import strutils  # `%` operator

      proc inferTypes(a, b: auto) =
        echo "$1 $2" % [$a, $b]

      proc main() =
        inferTypes(25, 30)
        inferTypes(1, "hello")
        inferTypes(4.4, 7)
        inferTypes("cat", 9.5)

      main()

When the above code is compiled & run, it will produce the following output:

      25 30
      1 hello
      4.4 7
      cat 9.5

<comment><USER17> How does Nim hold up if you put "auto" everywhere you possibly can in a non- trivial program? (Tone note: Straight question. I have no idea and am honestly interested.)

Context: Type inference is hard. Hindley-Milner is famous for making it possible, but the farther you stray from it, the harder it gets to do with no human-added annotations. I hope the authors of "Big Bang" are intimately familiar with the issues involved or they're going to be in trouble. If they aren't they ought to correct that.

I'd also suggest that the Haskell community would be happy to share their experiences on that front if they are asked, and unless the authors are already experts, they _really really_ should ask. Only good things can come from asking.

<comment><USER16> I don't know, I'm sorry.

(The majority of my professional & personal programming has been along the Shell-Python-C-C++ axis. My preference for static types increases approximately logarithmically with the size of the program; historically, I preferred Python for most quick scripting needs, but for larger programs, I was glad of static types, so I would switch to C++.)

Now, Nim has replaced C++ for me completely (and also expanded downwards into the upper end of Python's territory). The work I'm currently doing in Nim is well into the "I prefer static types" area of the spectrum.

Maybe some of the Nim core devs would have more experience with this situation.

IIRC, several of the Nim core devs are familiar with Haskell, and consider it to be one of the reference languages guiding aspects of the Nim language design.</comment></comment></comment></comment></comment>


Ask HN: Make a cool about page for startups - <AUTHOR>

For my new startup I'm trying to design & develop an about page that doesn't look boring. But I find it really hard.
I know things like mission, goals, since when you're around, etc. should definitely be included. But how can you actually make those not boring? I got some text right now, I tried to keep it as short as possible, but I don't know what to do with it really. Add some icons? Maybe...
Also, in the tips on websites I mostly read stuff like "include your clients", "list your employees and let them write their bio", ... But we can't really do such things, since we are still a startup and it's just me and my co-founder working for the company and we don't really have any clients yet? I think those things would be really cool to add, since your visitors can really make a connection with your company in that way, but again, I'm not sure how to do this. <comment><USER2> Lead, don't follow...remove remove remove until there's nothing to remove...</comment>


Unreasonable Ineffectiveness of Machine Learning in Computer Systems Research - <AUTHOR> https://www.sigarch.org/the-unreasonable-ineffectiveness-of-machine-learning-in-computer-systems-research/ <comment><USER2> > Driverless long-haul trucks are apparently just a few years away, and the > main worry now is not so much the safety of these trucks but the specter of > unemployment facing millions of people currently employed as truck drivers.

No, no they're not. We have some lane tracking in good weather etc., but we are still _decades_ (or more) away from full level-5 autonomy that would make drivers behind the wheel unnecessary.

But it only goes to show that not even computer science experts are immune to marketing hype and well funded PR campaigns. :)

As for the unreasonable ineffectiveness - it's not just in systems research. ML can be very effective in some areas (especially when there is a ton of training data), but many areas of human endeavor are hard to model via function approximation techniques like those used in most of ML.

<comment><USER3> > but we are still decades (or more) away from full level-5 autonomy

Decades? Are you really sure? This assumption seems quite uninformed to me. The Grand Challenge was 12 years ago, from then we evolved from Level 1 to Level 3. Back then there was no data, computing power wasn't available as today, chips (&sensors) can be designed an built within a few months or even less these days for far less money. Machine Learning and Computer Vision made many advances in these 12 years (talent in these fields is now easier to acquire as more people can learn about it these days). We are at a completely different point these days. Level 5 is not the matter of _decades_ , 5 years, 10 max. But not _decades_.

<comment><USER4> I disagree. I think the higher levels are many orders of magnitude more difficult.

<comment><USER5> Volvo is supposed to be rolling out a few level 4 cars to the public this year. (Level 4 being no human attention required on certain roads that the car knows, if there are problems it drives to a safe place to park and pulls over). [http://www.theverge.com/2016/4/27/11518826/volvo-tesla- autop...](http://www.theverge.com/2016/4/27/11518826/volvo-tesla-autopilot- autonomous-self-driving-car)

</comment></comment></comment></comment><comment><USER6> I think this is because the kinds of problems that arise in system design are logical and symbolic in nature and the current crop of "AI" has no symbolic reasoning capabilities. All the current hype is about pattern matching. Very good pattern matching but just pattern matching nonetheless. Whereas when constructing a compiler or a JIT it's more like what mathematicians do by setting down some axioms and exploring the resulting theoretical landscape. None of the current hype is about theorem proving or the kinds of inductive constructions that crop up in the process of proving theorems or designing compilers and JITs.

For an example of the kind of logical problem optimizers solve you can take a look at: [https://github.com/google/souper](https://github.com/google/souper).

So I don't see how you can take the current neural nets and get them to design a more efficient CPU architecture or a better JIT.

<comment><USER7> It would actually be very straightforward to do so if the costs of testing solutions weren't so high. CPU architecture and JIT code can both be represented as unstructured (non-tabular) data. I even recall a circuit having been optimized by a genetic algorithm a while ago in an experiment. I also recall using LSTM to generate valid code from IIRC examples in Linux. Superiptimizatiom is also a relevant topic.

We just need better simulation tools or more resources.

<comment><USER8> Genetic algorithms are not neural nets though.

<comment><USER9> And they're generally worse than simulated annealing.

</comment></comment></comment></comment><comment><USER10> Cute title but the post didn't really address either reasonableness or effectiveness, but mostly claimed that the potential has not yet been realized. It's a pet peeve of mine to see these hackneyed joke titles referencing famous papers, "considered harmful" is another case in point. Let's just stick to descriptive titles.

<comment><USER11> It imitates an old paper title: "The Unreasonable Effectiveness of Mathematics in the Natural Sciences" by Eugene Wigner (1960)

<comment><USER12> I thought Wigner was actually paying homage to Karpathy.

<comment><USER11> Few know that Karpathy was using a previous meme.

</comment></comment></comment></comment><comment><USER13> The perceptron scheme for branch prediction (full paper) [1] probably works because it uses far more memory for branch history than the usual approaches. It's not doing a better job with comparable resources.

[1] [https://www.cs.utexas.edu/~lin/papers/hpca01.pdf](https://www.cs.utexas.edu/~lin/papers/hpca01.pdf)

</comment><comment><USER14> Indirectly they have helped quite a bit. Some of the most advanced mathematical and symbolic solvers (MIP, IP, LP, CP, SAT, SMT) have slowly been incorporating machine learning to advance their capabilities. Their use cases in these solvers include: branch prediction, branch selection, constraint evaluation order, solver type selection, search strategy selection, cost estimations for column generation strategies, problem classification, solve time estimation, etc.

And since advancements in our abilities at solving symbolic and mathematical problems have directly enabled the current research in PLT and formal systems, I see no reason to discount the impact ML has had in pushing that frontier.

</comment><comment><USER15> Perhaps this paper provides an explanation? [https://arxiv.org/pdf/1608.08225.pdf](https://arxiv.org/pdf/1608.08225.pdf)

"The exceptional simplicity of physics-based functions hinges on properties such as symmetry, locality, compositionality and polynomial log-probability, and we explore how these properties translate into exceptionally simple neural networks approximating both natural phenomena such as images and abstract representations thereof such as drawings."

<comment><USER16> This is really good. Deep learning right now is giving off a kind of illusion of domain-independent general intelligence that can solve any problem, so it would be really helpful to have some theoretical characterization of the specific problem domains it's good at and ones it's not good at.

</comment></comment><comment><USER17> Branch predictions are an interesting use, although I'm wondering how expensive a misprediction really is.

But this:

" _Another example is the use of regression techniques from machine learning to build models of program behavior. If I replace 64-bit arithmetic with 32-bit arithmetic in a program, how much does it change the output of the program and how much does it reduce energy consumption? For many programs, it is not feasible to build analytical models to answer these kinds of questions (among other things, the answers are usually dependent on the input values), but if you have a lot of training data, Xin Sui has shown that you can often use regression to build (non-linear) proxy functions to answer these kinds of questions fairly accurately._ "

I'm not sure whether I am fascinated or horrified.

<comment><USER18> Wikipedia reckons 10-20 cycle penalty for a branch mispredict (which sounds plausible given it fills the pipeline with useless junk). Given that branches are quite common, that's painful enough to want to avoid, but definitely not so painful that you'd want to devote as much silicon to solving it as you do to, say, L1 cache.

I do recall a bit of research (published by a Nokia R&D team I think) that reckoned you could get a mostly-ok performance estimate by tracking about half a dozen indicators including instructions executed, cache misses, tlb misses and brancb mispredicts and weighting them appropriately. The trouble there is nobody wants a performance model that's right 90% of the time but significantly wrong 10% of the time with no way to tell if the workload you want to test is in the 10%. But it's an indication of the importance of branch prediction still, I think.

</comment></comment><comment><USER19> Keith Winstein & collaborators have good work about using ML to train TCP's congestion control in different scenarios: [http://web.mit.edu/remy/](http://web.mit.edu/remy/)

</comment><comment><USER20> I guess computer is much more deterministic than what is required for ML to be useful.

ML, in a very inaccurate way, can be seen as:

1.We have observations and conclusions.

2.We don't know exact those observations leads to the conclusions.

3.The assumed procedure that leads the observations to conclusions is called model.

4.With enough pairs of (observation, conclusion), we can train a good model that is good enough to make good decision on future observations.

Problem for traditional computer science is that, the system is so deterministic that we know EXACTLY how it works on instruction level, while ML is good at dealing problem that is inherently probabilistic.

</comment><comment><USER21> The latter part of the article seems to focus on deep methods not influencing lower level system architecture and design. Perhaps the reason is that those systems and problems are fundamentally different than the dynamics systems that NNs are finding so much success in. In short, compiler, programming and architecture are very formal exact systems, while driving, tts, stt, image association, etc are nowhere near as controlled of environments.

</comment><comment><USER22> To contrast this opinion, a promising research project from CMU that uses an RNN to manage a database based on "forecasted" workloads (I know it's not _quite_ architecture, but still): [http://pelotondb.io/](http://pelotondb.io/)

</comment><comment><USER23> Perhaps the biggest hurdle in this regard is the approach to machine learning. Nearly everything I have seen on machine learning is a primer on big data followed by a series of algorithms on making the best and smartest decision upon that mountain of data.

This is completely the wrong approach. Machine learning can be done on a dime, provided the proper nurturing and environment, but you have to be willing to make some concessions.

First and for most you have to be able to write a program that can make a decision. A simple "if" condition is sufficient.

Secondly, that decision is open to modification by asserting the evaluation (the "if" condition) against its result. In this regard the logic is fluid opposed to a series of static conditions written by humans hoping to devise organic decisions.

Finally, the decision is allowed to be completely wrong. Wrong decisions are better than either no decision or the same decision without deviation. This is how humans learn and it should be no surprise that computers would benefit from the same approach.

The key to getting this right is bounds checking and simplicity. A decision must find a terminal point in which to stop improving upon its outcome, and a narrow set of boundaries must be affirmed to prevent unnecessary deviation. It is perfectly acceptable if some grand master must occasionally prod the infantile program in the right direction. This is also something that people do to other people who are learning.

If you can do that you have machine learning. You don't need big data to get this. You certainly don't need complex transportation machines or voice activated software to validate it. AI on a dime. If you can do it on a dime you can certainly do it with a multi-billion dollar budget and thousands of developers.

<comment><USER24> You're advocating an evolutionary approach, correct? Doesn't such an approach need lots of examples to trial, before it generalizes broadly?

"Big data" is often shorthand for "lots of examples", no?

<comment><USER7> The poster is referring to control theory (often seen in ML as reinforcement learning), while also touching on the explore-exploit tradeoff in optimization more generally.

</comment></comment></comment><comment><USER25> OT: How and why is this page overriding my control key, and how can I stop it from doing that?

I use ctrl+scroll wheel to zoom and it is very annoying when that behavior is overridden.

<comment><USER26> I think rather than overriding your control key, it's overriding scrolling. It loads up some smoothscroll javascript, and there's likely a bug someplace that's interfering with the ability to zoom. Holding control and pressing +/\- to zoom in/out should still work.

<comment><USER25> Thanks

</comment></comment></comment><comment><USER27> Since when the belief that "a silver bullet exists for computer science research" becomes a thing?</comment>


The End of the TV Writers' Room? - <AUTHOR> https://www.vanityfair.com/hollywood/2018/08/the-end-of-the-tv-writers-room-as-we-know-it-mini-rooms <comment><USER3> It sounds like American networks are flirting with the British way of writing TV.

The British version of The Office only ran for 14 episodes and was wholly written by two people, versus 201 episodes and 40 credited writers for the American version. The original House of Cards only ran for four episodes. Even long-running series tend to be produced in much smaller quantities, because the norm is six episodes per season rather than 22. You can try out a much wider range of ideas if you just make less of each thing. It isn't as immediately profitable, but some of those ideas will become export hits. Not every programme needs to be mass produced.

<comment> jon-wood I find the British model of 6-8 episode series, with a low possibility of renewal, leads to much better TV in my opinion. If you have to produce 20+ episodes of a series then your core story ends up being dragged out in 5 minute doses across all those episodes, and you inevitably end up with slow episodes where nothing much happens.

Compare for example Line of Duty with The Shield. Both on the surface dramas about corrupt police officers, but Line of Duty is relentless in its plot development - there's barely a sentence that doesn't lead somewhere, whereas The Shield has whole episodes with nothing much of consequence.

<comment><USER4> Id disagree with the British model having a small number of writers or just one can lead to some very poor scripts getting made - where a collective writers room would have either fixed or killed and bad script.

Also concentrating power in that way can lead to bullying eg why Christopher Ecclestone left Dr who or Russel Davis suiciding torch wood.

<comment><USER5> >> small number of writers or just one can lead to some very poor scripts getting made

You're right, but on the flip side it allows for new ideas, which are what the best TV shows have. I'll take a wide distribution of quality over everything clumped around the mean.

<comment><USER4> Having a writers room doesn't stop new ideas getting made in the UK inertia and prejudice against "genre" fiction pay a bigger part.

<comment><USER5> I still expect to see better, more original scripts from individuals compared with collaborative efforts. That's my experience from science fiction at least, where many good authors have paired up to write a book and it is nearly never as good their individual works.

You could be right about prejudices holding back the UK TV industry. I don't know much about that.

</comment></comment></comment></comment></comment></comment><comment><USER6> Good riddance.

Some of the absolute best television shows I have ever seen have all been planned and executed by a single script writer with a vision and an idea for a single story spanning over X seasons.

And the absolute best tv series I have ever seen didn't even bother with standard length episodes, they vary between 15 and 45 minutes depending on the content that needs to go into the episode that week. Turns out that if you get rid of filler B-plots or C-plots, the entire series becomes much better.

<comment><USER7> Would you mind naming the 'absolute best tv series'? I'm not a big fan of television, but that sounds interesting.

<comment><USER8> Babylon 5 for example. It's still some of the best Sci-Fi and I regularly rewatch it every few years.

<comment><USER9> I'm a big B5 fan, but you have to admit there's an awful lot of filler in there.

(Also, it definitely wasn't what GP was referring to, since B5 didn't vary its runtime appreciably.)

<comment><USER6> B5 was definitely in the first larger group I was thinking about, because it had a single writer with a vision. I agree that there's an awful lot of filler in there. Imagine what B5 would have been like without the stifling fixed- length episodes and need for filler B-plot and C-plot?

One show I was also thinking about was of course Game of Thrones. It has varying length episodes though, but they're all still pretty close at the one- hour mark.

It's interesting that no other show on Netflix or HBO, that wouldn't need to be bound by fixed-length, still isn't. There should be room to experiment more there.

<comment><USER9> I'm not sure there _was_ a need for filler B-plot and C-plot. There's no particular reason it needed to be planned as 5 seasons if there wasn't enough A-plot to fill 5 seasons.

Hell, _Severed Dreams_ felt like a season's worth of plot all on its own, and all the better for it.

</comment></comment></comment></comment></comment></comment><comment><USER10> I'm weary of anything that enhances studios ability to exploit talent - <USER2> seems like a great way to get writers by the balls the way they have they've got the VFX industry. The company that did the CGI for Life of Pi (won best visual effects) promptly went out of business and that's typical for VFX firms on major projects because studios hold all the cards. They'll find a new/desperate shop that will agree to an absurd contract with infinite revisions. Writers have a strong guild though so hopefully they make sure this doesn't turn into a race to the bottom from writing talent.

<comment><USER11> The writers have a union which should be able to help with this scenario, yes?

Does the VFX industry have a similar thing in Hollywood?

<comment><USER12> Technically it's a guild, not a union. It's good for things like providing health coverage and making sure writers get paid and get credit. From what I understand, it has not been great at ensuring sane working conditions.

<comment><USER13> I met up with a pro TV writer I know and he said he was so happy the season was over because he could finally go to a mattress store and buy a new mattress. He said he didn't have time to do it for the last X months.

Basically being a TV writer is like working in video games; there's another person right behind you who would love to have your job because of the perceived glamor so terrible conditions persist.

<comment><USER14> Hollywood writers are glamorous? Hilarious. I've lived most of my life in SoCal and that's not really the general perception. Even movies about writers like Saving Mr. Banks don't depict it as a glamorous life, though Their Finest does a little bit.

Ultimately what drives screenwriters is their need to create. For those of us who do creative writing, be it for screen, dead tree, or compiler, there's this internal need for the work to make itself manifest. We're just the conduit for the story. That's why people put up with the conditions in the writers room, it's all for the story.

</comment></comment></comment></comment></comment><comment><USER15> Whether you're producing content or buying it, those involved literally don't know how to make shows any better, given the constraints they have (get millions of viewers per show, make lots of money). They've found a way to optimize producing the shows that will make money: squeeze efficiency where you can.

And that will continue as long as people contribute to their business model, which is the most money and the most eyeballs. You will never get a reliable stream of quality, unpopular, arty, amazing shows, because they aren't the business model. How do we change this?

Fund different business models. Fund a whole new content development platform that distributes funds to productions that won't make much money or get many eyeballs, but will create more diverse content. As those become more popular, do deals with content distributors to help fund it.

I don't think there's enough people interested in quality content for this to work without serious budget-pinching, so I don't think it'll happen. You could try to create the platform and just hope the viewers/funders will come. But unless someone disrupts content creation, everyone will continue to be beholden to whomever can pay for content.

Another alternative is we stop paying millions to stars of hit shows, or some other production change, but that seems even less likely...

<comment> jon-wood I don't know how viable this is for TV vs books, but crowdfunding seems like a good model to get more niche stuff funded, maybe even an episode or two at a time.

</comment></comment><comment><USER16> > "You're budgeting and trying to imagine, 'What are we going to need for the > last episode of this season that hasn't been written yet?' " he says. > "Eventually, it comes and smacks you in the face."

> Working out the story arc in advance gives a much better sense of what the > show requires in terms of sets, locations, cast, and all the other expensive > variables that go into creating a fictional world.

Its absolutely crazy US TV is developed in this way: make a pilot knowing nothing about any story arcs, and just hope you can budget and bluff your way to a finale?

It's blindly obvious it has been done this way. Hopefully this change will improve plots.

</comment><comment><USER17> Flashes of design sprint[1] came to mind as I read this.

The idea of planning a season, or more, before filming makes sense to me, especially as more shows become serialized. "Westworld" would not have worked if the writers did not have at least the first two seasons planned out. The same goes for "Breaking Bad" and "Better Call Saul", although I'm not sure to what extent Vince Gilligan planned out the five seasons of BB or has planned the five seasons of BCS (a prequel to BB, by the way).

[1] [http://www.gv.com/sprint/](http://www.gv.com/sprint/)

</comment><comment><USER18> It's funny that there are US production teams considering the British way of writing TV episodes given that over here in the UK, there's talk of a few shows like Doctor Who moving to the US model instead:

[https://www.radiotimes.com/news/2016-05-03/doctor-who- consid...](https://www.radiotimes.com/news/2016-05-03/doctor-who-considering- us-style-writers-room-under-chris-chibnall/)

Would be kind of amusing if over time, the British way of writing episodes became the American one and vice versa.

</comment><comment><USER19> > Zlotnik compares his team to "venture capitalists in a creative medium, > where the businesses are the shows. The [writers'] room is part of a system > of staged investment that manages the risk."

It's interesting that the article makes this analogy, then compares mini-rooms to the gig economy. Are startup accelerators promoting lean methodology not exactly the same concept?

The difference between getting into YCombinator and being "accepted" as an Uber driver is that the former has significant upside potential. Is this true of a scriptwriter in a mini-room? Very possibly, from a career progression perspective. But there also may be some exploitation - is your writing credit for Show That Hit It Big worth less in a world where new scripts are churned out by other mini-rooms daily?

Makes you wonder what shows would be like if scriptwriters were given equity packages. But that wasn't ever on the table. With or without this system, it's tough breaking into screenwriting. This doesn't change that - it's just an optimization by the studio itself on how to use its human resources.

</comment><comment><USER20> JokeBot 2000 writez all.

<comment><USER21> That AI Winter can't come soon enough.</comment></comment>