Working Hard. The biggest bullshit. - <AUTHOR> Sorry to interrupt, but I have a question. Do you really feel as if YOU working hard is ultimately going to make you successful. What I am saying is that would it not be smarter< even if you are building a startup, to find individuals who just are crazy about working and pay them the buck to do it. I also see lots of people who work hard, but their financial situation just cannot get better, although they do not spend lots of money. I am seriously re-thinking this working hard thing.
<comment><USER2> Empirically the answer seems to be that working hard is a necessary if not sufficient condition for doing great things, and for getting rich in particular.

It's an interesting question why you can't simply find suckers who like to work hard, and get rich off their efforts while you sit back and do nothing. One reason is that they'd have to be stupid to let you do that, and it's not much use having stupid people working for you, no matter how hard they work.

Another reason is that having people work for you is not zero work for you. You have to tell them what to do, and often this requires a great deal of effort. In fact, it's probably not possible to have good ideas about what to do in some field without at some point having worked hard in it yourself.

<comment><USER3> This doesn't truly answer your objections, but one company I've always been fascinated by is McDonald's. It's always amazed me that they can take relatively uneducated individuals and get them to turn out a hot meal every 90 seconds.

I'm convinced that the real money comes from designing good systems. I once heard that the reason McDonald's color codes the register buttons is so that even employees who are basically illiterate can hit the right button quickly and accurately. Brilliant.

The problem with being stuck in a system like McDonald's is that even though it makes the least skilled/productive people much more efficient, it also puts a cap at how efficient you can ultimately get. I suspect the most successful people are the ones that are able to recognize when they are trapped in someone else's system, especially if it's inefficient, and break out of it.

I suspect that's the key to success in entrepreneurship too, designing good systems. What most people don't realize is that the money doesn't come from mastering systems, but from designing and creating them. The guys running scientology are making a lot more money than the guys trying to get to Thetan level whatever.

Not that designing systems isn't work, but at least it's the kind of work that one might enjoy even if one doesn't enjoy toiling away in traditional institutions.

It's hard to explain well since no one ever talks about this stuff, but hopefully this post sort of makes sense.

<comment><USER4> Read Jim Collins' _Built to Last_. His thesis is that visionary corporations don't result from markets or products or ideas, but from _cultures_. Entrepreneurs whose companies survive for centuries work very hard to create a whole system of values and beliefs and purpose and then institutionalize them into every aspect of the organization. Clock-builders, not time-tellers.

[http://www.amazon.com/Built-Last-Successful-Visionary- Compan...](http://www.amazon.com/Built-Last-Successful-Visionary- Companies/dp/0887307396)

<comment><USER5> Fantastic recommendation - I find Jim Collins' books very informative and practical; tons of great useful nuggets

<comment><USER6> I read 'Good to Great' and wasn't impressed. It's not bad... but I wouldn't call it "great", either. He strikes me as some sort of "management guru", who is very unhackerish compared to other business books that actually introduce clever ideas.

<comment><USER7> I know we're diverging from the original topic a bit, but I'd like to make a couple of comments about 'Good to Great.' I enjoyed it solely on the fact that there was a tremendous amount of research and data mining involved. He set it up as close to experimental standards as you could get in that area and pulled out the threads with the highest correlation. Adding buzzwords like hedgehog to it, however, is another story.

Since I have a mild fetish for management books, I've drawn a couple conclusions on the whole genre itself. 'Clever ideas' are subjective. I feel that if a book gives you a straight-line recipe to success, then the message has failed because it has overlooked the nuance and gray(grey?) areas that is associated with running organizations and working with people.

Enough of this; back to the topic at hand.

<comment><USER6> Interesting - I'd actually like to discuss further, but your profile has no contact information. Email me at davidw atttttt dedasys.com

</comment></comment></comment></comment></comment></comment></comment><comment><USER8> Well I don't know if this answers your questions, but here's what I think.

"Working hard", by itself, isn't enough to get you rich, and it can often take you entirely in the wrong direction. You can work your ass off and read business books like "Good to Great" or techie books like "Programming Collective Intelligence" (which I bought and think is good), and 10 years later you can still be sitting on your couch, watching TV, thinking "I worked hard, why am I still not a rock star?" I can work my ass off and implement a new Web application in 3 hours, but if nobody uses it I'm still a failure, at least in an entrepreneurial sense.

For every effect there is a logical sequence of causes. If you want to launch a successful company, you have to follow a finite sequence of steps (think of idea, write good code, listen to user feedback, etc.). There is an infinite pool of actions that are NOT part of the sequence of steps that will not help you AT ALL towards starting a great company. The really challenging (yet necessary) part is figuring out what these useless activities are, and avoiding them. Figure out what exactly is necessary and do those things. Don't BS yourself and think "if I just work hard at these X things that everyone else says are important, I will some day make it big."

But I think even if you've figured out exactly what you need to do, a lot of ENERGY and ACTION is needed. Nothing important gets done without a lot of action.

"Working hard" is a bad term, because it sort of implies having a miserable time. I love tennis. When I play tennis I'm running all over the court and diving to make tough shots. I'm exhausted after a match. But I don't consider this working hard, I consider it high-energy playing. I think it's important to not only choose an end result you want, but also to find a process/means for getting to that end result that is so enjoyable that it doesn't seem like working hard at all, but rather like a high-energy game of tennis.

</comment><comment><USER9> You're averaging 11 posts a day, so I doubt you are working very hard.

<comment><USER4> 11 posts takes about 15 minutes if most of the post are one-liners or one- paragraphers...

<comment><USER10> That assumes that you aren't reading anything prior to posting. My guess is he blows 30-60 minutes a day.

</comment></comment></comment><comment><USER11> Effort is important, but knowing where to make an effort makes all the difference.

Tapping with a hammer ....... $ 2.00 Knowing where to tap ........ $ 9998.00

[http://fridayreflections.typepad.com/weblog/2007/08/knowing-...](http://fridayreflections.typepad.com/weblog/2007/08/knowing- where-t.html)

<comment><USER8> Whoa... that hammer quote is a stroke of brilliance.

</comment></comment><comment><USER12> I fear it has been said a hundred times. But its still right:

Don't work hard, work smart!

If you don't use your brain to plan, replan and think again, even hard work will get you nowhere (or at random places).

Well, surely working hard and working smart can be combined. But there is a limit: I usually stop coding, after it takes me too long to find the right algos. Sure I can go on then with stupid things like taxes, telephone calls and so on. But if I do, I have to pay back for that the next morning.

So basically I would answer the original post with: think!, then work as hard as you can, but get your breaks. All has to be well balanced or you'll burn out. Best is, if you find things that relax and that you'll enjoy while doing more work (having beer with your co-founders ;o).

</comment><comment><USER13> A lot of people do the mental equivalent of moving a pile of bricks 20 feet to a new pile one by one. It may be hard, but it's also ineffective.

When I think of working hard in an entrepreneurial context I'm thinking of someone who's breaking down barriers, being relentless, striving for greatness (not just "good enough"), taking on tasks others would be afraid to, etc. Generally just pushing beyond the boundaries of what most people are willing to do.

I know a few people who are ridiculously successful and they _all_ work (or worked) so hard that your average person couldn't handle their workload for even one week.

<comment><USER14> Right. I think the poster is missing half of the equation. I know lots of people who work really hard at what they do. But what they do just keeps the afloat, so they can go back in the next day and work really hard again.

The other part of the equation is being smart, watching for better opportunities, working on things on the side, or even quitting your job and living off of ramen while founding the Taginatr2000.com.

</comment></comment><comment><USER15> "As for the poor, they do not appear in Locke's little book at all."[45] In his "Essay on the Poor Law," Locke turns to the education of the poor; he laments that "the children of labouring people are an ordinary burden to the parish, and are usually maintained in idleness, so that their labour also is generally lost to the public till they are 12 or 14 years old."[46] He suggests, therefore, that "working schools" be set up in each parish in England for poor children so that they will be "from infancy [three years old] inured to work."[47] He goes on to outline the economics of these schools, arguing not only that they will be profitable for the parish but also that they will instill a good work ethic in the children.[48]"

Contrast this with what he suggests to an Aristocrat in Some Thoughts Concerning Education which should be taught to all children regardless of class.

The point; slaves are taught a "good work ethic"(the same as "working hard") and the ruling class are not.

[http://en.wikipedia.org/wiki/Some_Thoughts_Concerning_Educat...](http://en.wikipedia.org/wiki/Some_Thoughts_Concerning_Education)

</comment><comment><USER16> There are ways to get rich without working hard: most of them are all about taking a big risk. Gambling, robbing banks, that sort of thing.

But as pg said, working hard is necessary for doing something great.

Greatness seems to require a very focussed hard work, though. You not only have to work very hard, but on exactly the right things. Simply putting in long hours coding probably isn't enough.

<comment><USER17> Robbing a bank successfully takes hard work too, just of a different kind.

</comment></comment><comment><USER18> First off, congrats on a completely misleading title.

I feel like your question overlooks something, namely that if your goal is to become successful, then until you _do_ , you probably won't have the "buck" to pay your crazy smart hard-working individuals their worth to work for you. In order to acquire the money to pay them (and them is plural, remember) you're going to have to work for it. And working hard will probably get you that money faster than not working hard.

Finally, your claim about the people whose "financial situation just cannot get better" intrigues me. Who are these people? Do they have extenuating circumstances causing this? Do you find yourself in those same circumstances, or why are you so ready to align yourself with their plight?

</comment><comment><USER19> You are just changing what you are working hard on.

Instead of working hard on programming, you are now working hard to get a team of people to create your idea the way you want it.

If you think that is easy, not a chance. Managing people is a lot more work then programming. With programming it is you and the computer. You tell the computer what to do and most of the time the computer complies.

With people, when you tell them what to do they are way less cooperative then a computer and if you think people are just going to be "on the same page" as you are and just come up with stuff that matches what you wanted.. well I have tried that and it doesn't work.

</comment><comment><USER20> It is called capitalism. You put your capital to work instead of working yourself.

Unfortunately, if you don't already have capital to pay people, you are going to need to build up capital by working... hard.

I think a better way to look at the situation is by separating hard work and difficult work. Godin has a great post on this topic which he just republished Monday. [http://sethgodin.typepad.com/seths_blog/2007/09/labor- day.ht...](http://sethgodin.typepad.com/seths_blog/2007/09/labor-day.html)

</comment><comment><USER21> If you push people hard enough...

... they will either:

\- not be as productive (obvious)

\- start being deceptive about what they're actually doing (to avoid repercussions)

\- finally, they'll leave for greener pastures

I realize you're coming at it from a personal perspective... but it's something for startup founders to keep in mind as they bring on employees / non-founders.

There's a great example of this happening in practice in this video: <http://www.youtube.com/watch?v=zuJU1dzc5kM>

</comment><comment><USER22> To demonstrate how hard I'm not working right now, I'd like to point out that the phrase "YOU working hard" is grammatically incorrect. I think it should be "YOUR working hard" because "working" is a gerund. I could be wrong. I should get back to work now.

</comment><comment><USER23> I think I can sum up your question - and the answer to your question - in one word:

Leverage.

Yes, it's often used as an empty business buzzword, but that doesn't mean there isn't a lot behind it.

</comment><comment><USER24> want to get rich? start a religion. oh nevermind...you still have to work hard to get early adopters... <i>just like any other thing</i>

</comment><comment><USER25> They say: One who works, has no time to make money.

</comment><comment><USER26> Working hard is for suckers. Look at cats and hot chicks - they don't work at all and yet they find people to bust their rear ends to provide food and housing for them. Hard work is for amateurs and suckers. Pros are in it for the money. The best thing to do is to find some suckers and convince them you have a secret magical system that helps their hard work translate into success. But first they have to subscribe for $199/month. And you'll automatically rebill their cards unless they send you a letter written backwards in mandarin chinese which replaces every 7th word with every fourth. Then just sit back and do nothing and you'll get rich. Either that or convince them of some impending doom that only you know how to escape ... provided of course that they will demonstrate their worthiness by parting with their material goods - which of course you'll safekeep for them.

<comment><USER27> (you're probably just being sarcastic but) no offense, being a successful shyster takes a lot of hard work and planning too...

the only difference is that it's a lot more exciting for certain people/scum. which brings me to another point: hard work doesn't feel like hard work if it's your passion

"Tomorrow will be the most beautiful day of Raymond K. Hessel's life. His breakfast will taste better than any meal you and I have ever tasted."

<comment><USER28> You have a good point about hard work and passion. You have also just provided one of the best quotes in Fight Club. Good job!</comment></comment></comment>


Ask YC: Grabbing content - <AUTHOR>

First of all I would like to thank some of you who offered some life saving advice for my previous question.
Now that we have pulled up our socks and are making some headway, we have run into our first content problem.
We are building a business listings directory for a niche market. We have identified a bunch of ad-hoc sources for sourcing content. We have manually gathered content for our mockup, but to take it to the next level we need a lot more content than what we have going.
What are the different techniques used to gather content? It would be great if some of you have had specific experience in gathering content for some kind of a business listings directory.
Looking forward to some valuable insight!
Thanks again! <comment><USER2> My favorite technique is:

wget URL > HTML tidy HTML > XHTML xslt [identity transform based content extraction] XHTML > XML XML > DB

The whole process glued together with PERL or shell scripts. Depending on how you construct your content extraction, this technique can weather lots of the inevitable content style changes and easily adjusts when changes need to be made.

</comment><comment><USER3> Hmm, I really don't know how to help. I did just inquire into something not specifically related but might be randomly useful.

There's a company called DeepData @ <http://www.deepdata.com> which might be of some use? They have APIs for business listing queries, and may be able to help on some categorizations.

</comment><comment><USER4> If you mean screen-scraping, isn't this roughly what you're looking for?

<http://www.crummy.com/software/BeautifulSoup/>

</comment><comment><USER5> Just be very very careful if you grab any application forms;-)

</comment><comment><USER6> Dapper.net is your friend

</comment><comment><USER7> lixto</comment>


State of OpenSource Security - <AUTHOR> https://snyk.io/stateofossecurity/ <comment><AUTHOR> A test of 430,000 sites showed that 77% of them run at least one front-end library with a known security vulnerability, That's crazy must be a better way to handle vulnerabilities</comment>


Ask HN: Facebook/abc.xyz referrals in Google Analytics, spam or real? - <AUTHOR> I have also got 2-3 referrals from http://abc.xyz :-D This is the parent company of Google! No way they are going to link to my Go language book. I also got spam links asking me to vote for Trump. How do I know which referrals are genuine? I know http://github.com referrals are real ones, but does facebook have any internal HN type tool maybe? where the book was posted?<p>my book's link is http://leanpub.com/antitextbookGo/; I also got "referral" from awesome-angularJS's github project, the funny thing is, why would angularJS have link to a Go language book which has exactly 0 lines of Angular in it. I am really confused since this is the first time I am using Google Analytics <comment><AUTHOR> OP here;

[http://leanpub.com/antitextbookGo/](http://leanpub.com/antitextbookGo/) This is the link to which I have added Google analytics,

#website; sessions

abc.xyz ;20

facebook.com ;29

thenextweb.com/contributors/a-russian-trump-fan-is-celebrating-by-hacking- google-analytics/ ;23

buketeg.xyz ;10

85599130-1.compliance-don.xyz ;9

addons.mozilla.org/<>/iloveitaly; 10

There are countless such websites which clearly are fake, but few don't look like fake

I am not naive to web development, I know how easy it is to do referral scam, I would like to know if this is a new type of scam or if I am new to it. (p.s. if people at fb really have internal HN where my book was published then it'd be cool)</comment>


Peter Thiel Insider Picked to Oversee Trump's Defense Department Transition - <AUTHOR> https://theintercept.com/2016/11/28/peter-thiel-dod/ <comment><USER2> Truly an amazing million dollar investment on Thiel's part. The Trump presidency is his next unicorn.

Looking forward to America becoming a kleptocracy just like Putin's Russia, with Thiel and friends leading the charge.

<comment><USER3> > Looking forward to America becoming a kleptocracy just like Putin's Russia, > with Thiel and friends leading the charge.

also like Obama, who appointed a whole bunch of people who donated large amounts of money to the DNC to ambassador and other high ranking positions?

[http://observer.com/2016/09/wikileaks-guccifer-2-0-obama- sol...](http://observer.com/2016/09/wikileaks-guccifer-2-0-obama-sold-off- public-offices-to-donors/)

</comment></comment><comment><USER4> So Palantir is going to analyze all Americans' data. Sounds great. Just great.

</comment><comment><USER5> Previous discussion on why normal, "experienced" very much in the wrong way people were not picked for this: [https://news.ycombinator.com/item?id=13040395](https://news.ycombinator.com/item?id=13040395)

TL;DR: "neo-cons" \+ Reagan's 1980 transition staff are all probably dead or seriously retired by now.

<comment><USER6> Traditionally experienced conservative, but non-neoconservative, defense experts are not particularly hard to find, even with the neoconservative domination of the Bush-era national security apparatus.

OTOH, it might be that finding such people willing to work for Trump's team may be quite difficult.</comment></comment>


Boosted Boards founders launch heavy-duty scooter renter Skip - <AUTHOR> https://techcrunch.com/2018/05/17/skip-scooters/ <comment><USER2> Instead of the 'park it anywhere and inconvenience everyone else model' of deployment, the dockless scooter companies should advocate for dockless scooter dedicated parking spots/landing zones around the city.

The dockless scooter companies should negotiate (and pay a daily fee to) the cities to convert say every 100th car parking spot into a dedicated spot to park your scooter/bike.

I think this would be fair as one existing car parking spot could serve 10-20 dockeless scooters/bikes [1]. And it would require no effort other than repainting the parking spots and the city revenue (i.e $20 per day per spot) would be about the same if not slightly more.

[1] [https://www.citylab.com/transportation/2017/06/bike-share- do...](https://www.citylab.com/transportation/2017/06/bike-share-dock-parking- space-citi-bike-new-york/531936/)

[additional edit] Or alternatively, the city could just mark/paint out dedicated spots on the existing sidewalk that are currently underutilized (i.e. out of the way of existing pedestrian traffic) and that ends up leading to more city revenue in addition to car parking.

<comment><USER3> Are these things really much of an inconvenience to you? The vast majority of the ones I've seen are parked in reasonable locations.

This could be further improved by better education of users, requiring the user take a photo of their parking job, easily allowing other people to submit complaints, and warning then banning repeat offenders.

<comment><USER4> It irritates people to have brightly colored bike-shaped ads strewn about residential, non-commercial neighborhoods.

<comment><USER5> And this is different from brightly colored car-shaped ads how?

<comment><USER4> They're novel. They are in unexpected places such as sidewalks, rather thank locked up like basically any privately owned bicycle. Depending on the neighborhood, there may be no car-shaped ads at all.

</comment></comment></comment></comment></comment><comment><USER3> As a long time Boosted Board fan, and someone who's underwhelmed by the current crop of rental scooters, I'm interested to try this.

I'm also somewhat surprised Boosted Boards themselves haven't released other light electric vehicles like scooters and bicycles. I'd guess the market for non-skateboard electric vehicles is much larger, and a lot of the expertise they have should transfer.

I think a lightweight (~15 lbs) commuter scooter with ~5 mile range, ~16 MPH top speed, maybe an integrated charger and shoulder sling, and Boosted Board's design and quality would kill it.

<comment><USER6> I remember Boosted's mission from the beginning being something like "improving last-mile personal transportation." My hunch is that the electric skateboard market is bigger than they expected so the company just keeps growing with one product category.

</comment></comment><comment><USER7> All the ethics and controversy of ScooterGate aside, this is a good move for those who like these scooters.

I've ridden them in SF a few times and they are extremely underpowered. When you hit the slightest incline they go <10mph. I have consistently wondered why they can't be as fast as Boosted Boards, which seem to fly up any hill in San Francisco. So ... case closed I guess.

</comment><comment><USER8> we have not reached peak scooter yet. and that's a good thing. i say bring them all on, they have really solved a good balance of personal but light transportation

<comment><USER9> I have moved from Paris to San Francisco relatively recently.

The transportation system is pretty poor here compared to Paris.

I don't want to own a car. Driving has always been a chore to me and inferior to all the other modes of transportation where I don't have to focus on the road.

I am lucky enough to live 20 minutes by foot from my workplace.

I bought a boosted board, it is great when I want to commute a little bit quicker or need to go to the other side of the city.

pros : way faster than walking, can be hooked on my bag. cons : not very stable, you have to share the road with cars on many streets.

A scooter sounds interesting. With bigger wheels, it should be more stable, and it should still be possible to carry it.

</comment></comment><comment><USER10> Is there really that much money to be made from rent-able scooters? I imagine a business can be made out of it but this level of competition seems amazing to me. There is no differentiation from the products.

<comment><USER11> This post on Bird's unit economics is pretty good. [https://techcrunch.com/2018/04/10/how-to-understand-the- fina...](https://techcrunch.com/2018/04/10/how-to-understand-the-financial- levers-in-your-business/)

<comment><USER2> Interestingly since charging the electric scooter is such an additional complication w.r.t. unit economics, it'd be great to see them just make them kick scooters (i.e. foot powered with no motor like the OG Razor scooter) with the only electronics being the GPS/cellphone locator unit?

You then mitigate the speed/helmet concerns somewhat as well.

<comment><USER12> companies like [http://immotor.com/](http://immotor.com/) are trying to solve that with swappable batteries that the user owns rather than the rental service.

interesting idea, but i haven't really thought through it to figure out if it makes more sense or not.

</comment></comment></comment></comment><comment><USER13> I would love to actually just buy one of the scooters; I'm looking at getting something robust that can handle gravel, water, dirt, etc in questionable Stockholm weather.

</comment><comment><USER14> Awesome. I love they designed the scooter to facilitate a service model right from the get-go.

Personally, I'm bearish on the light electric vehicle sharing model due to skepticism on the unit economics.

Skip will also be a great piece of evidence for large automakers thinking of running their own ride-sharing services on their own automobiles.

</comment><comment><USER15> Electric scooters are interesting but this is a straight up advertisement.

</comment><comment><USER16> Looking forward to Casey Neistat's upcoming review of this.

</comment><comment><USER17> All it takes is one accident or false move and you will have years of pain (and potentially medical procedures and/or drugs) if not death.

<comment><USER3> Yup, probably safest to just stay inside.

</comment></comment><comment><USER18> Everyone I know (including myself) who has ridden a dockless rentable electric scooter has found it the inferior personal transit option in SF. What exactly is making these founders think this is a worthwhile option?

The bikeshares, the electric bikes, even boosted boards are all great. A personal non-electric scooter is great. They have designated places to go, or you bring them inside with you.

The public nuisance (lack of sensible parking), the safety concerns/lack of rules (ride on sidewalk or street, helmet or no, etc), the 10mph cap (slower than a manual scooter), and mediocre economics make it seem worse than everything. What are the pros that people care about?

<comment><USER19> Everyone I know who has ridden a dockless rentable electric scooter has absolutely loved them. I've used them to commute to/from work (what would normally be a 20 minute walk turns into <10 minutes if I'm in a rush), quickly return home from ~3 miles away after running into Golden Gate Park (too sweaty for a Lyft), and running errands to destinations a bit out of walking distance during rush hour when public transit/Lyft would be painfully slow. The added benefit is that the scootering was more fun and less or comparably expensive to the alternatives.

Scooters have advantages over bikes as well for this type of usage. It is far easier to switch to pedestrian mode (i.e. walking next to the scooter) when you need to navigate a crowded cross walk or slow down suddenly and get up on the sidewalk when an aggressive taxi/Lyft/bus stops short in front of you or turns across you. Your center of gravity is lower than on a bike lessening the impact from falls in some cases.

The parking debate seems pretty bimodal and has been debated seemingly endlessly already...

The safety concerns complaint is interesting. Lots of bikers already ride without helmets but no one seems to care/complain about them too much. Lots of complaints about safety riding on the sidewalks, but I haven't seen much hard evidence that collisions are actually happening en masse.

A 10-15 mph cap in SF seems totaly fine for what is meant to be a casual, relatively short distance means of travel.

How are you concluding that the economics are mediocre?

<comment><USER18> These seem like decent points. My reaction to them is mostly from riding my own manual scooter prior to the electric ones became prevalent in SF, and finding the electric share experience inferior in every way, as opposed to bikeshares being superior to owning my own bike.

Re: economics - My scooter is cheap (second hand for $10, new for $~40), and I wouldn't mind even if I lost it -- which is unlikely given I can just bring it into wherever I'm stopping instead of locking it outside -- so even though $1-2 per ride is cheap for transit, merely 5-20 rides later it's past the economics of scooter ownership, with few of the advantages of using a share program.

\- owning a personal kick scooter and keeping it in my apartment really doesn't take up much space (I just leave mine on top of a shoerack next to the door once collapsed)

\- it requires 0 maintenance

\- I average over 10mph by being able to go significantly faster on even a slight downhill on a bike lane. (I hear some scooters go up to 15, that's much more decent)

On the other bikeshares provide me all those pros. [edit: formatting]

</comment></comment></comment><comment><USER20> Electric scooters/skateboards and e-bikes are a menace to society. I'm a NYC cyclist whose travels went from terrifying to death-defying ever since e-bikes became commonplace here. These things travel at a flat 20mph in total silence. Such speed is beyond what most plain old cyclists can achieve by simply pedaling. On a daily basis I'm forced to dodge when a e-bike/board/scooter rider recklessly passes me with not an inch to spare. These people treat common courtesy and traffic laws as loose guidelines meant for someone else.

There are good reasons why personal electric vehicles are illegal in New York: they're unsafe for everyone involved without proper licensing of operators and safety inspection of vehicles. Drivers of motor vehicles are obligated to carry insurance and are held accountable for their offenses under the law, but operators of e-bikes/boards/scooters will kill or maim another person just as well as a car will, but will face no consequences except for maybe, possibly a civil lawsuit... assuming they're ever caught.

Now yet another hyped up startup is making an attempt to profit at the expense of my and my family's safety. I will not allow this, and will continue my citizen's campaign against illegal vehicles in my city. No quarter will be given until they're all gone. I consider e-bikes/boards/scooters a physical assault against my personal safety, and respond just like I would respond to any physical assault.

<comment><USER21> > respond just like I would respond to any physical assault.

Do tell.

<comment><USER22> I raise my arms in an X-formation to absorb the blow. Then I take off my sunglasses, look them dead in the eye, and coolly give them a piece of my mind, terminated with a small wink to let them know I'm not going to escalate any further if they don't want to. But, heh, usually they comply like puddy in my hands due to my tall muscular 5'10 build. Not usually a problem. ;)</comment></comment></comment>


Timelessness - <AUTHOR> http://einfall.wordpress.com/2008/09/13/timelessness/

<comment><USER2> Dani,

I really enjoy your essays, you've got a very interesting blog. Keep up the good work. Hope all is well at LightSail.

<comment><AUTHOR> Thanks for your encouragement :-)

</comment></comment><comment><USER3> i'm impressed by your brevity. a lost art for sure.

real-time control is one place where science is beautiful and fun.

<comment><AUTHOR> A topic you know something about?

<comment><USER3> just enough to appreciate it. i didn't have the math skills to be a controls engineer, but i did design electromagnetic devices for a while, and totally envied the controls people who got to tie all the pieces together - electrical, electronic, mechanical, etc.

my favorite example is the video of the apollo lunar module making adjustments as it approached the command module.</comment></comment></comment>


Apple is more excited about the iPhone 5C and so am I. - <AUTHOR> http://blog.joshkerr.com/apples-midrange-iphone-5c-appeals-more-to-me-than-the-top-of-the-line-5s <comment><USER2> Yes, Apple want you to buy a 5C, not a 5S. This way, they get to use the 5S as the limited-first-run of the parts that will go into the 6C (when we'll have a 6S that's a limited-first-run of the 7C, and so on.)

Let me just quote my previous post, it's equally applicable here:

> Apple has never been able to make enough iPhones of each successive > generation for people to get their hands on during the first few months > after release. Whereas, once they've ramped up production, they tend to have > all these parts left over in the pipeline that they have to sell off as N-1 > gen hardware to price-sensitive late adopters. (This is also why the iPod > Touch was originally created, to serve as a sink for N-2-gen parts.)

> Now, Apple are trying to shift the purchase frenzy to the N-1 gen, and > position their new gen-N tech -- whose production hasn't yet been ramped up > -- as something for early-adopters only. In other words, to switch from an > (N-1, N) view of the world, to an (N, N+1) view.

> Everything in the S will filter down to the C of the next gen. They'll get > their pipelines saturated with 5S parts just in time to wrap them in plastic > and call them 6C parts. As long as more people buy Cs than Ss, this works > out perfectly.

This is their strategy, working out perfectly.

<comment><USER3> You talk about the "S" and "C" iterations but what about the "normal" phones? What about next year? The iPhone6 is supposed to come out.

I still wonder how they will position themselves now. Before, they had it simple. Last years model and the new one. And the new one was divided into "This year the spec bump of the old model, next year a brand new phone"

But now, they canned the "old model" and went with the "C" variant. So right now, there is the 5C and 5S. So what'll it be next year? Do they then use a 3 device line up? Will it be: 5C,5S and 6? And the year after that: 5S, 6C and 6S?

</comment></comment><comment><USER4> _> I disable the flash on every piece of camera equipment I own. In my book, there is never a scenario where it is okay to use a flash, if it is too dark to take the photo, then don't take it._

That's about 90% bathwater and 10% baby.

Yes, the vast majority of the time ordinary people use their flash, they really shouldn't be. But there _are_ genuine situations where even snobs like us to need to just suck it up and break the no-flash streak.

The most obvious is when you're _not_ trying to take a beautiful photo; you're just trying to take an accurate one -- like inside a crawlspace where something needs to be repaired.

And then there are the times when you only get one chance to capture something amazing, and there's not going to be time to set a long exposure and pray for no-blur. This happens a lot when children or animals are involved:

[http://www.flickr.com/photos/18575617@N03/2281710004/sizes/z...](http://www.flickr.com/photos/18575617@N03/2281710004/sizes/z/)

But even in broad daylight with relatively still scenes, there are still flashworthy situations. For example, when all the light is behind the subject:

[http://www.flickr.com/photos/18575617@N03/2408341744/sizes/z...](http://www.flickr.com/photos/18575617@N03/2408341744/sizes/z/)

(That one is from the days before HDR -- if the flash hadn't fired, either the subjects' faces would have been underexposed, or the sunny background would have been overexposed.)

Now, just to give equal time to the opposing side, this non-flash photo would have been absolutely ruined if the flash had been used:

[http://www.flickr.com/photos/18575617@N03/2407218105/sizes/z...](http://www.flickr.com/photos/18575617@N03/2407218105/sizes/z/)

<comment><USER5> I agree. Furthermore, it's better to use the flash than not capture the moment at all.

> In my book, there is never a scenario where it is okay to use a flash, if it > is too dark to take the photo, then don't take it.

Statements like this reminds me of audiophiles that refuse to listen to music on "substandard" audio gear. Real music lovers enjoy the content, and would rather have poor quality than no music at all. They love the music and not the equipment. The same applies here. No photo vs photo with suboptimal lighting, I'll take the latter.

</comment></comment><comment><USER6> _I'm really happy with my iPhone 5 running iOS 7. It is plenty fast, has great battery life, takes great photos and runs all of my favorite apps. Put those phone guts into a much more exciting shell, make it more comfortable to hold, increase its battery life and you've got a good upgrade._

Or, spend ten bucks and get a blue plastic case.

<comment><USER7> I think with most Apple products and Apple "fan boys", they try hard to justify Apple's overpriced new products, which usually have features that are way behind other phones.

Every year a new phone is released, which has marginal improvements over the previous ones. But you know what? People will _still_ buy it and Apple knows that very well.

<comment><USER8> The iPhone has many features that best android phones, and I'll give you the most significant in my eyes. My iPhone gets OS updates when the newest version is released. This is not true of the vast vast majority of android phones.

<comment><USER9> Please point me in the direction to a cost-comparative (to the iPhone) Android device to an iPhone that has not received updates.

At least Android devices give you the option to upgrade. I've been forced into iOS upgrades on devices we use in the enterprise that have been made obsolete because of new bloat we didn't need (nor can we downgrade).

<comment><USER8> I had a droid x2 and it didn't get 3.0 despite being priced similar to an iPhone and it was less than a year old when 3.0 was released. This is very common with android phones.

<comment><USER10> Android 3.0 was a tablet only release.

<comment><USER8> I didn't mean 3.0, I meant any version from then on. For a phone that came out in may 2011, it was infuriating to be stuck on 2.x.

Other than nexus phones, all android phones are at the mercy of the carrier and manufacturer. None of them get updated on the day a new version of android comes out, most experience long delays, and many see no major version bump.

When iOS 7 comes out next week there is no doubt I will be able to install it on my iPhone.

</comment></comment></comment></comment></comment></comment></comment><comment><USER11> > I disable the flash on every piece of camera equipment I own. In my book, > there is never a scenario where it is okay to use a flash, if it is too dark > to take the photo, then don't take it.

I think the fact that you (and I) don't trust current camera flashes to make photos worth keeping is actually an argument that it's a problem worth fixing. It remains to be seen how successful the 5S flash will be, but I think it's cool they're trying. I'm also glad Apple made the sensor elements larger rather than cramming in more mega pixels. Normally that's a concern of the geeky DSLR consumers, and point & shooters suffer with noisy low-light performance and more pixels than they need.

<comment><USER12> I welcome improvements in the iPhone flash, because there are cases in which I'm forced to use it. But the biggest problem is also the hardest to solve: the flash is too close to the lens. A flash right next to the lens will always look bad, no matter what you do. Being able to pivot and bounce it is even better, but just moving it a few inches away makes a big difference. Unfortunately, all of those things are fairly incompatible with the need to make phones that fit easily in peoples' pockets.

<comment><USER13> Moving it a few inches away isn't inherently impossible, since you could put the flash at the other end of the phone from the lens. At the minimum it'd be a lot more awkward to use, though.

</comment></comment></comment><comment><USER14> Apple made a lot of compromises in designing the 5C to make it cheaper, but they completely failed to make it cheap! It's still more than two and a half times the price of a Nexus 4 unlocked. Unlocked sales for the 5C will be pitiful. Under a multi-year contract the 5C isn't substantially cheaper than the 5S, so people who want the best phone will shun it. The iPhone 5C is going to fail utterly in the market segment it's designed for.

However, I do see a couple of possible markets where the 5C may succeed. There's the Hello Kitty "anything in pastels is _squeeeeee_ " crowd. This market includes little girls, people buying phones for little girls, and Josh Kerr apparently. _IF_ the 5C proves more durable than the 5S it might also capture the tough-wear crowd that wants a phone that doesn't have to be hidden inside a giant blob of silicone to survive a 6" fall onto concrete. Let's face it, not many people are going to want the cases Apple is trying to sell for the 5C. I'm just speculating about the 5C's durability though. Plastic phones might not feel as nice in the hand, but if they use high quality plastic and are well built they do tend to survive better than phones made entirely out of metal and glass.

Will it sell? Never underestimate the Hello Kitty crowd!

<comment><USER15> I wish you could have written this without the ad hom implication that men who like certain colors are necessarily both effeminate and puerile. If Josh Kerr fucking loves pastels that doesn't make him any less of a man.

</comment></comment><comment><USER16> This is more of a fanboy ejaculatism than anything else. Why is this even on the frontpage?

</comment><comment><USER17> I find it really difficult to care about the iPhone when a wonderful fully- unlocked Nexus 4 can be acquired for $199.

<comment><USER18> I find it really difficult to care about the Nexus 4 when it doesn't run iOS apps.

For me, personally, that's worth paying extra money for, discounting all other differences at the hardware level, and it seems to be worth it for millions of other people too.

You have to understand that there are people in the world that make a solid value judgement that the iPhone is what they want and they can afford it, it's not bad value for their money, they want iOS, they don't mind being locked in to Apple's world _and_ that those are very sane choices to make.

That reasoning works, even in the face of a $199 unlocked Nexus 4.

</comment></comment><comment><USER5> > For practical purposes, 64-bit means that the processor in the phone can > address more than 4GB of RAM. The 5S has 2GB of ram so it isn't even taking > advantage of it.

Interesting observation on the 64-bit processor. From an enduser's point of view, what advantage does a 64-bit CPU have?

<comment><USER19> It is the naive observation that is far too common. The A7 is about ARMv8, which is an evolutionary step over ARMv7. The 4GB thing is the least relevant aspect of it right now.

<comment><USER20> Apple isn't talking about ARMv8 though, they are talking about 64 bit.

<comment><USER19> They are talking about improving the processor, and ARMv8 is a (or rather _the_ ) 64-bit ARM instruction set. Should Apple have instead talked about registers and wider SIMD, better double floating point, etc? 64-bit is just shorthand for that.

<comment><USER2> Exactly. We went through this exact same marketing with game consoles, years ago. The N64 was a _64-bit game console!_ What did that mean? Who cares, it's four times as much as a SNES, and twice as much as a Playstation!

</comment></comment></comment></comment></comment><comment><USER21> This makes no sense. Why would Jony Ive, supposedly one of the greatest industrial designers, and Apple, one of the greatest consumer products companies, be so darn excited about rereleasing a 1 year old product in 5 colors for 15% less?

Do people care about the color of their phone? Not enough color choice is not something I've heard as a complaint about the iPhone 5.

Are the millions of cases available not a reasonable solution for those who care about color?

Would I really buy an iPhone instead of an Android because I can get it in yellow and it ships with a yellow background screen?

The more likely explanation for the marketing difference is the lack of production capacity for the 5S and/or lower margins.

</comment><comment><USER22> _> I think the iPhone 5C is the perfect realization of hardware and software running harmoniously on a single device._

Wow, really? The iPhone 5C hasn't even shipped yet!

</comment><comment><USER23> I will buy a 5S for the fingerprint sensor, as soon as its functionality gets exposed to LastPass, and assuming it's as secure as Apple says it is. Apple all but enforcing weak password security has made browsing on iOS unworkable for everything except quick Wikipedia lookups. It was to the point that I would have switched to Android otherwise.

As it is I'm skipping the iPad Mini and getting a Nexus. Unless Apple suddenly comes out with one tomorrow with both Retina and fingerprint sensor the Mini is useless for everything but as a Kindle reader.

I am less worried about the NSA having my fingerprint as I am about crackers getting my passwords. If I was worried about that I'd go all Snowden and keep my phone in the freezer. But if even one instance of fingerprint information leaking out of the box Apple's keeping it in on the CPU surfaces, I may never trust Apple with that information.

</comment><comment><USER24> "The iPhone 5C ships with iOS 7 pre-configured to use background wallpaper that matches the color of the Phone. Little touches like this really differentiate Apple's products from its competitors"

Have you heard of Lumias?

</comment><comment><USER25> I'll be excited about an iPhone as soon as I am able to sideload an app, install a white/blacklist. I'd be quite excited if I were also allowed to run arbitrary apps in the background (listening to sockets even when not VoIP), and add arbitrary interactive views to the Notification Center.

A plastic colored body does not appeal to me in the least.

<comment><USER26> Congratulations, you are not the target market! You may now choose from the many available models of android and windows phones instead of making impotent comments on social networks. :)

<comment><USER25> No, I like making comments about it on social networks. If I get even one person on board with freedom it's a win. If I get enough people on board that Apple feels pressure and I can use unjailbroken iOS without these restrictions that would be great, I actually like iOS when it's jailbroken. I know that's a pipe dream but whatever.

<comment><USER27> Inform enough people and Apple will be forced to open up or go out of business, with either being an acceptable option. A couple of more years the way the market is going and they're going to be driven to irrelevance, which is also acceptable.

</comment></comment></comment></comment><comment><USER28> I don't get iPhone 5C at that price, too close to 5S.

Even if you don't use the Touch ID and camera flash, the improved camera sensor size/aperture and processor speed in iPhone 5S is enough to justified the price difference.

</comment><comment><USER29> Apple also touted the G5 as the first 64 bit workstation but neglected to see that many of us were running 64 bit SPARC workstations at the time. Not to mention SGI boxen.

</comment><comment><USER30> Why do people revel in broadcasting their ignorance and shallowness?

<comment><USER9> ...kind of like your post?

</comment></comment><comment><USER31> To me, I think Apple is trying to move into smart house industry.

</comment><comment><USER32> No black iPhone5C, Jony I am disappoint.</comment>


Millennials' share of the U.S. housing market: Small and shrinking - <AUTHOR> https://www.washingtonpost.com/business/2020/01/20/millennials-share-us-housing-market-small-shrinking/ <comment><USER2> Upsides to renting

\+ often all-in/TCO cheaper than owning

\+ less liabilities if/when expensive items break; landlord is in-charge of must repairs and expenses

\+ easier to move

Upsides to owning

\+ neighbors are further away

\+ potentially quieter

\+ customization (upgrades, ability to modify, holes in walls)

\+ able to purchase reliable/cheaper over lifecycle (TCO) items

\+ feeling of dominion/stability

There are other options too: rubbertramp/vanlife, leathertramp, oceantramp, go expat in cheaper countries</comment>


Millions of Ordinary Americans Support Donald Trump. Here's Why - <AUTHOR> http://www.theguardian.com/commentisfree/2016/mar/07/donald-trump-why-americans-support <comment><USER2> This article did a great job. It itself did everything that drives the support behind Trump.

There is in my mind only one candidate who stands a chance at this point in beating Trump in a national campaign: Bernie Sanders.

This is because Sanders understands something that none of the other politicians do. If you call Trump racist, sexist, homophobic, or whatever he will only grow stronger. You need to oppose him on his issues and not from a position of authority.

Trump gains his appeal from being a genuinely good entertainer, having recovered from many bankruptcies, how he speaks to his supporter, and his anti-establishment nature. He presents himself, and also acts as if, he is just the average guy even going so far as to call people up to the stage. This strengthens his presentation that he is not a "bought" candidate.

When someone from the establishment criticizes him, they validate all of his claims. He is the anti-establishment character: the only people who can push against him are people who aren't part of the establishment.

Remember that all press, is good press.

If you stick to the issues, don't let him get any sound bytes in, and stop talking about how evil he is 24/7 then he will no longer have any power in the race.</comment>


Go2 and complex numbers removal - <AUTHOR> https://github.com/golang/go/issues/19921 <comment><USER2> The issue was opened 11 Apr 2017, and closed today.

<comment><AUTHOR> Right, closed just few hours after being published here. Good decision.</comment></comment>


NASA is opening up hundreds of patents to inventors, for free - <AUTHOR> https://www.washingtonpost.com/news/the-switch/wp/2015/10/07/nasa-is-opening-up-hundreds-of-patents-to-inventors-for-free/ <comment><USER2> Horribly misleading title - they just waive the initial cost to acquire the patented technology, but once a business starts selling a product they collect their usual royalties:

> _Once the company starts selling a product, NASA will collect a standard net > royalty fee. This money goes first to the inventor and then to maintaining > the agency 's technology transfer activities and technology advancement._

source: [http://www.technology.nasa.gov/startup](http://www.technology.nasa.gov/startup)

</comment><comment><USER3> Why are NASA inventions not in the public domain, like any other work of the US government?</comment>


Show HN: Octofication, GitHub notifications for Chrome - <AUTHOR> https://chrome.google.com/webstore/detail/octofication/ooicpiadbdjcbbhamalpmdkececehgil

<comment><USER2> Neat little extension. Cute icon.

One thing I would suggest would to make the login and no notification pages smaller. For what they say, they are awfully big.

</comment><comment><USER3> Very useful, been looking for this for a bit! Thank you and well done.

</comment><comment><USER4> Happy to see fellow Malaysian on the front page</comment>


Ask HN: How can a front-end developer dive into machine learning? - <AUTHOR> I am a Computer Systems Engineer who is good at front end development. I want to learn machine learning next. How can a front end developer utilize machine learning? <comment><USER3> In Feb 2015 I quit my job as a front end developer to learn more about machine learning.

First, I went through the Recurse Center, which is a 3 month program sort of like a writing retreat for programmers. I learned a lot about Python and AWS in that time, and got an internship as a data engineer.

In that Fall, I started a computer science master's. I've taken mostly courses in machine learning including: Machine Learning Theory, Deep Learning, Probabilistic Graphical Models, NLP, and GPUs. I've collaborated with two professors on research papers, which has definitely been the highlight of my degree although I definitely think the courses were necessary as I continue to use the information that was covered.

Finally, I'll be starting this summer as a research engineer doing deep learning! This process took me 2.5 years, but I feel very prepared for my new role. It probably is possible to do this faster by joining a program like Metis or Insight, which prepare you for data science like jobs w/in 3 months. I would say that approach is slightly more challenging / high risk. If you really want to go into machine learning, I'd say doing the degree is a more surefire approach, granted it's more expensive in time and money.

<comment><USER4> I work in machine learning and I don't think a 3 months course is going to transform you in a machine learning engineer. I think you need at least the time you took. You need to learn the maths, statistics, etc. Apply those tools to real problems, get some experience with different problems and techniques. And, finally, be involved in some type of research. Because machine learning is evolving so fast, you need to learn how to read a paper, how to understand it, and how to apply it to your problem. I see no way to do everything it in 3 months. You need at least a couple of years and be lucky to find a job related, so you put your first step in the industry.

And congratulations for you achievement and your bravery to quit the job!

<comment><USER5> As a computer vision/ML applications engineer I disagree with this. What you describe is someone who is actively implementing cutting edge tech.

That is VERY different than what 99% of people should be doing with ML which is: Spinning up some K80s on Azure, installing TF/CUDA/OpenCL, pulling existing pre-trained models off the shelf, and running inference on a novel data set.

That's how you get into it as a garden variety dev.

Otherwise, go for the PhD if you want to actually make new stuff.

<comment><USER6> I would definitely extend this to running training as well, but I agree with the concept - for most people, it should be either transfer learning to adapt existing models to their data, or running training from scratch with currently known best practice methods, NN architectures and hyperparameters, but doing it on their particular datasets. Possibly by using mostly existing code and modifying mostly the data input/output routines.

<comment><USER5> I think you need a bit more competence to get into the training realm though, because it's a bigger step to create a new model - especially the hard step of data labeling.

Unless you have a novel data set and a way to quickly train you're probably better off using existing trained models in most cases.

I agree with the transfer learning piece wholeheartedly though.

<comment><USER6> Data labeling isn't _hard_ , it's _labor intensive_ , which is an entirely different resource. If the business goal is valuable enough, then a non-tech manager without any special expertise can organize twenty man-months of grunts to do the labeling, three man-months of cookie-cutter junior dev work for tools of labeling and data management, and a single man-month of an external consultant with proper expertise to write sensible guidelines on how the labeling should be done and supervise the process. All of which will cost something comparable to a the annual cost a single ML developer.

Training models often is tricky, but it's not _that_ hard, my experience shows that decent undergrads learn to train standard models on their own datasets after a single one semester course, and train quite difficult models after two semesters; so teaching/learning basic ML takes comparable time and effort to e.g. teaching/learning basic JS frontend development.

So if some company's IT department has some minimum ML skills, lack of expertise shouldn't be preventing them from training models. And even more so, using your own data (IMHO) is the whole point of adopting ML; if the problem is _so_ generic that you don't need to adapt it to your data, then you shouldn't be learning to use ML but rather buying and integrating a SaaS API run by someone else.

<comment><USER5> _it 's labor intensive_

Which is a form of hard...for example if you need 60,000 semantically labeled images, you need to train people to know how to do that specific of labeling and then have them do it, then QC the data, break it up into training and validation sets etc...

Don't forget that this advice is for a front end dev who hasn't ever touched caffe or torch or whatever. In many cases it takes new people a week to set up drivers and an environment on a GPU.

</comment></comment></comment></comment></comment></comment></comment><comment><USER7> Your question says "utilize" machine learning. I was in the same position. It struck me that rather than go off and try to catch up with half a century's research, and contribute nothing, I'd be better off utilizing machine learning in my front-end work.

So I have started using IBM's Watson platform, and some of Google's AI tools. I was specifically interested in speech processing applications (I have some background in signal processing and audio, which helps a little), and I've found the Watson stuff particularly useful.

At the end of the day, if depends on your motivation. If you really want to become a true expert, stop reading this and start studying. Otherwise, I think there does exist a significant "gap in the market", as it were, to build useful front ends to these technologies, which currently exist as raw APIs.

In terms of career prospects, I have already met several Watson consultants, who do exactly that, and charge top dollar for it. The plain fact is, it doesn't take very much to be considered an AI "expert" in the current climate. And you're probably more likely to get there quickly by standing on the shoulders of giants than by dedicating your life to a PhD.

<comment><USER8> I have no clue about Watson and Machine Learning, I mean I've talked to people and quickly glanced at the platforms. But in terms of the speech processing, does that not require any back-end at all? I'm assuming your using the browsers API to capture sound and then passing this off to Watson in some back-end method to analyze? Or can this seriously be done all fron-end? Pretty amazing if so.

<comment><USER9> It's not that the actual ML computation is done client side -- that would be way too slow. With Watson and many other ML platforms, you're leveraging someone else's (IBM, Google, Microsoft, Amazon, etc.) computing power.

Some people have made javascript-based ML models to run in the browser (I think some were made for this course[1]), but these are for educational purposes rather than actual use.

[1]: [https://cs231n.github.io/](https://cs231n.github.io/)

</comment></comment></comment><comment><USER10> I am a long time developer who is trying to move into data science / ML.

What I've found after taking off for 12 months of self-study, is that it quickly dissolves into _you must know math_. As far as I can tell, its take a problem, map it into a vector space, then use the full power of mathematical analysis on it .

There is a huge push by large companies to make AI as a service though, and for that, you only really need to know how to use the APIs.

Which makes me wonder, why in hell have I done burnt through all my savings for this. Sure I have a new found love for math, but I'm not going to be accepted as a mathematician ever without the rigor of a formal education, and if I just wanted to use APIs ... I could have continued to do what I was doing.

<comment><USER11> I'm a little confused -- how does learning math involve burning through savings?

<comment><USER12> If you live in an expensive place like San Francisco, you burn through savings by default without a job.

<comment><USER11> Ah... I didn't catch the "without a job" part...

</comment></comment></comment></comment><comment><USER13> I don't usually comment on HN but when I do...

Kidding aside, I've seen these kinds of posts so many times and I want to make sure that for those that are thinking "strategically" about their profession, career and passions then I would advise to buckle down with a good BS in Math at a minimum (or CS).

But why? Im your older self telling you that you will grow to really really like and enjoy programming, computers, tech etc. and may want to continuously dive deeper. And when you attempt to do that it all comes down to Math. So save yourself a ton of time and money and just do it, close all your browser tabs, cobble together all your transcripts and get into a Math program (if you already have, go get a MS in Math at a uni that has a strong CS program).

<comment><USER14> That's exactly how i feel. I dropped out of school at 19, taught myself to code, went to a hack school, and have been working as a rails dev for the last 4-ish years. I realized I wanted to go into ML, and instead of trying to take shortcuts, at 24 I enrolled in my local community college to finish a degree in comp-sci. Looking at 8+ math classes (from Geometry to Calculus 3), but the only reason why I'm doing it is because I believe it will be worth it in the long run.

The only thing that sucks about it is all the GE classes I have to take before I can even start my fun Comp-Sci classes.

<comment><USER15> Hey its me, your past. Really though, I'm currently 20 and dropped out of school last year (GE's kicked my ass), and started working full time as a prototyper. I know I am probably going to have to go back at some point, but what really made you decide to go back? Was it for a higher payroll, or better fulfillment?

<comment><USER14> I want to master my craft, and i believe the way to do that is to have the application and the theory. being a rails + mobile dev teaches a bunch about the application side of programming and I find it fascinating.

In terms of the age thing, I get it. At 19 I hated being at school because I wanted to be making $20+/hour working on websites. But now at 25 and right under 6-figures, I see that I can simply work during the day and then go to school at night. It just means I have to do that for 4+ years. I started at 24, but I will not be done until I am 30. So, if I would have done it right the first time, and not dropped out, I __might__ have been further.

I don't regret it tho. I rolled the dice and although I was trying to hit the start-up lottery at that age (why I got into programming), I ended up on a great career with incredibly valuable experiences that I would not have had if I didn't drop out at 19.

8/10 would do again.

</comment></comment></comment></comment><comment><USER16> Dan Shiffman created a Youtube playlist titled "Intelligence and Learning", which might be a good place to start.

This is the link to his channel [https://www.youtube.com/user/shiffman](https://www.youtube.com/user/shiffman)

<comment><USER17> Siraj Raval and Dan Shiffman are THE best, when comes to teaching.

</comment></comment><comment><USER18> A lot of bootcamp / self-trained devs went into front-end JS for the money. Now that it's saturated, time to move onto the next buzz word. A foundation of CS fundamentals will still be lacking.

<comment><USER19> You say "for the money" like it's a bad thing. A job is where you trade labor for money, no need to foist anything else onto it.

<comment><USER18> It's great to do jobs for money, I just think programming isn't something you can pick up in an afternoon and be good at, like mowing lawns or making sandwiches. I can't trust the journeymen for high tech jobs, maybe for editing wordpress or something.

</comment></comment></comment><comment><USER20> I'm about a year into a transition from traditional software dev / engineering leadership to ML / robotics engineering, starting with 6 months off of personal study and now continuing study while on the job as a research engineer. I wrote about my experience and give advice on how to approach learning ML here:

[http://karlrosaen.com/ml/](http://karlrosaen.com/ml/)

</comment><comment><USER21> You can check out this guy:

[https://www.youtube.com/watch?v=h3l4qz76JhQ](https://www.youtube.com/watch?v=h3l4qz76JhQ)

[https://www.youtube.com/watch?v=2FmcHiLCwTU](https://www.youtube.com/watch?v=2FmcHiLCwTU)

His videos are pretty dense but if you know Python and some basic ideas about neural networks you should be able to follow through.

I'm a plain dev, gone fullstack, gone frontend and it was pretty fun for me to reproduce and get to understand what's happening.

</comment><comment><USER22> [http://course.fast.ai](http://course.fast.ai)

Its hands down the best course to get your hands dirty with latest , state of art stuff, and then learn how it works. It has completely different approach to most courses. It is top down.

Do this first, you can immediately apply it to cool stuff like image classification , nlp etc.

The assignments have additional resources where you can get into more detailed math (but the course doesn't dumb things down, but gives more intuitive explanations) and dive even deeper.

</comment><comment><USER23> Hey honest question.

I have worked with ai and nlp guys. How i have seen this works out: there is a problem x. They get the best most recent respected research on the problem x. They implement it most of the time it's on (github).

If it doesn't solve the problem at hand they shrug their shoulders and say something like "it is the standford nlp parser can't do better than that!"

the concept "getting into ai" \- I am confused. We need more people to git clone ai repos? Or are these people truly interested in ai research - at that point they should be looking at a phd.

Then people pile on: "learn how an nn works!" Uh why? Anyone can git clone and setup nodes. I am missing something. Please help.

<comment><USER24> So I have been doing what I shall call applied machine learning since I was in college when I built an ad classifier for a web crawler I was building at the time. I made the real transition while working in the search team of a web company almost 10 years ago now.

Let me first say that I am unlikely to ever design a new novel algorithm like an SVM kernel. I have however studied ML theory extensively and have a good grasp of the underlying math. I also had the advantage of working in medical research starting in high school and even before college I had learned a lot about statistics and was comfortable using a tool like SPSS to perform ROC analysis as well as gaining a solid understanding of what real statistical rigor was.

I, and those I know and work with, do a lot more than clone some repos from GitHub and see if they work. Typically there is some sort of a business problem that needs solving. Sometime we know of an approach that will work but often there is a literature survey that needs to be conducted to see if anyone has solved a similar enough problem and written about it. I am comfortable reading ML/NLP literature and evaluating the methodologies described. Often there is some open source stuff to get us started but rarely (I can't think of any, but its early in the morning) have I been able to put together a complete solution without solving some difficult problems on my own.

If I were to give someone advice it would be probably not the advice that they would want but here goes. I assume that the person would already have a solid mathematical foundation like engineering calculus. 1\. Start by getting solid foundation in statistics and probability. 2\. You will need a foundation in linear algebra. 3\. Find a mentor(s) that can help you with both the theoretical side of ML and the applied side. In my case they were different people. 4\. Implement some learning algorithms from scratch. I build a NN library a long time ago. I never used it in a production application but the learnings it gave me are still invaluable. 5\. Read the research. You need to feel comfortable picking up a paper, understanding it, and evaluating whether you should believe the authors or not.

Maybe there are shorter roads. Personally I don't believe so. I was lucky to be paid to learn these skills through my career. I am sure there are people who are smarter than me or who can just learn by reading. I learn by doing. But this has led to success for me and I think gave me the ability to succeed in different environments, using different technologies, and long before the entire world was so enamored with deep learning.

<comment><USER25> do you have advice on whether it is worth going back to school if your goal is to build novel and useful software/ai tools using deep learning - <USER2> necessarily improving the algorithms themselves. Would you expect to still need those 5 things you listed as advice.

</comment></comment></comment><comment><USER26> [http://www.fast.ai/](http://www.fast.ai/) is a mooc, Coursera is a mooc that has courses on the topic also there is udemy, the knowledge you retrieve depends on the effort you put in, just like front end development

</comment><comment><USER27> I did Udacity's Deep Learning and Machine Learning courses and found them to be a really great introduction, and enough for me to continue on my own. I'm not a front-end dev, but also definitely not a mathematician or computer scientist. You should be able to learn enough to do some cool stuff.

\- Make sure you know Python well - pretty much everything interesting in ML is in Python. If you already know JS this shouldn't be too difficult.

\- Learn you some classic ML using scikit-learn and some online course.

\- Learn Deep Learning using TensorFlow, Keras and/or PyTorch with one of the online courses.

\- Get in the habit of reading new papers in ML (which are out on a daily basis) and replicating the results.

\- Start working on some cool, original stuff.

<comment><USER28> From where to get the latest papers?

<comment> kiril-me Cornell University Library [[https://arxiv.org/](https://arxiv.org/)] has a lot of publicly available papers. You need to find the domain that you are interested in and start looking. Usually, papers describe relative works and state of the art algorithms. So you can start with one algorithm and go deep.

Books: [https://medium.com/iambot/tagged/books](https://medium.com/iambot/tagged/books)

Also, you can join communities.

[https://deeplearningstudies.slack.com](https://deeplearningstudies.slack.com)

[https://www.facebook.com/groups/1892696574296664/](https://www.facebook.com/groups/1892696574296664/)

</comment></comment></comment><comment><USER29> How can you utilize it? It's not "front end" per se, but you can use it to build recommendation systems and predictive models for users. Basically, anytime you need or want to make a decision involving some amount of uncertainty.

</comment><comment><USER30> it depends if you want to be a data engineer (lots of need for that), software engineer that developers code for data scientists (Software Engineer: Machine Learner - also lots of demand), or a data scientist.

the latter, 'might' be a challenge. I started looking into linkedin profiles of data scientists of top tech companies after I realized there were more wharton mba's as data scientists than there where people who mastered in CIS in my program.

So far, 50% had a masters/phd in statistics, 25% Information / Data Science, 25% business background. However, I am still looking into it as my sample is small and I have selection bias in my sampling.

<comment><USER31> That's probably because "data scientist" isn't a job description, it's a trendy title for a whole constellation of jobs that involve looking at data. I would expect that the Wharton grads are probably doing work that is fundamentally different from most people with hardcore academic stats backgrounds.

<comment><USER3> I think this sentiment is roughly correct. It's basically the same for programmer as there are many jobs that involve programming. Note that data science doesn't necessarily mean machine learning, and there are lots of data scientists who do powerful work with stats that doesn't involve anything that looks like ml.

The jobs for people working in machine learning tend to fall into: software engineer (machine learning), research engineer, and research scientist.

Yann LeCun has an excellent Quora Q&A where he delves into the differences of the roles. I do not have the link on hand but it is probably easy to find.

</comment></comment></comment><comment><USER32> Just like you use socketio api without knowing about tcp ip or network routing protocols. You can treat machine leaning as an api which does things. You don't need to take a machine leaning class. Just treat it as an api.

And you never know, you might be already using some api which does machine learning.

</comment><comment><USER33> Check out this excellent walk through by Per Harald Borgen: [https://scrimba.com/casts/cast-1980](https://scrimba.com/casts/cast-1980)

<comment><USER34> This is great.

</comment></comment><comment><USER35> One word: [https://www.coursera.org/learn/machine- learning](https://www.coursera.org/learn/machine-learning)

<comment><USER36> That isn't a word, per se bruh.

<comment><USER37> per se: in and of itself as such: exactly.

you meant as such.

</comment></comment></comment><comment><USER38> I literally just grabbed the last (at the time) machine learning paper that'd crossed my feed, and started reading.

Whenever I encountered something I didn't know, I googled around until I had even a vague idea.

That said a) I don't know how do-able that would have been without the math background I've already got, and b) I haven't gotten much further in that reading that one paper ...yet.

Regarding the math - The only thing I know for sure to point you at is multivariable calculus, which is also (probably) the most useful math class I ever took. (It helps that the teacher was completely amazing). The set of concepts it introduced me to are amongst the most useful things I ever learned, and it's that understanding that has always given me a leg up on jumping into things at the deep end.

(I took multivar from UC berkeley in, I think, spring 2002? I went looking for video lectures, but did not find what I was looking for...)

</comment><comment> kiril-me Cornell University Library [[https://arxiv.org/](https://arxiv.org/)] has a lot of publicly available papers. You need to find the domain that you are interested in and start looking. Usually, papers describe relative works and state of the art algorithms. So you can start with one algorithm and go deep.

Books: [https://medium.com/iambot/tagged/books](https://medium.com/iambot/tagged/books)

Also, you can join communities.

[https://deeplearningstudies.slack.com](https://deeplearningstudies.slack.com)

[https://www.facebook.com/groups/1892696574296664/](https://www.facebook.com/groups/1892696574296664/)

</comment><comment><USER39> I'm a front end developer who was in a similar position as you! I'd highly recommend giving this playlist a shot: [https://www.commonlounge.com/discussion/81f5bbcfea4e44b9b2bd...](https://www.commonlounge.com/discussion/81f5bbcfea4e44b9b2bd081d1ea536ac/main)

From the description: These tutorials have been chosen to maximize learning curve, i.e. learn the most in the shortest amount of time and cover topics from basic deep learning all the way to research done within the last 1 year.

They cover significantly more material than a typical deep learning course and took me lesser time. Good luck!

</comment><comment><USER40> Check out metacademy for some roadmaps that address precisely this question: [https://metacademy.org/roadmaps/](https://metacademy.org/roadmaps/)

</comment><comment><USER41> I would suggest running through a tutorial or two using one of the hosted machine learning options (Amazon has one I am working with right now).

This will let you focus on the essence of machine learning (data gathering and cleansing, interpretation) rather than the mechanics. The gathering of data and building of intuition about results are by far the hardest parts of machine learning, in my experience and reading. This is especially true if you are just getting started.

Plenty of time to focus on the mechanics later.

(Full disclosure, I am working on a ebook about Amazon machine learning link in profile.)

</comment><comment><USER42> I'd recommend looking into Azure's offering if you want an environment that's already setup, free to play with, and has some tutorials and templates [https://azure.microsoft.com/en-us/services/machine- learning/](https://azure.microsoft.com/en-us/services/machine-learning/)

If you want to get more hands on, I'd look into Tensorflow. You can search github for popular projects using it to get some ideas. I haven't played with it in awhile.

</comment><comment><USER43> Find a company with a data-science department, which needs engineers to industrialize their proof-of-concepts (POCs). The POCs might often include front-end work. Once you are in, learn machine-learning on the side and try to transition into a machine-learning role.

EDIT: On top of it, if software engineering is your strength (testing, automatic deployment, etc), data-scientists will also benefit, since you can show them how to professionally develop a sw product.

<comment><USER41> I have a friend applying for a job doing just this: being the software developer to help the data science folks operationalize their models.

Not sure how applicable this is to a straight up front end developer, though. I would definitely suggest learning Python if you head this route. R would be good as well.

</comment></comment><comment><USER44> The Andrew Ng's course on coursera. Then some Udacity.

</comment><comment><USER45> It don't answer you but it is related in some way. I saw some days ago a js library to implement neural networks. I didn't dive in it but it looks like something to do simple things on browsers [https://synaptic.juancazala.com/#/](https://synaptic.juancazala.com/#/)

</comment><comment><USER46> I have a slightly older article series on Machine Learning in Javascript, teaching algorithms assuming only JS knowledge: [https://www.burakkanber.com/tag/ml-in- js/](https://www.burakkanber.com/tag/ml-in-js/)

</comment><comment><USER47> I can't recommend [Practical Deep Learning for Coders]([http://course.fast.ai/index.html](http://course.fast.ai/index.html)) enough. I am going currently through it and it is extremely educational and practical.

</comment><comment><USER48> I have couple of links here. [https://medium.com/@boraturant/ai-ml-dl- links-5cf596fd29a1](https://medium.com/@boraturant/ai-ml-dl- links-5cf596fd29a1)

</comment><comment><USER49> Hey, I'm in a similar boat. Is your motivation more curiosity or career- related? Do you really like front-end code? I'm finding that it's an easier path to get into ML through conversational UX.

</comment><comment><USER50> Related question: If I only had an MIS undergrad degree (some 200 level stats and no other math) and wanted to get into machine learning what kind of math courses would I need to pick up to become proficient?

<comment><USER51> > what kind of math courses would I need to pick up to become proficient?

look ML courses and their prereqs.

</comment></comment><comment><USER52> A front end developer can most easily jump into machine learning by reading the docs for a machine learning API then querying it. Won't take years and years and no PhD needed either.

</comment><comment><USER53> have not finish it but it seems good, you could check it out, this tutorial uses processing for teaching neural networks and it relays on processing's graphic feedback to visualize what is happening [https://medium.com/typeme/lets-code-a-neural-network-from- sc...](https://medium.com/typeme/lets-code-a-neural-network-from-scratch- part-1-24f0a30d7d62)

</comment><comment><USER34> Anyone here have any good book recommendations?

<comment><USER54> I found "An Introduction to Statistical Learning with applications in R" to be very useful.

</comment></comment><comment><USER55> Learn statistics...

Generally speaking though, it's easier to go from maths/stats -> programming than the other way.

</comment><comment><USER56> There are a few ways to go about this.

One is very programmer heavy. There's a lot of data processing that goes into machine learning, but you can't quite separate the two. Often, the programmer who prepares and processes the data needs to write the code that actually runs the model and parses the result, and this means you benefit from more understanding of machine learning. That role is most likely the best one available to programmers who don't have much mathematics background in this area.

To really get into machine learning itself as a data scientist, though... I do think it requires some math. There's a reason a large percentage of people who work in this field have an MS or PhD in a very quantitative field. And I don't just mean algorithm designers - to really be able to explain the difference between naive bayes, random forest, neural nets, and logistic regression, it helps enormously to have a background in math.

To illustrate this, I've taken two coursera courses on data science. They were both excellent, but approached from different angles. Bill Howe's data science class involved an exercise to use a random forest to do some classification, but the focus was on calling the scikit-learn library. We did of course review the algorithm, but not in mathematical depth.

Andrew Ng's course on machine learning got into implementing the algorithms (with a language called Octave, which honestly I didn't like much, but that's a completely different topic where plenty of people would disagree with me). To do that class, honestly, I'd just ask if the terms "vector calculus", "matrix of second order partial derivatives" or "logistic function" mean something to you. It's ok if you can't define these things on the spot, but was there a time when you could? You can get up to speed, but I'd say if you haven't taken basic calculus through differential equations (with linear algebra), then you won't be able to understand this material.

I've been impressed with how well people learn on their own, picking up a lot of math as they go along. And I don't think you need to be able to implement these algorithms yourself to use them meaningfully. But if you're going to be deciding what kind of model to use, even if you're using libraries to do it (and most people who can implement these algorithms would still use a library), I think that you do need to be able to describe how a neural net work vs random forests vs logistic regression vs naive bayes. There is a side of this that is very math-y as well.

On the bright side, we live in an era of amazingly available learning material. I personally think a dedicated person can probably learn calc, linear algebra, and differential equations through web-based coursework now.

SO overall, I'd say - start on the data side as much as possible, leveraging your programming skills. While doing this, keep getting more exposure to ML algorithms, and make sure you are taking a coursera or other web-based class on the side.

</comment><comment><USER57> face first, feets pushing

</comment><comment><USER58> If you really want to learn machine learning you should pursue a PhD. You might be able to do very simple stuff and follow tutorials because there are very high level tools around, but to really know what's going on and how to use it to produce new products and services, you need to master it properly.

<comment><USER59> This is not true and harmful to the progress of the field: [http://www.fast.ai/2017/03/17/not-commoditized-no- phd/](http://www.fast.ai/2017/03/17/not-commoditized-no-phd/)

<comment><USER58> Well the article builds around a very superficial view of ML.

If you want to do simple recommendation systems or spam filters than O.k. Those are solved problems, hence commoditized.

If you want to build novel things, you really need academic-grade ML.

If you want another argument, I came from working in VC and startups, and they think they understand ML. Boy, they really don't. They are like kids pretending to play a guitar that can't strike a single chord right.

<comment><USER60> Different approaches suit different people and PHD is a relatively specialized route. It's good to have people targeting similar goals with different approaches.

For an anecdote, I recall hearing one of the Kaggle founders mention that many of their bounties are won by non-statisticians/ML-ists. Producing novel (in the academic sense) stuff is unlikely outside of an academic setting, but producing products or solving problems is do-able.

Edit/comment: no need to downvote rsrsrs86 people. He's putting forward a position and defending it, not trolling. If you disagree, then disagree. The whole point of a thread like this is hearing people's take. Surely, PHD is a valid suggestion.

<comment><USER58> Kaggle competitions are very restricted in the sense that they are supervised learning problems. This typically results in applications in analytics. This should o.k. be easier to get into.

But ML can do much more than analytics., and much more than supervised problems. And the great problems to be solved are not supervised problems. They involve learning as you go, without a clean database with examples to learn from. They are adaptive problems.

You might optimize prices in an online retail player by trying to estimate supply and demand curves, but you will fail, and the best way to do it is not much different than teaching a neural network to play video games, but is fundamentally different from supervised learning and regressions.

ML can do self-driving cars, it can build drones that learn to fly, it can translate horses to zebras, it can play defeat humans at Go, it can make guitars sound like pianos.

There is a lot of technique and theory into framing any problem as a problem that can be solved by machine learning. Machine learning is generally not feasible unless you restrict the problem properly.

</comment></comment></comment></comment></comment><comment><USER61> For starters, what's your math background like? If you're interested in seriously pursuing ML theory, I'd recommend having at least 2 semesters of stats and 1 semester of linear algebra fresh under your belt. Don't short change yourself here, because any remotely advanced material will be incredibly frustrating (aka constant backpedaling) without the requisite math intuition.

One approach might be to tackle a MOOC like Andrew Ng's coursera offering (which isn't very math heavy) while simultaneously brushing up on your stats and linear algebra (mostly stats tbh). Even if you end up just focusing on implementation, I think this will be time well spent.</comment>


Ask HN: Innocuous News? HN is now IN? - <AUTHOR>

What gives? <comment><USER2> I predicted this post here <http://news.ycombinator.com/item?id=576332>

<comment><USER3> Somebody get this guy a medal!

Lol. Maybe they can make the original post a sticky so people won't keep asking the same thing? Is that even possible on HN?

</comment></comment><comment><USER4> Even though it will be changed back, I will still say I don't like it. Hacking and IT don't need political correctness, they need freedom of expression.

Ask yourself, "What would Katt Williams say about this change?" He would probably say something like - "Hacker Please!"

</comment><comment><USER5> This thread will tell you all you need to know:

<http://news.ycombinator.com/item?id=575815>

</comment><comment><USER6> not interesting, stimulating, or significant; pallid; insipid: an innocuous novel

(third definition at <http://dictionary.reference.com/browse/innocuous>)

Should we take a vote on a new name?

<comment><USER3> Or we could just go back to hacker news. And stop sweating what other people would think about us reading it.</comment></comment>


Pattern Calculus - <AUTHOR> http://lambda-the-ultimate.org/node/3695

<comment><USER2> "By contrast, a small typed pattern calculus is able to support all the main programming styles, including functional, imperative, object-oriented and query-based styles..."

I recall Stephen Wolfram advocating this with Mma nearly 20 years ago.

</comment><comment><USER3> Has anyone here played with the bondi language? What's it like?

<comment><USER4> The site referenced seems to be down, but here's another page with some more links (including some bondi source): <http://www- staff.it.uts.edu.au/~cbj/patterns/></comment></comment>


Graphene contact lenses could give you infrared vision - <AUTHOR> http://www.nature.com/nnano/journal/vaop/ncurrent/full/nnano.2014.31.html

<comment><USER2> will_brown, your title has nothing to do with the article.</comment>


Parquet: Columnar Storage for Hadoop - <AUTHOR> http://parquet.github.com/

<comment><USER2> Is it just me or is that website not readable without getting a headache. Deep blue on left to white on right? Seriously?

<comment><USER3> I'm not sure where you are looking but the Parquet home page looks mostly brown and reddish to me.

</comment></comment><comment><USER4> Wow, they already replaced trevni? That was quick.</comment>


U.S. Is Net Oil Exporter for First Time in 75 Years - <AUTHOR> https://finance.yahoo.com/news/u-becomes-net-oil-exporter-220000624.html <comment><USER2> The US has doubled their oil production since the 2000s. Trends like these aren't easy to grasp by just watching the news. If only there were maps and charts we could read.</comment>


Twitter wins rights to stream NFL Thursday night games - <AUTHOR> http://espn.go.com/nfl/story/_/id/15138355/twitter-wins-digital-streaming-rights-nfl-thursday-night-games <comment><USER2> Yahoo!, one of the original pillars of Internet media, is being carved up right now.

So it seems fitting that Twitter, one of the pillars of social media, is trying to adopt their business model: try everything, specialize at nothing.

At the same time, I like the idea of non-traditional television providers gaining access to live sports - just one more nail in the coffin of multi- channel television. That said, I can't help but think Amazon or Netflix was better position to provide the best experience for this.

</comment><comment><AUTHOR> Sports is one of the last hooks keeping people from ditching cable television.

I imagine if we see more deals like this one—especially with the NFL—the number of cable cutters will continue to accelerate.</comment>


WireGuard merged to net-next, on its way to Linux 5.6 - <AUTHOR> https://lists.zx2c4.com/pipermail/wireguard/2019-December/004704.html <comment> m-p-3 Another discussion here [https://news.ycombinator.com/item?id=21741133](https://news.ycombinator.com/item?id=21741133)</comment>


Karma? - <AUTHOR> I have posted interesting content and tried to comment in an intelligent fashion, but I still only have 1 Karma?  What am I doing wrong? <comment><USER2> Looking at your submissions - while interesting, wouldn't warrant up vote from me. I tend to up vote as a sort of bookmark or if I feel something should get more exposure (try to drive it to the front page). Posts I up vote are generally related to cool projects / projects that could help me in my work or discussion topics I feel strongly about.

There is definitely a target audience that is the HN community - so while something might be interesting, it doesn't necessarily mean it might be popular - ie: theres a lot of posts today about the Tesla Model 3 but few up votes if any on any of them because it's all speculation until it actually happens.</comment>


Ask HN: Is Learning COBOL a Good Idea - <AUTHOR>

I'm currently tossing around the idea of learning COBOL. The reason this has been on my mind is because its a very very old language. It seems that it has long since been out of the lime light for a long time but, many companies still use old legacy COBOL code. As the years go on I feel COBOL will become more and more in demand as many younger developers and students are no longer bothering to learn it but there are still systems running it and there are still people who are paid quite will to maintain it.<p>What does HN think? <comment><USER2> I actually have used it, and I'd recommend that you steer clear.

About twelve years ago, I had to translate 30k lines of Micro Focus COBOL into Delphi (object-oriented Pascal). It ran on Solaris and relied heavily on memory-mapped files (it called mmap() through an FFI). While I got the project done, it was a nightmare to work with. Why?

\- Most COBOL programs generally use global variables everywhere. I suppose that some newer dialects exist that allow you to limit variable scope slightly, but that doesn't really help because no one writes new COBOL programs anymore.

\- Want to install the compiler? Call our support line. (They probably thought that leaving bugs in their installer constituted some sort of anti-piracy measure.)

\- Want to define a function? No problem! Just open a new file and type half a page of boilerplate code.

\- Want to recompile even a simple COBOL program? No problem! Just wait a minute!

\- Want to recompile a moderately-sized COBOL program (30k lines)? No problem! Just wait five minutes!

\- Want to buy a COBOL compiler for Solaris? That'll be $2995 for a single- seat development license. Don't even think about trying to run the resulting binary in anything like a production environment because that costs many tens of thousands more. (Maybe you're thinking that you'll just use a competing company's compiler. Well, you thought wrong! On the platform I was stuck with, Micro Focus was the only game in town.)

\- Put COBOL on your resume, and people will rightly think less of you. I will think less of you.

We live in a golden era, where you can do almost anything you want. We have gobs of RAM, lots of cores, fast internet connections, great programming languages, etc. -- all things that were barely imaginable to the brightest minds of 50 years ago. Why saddle yourself with the language of that generation's dullest minds?

tl;dr: Please, just let this stinking heap of a language die!

</comment><comment><USER3> Depends on what you want to do career-wise, and what's going to make you happy.

There are billions of lines of COBOL running out there, doing all the unhip, non-HN, bread and butter IT/MIS types of things that keep the world running (like getting your paycheck right, or processing your insurance claim, or calculating your utility bill). Because many of these applications incorporate decades of tribal knowledge and accumulated tweaks, all the "COBOL needs to die" nonsense in the world will not make them go away in my lifetime, and probably not in yours. And since the cool kids keep hearing that COBOL is the ultimate in looser languages, you are correct that there's a declining pool of COBOL talent. One of my clients even funded the CS department at the local CC so that there would be a COBOL/Mainframe oriented degree track to develop talent.

Up side: You'll be in a field with declining talent but not equally declining demand and will generally be able to find a job and be paid well (though don't count on off-the-charts compensation). You'll usually work in mature, stable operational environments at large, established companies. You'll almost always be involved in core business processes, making you less likely to be the subject of cuts when budgets get tight.

Down side: There are no COBOL start-up companies; you won't be working on cutting edge stuff. "Modify the taxation calculator to reflect changes in the FY2015 tax code" would be typical projects. Stable also usually == staid & bureaucratic, and if that sort of work environment annoys you, you won't be happy. COBOL is a horrid, ugly language especially if you're used most anything else. COBOL is often tied to IBM Mainframes, so it's somewhat rare to not have to also know something about z/OS, CICS and other IBM-isms. Hipsters and brogrammers will judge you negatively not because of your talent or accomplishments, but because you solved problems with COBOL, instead of their choice of cooler languages (I get this some times having done a fair bit of FORTRAN back in the day by people who ignore how cool the problems we were solving were).

So if career stability is important enough to give up on faster paced, more cutting edge environments, then you might give COBOL a go.</comment>