Statistics: Losing Ground to CS, Losing Image Among Students - <AUTHOR> http://blog.revolutionanalytics.com/2014/08/statistics-losing-ground-to-cs-losing-image-among-students.html <comment><USER3> I disagree. Statistics is not "losing image" among students. It's the other way around: _more and more students and software developers have become interested in statistics_ , inspired by the successes achieved by machine learning and AI researchers. Because machine leaning _is_ statistical learning. It's almost impossible to build a machine learning system without learning at least some statistics!

As evidence of the growing popularity of statistics, consider that this submission made the front page on HN.

The "losing image" meme applies, not to the discipline of statistics, but to _staid academic departments_ who have lost power and prestige. They don't like that their subject has become popular in a different department (CS), outside their purview.

PS. I would add that many (but not all) statistics departments have brought this upon themselves. For a long time, their approach to real-world, practical applications has been to apply _existing_ models in ways that can be readily interpreted by human beings, e.g., with "explanatory variables." Meanwhile, machine learning and AI researchers have been experimenting with, testing, and deploying _new_ models and _new_ ways of 'scaling up' old models, focused on making accurate predictions on previously-unseen data. _Of course_ students find the latter more exciting.

</comment><comment><USER4> Talk about head in the sand. CS is subsuming stats (not really) because computers are a, if not the, major driving force begind statistical advances. L1 regulation became a fad in stats because computational optimization could efficiently solve this non-smooth optimization problem. Engineers are now advancing algorithms and moving it to real world use, while statisticians are still re-searching their field and finding applications using existing algorithms. There is nothing wrong with application, but this means that they are not at the forefront of research and more importantly they have to wait for other people to solve their problems because they are no capable of inventing novel algorithms. When the world at the large relies on computers for every little thing and on computer scientists for a lot of scientific advances, is it really surprising that computer scientists are more prominant?

And computer scientists have plenty of rigor. NIPs conferrence is an AI/neuroscience conference that is now prestigious in stats as well. Some machine learning experts lack mathematical foundations but statisticians are not really good mathematicians either. Being able to regurgitate stat theorems and prove convergence in expection under two dozen conditions (no kidding; read average JASA papers) are not the hallmark of analytic rigor. Average stats Ph.D., even from top programs, lack thorough understanding of measure theory. Just look at standard stat textbooks; examples are aplenty (using statistical software), but proofs are nowher to be found. It is amazing how many statisticians are still using pseudo-inverses in numeric computation.

The statistical education is a problem. The biggest factor being the very software this article is promoting: R. It is an atrocious monstrocity. Perhaps statisticians can live with it. Fine, but don't expect good programmers coming out of using R for five years. Python would be far better. In the end, statisticians are not professional programmers, the same way accountants are not. I don't see too many accountants fretting CS is subsuming their field. Let us not forget, most statistical methods have their roots in other science and engineering fields, and they are not particularly bothered.

<comment><USER5> Here is a math major's perspective (I also have a C.S. master's from Stanford, a decent school for computer science).

The real reason statistics is losing to machine learning is M&M: money and marketing.

Money is a huge factor when kids choose majors in college. Many of them have student loans to pay off, and for many of them, getting a high-paying job post college is a serious consideration. In this regard, statistics is a great major, but computer science is flat-out ridiculous. When big tech companies are offering a Stanford graduate with only a few summers of programming internships under the belt for 150k/year, naturally a lot of kids are lured into computer science.

Then, once they start studying computer science, they discover this thing called machine learning. While I do think there is a difference in emphasis between stats and machine learning, the fundamentals are same, except the nomenclature sounds much cooler in machine learning. Nonparametric inference sounds esoteric and cryptic, but unsupervised learning sounds futuristic and cool.

The biggest problem with both statistics and machine learning education, I would say, is their lack of emphasis on mathematical foundations. When I was in college, CS229 (Introduction to Machine Learning) was touted to be the hardest class at Stanford. Having helped my friends wade through CS229 problems (a lot of which comes down to wading through linear algebra and multi-variable differential calculus), I do not think this is remotely true: CS229 is hard because it attracts students who do not have the requisite mathematical maturity to learn statistics/machine learning in a serious way (I am not even talking about the real-analytic foundation of probability and calculus but rudimentary linear algebra and chain rules).

Also, I do think CS229@Stanford is a great class that brings theory and practice together =)

As for R/Python/Matlab/etc., I will let the zealots argue what's best. To me, they are like statistics and machine learning: similar with different emphasis.

<comment><USER6> > The biggest problem with both statistics and machine learning education, I > would say, is their lack of emphasis on mathematical foundations.

Any recommendations for someone who'd like to pick up sound (if modest) foundations?

<comment><USER5> Yes. For example, to follow CS229's lecture notes, the following material should suffice.

\- Linear Algebra Done Right by Sheldon Axler

\- Linear Algebra by Steven Levandosky

\- Linear Algebra and Multivariable Calculus, and Manifolds by Ted Shifrin (you probably do not need the manifolds part)

Also, as paradoxical as it may sound, CS229 lecture notes themselves. The key here is to verify the details painstakingly. In math, we joke that whenever a book says something is "trivial" or "easy to verify" or "clear to show", it is a substantial exercise in disguise.

<comment><USER7> Yes, the whole "trivial exercise" thing is in itself a big problem with how mathematics is taught. Someone who's struggling just to understand the text could probably really use a worked-out example.

<comment><USER8> That's the wrong interpretation. In math, a 'trivial' or 'clearly' is supposed to indicate that a certain detail is important to understanding the subject. And if it isn't clear or trivial then the reader is expected to delve deeper before moving on.

The "big problem" is that math is taught in a restricted time scale - <USER2> semester, just like all other standard courses. In reality it might take someone many weeks or months to pick up a certain piece of measure theory or hilbert spaces. Math builds upon itself and when it is rushed there is a lack of connection and then ultimately failure to understand.

This is why MOOCs are a great boon for many, as they allow more freedom to self-dictate the pace.

<comment><USER7> If that's what it really means then a perfectly good way to say it is: "To test your understanding, make sure you can do this exercise before continuing."

I suspect many mathematicians _like_ being intimidating.

</comment></comment></comment></comment></comment></comment></comment><comment><USER9> I'm surprised that they didn't mention Leo Breiman's famous paper "Statistical Modelling: The Two Cultures"[0]. A worthwhile read for anyone interested in the topic. I could sum it up, but the abstract does a better job:

\</comment><comment>----

Abstract. There are two cultures in the use of statistical modeling to reach conclusions from data. One assumes that the data are generated by a given stochastic data model. The other uses algorithmic models and treats the data mechanism as unknown. The statistical community has been committed to the almost exclusive use of data models. This commitment has led to irrelevant theory, questionable conclusions, and has kept statisticians from working on a large range of interesting current prob- lems. Algorithmic modeling, both in theory and practice, has developed rapidlyin fields outside statistics. It can be used both on large complex data sets and as a more accurate and informative alternative to data modeling on smaller data sets. If our goal as a field is to use data to solve problems, then we need to move awayfrom exclusive dependence on data models and adopt a more diverse set of tools.

[0].[http://tuvalu.santafe.edu/~aaronc/courses/5352/readings/Brei...](http://tuvalu.santafe.edu/~aaronc/courses/5352/readings/Breiman_01_StatisticalModeling_TheTwoCultures.pdf)

<comment><USER4> Fantastic paper. I wonder what "irrelevant theory, questionable conclusions" he had in mind.

</comment></comment><comment><USER10> In my opinion, one big reason in the fall of statistics is the asymmetry in curriculum.

If you are in CS/Machine Learning, you will be forced/suggested to take a few statistics/probability courses, typically up to a regression course. These are good enough to get you started and familiar with statistical concepts and methodology.

However, if you come from a typical statistics program, programming/computing courses are almost always never required. Professors in probability theory/statistics often do not have great computing skill, they might know enough to get around in R/Mathlab, and teach you the basics in these tools, but a lot of times you are left in the dark in terms learning how to code.

If your career goal is an analyst, whose job is report oriented, you can get by by knowing just enough R (SAS, Matlab etc.) scripting. As your deliverables are generally fancy LaTex pdf (or presentations). But current job market pays and values more of a "data scientist", whatever that terms mean. This type of roles typically requires solid software engineering background, and requires you to be able to work with software developers and commit to the same codebase on a regular basis. The lack of programming/computing skills of a stats majored students will be a huge minus for this type of responsibilities.

<comment><USER11> Granted, I started college 30+ years ago, so my perspective may be outdated, but I fear that it might not be. What you say about "asymmetry" struck me because it seems like a more widespread issue. Some anecdotes from my college years:

* The CS students did all of their work on the college mainframe, and there was a pervasive attitude that personal computers were toys.

* The earliest adopters of PC's, and of using computers to solve problems rather than as objects of study, were in the physics department.

* Math required students to learn programming, but only one course used it -- numerical analysis.

I wonder if doing symbolic math by hand still dominates math education. Of course I enjoy doing it as a form of recreation, but I think that my use of math in my subsequent career would be stunted, had I not taken an interest in programming outside of the math department.

I also wonder if "the hot stuff in discipline A is being taught in department B" is just a feature of the chaotic nature of scholarship and research, meaning that a student needs to explore more than one field in order to really be top notch in their main chosen field.

</comment></comment><comment><USER12> Here's another relevant HN discussion about the relationship of CS and Statistics:

[https://news.ycombinator.com/item?id=6630491](https://news.ycombinator.com/item?id=6630491)

The boosting, random forest, support vector machine, and deep learning ideas should have been developed in Statistics. Why weren't they?

It's much deeper than TI-83 calculators and cookbook AP courses!

<comment><USER13> Boosting was, I suppose, invented by Schapire and Freund, both CS guys, but its most successful form, gradient boosting was invented by Friedman, a stat professor. Random forests were developed by Breiman and Cutler, both stats professors. SVM's probably weren't developed in statistics because they don't have a probability model.

<comment><USER12> It sounds like you're challenging my central claim: conventional statistics has largely ignored the impact of computational technologies on inference, and dismissed the resulting failure as a mere marketing problem.

You are wrong.

Boosting is a generic technique, and reducing it to gradient boosting and then claiming it as "mainstream statistics" is silly. Boosting is _not_ , even today, part of the standard statistics curriculum, despite its wide utility. But the bootstrap is. I wonder why?

Describing Leo Breiman as a stats professor is similarly twisted. He was always an outlier in traditional statistics; in fact, he left a position in the math department at UCLA because he didn't fit in, and began independent consulting, during which time he developed CART. And during his time, years later, as a bona fide stats professor at UC Berkeley, he spent considerable effort overcoming a tendency of mainstream stats to ignore the implications of computing. For instance, he was one of a small number of stats professors who was active in the NIPS community. (His bio at [http://projecteuclid.org/download/pdf_1/euclid.ss/1009213290](http://projecteuclid.org/download/pdf_1/euclid.ss/1009213290) is absolutely fascinating, see pages 195-6 for his feelings about conventional statistics.)

And don't forget that a lot of the popularity of CART was due to the C4.5 implementation, due to Ross Quinlan, a CS professor with a physics background, which allowed easy building of classifiers on top of trees. Which points out another thing, that these advances have in many cases not come from, strictly speaking, _computer science_ ; physicists and engineers have been very influential.

SVM's were developed by Vladimir Vapnik and collaborators, who came out of a Russian math/physics background. Who cares whether SVMs have a model or not? This is an example of the myopia that afflicts old-fashioned statistics. SVMs are highly effective predictors with deep mathematical properties and admit a statistical analysis of their performance and properties. (You don't think every statistician who uses logistic regression really believes this is a _model_ for the data generation process! Yet logistic regression is part of the stats canon.) The SVM analysis ("large margin classifiers") largely took place, again, outside mainstream statistics.

We could say the same thing about neural networks.

And we could say the same thing about deep learning.

NNs: 1980s-1990s. SVMs: 1990s-2000s. Deep learning: 2000s-2010s. In ten years, we'll be adding another example to the list, because this myopia is not just poor marketing skills.

</comment></comment></comment><comment><USER14> My first stat class was an introductory statistics class that was geared towards biology students. It was pretty terrible.

When I got into CS grad school I took more rigorous statistics courses and fell in love. I mean, head over heels in love. I tried to transfer out of the CS graduate school and into to the statistics graduate school. Because I would lose funding and would be starting over I instead decided just to quit when I was awarded my masters instead. (Now I just spend my days code JavaScript and CSS...what happened..)

If I would have been exposed to statistics earlier in life I'm absolutely certain I would have majored in it in addition to computer science.

</comment><comment><USER15> I did three terms of statistics combined at two different universities and then took the EdX statistics course a few years later. I was not impressed with the way it's taught at any of those places - if that's the usual way to tech statistics, I'm not surprised it's not very popular.

They say "This "engineering-style" research model causes a cavalier attitude towards underlying models and assumptions." \- but CS should be easy to verify. Everything I ever did for the CS courses could be looked at and explained exactly why it's right in simpler terms than what I did in the first place. With statistics I get a feeling it's completely the other way around - you can get some data, answer a question and it takes really complicated explanation to prove what you did is right. If you do simple mistake like taking one tailed instead of two tailed test, you still get a result - but how can you actually test it's not the right one? I was never taught or could find any way to do that.

In geometry you can fairly easily throw some more equations at the problem and see your solution matches them too. In CS you can prove some thing in multiple ways, or throw enough data at some algorithm that you can see that both results and complexity bounds are what you expect them to be. The statistics courses I've seen resulted in many people who can go through the usual assignments, but couldn't tell if what they got back was right or wrong.

</comment><comment><USER16> As an academic statistician running the largest data science program in the world (10,000+ have completed a class) I think that CS/Data Science only pose a threat to statistics if we don't adapt. I wrote about it here. [http://simplystatistics.org/2013/04/15/data-science-only- pos...](http://simplystatistics.org/2013/04/15/data-science-only-poses-a- threat-to-biostatistics-if-we-dont-adapt/)

</comment><comment><USER17> I'm not sure what the difference between statistics and machine-learning is supposed to be, seriously. The theoretical foundation of any machine learning algorithm is probability theory and statistics.

Is machine learning implied to be a more "hands on" approach, to focus more on learning a set of techniques than on learning the theoretical underpinning of such techniques? Is it basically "statistical engineering"?

The least charitable interpretation would be that it's just a hipper term for it.

This article pretty much sums up my impression of the divide: [http://brenocon.com/blog/2008/12/statistics-vs-machine- learn...](http://brenocon.com/blog/2008/12/statistics-vs-machine-learning- fight/)

</comment><comment><USER18> After reading the title I spent some time thinking about what the causes might be before reading the article. To my great amusement the reasons I consider machine learning to be a more vital field than statistics were all given as "structural problems with the CS research "business model"". :)

Take the great recent success of deep learning. This is a method that could have never arisen in statistics, because at this point in time it has no (well, very little) supporting theory. But it works! Given a decade or so we'll likely have a good theory but we shouldn't let lack theory hinder development of applications, and in machine learning we generally don't.

The classic Brieman "Two Cultures" paper is very relevant here.

<comment><USER19> The irony is conventional scientists forgot was it was to be scientists ! the best science was created without knowing what you were creating.

</comment></comment><comment><USER20> His attitude seems to be more "slow CS down" than "speed Stats up". He talks about "let's consider the problems in CS first", but then never gets around to seriously discussing the problems of the Stats profession.

The most he seems to admit to is "Stats hasn't been awake, hasn't been present", like it's some kind of defendable excuse. It's a major problem! If you don't show up to the game, you can't say anything about the "competition" playing unfairly or not playing by the right rules or what have you.

We've seen this before in all kinds of discussions. "Them damn kids and their Rubython on Djrails think they invented everything." It paints a picture of some new, young beast hunting down and devouring the old guard bit by bit in some kind of cruel game designed to torture body _and_ mind. Which is completely ridiculous. The new gang never "attacks" the old. They just walk away.

For all sets of tools, the reason people prefer one over another is because they are finding answers quicker and more easily with A rather than B. Nobody cares about the purity of form (or in other words, the ones who do care are _statistically insignificant_ ). They care about getting work done.

None of this is to say that working on theory is inferior or superior to working on applied use and engineering. They are all equally valid pursuits. But we all need to be able to admit when people don't share our passions, and not blame them for having their own.

Show up to the game. The game is industry. It's work. Stop focusing on academia as the only legitimate outlet for graduating mathematicians and statisticians. Talking about "adult supervision" is really just armchair quarterbacking. Sit in a meeting with a person with a 30-year-old, unused history degree with _absolutely zero_ understanding of statistics--thinks ALL math is ivory tower bullshit not worth the paper the textbooks are printed on --and explain to him why his desired results can't be obtained from his current data. See how much your statistical models stand up to "let's turn that can't into a can" or "can't is unacceptable, we're not going to leave here until we figure it out".

</comment><comment><USER21> Statistics is taught wrong from the get go. Who cares about z-tables and chi values? It distracts from the important underlying laws of probability. Statistics has never been more important, and yet the vocabulary is badly outdated leaving students with a huge mountain of knowledge to climb before reaching anything of real utility.

<comment><USER22> I don't quite agree, as the standard normal and Chi squared distribution are obviously important to understand. I think what you mean is that teaching stats based on hypothesis testing on simple experiments with small samples is not the best way to introduce students to this world. But the history of statistics was heavily influenced by designed experiments, while most of the data that is out there is observational. That is hard for older generations of statisticians to swallow.

</comment></comment><comment><USER23> I was expecting a statement like:

"Based on a random sample of institutions surveyed, Statistics is losing ground to CS at a relative mean rate of 5.7% per year (by junior and senior year undergraduate enrollment headcounts, taken over the past five years from the institutions sampled), with a 95% confidence interval of +/\- 0.3%."

Ironically, no.

</comment><comment><USER24> Tisk, tisk Norman. A good statistician shouldn't use anecdotal evidence with a sample size of one to support his/her points. If I picked up a random stat pub, I bet I could find some missing citations as well.

</comment><comment><USER25> I wonder if CS should be divided into heavy research and then a serious minor to be applied to other majors.

CS of applications should augment other fields of study. CS of research should be the heavy duty stuff.

</comment><comment><USER26> im currently reading "discovering statistics using R" ..even though we're using python and ipython at work - ipython is simply fantastic! -- hope to integrate the R support magic soon too.</comment>


Building a distributed social network? You're doing it wrong. - <AUTHOR> http://benwerd.com/2010/06/building-a-distributed-social-network-youre-doing-it-wrong/

<comment><USER2> Of note is that most of what are called "distributed social networks" are actually federated social networks.</comment>


Alexa: A dating bot for Facebook Messenger - <AUTHOR> http://www.meetalexa.com/ <comment><USER2> Branding it the same name as Amazon Echo's chat agent (also Alexa) seems like trouble...

<comment><USER3> And Amazon's other service, found at alexa.com

<comment><USER4> These, in turn, ultimately owe their name to the Library of Alexandria:

[https://en.wikipedia.org/wiki/Alexa_Internet#Operations_and_...](https://en.wikipedia.org/wiki/Alexa_Internet#Operations_and_history)

<comment><USER5> Which owes its name to Alexander the Great.

<comment><USER6> Who in turn owes his name to Alexander II of Macedon.

<comment><USER7> No, to Alexander I of Macedon.

Alexander II was Alexander (III) the Great's uncle.

<comment><USER6> So what if he was his uncle? People are named after their uncles. Alexander II ruled well before the birth of Alexander III (during the boyhood of his father in fact) and would have been a perfectly logical namesake.

</comment></comment></comment></comment></comment></comment></comment><comment><USER8> I really don't understand why chatting is supposed to be a better interface for something like that. Tinder blew up because they innovated the simplest possible UI. The UI here is broken, notice that after the second "like" the user would have had to scroll up to like another one. Or they would have to memorize some chat commands like "main menu" or "more women".

</comment><comment><USER9> Misspelled "increase". Looks interesting though.

</comment><comment><USER10> So it's a menu system.</comment>


Rate my startup: AudioOrchard.com - <AUTHOR>

Hello HN,
We've just launched a tool for musicians that puts traditional home recording software online (i.e. Garageband or Pro Tools). The goal is to make collaborative music making super simple by…
1) Creating a free, cross-platform, tool that manages all files in the cloud, so working together is easy
2) Building a community into the tool so musicians can record music, invite others to add tracks, see other artists work, see how they made it, and even make "remixes" of it.
3) Make it simple to publish completed works to Facebook, Twitter, Sound Cloud, etc.
We've been working hard over the past year to create what we think is a MVP (minimum viable product) for the recording tool itself and are now focusing our efforts on the community/social side of the tool.
The question for those musically minded entrepreneurs on HM is, is the current tool sufficiently robust enough in its current incarnation to be useful? If not, what's missing?
Our tool is at http://www.audioorchard.com
Thanks :) <comment><USER2> Looks great from what I see from the homepage. (Silverlight doesn't work well with Chrome on Mac)

<comment><AUTHOR> Yeah, Chrome on my Mac always feels a little off (although I still use it most of the time). Hopefully Google and Microsoft can get that fixed soon :)

Any chance you can test it out with Firefox or Safari?

</comment></comment><comment><USER3> Clickable link <http://www.audioorchard.com></comment>


Dear Internet, please make a tool to help me remember useful HN threads - <AUTHOR>

Is there a tool to help discover and remember useful HN threads? All HN threads are useful, but some go off the scale. Like this recent thread asking "What source code is worth studying?"
http://news.ycombinator.com/item?id=879101
Bookmarks are not the complete solution, because I'd like to discover stuff I've missed, too.
If such a site does not already exist, and until it does, how about if we start a tag on delicious.com for this purpose? Maybe just hackernews, with the useful being implied. Or can anyone suggest a better tag, or better interim solution? <comment><AUTHOR> Another thread that was useful - color scheme tools:

<http://news.ycombinator.com/item?id=859010></comment>


Ask HN: Why do not candidates clarify questions during interviews? - <AUTHOR> I interview people a lot and I just do not understand how only a few people actually clarify questions before answering them. What usually happens then is that the interviewer has to put an extra effort into trying to make sure that she is not the confused one. The issue is that it also happens in a case of otherwise strong candidates.<p>How do you begin an interview to prevent that from happening? <comment><USER2> There are many terrible interviewers and companies out there who would treat asking for clarification as a weakness so I imagine many interviewees avoid asking for clarification to avoid appearing "weak".

As others have said make sure you start the interview clearly inviting questions, but also if the candidate still seems reticent (they might believe it's a trick offer) maybe engage in a dialog as they start addressing the question.

For example immediately ask an open ended question about some edge case "What do we think should happen if the tree only has a single node?" or something like that. By immediately beginning a dialogue you invite more discussion. But bear in mind many candidates just won't enter into a discussion, it's not how they work, so try not to hold it against them if they are otherwise perfectly suited.

</comment><comment><USER3> The interviewer should say something like (in a friendly tone) "we're not trying to catch you out. Please do ask for clarification if I haven't been clear with any of the questions", and start the interview with a discussion style question to allow a bit of chat back and forth between the candidate and interviewer, to tip it more into a conversation than an interrogation.

</comment><comment><USER4> Just to clarify, is there a technical lead or senior developer in these interviews?</comment>


Chrome will not be developing the Sidebar API (chrome.sidebar) - <AUTHOR> https://code.google.com/p/chromium/issues/detail?id=477424#c82 <comment><AUTHOR> Really disappointing ! This API would have made it possible to embed plugins like "Sidewise Tree Style Tabs" in a chrome window. But I understand their point. One should not forgo simplicity for additional features.</comment>


Ask HN: How Do I Inspire College/High-School Students To Work With Me? - <AUTHOR>

Good Afternoon and Happy Holidays Hacker News, Im from a very LOW ECONOMIC & TECHNOLOGY skill level community in NC and I want that to change.
I've acquired different funding for projects in order for students to work with me and therefore bring technology skills and money to the community, but nobody is interested.
Like why is that the case? especially when presently many people don't have jobs.
I know many of them are in debt and many of them are at a poor level in technology, but no one is interested. But what is so funny is that these are the same people that complain about how our government is unfair to them and how they've been given a bad hand.
I would think that, they would be more interested.
Back then when I was in college, if somebody had offered me a work/job where I can gain money and experience (in a field Im interested in), I would've jumped at the opportunity.
Am I targeting wrong students? Is it a mental or psychological issue? or Am I the problem? <comment><USER2> What is your sales pitch to them?

<comment><AUTHOR> Thank you for your response ghubbard.

My sales pitch is that

1\. I have projects available that can make them get money (and skills on their resume), since they (and I) really need it.

2\. Im ready to train them (as I'll be paying for their time as well), because although they are CS & IT Students, they've not been taught how to program good.

3\. Im willing to work on their schedule (since many of them are students)

4\. They can tell me what programming language or projects they'll like to learn (or interested in) and I'm willing to get what they want.

But still, it's almost as if everything I've done is a waste of time.

Is there something I don't know? Am I doing something wrong?

Am I crazy? They need money and I have a way of getting it for them, but yet no one wants to work. Like please, why is that?

<comment><USER2> Have you asked _them_ why? Maybe you're giving them too much choice.

<comment><AUTHOR> Oh okay. I'll check in on that ghubbard. I appreciate your response. Have A Good Sunday & A Great Weekend.</comment></comment></comment></comment>


A Return to Linux on the Workstation - <AUTHOR> http://cuddletech.com/blog/?p=770

<comment><USER2> Even a Joyent tech admits Ubuntu is a more pleasant experience. Thought this was interesting, but all his points are not isolated to the desktop experience. Pretty much all the reasons he switched to linux affect the building and maintenance of servers too. I think it just boils down to community support.

<comment><USER3> I will read the article later, when I have more time, but many geeks that only know Linux and eventually BSD, have no idea how painful commercial UNIX systems can be.

Several of our customers have HP-UX systems that look like plain System V systems straight out of the 70's.

<comment><USER4> That was my first response to Linux when I was first trying it out. Back in the mid 1990s.

At the userland level especially, it didn't suck, and (with the GNU userland, windowmanagers, etc.), in fact, sucked radically less than stock commercial Unices I'd been using at the time (Sun, HP, AT&T, Data General, BSD).

The situation's only gotten much, much better.

I'll occasionally find myself in situations where I'm connecting to commercial Unix boxes (was a semi-recent shop where a fair number of staff still ran CDE desktops), and, really, it's painful. Doable, but painful.

</comment></comment></comment><comment><USER5> After using Archlinux for a year I am back on Ubuntu 12.10. Ubuntu 12.10 is an absolute bliss. It is a complete hassle free desktop environment. If Ubuntu had support of hardware and software developers, it is definitely ready for some serious competition.

On the other hand, I am not so happy with Linux kernel which is being updated every 3 months yet it performs terribly when it comes to power management. There is a new power regression bug every few months.

<comment><USER6> Weird, I'm on Arch and it's no more hassle than Ubuntu. Do you use a custom WM/etc?

<comment><USER5> I have tried several WM from DWM, Awesome to XFCE4 and Gnome. There are things that Archlinux community doesn't want to do as a part of their philosophy. I am not saying it is wrong. I just like already set up distribution for now.

</comment></comment></comment><comment><USER7> Ahem, on the _desktop_. And the switch was from OSX.

I don't much care either way - I use OSX on my workstations and Ubuntu on my servers - but the title seems overhyped for such a simple article.

<comment><USER8> But at the end I didn't understand because he wrote "In the end, 3 days later I had been issued a replacement MacBook Pro which I got just as Mountain Lion released." So he went back right?

<comment><USER9> Right. He, like most of the tech world, is using a Mac on his desktop.

<comment><USER10> Proof of that?

</comment></comment></comment></comment><comment><USER11> I have installed Ubuntu Linux 4+ years ago, basically when its multimedia capabilities started to work in a fresh installation. I have never felt any necessity to come back to OS X/Windows since then.

<comment><USER12> Once Steam comes to Linux, I don't see many reasons why a lot of gamers won't switch over. Many of the people I who use Windows to develop in Python/Ruby/etc use either a VM or SSH into a Linux box, simply because they want to game. I think many people will not want to pay the Microsoft tax, especially for Windows 8.

<comment><USER13> The only games that will be on GNU/Linux, at least at first are Valve games and crappy indie flash games. While it's the first step, most developers aren't on board. Yet.

<comment><USER14> Most of the Humble Indie Bundle games seem to be available on Linux and I wouldn't call them crappy nor flash. But yeah, the big titles rarely work. Valve's work on porting their Source engine to Linux will certainly help this.

</comment></comment></comment></comment><comment><USER15> SmartOS looks pretty interesting (the thing he works on as mentioned in TFA). Do people have opinions about it?

</comment><comment><USER16> Headline is a cheap clickbait.

<comment><USER17> You appear to assume that obtaining clicks from HN readers through subterfuge is the author's goal.

I don't think that's borne out by the content of that post or of other posts. It's more likely that he's just writing for his regular audience, for whom the title expresses exactly what's happened: he's changed the operating system on "the Workstation" to Linux.

<comment><AUTHOR> I should apologize, I submitted the article with a different headline: "Joyent sysadmin switches to linux". I contemplated using Ben's full name but I thought better of assuming everyone knew who he was. My title was an attempt to summarize the article, which I will avoid doing in the future.

For those who do not follow his blog, Ben was a major OpenSolaris proponent and even served on the council before the Oracle buy out. I never thought I'd see the day he replaced a solaris machine with linux.

Now I should also mention that Ben thought Sun got distracted by focusing on OpenSolaris as a OS for developer workstations. Thus I think it would be inaccurate to call the OpenSolaris forks dead, I'm sure he still prefers Solaris on servers.

<comment><USER18> I actually prefer your headline because I think it gives context to the post. Joyent has been promoting Solaris in a world where _everyone_ else appears to be using Linux.

</comment></comment></comment></comment><comment><USER19> So... what's a 'workstation' these days? Every desktop machine has the 3M: (at least) a megapixel display, (at least) a megabyte of RAM, and (at least) a 1 MIPS processor, and that's in addition to little things like graphics cards and Ethernet (and/or WiFi) hardware, both of which were defining features at one point.

<http://en.wikipedia.org/wiki/3M_computer>

Does the definition of workstation come down to what the computer's used for at this point?

<comment><USER20> I'm torn apart whether this is a good or a silly question. On one hand it's an interesting nitpick on the change of labels / language, on the other hand it feels like Gordon E. Moore should poke you a bit with a stick or something.

In other words: Labels change, especially in IT. Duh?

<comment><USER19> That doesn't answer my question, which is: What has the label changed _to_?

<comment><USER20> I'd look at it like this: A workstation is what you use when computation power and/or memory bandwidth is a limiting factor of your work. This holds true for:

\- developers of software with long compilation time

\- 3d animators / professional video editors

\- scientists that want to run simulations

\- engineers that want to run simulations

For these people, a computer is never fast enough and they get a tangible benefit for every speedup - which is why they always have the best performing gear. And the (multicore) performance of the best gear is always [CONSUMERGRADE_PC * X].

I'd set X=3 and then you have yourself a workstation. (For example 16 core Xeon vs. 4 core i5.

For that reason I don't think it really makes sense to define workstation in an absolute number of Gigaflops/Hertz/Byte. It's just the cutting edge of desktop computing that's still feasible for work (e.g. overclocked CPUs to 8Ghz with liquid nitrogen cooling don't count, neither do machines with say more than 2kW heat - that's when you need a computing cluster).</comment></comment></comment></comment>


Ask HN: Remote work and benefits - <AUTHOR> Question for remote workers in the US.  For those who work for a company based in state A and live in state B, how does it work for your benefits?  Especially health coverage.  Different states have different requirements/laws/regulations.  Is it sometimes a problem if the health insurance company provided by your employer doesn't operate in your state of residence? <comment><USER2> My employer uses Trinet for payroll and health insurance. In theory it's a good idea, but for some reason they only offer terrible health plans in my state. It was so bad I opted to buy health insurance on the open market, despite it coming from post-taxed income.

</comment><comment><USER3> My employer does payroll and benefits through a 3rd party provider. This provider has a nexus in my state, which allows me to claim local taxes as well as get a healthcare plan that meets my state minimums as well as has providers in my area.

Technically I'm a dual employee of the 3rd party payroll company and my employer (My W-2s have the payroll provider listed).</comment>


The 'Terminator' of startups says he's seeing two to four wind-downs a week - <AUTHOR> https://techcrunch.com/2017/03/20/the-terminator-of-startups-says-hes-seeing-two-to-four-wind-downs-a-week/?ncid=rss <comment><USER2> Here's him making the same exact kinds of claims in 2011. He called himself (or claimed that people called him) the 'undertaker' back then: [https://www.pehub.com/2011/08/silicon- valley%E2%80%99s-under...](https://www.pehub.com/2011/08/silicon- valley%E2%80%99s-undertaker-%E2%80%98we%E2%80%99re-anticipating-a-major- fallout%E2%80%99/#)

<comment><USER3> "We are busy as beavers. We're doing two to four deals a week."

You weren't exaggerating.

<comment><USER4> "MP: We're seeing two to four companies wind-down a week, which we've never seen before."

...except for the last time we saw it.

</comment></comment></comment><comment><USER5> Looks like I'm getting a new chair soon. The best thing about the Dot Com bubble bursting was my cheap Aeron. I'm ready to upgrade.

<comment><USER6> There is an upgrade from an Aeron?

<comment><USER7> Embody, also by Herman Miller.

<comment><USER8> At $1500 for a chair, no wonder these startups are failing.

<comment><USER7> Something something it's literally impossible to hire engineers who aren't showered in toys and treats.

But yeah, I generally agree with you. I like my fancy chair because I spend an awful lot of time in it every day, and I'm tall (6'6"), so most chairs are pretty uncomfortable for me and my lower back already sucks.

<comment><USER9> there was an article a while back about how any type of exercise movement, weight lifting to running to walking would ease up lower back pain.

Ultimately comes down to strengthening those muscles. Sitting all day atrophies the muscles. I had lower back pain for the first 30 years of my life, then started deadlifting and the back pain is gone. g'luck

<comment><USER10> Of course, the most common injury when deadlifting form slips is probably lowerback related.

I still think everyone would benefit from some strength training! Any strenuous physical activity has risk from injury, but I think that risk is better to accept than the risk from doing nothing. But it's worth looking up some powerlifting videos on YouTube for good form and cues. (I've seen deadlifting videos from non-powerlifters, and I have not been impressed.) This is a good one: [https://www.youtube.com/watch?v=d5eGGZXb0Is](https://www.youtube.com/watch?v=d5eGGZXb0Is) Layne Norton is a smart, thoughtful guy and an elite powerlifter.

<comment><USER8> Layne Norton is smart and thoughtful. There are others as well. Basically I learned a million time more from these guys than I ever learned in all the yoga classes I ever took.

</comment></comment></comment></comment></comment></comment></comment></comment><comment><USER11> Key passage for me

>MP: We sell a lot of patents, maybe more than anyone else. Usually, the only way to pay back [lenders'] loans is to sell the patents. So people aren't paying what the company would have been worth but usually enough to pay back that secured debt.

So many companies saying they'll only use their patents defensively... may find that their patents will fall into the hands of someone who doesn't share their perspective.

<comment><USER12> Companies seldom care what happens after they're dead. Since we do care, we take their promises with a grain of salt.

</comment></comment><comment><USER13> If there isn't yet a _Silicon Valley_ episode featuring a thinly veiled expy of this gentleman, I can't imagine why not.

<comment><USER14> Yeah, one can imagine him coming into the house uninvited and starting to examine their equipment and various other assets.

<comment><USER15> Vampires can't enter a house uninvited.

<comment><USER8> Really, this guy is not a vampire. He's the grim reaper.

I worked for a startup that got shutdown. Don't like failure? Don't work in Silicon Valley. Anyways, Cisco (our probable exit strategy) leaned ever so slightly in the other direction and the VCs pulled our funding. But they also sent in a turnaround guy to reposition us for either the direction Cisco ever so slightly leaned in (soon to lean back) or something else.

Guy was the biggest asshole in the world. Moving the company to Microsoft Exchange is not a worthy accomplishment for a turnaround executive. Two weeks later, he shut us down for good but not before putting his 8 months pregnant wife on the insurance plan. This was a strange detail given that he was worth about $20M.

This guy had taken the job to shut us down. He then brought in a squadron of his buddies who shut us down properly and put a bow tie on it only after laying off everyone else. Basically, he sucked a lot of money out of the VCs. I occasionally Google'd him to figure out if he'd ever done anything afterwards of any merit. Nope.

 _That_ guy was a vampire.

<comment><USER16> This is why the word "suit" exists. There's smart people who contribute value and build companies, clever guys who make money skimming off the top off smart people via finance, and then there are suits who leach off the other two, with no redeeming value.

</comment></comment></comment></comment></comment><comment><USER17> Activity of restructuring firms is a good leading indicator of what's happening in private markets. These types of guys get involved well before public announcements of companies like Quixey. [0] It's also a very valuable service. Silicon Valley does well when people and assets from failing companies are freed up to join hyper-growth companies. Lenders are also more willing to take on startups as customers when they are confident that liquidators can get their money back.

[0] [http://www.bizjournals.com/sanjose/news/2017/03/09/quixey- al...](http://www.bizjournals.com/sanjose/news/2017/03/09/quixey-alibaba- funding-mountain-view-shutting-down.html)

<comment><USER18> All this needs is a "This is good for Bitcoin." and we've reached full success-fail.

<comment><USER19> It's actually a core and well supported part of capitalism - people hire more when firing is easy, lenders are more willing to lend in industries with repo men, etc. It's kind of morbid, but rich ecosystems like the Amazon mostly exist because dead stuff is rapidly recycled. The economy is no different: slowing down the cycle is incredibly distortionary.

<comment><USER18> Yeah, private equity is a natural response to business failing in the wake of a bubble, but bubbles themselves are market distortions caused by insane valuations.

Highlighting the value of reclamation just draws the heat off of the irrationality of the tech bubble. Amazon isn't a rich ecosystem, it's oligarchic.

And, indeed, any systemic failure in a capitalist economy gets explained away by saying that some cadre of vultures will exploit arbitrage until the gap closes. No one really questions the systemic failures per se. The economy happily creates this surplus in full anticipation of it being scrapped and fed to vultures. Banks repossess cars and capitalists think "working as intended" instead of "there's so many delinquent car loans that there's an industry of repo men and maybe that's significant." This criticism extends similarly to the tech bubble.

<comment><USER20> >Amazon isn't a rich ecosystem, it's oligarchic.

I think the gp meant [https://en.wikipedia.org/wiki/Amazon_rainforest](https://en.wikipedia.org/wiki/Amazon_rainforest)

<comment><USER18> _facepalm_ Thanks.

<comment><USER20> The context mixes the metaphor and the pun(intentional or not).

</comment></comment></comment></comment></comment></comment></comment><comment><USER21> I've been saying that since mid-2015 we've been seeing a slow down in the startup sector overall. Last year it was clear that new startup creations isn't keeping pace with previous years and shutdowns are more common. I think 2017 will be a disastrous year for startup formation and venture investment. SXSW this year was diminished from what it was in past years and it's clear that there's a pullback across the board.

<comment><USER22> I was at SXSW but didn't have as much time for Interactive sessions as in past years. Can you expound on your SXSW observation?

I did notice the trade show seemed dominated by the country pavillions like I don't remember before. It made me wonder if they were basically bussing in international startups to cover up for the general lack of startups that wanted to buy a booth.

<comment><USER21> First, from what I am hearing, there was overall lower number of badge sales for SXSWi. The sometimes large attendance at sessions was masked by the fact that SXSW now allows any ticket type entrance to various events, but only prioritizes certain badge holders.

Second, overall attendance at unofficial events was significantly lower from previous years. Previously you faced huge lines for most parties, but many such lines, if any, cleared quickly this year.

Third, investment by startups in their own official events was significantly lower this year. Startups either attended without running their own events or participated in other party's events instead of running their own events.

Fourth, many large brands decided not to participate this year. Microsoft and Samsung were both absent for the first time in a long time. And many other companies scaled back their events.

Fifth, as you mentioned, the exhibition hall was dominated by non-startup parties moreso than in years past. This indicates that startups are not investing in SXSW as much as they had before.

In general, attendance felt lighter this year vs. 2016, and definitely vs. 2015 and 2014, which felt like a peak year for SXSWi.

While these are SXSW-specific numbers, I would wager that you are seeing similar slowdown with other startup-focused events and conferences. Meetups are more lightly attended, and certainly less sponsored.

It feels like the burst in activity is slowing. Not gone. Just slowing.

<comment><USER23> At the same time, it's also reasonable to ask how much of it is just SXSW getting past its Sell By date. I don't really have an opinion one way or the other but a lot of events seem to go through a certain natural cycle even when the environment they're operating in remains basically healthy.

<comment><USER21> While I agree that perhaps SXSW has "jumped the shark", at least the interactive part, you'll find that enrollment and participation at many other startup-focused events is declined in 2017 year over year versus 2016. I would bet that Web Summit, CollisionConf, TechCrunch Disrupt, and others are not as full as they used to be or are facing similar challenges with startup numbers, and sponsorships especially.

<comment><USER23> The events I attend which tend to be in the cloud-native infrastructure and agile development spaces (DevOpsDays, kubecon, OpenStack Summit, OpenShift Commons, a lot of the Linux Foundation events) have been really well-attended over the past year or two. Selling out, doubling venue size and then still selling out next time. But, although a lot of startups are at these events (as are a lot of bigger companies), they're not really oriented toward the classic SV social/mobile/web startup crowd.

</comment></comment></comment></comment></comment></comment><comment><USER24> Here's an idea. Go talk to these guys when you're starting your company. These guys are seeing all the failures, so they should have a decent grasp of what NOT to do.

Discounting for the fact that they're only seeing failures.

<comment><USER25> There's some innocence in your comment which I wish I still appreciated. I think the grittier reality is that a lot of these startups were based on generally mediocre ideas, but were funded anyway because of relationships and contacts.

I cringe every time I see another "Come help us change the world at..." or "Come help us reinvent blah blah" here on HN. Instant red flag to me, but I'll be damned if they didn't get funded anyway.

<comment><USER26> I really wish I remember where I had heard it or maybe read it, but it was an investor's advice to new investors: "If the (startup) company comes in to the (investor) meeting with lots of color and excitement, turn him down. The guy that comes in and nearly bores you to sleep with his spreadsheets, he's the one to invest in."

<comment><USER25> Imagine how many good ideas may have been lost because those pitching them didn't have any connections? They don't even get the chance to be boring.

</comment></comment></comment></comment><comment><USER27> While this is certainly a click-worthy title, I'm much more interested in seeing whether the relative number of startup failures is increasing, rather than looking at this in absolute terms.

2015 was a banner year for startup funding, and you would expect many of those war-chests to be running low right around now.

</comment><comment><USER28> So where is the ping-pong index at?

[https://www.wsj.com/articles/is-the-tech-bubble-popping- ping...](https://www.wsj.com/articles/is-the-tech-bubble-popping-ping-pong- offers-an-answer-1462286089)

</comment><comment><USER29> Isn't this a very healthy statistic? In theory, not all companies are meant to last forever. Heck, no company should last forever.

Infact, I would argue that he should be doing 7-8 a week. Clearing out the deck and making way for the new is a good way we can maintain a long term healthy eco system.

Frankly, many iffy biz models that escape startup death aren't survivors but are just long term zombies that are propped up by investor stupidity with no clear path to sustainable growth.

</comment><comment><USER30> Alternative title: The _Chicken Little_ of startups sees a downturn coming every year for the last 25 years.

<comment><USER31> A broken clock says the right time twice a day. The whole point is _when_.

</comment></comment><comment><USER32> "Shut down 3 or 4 unicorns" \- wonder who they were - and I find it a bit funny that he doesn't seem to know the exact number. Or maybe he's just trying to be less specific on purpose.

<comment><USER33> Valuation is very much a vanity. You can inflate it in a variety of ways. By the time the companies get to him, I think the joke is that you can't tell whether it was actually a unicorn or not. If common stock is worth 0, nobody cares anyways.

</comment></comment><comment><USER34> The number of wind-downs a week is presented as interesting, but there's no indication how this represents the health of startups in general instead of his businesses ability to get customers.

<comment><USER35> In the title, no.

The indication (more than normal) is given in the response to the first question, from which the article's title is taken:

> MP: We're seeing two to four companies wind-down a week, which we've never > seen before. I think more [investors] are taking the Sequoia Capital > approach, meaning if something isn't working, they're moving on

edit: I think the parent was slightly edited but apologies if I simply misread.

<comment><USER36> The question should be is the rate of churn of startups going up.

One might interpret the facts as presented to indicate that startups are fashionable, and more people are starting them. A certain percentage are likely to fail.

<comment><USER37> This can happen BECAUSE economy is shit.

I am in Brazil, right now completely unemployed (technically I never was employed in first place, so I don't count as unemployed in statistics), and out of work.

So I am thinking of what business to start, this time... (I legally own 3 business, and I think I am heir of more 3 or 4) Because the ones I have none are profitable at all, and I can't find any work (not even supermarket cashier).

And I noticed, that on social sites (Facebook for example) that shows employment, lots of people are becoming business-people too, for example I noticed scores of 18 year olds that own some business selling cakes, or making clothes, or other stuff like that.

It is basically desperation, people can't find jobs, so they start their own business out of desperation, and of course most of these will fail anyway, from the smallest one (someone selling cupcakes from their garage) to the biggest ones (venture-backed stuff post-IPO...)

<comment><USER38> > _And I noticed, that on social sites (Facebook for example) that shows > employment, lots of people are becoming business-people too, for example I > noticed scores of 18 year olds that own some business selling cakes, or > making clothes, or other stuff like that._

I'm not sure how it is in Brazil, but in some parts of the US, there are many young adults 18-22 who start businesses like this. They either start right out of high school or drop out of college, create an LLC, then start a business. Once every so often, someone does well. They make it 5+ years before calling it quits for various reasons. Most of them fail right out of the gate.

The reality of, "Oh crap. This is my business. I can do whatever I want, but I am literally responsible for everything - including generating income" hits. They see that it's not so easy bootstrapping a photography business or cake business from scratch when there is no formal training in the area. No one wants photos that are washed out when a professional can do it for a few dollars more. No one wants a cake that can be replicated by spending 1/3 the price for a store bought cake mix.

Then they stop actually participating in the business, but their social media profile still says "Owner of Pop Flash Photography" or "Sole Proprietor of Wake n Bake Cakes".

One thing that some CS students in my area have done is to start an LLC once they've written something demoable. It doesn't have to be new or innovative (could be a simple calculator app that's on the app store), but if they can distribute it for use, they license it and publish it under the LLC. They make business cards for the LLC. Then they say things like, "I'm the Founder/Owner of Living Room Launched Software. We have a small app on the app store. Here's my card. Contact me if you need something."

This isn't to knock young people who have the entrepreneurial spirit! I think it's great for people to explore what they want to do in life, and it's better to fail fast straight out of HS than to blow their life savings in their mid-40s. Some kid fresh out of high school looking to get rich quick off of something easy is markedly different from the churn discussed in this article.

</comment></comment></comment></comment></comment><comment><USER39> "The price of consolidating the companies is too high."

I find this fascinating, as it really speaks to my experience in small companies. The thing you actually create -- code, docs, self-serve product, sales and marketing collateral -- isn't particularly valuable. Most of the knowledge is tribal knowledge, and you're all just spending time trying to expand that. It's amazing how far you can go, and have to go, with the informational equivalent of duct tape and string.

</comment><comment><USER40> This is a natural byproduct of the evolution of tech sector becoming THE world leader. We are no longer a small mammal outrunning giant reptiles, we are now the dominant force shaping the future of LIFE. This is a smart corrective measure to prevent a bubble. The best firms have learned that only scientific innovation, technical genius, and world-class leaders deserve capital for their ideas.

</comment><comment><USER41> > It's across the spectrum really, B-, C-, D-, E-stage companies. We've shut > down three or four unicorns.

Anyone know what these 3 / 4 "Unicorns" are?

Also, isn't the term "unicorn" use to refer to wildly successful startups that have very low chance of failing? This sounds like an oxymoron...

<comment><USER42> Unicorn typically refers to a startup that at one time had a valuation of over $1 billion.

</comment></comment><comment><USER43> I would much rather Mr Joyboy ("The Loved One", 1965) be my undertaker. [https://www.youtube.com/watch?v=-edybFkBiO4](https://www.youtube.com/watch?v=-edybFkBiO4) ... what class!

</comment><comment><USER44> This may be a good thing for new start-ups. If investors are not "throwing good money after bad" they may have more fuel in the tank for new bets. Each massive round in a unicorn can fuel ten series A rounds.

</comment><comment><USER45> tl;dr: watch your runway, pivot earlier, capture customers aggressively.

</comment><comment><USER46> where can I find a complete list of unicorns trading on the exchange? seems like a great year to short them all.

</comment><comment><USER47> What does it mean to be the terminator of startups? Do you find the Sean Connor of startups and try to kill him? ...Or save him?

<comment><USER48> They're definitionally private companies. So you can't. Would that one could...

<comment><USER48> Sorry, this belongs on brilliantcode's comment above. Mobile client and poor connection and fat fingers.</comment></comment></comment>


Ask HN: Do you learn more from Tutorials? - <AUTHOR>

Most tutorials tend to provide a step by step solution. But I feel like I would learn more if I had to figure things out on my own. Does anyone else feel the same way? <comment><USER2> For me, a good tutorial explains the "why" and not just the "how" or "what". So even though step by step tutorial can be useful, if they don't teach the why, I don't find them too effective.

Also about figuring things on your own, the point of any good tutorial is to give you directions. You still have to figure things out on your own. You always do. But a good tutorial can at least tell you where to look to figure a specific thing out.

</comment><comment><USER3> I' a pretty visual person myself. So I find that watching a video tutorial and then immediately trying to put it into practice myself seems to be a winning combo. But of course, everyone is different and some learn more effectively with other means (reading, audio, etc.).

</comment><comment><USER4> I arguably learn things more _thoroughly_ slogging through the docs. But only if it is important enough to justify the much higher initial effort, otherwise I don't learn them at all.

However, I learn more things faster from tutorials because they(assuming they're any good) give a high level overview and show how the pieces fit together to actually get something done. That is something a lot of official docs do poorly or not at all. And if it is a topic I'm actually interested in and use regularly, I'll eventually spend plenty of time with the official docs & learn it in detail.

</comment><comment><USER5> It really depends on what I am trying to do and the type of tutorial.

The more detailed the better and if it explains pit-falls as well it's nice.

But if you considering SO a "tutorial" or something like fixing small problems - then no I don't learn much it's mostly quick fixes and I'm off.

It comes down to the details and how much time an author puts into a post - Quality comes secondary (ie: authors primary spoken language)

</comment><comment><USER6> I learn from the kind if tutorials that are linked from HN, but if I google for a tutorial many have little useful information but lots of ads.

My own tutorials often focus on underlying principles rather than srep-by-step solutions. If the solution doesnt apply bevause the problem is different, say because a new release changed the UI of the software that the tutorial discusses, understanding the principles may enabke you to work out your own solution.</comment>


Red Hat Enterprise Linux 7.2 beta is out - <AUTHOR> http://www.zdnet.com/article/red-hat-enterprise-linux-7-2-beta-is-out/

<comment><USER2> Oh my. Red Hat 7.2. And my first Linux was Red Hat 7.2 :)

Who would have thought.</comment>


Automate your Ad Analytics Spreadsheets - <AUTHOR> http://spreadsheets.datanitro.com/

<comment><USER2> This is such a necessary service. I had a client where we banged our heads against the wall trying to automate this - we basically had to hire someone specifically to put these together every week.

</comment><comment><USER3> Looks interesting. Fighting with Google API can be a real PITA from my limited experience.

Can you guys help us figure out the kind of stats we _should_ be getting out of FB/Google? Also, would be nice if you can push stats to our graphite server. Is something like this (or maybe webhooks) possible?

<comment><USER4> We can help with both of those. Send me an email (firstname.lastname@example.com) and I'll get you set up.

</comment></comment><comment><USER5> I'm curious what the demand for these kinds of things are in the ad industry. I'm working on a major external reporting intake system/management/analysis platform for the publisher I work for that involves parsing reports/accessing APIs and aggregating the information into one place.

The big players (optimizers) seem to be doing about the same thing, but with their own value added features. I've literally seen no open source or independent platform do this though, and I'm not interested in paying for a vendor locked in solution. I've started working on a project on my own that does this, but I'm not really motivated by it because I don't know enough about the industry to see if its a problem for other publishers.

<comment><USER6> There is demand for this. However any reasonably large site is using way more than just these ad networks to power these sites. They would want to hook into several of the bigger ad exchanges (who in turn hook into hundreds of ad servers, networks, DSPs, etc) to get their data.

<comment><AUTHOR> Thanks for your comments. I would love to hear more about what you think of the space ryen - if you have a chance, can you please email me at email@example.com?

</comment></comment></comment><comment><USER7> In all honesty, I don't see who would use a service like this apart from very small time advertisers, but It'd be interesting in knowing how you piece together the reports, is it automated?

</comment><comment><USER8> Great idea for a startup! I've struggled with this in the past.

</comment><comment><USER9> This looks very straightforward. What API's do you support?

<comment><AUTHOR> We support Google AdWords, Google Analytics, Facebook Ads, Twitter Ads, and Bing Ads.

If you need to get data from somewhere else, just email us (email@example.com), and we'll figure this out for you.

</comment></comment><comment><USER10> does a client send you their entire adwords campaign?

<comment><USER4> No, only what's needed to update their model. We also don't use the data in any way other than to send updates - we don't even store it on our servers.</comment></comment>


Ask HN: Who do you use for donains/DNS? - <AUTHOR>

Any recommendations? I'm with Mediatemple right now, but I'd like to have my domains separate to my hosting. <comment><USER2> namecheap.com, best domain name registrar by lifehacker

<comment><USER3> +1. I've used network solutions and godaddy before, but namecheap has provided the best UI, customer service, and even price. Very tech friendly and very little bait-and-switch popup marketing.

</comment></comment><comment><USER4> I use godaddy, but I'm not impressed. Just haven't got around to transferring. I would recommend you go with someone else.

</comment><comment><USER5> I use FastHosts, had no issues and DNS records get updated pretty quickly whenever I make changes.

</comment><comment><USER6> I really like DNSimple.com. I've been a customer for over a year and highly recommend it.

</comment><comment><USER7> I've been using Moniker for years and their bulk domain management is solid.

</comment><comment><USER8> Hover.com and 10dollar.ca.

</comment><comment><USER9> gkg.net - Reasonably priced, and they support DNSSEC.

</comment><comment><USER10> dnsmadeeasy.com

</comment><comment><USER11> cloudflare.com and gandi.net

</comment><comment><USER12> PairNIC.</comment>


Ask HN: Fast vs. perfect, semantic front-end code for MVP? - <AUTHOR>

Just wondering where web designers/front-end developers draw the line between quickly prototyping code for their startup (perhaps using something like Twitter's Bootstrap framework), and having long-lasting, semantic code that won't need to be written in the short term?<p>This is my first startup and I'm finding it difficult to know when work is 'good enough' rather than obsessing on the details as is customary when freelancing. <comment><USER2> It's always "good enough", except when it isn't. :)

Seriously, though, startups -- and websites in general -- are continually moving targets. Your requirements are always changing, your user base and their needs are always changing.

You ever see the inside of a FedEx truck? Eeek. Focus on what your customers need, over-deliver on that, and keep the rest together as best you can.

You will learn how to manage your business properly with time, and you'll have time as long as your customers are happy.

</comment><comment><USER3> I'm not sure why you'd treat your own product any different from how you treat freelance work?

What if the freelance client was someone building an MVP? :)</comment>


Show HN: Walnut – Best videos from Reddit in last 24h - <AUTHOR> <comment><USER2> There is no link provided</comment>


Ballistic Missile Alert System Redesigned - <AUTHOR> https://medium.com/@fairpixelsco/redesigning-hawaiis-emergy-alert-interface-in-the-open-91c6318a7045?m <comment><USER2> Good points and prototype design. Things of such importance should be designed to help remove as much human error as possible.

For example, the big red button in modern data centers, they are purposely behind a somewhat weighted glass or plastic casing with clear instructions on it's purpose that has to be manually opened and lifted in order to be pushed. It is a button that cannot be accidentally pushed, unless there is no casing which would be a major design, engineering, management and implementation flaw just waiting to accidentally be pushed.

There should be extremely high standards of ease of use and good UX/UI any emergency system, it should remove the capability of accidentally being engaged in a live environment that goes out to the public. If that system is engaged then there should be no doubt it is a true emergency and the individual or individuals operating the system engaged it on purpose.

For testing, there should be a clear and informational way to know the system is in testing mode versus production mode. Many experienced engineers have seen what happens when test and production are not separated properly (junior engineer wipes out or causes a disruption in production by properly following a documented process that should have been updated to insure that production, test, staging and dev cannot talk/hurt each other).

It is good that this large issue is being addressed, and I hope the entire government takes steps to improve interface design to the emergency systems as when they are needed it should be extremely simple for the operator to insure they are not making any mistakes and should be very difficult for an accident to occur.

</comment><comment><USER3> Is it really true that something solely based in Hawaii can raise the alarm? I would think there would be several systems involved (e.g. NORAD). How is it that this could be the result of a bad interface and a "fat finger"? I know... malice vs. incompetence... but in this case the incompetence seems a bit too unlikely / too much. I think it is either incompetence at a larger scale, or it is a hack... but perhaps I am placing too much faith in our national missile defense systems.</comment>


Google server farms to be used in solving the Najdorf Sicilian defense - <AUTHOR> http://www.chessbase.com/newsdetail.asp?newsid=8047

<comment><USER2> The title is incorrect. It was an IBM cluster...</comment>


The Great American Bubble Machine (Goldman Sachs exposé) - <AUTHOR> http://sites.google.com/site/disclosuredelta/ <comment><USER2> This article has been submitted probably a dozen times, each time failing to spark a discussion. I assume in the context of what's going on with the SEC, it's more appealing now.

That being said, this article -- like most of Taibbi's -- is a well-written, entertaining narrative that sews together selective elements to construct something plausible. Go one step further by looking at the environments of each "bubble" to get a less filtered context -- what other companies were doing, what economic forecasters were saying, etc -- and it falls apart quickly.

Goldman Sachs is a company comprised of smart people. Sometimes elements of the company get involved in shaddy things. That happens everywhere -- finance, government, gumball manufacturers.

Interestingly, John Galbraith's chronicle of the crash of '29, did detail perhaps the most egregious of the young company's offenses. He also said: _Our political tradition sets great store by the generalized symbol of evil. This is the wrongdoer whose wrongdoing will be taken by the public to be the secret propensity of a whole community or class. We search avidly for such people, not so much because we wish to see them exposed and punished as individuals, but because we cherish the resulting political discomfort of their friends. To uncover an evil man among the friends of one's foes had long been a recognized method of advancing one's political fortunes. However, in recent times the technique has been greatly improved and refined by the added firmness with which the evil of the evildoer is now attributed to friends, acquaintances, and all who share his way of life._

</comment><comment><USER3> Dupe:

<http://searchyc.com/bubble+machine>

Incidentally, Megan McArdle's takedown of the article is more interesting than the article itself (and I actually like Taibbi, when he's not writing about finance):

[http://www.theatlantic.com/business/archive/2009/07/matt- tai...](http://www.theatlantic.com/business/archive/2009/07/matt-taibbi-gets- his-sarah-palin-on/21084/)

<comment><USER4> I think most people except those who are compromised by Wall St. influence consider Megan McArdle more of an apologist for Wall St. and laissez faire in general than as someone who did any sort of "takedown". Read closely, it's a pretty weak article, it's whole premise rests on criticisms of the delivery and attempts to bring reasonable doubt to a debate that has very little reasonable doubt about systemic abuse of public good faith by the big financial players.

<comment><USER3> I'm a liberal regs-enthusiast cap-D Democrat who's been reading McArdle since she landed at the Atlantic (so: awhile) and I just don't agree with your assessment of her. She's going to sound like an apologist because she's a UofC-trained conservative-leaning libertarian. That doesn't make her an actual apologist.

In fact, when you read the article, you probably saw that she concluded it by saying that GS deserves the populist outrage (she hasn't been talking down the recent SEC suit, either). I think it's hard to argue that she's criticizing Taibbi because of ideology.

No, her problem with Taibbi is that he doesn't appear to know what he's talking about. These aren't critiques on "delivery". Taibbi's article is premised on the idea that GS orchestrates calamities to profit from them. He appears to have drawn that conclusion from the fact that GS has survived many calamities. But McArdle appears to be correct: GS survives calamities because they don't go all-in the way firms like Lehman, Bear, and (it's alleged) Merrill did.

If GS is no worse than Lehman, Merrill, Morgan, or Citi, Taibbi's argument is damaged. If GS comes out ahead of them because it acted more carefully and less irresponsibly than those banks, Taibbi's argument is more or less destroyed. The fact that Taibbi doesn't seem to know the difference between a CDO and an IRS is amusing but besides the point.

</comment></comment></comment><comment><USER5> I posted this 5 days ago but no upvotes or comments. It might be the plaintext format that makes this version more appealing, because it is a large read.

</comment><comment><USER6> Definitive worth the read if economy is your cup of tea. Thanks for posting.</comment>


Testing OmniSci on web analytics workload - <AUTHOR> https://presentations.clickhouse.tech/original_website/benchmark.html#[%22100000000%22,[%22ClickHouse%22,%22OmniSci%22],[%220%22,%221%22,%222%22]] <comment><AUTHOR> Instructions, scripts and log can be found here: [https://github.com/ClickHouse/ClickHouse/tree/master/benchma...](https://github.com/ClickHouse/ClickHouse/tree/master/benchmark/omnisci)</comment>


Ask HN: Could reinforcement learning be used to create vaccines? - <AUTHOR> My understanding is that reinforcement learning can be used to train neural networks to become chess masters.<p>Would it be possible to pit a virus generator NN vs a vaccine generator NN to create vaccines? Seems like you could train a NN that could generate a vaccines to combat viruses that we haven't even seen yet. <comment><USER2> Anti-viral vaccines are not anti-viral drugs. The main problem is not figuring out an exploit in the virus biology that could be leveraged by a pharmacological agent to disrupt the viral life cycle. The main problem is

1\. finding a part or form of the virus that elicits a strong immune response, which

2\. does not cause illness on its own.

Optimization criteria 1. and 2. are in direct conflict with each other as our immune systems are pretty efficient at ignoring stuff that is foreign but not harmful (otherwise you would develop an immune reaction to all kinds of things which we would then call an allergic reaction). So you need to find the right balance, and what that balance is depends on the human response.

<comment><USER2> Now that does not mean that one could not think about automated ways of finding vaccine candidates, but instead of modelling (implicitly or explicitly) viruses and vaccines, you would need to model virus-derived particles and human immune responses (plus the effect of adjuvants, etc.).

<comment><AUTHOR> Good information, so we would have a Neural Net that takes a vaccine input and predicts the human (or animal) immune response, and another neural net that generates the vaccine inputs. I suppose the problem would be lack of labeled training data, which is why I was thinking about the chess example which works by simply knowing the rules of the game.</comment></comment></comment>