China growth fuelled by cheap debt rather than corporate profits - <AUTHOR> http://online.wsj.com/article/SB10001424052970204619004574319261337617196.html

<comment><USER2> In my dealing with China's economists, I have found them to be very well educated and conservative. Just what you'd want/expect from an economist. I also found the government leaders listen to them. Possibly more so than we have done in the U.S.

I doubt they have any choice at this point but to leverage debt to keep the economy fueled. Lets hope they find a better balance and know when to reign it it than the U.S. did. All said, its probably a good thing they are in the debt game as it keeps the playing field leveled.

They have reigned in the housing market a bit. For example, they keep adjusting the amount you have to put down on a home. What used to be 15% to 30% in some areas has gone to 30 or 50%. Yep, you could never buy a home in China for 5% down!

<comment><USER3> Here in Beijing it's been interesting to watch the effects of stimulus on the housing market. The government is promoting growth by giving lots of cash to larger organizations and entities, which in turn give them to a couple of people who quickly buy real estate. The result is that prices are rising and falling in large amounts quickly depending on what the government is doing. (On a side note, most of the Party members I've known have been involved in real estate, so I don't think it's accidental that there are systemic pressures to support the real estate market.)

That said, home ownership is still out of the reach of most of the common people.

I think the article is about dead wrong. China has a problem with its banking sector, And the stock markets are ridiculous since corporate profit is tied to real estate prices. But China still does not have a problem. The four big banks will be bailed out of their losses by the center (the last giveaway was 54 billion) and when you think of it this way, China isn't leveraging debt so much as spending credit to keep itself afloat.

The ignorance at the WSJ is hilarious. Who is keeping them in business?

<comment><USER4> "The ignorance at the WSJ is hilarious. Who is keeping them in business?"

Rupert Murdoch.

</comment></comment></comment><comment><USER5> What this article is saying is that in the future, I'm guessing 2 to 5 years, China could see a credit bubble and misguided investments.

If you're an economist this is obvious. And as the article states, the Chinese leadership knows it as well. They had been tightening credit before the recent downturn.

Therefore it is a very reasonable assumption that China will start tightening again in 2 to 5 years when the global economy recovers.

In other words, this article gives no new or insightful information.

<comment><USER6> You are correct that the author argues that restrictive government policies ameliorated some of the effects of very low interest rates. But on a close reading of the article, even this author, who seems to think government control of the economy is doing very well so far, concedes that there is already "misguided investment."

<comment><AUTHOR> Private Chinese firms are already complaining that they're being squeezed out by their state owned counterparts (who are getting the bulk of the bank financing and government stimulus funds): [http://www.chinastakes.com/2009/8/china-stimulus-plan- critic...](http://www.chinastakes.com/2009/8/china-stimulus-plan-criticized- for-crowding-out-private-sector.html)</comment></comment></comment>


Ask HN: Ethics and laws regarding scraping websites? - <AUTHOR>

What are the ethics and laws regarding scraping internet sites?
For one, we have robot.txt: http://en.wikipedia.org/wiki/Robot.txt
Is that the only thing that prevents(or prohibits) a robot/spider from scraping a site?
Can there be copyrighted material that is not allowed to be scraped?
I ask because I want to search/scrape a few hundred pages with similar content and present those results all the way to the buying of the product. I.e. not just like if you google you get a page that has the information. I want to present them with the options(maybe in a ranked order) where  one click is needed to choose the product to buy, pretty much. Or one to search, one to choose and get to the productsite where you obviously have to do some more confirmation but you get the point... <comment><USER2> I think this is a very grey area. If it's on the internet it's scrapable. Does that mean you can use it? You really need a lawyer.

Take for example Amazon reviews. They are there to be scraped but some of them are not available via the web service API due to copyright restrictions. That's a clear sign to me that they don't wouldn't want this information scraped. I'm not sure how a judge would see it though.

Another example is is licensed material. Take TV listings. They are listed all over the place but they are still under copyright so you can't just scrape them and use them on your site.

IANAL but my basic rule is that I scrape if I think I am going to offer the scraped sight something in return (usually traffic). So if it's mutually beneficial I feel OK. That doesn't mean it's legal though :(

<comment><USER3> Only the layout of the TV listing information is copyrighted. You can't copyright facts.

<comment><USER2> This seems reasonable. However I seem to remember that the actual listings in the UK are 'owned' by some central authority (I forget who but it's got something to do with the Radio Times) and you license the right to publish them.

While it's a fact that a show is on it's also content owned by some authority. This is where lawyers make their money.

<comment><USER3> My apologies, the copyright laws of the UK are quite different than those of the US. I don't know whether or not facts can be copyrighted there.

</comment></comment></comment></comment><comment><USER4> So, copyright law doesn't care whether you scraped the data or acquired in some other way. That is, either you can use the information under copyright law, or you can't. If you can't use it, then getting it through some method other than scraping won't help you -- and if you can use it, then scraping won't change the legality.

So you need to make sure there isn't a copyright violation, which is going to depend on the specific information you're looking at.

There's still a potential problem, though, unrelated to copyright. After eBay v. Bidder's Edge, it can be trespass to chattels to scrape data in violation of a site's TOS. In the eBay case, the court held that it was in violation of trespass law because the eBay TOS prohibited robots... so it would have been fine if Bidder's Edge had taken the data manually.

Basically, you need to make sure the data isn't copyrighted, and you need to make sure that scraping the data isn't in violation of the site's TOS.

</comment><comment><USER5> Search engines all scrape... Titles, meta data, and some or all of the content.

I think a good rule of thumb is to consider whether the scraping target will benefit from being scraped. Most sites are delighted to be scraped by Google. Will your scraping drive sales/visitors to the target? Or will it cost sales/visitors? Will you link back (which helps them from an seo standpoint)?

<comment><USER6> Search engines do not all scrape.

They scrape if the owners have indicated they wish their site to be scraped by not forbidding access via the robots.txt file.

<comment><USER5> Um, so yeah-- they all scrape-- like I said.

But no, they don't scrape all sites.

</comment></comment></comment><comment> a-priori Ethically, I see no problem is doing any scraping as long as you credit the source and obey the robot.txt file. I figure, they were the ones who made the information freely available on the web.

Legally, however, it's a whole different beast that I'm not qualified to talk about.

</comment><comment><USER7> You should look into the DMCA safe harbor provisions for Internet Service Providers. The basic idea behind them is that, if you comply with certain requirements (establish a DMCA officer, respond to takedown notices, etc.), then your service will be immune from many sorts of copyright infringement claims.

This is not legal advice, and it's not even a very good picture of the DMCA safe harbors, but hopefully it's enough to point you in the right direction. The Electronic Frontier Foundation has good resources on this, for example:

<http://w2.eff.org/bloggers/lg/faq-ip.php>

</comment><comment><USER8> IANAL, but...

To reuse copyrighted content, you have to consider fair use eligibility and such -- but fortunately, not all data is locked down by copyright.

One of my favorite ten USSC rulings:

[http://en.wikipedia.org/wiki/Feist_Publications_v._Rural_Tel...](http://en.wikipedia.org/wiki/Feist_Publications_v._Rural_Telephone_Service)

""" It is a long-standing principle of United States copyright law that "information" is not copyrightable, O'Connor notes, but "collections" of information can be. Rural claimed a collection copyright in its directory. The court clarified that the intent of copyright law was not, as claimed by Rural and some lower courts, to reward the efforts of persons collecting information, but rather "to promote the Progress of Science and useful Arts" (U.S. Const. 1.8.8), that is, to encourage creative expression. Since facts are purely copied from the world around us, O'Connor concludes, "the sine qua non of copyright is originality". However, the standard for creativity is extremely low. It need not be novel, rather it only needs to possess a "spark" or "minimal degree" of creativity to be protected by copyright. ... In the late 1990s, Congress attempted to pass laws which would protect collections of data, but these measures failed. By contrast, the European Union has a sui generis (specific to that type of work) intellectual property protection for collections of data."""

<comment><USER9> It is rumoured that some encyclopedias give biographies of ficticious people to enforce collective copyright. This wouldn't concern you if wanted information about a historical figure. However, it would be very problematic if you worked for a rival encyclopedia or wanted to make your own website of the encyclopedia.

<comment><USER10> Dictionaries have long done the same thing with regard to definitions.

<http://en.wikipedia.org/wiki/Copyright_trap>

<comment><USER9> I didn't know that dictionaries also do it. Nor did I know that fake entries were also known as mountweazels (after the surname of a ficticious entry) or nihilartikels. How very cromulent ( <http://en.wikipedia.org/wiki/Cromulent> ).

</comment></comment></comment></comment><comment><USER11> Definitely check the TOS of the sites you plan to scrape. Some data, in particular factual data, isn't copyrightable, but that doesn't necessarily give you the rights to automatically collect it from any place you can find it. Businesses spend a lot of money and time collecting and hosting data, so they can get pretty particular about how you're allowed to use their service.

Here's an excerpt from the yellowpages.com TOS. Without it, their company names, addresses, and phone numbers would be more or less fair game for any competitor:

"You are prohibited from data mining, scraping, crawling, or using any process or processes that send automated queries to the YELLOWPAGES.COM Web site. You may not use the YELLOWPAGES.COM Web sites to compile a collection of listings, including a competing listing product or service."

</comment><comment><USER12> Depends on what and how you intend to use the scraped data for. I'd recommend you also check the TOS of the site you intend to scrape. Many sites explicitly say how their data can be used and how you should scrape it. Some sites also ban IPs that are scraping them (they assume it's a DoS type of an attack.

<comment><USER13> you should have a sleep between requests to avoid overloading their servers.

i don't know how much delay is best. does anyone else know?

<comment><USER14> I think the rule of thumb is roughly 1 request every 2 seconds.

</comment></comment></comment><comment><USER15> If the websites you scrape don't want you to do so, they will update their robots.txt and ban you. If you don't respect that, they will ban your IP and/or take legal action.

So just try not to piss them off.

<comment><USER16> If the websites don't want you to scrape, use proxies and botnets!

</comment></comment><comment><USER17> You just need to respect copyright. So if they explicitly allow you to scrape the data, or if you're operating within the "Fair Use" doctrine, then you're fine.

(IANAL)

</comment><comment><USER14> Apart from the copyright issues, if you're going to scrape or crawl someone's site.. it's best to be polite about it.

I wrote a web crawler a few years back and at the time I didn't really understand the implications of having a crawler grab 20+ pages concurrently from a site.

I learned pretty fast when I found a few sites had banned my crawler.. Oops, sorry guys!

</comment><comment><USER18> This might be informative: [http://blog.screen- scraper.com/2008/04/21/screening-scraping...](http://blog.screen- scraper.com/2008/04/21/screening-scraping-ethics/)

</comment><comment><AUTHOR> but if we take google for an example. they give the serached sites something in return, they make the easy to find. but how does the google spider know that it can index the site? it checks for robots.txt obv but does it check for Copyright?

<http://en.wikipedia.org/wiki/Fair_Use> fair user seems to be only in the US, i potentially want to reahc the whole world even if the USA is a good place to start.</comment>


Decline in readers, ads leads hundreds of newspapers to fold - <AUTHOR> https://apnews.com/0c59cf4a09114238af55fe18e32bc454 <comment><AUTHOR> As I commented at other thread yesterday, because your apps and your phone know who you are and what is your home town, they deliver targeted ads. Who needs a local newspaper if Candy Crush delivers the ad for a local business? Your local newspaper competes with Candy Crush for ad revenues. All thanks to surveillance capitalism business model of Google etc. As simple as that...</comment>


Linux and open source developer tools you should not overlook - <AUTHOR> http://blogs.techrepublic.com.com/10things/?p=579

<comment><USER2>
        GDB isn't really a developer tool,

How is gdb "not really a dev tool"? The article is just a bad list of IDE's and html editors with gcc,make and gdb mixed in, not very useful or insightful. I was hopeing to learn about some cool unix tools and stuff. GCC, make and gdb are cool, but im sure there are better articles about them.</comment>


Call My Red Phone - A simple cheap business line with flexible options - <AUTHOR> http://www.callmyredphone.com

<comment><USER2> I'm assuming you're using Twilio for this (if not, please correct me). How can you charge 2c a minute when it costs more than this from Twilio if the user is doing forwarding? Do you just hope that the monthly charge makes up for the deficit?

<comment><AUTHOR> Yes, we use Twilio.

Our goal to start is to see how people are using the application in terms of minutes and call styles. After that, we'll probably tweak the plans to allow plans to have minutes built in. We figure most customers won't use the lines heavily as a main point of contact (like a Startup who uses us as a contact us line). This way we can build minutes into the plans and still turn a profit.

</comment></comment><comment><AUTHOR> And, for what its worth, we launched toll-free phone numbers this morning.</comment>


The woman who accidentally ticked: 'I am a terrorist' - <AUTHOR> https://www.bbc.co.uk/news/uk-scotland-45678517 <comment><USER2> Because a terrorist would actually answer yes on such a Form. /s

Same goes for the old form that asks if you were part of the nazi party in 1939-1945 even thought you were born much later.

These forms are such bullshit and are only here to give authorities leverage against you at a later date incase you did purger yourself. Then they have it in writing that you lied.

<comment><USER3> > These forms are such bullshit and are only here to give authorities leverage > against you at a later date incase you did purger yourself. Then they have > it in writing that you lied.

They're not bullshit. The forms' design is what gives the government unambiguous grounds to revoke the citizenship of foreign war criminals, once their crimes are discovered. The fact that they lied during their naturalization means it was _never_ actually valid.

<comment><USER4> >The forms' design is what gives the government unambiguous grounds to revoke the citizenship of foreign war criminals, once their crimes are discovered.

No, they are what makes the person (even if they never apply for citizenship; the form has nothing to do with citizenship applications) unquestionably a criminal under US law if they've ever done the thing asked about in the question and denied it on the form, because US law doesn't unconditionally exert universal jurisdiction over all instances of the international crimes identified, but it does over lying to the US government.

</comment></comment></comment><comment><USER5> I recently (as of about 5 weeks ago) stopped working in gun sales. (Relevance in a moment.).

While I think this case is absolutely outrageous...

There's a form called the 4473 that you fill out when you purchase a firearm in the US. (Among other forms depending on state ).

This form has a bank of questions such as "are you the actual purchaser of the firearm" to "are you an addict and illegal user of drugs". And while you would think no one would answer "yes" to the wrong questions, people do.

I will also say, I wish literacy (is that the right word here, it's very early in the morning where I am at ) was a requirement. A frequent question I would get for one question, which read "have you ever renounced your US citizenship" would be variations of "is this asking me if I'm a Mexican?" Or "re-no-unsed, is that like a place?"

Though perhaps I need to be less judge mental. I denied a lot of sales.

</comment><comment><USER6> This is an excellent example of one of the three pillars of classic test theory (the basis of which such forms _should_ be based), validity:

Is this parameter measuring what I want to measure?

This is an examplariously hilarious case of absolutely not. Saved.

</comment><comment><USER7> Note that 'terrorist' is a larger group of people than some might imagine. My favorite example is the "Animal Liberation Front", a group of militant self- proclaimed animal lovers that, after burning down enough buildings, got on the FBI's "domestic terrorist" list.

So if you are member of the ALF, you'd have to tick this box; Otherwise it'll be another item on the list you can get arrested for.

[1] is a list of organizations on the US' 'domestic terrorism' list.

[1] [https://en.wikipedia.org/wiki/Domestic_terrorism_in_the_Unit...](https://en.wikipedia.org/wiki/Domestic_terrorism_in_the_United_States#Terrorist_organizations)

</comment><comment><USER8>
        [ ] Yes, I am a psychopathic axe murderer looking for my next victim.

        [ ] Yes, I am avoiding taxes that would make the Panama Papers blush.

        [ ] Yes, I am a going to Bermuda to marry my underage girlfriend.

        [ ] Yes, I am a terrorist, the bombs are at home.

_I dunno which to pick, maybe all but the girlfriend one. Which did you choose, deary?_

</comment><comment><USER9> This was without a doubt my worst fear when filling out the visa application form. I went back and rechecked five times, just to ensure I didn't actually declare myself a terrorist.

Now, I know what happens.

</comment><comment><USER10> Can you imagine if your browser or some add-on decided you could auto-fill info. on a form like that, and inappropriately checked the box or filled the radio button or whatever?

<comment><USER8> Add AI, automation and thought UIs... thoughtcrime is upon us.

Moreover, imagine illicit drones that have weapons or explosives that operate with AI and thought-control... you think about killing your boss, but you forgot to think "you didn't mean it" that the drone shouldn't do it. Whoops.</comment></comment>


How to implement a online store? - <AUTHOR> Which technologies, tools and programming languages would you use?<p>This seems to be quite common interview question and i would like to know how would HN people answer to this! <comment><USER2> With counter questions of course: number users, number of product, what backoffice (whitelabel shipping?) already exists. I18n (languages, currencies, shipping providers, payment providers). Level of customization and editorial control needed etc etc.

</comment><comment><USER3> I'd say if it comes up in an interview you should answer it how _you 'd_ answer it. That'll save a lot of time.

<comment><AUTHOR> Sure, but this time i'm interested what would other people answer</comment></comment>


Deep learning job postings have collapsed in the past six months - <AUTHOR> https://twitter.com/fchollet/status/1300137812872765440 <comment><USER2> I've worked in lots of big corps as a consultant. Every one raced to harness the power of "big data" ~7 years ago. They couldn't hire or spend money fast enough. And for their investment they (mostly) got nothing. The few that managed to bludgeon their map/reduce clusters in to submission and get actionable insights discovered... they paid more to get those insights than they were worth!

I think this same thing is happening with ML. It was a hiring bonanza. Every big corp wanted to get an ML/AI strategy in place. They were forcing ML in to places it didn't (and may never) belong. This "recession" is mostly COVID related I think - but companies will discover that ML is (for the vast majority) a shiny object with no discernible ROI. Like Big Data, I think we'll see a few companies execute well and actually get some value, while most will just jump to the next shiny thing in a year or two.

<comment><USER3> "Like Big Data, I think we'll see a few companies execute well and actually get some value, while most will just jump to the next shiny thing in a year or two."

Here's another aspect - in many places nobody listens to the actual people doing the work. In my last job I was hired to lead a Data Science team and to help the company get value of Stats/ML/AI/DL/Buzzword. And I (and my team) were promptly overridden on every decision of what projects an expectations were realistic and what were not. I left, as did everybody else that reported to me, and we were replaced by people who would make really good BS slides that showed what upper management wanted to see. A year after that the whole initiative was cancelled.

Back in 2000 I was in a similar position with a small company jumping on the internet as their next business model. Lots of nonsense and one horrible web based business later, the company failed.

It's the same story over and over again. Some winners, lot of losers, many by self-inflicted wounds.

<comment><USER4> If you think about it, that's the natural outcome. Why? Because people in corporations don't have the incentive to benefit the business but to progress their careers and that's done through meeting the goals for their position and make their upper ups progress with their careers too.

So essentially, you have a system where people spend other people's resources for living and their success is judged by making the chain link above happy. In especially large companies it's easy to have a disconnect from the product because people in the top specialise in topics that have nothing to do with the product. If the people at the top want to have this shiny new thing that the press and everyone else is saying that it's the next big thing, you better give them the new shiny thing if you want to have a smooth career. In publicly traded companies, this is even more prevalent because people who buy and sell the stocks would be even more disconnected from the product and tied to the buzzwords.

The more technical minded people who have the hunch on tech miss the point of the organisation that they are in and get very frustrated. It's probably the reason why startups can be much more fulfilling for deeply technical people.

<comment><USER3> >If you think about it, that's the natural outcome. Why? Because people in corporations don't have the incentive to benefit the business but to progress their careers and that's done through meeting the goals for their position and make their upper ups progress with their careers too.

This is one of the reasons I roll my eyes whenever I read something like "McKinsey says 75% of Big Data/AI/Buzzword projects do not deliver any value." What's the baseline for failing and/or delivering zero value because those projects were destined to fail?

<comment><USER5> > because of silly management decisions?

The whole point is, from their point of view those decisions are rational. It's much more lucrative from their (managers') personal point of view to develop a smokes-and-mirrors looks-good-on-ppt AI project. To be safe from risk, don't give the AI people too much responsibility, let them "do stuff", who cares, the point is we can now say we are an AI-driven company on the brochures, and we have something to report up to upper management. When they ask "are we also doing this deep learning thing? It's important nowadays!" we say "Of course, we have a team working on it, here's a PPT!". An actual AI project would have much bigger risks and uncertainty. I as a manager may be blamed for messing up real company processes if we actually rely on the AI. If it's just there but doesn't actually do anything, it's a net win for me.

Note how this is not how things run when there are real goals that can be immediately improved through ML/AI and it shows up immediately on the bottom line, like ad and recommendation optimizations in Youtube or Netflix or core product value like at Tesla etc.

The bullshit powerpoint AI with frustrated and confused engineers happens in companies where the connection is less direct and everyone only has a nebulous idea of what they would even want out of the AI system (extract valuable business knowledge!).

<comment><USER6> I think the problem a lot of places has been wanting "appealing" ML/AI solutions. The kind you write papers about and put on Powerpoints.

The useful AI/ML isn't glamorous, it's quite boring and ugly. Things like spam detection, image labeling, event parsing, text classification.

It's hard to get a big, shiny model into direct user facing systems.

<comment><USER5> What would you categorize as shiny in this case? "spam detection, image labeling, event parsing, text classification" can be implemented in lots of ways, simple and shiny as well.

Either way I don't think it matters too much because people can't really tell simple from shiny as long as the buzzword bullet points are there.

The point is rather that the job of the data science team is to deliver prestige to the manager, not to deliver data science solutions to actual practical problems. It's enough if they work on toy data and show "promising results" and can have percentages, impressive serious charts and numbers on the powerpoint slides.

I've heard from many data scientists in such situations that they don't get any input on what they should actually do, so they make up their own questions and own tasks to model, which often has nothing to do with actual business value, but they toy around with their models, produce accuracy percentages and that's enough.

</comment></comment></comment></comment></comment></comment></comment><comment><USER7> According to data from Revealera.com, if you normalize the data, the % of job openings that mention 'deep learning' has actually remained stable YoY: [https://i.imgur.com/sDoKwD0.png](https://i.imgur.com/sDoKwD0.png)

* Revealera.com crawls job openings from over 10,000 company websites and analyzes them for technology trends for hedge funds.

<comment><USER8> I think the fact the original tweet was not normalized in this incredibly obvious way is at least one valid reason companies could use less deep learning folk

<comment><USER9> The original tweet is one of the authors of TensorFlow (specifically Keras, a large section of the API for v2), if it's any indication of the quality of the framework.

</comment></comment></comment><comment><USER10> Something I've learned: when non-engineers ask for an AI or ML implementation, they almost certainly don't understand the difference between that and an "algorithmic" solution.

If you solve "trending products" by building a SQL statement that e.g. selects items with the largest increase of purchases this month in comparison to the same month a year ago, that's still "AI" to them.

Knowing this can save you a lot of wasted time.

<comment><USER11> Any sufficiently misunderstood algorithm is indistinguishable from AI.

<comment><USER12> In my AI class in college, we learned about first order logic. To me it didn't seem like we were really learning AI but I couldn't quite put my finger on it. I guess it's because it made too much sense so in my mind it couldn't be AI.

<comment><USER13> This is basically a form of the AI effect[1]:

> The AI effect occurs when onlookers discount the behavior of an artificial > intelligence program by arguing that it is not real intelligence.

[1]: [https://en.wikipedia.org/wiki/AI_effect](https://en.wikipedia.org/wiki/AI_effect)

<comment><USER14> Ah yes, the AI version of the no true scotsman fallacy

</comment></comment></comment></comment></comment><comment><USER15> Most data-related problems, or extraction of knowledge from data, simply doesn't benefit from Deep Learning.

In my experience, what many organizations lack is simple but high-quality "Business Analytics": Reporting & dashboards are developed that look good but jam too much information together. It is often the wrong information:

Something is requested, and the developer develops exactly what was asked. The problem is that it wasn't what was _needed_ because the person making the request couldn't articulate the question in the same terms the developer would understand. The request will say "Give me X & Y" when the real question is "I want to understand the impact of Y on X". The person gets X & Y, looks at it every day in their dashboard, and never sees much that is useful. The initial request should always be the start of a conversation, but that often doesn't happen. A common result are people in departments spending tons of time in Excel sorting, counting, making pivot tables, etc., when all of that could be automated.

This is part of the reason why companies often go looking for some new "silver bullet" to solve their data problems. They don't have the basics down, and don't understand the data problems well enough to seek out a solution.

<comment><USER16> I think we're starting to see peak managerialism. The latest wave in stats has shown more than anything that a significant shortfall in basic statistics knowledge makes it almost impossible to make good decisions with vast amounts of data.

Without the skillsets to work with and then understand that data, they are forced into this long process of asking for data to be put into reporting and dashboards and then once they finally get them, either fixating on the limited metrics it provides while being oblivious to other context not in front of them, or to instead forced to start another long iteration to adjust that reporting and dashboards.

We've gone almost 30 years believing management was the sole skill required to manage teams and companies, but dealing with the new era of data is starting to show the limits

</comment></comment><comment><USER17> I observe the state of the art on most Nlp tasks since many years: In 2018,2019 there was huge progress made each year on most tasks. 2020,except for a few tasks have mostly stagnated... NLP accuracy is generally not production ready but the pace of progress was quick enough to have huge hopes. The root cause of the evil is: Nobody has build upon the state of the art pre trained language: XLnet while there are hundreds of declinaisons of BERTs. Just because of Google being behind it, if XLnet was owned by Google 2020 would have been different. I also believe that pre trained language have reached a plateau and we need new original ideas such as bringing variational autoencoder to Nlp and using metaoptimizers such as Ranger.

The most pathetic one is that: Many major Nlp tasks have old SOTA in BERT just because nobody cared of _using_ (not improving) XLnet on them which is absolute shame, I mean on many major tasks we could trivially win many percents of accuracy but nobody qualified bothered to do it,where goes the money then? To many NIH papers I guess.

There's also not enough synergies, there are many interesting ideas that just needs to be combined and I think there's not enough funding for that, it's not exciting enough...

I pray for 2021 to be a better year for AI, otherwise it will show evidence for a new AI progress winter

<comment><USER18> I do not agree with this. I work heavily with NLP models for production in the Legal domain (where my baseline is where a 8GB 1080 must predict more than 1000 words/sec). This year was when our team glued enough pieces of Deep Learning to outperform our previous statistic/old ML pipeline that was been optimized for years.

Little things compound such as optimizers ( Ranger/Adahessian), better RNN ( IndRNN, Linear Transformers, Hopfield networks ) and techniques (cache everywhere, Torch script,gradient accumulation training)

<comment><USER19> Interesting. What's the main goal(s) of your NLP models?

<comment><USER18> We work on multiple models, all related to legal proceedings and lawsuits, such as: \- Structure Judicial Federal Register texts \- Identify entities in Legal texts (citation to laws, other lawsuits) \- Predict time to completion, risk and amount due of a lawsuit \- Classifying judicial proceedings to non lawyers

<comment><USER20> How accurate is your prediction of time/risk/amount? How useful is identifying entities or classifying proceedings?

</comment></comment></comment></comment></comment><comment><USER21> While this sounds plausible and has a lot of "prior" credibility coming from someone as central to deep learning as François Chollet, I'd love to see corroborating signal in actual job-posting data, from LinkedIn, Indeed, GlassDoor, etc. Backing up this kind of claim with data is especially important given the fact that the pandemic is disrupting all job sectors to varying degrees.

As you can imagine, searching Google for "linkedin job posting data" doesn't work so great. The closest supporting data I could find is this July report on the blog of a recruiting firm named Burtch Works [1]. They searched LinkedIn daily for data scientist job postings (so not specifically deep learning) and observed that the number of postings crashed between late March and early May to 40% of their March value, and have held steady up to mid-June, where the report data period ends.

There's also this Glassdoor Economic Research report [2], which seems to draw heavily from US Bureau of Labor Statistics data available in interactive charts [3]. The most relevant bit in there is that the "information" sector (which includes their definitions of "tech" and "media") has not yet started an upward recovery in job postings, as of July.

[1] [https://www.burtchworks.com/2020/06/16/linkedin-data- scienti...](https://www.burtchworks.com/2020/06/16/linkedin-data-scientist- job-postings-stabilizing-is-recovery-around-the-corner/)

[2] [https://www.glassdoor.com/research/july-2020-bls-jobs- report...](https://www.glassdoor.com/research/july-2020-bls-jobs-report/)

[3] [https://www.bls.gov/charts/employment- situation/employment-l...](https://www.bls.gov/charts/employment- situation/employment-levels-by-industry.htm)

<comment><USER22> Here are some data points from March. [https://towardsdatascience.com/whats- happened-to-the-data-sc...](https://towardsdatascience.com/whats-happened-to- the-data-science-job-market-in-the-past-month-88c748a4cd25)

<comment><USER21> I actually found this, but decided not to post it because it only captures the first few weeks of post-crisis patterns, and doesn't contextualize any of the deep-learning-specific job losses against the broader job market, which as we all know was doing the same thing, directionally. It would be really cool to get an updated report of that level of detail from the author, who seems active on Twitter ([https://twitter.com/neutronsneurons](https://twitter.com/neutronsneurons)), but not Medium: that April job report is his latest article.

</comment></comment></comment><comment><USER23> I feel like it was also a classic case of running before we could crawl. Jumping from A to Z before we could go from 0 to 1.

I work at an Residential IoT company, there are quite a few really valid use cases for Big Data and even ML. (Think about predictive failure).

We hired more than one expensive data scientist in the past few years, and had big strategies more than once. But at the end of the day it's still "hard" to ask a question such as "if I give you a MAC Address give me the runtime for the last 6 months".

We're trying to shoot for the moon, when all I've ever asked is I want an API to show me indoor temp for particular device over a long period.

<comment><USER24> Everyone wants to fire up Tensorflow, Keras and PyTorch these days. Fewer people want to work in Airflow and SSIS, spend days tuning ETL, etc. This is the domain of data engineering, which bridges software engineering and data science with a dash of devops. I've been working in this field for a couple of years and it's clear to me that data engineering is a necessary foundation and impact multiplier for data science.

<comment><USER25> Don't forget data cleaning. A huge issue I've seen is just getting sufficient data of a high enough quality.

Also, (for supervised classification problems) labelling is a big problem.

It is almost as if we need a "data janitor" title.

<comment><USER26> Phht you don't want to call it data janitor; no-one good will want that title. At least call it Data Integrity Engineer or something reasonably high-status.

<comment><USER27> At my company we have just created a position called Data Steward.

</comment></comment></comment></comment></comment><comment><USER28> Meh, only for people who bought into the hype without real use cases. Which I agree may be numerous.

In my company though, we've been applying DL with great success for a few years now, and there are at least five years of work remaining. And that's not spending any time doing research or anything fancy: just picking the low- hanging fruit.

<comment><USER29> I think many companies have real problems, but find that DL ends up being a poor solution in practice for various reasons.

You need not only real use cases, but use cases that happens to well with DL's trade offs and limitations. I think many companies hired with very unrealistic expectations here.

</comment></comment><comment><USER30> I managing some teams right now that do a mix of high-end ML stuff with more prosaic solutions. The ML team is smart, and pretty fast with what they do, but they tend to (as many comments here have mentioned) focus on delivering only PhD level work. This translates into taking simple problems and trying to deorbit the ISS through a wormhole on it rather than just getting something in place that answers the problem.

In conjunction with this, it turns out 99% of the problems the customer is facing, despite their belief to the contrary, aren't solved best with ML, but with good old fashioned engineering.

In cases where the problem can be approached either way, the ML approach typically takes much longer, is much harder to accomplish, has more engineering challenges to get it into production, and the early ramp-up stages around data collecting, cleaning and labeling are often almost impossible to surmount.

All that being said, there are some things that are only really solvable with some ML techniques, and that's where the discipline shines.

One final challenge is that a lot of data scientists and ML people seem to think that if it's not being solved using a standard ML or DL algorithm then it _isn 't_ ML, even if it has all of the characteristics of being one. The gatekeeping in the field is horrendous and I suspect it comes from people who don't have strong CS backgrounds wrapping themselves too tightly against their hard-earned knowledge rather than having an expansive view of what can solve these problems.

<comment><USER31> Get your math and your domain knowledge straight and you can do a lot with little. Lots of programmers want to be ml engineers because the prestige is higher because you normally take in PhDs. The big problem is hype, people are throwing AI at everything as...garbage marketing. It's at the point where if you say you use AI in your software title, I know you suck, because you aren't focusing on solving a problem you are focusing on being cool which will never end well.

</comment></comment><comment><USER32> There's a lot of what I call "model fetishism" in machine learning.

Instead of focusing our energies on the infrastructure and quality of data around machine learning, there's eagerness to take bad data to very high-end models. I've seen it again and again at different companies, usually always with disastrous consequences.

A lot of these companies would do better to invest in engineering and domain expertise around the problem than worry about the type of model they're using to solve the problem (which usually comes later, once the other supporting maturity pieces are in place)

<comment><USER33> This is why my interview question focuses around applying linear regression to a complex domain. It weeds out an enormous number of candidates.

There are 5 ML models that we maintain where I work, and none of then are more complicated than linear regression or random forests. Convincing me to use something more complex would take an enormous amount of evidence. Domain knowledge is king.

</comment></comment><comment><USER34> This is an anecdote with no data. And the entire global economy is in a recession, so the fact deep learning might have fewer job postings isn't particular notable.

I'll note that in my personal anecdote, the megacorps remain interested in and hiring in ML as much as ever.

<comment><USER35> He's now posted a follow-up analysis of LinkedIn Job postings: [https://twitter.com/fchollet/status/1300417952211034112?s=20](https://twitter.com/fchollet/status/1300417952211034112?s=20)

<comment><USER36> Would be interesting to see this dip relative to other tech subfields like javascript/react or even data science and other such keywords. Does anyone know of a public LinkedIn dataset?

The author disables tweet replies so I'm not sure where they get their numbers from.

</comment></comment></comment><comment><USER37> Missing in the original chart/data: have ML/DL job postings decrease more or less than other comparable job categories (programming, business analyst, etc.)

<comment><USER38> Great point. Not as good point: is looking for pytorch and tf the right measure?

<comment><USER39> In his tweet I thought he made it clear he wasn't predicting an AI specific slowdown but a universal recession due to Covid?

<comment><USER40> He's wrong and using not the best data for such an assertion.

Data science jobs are not slowing down, though they're not really increasing either.

In comparison since 2016 software engineering jobs revolving around building up systems for data scientists have increased 6 fold, maybe even more since I last looked.

</comment></comment></comment></comment><comment><USER41> 99% of the time you don't need a deep recurrent neural network with an attention based transformer. Most times, you just need a bare-bones logistic regression with some carefully cleansed data and thoughtful, domain-aware feature engineering.

Yes, you're not going to achieve state-of-the-art performance with logistic regression. But for most problems the difference between SOTA and even simple models is not nearly as large as you might think. And two, even if you're cargo-culting SOTA techniques, it's probably not going to work unless you're at an org with an 8-digit R&D budget.

</comment><comment><USER42> I know very little about the DL/ML space, but as a full-stack engineer it feels like most companies have tried to replicate what FAANG companies do (heavy investment in data/ml) when the cost/benefit simply isn't there.

Small companies need to frame the problem as:

1) Do we have a problem where the solution is discrete and already solved by an existing ML/DL model/architecture?

2) Can we have one of our existing engineers (or a short-term contractor) do transfer learning to slightly tweak that model to our specific problem/data?

Once that "problem" actually turns into multiple "machine learning problems" or "oh, we just need todo this one novel thing", they will probably need to bail because it'll be too hard/expensive and the most likely outcome will be no meaningful progress.

Said in another way: can we expect an engineer to get a fastai model up and running very quickly for our problem? If so, great - if not, then bail.

ie: the solution for most companies will be having 1 part-time "citizen data scientist" [1] on your engineering team.

[1]: [https://www.datarobot.com/wiki/citizen-data- scientist/](https://www.datarobot.com/wiki/citizen-data-scientist/)

</comment><comment><USER43> The way I see it, only those companies that had already been using a data oriented approach to business can really reap the benefits of ML. From a company's point of view, ML/AI should be a natural evolution of an existing tool set to better solve problems they have been trying to solve in the past using deterministic methods and then statistical methods, etc. Any other project that is diving right into ML is likely to fail because

1\. There's no clear problem statement. They have never formulated one and now trying to bolt ML on to their decision making.

2\. They don't have well catalogued data for engineers/scientists to work with because they never tried to do rigorous analysis of data before ML became a thing.

3\. Managers have no idea how to deal with data driven insights. What if the results are completely unintuitive to them? Are they going to change their processes abruptly? What if the results are aligned with what they have been always doing? Is it worth paying for something that they have been doing intuitively for decades?

I'm not a data scientist. But the biggest complaint I hear from my colleagues is that they lack data to train models.

<comment><USER44> Yeah, you really shouldn't conform data to the problem. It's more an emergent silver gun than a constructed silver bullet.

</comment></comment><comment><USER45> Isn't it the same pattern every 10 years or so for "AI" related tech ? Some people hype tech X as being a game changer - tech X is way less amazing than advertised - investors bail out - tech X dies - rinse and repeat.

[https://en.wikipedia.org/wiki/AI_winter](https://en.wikipedia.org/wiki/AI_winter)

<comment><USER46> This is more akin to the Internet bubble than the previous AI winter. The technology is valuable for business, but the hype is huge and companies aren't ready for it yet.

</comment></comment><comment><USER47> AI has a business problem.

Very few businesses I know actually have a deep learning problem. But they want a deep learning solution. Lest they get left out of the hype train.

<comment><USER46> Blockbuster didn't have an Internet problem.

<comment><USER48> Dentistry didn't have a sledgehammer problem and, after all these years, it still doesn't.

</comment></comment></comment><comment><USER49> "This is evident in particular in deep learning job postings, which collapsed in the past 6 months."

Have they? Specifically, have they "collapsed" relative to the average decline in job listings mid-pandemic?

</comment><comment><USER50> I am a DL researcher at a top industry lab.

I'm completely unsurprised by this. Regularly, at lunch, I'll ask my coworkers if they know of any DL applications that are making O($billions), and no one knows any outside of FAANG.

FAANG is making an insane amount of money due to DL. Outside of them though, I don't know who's making money here. When I was interviewing for jobs, there were a ton of startups that were trying to do things with DL that would have been better done with a few if statements and a random forest, and that had a total market size in the millions.

I think that, eventually, there'll be a market for this stuff, but I'm not convinced that it's anywhere near being widespread.

I was also a consultant before my current role. The vast majority of non-tech firms don't have their data in well organized + cleaned databases. Just moving from a mess of Excel sheets to Python scripts + SQL databases would have made a HUGE difference to the vast majority of clients I worked with, but even that was too big of a transformation.

Basically, everyone with the sophistication to take advantage of DL/ML already has the in-house expertise to do it. There's almost no one in the intersection of "Could make $$$ doing DL" && "Has the technical infrastructure to integrate DL".

</comment><comment> ur-whale That may be true in the research arena (where Mr Chollet works), but I don't think that's the case in terms of where deep learning is actually applied in industry, nor will it be the case for years to come IMO.

It's just that much that needed to be invented has been invented and now it's time to apply it everywhere it can be applied, which is a great many place.

</comment><comment><USER51> Some context, for those unfamiliar: [https://en.wikipedia.org/wiki/AI_winter](https://en.wikipedia.org/wiki/AI_winter)

<comment><USER52> The poster explicitly states he does not think this is indicative of AI winter.

<comment> the-dude Mentioning context does not mean the OP assumes equivalence.

It is context.

</comment></comment></comment><comment><USER53> I've been using voice commands on my android phone, in situations where I can't use my hands. Most often all I want to do is.

1\. Start and stop a podcast.

2\. Play music

3\. Ask for the time

The phone understands me, but then android breaks the flow, so I have to use my hands.

1\. It will ask me to unlock the phone first? I have gloves and a mask on. It won't recognize my face, and my gloves don't register touches. Why do I have to unclock the phone to play music in the first place.

2\. It gets confused on which app to play the music/podcast on. Wants to open youtube app, or spotify, and so on ...

3\. Not consistent. I can say the same thing, and sometimes it will do one things, and another next time.

4\. If I'm playing a video, and I want to show it full screen. I have to maximize and touch the screen. Why can't it play full screen be default.

<comment><USER54> I have similar with my Google Home; it can play Netflix but can't work out BBC iPlayer most of the time. And many times if I ask it to play music, it'll give an error saying it can't play on YouTube Music because I don't have a subscription, even though my default music player is Spotify in my account.

</comment></comment><comment><USER55> My favorite joke on this is "The answer is deep learning, now whats the problem?"

<comment><USER40> labeled data

</comment></comment><comment><USER56> It's not exactly a great year for extrapolating trends about what people are doing with their time. I wonder how much of this is 2020-specific and not just due to the natural cycle of AI winters.

<comment><USER57> at least some is pure 2020. we want to hire, we can't right now.

<comment><USER58> Why not? I would have thought it was a buyer's market now with all the layoffs.

<comment><USER59> There's tons of layoffs because businesses are doing _really badly_. Current cashflow may not support another developer. Future cashflow doesn't look that great in any B2C market, either, and the B2B markets will start to look slim pickings not too far after that.

<comment><USER58> I'm not sure I follow. My question was directed toward OP, whose company is hiring, which presumably means they are doing well. Can you please clarify?

</comment></comment></comment></comment></comment><comment><USER60> Finally! Big companies need to realize they must understand what what they are doing with technology to get any value of out it.

They've long resisted that, of course, but I'm pretty sure half the popular of deep learning was it leveled the playing field, making engineers as ignorant of the inner-workings of their creations as the middle managers.

May the middle-manager-fication of work, and acceptance of ignorance that goes with, fail.

\-----

Then again, I do prefer it when many of those old moronic companies flounder, so maybe this is a bad thing that they're wising up.

</comment><comment><USER61> Deep Learning has become mainstream. The place work at actually uses 2 unrelated products based on NN.

</comment><comment><USER62> I imagine this correlates to the "blockchain" postings.

</comment><comment><USER63> I would have expected a comparison to job postings in general: how do deep learning job postings compare to job postings for any kind of technical position?

</comment><comment><USER64> Meanwhile, the academic job market, certainly in my area, ie linguistics/computational linguistics, has collapsed, too. A colleague did a similar and equally nice analysis here: [https://twitter.com/ruipchaves/status/1279075251025043457](https://twitter.com/ruipchaves/status/1279075251025043457)

It's tough atm.

</comment><comment><USER65> Companies trying to add machine learning to everything they do like if that's going to solve all their problems or unlock new revenue streams.

80 or 90% of what companies are doing with machine learning results in systems with a high computing cost that are clearly unprofitable if seen as revenue impacting units. Many similar things can be achieved with low-level heuristics that result in way smaller computing costs.

But nobody wants to do that anymore. There's nothing "sexy" or "cool" about breaking down your problems and trying to create rule-based systems that addresses the problem. Semantic software is not cool anymore, and what became cool is this super expensive blackbox that requires more computer power than regular software. Companies have developed this bias for ML solutions because they seem to have this unlimited potential for solving problems, so it seems like a good long term investment. Everyone wants to take that bus.

Don't get me wrong. I love ML, but people use it for the stupidest things.

</comment><comment><USER66> Data science and ML In big companies are pulling resources away from the real value add activities like proper data integrity, blending sources, improving speed performance. Yes Business Intelligence is not cool anymore. Yes I also call my team "data analytics". But let's not forget the simple fact that "data driven" means we give people insights when and where they need them. Insights could be coming from an sql group by, ML, AI, watching the flying of birds, but they are still simply a data point for some human to make a decision. That means we need to produce the insight, being able to communicate it to people, have the the credibility for said people to actually listen to what we are saying. Focusing on how we put that data point together is irrelevant, focusing on hiring PHDs to do ML is most likely going to end in a failure because PHDs are not predictive of great analytical skills, experience and things like sql are much better predictors.

</comment><comment><USER67> On the plus side, ML systems have become commoditized to the point that any reasonably skilled software engineer can do the integration. From there, it really comes down to understanding the product domain inside and out.

I have seen so many more projects derailed by a lack of domain knowledge than I have seen for lack of technical understanding in algorithms.

</comment><comment><USER68> Every company of course is very different, but I have seen that companies understood that fro Deep Learning you need a Pytorch or TF expert or maybe some other framework and most of these experts already work in Google/Facebook or any other advanced companies (NVIDIA, Microsoft, Cruise, etc), hiring is very difficult and cost is high. Then you can start using regular SQL and/or AutoML to get some insights. For a large number of companies that's enough. When there is so much complexity, such as DL modeling there's little transparency and management want to understand things. After COViD time will tell, but my take is that only a few companies need DL.

</comment><comment><USER69> I'm currently a masters student and I'm rather glad I opted not to take a specialized degree such as Machine Learning, taking on computer science instead. All this discussion about DS, ML, AI (and even CS) becoming over- saturated has made me rather wary and I worry that I'm choosing the wrong 'tracks' to study (currently doing ML and Cybersecurity as I genuinely am interested in those fields). I won't be graduating until next year but I'm forcing myself to be optimistic that the tech job market will be in a better place by then.

</comment><comment><USER70> In general does anyone know if its a good time to look for a new dev job? I was really going to move this year, but it seems sensible to wait. Just sucks to see friends with RSUs going up in value so quickly.

<comment><USER71> No harm in having a recruiter or two feed you opportunities on a regular basis to interview at (just be up front with them that you're holding out for a solid fit for your criteria). Better to have a job while interviewing than be under pressure to accept the first half decent thing that comes along.

</comment></comment><comment><USER72> For most companies ML is just part of the long term strategy, with covid priorities have shifted from long term R&D to short term survival, so I don't see anything out of the ordinary here

</comment><comment><USER73> A lot of thee c folks aren't tech folks or even math folks. They want to try to use deep learning to do prediction or get some insight when something as simple as regression would have worked.

<comment><USER74> what's particularly surprised me is how effective gradient boosting is in practise. I've seen so many cases of real world applications where just using catboost or whatever worked ~95% as well or even just as well as some super complicated deep learning approach and it saves you ten times the cost

<comment><USER75> To be fair, if you're willing to write code to perform feature engineering for you, you can often replace the complicated boosting approach with a much simpler regression model.

Turtles all the way down, I guess.

</comment></comment></comment><comment><USER76> Deep Learning has been so commoditized and compartmentize over the past 5 years, now I think average SDE with some basic understanding of it can do a reasonable job in application.

</comment><comment><USER77> There will always be Snake Oil salesmen and hence Snake Oil..

</comment><comment><USER78> I don't think anyone should freak out when they see a tweet like this: deep learning is just one particularly trendy part of ML, which is just one piece of data science, which is just one job title in the "working with data" career space. I think that most people with backgrounds or interests in DL are very well equipped to participate in the (ever more important) data science world.

</comment><comment><USER79> Booms imply crashes. Anyone who is surprised at this couldn't be smart enough to be a good machine learning engineer.

</comment><comment><USER80> No question ML is powerful and can do great things. Also no question a lot of companies where just throwing money at stuff for fear of being seen as behind in this space. When the going gets tough such vanity efforts are the first things to go.

Teams adding measurable value for their companies should be fine but others might not be.

</comment><comment><USER81> In my industry (research), we still have a strong line of business. Some commercial clients have killed their contracts with us to save money during the COVID era, but government contracts are still going strong. In areas where there's a clear use case I think there is still work to go around.

</comment><comment><USER82> My belief in an AI breakthrough is so strong that I would invite another AI winter to try to play catch up

<comment><USER83> What is your belief based on?

</comment></comment><comment><USER84> The graph means very little without a comparison line of "all programming jobs" and/or "all jobs."

</comment><comment><USER85> Out of curiosity: are there job postings that did not "collapse" over the past six months?

</comment><comment><USER86> Good riddance. The majority of it is snakeoil, relabeling and "smoke and mirrors". A lot of smart or lucky people made a lot of money, a lot of dumb people with power over money lost... probably insignificant amounts of it.

</comment><comment><USER87> ML/DL is at the exploratory phase for most companies. I have no surprise when seeing this post. Nevertheless, this also open new opportunities in other domains and new kind of business based on data. I have no doubt.

</comment><comment><USER88> Is there a LinkedIn tool that allows you to make similar trend plots as shown in the Twitter thread, or has the author been archiving the data over time?

</comment><comment><USER89> Unless you're doing ML/DL/etc _research_ then what you're really doing is engineering, like always.

</comment><comment><USER90> the fact that he doesn't allow people to answer his tweets making data-less claims like this is really a problem

<comment><USER91> He labels anyone who criticizes him as a troll. Unfortunately he is a public figure in the ML space and does have his share of trolls, but doesn't take too well to even well thought out replies.

<comment><USER90> he's so French, in the worse way possible. I say that as a French person myself

<comment><USER92> Also his analysis is shoddy. He shows an absolute decrease in DL job postings since covid hit, and claims that DL is in decline irrespective if other fields like SWE are also in a similar decline. Utterly surprised by the analysis given the data.

</comment></comment></comment></comment><comment><USER93> covid has certainly sped up the transition to the "plateau" state in the ML/DL/AI hype cycle

</comment><comment><USER94> Is that a worldwide trend, or is it based on US data? That's not clearly stated in the tweet.

</comment><comment><USER95> How does that compare to job postings overall? Those would have fallen off a cliff as well.

</comment><comment><USER96> If you ever talked to one of the self proclaimed 'AI experts,' you know why.

</comment><comment><USER97> Sufficient DL frameworks are now in the cloud and it is mostly an engineering problem.

</comment><comment><USER98> I guess nobody's model... _puts on sunglasses_ ...predicted this event.

</comment><comment><USER99> I have seen ML and big data crowd out remote openings though.

</comment><comment><USER100> And yet data center spend has gone through the roof. Why?

</comment><comment><USER101> Why was this headline changed?

</comment><comment><USER102> I believe this to be an obvious that the Singularity has already occurred.

</comment><comment><USER103> memento mori: [https://en.wikipedia.org/wiki/AI_winter](https://en.wikipedia.org/wiki/AI_winter)

</comment><comment><USER104> Citation needed.

</comment><comment><USER92> This needs to be normalized to "job posting collapse in the past 6 months" unless you expect DL jobs to grow while everything shrinks? I'm somewhat surprised by the analysis from someone's who's "data driven." I mean, he even says so as much in the twitter thread:

"To be clear, I think this is an economic recession indicator, _not_ the start of a new AI winter."

So, looks like he discovered an economic recession.

<comment><USER7> If you normalize the data, there is absolutely 0% change in the # of job openings for deep learning: [https://i.imgur.com/sDoKwD0.png](https://i.imgur.com/sDoKwD0.png)</comment></comment>


The next generation of spreadsheets will be a database - <AUTHOR> http://www.infoworld.com/article/2927917/database/the-next-generation-of-spreadsheets-will-be-a-database.html <comment><USER2> I've been hearing versions of this, with slightly different justifications and details for why databases would replace spreadsheets, for, well, almost as long as there have been desktop databases.

Really, I think it misses the point. Databases are about storing data, and admit a wide array of front ends, and there are plenty of tools _now_ to use spreadsheets as a front-end for databases (most spreadsheets have basic tools for this built in, and more advanced ones are available.)

Spreadsheets are about viewing/manipulating data, and can both natively store data (in what is, necessarily, a database, though perhaps not a relational one) or connect to foreign sources for it.

"The next generation of spreadsheets will be a database" is a silly statement, for that reason: its possible the next generation of spreadsheets may have a particular kind of database (say, relational) as a native, internal storage system and expose tools for working with it. Or the next popular database front end -- the next Access -- may be a general purpose spreadsheet with the right tools.

And maybe spreadsheets, as a viewing and interaction tool, will be replaced with something with a different more mobile/touch-friendly UI paradigm, as the article seems to suggest (but doesn't really present much of a case for -- making vague suggestions about how spreadsheets don't work on touch [that remind me of the kind of hyperbolic descriptions of why current solutions don't work typical of infomercials] without any suggestion of what concrete elements would work better is _not_ making that case.)

<comment><USER3> Here's some visual illustrations of why spreadsheets don't work well for organizational use cases: [https://www.youtube.com/watch?v=zttu1JNgRck](https://www.youtube.com/watch?v=zttu1JNgRck)

Joel Spolsky also has a great essay about this: [http://www.joelonsoftware.com/items/2012/01/06.html](http://www.joelonsoftware.com/items/2012/01/06.html)

"What was I talking about? Oh yeah... most people just used Excel to make lists. Suddenly we understood why Lotus Improv, which was this fancy futuristic spreadsheet that was going to make Excel obsolete, had failed completely: because it was great at calculations, but terrible at creating tables, and everyone was using Excel for tables, not calculations."

<comment><USER4> On the other end I work in a company where excel is often used as a "development" platform, internal tools, VBA codebases etc etc. It's terrifying to see what a well intentioned mathematician can do with Excel, VBA, and 50meg of data. (Mostly just create a spreadsheet that crashes a lot tbh).

Excel is used for a great many things, lists, calculations, analysis, charts, storage, and all of the possible mixtures of the two.

</comment></comment></comment><comment><USER5> This is an advertorial and should be labelled as such:

"My company, Airtable, was founded to address this need."

<comment><USER6> It's a very useful article, and well-argued, but I agree, the format jarred. I'm not sure however that it's worse in substance than many: just that he's been rather unsubtle a bout it

<comment><USER3> Hi, author of the post here. Apologies if the format of the article was confusing or misleading. I will note that this article was posted under InfoWorld's "New Tech Forum", which is specifically for authors from industry to write guest posts.

Here's an example of another article from the New Tech Forum: [http://www.infoworld.com/article/2607260/database/how- databa...](http://www.infoworld.com/article/2607260/database/how-database- virtualization-works.html)

</comment></comment></comment><comment><USER7> The majority of end users have the ability to understand a list in the style of a spreadsheet, which then becomes a simple and effective tool. But sometimes there need goes beyond the spreadsheet.

As soon as you add relationships, such as 1-to-many, the average end user is lost. Not just struggling a little, but completely bamboozled. As programmers we often forget that even something as simple as that is a road block for almost all end users. Teaching users about relationships is a dead end, about 90% will never get it. Especially when they are trying to learn about it from a website product.

Instead I think 'airtable' would be better placed marketing to web developers and IT departments. Every company I have worked at has requests from other departments wanting a simple database. Most companies are full of these simple internal databases. A tool that allows us to quickly (~1 day) setup a solution and then get rid of the request would be a boon. You should be targeting web developers and IT professionals with your solution.

</comment><comment><USER8> Way back in 1986, there was Javelin.

Wikipedia's actually got a good article on it: [https://en.wikipedia.org/wiki/Javelin_Software](https://en.wikipedia.org/wiki/Javelin_Software)

But consider this: if they hadn't mistimed their IPO to be the day after the Crash of 1987, they could easily have been the dominant paradigm in spreadsheets and small databases.

<comment><USER9> Interesting, thanks. However, I'm not sure most end users were going to figure this out any sooner than a relational database:

 _Unlike models in a spreadsheet, Javelin models are built on objects called variables, not on data in cells of a report. For example, a time series, or any variable, is an object in itself, not a collection of cells which happen to appear in a row or column. Variables have many attributes, including complete awareness of their connections to all other variables, data references, and text and image notes. Calculations are performed on these objects, as opposed to a range of cells, so adding two time series automatically aligns them in calendar time, or in a user-defined time frame._

Maybe it's just not well-explained by the Wikipedia authors, or it makes more sense if you see it ...

</comment></comment><comment><USER10> This article kind of misses the forest for the trees. The reason why spreadsheets took over for databases is because the spread sheet was easy and put less restrictions in the way.

So creating a spreadsheet that is not as easy in order to support real database like functionality is going to end up the same way...

</comment><comment><USER11> I agree with this. My group uses google sheets to manage a very complex collaborative process which is highly record oriented and requires relational semantics and database-like integrity controls, but which also needs spreadsheet-like sorting, grouping, formula work, rapid data entry, pivot tables, and so on.

We kind of work around spreadsheet limitations now by having a separate google sheet hold source data in various tabs (one for each relational table), then importing data from that sheet into the working spreadsheets (where we do enrichment and analysis) using various lookup and query functions. It's precisely as fragile as the attention span of the most easily distracted team member.

<comment><USER12> Have a look at Quantrix. It's a spreadsheet that separates data from formulas and reporting. Database access is built in.

It a hidden gem of many finance departments.

<comment><USER11> Thanks - was not aware of this at all. Will check it out.

</comment></comment></comment><comment><USER13> Dabble DB[1] was a really cool business and implementation of "really easy to use databases".

[1] [http://en.wikipedia.org/wiki/Dabble_DB](http://en.wikipedia.org/wiki/Dabble_DB)

<comment><USER14> I was hoping someone else would remember that. It got bought up and shut down by Twitter (they were after another project by the same team I recall).

Sad. It looked like it could have been a game-changer.

</comment></comment><comment><USER15> TreeSheets is worth a look, open-source for Windows, Linux and Mac (beta): [http://strlen.com/treesheets/](http://strlen.com/treesheets/)

      It's like a spreadsheet, but more suitable for complex data
         because it's hierarchical.
      It's like a mind mapper, but more organized and compact.
      It's like an outliner, but in more than one dimension.
      It's like a text editor, but with structure.

</comment><comment><USER16> I'm in the database camp but I can see the use case for spreadsheets.

The big problem is unless you are very disciplined it is easy for a large spreadsheet to become unmaintanable. I've come across this a lot when I've been approached by people in my work place asking me to automate a spreadsheet for them. Inevitably someone will insert, delete or just move a column around and the whole fragile mess will come crashing down.

I've found when someone comes to me and says "I spend X amount of time every morning updating this spreadsheet can you automate it for me." The answer is almost always to migrate to a database.

Spreadsheets also seems to encourage people to lazily paste data in without much thought. I've seen some real nightmares - spreadsheets that are 100's of mb in size and contain almost a complete copy of portions of the company's database inside them, which take minutes to open and load.

Pasting data has some other disadvantages as well over time minor changes sneak in due to the mutable nature of cells in excel. Which leads to phone calls like "Can you check the figures for last FY Mike our Accountant is telling me X while Bill our Engineer is convinced it should be Y" eventually you find out Mike is getting his data out of a giant spreadsheet and one of the cells has 'inadvertantly' been modified so it no longer agrees with the source database. Eventually you learn to head off phone calls like this with "The number in the database is correct if you sourced it from anywhere else its likely to be wrong." But still frustrating to deal with.

</comment><comment><USER17> I can totally see this, as it is our analysts use Toad for Oracle and directly edit tables through that. There is a "data view" that looks quite similar to excel.

</comment><comment><USER18> If there was a decent way to hook up databases to spread sheets we probably would use it at my 3 month old startup.

As it is, I opened a json endpoint so that Zapier can scrape our orders and fill it into the next-blank-line in a google doc.

Is there a better way to satisfy this use case?

<comment><USER3> Hi, I'm a cofounder of Airtable. We have an alpha version of a Zapier integration, and also an API to directly push your order data into Airtable. The benefit of using Airtable over Google Sheets is that you get true database features without losing the speed and flexibility of a spreadsheet interface: foreign key linking, rich field types, multiple views, better collaboration and mobile access, etc.

</comment></comment><comment><USER19> It's interesting how "spreadsheet" interface is very very popular. Even the latest enterprise planning software[1] is using it as well.

[1] [http://bit.ly/1dKmrF5](http://bit.ly/1dKmrF5)

</comment><comment><USER20> One of the problems with spreadsheets is that they don't have a concept of table. Tables are _emulated_ in spreadsheets using blocks of cells. Spreadsheets make no difference between a column header and a column value.

</comment><comment><USER21> I've wanted something like this for years. So many times I've tried to use a Google spreadsheet to store data, because I needed the web-based UI and mobile access... but it never works out well, because it's just not a relational database. Airtable seems to get it right.

Also, the API docs being automatically customised per-table is awesome.

</comment><comment><USER22> Kind of ... I think that instant tabulR data of the sort you find in an HDF5 hooked up to distributed backend so my changes and yours are merged managed or thrown seems the likely "future" solution. If that's anything like aorta me please shout details

</comment><comment> noir-york Can/will airtable support pivot tables?

I love Excel - I wonder how it was possible to analyse a business before it came along - and the primary reason is pivot tables. Pivot tables are an awesome tool.

It should be possible to replicate pivot table functionality using SQL semantics.

</comment><comment><USER23> When this says generation, what time frame are they referring to? I've been reading about this sort of thing for the last twenty years. Hasn't happened. What has happened is people asking for more spreadsheet integrations.

</comment><comment><USER24> What drives me crazy is spreadsheets as a reporting / BI tool. Just because Excel does graphs and charts, and everyone knows how to use Excel, the result is that everyone only knows how to make graphs and charts in Excel.

</comment><comment><USER25> Looks like it doesn't support any kind of "group by" functionality.</comment>


Show HN: Live HTTP Headers for Chrome (based upon FF plugin, includes search) - <AUTHOR> https://chrome.google.com/webstore/detail/ihnbjlgmejamphehjjdlbmeiphikmlmc/

<comment><USER2> Awesome! When I switched from FF to Chrome, I was really disappointed that many of the extensions I used for web development weren't yet available on Chrome. It's great to see more of these extensions brought over. Thanks!

<comment><AUTHOR> Thanks! I'd been waiting on this for 1-2 years, figured it was about time ;)</comment></comment>


New sub-reddit devoted to datasets - <AUTHOR> http://www.reddit.com/r/datasets/

<comment><USER2> Delicious is a pretty good place to look for bookmarked data sets.

<http://delicious.com/sidmitra/datasets>

<comment><USER2> Also,

A lot of conversation around data sets happens on Google groups such as "Get the info"

<http://groups.google.com/group/get-theinfo>

<comment><USER3> Also, <http://delicious.com/popular/datasets>

</comment></comment></comment><comment><USER4> I've been collecting a few myself

<http://datavis.slinkset.com/>

</comment><comment><USER5> There's also www.reddit.com/r/opendata

</comment><comment><USER6> Great resource. I've already found some stuff I've been looking for for a while.

</comment><comment><USER7> I like how the top link right now [1] is nicely meta.

[1] <http://www.reddit.com/r/datasets/.xml></comment>


U.S. to mandate design changes on Boeing 737 MAX 8 after crashes - <AUTHOR> https://www.reuters.com/article/us-ethiopia-airplane-usa/u-s-to-mandate-design-changes-on-boeing-737-max-8-after-crashes-idUSKBN1QS2CL <comment><USER2> >U.S. Transportation Secretary Elaine Chao told reporters regulators would not hesitate to act if they find a safety issue.

In any other administration, Republican/Democrat, I'd believe them. This one? With Chao being McConnell's wife.

This makes me feel uncomfortable.

<comment><USER3> ...and how does that make her unqualified? Because you don't like Mitch McConnell? Does McConnell have some connection to Boeing I'm unaware of? Because otherwise I don't see how it's a relevant fact, at least not in this case.

<comment><USER2> Airplanes are becoming far too complex to fly. Pilots are no longer needed, but rather computer scientists from MIT. I see it all the time in many products. Always seeking to go one unnecessary step further, when often old and simpler is far better. Split second decisions are needed, and the complexity creates danger. All of this for great cost yet very little gain. I don't know about you, but I don't want Albert Einstein to be my pilot. I want great flying professionals that are allowed to easily and quickly take control of a plane!

</comment></comment></comment><comment><USER4> Grounding the aircraft as a precaution would have been a wise decision especially considering that lives once lost cannot be recovered. Monetary losses, however, can be.

<comment><USER5> Grounding these planes would mean bringing in many dozens of out-of-service airplanes from storage.

</comment></comment><comment><USER6> > mandate that Boeing Co implement design changes by April that have been in > the works for months

I assume that just before this announcement the FAA had a phone call with Boeing confirming the will have the fix ready by April. This way the FAA gets to looks tough without harming Boeing in any way.

<comment><USER2> Why should they care how they want to look appear? It seems we lack people willing to behave on principal.

What is the downside to not grounding planes that have killed 300 people until April when the fix is ready and we can get pilots training? Where in the constitution does it say we must protect shareholders of airlines and save them 3 weeks of lost revenue from ...(checks notes)... 30 planes ...(Southwest)?

If something happens between now and then on this airplane, and it may not even be related to the issue, how much trust is lost in the United States of America and its aviation industry. it just seems quite short sighted.</comment></comment>


Goodbye Microsoft, Hello Facebook (2010) - <AUTHOR> http://worldofsu.com/philipsu/goodbye-microsoft-hello-facebook/

<comment><USER2> This was posted in 2010. A little long-winded for sure, but it's good to hear someone would have such positive things to say coming out of a 12-year tenure at Microsoft (I have a few friends headed there after graduation).

I remember reading this post of his titled "Ten Things I Hate About Working at Facebook" ([http://worldofsu.com/philipsu/2012/08/ten-things-i-hate- abou...](http://worldofsu.com/philipsu/2012/08/ten-things-i-hate-about- working-at-facebook/)). It does sound like he's having a blast there- it'd be interesting to read a follow-up comparing the two companies.

<comment><USER3> I didn't read through the comments of his "Ten Things" post, but it certainly reads to me as something wholly ripe with sarcasm, in fact mocking what is truly wrong with much of corporate America by complaining about these "flaws" found in Facebook.

<comment><USER4> It was entirely facetious, which was missed by many here when it made an appearance on the front page [1]

[1]: <http://news.ycombinator.com/item?id=4384029>

</comment></comment></comment><comment><USER5> FYI: If you fast-forward to 2013, Philip Su is now heading the Engineering office at Facebook London. He's a really nice guy and great culture fit for the company.

Source: I work at FB. Also, just use the Googles...

<comment><USER6> Or the Facebooks ... <https://www.facebook.com/the.philip.su>

</comment></comment><comment><USER7> One theory was that he was the original Mini Microsoft. No clue if true, but the quality of mini's posts dropped around the same time he left.

</comment><comment><USER8> There is so much damn advice that comes down to "be awesome, don't suck." It's not interesting, we need more advice for those that aspire to be average.

</comment><comment><USER9> I clicked the link thinking of another article having opinion of how Facebook is becoming more like Microsoft(not hot, not cool). After reading the article though, it felt good that people working there have good feeling about Microsoft. I have lots of senior friends going to Microsoft this year.

</comment><comment><USER10> His August 2012 post is a little less than positive -- <http://worldofsu.com/philipsu/2012/08/>

_edit_ : I'm an idiot; brain fart on a Saturday at the office

<comment><USER11> Facebook conduct job interviews in a hot tub? That could feel a little awkward.. especially since programmers not generally known for body confidence.

<comment><USER12> You missed the bit that says > "[Ed: This is completely untrue, in case this fantastical point seems plausible at first. I installed a hot tub (non- functioning) as a conference room in Facebook Seattle. Interviews are never done in them.]"

</comment></comment></comment><comment><USER13> He gave a great talk at the HNLondon Meetup on Wednesday. He's a really entertaining speaker:

<http://vimeo.com/60896034>

</comment><comment><USER6> Timestamps are a helpful thing to put on blog posts.</comment>


WHO haunted by old tweet saying China found no human transmission of coronavirus - sahin-boydas https://nypost.com/2020/03/20/who-haunted-by-old-tweet-saying-china-found-no-human-transmission-of-coronavirus/ <comment> sahin-boydas [https://twitter.com/WHO/status/1217043229427761152](https://twitter.com/WHO/status/1217043229427761152)

Preliminary investigations conducted by the Chinese authorities have found no clear evidence of human-to-human transmission of the novel #coronavirus (2019-nCoV) identified in #Wuhan, #China🇨🇳.

</comment><comment><AUTHOR> > It is an open secret among international diplomats and public health experts > that WHO is "not fit for mission" (as one of them put it to me), riddled > with politics and bureaucracy. Given its previous failures and the warning > that was Sars, its leadership has no excuse for reacting so oddly, and so > tardily, to the current crisis.

[http://www.rationaloptimist.com/blog/who-must- answer/](http://www.rationaloptimist.com/blog/who-must-answer/)</comment>


Adobe Brings Flash to HTML5 Canvas - <AUTHOR> http://www.readwriteweb.com/archives/flash_now_importable_to_hmtl_canvas.php?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+readwriteweb+%28ReadWriteWeb%29

<comment><USER2> I saw now link between the video embedded and the supposed feature of flash and HTML5, just an animation FPS comparison.

<comment><USER3> The way I understood was that the content (animation shown in the video) was created in Flash and exported for HTML5/Canvas. Meaning you can create content in Flash (the animation package) and export it for HTML5 making Adobe less depended on their Flash Player platform.

The video was demonstrating that the exported content runs slower in HTML5/Canvas than in Flash Player.

<comment><USER4> The content shown was hand written for the three different platforms (Flash, SVG and Canvas). The conclusion of the author was that Flash was still faster/better/more consistent for most things of this type (even though his original Flash version crashed several browsers!).

The video was someone extending those tests to his phone and a newer version of Flash. It has nothing to do with Canvas export from CS5, so I don't know why this blog confusingly linked the two.</comment></comment></comment>


Bonuses, Power and Inequality - <AUTHOR> http://stumblingandmumbling.typepad.com/stumbling_and_mumbling/2009/02/bonuses-power-and-inequality.html

<comment><USER2> This is very close to what the mutualists say: [http://mutualist.blogspot.com/2008/11/organization-theory- la...](http://mutualist.blogspot.com/2008/11/organization-theory-latest- chapter.html) I must admit I am convinced on many aspects of their critique of the current system - but still I don't buy their solutions.</comment>


OCaml 4.04.0 released - <AUTHOR> https://sympa.inria.fr/sympa/arc/caml-list/2016-11/msg00010.html <comment><USER2> I apologize for the plug, but I invite folks who like OCaml to check-out Tezos, a project for a self-amending blockchain in OCaml. [http://github.com/tezos](http://github.com/tezos)

I think it's a really neat piece of engineering that demonstrates the strength of the language. We have some really nice uses of the GADT and we try and squeeze as much guarantees as we can out of the type system. The modules give us a natural way to follow the principle of least authority by restricting the visible surface of each implementation layer as much as possible.

Some of the core Tezos developers are also behind the recent mooc and work on the OCaml compiler.

</comment><comment><USER3> Damn, still no modular implicits. And no multicore yet, either. Seems like they keep getting pushed back.

At least BuckleScript is progressing quickly.

<comment><USER4> My first reaction was to check for multi-core. I've been hearing its around the corner for years.

</comment></comment><comment><USER5> Just finishing OCaml course at Paris Diderot

www.fun-mooc.fr/login?next=/courses/parisdiderot/56002S02

Really cool language and opened my eyes on how some constructs are being used.

Multicore is definitely must for functional languages.(sorry random thought)

<comment><USER6> > Multicore is definitely must for functional languages.(sorry random thought)

...why?

<comment><USER7> May be worth exploring this from the other side too. Why do you think it is ok for a programming language to lock people into to using just 1 core per process?

<comment><USER8> I think most would agree that multicore capability is desirable, but it's clearly not a "must" as ocaml has been without it all of this time and many have used it to good effect regardless (of course it might be a must to certain people who need it for their use case). Also, the same restriction that prevents multicore in ocaml (the GIL) also affects other languages which are obviously useful, such as Python. So to answer your question it's clearly "ok" since it doesn't prevent people from productively writing effective and useful software in it, although it is of course a restriction which many would like to see go away.

</comment></comment></comment></comment><comment> nv-vn Glad the release cycle is speeding up, but kinda underwhelmed by the lack of changes which have been floating around in "for the next release" limbo here for a while (multicore, modular implicits, etc.). Either way, lots of great work went into this and I'm happy to see the state of OCaml continuing to evolve for the better. Thanks for all the hard work!

<comment><USER9> I feel your pain, but the features will land when deemed stable. Without more frequent releases, those features would get into a release even later, so more frequent releases are a win either way.

</comment></comment><comment><AUTHOR> The changes are explained with some examples here [0]. Flambda is finally on, but comes with a regression :( [1].

[0]: [https://github.com/gasche/ocaml-releases-change- explanation/...](https://github.com/gasche/ocaml-releases-change- explanation/wiki/4.04.0-changes-explanation) [1]: [https://sympa.inria.fr/sympa/arc/caml- list/2016-11/msg00000....](https://sympa.inria.fr/sympa/arc/caml- list/2016-11/msg00000.html)

</comment><comment><USER10> I would like to see a language like haskell with no delayed or lazy evaluation so that one can better predict performance. Haskell has a great community and a charm that I would like for Ocaml. Don't know if Jane Street is still advocating their Ocaml core or kernel. F#, ml, haskell and OCaml, and a strong language for parallelism whose name I don't remember are hints that we need a better language. Haskell is not prepared for real world :), and OCaml is a turtle moving target.

<comment><USER11> Haskell has strict extension that removes all delayed/lazy computation from your code. Why do you think it's not ready for real world? It's used by numerous companies around the World for different kinds of software development, starting from formal verification and ending with web sites. Same for OCaml, both languages are very powerful.

</comment></comment><comment><USER12> I really like ocaml, but it could do with better community management I think. Eg it's been more than one week that the links of the news section on the homepage result in a white page. This might deter newcomers to even try it.

<comment><USER13> The community efforts are largely volunteer-driven by many people who also have day jobs (including me). Indeed, things could be better but given how much does get done, I'm willing to cut them some slack.</comment></comment>


MH17: Russia 'liable' for downing airliner over Ukraine - <AUTHOR> https://www.bbc.com/news/world-europe-44252150 <comment><USER2> This video showing the hearses from just one of the flights bringing back bodies to the Netherlands is so powerful:

[https://www.youtube.com/watch?v=P2NF45JfUkk](https://www.youtube.com/watch?v=P2NF45JfUkk)

We (the rest of the world) must bring Russia to justice over this.

</comment><comment><USER3> So, what can anyone do to Russia? Russian tech talent is St. Petersburg is still making 10K euros a month.

How has embargos done anything?

<comment><USER2> How about notching things up to NK levels? Russia has clearly demonstrated themselves to be a hostile state.</comment></comment>


Ask HN: Payment processing for International customers? - <AUTHOR> I live in India. I am looking for a solution for payment processing for my side project t.<p>I haven't registered my company yet. FROM the blogs and articles on other websites it seems that I can use PayPal but again I would like to know if there's better alternate for PayPal? <comment><USER2> If you need Cc processing PP is one of better, but have high commission fee. Google and you can find a lot of CC processors for India too.

<comment><AUTHOR> Thanks :)</comment></comment>


IBM show POC of social login attack [video] - <AUTHOR> https://www.youtube.com/watch?v=kC0s3S00Dmk

<comment><AUTHOR> Just got linked to this, feel it's probably already been on HN and I missed it last week but couldn't find any submissions so posting in case it hasn't been.</comment>


Developer solution to be used with iPad Pro? - <AUTHOR> I'm finding the iPad pros pretty intriguing.  Are there any solutions for coders?  Usually I use VSCode. <comment><USER2> Since there isn't anything quite like Termux for iOS (please correct me if you find something), I would recommend simply using the iPad to connect to a remote machine, through SSH or otherwise. That way you get the full power of the Unix shell alongside the convenience of iOS apps.

You could even configure your remote machine so that files you edited with SSH were also available over WebDAV or (secure)FTP, since many apps support read/write from those servers.

I'm not sure you're going to get the experience of Visual Studio Code from the command line, but if you're really hooked on it as an editor you could use VNC and take the speed/responsiveness penalty.

</comment><comment><USER3> Juno (a Jupyter client), Prompt, Textastic, CodeBucket, Working Copy, Pythonista, Coda and Cloud Console.

But I'd love a VSCode app for iOS to write code and submit to git from one single app.

</comment><comment><USER4> Blink Shell and VPS is the only real way to do any programming on iPad.

However, I love the form factor of 10.5" so much that this has become my preferred of way of doing any "fun" programming. For work and serious projects, I still fire up MBP.

</comment><comment><USER5> I really like Blink Shell, Charles Proxy, and Pythonista. One nice thing about Pythonista is that you can trigger scripts from Shortcuts (neé Workflow) which has come in handy for hacky workarounds.</comment>


Stop writing bad web copy, startups - <AUTHOR> http://blog.webcopyplus.com/2009/01/07/web-writing-the-good-bad-and-ugly/ Basic advice that is violated over and over again. Kathy Sierra also had a lot to say about this on http://headrush.typepad.com/. <comment><USER2> Nice job adding the word "startups" to the title... It's sort of like adding "in bed" after reading a fortune cookie. Soon everybody will be doing it.

</comment><comment><USER3> > This is where you hire a copywriter for your website and he or she fills the > pages with cleverly crafted, delightfully delectable prose that oh-so- > playfully sing and dance.

Prose is singular. The plural of prose is proses, though it's not frequently used. If you're going to chide authors on style, make sure you've got the grammar correct first. Also as a counterexample, I'd like to point out that woot.com typically exhibits playful prose to great effect. Honestly, I probably visit more for the excellent ad copy than any other reason.

<comment><USER4> Based on the grammar, "that playfully sing and dance" refers to pages. The grammar works fine.

But it's clumsy, and should be _This is where you hire a copywriter for your website who can fill cleverly-crafted prose into pages that sing and dance._

</comment></comment><comment><USER5> I highly recommend: <http://jasmineyoungediting.com> for affordable rapid turn-around copywriting, editing, proof-reading, etc...

</comment><comment><USER6> They don't take their own advice; their homepage is so busy!

<http://www.webcopyplus.com/index.html>

</comment><comment><USER7> I'd like to add that good editing can make or break the copy, no matter who it's targeted at.

Their != they're != there Your != you're Then != than Of != have

<comment><USER8> Absolutely. I was recently considering an ebook about good web copy and read the following in the preview: "it's purpose is". That was a deal-breaker for me.

</comment></comment><comment><USER9> Does anyone know what the cost for this service is - say for approximately 300 words, 600 words, and so on?

(rough idea)

Love this!

<comment><USER10> It depends on a wide variety of factors. (And, in general, the more willing someone is to offer you a no-questions-asked quote, the more likely it is that their writing is, well, a commodity.) If I were bidding on something like that, I'd want to know:

* Whether it requires any special industry/technical knowledge.

* Who your audience is.

* Whether the page is designed to convert, or just informational.

* Your future content needs.

I've charged as little as $50 for that kind of writing (to an audience of laypeople, as part of a larger project, with no effort to sell) or as much as $500 (selling a to a narrowly-targeted audience acquired through PPC spending, as part of a one-time project).

I do write this kind of thing, by the way; email address is in the profile.</comment></comment>