Why Diaspora will win - <AUTHOR> http://www.downloadsquad.com/2010/05/21/why-diaspora-will-win

<comment><USER2> All they need to do is to re-implement facebook (trivial, right, after all it's already been done at least once), woo away 400M users from their direct competitor that has very big backers, against an established network effect (that's trivial too, shouldn't take more than two weeks, after all once you have the first couple of million the rest will follow), they have to do all this without _any_ privacy mess-ups or their credibility is shot (and they are all big names in the cryptography scene so we can see that one as in the bag) and to top it off they have to stay good friends through all this.

Really, what could possibly go wrong.

On top of that, they have decided to solve those problems in facebook that do not really need solving, after all, if the only thing they did different was to default all privacy settings to _do not share this_ they'd be doing just fine.

To paint this as Diaspora vs Facebook and to suggest they will 'win' is to make it impossible for them to succeed at any level. This just makes it _harder_ for Diaspora because it raises the bar at which they will be considered a success to the 'impossible to meet'.

If they attract a 100K userbase that is happy they ought to be able to call it a success. But that's no longer an option with bullshit-titled articles like these.

Leave them alone, support them, give them resources but don't raise that bar any higher, please.

Writers like this are setting these kids up for failure, no matter how successful they are.

</comment><comment><USER3> I see Diaspora kind of like a third-party candidate in an election (in the US). They won't win, but it's not really about that. It's about forcing the big guys to take a better stance on their one important issue.

<comment><USER4> I also see Diaspora like a third-party candidate in a US election, but in a different way:

They won't win, they never stood a chance, and beyond a small legion of die- hard supporters, never had any public traction with the public beyond "oh hey, did you know what's-his-face is running?". And like most obscure third party candidates, they will impart no significant force upon the major candidates' policies or views.

Sorry to be a debbie downer, but I have a hard time treating seriously a team that has no track record nor even the slightest bit of code written. Come on, we've been around that block to many times to be fooled by this, haven't we?

Don't get me wrong, it may very well be that some agile startup will come along and eat Facebook for lunch. Hell, it'd be great to see some real competition in this sphere - but from everything we've seen of Diaspora, there's no real reason to expect them to do anything but sit around, write a bit of code (if they even get to that) and then fizzle out.

<comment><USER5> Honestly, I don't think it matters who the developers are or what their coding expertise is at this point. They were there in the right place at the right time for the media to latch on to their story and to bring those issues into the light. They could take the money and run away laughing at this point, and they still will make an impact on Facebook because the media were able to point to their business model and say "See, things could be different!". Yes, they have ideas that have been tried and failed before, but their story is "sexier" and better timed than those. It's not so much the people or the code, but the position and timing.

<comment><USER6> The problem with latching on by the media is that goes away as quickly as it comes.

The main problem with Diaspora is that they're focusing on the technology, and on the servers. The real solution to the "Facebook problem" lies with the end users, not with the servers, however decentralized they were.

I'm pretty sure Diaspora will produce an application. And I'm pretty sure only a handful of early adopters will set up the servers, and it will be used by even fewer zealots. The "real" users -- who are a majority on Facebook -- will never switch.

And don't forget that there already is a project doing precisely what they intend to build: <http://opensource.appleseedproject.org/>

<comment><USER7> And they're not the only ones:

<https://openqabal.dev.java.net/>

(disclaimer: OpenQabal is a project I started).

I'm pretty sure there are a few other projects out there as well, doing more or less the same thing(s). If everybody working on one of these projects could get in sync, and agree to common protocols and formats, so that they could all interoperate, it would make it that much more likely for the federated / decentralized approach to catch on. I'm not sure it'll happen though. Nobody seems to really be talking about it (yet).

</comment></comment></comment></comment></comment><comment><USER8> I hate that diaspora got so much money. I don't think these guys are proven enough, nor will their solution be the one that eventually be the best. I also _fear_ that they will screw it up and the pro-facebook crowd will declare it dead.

<comment><USER2> People can give their money away as much as they want.

Why would you hate it ? Are you jealous ?

The problem is that the amount of money they received is a pittance compared to what is needed to take on FB, and it will take a lot more than _just_ money.

They're in a dangerous zone of having high expectations and no product, it reminds me very much of the cuil announcement, and I fear it will end more or less the same.

edit: dear downmodders, I know that you don't like my position on this but wishing it to be so doesn't make it so.

<comment><USER8> I hate they got so much, because they're much less likely to succeed when they can plan huge from the start. 150k will feel like they can do more then they really can. Where something like 30k would inspire leanness.

</comment></comment></comment><comment><USER9> On the one hand, I think the aspect of Diaspora-the principles of user- controlled content (i.e. It's mine, and I use it however I want vs. It's mine, but it's on their infrastructure) is great.

On the other hand, I think that banking the success of their product based on the premise that "we are the anti" Facebook is dangerous, possibly lethal. Build what people want, cater it to their wants; don't spend your campaign time telling us what the other guy ISN'T doing.

<comment><USER10> Yeah, seems like Facebook is in a position to gain by making some highly publicized moves toward assuring privacy... get that in a couple of news cycles and people will assume it's a fixed-enough problem (whether it is or not).

</comment></comment><comment><USER11> Why Duke Nukem Forever will win game of the year 2011.

</comment><comment><USER12> I'm expecting Google to roll in and take over any day now. Social networking almost overlaps with their existing turf already. I seriously doubt Buzz as it exists now is as far as they'll go.

We're clearly moving to a more federated system and I'm not convinced that a dorm room startup can pull off an industry unification move like this. They don't have the traction.

</comment><comment><USER13> yeh, so this post was as bad as the other. In fact it's probably worse for being so speculative :)

Beyond the fact this is unlikely to be the "next Facebook" there isn't a lot we can do now except wait and see.

</comment><comment><USER14> What about all the other facebook-replacement projects? Why didn't they "win"?</comment>


Ask HN: How can I contribute more to HN? - <AUTHOR> http://news.ycombinator.com/threads?id=raganwald

<comment><AUTHOR> In light of the effort to improve HN, I 'd like to politely (and humbly) ask for some feedback. What can I do to make HN a better place? I'm not looking for feedback on specific comments (we have downvotes and relies for that), but general advice.

Comment less? Be more technical? Be less arrogant? Be more polite? Stay on topic? Anything else?

I have quite a few karma points, but I often say that moderation is most needed when something is popular but unhelpful to HN. In the same spirit, I am thinking that feedback is most helpful for people like me who have a lot of karma but could be even better HN citizens.

Please? And thank you!</comment>


Free Software Foundation Asks Microsoft to Open Source Windows 7 - <AUTHOR> https://www.theregister.co.uk/2020/01/24/windows_7_open_source/ <comment><USER2> I'd imagine there's too much code overlap with Windows 10 for Microsoft to want to do this.

</comment><comment><USER3> It will never happen, but one can dream :)

<comment><USER4> Maybe 30 years from now haha

</comment></comment><comment><USER5> This is the best idea since the FSF bought Facebook. [1]

[1] [https://www.fsf.org/blogs/community/free-software- foundation...](https://www.fsf.org/blogs/community/free-software-foundation- announces-crowdfunding-campaign-to-buy-facebook)</comment>


Tinkerers by David Brin - <AUTHOR> http://forward.msci.org/tinkerers/graphicnovel.html

<comment><USER2> I'm trying to figure out if the character introduced on this page is Cap'n Crunch, Kenny Rogers, or the Woz: <http://forward.msci.org/tinkerers/images/page6.jpg></comment>


The Soviet Union's Flawed Rival to Concorde - <AUTHOR> http://www.bbc.com/future/story/20171018-the-soviet-unions-flawed-rival-to-concorde <comment><USER2> The article misses (although hints at the cause of) a very important difference: Concorde's ability to supercruise, that is, cruise supersonically without using afterburners. The Tu-144 needed to use afterburners during the entire supersonic phase of flight, whereas Concorde just needed them to accelerate. Concorde was a notorious fuel guzzler but only during subsonic flight and ground operation; she was actually quite efficient at Mach 2.

<comment><USER3> Indeed.

Concorde's largest downfall wasn't technical, it was social. The original expectation was that she would rapidly climb, go supersonic over land, and supercruise all the way to her destination. If this was possible it was reasonably economical.

Problem is that sonic-booms over populated areas are a big "no no." So Concorde was forced to wait until she got out over the atlantic to supercruise, which meant more time spent in her inefficient subsonic configuration and longer flight times.

In 1976 the US actually banned Concorde due to noise complaints. The ban was lifted in 1977 after changes mentioned above.

<comment><USER4> The Concorde was also really expensive. I was able to fly on it twice, once each direction, near the end of their operations. I only flew on it because they'd announced they were shutting down and I wanted to fly supersonic before they went out of business. It was very expensive.

I think it was in '03 and the price was just something like $12,000 for the two flights. It was high enough that I put it on a credit card. The prices had a lot of variation and I flew first class but they had a higher class that they called Concorde, as I recall.

<comment><USER5> Yes, it was a premium product. But if you're a banker/lawyer going between NY and London a lot and your billable rate is $crazy, it's super good value.

I think there is an enormous market for something like Concorde now, much more than there was before. The amount of super rich has increased massively (and each of them have way more money). I think you'd even see executives using it over their own private jets for the speed increase - which means tickets could be crazy expensive, as operating a jet is very pricey.

I suppose it'd depend a bit on if you could get WiFi on it though. Wonder if that is possible at supersonic speeds?

<comment><USER6> R.E.G. Davies worked out why this isn't as powerful as you think because the earth is round. The justification for that expensive ticket is that you can have your meeting without getting jet-lagged. So, basically, leave your house at a reasonable time, get to a meeting on the other side of the Atlantic at a reasonable time- that's worth an awful lot of money. If you have to fly out the night before anyway, what justifies the extra money versus subsonic transport?

So let's imagine you live in New York and have a meeting in Paris- and let's say, for the purpose of argument, that it's actually a transporter, instantaneous travel from airport to airport. You leave your house at 6AM, get to the airport at 7AM. Its an international flight, so you need to get to the airport early, so your flight leaves at 8AM. You instantaneously arrive in Paris, but that's six hours time zone different: 2PM in Paris. Two hours to navigate through customs and the airport and it's 4PM, an hour to do ground transport to your meeting and it's already 5PM.

So even with exactly zero travel time between JFK->CDG your meeting can't be conducted at normal business times. So if your meeting still requires traveling out the day before and dealing with jet lag and all the rest, there just isn't that much benefit to justify the costs.

It might make sense for a business jet, so long as the costs are really not much more than a GV, or if it's primarily about prestige anyway.

<comment><USER7> Two hours for customs? If you join the Global Entry Program it only takes a few minutes--you check-in at a kiosk and go to a special lane. Great if you have kids. You also end up enrolled in the Trusted Traveler Program, which puts you in the expedited lane for terminal security.

As for the wait time at the airport: if you're buying premium tickets the airline doesn't care how late you arrive as long as you don't delay the flight. IIRC, at least for domestic travel TSA requires that all checked luggage be presented at least 30 minutes prior to departure, so that's the only hard time limit. Maybe it's earlier for international travel, but for a single day itinerary you presumably wouldn't have any checked luggage.

<comment><USER6> So far as I know, France has no equivalent of Global Entry. It's also not universal advice, even if you were flying into the US- it's only available to citizens of certain countries. (France is not one of them, though Germany's EasyPass is.)

It would be faster to get through the CDG customs lines if you had a Schengen citizenship, since they could get into the the Schengen lines, but that's not really particularly useful advice.

</comment></comment></comment></comment></comment></comment></comment><comment><USER8> This 6min video does a great job on explaining the differences between Concorde and Tu-144 and why Tu-144 was so bad at many things. [https://www.youtube.com/watch?v=VFWbuKr5-I8](https://www.youtube.com/watch?v=VFWbuKr5-I8)

<comment><USER9> Odd coincidence how this "news" article popped out of nowhere, after this video just had brought the Tu-144 into the focus again.

</comment></comment><comment><USER10> That article had quite a few typos, especially for a BBC article:

> "There was the space race and the race to put a man on the Moon race > happening at the same time.

> The Nasa flights spelled the end of the Tu-144's flying career. Despite the > refinements added by _Nasan_

> The Nasa programme _bought_ the Tu-144 back from the dead

<comment><USER11> The last one might be a pun.

</comment></comment><comment><USER12> I think they hoped to get it out of the door to try things on and then replace with gen-2 supersonics which were no doubt to arrive.

As you know they never materialized due to lack of reasonable economic demand.

I think the sadder story are Soviet high-speed trains. There were one or two, they did some speed records, worked on some experimental routes and never got wide adoption.

Only in the last 15 years there's finally some viable high-speed options. Including one of Soviet designs incidentally.

<comment><USER13> Seems like there's a commonality with Britain there. We had a world-leading high-speed train design (the APT), rushed into a press demonstration 6 months before it was ready; the project was cancelled but the technology found its way to Italy and then, more recently, back into trains that are now used on the route for which the design was originally intended.

<comment><USER14> >the technology found its way to Italy

Is this also related to the problematic and eventually scrapped Dutch high- speed Fyra trains?

<comment><USER13> They have a common heritage. The APT design was developed into the FS ETR 400 which was a collaboration between Fiat Ferroviaria (who have gone on to produce the Pendolino, the train I was talking about, and are now part of Alstom) and Ansaldo (who, as AnsaldoBreda, produced Fyra; now part of Hitachi).

<comment><USER15> Note the ETR 450 uses hydraulic actuators, as do the later Pendolino models, whereas the APT used electromagnetic actuators. (I'm unaware of any ETR 400 having ever existed; the prototype ETR 401 did, but that used passive tilt rather than the active tilt developed by BREL.)

SIG, then owned by Fiat Ferroviaria, built the electromagnetic actuators used by the Swiss ICN and the British Class 390 "Pendolino".

</comment></comment></comment></comment></comment><comment><USER16> If you're ever around Stuttgart/Frankfurt area, there's a couple of really amazing Technical Museums that are sister facilities. One is in Sinsheim and one is in Speyer and are known as the Teknik Museum Sinsheim/Speyer. They're full of really awesome exhibits - heaps and heaps of cars, trains, aircraft, military equipment, etc. Due to it being a late plan, I spent a very rushed 4 hours in each of them and that wasn't nearly enough time - at times I was literally jogging between the exhibits trying to absorb it all. They are really amazing places to check out.

Anyway, specifically of interest here is Teknik Museum Sinsheim [1], which has both a Concorde and a Tu-144 on one of the hall roofs, next to each other. You get to climb up and walk through them, look up from under them, etc. but it's really cool to just look at all the little differences between them. Here's [2] a cool website I just found that shows a side by side comparison of the two jets.

The one that caught my eye immediately, which you can see on that page as well as this [3] wiki image, was that the air intake on the Tu-144 is _far_ longer, necessitating the rear bogies to fold up into the air intake. I can't begin to imagine how they handled that without seriously messing with the air flow (and I can't find any diagrams of the airflow like with the Concorde). Wiki apparently says it was due to design misconceptions on the Soviet side and they were eventually shortened significantly. Also, the engines sit far more inboard on the Tu than the Concorde, presumably so they can get that additional length of intake up the fuselage/wing structure. Alternatively, it wouldn't surprise me if that's also part of its military heritage coming through.

The Tu-144 is a pretty fascinating aircraft, even beyond the story behind its development and competition with the Concorde. However, it's really cool to compare the pair and see the differences because it makes you wonder why they differ in those ways - whether it's a case of the Soviets thinking they should replace/modify the Western design with something better, or whether they took multiple concepts including the Western design and sort of mashed them together. It's truly a fascinating aircraft, despite its failures.

[1]: [https://sinsheim.technik-museum.de/en/](https://sinsheim.technik- museum.de/en/)

[2]: [http://www.hitechweb.genezis.eu/concorde_vs_tu-144.htm](http://www.hitechweb.genezis.eu/concorde_vs_tu-144.htm)

[3]: [https://en.wikipedia.org/wiki/Tupolev_Tu-144#/media/File:Tup...](https://en.wikipedia.org/wiki/Tupolev_Tu-144#/media/File:Tupolev_Tu-144_CCCP-77102_LEB_02.06.73_edited-3.jpg)

<comment><USER17> The sister museum in Speyer (not far away) has a Buran, a Soyuz capsule, and a nicely prepared Boeing 747 among other thing. If you are going to visit, you should try to visit both.

<comment><USER18> The _Europa Park_ theme park a little bit further south, while not a museum, has a rebuild _Mir_ used for cosmonaut training.

[1]: [http://www.ep-fans.info/?id=1540,12,11](http://www.ep- fans.info/?id=1540,12,11)

</comment></comment></comment><comment><USER19> > _The space race undermined the Tu-144 programme by shifting the Soviet focus > towards long-range rocketry and high-altitude missiles, and away from > supersonic bombers, effectively forcing the Soviets to develop the Tu-144 as > a standalone civil aircraft programme._

This is the key to understand this plane. Soviet goverment considered bombers to be obsolete by rockets. Designers had to pull many tricks to keep supersonic bomber program running.

One way was to develop civil/cargo plane. Other was to call it 'rocket carrier' (long range missile launcher).

</comment><comment><USER20> I'm surprised that nobody has yet mentioned Boeing's SST effort. It was a much more ambitious aircraft, larger and faster. Prototypes were under construction. There were orders for 122 planes.

Development was expensive, and Boeing was relying on considerable contributions from the US government. The project was cancelled in 1971 amid cost and environmental concerns.

[https://en.wikipedia.org/wiki/Boeing_2707](https://en.wikipedia.org/wiki/Boeing_2707)

</comment><comment><USER21> I was surprised to find no mention of Tu-160, which is a supersonic strategic bomber currently in operation. It builds on Tu-144 design. [https://en.m.wikipedia.org/wiki/Tupolev_Tu-160](https://en.m.wikipedia.org/wiki/Tupolev_Tu-160) One of the advantages of flying Mach-2 is to provoke the increased wear of the intercept aircraft at peaceful times, or so they say.

</comment><comment><USER22> Maybe someone with domain knowledge is around here: could we make a spacious long range super sonic airplane today, with our modern tech? Think something a bit smaller than the 747, for example.

Why isn't it considered viable? The world is 2x richer than the last time the Concorde flew...

<comment><USER23> [https://medium.com/lift-and-drag/youre-too-cheap-to-fly- fast...](https://medium.com/lift-and-drag/youre-too-cheap-to-fly- faster-7885a299bca2)

The major pressure has been to make aviation cheaper, not faster.

<comment><USER24> Right. And for those already willing/able to forgo the cheaper for more comfort (but not much speed), there are already well-established options including lay-flat seating for international routes and even private plane rental.

<comment><USER23> Honestly, I can't say myself that if I had the money, that I'd choose getting there a few hours earlier instead of feeling like a human being when I arrived.

</comment></comment></comment></comment><comment><USER25> Less long form information about the vehicle: [https://en.wikipedia.org/wiki/Tupolev_Tu-144#Paris_Air_Show_...](https://en.wikipedia.org/wiki/Tupolev_Tu-144#Paris_Air_Show_crash)

</comment><comment><USER26> > _Its first flight comes three months before Concorde takes to the air._

So, a copy was rushed into testing before the original was ready. This could explain why there were flaws in the design...

</comment><comment><USER27> It was a direct ripoff of concord only it was a POS that crashed on. It's air show debut. It never flew commercially more than a half dozen times.

</comment><comment><USER28> I'm confused.

The standard reason given it failed was the plans were leaked on purpose to the Soviets that had flaws to try (successfully) to make it crash.

I'm happy it's an urban legend, but it was up there with the NN Tank story and the Gas Pipeline story (yet to be disappointed that this too is legend) as a common pre-WWW engineering story.

Why no mention? Even to nix the legend or for old times sake.</comment>


Windows 0 day published on Twitter - <AUTHOR> https://arstechnica.com/gadgets/2018/10/another-windows-0-day-flaw-has-been-published-on-twitter/ <comment><AUTHOR> I don't know how severe this bug is, but the person who posted it seriously needs to see a therapist/doctor and talk to someone about their depression.

If you're experiencing these kinds of mood swings, understand that there are people who can help. You don't have to live your entire life that way. Acknowledging and seeking treatment are important first steps.</comment>


Ask HN: How best to socialize my new side project? - <AUTHOR>

I'm in the process of applying the finishing touches to a new site I've developed.  I should be soft-launching it next week, taking a couple of days to tweak, test, and add some initial data to it.  After that, it's time to do a formal launch!
The question is...  how?  This is my first "startup" endeavor.  The site itself is niche-focused.  Technology.  Primarily developers, sysadmins, and the link.  Aside from posting an announcement here on HN, how else can I get the word out?
I've got a Twitter account already setup for it.  It has a handful of followers, but no one influential enough to really drive traffic to the site.  Where else can I turn to put the word out?  Are there other social news sites I should post to?  Web directories?  Any advice?
Since this is my first outing on my own -- despite a 15 year career in technology -- I'd REALLY appreciate any advice, pointers, tips, etc. that you more experienced readers can provide. <comment><USER2> It is difficult especially for the techies. Can't help you much except for wishing you luck. In my case, I got about 50% replies to the emails I had sent but it's not even a tip of an iceberg.

Do you know of any people in the niche? Chances are we're going to like your site because techies (don't like the word "geeks") get excited about everything technological.

<comment><AUTHOR> I do. I'll certainly reach out to them to aid in getting traction. It always helps to get the word out as much as possible, though.</comment></comment>


Greenwald: I'm working on several new significant NSA stories. - <AUTHOR> http://www.theguardian.com/commentisfree/2013/sep/07/nsa-encryption-us-uk-press-freedoms

<comment><USER2> "In Europe this week, President Obama has been making similar claims when asked about NSA spying, repeatedly assuring people that NSA surveillance is overwhelmingly devoted to stopping terrorism threats."

They can't say anything consequential without a qualifier. "Overwhelmingly devoted" means that surveillance is also deployed for other reasons.

From Der Spiegel: "But the UN and the EU are also listed as espionage targets, with issues of economic stability as the primary concern. The focus, though, is also on trade policy and foreign policy (each rated "3") as well as energy security, food products and technological innovations (each rated "5")." [http://www.spiegel.de/international/world/secret-nsa- documen...](http://www.spiegel.de/international/world/secret-nsa-documents- show-how-the-us-spies-on-europe-and-the-un-a-918625.html)

</comment><comment><USER3> I hope Greenwald is being careful. I worry about him ending up like Michael Hastings.

<comment><USER4> Does Greenwald have a history of drug abuse? Did he wreck a car while drunk in his teens? Is is family actively trying to get him into detox?

[http://touch.latimes.com/#section/-1/article/p2p-77098131/](http://touch.latimes.com/#section/-1/article/p2p-77098131/)

</comment></comment><comment><USER5> I hope all of these companies will lose a lot of customers due to these revelations. Their cooperation with NSA to weaken their security software while selling it as "very secure" to their customers is disgusting, and it's irrelevant whether they did it voluntarily or not. There's always a choice, and they have no excuse.

If you still believe in such things as "American values" and "patriotism", this is the exact opposite of that: un-American, and unpatriotic.</comment>


Amazon EC2 Server Used in Sony Attack - <AUTHOR> http://www.bloomberg.com/news/2011-05-13/sony-network-said-to-have-been-invaded-by-hackers-using-amazon-com-server.html <comment><USER2> I get ssh based attacks from EC2 a lot, I've blocked completely their ip ranges from servers I know should never communicate with EC2.</comment>


Demonstration of Picasso canvas fingerprinting - <AUTHOR> https://antoinevastel.com/browser%20fingerprinting/2019/03/21/picasso-canvas-fingerprinting.html <comment><USER2> Interesting! In case you are wondering: the fingerprint is the hash printed above the rendered picture, and it should be unique per device class (because of the hardware / driver / OS / browser / ...).

</comment><comment><USER3> I think this would be more useful with some comparison points, so:

* Firefox/Linux/AMD and default settings gives me 4dd1c8196b5a7f7fd5faecadd398a31e

* Chromium/Linux/AMD and default settings gives me 3a255645100c90693ae68d2e121f64fb</comment>


AI in drug discovery is overhyped: examples from AstraZeneca, Harvard, Stanford - <AUTHOR> https://medium.com/the-ai-lab/artificial-intelligence-in-drug-discovery-is-overhyped-examples-from-astrazeneca-harvard-315d69a7f863 <comment><USER4> 1) As someone who does NLP and AI for Evidence Based medicine [1] I can only agree. I build natural language processing tools, together with the team, for EBM. I have /no/ idea what to do next. Please. Help me.

2) Rant: here's the thing, we have SVMs, LSTM, BiLSTM-CRF, Knowledge Graphs, 28M articles, deploy infrastructure, tests, you know all the cool stuff. We can't sell it. It's PubMed this, PubMed that. There's no breaking that spell. So instead of building a better search engine, we focused on better analytics. Is it cool, sure. Is it as good as all the KoL stuff from LexusNexis, nope. Medicine is /hard/, if you think your app is hard: medicine is /harder/. Am I trying to cover my ass, sure. But seriously, deal with everything being non- standard and proprietary for long enough and you'll feel the same way. 80% of my job is saying "no" to people who aren't going to pay in the first place (Sanofi, Novartis, etc). They don't care. I'm just going to ride this out, throw some blockchain in somewhere where it makes absolutely 0% sense, call it a day. Seriously, NLP/AI for medicine: it's crap 100%. And, I'm a "senior developer" with publications behind my ass on this. I don't know what to do instead of riding the hype train. Seriously: anyone out there that knows what to do with 100M+ medical publications in a 100M+ knowledge graph, be my guest: I'd hire you in a heart beat. But it's hard and there is nothing to be gained AFAIK.

[1]: [http://growthevidence.com/joel-kuiper/](http://growthevidence.com/joel- kuiper/)

<comment><USER5> Imagine you were given raw data from the large hadron collider and you were asked to find interesting things in the data. What would you do ?

Any serious scientist would see the folly of trying to run only machine learning algorithms to find the Higgs boson. What you would need (in addition to ML algorithms for processing) is a good theory of what you're looking at. This also means just applying computer science methods to the problem isn't going to work, you need to inject theory from physics.

For drug discovery this means you need to actually do biology at some point in order to make progress. ML strategies involve tight loop around trying different models, implementing them, and checking to see if it does better than other models. What's needed is then good experiments to test your models and good models to make novel predictions.

There is no getting away from wet lab experiments in biology if you're going to make any significant advances. The theory just isn't there to do purely computational work.

<comment><USER4> Exactly, and that uncovers the folly in this: what are you trying to do. Often enough it's very specific, and requires actually going out into the world. Sometimes the literature is already there. But nearly always it requires some form of analytical thought. "I want X because I need to prove Y". There is no AI magic sauce around that. It means that the people interacting with the "user friendly" interface need to know, and need to know how to get there somehow. That's rarely the case. And from a product perspective it makes things very hard: you get bombarded with very specific requests that require time and effort to build UIs and models for, that at the end the of the day only solve that specific question. Instead, it would be better to equip people with the tools needed to solve the meta-question so to speak, but that meta question is much much harder (at least, I've been trying but no success yet). As a funny anecdote: my medical knowledge had exploded. But I see that as a symptom of the broader problem: I know how to do /some/ things because I've gained that domain knowledge, but I can't teach the system yet to solve the "general" problem because a) I don't know what it is b) there is no data to get there c) what even is the UI for that.

<comment><USER6> Is this a fair way to express your problem?

You're being asked to use AI to replace an entire process all at once, rather than to assist with or replace a step in that process.

Instead of being asked "how do I solve this problem" it would be nice if you were asked "can you replace this thing I have to do all the time?"

Maybe this doesn't apply to your work, but if I were in your position I'd talk to the human that does the human version of the process and break down everything they do to get from A to Z into steps. If you can start by just replacing or augmenting a single step, you're adding value right away and you've also built a part of your eventual A to Z machine, right? For example, your search engine idea is a good direction. Can you automate the process of finding relevant articles? That would help the humans but would also help whatever AI you're working up to, right? Then maybe further in there's some other process involving physics modeling or something that is clear enough in scope to do programmatically.

I do much simpler work, but I find it's much easier to automate processes one step at a time rather than all at once.

</comment></comment></comment></comment><comment><USER7> As someone who has worked on AI for drug discovery, I would say that the title is correct, but not for the reasons stated. There is also some annoying speculation in this document that is completely incorrect, and unsupported, so I caution people when reading it.

Anyway, the primary reason that AI for drug discover is overhyped is that the sort of problems AI is good at solving don't line up well with the unsolved problems in the drug discovery pipeline.

This article, for example, focuses a lot on lead generation. Lead generation is the easiest aspect of the problem to tackle using AI, and so most people doing research start out trying to build a foundation in this space. However, it doesn't actually represent the majority of the cost.

Drug makers typically spend about ~$800M on failed drugs for every ~$900M in revenue. They aren't spending that $800M on leads, finding leads is fairly easy. They are spending that money on drugs that fail in Phase 2 and Phase 3, which is more about off-target side effects, bulk formulation and synthesis, patient population differences, drug-drug interactions, etc.

It would be nice having better leads, but there aren't a shortage of them that look good in vitro or even in vivo. It isn't until much later in the pipeline that the costs really add up, and failures there are expensive. If we could solve off-target side effects using AI, then we'd be in a whole different ballgame. Having banged my head against it for a while, I think it is possible, but will take a huge amount of investment.

The work this article talks about is more foundational, which is necessary but should not really be taken as anything more.

<comment><USER8> that is a great point.

for those not familiar with where the money really goes in R&D, and where the biggest opportunities for improvement are, check out these charts [1] and [2] from the seminal paper on calculating the cost of getting a drug approved [3].

the biggest areas to improve R&D productivity lie in 1) picking more validated targets to reduce Phase 2 and 3 failure rate and 2) reducing the cost of lead optimization (basically the process of turning a compound that has the desired impact on the target (target is a molecule implicated in disease that you want to effect with a drug) into a molecule with drug like properties (ie gets where you need it in the body, is safe, can be manufactured and delivered efficiently))

im not an AI person, but could AI play a role here? there are plenty of validated targets that are "undruggable"; could AI help find as-yet- undiscovered molecules that could engage these targets? or could AI somehow make the med chem / lead optimization process easier?

[1] [https://media.nature.com/m685/nature- assets/nrd/journal/v9/n...](https://media.nature.com/m685/nature- assets/nrd/journal/v9/n3/images/nrd3078-f2.jpg) [2] [https://media.nature.com/m685/nature- assets/nrd/journal/v9/n...](https://media.nature.com/m685/nature- assets/nrd/journal/v9/n3/images/nrd3078-f3.jpg) [3] [https://www.nature.com/articles/nrd3078](https://www.nature.com/articles/nrd3078)

<comment><USER9> The primary missions in drug development are efficacy and safety. AI can help answer clear well-formed efficacy questions like, "Does this molecule fit a chosen target molecule"? But it can't help with bigger efficacies like, "Will hitting this target make a _sufficient_ difference in managing this disease"? Or _any_ safety questions like, "Does the molecule also hit any other molecules somewhere in the body (or population) that might screw up something else"? And unfortunately it's safety failures that eat up 90% of drug development costs (esp. in Phase III).

Until Wall St (or Sand Hill Rd) understands that domain-agnostic low-info approaches like AI are incapable of answering complex questions that require teams of PhDs steeped in decades of doing both chemistry and biology, the notion of CADD will continue to miss the mark and waste megabucks.

</comment></comment></comment><comment><USER10> The fundamental problem with AI (here meaning, neural-network based applications) applied to biology is that _pattern recognition_ doesn't get you very far. When designing a small molecule drug, for example, the goal is to find a molecule that will slot into some protein (or other biomolecule) of interest in just the right way; whether it does so is dependent on shape in complex and impossible-to-predict ways. As an analogy, take the task of making a key (drug) to open the front door of an apartment across town (disease- relevant target); your "training data" (known drug-y molecules) are the keys for a bunch of other apartments sampled mostly randomly from around town. Obviously, you can make any number of plausible key-oid objects that would pass visual inspection as potential keys to the target, but even a godly- intelligent post-Bostromian superAI couldn't _solve_ the problem in any principled way. You just need information about the actual lock you want to open, plain and simple.

There are many, many, _many_ other open problems in biology, of course, ranging all along the conceptual gamut, so I can't say there's _nothing_ where AI would be a killer app. And there's also the sort of last-ditch null argument that, well, being smart helps humans do biology, so computers that are smart must be useful _somehow_. But typical progress in this field is from patiently acquiring new data. There's no point where you know "enough" of the picture to infer the rest with any degree of certainty- the degrees of freedom in the "design" of biological systems is simply too huge.

<comment><USER11> I'm not sure this is a great analogy. If we didn't know anything about the locks (target binding sites) I would agree with you, but there is no reason to enforce that restriction. A pipeline that given a target binding could predict a number of potential drug structures with high accuracy would be extremely useful. Going with your analogy, this would be like training a net using a database of lock structures and their corresponding keys, and then having it predict a key for a given lock. That seems fairly doable for current ML techniques.

Obviously actual drug design is more complicated than this, and you have to consider side effects. But I don't think it's as hopeless as your analogy.

<comment><USER10> > Going with your analogy, this would be like training a net using a database > of lock structures and their corresponding keys, and then having it predict > a key for a given lock.

If we have that, then the problem is entirely trivial- just look at the lengths of the tumblers, and give the key teeth that match. And so it is, with the corresponding increase in complexity, in biology- given a complete and confident structure of the target, we have (non-AI, reasonably reliable) chemical modelling methods to see whether and how a given compound will bind; iterate molecules until you find one that works and you've found your "key" quite cheaply. So, there's another limitation to the practicality of AI- when it's not irreducibly complex and intrinsically unknown, biology is usually mechanistic and deterministic enough that conventional approaches can reason about it quite effectively without resorting to anything so fancy as a neural network.

Biology is almost always more complicated than a given description, so yes, the real problem is more complicated. As you mention, the key we create must not only fit the target lock, but it must also not fit any _other_ locks that might cause problems. Perhaps AI methods could try to make a working key that is un-key-like as possible, to make it unlikely to fit off-target locks; potentially useful. But, absent knowledge of the various perverse configurations that locks in the wider world can have, the confidence the AI method's inferences are very limited- roughly speaking, it's attempting to make a classifier for a population that is mostly unknown. We might hope for a severalfold increase in the number of successful drug candidates, in the best case, but not a fundamental disruption of the pharmaceutical research process.

<comment><USER11> > given a complete and confident structure of the target, we have (non-AI, > reasonably reliable) chemical modelling methods to see whether and how a > given compound will bind; iterate molecules until you find one that works > and you've found your "key" quite cheaply.

I am a researcher in the computational chemistry/biophysics field, and from what I can tell this is not the case (would love to be corrected if I am mistaken; my research is not directly related to drug design). There are various computational ways to to test whether a molecule will bind to a target site, but they are either too inaccurate or too computationally expensive to claim that this problem is "solved" by chemical modeling methods. Pharma companies are still wasting money sending drug structures that have been validated by computational methods to organic chemists to synthesize that end up not binding as expected.

</comment></comment></comment></comment><comment><USER12> AI can't help much in drug discovery (i.e lead generation) because the pharamecutical industry is suffering from Eroom's law ( [https://en.wikipedia.org/wiki/Eroom%27s_law](https://en.wikipedia.org/wiki/Eroom%27s_law) ). It's a play off Moore's law (spelled backwards), but the fundamental problem is the pharma/medical industries do not understand biology from first principles, unlike hard physical sciences, such as computer hardware engineering.

First principles understanding of computing hardware allows chip manufacturers to create novel, new chips from scratch, as we fully understand the physics behind electricity, how logic gates work and signals propagate through physical mediums. Lock most college students in a room with resistors, diodes, wire, etc, and reference material, and they could recreate basic circuitry and very simple computers. You can not do the same with biology - no student or expert, given unlimited resources, equipment, or reference material can construct a new living cell from scratch (proteins / molecules). This is not a slander of those sciences and scientists, but there is simply a huge gap in how well we understand the basic principles of life and how well we understand the basic principles of computing.

Throwing more resources / computing into "drug discovery" is like trying to build chips by wiring computer parts differently. Occasionally it might work and produce a "useful" result, but it's fundamentally a broken approach.

<comment><USER13> YES. Exactly this.

It's fine to make approximations to avoid exponential scaling, but applying function approximators essentially randomly won't get you anywhere. This is then compounded by the fact that the functional framework you're starting from is not a first-principles approach.

Until there is QMC for drug discovery, it will all be hype.

<comment><USER9> _Mostly_ hype. Yes, automating drug discovery to any extent is utterly hopeless, and as likely to impact the pharma business any time as autonomous killer robots overrunning the battlefield -- Not In My Lifetime.

But AI definitely has a near term future in addressing well formed questions like specific assays or searching for well-constrained targets, like ligand matches. The trick is for the AI contributor TO LEARN SOMETHING ABOUT THE DAMNED DOMAIN. Unless the chemist/biologist is intimately involved in the task, the AI provider is shooting blind. But with many wise eyes on the ball, even the hardest problems becomes a lot more assailable.

[I say this as someone who processes images and analyzes data within a big pharma, and has seen several grand IT plans fail (like systems biology disease modeling) and many small & specific scientist-assistance tasks succeed.]

</comment></comment></comment><comment><USER14> Responding to his issues with Vijay Pande's work (I'm not affiliated), graph convolutions really are better than char-RNN for this. There's a good theoretical motivation for why, and people have spent a lot of time trying to find better embeddings of chemical space. (Admittedly, still not great - <USER2> wouldn't dispute the overall thesis that AI in drug discovery is still very early.)

I did a project a year or so ago to reimplement one of Aspuru-Guzik's papers, a variational autoencoder, ([https://github.com/maxhodak/keras- molecules](https://github.com/maxhodak/keras-molecules)) and when I did that I compared to char-RNN and the VAE did get much more interesting results. I also saw results from other people around that time showing that using graph convolutions on the front end instead of one-hot encoded SMILES strings was even better.

Also, as for OP's objection about GANs not working because it's a "perfect discriminator," this is an obvious result that becomes apparent after spending 20 seconds with the problem. (Eg, this thread here: [https://github.com/maxhodak/keras- molecules/issues/55](https://github.com/maxhodak/keras-molecules/issues/55)) I haven't read the Harvard paper referenced but I'd be absolutely shocked if this was lost on them. There are definitely ways to work through it.

<comment><AUTHOR> I am not sure to fully understand your remark, but if you have a benchmark graph convolutions vs. char-CNN, it would be great to write your result and post it on Arxiv. Pande will be interested ;) The problem is not with the theoretical motivation, but with the empirical confirmation.

I also agree that there are many ways to work through the perfect discriminator problem for ORGAN. But it remains to be done (afaik).

<comment><USER14> I misread char-CNN as char-RNN in the linked article. I don't know of results that compare graph convolutions to char-CNN. If a comparison were to be made, what metrics should it focus on?

<comment><AUTHOR> See MoleculeNet ;)

</comment></comment></comment></comment><comment><USER15> For the most part, I agree with the article's premise.

But they were pretty unfair to the Stanford lab (Vijay Pande). The Stanford team is using graph convolutions to process molecular structures. The article complains that the Stanford team is biased because they are not using sequences to represent the molecular structures. But this actually makes a lot of sense because molecular structures _are_ graphs. The article then goes on to say that the Stanford team has an agenda to get everyone to use deepchem instead of TensorFlow or PyTorch. But TensorFlow _is_ a dependency in deepchem, and deepchem looks like a bunch of useful tools wrapped around TensorFlow.

I'm not buying the articles arguments

<comment><USER16> Agreed. Just because you don't compare your model to another model doesn't mean you're hiding something and it's irresponsible to pretend that's the case. Perhaps the author should have run char-cnn against data instead of making baseless accusations.

On the whole, a lot of the arguments are realistic of ML in general. There is far too much focus on individual problems and the accuracy measure without enough focus on the interpretation of the accuracy, how well it will really generalize, etc.

<comment><AUTHOR> In general, you are right. But in this particular case, char-CNN is the standard used by many people, Stanford included. It is not sophisticated at all.

Look at the big list of exotic models they tried: [http://moleculenet.ai/models](http://moleculenet.ai/models)

For me, this omission is a negligence. It is selective laziness.

The author (me) does not have 20 PhD students, postdocs and startuppers under his hand, to do the job for Stanford. With my limited resources, it is more cost-effective to do my job against Stanford ;)

<comment><USER17> It sounds like you have axe to grind against well-funded professors, not that you actually have a valid argument.

<comment><AUTHOR> This professor gave an argument in his paper. I gave a refutation. If you agree with him that char-CNN was a sophisticated model in early 2017, then you are not well-informed about the situation in deep learning.

The MoleculeNet co-author gave another argument in the comments. My refutation is that you can't claim to lack time after 8*20 man-months have passed.

</comment></comment></comment></comment></comment><comment><USER18> DeepChem developer and MoleculeNet co-author here.

We are NOT trying to get vendor lock in for users of DeepChem. If there are complaints about lock in using our tools please file a github issue and we can work on trying to create an open easy to use API for everyone.

<comment><AUTHOR> From a user viewpoint, Deepchem would greatly benefit from being a better team player with lower-level (Tensorflow) or other (Pytorch) frameworks. The pace of research (in NLP in particular) is too fast to make it realistic to port everything in Deepchem without an unreasonable delay.

Does it fit the Deepchem agenda? That's another question ;)

<comment><USER18> Thanks for the feedback (I love feedback). While that is a good high level goal the devil is in the details of how to make it happen. Here are some doable ideas in the medium term which might help.

1) Tutorial and documentation on how to access the raw Tensorflow graph when using DeepChem

2) Tutorial of combining raw Tensorflow with our existing chemistry specific layers

3) Different documentation quick-start sections for ML practitioners and application practitioners.

4) Better overall documentation of our Chemistry Specific layers.

Would these ideas have made a better first user experience for you?

<comment><AUTHOR> Yes, especially 2)

</comment></comment></comment></comment><comment><USER19> The article mention the name of a new startup too many times to be convincing.

<comment><AUTHOR> Sorry for this inconvenience. Content without ads is rarely free.

</comment></comment><comment><USER8> This comment is related to AI in healthcare services, not drug discovery specifically, but is illustrative. I recently went to a talk by a sr exec at one of the biggest health systems in the country. They were evaluating AI tools to predict which patients would die in 18 months so they could design the most patient friendly and cost effective end of life plan. None of the algorithms they evaluated outperformed simply asking a doctor which patients she thought would die in the next year

</comment><comment><USER20> Of course, "these days" (scine ~2014 by my reckoning) AI is synonymous with Deep Learning (in the lay press anyway) but there are actually other techniques still being researched, specifically theory- and knowledge-driven techniques, from the field of symbolic machine learning.

Such techniques have long been used in medicine and biology with great success, very notably in the example of the Robot Scientist, a system that automates the scientific process end-to-end, in a biology context.

This is from the abstract of the Nature paper, from January 2004 [1]:

      The question of whether it is possible to automate the scientific process is of both great theoretical
      interest1,2 and increasing practical importance because, in many scientific areas, data are being generated much
      faster than they can be effectively analysed. We describe a physically implemented robotic system that applies
      techniques from artificial intelligence3,4,5,6,7,8 to carry out cycles of scientific experimentation. The system
      automatically originates hypotheses to explain observations, devises experiments to test these hypotheses,
      physically runs the experiments using a laboratory robot, interprets the results to falsify hypotheses
      inconsistent with the data, and then repeats the cycle. Here we apply the system to the determination of gene
      function using deletion mutants of yeast (Saccharomyces cerevisiae) and auxotrophic growth experiments9. We built
      and tested a detailed logical model (involving genes, proteins and metabolites) of the aromatic amino acid
      synthesis pathway. In biological experiments that automatically reconstruct parts of this model, we show that an
      intelligent experiment selection strategy is competitive with human performance and significantly outperforms,
      with a cost decrease of 3-fold and 100-fold (respectively), both cheapest and random-experiment selection.

There's more info and especially links in the Wikipedia article [2].

_____________

[1] Functional genomic hypothesis generation and experimentation by a robot scientist, [https://www.nature.com/articles/nature02236](https://www.nature.com/articles/nature02236)

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

</comment><comment><USER21> AI in X is overhyped. For all X.

<comment><USER22> Was jumping on to say exactly that. We are most likely somewhere close to the "Peak of Inflated Expectations" for the current iteration of AI. It's useful, just not a magic bullet to solve all problems.

[https://en.wikipedia.org/wiki/Hype_cycle#/media/File:Gartner...](https://en.wikipedia.org/wiki/Hype_cycle#/media/File:Gartner_Hype_Cycle.svg)

</comment></comment><comment><USER23> I'm not qualified to discuss drug discovery, but my eye was caught by the authors statement that char-RNN on SMILES strings might outperform the use of a coulomb matrix. IMO this is really misguided as a SMILES string does not contain specific geometrical data (bond angles, dihedrals etc) of a compound...

Learning the grammar of smiles-representation is not the same as learning to use inter atomic distances.

<comment><USER17> SMILES implicitly stores the geometry data. It's technically true it doesn't encode a unique 3D structure (for a rigid molecule), but you could convert the implict bonds to standard-length ones, embed the molecule in 3D space, and minimize it (that's precisely what SMILES to 3D structure systems do).

</comment></comment><comment><USER24> calling any existing technology "AI" is overhyped

<comment><USER25> I don't know about " _any_ existing technology" but in any aspect of drug discovery with which i'm aware, i'd agree. My job amounts to using statistics and linear programming to winnow drug candidates out of databases of hundreds of millions of small molecules, (sometimes: "chemoinformatics"). And i've lost count of how many times i've had the marketing department insist on calling what i do "AI". (and let me assure you, as far as pushing back against marketing is concerned "all resistance is futile")

<comment><USER26> totally random thought: do you have pictures of molecules by any chance - <USER3> be a good source to train some CNN on it and existing drugs.

Disclaimer: I have zero experience in drug discovery.

<comment><USER25> I don't know how it can help train whatever 'CNN' stands for (i'm guessing not the news network, but something Neural Network?) Yet there are vast sources of various ("pictures") representations of small molecules all over the internet; from line-drawings, 2d stick models[1] to those providing rotation to space filling models[2]. As a very personal aside, neural networks are just a means of doing curve fitting without necessary learning anything.

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

[2] [https://www.cancerquest.org/patients/drug- reference/morphine](https://www.cancerquest.org/patients/drug- reference/morphine)

<comment><USER27> It stands for convolutional neural network, which is a NN architecture that works well on image data. They use CNN's in object detection and other computer vision related tasks.

</comment></comment></comment></comment></comment><comment><USER28> Anyone who has actually built software using what the masses think is 'artificial intelligence' knows exactly how artificial and overhyped ML and AI are. Neural networks are a far cry from the magic they've been made out to be, and are nowhere close to as sophisticated as the brains of most animals. Cherry picking results makes for good marketing material, but that's pretty much where the usefulness ends.

We still don't even understand how exactly brains work.

<comment><USER29> Neural networks really are pretty magical for a lot of image based problems these days. The impact for text problems is so far decidedly not magical, though there are starting to be some tantalizing results in my opinion.

<comment><USER28> More gimmick than magic. At best they can do a crappy job of emulating specific human behaviours on carefully curated training datasets.

<comment><USER30> What about Alpha Go Zeros approach of reinforced learning based on self play? Arguably the game of go has really simple, explicit rules, and evaluating the final end state is also easy, but it shows that with enough compute NNs can identify important features without supervision or labeling.

<comment><USER28> What about what about

Given enough time, energy, and computing power, you could just precompute every possible outcome and call it a neural network. You'll be praised as a genius.

<comment><USER31> Ah yes, I forgot. Deepmind just secretly computed all 10^100 board states for alphago...

Neural nets have been very successful for a lot of things while also being overhyped, but dismissing it all as marketing hype is throwing the baby out with the bathwater.

</comment></comment></comment></comment></comment></comment><comment><USER32> AI is overhyped is probably an equally valid title.</comment>


I love SF but I needed a break. Photos from my coast-to-coast motorcycle trip - <AUTHOR> http://imgur.com/a/euaD5

<comment><AUTHOR> I also did a write up about my packing list and other details, in case anyone is interested in taking a similar trip but isn't sure what to do: [http://www.theantimba.com/america-youre-one-beautiful- bitch-...](http://www.theantimba.com/america-youre-one-beautiful- bitch-9000-mile-47-day-motorcycle-journey/)</comment>


Joe – CLI Python tool for generating .gitignore files - <AUTHOR> https://github.com/karan/joe <comment><USER2> You may want to rename your project.

[http://joe-editor.sourceforge.net/](http://joe-editor.sourceforge.net/)

<comment><USER3> They already are [https://github.com/karan/joe/issues/30](https://github.com/karan/joe/issues/30)

They were going with gi but that conflicts with gitignore.io

Someone else thought gi-joe and that is an Awesome name!

</comment></comment><comment><USER4> Been using [https://www.gitignore.io/](https://www.gitignore.io/) with a lot of success

<comment><USER5> There is a cool oh-my-zsh plugin for this:

[https://github.com/robbyrussell/oh-my- zsh/blob/master/plugin...](https://github.com/robbyrussell/oh-my- zsh/blob/master/plugins/gitignore/gitignore.plugin.zsh)

<comment><USER3> [https://www.gitignore.io/docs](https://www.gitignore.io/docs)

gitignore already has this and no need for a plugin.

$ echo "function gi() { curl -L -s [https://www.gitignore.io/api/\$@](https://www.gitignore.io/api/\\$@) ;}" >> ~/.zshrc && source ~/.zshrc

</comment></comment></comment><comment><USER6> Alternatively

        gitignore() {
            wget \
            https://raw.githubusercontent.com/github/gitignore/master/Global/OSX.gitignore \
            https://raw.githubusercontent.com/github/gitignore/master/$1.gitignore \
            -O .gitignore
        }

</comment><comment><USER7> positive feedback: pretty well-presented, with a nice readme

negative feedback: the time required to run joe (including remember that it exists, possibly installing it, how to invoke it correctly) is going to be similar to the time required to just manually write a ignore file / copy and edit one from a previous project in the same language.

</comment><comment><USER8> What advantage does this have over just grabbing the file from [https://github.com/github/gitignore](https://github.com/github/gitignore) directly?

<comment><AUTHOR> Saves time, I guess. The same reason you'd wanna use something like `howdoi` to get StackOverflow answers directly inside the terminal.

[http://stackoverflow.com/a/14286317/544059](http://stackoverflow.com/a/14286317/544059)

</comment></comment><comment><USER9> 1 line shell function that does the same thing:

[https://github.com/caarlos0/dotfiles/blob/master/git/aliases...](https://github.com/caarlos0/dotfiles/blob/master/git/aliases.zsh#L22-L24)</comment>


Want to make good business decisions? Learn causality - <AUTHOR> https://multithreaded.stitchfix.com/blog/2019/12/19/good-marketing-decisions/ <comment><USER2> For those of you interest in learning more about causality, I just announced on my mailing list ([https://data4sci.com/latest](https://data4sci.com/latest)) that I'm working on a series of blog posts covering the contents of Judea Pearls's Causal Inference in Statistics - A Primer ([https://amzn.to/39G5lWl](https://amzn.to/39G5lWl), affiliate link) using Python.

</comment><comment><USER3> Super interesting article. I can recommend Pearl's Book of Why to learn more about it

Although to me it's still quite difficult to apply the theoretical knowledge to day-to-day applications. Any references or ideas?

<comment><USER4> I find that as well. I've also read Book of Why but it's difficult to know when and how to use them. Perhaps it's just more practice of writing causal diagrams, as Pearl seems to do with ease, just due to the years of practice he's had. Something like programming, I guess.

</comment></comment><comment><USER5> Here's a relevant video for causality:

[https://www.youtube.com/watch?v=1dWjKkF0Zi4](https://www.youtube.com/watch?v=1dWjKkF0Zi4)

<comment><USER6> Well, there goes the afternoon.

<comment><USER7> Causality.

</comment></comment></comment><comment><USER8> Nice article. But playing and learning, thinking and debating about causal loops is Better imho. So I created a loopy clone: [https://nocomplexity.com/causalloopdiagram/](https://nocomplexity.com/causalloopdiagram/)

</comment><comment><USER9> Note that the article subtly admits that you can subvert the objectivity in your model-based decisions by making your subjecive choices of how to build the graph.

<comment><USER10> I think the graph itself is useful for that exact reason. It lets us see the the specific model assumptions and generate the testable conditions.

</comment></comment><comment><USER11> This is common sense. Nothing more than you would learn in an introduction to regression analysis class.

Still helpful to those not familiar with it.

*edit: To those downvoting, is there anything especially insightful that I missed?

<comment><USER12> I didn't downvote, but your comment came across as dismissive.

Personally, I think there's a world of difference between the aphorism of "correlation does not equal causation" and actually understanding how causation works. I understand the former quite well, but haven't much a clue about the latter. (And I'm not alone!)

<comment><USER11> Got it.

I left my comment for the benefit of those who have learned regression. I read the article expecting something new, seeing how highly upvoted the post was on HN, but realized it was basic information.

If they removed the symbols from the article, I bet the article would be accessible to way more people.

Regarding causation, I think the essential points are:

\- there is never any true causation (e.g. Even though it is always dark when I close my eyelids, I can never be sure that closing my eyelids causes the darkness. How can I know that the next time I close my eyelids, it won't be something other than dark?)

\- you can see if a correlation may have a causal relationship by applying common sense to the chronology of events (if darkness correlates highly with closed eyelids, and if I notice that, chronologically, darkness has always followed closing my eyelids, I can be more confident that the relationship may be causal. Stats help formalize this process.)

<comment><USER13> Try not to make comments that an article is trivial and common sense when you admittedly missed 50% of the article, and of that 50% you weren't familiar with any of the concepts.

Essentially that's reading an introduction, saying you knew everything in the introduction and then claiming the article has no content.

<comment><USER11> That's unfair. Most of the article is basic.

The parts that I am unfamiliar with are the second paragraph in the "What is my data telling me?" section where they describe DAG, and the last paragraph in the "Can't I just use XGBoost?" section, where they introduce TMLE.

Even then, DAG seems like a very straightforward concept that would be useful to have in your toolkit, but is probably something most thoughtful people do without explicitly thinking about it when doing regressions.

And the TMLE paragraph is essentially jargon that you would need to do your own research to actually understand. Why couldn't they spend the article describing this, as it seems that this what their value add as a service is.

Overall, this piece seemed more intended to market themselves ("Look! We do more complicated regression than you know how to do, and we're not going to explain it in any depth.") instead of well meaning teaching. Of course, I'm probably seeing bad intentions when there aren't any, so I am likely wrong.

<comment><USER13> > That's unfair. Most of the article is basic.

> Even then, DAG seems like a very straightforward concept that would be > useful to have in your toolkit, but is probably something most thoughtful > people do without explicitly thinking about it when doing regressions.

I appreciate the honest response. To elaborate a bit, I think you may be mixing up the point of the article with the fact it starts with a low baseline of expected familiarity / knowledge to be able to bring more readers along.

My read is that the purpose of the article is to explain how to perform more accurate blocking and be explicit about you model assumptions by using DAGs. Followed by why the common tool everyone reaches for of linear regression is the wrong tool in this case, and TMLE is better.

That's the meat of what the article builds to, and is 9 out of the 30 paragraphs. Roughly 1/3rd (not 1/2 as guessed).

> Why couldn't they spend the article describing this, as it seems that this > what their value add as a service is.

Agreed, but I think this is a different type of article. It's (in my opinion) and article saying "hey, there is a problem or shortcoming in what you're doing today. Gives justification as to why many people do have this problem, and then says "These are the tools to help you fix it."

A person unaware knows what to look into to solve their problem. Presumably a treatment on the solutions doesn't fit in a blog post - and it doesn't have to. There is value in identifying a person's problem and telling them how to learn about the solution. Even without fully detailing the solution. And I think this is your main criticism with the post, is that it wasn't a post to just present the solution in full detail.

That said, I don't want this to seem like im beating you up - but rather show that there are many different ways to get value and information on a topic without a detailed treatment of a solution.

And cycling back to your original point, I agree, I think the reason they don't go into full detail on the solution is because at that point the amount there'd need to cover to do a full treatment on the solution ends up having the cost outweigh the value in a blog post that is motivated to bringing people to their company/service.</comment></comment></comment></comment></comment></comment>


SHARE: Blogs: Views on the Mainframe Skills Gap, Generation by Generation - <AUTHOR> http://www.share.org/p/bl/ar/blogaid=588 <comment><USER2> Won't comment on this article or it's relevance for current professionals..but did have an interesting conversation with a fellow who had been working with mainframes forever in a small colo in Norcross Ga.

He noted that his maintenance|upgrade work on legacy mainframes and architectures were typically week long affairs that brought him about 8-10K. He also noted that he knew all of the people in the US who could perform that work by name.</comment>


Regular Expressions Will Stab You in the Back - <AUTHOR> http://tech.blog.cueup.com/regular-expressions-will-stab-you-in-the-back

<comment><USER2> If you're really worried about the worst-case behavior of regular expressions be sure to select an engine designed specifically to not choke on pathological inputs, such as Russ Cox's RE2:

<http://code.google.com/p/re2/>

The rather interesting analysis is here:

<http://swtch.com/~rsc/regexp/regexp1.html>

Not that it's a free lunch, since you're gaining a constant amount of overhead in exchange for a sane growth pattern.

EDIT: Of course the article would mention RE2. That's what I get for reading the comments before TFA...

<comment><AUTHOR> This gets a section in the article, but it's worth repeating, since RE2 is just that good. It's as close to Perl-compatible as a non-backtracking regular expression engine is likely to get, pleasantly fast, and stable enough for Google to use in production.

Regarding speed, I've actually found that re2 is faster on some non- pathological regexps, and a little slower on most. As usual, if you care about this, benchmark it. I've managed to get some easy 2x speedups by switching from Python's re to re2, even though re is usually faster.

<comment><USER2> Good to hear that RE2's constant factor doesn't seem too large, I know the Rust guys were looking to potentially use it as the regex engine in their stdlib, but were torn on whether that constant factor would prove troublesome.

</comment></comment></comment><comment><USER3> Or they won't as everyone who worked on perl based website from 2000 will tell you. This is a massively overstated title and lede.

However the solutions given, if you do end up with this problem, are good and detailed.

They did miss perl's solution, Special Backtracking Control Verbs : [http://perldoc.perl.org/perlre.html#Special-Backtracking- Con...](http://perldoc.perl.org/perlre.html#Special-Backtracking-Control- Verbs)

<comment><AUTHOR> Perl also has some clever memoization which handles a lot of cases that choke less mature regular expression engines. But neither of these is a satisfying solution; backtracking control verbs can only help you if you use them, and the memoization is not the solution to all woes, as the benchmark in the "Performance" section of this article shows:

<http://swtch.com/~rsc/regexp/regexp1.html>

The sky isn't falling, but it's still possible to get struck by lightning. (And of course, you're vulnerable to DoS attacks if you run user-supplied regexps with backreferences -- this is provably NP-hard.)

<comment><USER4> Not to gainsay everything you've written here, because I think that I think there's plenty of room in my universe for new regex engines, but the article is five years old and benchmarks Perl 5.8.7.

We are of course up to 5.16.1 these days, and I'd be more inclined to pay attention to benchmarks if they were performed with remotely current versions of the software.

Otherwise, thanks for the enlightening post.

<comment><AUTHOR> Just ran the same benchmark on Perl 5.12.3 (because it came installed by default on my machine), and got the same exponential-time matching behavior. If you have a more recent version of Perl than I do, I'd be interested in hearing the results. This takes about 10 seconds on my machine, but should be nigh-instantaneous if they've fixed the problem in a later version of Perl:

        "aaaaaaaaaaaaaaaaaaaaaaaaaaa" =~ /a?a?a?a?a?a?a?a?a?a?a?a?a?a?a?a?a?a?a?a?a?a?a?a?a?a?a?aaaaaaaaaaaaaaaaaaaaaaaaaaa/

<comment><USER4> I have 5.8.8, and I have 5.14.2. I've also taken the liberty of converting the string of a?'s into a{0,27}.

I recognize that you're trying to demonstrate pathologically bad behavior, but as written, you've got a pathologically bad regex.

Note the speed up from 24 & 30 seconds to seven one-thousandths of a second in both cases. Again, I like the idea of a regex engine with different design considerations than Perl's.

Your article would have been much better if it has used a class of pathological data with real world examples, and not used contrived examples that I rewrote in less time than it took to run.

So here's my results:

5.14.2:

         $ time perl -e 'print "aaaaaaaaaaaaaaaaaaaaaaaaaaa" =~ /a?a?a?a?a?a?a?a?a?a?a?a?a?a?a?a?a?a?a?a?a?a?a?a?a?a?a?aaaaaaaaaaaaaaaaaaaaaaaaaaa/ '
         1
         real    0m24.227s
         user    0m24.210s
         sys     0m0.008s

         $ time perl -e 'print "aaaaaaaaaaaaaaaaaaaaaaaaaaa" =~ /a{0,27}aaaaaaaaaaaaaaaaaaaaaaaaaaa/ '
         1
         real    0m0.007s
         user    0m0.004s
         sys     0m0.004s

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

5..8.8:

         $ time perl -e 'print "aaaaaaaaaaaaaaaaaaaaaaaaaaa" =~ /a?a?a?a?a?a?a?a?a?a?a?a?a?a?a?a?a?a?a?a?a?a?a?a?a?a?a?aaaaaaaaaaa                               aaaaaaaaaaaaaaaa/ '
         1
         real    0m30.065s
         user    0m30.046s
         sys     0m0.016s

         $ time perl -e 'print "aaaaaaaaaaaaaaaaaaaaaaaaaaa" =~ /a{0,27}aaaaaaaaaaaaaaaaaaaaaaaaaaa/ '
         1
         real    0m0.007s
         user    0m0.004s
         sys     0m0.004s

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

<comment><AUTHOR> Real-world examples tend to be subtler, and not quite as easy to explain in an introductory article. Of course you wouldn't really use the for-demonstration- only regex I mentioned. Instead, you would write something reasonable-looking that works just fine unless it encounters a highly unlikely bad input. That's the way I've always seen it happen.

<comment><USER4> In the "real world" I usually try to optimize for the average case, and deal with the "highly unlikely bad input" as a special case.

I'm still impressed with the new regex engine, but I have a pathological hatred of contrived examples.

It's not you, it's me.</comment></comment></comment>


What is the Technological Singularity? - <AUTHOR> http://thenextweb.com/industry/2011/06/19/what-is-the-technological-singularity/ <comment><USER2> The singularity is _not_ near.

Kurzweil continues to pitch his prognostications for strong AI so that he can sell more books and speaking engagements. Despite experts in the fields of biology [1] and cognitive science telling him that we just don't understand these things very well, he professes that the key discoveries lie just around the corner and that we should just wait. You know, there's probably a business in selling some of his followers rapture insurance...

I'm a systems biologist who is at an intersection of some of the various fields where Kurzweil makes his boldest predictions, and I'm willing to place some bets. Before we see this ludicrous explosion in technology and AI that he describes, I think we're much more likely to see all of these:

* A cure for AIDS.

* Whole-genome sequencing for under $100.

* A scale up and widespread use of tissue bioreactors.

* Identified causes and cures for neurodegeneration.

* Effective treatments and diagnostic tools for many or most forms of cancer.

* Artificial hematopoietic cells approved for human use (no more blood donations, etc.)

* Designer babies with hundreds if not thousands of selected markers.

These problems are easier than human-surpassing machine intelligence. While I don't have a problem with believing machines will one day be smarter than us, I find it offensive that this guy continues to sell us on it happening within our lifetimes. It almost certainly won't.

Kurzweil has almost no appreciation (and I would argue he has little understanding) for biological systems and their complexity. [2] We can barely understand a single cell, and yet we're supposed to understand the brain in only a few years!

The best part is, when is is called out on this argument, Kurzweil admits it. But he then proceeds to tell us silly biologists in the errors of our ways-- that we are missing his point entirely. Strong AI doesn't need understanding from the field of biology in order to happen. Consciousness can't be _that_ hard.

In closing, SMBC's take: <http://www.smbc-comics.com/comics/20100114.gif>

[1] PZ Meyers vs. Kurzweil - [http://scienceblogs.com/pharyngula/2009/02/singularly_silly_...](http://scienceblogs.com/pharyngula/2009/02/singularly_silly_singularity.php)

[2] [http://pipeline.corante.com/archives/2010/08/18/reverseengin...](http://pipeline.corante.com/archives/2010/08/18/reverseengineering_the_human_brain_really.php)

<comment><USER3> I sympathize with your position. Kurzweil seems to regularly makes strong remarks that shock me in their assertive naivety.

However, our species is incredibly poor at intuitively understanding exponential trends. Kurzweil's essential point is that our gut-feeling predictions of the future will be off substantially. Specifically, our intuitive sense of future progress significantly underestimates actual progress and the gap systematically widens as time progresses.

Neither you or I could have intuitively predicted twenty years ago the level of today's technological advancement. And we'd be even more wrong in the predicting the next twenty.

It's far too easy to dismiss Kurzweil's work as that of a superficial nutjob who wants to "sell more books and speaking engagements." Kuzweil's crunched the numbers. While he might not be right on everything, his predictions are based on data, but yours are based on human intuition that has historically been wrong.

I would be very careful of the knee-jerk reaction you display to Kurzweil's work. Keep in mind we are cognitively disabled at intuitively predicting exponential progress.

<comment><USER4> The problem is not all tasks are of linear difficulty. If building a 1% more intelligent AI takes 1% more computational power then he might have a point. However if building a 1% more intelligent AI takes 2x the computational power then you don't end up with a singularity because you don't get a strong feedback loop.

Consider starting at the low end of the scale booting windows 7 on a 2KHz CPU is about twice as fast as a 1KHz CPU and all things being equal faster processors let you get more things done but having a 1GHz vs 2GHz CPU has little effect on boot times. We might be able to build a 10x human level intelligence but if that can only build a 11x human intelligence and that can only build a 11.1x human intelligence you quickly hit a wall. Or for a more natural looking progression (10x, 15x, 18.75x, 21.09x, 22.4x). x sub 1 = 10; x(sub n) = x(n-1) * (2^n+1)/ (2^n)

<comment><USER5> The point of TS is that once you get to 1.0001x smarter than human you get to wall really quickly. If the wall is 2x or 11x, self-improving AI will reach it in no time. And we'll still have tremendous benefits from it, no matter how close the wall is to human intelligence. AI only need be a tiny bit smarter than humans to achieve things such as interstellar travel, eternal life, etc.

<comment><USER4> Slightly more intelligent than human is not the same as magic. Self improving AI is a nice theory but it assumes you can get better at everything at the same time without tradeoffs and that takes better hardware not just software. Science fiction is full of magic masquerading as technology, but it ignores the possibility that reality may not support that capability. Predicting the future requires more than simply extrapolating trends it also requires understanding physical limitations, early engines saw rapid increases in both HP and efficiency but that trend slowed down for a reason.

EX: Literal eternal life requires a universe that is capable of lasting that long. If the fundamental laws of physics limit either the age of the universe or the amount of useful work that can be done then you at some point everything dies. So no we are down from eternal to just really long life.

Also, 1.0001x a smart as a human is not really significant. 1,000 AI's 1.0001x smarter than a human is not going to be able to say beat 1 grand master at go without training or hundreds of years of preparation. I have no problem saying AI get's to quickly build better software, but building hardware at scale takes time.

</comment></comment></comment></comment></comment><comment><USER6> The thing I don't like about Kurzweil's singularity prediction is it kind of sounds like predictions in the 1960's that we'd all be living in space by now. Yet, here we are 50 years later still burning gas in our cars but we have phenominal GPS technology in our phones. People (even scientists) tend to overestimate the "deepness" of technological advancement and underestimate the "wideness". I predict that by the time a singularity occurs that it'll be much farther out in time and may not even be a distinguishable event.

<comment><USER7> A great example of this is Star Trek. They can travel at warp speed, but they can't use a communicator to send a video signal back to the ship, the captain has to beam down to see it himself...

<comment><USER8> In fairness, that's more of an "interesting TV" issue than a "failure of imagination" issue. A show about a bunch of people who sit around in a room teleoperating a robot probably would be a lot less interesting.

</comment></comment></comment><comment><USER9> It sort of annoys me that whenever people discuss technological singularities its always through Kurzweil's ideas. I feel that Verner Vinge, the guy who invented the term, had a much more interesting take on it, and there are other schools of thought as well. [http://singinst.org/blog/2007/09/30/three-major- singularity-...](http://singinst.org/blog/2007/09/30/three-major-singularity- schools/)

<comment><USER10> Indeed. Kurzweil is far from the only Singulitarian. And virtually all of them have more interesting things to say on the matter than he does.

</comment></comment><comment><USER11> _One only needs to look at history to see our capacity for rapid improvement in retrospect. One of my favorite metrics is life expectancy. In 1800, most people wouldn't expect to live past 30._

It's hard to take someone seriously writing about science who makes such a dumb mistake. The life expectancy for a 20 year old in 1800 was 64. I don't see how it can even pass the smell test for an intelligent person to assume that a couple hundred years ago people systematically keeled over at 30.

<comment><USER12> You are deliberately disregarding the improvements in modern medicine that caused such a dramatic drop in child/infant death. This seems like a pretty classic example of people not realizing how _wildly spectacular_ modern society is, because they're living in it.

</comment></comment><comment><USER13> We've already passed the technological singularity, we just don't realize it because we assume machine intelligence is going to act like human intelligence.

Look around, computers have been more intelligent than humans in many different domains for decades. Computers can solve complex equations billions of times faster than humans, they can play chess better than people that have dedicated their entire lives to mastering the game, they can sift through the information of billions of websites in hundredths of a second, they can answer obscure and complex questions better than the best Jeopardy players, they can accurately model extremely complex systems like the world's weather, and the list goes on and on and on.

But guess what, your calculator isn't plotting to kill you because unlike humans it hasn't been programmed to do that. There may come a time when someone is evil or careless enough to program these traits into a machine capable of acting on them, but this is a game humans (and life in general) have been playing for a very long time, and we're very good at it. Good luck programming a billion years of evolution based learning into your calculator.

</comment><comment><USER5> I'm skeptical about TS because it seems to good to be true (or too terrible), but every time I hear about it I can't help but think of this clip [1] from Neil deGrasse Tyson about how aliens would only have to be less than a percent smarter than us for us to look like peons to them. So it makes it seem inevitable that if strong AI comes to pass and AI is measures more intelligent than us, it's the end of human civilization and the begin of the era of machines. Our only hope is they decide to keep us around as pets, and are good owners. If so we'll be able to kick our feet back and enjoy eternity (should we choose to live it) debating iPhone vs. Android on Hacker News.

[1] <http://www.youtube.com/watch?v=w-uZZ7RdL5E>

<comment><USER14> It's an interesting thought experiment, but the 1% is an oversimplification - necessary, perhaps, in a general audience. The 1% difference he talks about is not phenotype (reasoning ability) but genotype (DNA). It's not hard to imagine a few critical changes such as more cortical folds and improved myelination leading to most of the phenotypic cognitive difference. These could be controlled in some key fractions of a % of the genome.

While it works as a metaphor, it's flawed as a general metric. A species in which Hawking-level ability is - if not average - at least commonplace, would require the average IQ to be 2-3 standard deviations higher (relatively) on the human scale. IQ being, itself, a rather flawed metric of course :)

</comment></comment><comment><USER15> It's amazing how similar Kurzweil's observations about technology are to Stafford Beer's same observations in the early 1970s. Beer even draws an exponential graph and talks about a succession of S-curve shaped technology paradigms.</comment>


Sequoia-Backed Chinese E-Commerce Company's Black-Hat SEO - <AUTHOR> http://www.digitalduediligenceadvisors.com/seqouia-fails-to-spot-milanoos-black-hat-tactics/ Yeah, from the folks who brought you that JC Penney story, and as seen on TC.<p>Happy to answer any questions from HNers on this. <comment><USER2> I think if folks here (or the public in general) knew how common this was, they'd be shocked and outraged. I can say with quite a bit of conviction, experience and data that this is incredibly common - even overwhelmingly so.

My conservative guess is that for 50-100K+ keyword searches where substantive commercial value exists in ranking well, 50%+ of the top 10 ranking pages have engaged in manipulative link practices. They're not usually this blatant (though plenty are), but penalization is rare and link spam is on the rise.

My sense is that rather than try to write algorithms to compensate, Google's webspam team has been focused on finding less manipulatable signals like social data (which is currently relatively clean), user+usage data (through Chrome, GG toolbar, GG analytics, etc) and more sophisticated citation analysis.

Still, it's hard to preach white hat SEO when black hat works so frequently and so well (and from outward appearances, Google rewards it).

</comment><comment><USER3> Actually, Google does care deeply about paid link spam. A very relevant example is the exposure of JCPenney a few months ago when the NYTimes investigated how they were getting so much traffic from Google (<http://www.nytimes.com/2011/02/13/business/13search.html>). Subsequently, Google penalized their rankings.

@fleitz - I agree that it's not "illegal" but unethical, probably. I disagree regarding "why would Sequoia care?". If they are earning a significant amount of their revenue and growth by relying on Google, then once they get caught their growth will slow and if due diligence in this arena wasn't done, Sequoia may have invested with an inflated valuation. Of course, if I were a VC, during due diligence something like "Google drives 75% of our inbound traffic + revenue" would set off all types of alarms and require further digging.

<comment><USER4> Historically, Google has made little effort to combat any type of spam _which generated revenue_.

When I used AdWords, I would say that 30% of the clicks where fraudulent and 90% of those came from Google "content" partners who where nothing more than link farm pages.

This has existed for the last 8+ years and Google has absolutely done nothing about it, except for odd case here and there.

They even went so far as setting up their own domain parking service (Google Domain Parking) to allow this type of behavior (and monetize it).

For the most part, Google only penalizes those who make Google look stupid... JCPenney.

</comment></comment><comment><USER5> It's a Chinese company. Google backlash isn't dangerous, I'm guessing they are aiming more towards Baidu optimization than Google optmization. I'm pretty sure Baidu isn't that regarding of black hat SEO.

</comment><comment><USER6> Wow, can't wait to see how this develops! Someone's about to lose their spot at the teat.

I know Matt Cutts checks up on Hacker News...

</comment><comment><USER7> I wonder if Google has a harder time with spam that isn't in English.

</comment><comment><USER8> Its pretty egregious.

I've heard folks say "C'mon, whats the harm the system is there to be gamed and they just play it better than others." Which I can't really argue with, I mean there isn't really a barrier to entry other than cash and willingness. And its a whole industry in its own right. But still it does leave one with a poor impression.

</comment><comment><USER9> If Google doesn't want this kind of behavior they can always change their algorithm. There's nothing illegal about most 'black hat' SEO tactics so why would Sequoia care?

Everyone takes the same risk that Google will change their algorithms, black hat SEO just ensures they'll stay at the top rather than artificially limiting the techniques they use to gain ranking to those that Matt Cutts approves of.

Google itself engages in 'black hat' SEO as defined by Matt Cutts which is why his team needed to change the algorithms and/or make them not apply to Google's own properties such as Google Local after the panda update.

I think this is possibly the best indication that black hat SEO works.

<comment><USER10> The issue is that Google is (or at least appears to be) actively pursuing this type of black hat SEO and trying to remove it from its search results. That's an uncomfortably high risk margin, especially since it seems that this company's business is highly dependent on those search results.

(Certainly, I wouldn't want to invest in a company that relies so heavily on black hat SEO, whether my own or someone else's.)

</comment></comment><comment><USER11> This post makes it sound like it never happens, but I know that a competitor of my company that is VC funded has played around with doing exactly the same thing -- buying links. I'm not sure that VCs even know how to do due diligence on such a thing.

<comment><USER12> > I'm not sure that VCs even know how to do due diligence on such a thing.

There is a nice consulting gig for someone.

</comment></comment><comment><USER13> White or black SEO? Where are the lines, nothing wrong about what they are doing. SEO is just what you can get away with. it is that tipping point, some push it to the limit and others don't.

</comment><comment><USER14> Google doesn't like link-buying, and it's within their rights to weight or punish that however they'd like.

However, the term 'black hat SEO' should be reserved for dishonest/unethical practices – not just those that don't meet one company's preferences.</comment>


Ask HN: Static hosting for $1/m. - <AUTHOR>

I'm thinking about it sometime, i'll try to launch a service of static shared web hosting starting at 1$/m or 5$/y, it will support integration with: google drive skydrive dropbox box.com github bitbucket amazon and other dummy services like ftp,sftp,git.svn....<p>My question is, there is a market for that? Thanks <comment><USER2> Seems like a decent idea IF you can make it real easy for me. Right now I host my website on S3. I would probably prioritize hooking into services that (unlike github) don't already have a built-in free way of hosting a site :)

And be careful you don't fall into the "unlimited hosting for a fixed price" trap.

Didn't there used to be a 3rd party Dropbox app that published a folder as a website? (Not the "Public" folder which, I suppose, is another option for some people).

<comment><USER3> How do you gzip your content hosted on S3?

<comment><USER2> Easy: I don't :)

You might be able to do it by generating the gzipped versions yourself and putting CloudFront in front of you site, but I haven't tried.

You could definitely do it with Cloud Flare. If you don't care about ancient browsers and bots that don't understand gzip, you could pretty easily just have gzip-only versions of everything.

</comment></comment></comment><comment><USER4> No, because there are enough services that allow for $0/m static hosting.

<comment><USER5> Sure, but I'm not expecting much from a free service in terms of uptime.

I wouldn't expect much more from a $1/month service, but I would expect better service.

<comment><USER4> GitHub Pages and AWS Free Tier are both effectively $0/month services with near 100% uptime.

<comment><AUTHOR> i'm building it with easy of use on mind, A totally clean interface, easy integration tools. And of course, it will be focused on stability, your site will run on many servers with redundancy and user managed cache of nearly everything</comment></comment></comment></comment>


V 2 of Big Timer, the countdown timer for meetings, standups, etc. Made with Vue - <AUTHOR> https://www.bigtimer.net <comment><AUTHOR> I'm not much of a programmer. I learned Vue and Webpack just for this project. Or actually: I wanted a project to learn how to make web apps and that turned out to be the most attractive way.

I hope it's useful to someone out here. Let me know how you would improve it!</comment>