Ask HN: Any way we can find the job opening and closing trend of any company? - <AUTHOR> Hello HN,
It would be interesting to see the data about when a company posts a job post and when it gets closed.
We can then use that data to create "trends" graph for that company.
From a candidate's perspective, this can help identify what time of the year is the best one to apply and more importantly when to start preparing for one. <comment><USER2> An interesting theory, but there'd be the following complications:

• Job websites are, for some companies first and foremost, a signal to the investment community. Hey look, series A, and now we have these 12 job listings open. Meantime they're trying to figure much more important things out than who to hire at that level (like who they can get who knows how to hire). Any emails sent in go straight to nowhere.

• Quantities are going to be wrong. If you want to hire a team of 5 engineers, you could have between 1 and 20 job listings (because different skill sets might be all you can find, but that'd be good enough, or you're afraid 10 identical listings will make you look desperate).

• Job websites are out of date. A company hires everyone it needs to, it moves along smoothly, job listings are still up for jobs already filled. Why bother taking them down? Maybe you'll get an application from a rockstar who'll work for $50k and no equity at the exact instant your leader puts in his notice.

• Job listings are also a political tool. Say you have an organization with 1,000 employees and 90% of them are low-skilled H1B's. While you might ordinarily need about 50-100 job listings to keep pace with employee turnover, putting 1000 job listings out there helps give cover to politicians to expand, rather than curtail, the H1B program. Recruiting someone is expensive. Pretending to recruit someone is almost free, and unreasonably effective. Vaporware for fun and profit.

For a candidate, the best time to apply for a job is when he or she needs a job, or wants a better job. There's no Rolling Stones/Satisfaction/Losing streak when it comes to applying for work.

There's no threshold for perceived competence with most of these companies- they're happy to leave positions unfilled whether they have 1 or 1 million of them on their jobs website, because there's an endemic irrational fear of making a "bad hire" at the expense of a fear of losing good ones.

</comment><comment><USER3> > From a candidate's perspective, this can help identify what time of the year > is the best one to apply and more importantly when to start preparing for > one.

It can also tell you which jobs are potentially "fake" jobs posted for some other purpose - to signal the market, to fulfill a requirement for the H1B they want to hire, etc.</comment>


Ellen Pao Says Kleiner Perkins Has Now Fired Her - <AUTHOR> https://allthingsd.com/20121002/breaking-ellen-pao-says-kleiner-perkins-has-now-fired-her/ <comment><USER2> I'm surprised it took so long, actually. What did she expect? Suing her employer makes her a massive liability to the firm. The only thing keeping her there was how a firing would reflect on the case and in the press. KPCB must've decided that the bad press was the better alternative (and rightly so, IMHO).

<comment><USER3> Exactly, I don't know why she didn't quit right away- even if it turns out her claims are completely true, can she still work in a place that she's (successfully or unsuccessfully) sued? And why would she want to? And why would you want an employee who's sued you? Is it standard to keep going to work after suing someone?

</comment></comment><comment><USER4> I have _no idea_ who this person is. From what I understand she sued her employer because she claims she "was shut out of promotions" and did (or did not) get fired. Then she used Quora to tell the world she was fired. Her employer replied saying she was not fired, but is "in a successful career transition".

I suppose she was done working _anywhere_. Who would want to hire someone like that?

<comment><USER5> I would hire someone like that. People who are not afraid to stand up and speak loudly when they feel that they have been mistreated are a valuable asset, assuming that you don't mistreat them. For everyone who runs their company on secrets, bullshit and idiocy, they are a terrible liability.

<comment><USER4> You said it exactly: "They _feel_ that they have been mistreated... It's a feeling and you can never control how someone feels. At her level I would expect her to be professional about it and communicate within the team about her feelings. I don't think suing and posting on Quora helps her or the company involved. That was 100% "feelings" as well. Not professional.

<comment><USER6> [redacted snark]

You are right that it's often _tactically_ the wrong decision to post about an ongoing dispute. I will give you that. I'd be surprised if any attorney would recommend for her to post on Quora about the termination while the suit was ongoing.

However, this one-sided "professionalism" whereby people are expected to keep the secrets of employers who fuck them over is disgusting. That expectation exists because there's a power relationship, and because society really hasn't advanced much out of the Dark Ages, and not for any good moral reason.

<comment><USER4> You are referring to people being slavish and excessively subservient to authority figures? I wasn't looking at it that way. I was merely thinking she would like to work somewhere else again. The way she publicly played this makes _her_ look unprofessional. If you have a _feeling_ you're being fucked over, make a case, collect the evidence and by all means, break the news. But don't go all emotional and start posting on Quora and then keep quit about the whole case. The way she handled this does not make her case stronger, only weaker.

<comment><USER6> Having seen whistle-blowing cases play out, there's never a socially "acceptable" to raise this sort of issue. No matter when you do it, people will say that you were "emotional", blew the whistle "too soon" (oblivious to the failed negotiations they _didn't_ see) and "bit the hand".

In Hollywood movies, people like whistleblowers.

Unfortunately, in real life, whistleblowers almost always get a negative reception, even from people who are supposed to be neutral because they have no obvious interest. (People just don't like bad news, full stop.) And the discussion usually gets to a smear against _how they are saying it_ (status reduction) rather than _what they are saying_. The goal is to create the appearance that: (a) this person didn't go through proper channels, and (b) therefore isn't worth listening to, and (c) deserves various unrelated adverse consequences (such as being blacklisted from future employment) that will make it harder to concentrate from the case at hand.

You're right that her employment opportunities in the future are damaged by this. That's the reality of the world we live in. It shouldn't be that way, but powerful people tend to protect their own.

<comment><USER4> You are absolutely correct. Ive seen local whistle blowers get seriously mangled for decades... The info user "michaelochurch" (top) just provided makes me think the company she used to work for is a huge bag of dicks. Her claims might well be relevant. At the end of the day her reputation (and future) could well be seriously damaged. You could ask yourself if that was worth it...

<comment><USER6> The danger of he-said/she-said contests is that peoples' perceptions (myself included) end up being biased heavily by their own attitudes toward power. This can be described as an emotional left/right that may or may not correlate with economics. (Many libertarians are emotionally leftist-- they despise centralized power-- but economically toward the right. They just happen to see governments as more of an enemy than corporations... and I disagree with them, but that's another story.) This whole mess is probably the result of a failed negotiation and we have no way of knowing who failed, miscommunicated, or did wrong.

If there isn't much hard evidence (and in this case, there's not) then people will tend to side with power if they're emotionally rightist and against power if they're emotional leftists. (I'm an emotional leftist, as you can probably tell.)

What you discover when you become a whistleblower is that about 70 percent of people are on the emotional right. They think they're in the center because they don't reject the whistleblower out of hand (instead, they complain that "she may be right, but she didn't use proper channels and _I_ certainly want nothing to do with this matter.") That's not always a bad thing; the powerful entity is known, and the whistleblower is a total unknown, and people tend to be afraid of unknowns. It does, however, make it very difficult to be an effective whistleblower. Worse yet, an even larger percentage of people in positions of power (such as future employers) are on the emotional right.

White-collar employment culture is emotionally rightist in the extreme; if your ex-company says you weren't a leader and you say you were, they're often taken to be right and you're wrong. It's extremely unfortunate and wrong that it's this way, but I don't see how it could be any different (surprise: high- ranking people in powerful institutions tend to side with power) but it is something one needs to be aware of.

<comment><USER7> >if your ex-company says you weren't a leader and you say you were, they're often taken to be right and you're wrong

This is true, the best solution is normally to go elsewhere and succeed. Whether that means at another company or on your own. People tend to have a lens through which they understand the world, once they have slotted you it is over (see: bozo bit, various cognitive biases, etc...)

</comment></comment></comment></comment></comment></comment></comment></comment></comment><comment><USER8> I like that the entire article is sourced from a Quora question.

</comment><comment><USER9> Sad story for all parties really. Taints everyone. Reminds of the Florian Leibert case.

</comment><comment><USER10> I know they say that there's no such thing as bad publicity, but sheesh!

I'm amazed that KP didn't just buy some quietude here. Clearly, much ax- grinding is going on.

</comment><comment><USER6> I don't think we'll ever know whether she's right or wrong. She could quite possibly be in the wrong, but I increasingly doubt it the more I read.

Here's why: her firm acted like a massive dick.

First, making employees sign a non-disparagement, arbitration-only agreement (except as a term in a severance negotiation) is vile and should be illegal. You're effectively threatening to fire someone (or rescind an offer) if he or she doesn't give up a basic right.

All of these "Here's what rights you would have if we fired you, but you have to give them up to work for us" terms (non-disparagement, non-litigation or arbitration-only, non-solicitation of employees) are unethical and only exist because most people can't afford to be without an income. They are contracts under duress and should be categorically stricken.

Second, rather than arguing that the discrimination claims are untrue by bashing the employee's performance, the appropriate thing to do would have been to establish with _actual data_ (not performance reviews, which can be fudged) that she wasn't experienced enough for the promotion, and that she would have been the least qualified person at that level if she had been promoted. The proper way to handle a bogus harassment claim (if it is that, and I doubt it given the way the firm has behaved) is to show the person's trajectory of promotions, and then show the average for a person of that experience, and to use hard data. "The average partner makes senior partner after 7 years. She was only here for 4." Or: "To make senior partner, we generally expect that a person bring in $20 million in business. She brought in $12 million." That's what you do if you're decent.

Also, the truth is that a company in that position will almost never need to air performance reviews. Unless this was a "lock-step" or expected promotion where it's professionally damaging not to get it, the onus is (or at least should be) on her to prove, using hard data, that she did deserve to get the promotion. If they were decent, they'd sit back and wait for her to make a case that she did deserve the promotion, and then argue against that.

Third, they are asking to have her pay their legal fees. They are a _venture capital firm_. When you're that rich, trying to scrounge money off of people who are quite possibly not rich is pretty debased: an inverse Robin Hood sort of thing.

Fourth, using performance reviews for anything other than direct feedback is immoral and, itself, constitutes legalized (but ethically illegitimate) harassment. If performance reviews are confidential between manager and employee, then that's how things should be. They should be direct feedback for mutual benefit, and nothing more. If they're aired to the public, that's wrong. That's effectively extortion.

<comment><USER11> How are employment contracts for 6-figure jobs "under duress"? KPCB isn't obliged to hire anyone. It seems entirely reasonable that they should be able to choose their own terms, and, similarly, if employee candidates don't like those terms, they don't take the job.

What you've done here is subtly shifted the frame of the discussion. You make it sound like if she didn't sign the contract, she'd be _fired_. That's not at all what happened, is it?. As a prospective hire at one of the most "prestigious" VC firms in the country, this person no doubt had many options for where to work next. Kleiner extended her an offer and with it an employment contract, right? If she didn't think she could keep the promises in that contract, she should have gone somewhere else.

I'm also not sure what their being a "VC firm" has to do with who pays legal fees. The rules aren't different for "rich" firms and "poor" firms. People make frivolous claims to both kinds of companies, and larger companies shouldn't have to shoulder the legal costs of fending off bogus claims simply because they're larger. Obviously, if KPCB is wrong about what happened, it's they who will be paying the legal fees. What matters is who's right and who's wrong, not which side of the conflict is easier to caricature.

I have no opinion about the sexual harassment charge itself, nor do I think that "non-disparagement" clauses should (or even can) prevent someone from litigating a sexual harassment or discrimination charge, or even from making a case to the public about them. No doubt KPCB's lawyers want that clause to shut down the drama, but there's a public interest issue involved in firms forcing employees out due to sexual discrimination or harassment.

The reality here is probably that you don't know anything about what happened with Ellen Pao and Kleiner Perkins, but that this is a message board and so you feel it's appropriate to litigate the issue with cartoons. I wish you'd not do that. It makes us all dumber.

 _Late edit: 5 minutes of Google research suggests that non-disparagement clauses are not enforceable in matters of discrimination and, broadly, in public interest cases of all stripes; similarly (you may have already known this), where non-compete agreements are valid, they are still not enforceable when they directly harm the public interest._

<comment><USER12> The problem is what happens when all employers follow suit with this arbitration garbage and it becomes standard practice, which it is becoming. Employers have all the leverage in these cases.

<comment><USER13> What is the problem with arbitration? If I were Ms Pao, I wouldn't want a jury trial. Jurors hate people who they think are looking for a handout.

Here is how it is going to play out: "KPCB is an investment fund, meaning we take money from groups such as a teacher's union retirement fund, and invest it in companies. The returns from those investments are paid back to the investor, ultimately helping secure the retirements of teachers. We pay our employees exceptionally well, because the stakes are so high. Small investment decisions can impact the bottom line of the fund by many millions of dollars".

Juror thinks: "I am not going to take from school teachers to pay Ms Pao because her feelings were hurt. I am sorry some guy harassed her, but that doesn't mean she wins the lottery. Hell, I'd let my boss kick me in groin every day if it meant I got her salary.".

Jury trials are a bad idea when the jury can't relate to the victim. Getting a fair jury is almost impossible in a sexual harassment case.

<comment><USER14> _> What is the problem with arbitration? _

The problem is that the company pays for the arbitor, who is usually an independent contractor hired on a per-arbitration basis. The arbitor is ecnomically pressured to side with the company, because siding against the company is likely not to result in repeat business. This effect is well- established and has been documented (I'll come back and post a reply with sources if I have the time).

 _Jury trials are a bad idea when the jury can't relate to the victim. Getting a fair jury is almost impossible in a sexual harassment case._

Getting a fair jury is not only possible, it's almost impossible _not_ to get a fair jury for a civil trial. Sexual mores and attitudes have changed significantly over the past several decades, to the point where sexual harassment is no longer perceived as acceptable in the general workplace by either gender.

Note that in male-centric fields (sports, programming, etc.) attitudes toward sexual harassment have not changed as much as they have in more gender- balanced fields. This skewed perspective on sexual harassment has made more than a few plaintiff's lawyers quite wealthy.

<comment><USER15> "The arbitor is ecnomically pressured to side with the company, because siding against the company is likely not to result in repeat business."

Arbitrators are typically agreed upon by attorney's of both sides or specified by a neutral higher authority (judge etc.)

<comment><USER16> Perhaps, but that does not disprove the quoted claim.

</comment></comment></comment></comment></comment></comment></comment><comment><USER17> SURPRISE!!</comment>


Go Dependencies Considered Harmful - <AUTHOR> http://fortytw2.com/go-dependencies-considered-harmful <comment><USER2> I greatly respect the Go community for subscribing to the language and ecosystem's opinionatedness, and resisting attempts to relax these rules and conventions. It's not for everyone, I know, but that's the banner they champion, and it works for them.

But, I can't support the attitude voiced in this blog post, that uses the technical realities of Go's broken out-of-the-box package management to justify a culture of NIH [1][2]. This is warped logic.

Are outside dependencies a liability? Yes! You weigh your risks before you rely on someone else's code. Does Go provide sub-par support for reproducible builds and dependency management out-of-the-box? Yes! But others in the community [3][4] have stepped up to bring Go packaging up to the standards that other languages' developers have come to expect.

[1] [http://c2.com/cgi/wiki?NotInventedHere](http://c2.com/cgi/wiki?NotInventedHere) [2] [https://en.wikipedia.org/wiki/Not_invented_here](https://en.wikipedia.org/wiki/Not_invented_here) [3] [https://docs.google.com/document/d/18tNd8r5DV0yluCR7tPvkMTsW...](https://docs.google.com/document/d/18tNd8r5DV0yluCR7tPvkMTsWD_lYcRO7NhpNSDymRr8) [4] [https://github.com/Masterminds/glide](https://github.com/Masterminds/glide)</comment>


Is the iPad a realistic restaurant point of sale device? - <AUTHOR> http://www.chompstack.com/blog/2010/05/26/is-the-ipad-a-realistic-restaurant-point-of-sale-device/

<comment><USER2> I'd expect them to get stolen in a heartbeat.

If a piece of gear in a restaurant is worth $500 and doubles as a consumer media device you can expect it to develop little legs and walk off all by itself within a week.

Proprietary and/or tethered devices with limited use outside of their intended area (such as maybe another restaurant ;) ) have much less chance of suddenly sprouting legs.

<comment><USER3> I would think so to, but there has to be a good way around this. My guess is that you bring the device out to place the order, so it never leaves your sight. I'd also expect it to be inn high end restaurants, so theft seems less likely than say pizza palace.

<comment><USER2> > so it never leaves your sight.

Let me guess, you've never waited tables :) ?

Keeping an eye on a $500 gizmo that's laying around untethered and that just _wants_ to run out that door is a lot harder than you think. Just take a look at trade shows where there is on-site security and all kinds of paranoia and _still_ the weirdest stuff gets stolen. The only relatively safe place in a restaurant is behind the bar, anything that gets carried in to the store is at risk.

And you can't exactly do a pat-down of your clientèle even if something is missing, a good number of them is not going to come back after being insulted like that. Lose-lose.

Just having those things on the premises is going to attract people that will try to lift one.

And if you 'nail them down' you can expect them to break because people will try to pry them loose.

Restaurants lose all kinds of cheap stuff, plates, cutlery, even the flower pots and the props occasionally disappear.

And then there is this problem:

[http://www.mercsystems.com/article_employee-theft- restaurant...](http://www.mercsystems.com/article_employee-theft- restaurants.php)

$500 items attract a crowd of people that will want to take them home.

</comment></comment></comment><comment><USER4> I could be wrong, but I always assumed that mobile POS devices weren't used in American restaurants for some non-technological reason. They're used elsewhere--- some big UK chain restaurants use them (Googling turns up some info on TGI Friday's system), and a bunch of restaurants in Japan use them, for example.

Are the existing solutions too expensive, or in some way insufficient, in a way that rolling a new one out of an Android tablet will fix?

<comment><USER5> For most restaurants in the US they are just too expensive compared to the alternative of having a low paid employee walk 15-30ft to a cash register. There's also a problem of paper receipts which many customers still demand. I have seen mobile POS devices with receipt printers. (ballpark $2-$3k as of a few years ago. Basically a Windows Mobile PDA attached to a little Epson receipt printer)

Total guess: Do waiters and service industry workers in Europe get paid a respectable wage? If you're paying someone a living wage saving the time spent going back and forth to the cash register is probably valuable enough to justify the cost of a mobile POS. Maybe the same deal in Japan?

<comment><USER6> I think the devices _delirium is talking about aren't point of sale devices per se.

In Japan, most chain izakaya (restaurants that serve a wide variety of food an drink) have devices that waiters punch your order into instead of writing it down or remembering it. This is especially useful there, as it is common to go with large groups of people (6 to 20+), and the menu often has about 100 different food items on it.

In addition to staff operated devices, some restaurants have consoles where you can order your food from by navigating a software menu.

</comment></comment></comment><comment><USER6> Why does this article consider the iPad in the first place? As the article points out, the device is too big and unwieldy in the first place. If you were to use an apple product, an iPod Touch is the first thing that pops into my mind.

<comment><USER7> I think the iPod Touch is too small to be effective as an order taking tool. Maybe something closer to a Nook size. After all, if you're a server and you need to take down kitchen instructions or find a drink fast, a slightly larger device is exponentially more useful. But in terms of weight and price point I think the iPod Touch is perfect.

<comment><USER8> Only because it lacks a stylus.

In at least a few places in Aus (Canberra Centre, Melb Airport, Elizabeth St Melb) I've seen ipod touch sized PDA's being used to take down orders at McDonalds.. (including special orders - they got my mcflurry + caramel topping down fine).

Maccas may have a slightly limited menu - but it's still pretty large compared to some restaurants.

<comment><USER7> McDonald's definitely does have a limited menu and with very limited customizations. I've worked at 17 restaurants (seriously, and I am aware of the jokes which could be made about this). Now imagine working at place where the customer says to you: "I'd like a martini, but dry, with 4 olives, shaken, and can I have Belvedere vodka? And not a chilled glass, please. And for my appetizer I'd like your asparagus soup, but without the croutons. And I'm allergic to garlic, so could I get that steak medium rare with extra crispy fries but without any garlic anywhere near it or I'll probably die? Thanks!" Now try that out on a PDA in real-time with a stylus. I've worked with those PDAs--Micros makes a good percentage of them--and I wasn't too impressed. But I'd like to code something better, on a better device.

</comment></comment></comment></comment><comment><USER9> If it's at all feasible, it will happen first in the retail Apple stores themselves. If not even Apple bother to showcase their own products in this setting, you can safely guess it's unworkable.

<comment><USER10> Assuming that Apple is the fount of all retail innovation is a bit unfair to the rest of the world, don't you think? Surely there exists a store somewhere that could make iPads work in this setting, even if Apple doesn't try.

Still, I agree with the post, and would expect a cheaper, more durable device to take whatever market exists. I know that programmers have been dreaming of making a successful wireless restaurant pad for a _very_ long time, so someone might just figure it out.

<comment><USER9> It's not a matter of being innovative—it's just that, if anyone has a reason to show off their "iPad bling", it's Apple themselves—as you can buy one right there after seeing it in use.

</comment></comment></comment><comment><USER11> A better application would be to bolt iPads below glass at every seat. Then ordering would require no server interaction. Nor would the payment (think PayPal, Google Checkout, etc). The cost savings to a restaurant owner in terms of hiring waitstaff would be significant.

<comment><USER2> The average restaurant has 50 seats or so, that's a lot of dough, also the iPads screen will not work under another layer of glass.

<comment><USER11> 50 * $500 = $25,000. This is about what I would expect a restaurant to pay for a single server in a year. It seems like it might pay for itself quickly.

If the iPad doesn't work under glass, then a cheaper alternative touch sensor could be used, as suggested by others in this thread.

<comment><USER2> As soon as you start talking about 'cheaper alternative touch sensors' you'll have to interface those with the ipad in such a way that it will still work, that could be a lot harder than it seems. Also, when you customize stuff the cost will go up quickly.

A typical 'asshole' and 'dirty fingers' proof point of sale terminal for in a restaurant goes for 5 grand or so.

But that's only one for all the tables you've got.

Also, don't underestimate the cost of writing the software and integrating it in to the workflow of the restaurant.

</comment></comment></comment></comment><comment><USER12> too expensive. square and the like make sense from the point that you already have a phone, and it's more peer-to-peer payments than a commercial solution.

<comment><USER7> Square is badass but it doesn't offer simple menu capabilities such as modifiers. Restaurants have fairly particular needs in terms of data structures. Also, doesn't Square only support personal accounts at this time and has specific weekly transaction limits based on a credit check? Additionally, Square can't control cash drawers, etc, and if you take in a lot of cash (restaurants often do), you need a place to put it and a way to correlate it at the end of the night. But seriously, Square has the most beautiful iPad app/control panel, and for the most part it is very intuitive to use.

<comment><USER13> Why can't they have a server-based plugin architecture to take care of custom menu capabilities?

<comment><USER7> No offense, but that may be the most abstract sentence on Earth. Certainly it is possible that they could write some software, possibly taking the form of a plugin architecture, to store menu modifiers (options and choices), and that it would likely be on their server, for download and use by their client app during customer menu creation. It is also possible that they could move to reading RFID cards, or form a softball league. So many possibilities.</comment></comment></comment></comment>


Modulus Acquired by Progress Software - <AUTHOR> http://blog.modulus.io/modulus-acquired-by-progress

<comment><USER2> I'm a user of modulus and a big fan of their service. It has been much more consistent than other Node.js PaaS providers that I've tried. Downtime has been almost non-existent and the level of service has been consistently high.

I hope that this new step in their growth will lead to an even better product without compromising what has made them so great up to now. To me that is, down-to-earth interaction and quiet and reliable service.

For those interested, Plunker ([http://plnkr.co](http://plnkr.co)) runs off Modulus and gets around 50k pageviews a day. The set-up is 1 servo for the static assets and embedded view, 1 servo for the api server and 1 servo for the real-time preview server.

The runner servo alone is serving 32k requests an hour and is below capacity.

</comment><comment><USER3> Congrats guys! I am also a user of modulus and they have a very competitive service.

</comment><comment><USER4> Modulus is the way webdev should be moving. It's just a superior experience.

</comment><comment><USER5> Modulus is great, can focus efforts on your product rather than server config</comment>


Mobile Ambients – process calculus for mobile computation (1998) [pdf] - <AUTHOR> http://lucacardelli.name/Papers/MobileAmbientsETAPS98.A4.pdf <comment><USER2> It's still so interesting to me that this never caught on, particularly in the "distributed age." This feels like a hidden gem in the research that has been covered up by the sands of time.

<comment><AUTHOR> Absolutely! I don't know the exact history and why it hasn't been applied much, and I'd be very curious to learn, but it's definitely a gem.

</comment></comment><comment><AUTHOR> tldr version: [https://en.m.wikipedia.org/wiki/Ambient_calculus](https://en.m.wikipedia.org/wiki/Ambient_calculus)</comment>


How to recognize AI snake oil [pdf] - <AUTHOR> https://www.cs.princeton.edu/~arvindn/talks/MIT-STS-AI-snakeoil.pdf <comment><USER2> I don't have time to read the entire paper but I would like to share an anecdote. I worked at a company with a well staffed/funded machine learning team. They were in charge of recommendation systems - think along the lines of youtube up next videos. My team wanted better recommendations (really, less editorial intensive) so the ML team spent weeks crafting 12 or more variants of their recommendation system for our content. We then ran that system for months in an A/B testing system that judged user behaviour by several KPI. The result was all variants performed equally well within statistically insignificant bounds. The best performing variant happened to be random.

Talking to other groups that had gone through the exact same process our results were pretty typical. These guys were all very intelligent and the code and systems they had implemented were pretty impressive. I'm guessing the system they built would have cost a few million dollars if built from scratch. We did use this "AI/ML" in our marketing so maybe it was payed for by increased sales through use of buzz words. But my experience was that in most limited use cases the technology was ineffective.

<comment><USER3> Since we are sharing anecdotes, I can report it's been 20 years of buying stuff on the internet and the combined billions of ad tracking research dollars spent by Amazon and Google have not yet come up with a better algorithm than to bombard me with ads for _the exact same thing I just bought_.

<comment><USER4> I just spent 15m on Amazon trying to prod the recommendation algorithm into finding something I actually wanted to buy so I could get above the "free delivery threshold".

Think about that. I wanted to spend money. I wasn't too fussy what it was. Amazon has a decade of my purchasing and browsing history.

And they still failed.

<comment><USER5> Amazon infuriates me. I regularly buy gadget-y bits like electronic components and peripherals. Probably at least once a month. Never see adverts for similar.

That ONE TIME I buy a unicorn dress for my 2 year old daughter? That's a lifetime of unicorn related merchandise adverts and recommendations for you!

<comment><USER6> Actually that's really interesting because it exposes a bias in their recommendation system. They must be heavily biased towards things with _mass appeal_ instead of specifically targeting user preferences, which is funny because it goes against the grain of the whole "targeted advertising" promise of ML. You'd think if anyone could get that right, it would be Amazon, yet..

</comment></comment></comment></comment></comment><comment><USER7> Over the years my heuristic has turned into: "Did the team formulate their problem as a supervised learning problem?" \- If not it's probably BS.

In longform if anyone is interested [https://medium.com/@marksaroufim/can- deep-learning-solve-my-...](https://medium.com/@marksaroufim/can-deep- learning-solve-my-problem-a-type-theoretic-heuristic-e57f4d1658f)

EDIT: I would consider autoencoders, word2vec, Reinforcement Learning examples of turning a different problem into a supervised learning problem

EDIT 2: Social functions like happiness, emotion and fairness are difficult to state - you can't have a supervised learning problem without a loss function

<comment><USER8> It's hard to verbalize this, most of it is "intuition" but I think it boils down to "supervised learning is BS."

Humans are smarter than computers. How can a human teach a computer how to do something when the human itself can't teach another human that something?

We haven't solved _that_ problem. The snake is eating its tail.

You can't teach a human how to do something when the methodology to do that is the student trying something and the teacher saying "Yes" or "No".

Well.... _why_? Why is it yes or why is it no? What is the difference between what the human or the computer, or in general, the student, _did_ and what is _good_ or _correct_? And then you still have to define "good" and many times that means waiting, in the case of the PDF linked to above, perhaps many years to determine if the employee the AI picked, turned out to be a good employee or not.

And how do you determine _that_? How do you know if an employee is good or not? We haven't even figured that out yet.

How can we create an AI to pick good employees if _human beings_ don't know how to do that?

Supervised learning isn't going to solve any problem, if that problem isn't solved or perhaps even solvable at all.

In other words, over the years, my heuristic has turned into, "Has a human being solved this problem?" If not, then AI software that claims to is BS.

<comment><USER9> Supervised learning in machine learning is nothing remotely like a human teaching anyone anything. It's a very clear mathematical formulation of what the objective is and how the algorithm can improve itself against that objective.

The closest analogy for humans would be to define a metric and ask a human to figure out how to maximize that metric. That's something we're often pretty good at doing, often in ways that the person defining the metric didn't actually want us to use.

<comment><USER8> > Supervised learning in machine learning is nothing remotely like a human > teaching anyone anything.

I disagree, I think it's exactly the same. As an example, a human teaching a human how to use an orbital sander to smooth out the rough grain of a piece of wood.

The teacher sees the student bearing down really hard with the sander and hears the RPM's of the sander declining as measured by the frequency of the sound.

The teacher would help the student improve by saying, "Decrease pressure such that you maximize the RPM's of the sander. Let the velocity of the sander do the work, not the pressure from your hand."

That's a good application of supervised learning. Hiring the right candidate for your company is not.

<comment><USER10> But that's not at all how "supervised learning" works. You would do something like have a thousand sanded pieces of wood and columns of attributes of the sanding parameters that were used, and have a human label the wood pieces that meet the spec. Then you solve for the parameters that were likely to generate those acceptable results. ML is brute force compared with the heuristics that human learning can apply. And ML never* gives you results that can be generalized with simple rules.

* excepting some classes of expert systems

<comment><USER8> One of the columns of sanding parameters is the sound of the sander.

</comment></comment></comment></comment></comment></comment><comment><USER11> The author says "AI is already at or beyond human accuracy in all the tasks on this slide and is continuing to get better rapidly" and one of his examples is "Medical diagnosis from scans". That is an example of precisely the sort of snake oil hype he's berating in the social prediction category.

In an extremely narrow sense of pattern recognition of some "image features", i.e. 5% of what a radiologist actually does, he's probably right. But context is the other 95%, and AI is nowhere close to being able to approach expert accuracy in that. It's a goal as far away from reality as AGI.

"AI" tools will probably improve the productivity of radiologists, and there are statistical learning tools that already kind of do that (usually not actually widely used in medical practice, you can say yet, I can say who knows but nice prototype). But actual diagnosis, like the part where an MD makes a judgement call and the part which malpractice insurance is for? Not in any of our lifetimes.

A radiologist friend complains that it's been 10+ years since they've been using speech recognition instead of a human transcriptionist, and all the systems out there are still really bad. Recognizing medical lingo is something you can probably achieve with more training data, but the software that sometimes drops "not" from a scan report is a cost-cutting measure, not a productivity tool. It makes the radiologist worse off because he's got to waste his time proofreading the hell out of it, but the hospital saves money.

<comment><USER12> Author here. I appreciate your criticism. What I had in mind was more along the lines of Google's claims around diabetic retinopathy. I received feedback very similar to yours, i.e. that those claims are based on an extremely narrow problem formulation: [https://twitter.com/MaxALittle/status/1196957870853627904](https://twitter.com/MaxALittle/status/1196957870853627904)

I will correct this in future versions of the talk and paper.

<comment><USER13> Then I shall write to you directly. I don't know how you can make the claim that automated essay grading is anything but a shockingly mendacious academic abuse of student's time and brainpower. To me, this seems far worse than job applicant filtering, firstly because hiring is fundamentally predictive, and secondly because many jobs have a component of legitimately rigid qualifications. An essay is a tool to affect the thoughts of a human. It is not predictive of some hidden factor; it stands alone. It must be original to have value; a learned pattern of ideas is the anti-pattern for novelty. If the grading of an essay can be, in any way, assisted by an algorithm, it is probably not worth human effort to produce. If you personally use essay grading software, or know of anybody at Princeton that does, you have an absolute obligation to disclose this to all of your students and prospective applicants. They are paying for humans to help them become better humans.

</comment></comment></comment><comment><USER14> My brush with AI snake oil:

I interviewed at a startup that seemed fishy. They offer a fully AI powered customer service chat as an off the shelf black box to banks. I highly suspect that they were a pseudo AI setup. LinkedIn shows that they are light on developers but very heavy on "trainers", probably the people who actually handle the customers, mostly young graduates in unrelated fields, who may believe that their interactions will be the necessary data to build a real AI.

I doubt that AI will ever be built, it's just a glorified Mechanical Turk help-desk. I guess the banks will keep it going as long as they see near human level outputs.

<comment><USER15> I have a feeling I know _exactly_ which company you're talking about...so it's either just that obvious, or there's more than one of these, or both!

<comment><USER16> It seems to be the latter. In fact, the trick (should I say, fraud?) is so common that they were even several articles about it in the press over the past two or three years. Even the famous x.ai had (and I guess still has) humans doing the work.

[https://www.bloomberg.com/news/articles/2016-04-18/the- human...](https://www.bloomberg.com/news/articles/2016-04-18/the-humans- hiding-behind-the-chatbots)

</comment></comment></comment><comment><USER17> My company is sourcing AI from MTurk. It's actually cheaper than running fat GPU model training instances. The network learns fast and adapts well to changes in inputs.

I envision the sticker "human inside" strapped on our algorithms.

<comment><USER18> You should emphasize that this is Organic AI. It's low carbon and overall greener.

<comment><USER19> Or keep calling it AI, and concede that AI stands for "actual intelligence" if someone asks you directly.

<comment><USER20> AI now is like Cyber was in the 1990s it's seems to be nothing but a buzzword for many organizations to throw around.

The term AI is used as if humanity now has figured out general AI or artificial general intelligence (AGI). It's quite obvious organizations and people use the term AI to fool the less tech inclined into thinking it's AGI - a real thinking machine.

<comment><USER19> Remember 5-ish years ago when IBM's marketing department was hawking their machine learning and information retrieval products as AI, and everyone in the world rolled their eyes so hard we had to add another leap second to the calendar to account for the the resulting change in the earth's rotation?

I suppose their only real sin was business suits. Everything seems more credible if you say it while wearing a hoodie.

</comment></comment></comment></comment></comment><comment><USER21> I worked at a place that was selling ML powered science instrument output analysis. It did not work at all (fake it till you make it is normal, was told). So there was a person in the loop (machine output -> internet -> person doing it manually pretending to be machine -> internet -> report app). The joke was "organic neural net." Theranos of the North! ML is a great and powerful pattern matcher (talking about NN not first order logic systems) right now, but, I fear we are going into another AI winter with all the over promising.

<comment> rm_-rf_slash We won't ever have an AI winter like in the 70s again. A lot of ML is already very useful across many domains (computer vision, NLP, advertising, etc). Back then, there was almost no personal computing, almost no internet, smol data, and so on. Stuff you need for ML to be useful and used.

So what if some corporate hack calls linear regression "AI"? The results speak for themselves. The ML genie is too profitable to go back in the bottle.

<comment><USER22> Didn't linear regression used to be called "AI" as recently as a decade ago?

<comment><USER16> It's still better in many cases than modern ML (especially if you incorporate explainability and efficiency as metrics of "better" next to the predictive power), so I wouldn't object much if a company called it "AI". In fact, if I learned that an "AI" behind some product was just linear regression, I'd trust them more.

</comment></comment></comment></comment><comment><USER23> What I dislike far more than the idea of using such systems to predict social outcome is that the usage of such systems is done behind closed doors. I would be much more willing to accept such systems if the law required any system to be fully accessible online, including the current neural network, how it was trained, and training data used to train it (if the training data cannot be shared online, then the neural network trained from it cannot be used by the government).

Independent companies using AI is far less a concern for me. If they are snake oil, people will learn how to overcome them. Government (especially parts related to enforcement) is what I find scary.

<comment><USER24> Using an association between features to make a prediction about something, rather than measuring the thing itself, is exactly what's meant by "prejudice." Even when the associations are real and the model is built with perfect mathematical rigor. ML is categorically unsuitable for government decisions affecting lives.

<comment><USER23> You seem to think this level of prejudice for prediction is wrong. Why?

If someone has killed 12 people, being prejudice about their chance of killing another and using that to determine the length of a sentence seems reasonable.

Even with something like a health inspection. Measuring how they store and cook raw chicken is about predicting the health risks to the public eating it, not about measuring the actual number of outbreaks of salmonella. And even if they were to measure the previous outbreaks of salmonella and use it to prediction the future outbreaks, that is still two different things.

</comment></comment></comment><comment><USER25> I read "Why are HR departments apparently so gullible?" and as someone who has worked in a corporate for 20 years I spotted my underwear.

The identification of facial recognition as problematic because of accuracy doesn't match my thinking. I believe that the key issue is that given a set of targets facial recognition systems will find near misses from the wider population of all faces offered as candidates, that they then flag as potential matches. This leads to real world problems (like innocent folks being arrested).

Automated essay grading and content recommendation are both very problematic because they do not account for originality and novelty. A lecturer grading an essay that is written my a strong mind from a different culture might be able to recognise and credit a new voice, a learned classifier never will. Similarly content recommenders have us trapped in the same old same old bubble, nothing strikingly new can get through.

<comment><USER26> ...I spotted my underwear?

What does this mean?

<comment><USER27> Interpretation 1: they pooped their pants

Interpretation 2: they introduced a non sequitur in their excitement of finding their underwear (perhaps they lost it)

Interpretation 3: when they had their flashback to having worked in corporate 20 years ago they recalled where they misplaced their underwear

</comment></comment></comment><comment><USER28> Top textual feature predicting snake oil: calling the product AI rather than ML.

<comment><USER29> That doesn't work. Everything is called AI these days and in mountains of bullshit there are also some actually useful results, these few are not snake oil.

<comment><USER30> Somewhere out there, a biotech R&D company has developed an effective penis enlargement treatment. Unfortunately they have been having some trouble reaching potential customers.

<comment><USER31> This is a tragedy for half of humanity. Actually, all humanity come to think of it

</comment></comment></comment></comment><comment><USER32> This presentation categorized AI-related tech with social outcomes as _fundamentally dubious_ , such as predicting criminal recidivism, predicting terrorist risk, or predictive policing. The rationals are that

(1) technically, the technology is far from perfect.

(2) Social outcomes like fairness are fundamentally difficult to state.

(3) Its inherent problem is amplified with the ethical/moral problems.

Of course many find it's unacceptable due to the ethical problems in a typical Western liberal democracy. But what if I'm an authoritarian who wants to find the tools of suppression, and I don't care the false positives or the ethical problems? Is it going to help my regime, or is it going to have the opposite effect?

I highly suspect that the answer is the former one. Fortunately, the technical limitations of "AI" means it's still more or less ineffective today, but it can only get better.

Therefore, I don't think AI with social outcomes are _fundamentally dubious_ , but rather, _fundamentally dangerous_.

</comment><comment><USER33> Lots of AI is actually large numbers of humans working on small bits of problems that are beyond our ability to automate. Not infrequently these are passed off on the outside as 'ai' startups. There are some good examples too where the companies that use machine learning properly and to good effect. Interestingly they don't blab about it because it is their edge over the competition and often just knowing that something can be done is enough to inspire someone else to be able to copy it.

So here is my own theory on how to recognize AI snake oil: if it requires advertising it is probably fake, if it is very quiet and successful it is likely genuine.

<comment><USER34> > if it requires advertising it is probably fake, if it is very quiet and > successful it is likely genuine.

This is true of almost any product being offered for sale. Good advice.

<comment><USER16> Agreed, and I personally use the following heuristic in my product evaluation: the heavier the intensity of advertising, the worse the product. After correcting for rough company size (bigger company = bigger baseline advertising budget), I found it to be quite accurate.

</comment></comment></comment><comment><USER35> The economic point of AI decision systems is that they can make an automated decision for $0.0001 of computer time, instead of $10 for a few minutes of an expert's time. For something like spam filtering, you obviously need the cheap solution. But you don't need that for the social interventions where you're making a decision about parole or something. You can spend $10 (or $1000) of people's time to make those decisions, because both their volume and impact is at human scale.

<comment><USER36> Moreover, the big advantage of AI is consistency. I.e. it does not get tired, bored, lose motivation, etc.

Hence it might better to get 70% correct answer all of the time, than 90% (human) some of the time.

<comment><USER37> On hard problems these AIs are doing no better than chance. Nor is a badly trained AI in any way consistent.

</comment></comment></comment><comment><USER38> Perhaps ML could be applied here to help filter out the barrage of AI snake oil. Funding, anyone?

<comment><USER39> You should fund it with an Initial Coin Offering for maximum credibility

<comment><USER40> Where can I buy in?

</comment></comment></comment><comment><USER41> I've worked on data related to healthcare and security (and sometimes both) for quite a while now, and I think there are a couple of general contextual themes, where, if present, means that you have to be extremely careful about applying "AI" (some kind of ML in most cases): (a) where there's a high cost for incorrect predictions (e.g. criminal recidivism, educational attainment, terrorist attacks, etc) (b) where causation is important (e.g. drug efficacy and safety, educational attainment, almost all of healthcare) (c) where you're in an adversarial domain (e.g. fraud, cybersecurity, security in general) (d) where high technical performance (precision/recall/F1/etc) isn't correlated with predictiveness of what you're actually looking for (much of healthcare)

In healthcare and security, there's starting to be an awareness of the snake- oil that's out there, but I still run into people regularly who ask for a magic algorithm that predicts patient outcomes or a security breach.

</comment><comment><USER42> I really wish we could stop using AI or ML for things in the "predicting social outcomes" category. Naming them more like "computational astrology" or "machine alchemy" would be a better fit.

<comment><USER22> "Astrology" (where mathematicians used to hang out before the scientific revolution, so no "computational"/"mathematical" qualifier needed) has been going on for decades in the financial and economic fields, so this one seems to be promised a bright future too!

</comment></comment><comment><USER43> Looking for a job as a "deep learning" PhD soonish, the amount of BS in this field at the moment has all my meters maxing out. Going through job listings is pretty exhausting when I'm constantly torn between laughing and crying...

</comment><comment><USER44> Excellent takeaways:

>AI excels at some tasks, but can't predict social outcomes.

>We must resist the enormous commercial interests that aim to obfuscate this fact.

>In most cases, manual scoring rules are just as accurate, far more transparent, and worth considering.

</comment><comment><USER45> From the slides:

Harms of AI for predicting social outcomes

• Hunger for personal data

• Massive transfer of power from domain experts & workers to unaccountable tech companies

• Lack of explainability

• Distracts from interventions

• Veneer of accuracy

Human behavior is not IID and these models will struggle and fail due to the fundamental statistical assumptions of modern AI techniques. I also agree that as a result, we will normalize the collection of personal data in the name social progress.

</comment><comment><USER46> Isn't "AI" pretty much snake oil. IIRC Artificial Intelligence used to mean a computer that could think like a person. But that just is not the case. Even with IBM ads with a computer talking to people saying it's going to fix the network and stop cyber attacks that is just complete nonsense. And it will probably always be nonsense because of course there's no way a computer can think like a person because a computer is not a person. It did not grow up and fall of it's bike and skin it's knee and take a road trip to the rock concert and meet someone and so on. AI is being used as a marketing term to compensate for the fact that sophisticated pattern recognition algorithms and the like are not particularly marketable even if they are useful.

</comment><comment><USER47> Once upon a time, I used to do research in the field of Wireless Sensor Networks, which used to be abbreviated into WSN. You can still find lots of research papers using the abbreviation in the title, circa 10-12 years ago.

It went through a hype cycle, and then people sort of moved on - into IoT. And IoT, naturally, just sounds better and is more accessible, plus the tech did catch up, so it became a lot more popular than WSN (the term).

I told a friend of mine that no one paid attention when the field was called WSN, and now it is the exact same thing, but IoT is taking off like crazy. (At that point, I had left research altogether). He said "Yes, and that's perfectly reasonable. If you don't invent new terminology every few years and manufacture some kind of new hype, the funding agencies stop sending you money."

This is a community which is based on the mantra of "making something users want". Once you realize that the upstream user here is the funding agency, and what they really want is to make bets on "cool stuff for tomorrow" rather than boring old 3-5 year old tech (such as WSN), the hype actually makes perfect sense.

Sure, there is still a need to separate the snake oil from the reality. But that is true of tech in general. I am not sure if AI/ML is particularly bad in some way.

</comment><comment><USER48> Here's the compressed decision tree:

Does it claim to have high performance on a task? Can humans quickly and cheaply verify that claim? If yes->no, then it is snake oil.

</comment><comment><USER49> Several references to simple linear models and even _improper linear models_ , so I will recommend this paper: Dawes, _The Robust Beauty of Improper Linear Models_.

[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.188...](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.188.5825&rep=rep1&type=pdf)

</comment><comment><USER50> It seems like at least half the tech people I talk to work with AI now... no matter their field.

<comment><USER51> Any type of heuristic search = AI. hand-made decision tree or lookup table = AI, Naive bayes = AI, KNN = AI. Uses numpy = AI. One employee in mobile app startup has graduate thesis in ML = AI startup. Approximate string matching in SQL query = AI. Not clear what the product is going to be = AI.

"Information is not knowledge. Knowledge is not wisdom. Wisdom is not truth. Truth is not beauty. Beauty is not blockchain. Blockchain is not AI. AI is THE BEST."

<comment><USER52> In my space (cybersecurity) I've heard "more than 2 joins" (in an SQL context) is ML.

<comment><USER53> So it's turning into a meaningless buzzword.

</comment></comment></comment></comment><comment><USER54> The classic Bone Tumor Differential Diagnosis program that Apple published for the Apple ][ in the original Apple Software Bank Volume 1 from 1978 sounds legit. But beware: it requires 32K of RAM, bigger than any other program in that volume! (Available on both cassette and 5-1/4" floppy disk for free from your local Apple dealer.)

[https://archive.org/stream/Apple_Software_Bank_Vol_1-2/Apple...](https://archive.org/stream/Apple_Software_Bank_Vol_1-2/Apple_Software_Bank_Vol_1-2_djvu.txt)

[https://archive.org/details/a2_Biology_19xx_](https://archive.org/details/a2_Biology_19xx_)

[https://mirrors.apple2.org.za/ftp.apple.asimov.net/documenta...](https://mirrors.apple2.org.za/ftp.apple.asimov.net/documentation/applications/misc/Apple%20Software%20Bank%20Vol%201-2.pdf)

Program Name: BONE TUMOR DIFFERENTIAL DIAGNOSIS

Software Bank Number: 001 1 4

Submitted By: Jeffrey Dach, M.D.

Program Language: APPLESOFT II BASIC

Minimum Memory Size: 32K Bytes

This program is intended for use by qualified medical practitioners. While the specific data are of interest only to those familiar with bone pathologies, the programming techniques may well interest a wide range of computer users.

INSTRUCTIONS

LOAD the program into APPLESOFT II BASIC, and type RUN. Follow the instructions displayed on the screen. The program asks a series of questions concerning radiographic and clinical details of the bone tumor in question. For each question, type the number of the appropriate answer and press the RETURN key. Finally, the program uses Baye's rule and a predetermined probability matrix from Lodwick (1963) to calculate the relative probabilities of 9 different diagnoses.

Some knowledge of descriptive terms for bone tumors is needed to answer the questions. Only a qualified physician should attempt to use this program as a diagnostic tool.

</comment><comment><USER55> Can we do blockchain next?

<comment><USER56> We don't even need AI for that. Here's some flawless pseudo-code:

fn is-snake-oil: return !(coin is BTC or coin is in stable-coin-list)

<comment><USER57> Sadly, that gives a false negative for when coin is BTC or coin is in stable- coin-list.

<comment><USER58> I think that was the joke

</comment></comment></comment></comment><comment><USER59> > Actually, Major Major had been promoted by an I.B.M. machine with a sense of > humor almost as keen as his father's.

Catch 22

</comment><comment><USER60> I once figured I can use ML to predict stock market movements and I went about buying 5 years of data from CBOE and started working on modeling. At one point I managed to get fairly accurate predictions of movements and I thought I had struck a lottery.

Turns out my training data had tomorrow's movement indicator in it which I accidentally added. Despite that dumb mistake it was only accurate up to 80% or so. That's when I realized Im better off with a random number generator.

<comment><USER6> I went down that path briefly too. After having trouble getting results, I realized I needed to check the fundamentals. What did it in for me was finally just doing a simple correlation analysis of earlier prices to later prices, and realizing there is NO CORRELATION. I realized, if there's nothing to predict, no amount of ML will magically figure it out for me, no matter how complex the indicator.

(I was just looking at single products.. I suppose this may be completely different when considering multiple products and markets and integrating external information like news sources.. not at all saying that ML can't be applied to the stock market, just that it's not nearly as simple as looking at previous prices to predict future prices, as everyone says... the point being, always check basic correlations!)

</comment></comment><comment><USER13> There is a particular form of statistical inference which is not well- understood as problematic. I will use my "smart" scale as an example. It takes weight and several impedance measurements, which are taken from a large population along with body composition truth measurements from DEXA, and the coefficients of the algorithm are determined by regression. It should be no surprise that the weight measurement overpowers all other measurements in the regression, and the impedance covariance is ill-conditioned, so really it's just a height and weight formula. My information gain from the impedance measurement is zero. The correct way to do the regression is in reverse, from more (relevant) information to less, so the parameters should be well- conditioned. This is assuming that DEXA contains all useful information to predict impedance. If not, forget about the whole thing. If that model works, then the reverse can be found through Bayesian optimization. You still have bias set by the covariance prior, but it is at least known, and you can give information about how it is affecting the result.

</comment><comment><USER61> It's a bit of a harsh heuristic, but after working now on several projects involving ML/AI and reading and watching about the experience of others in the industry too, I've come to associate most claims of ML as snake oil.

In industry today, I believe very few businesses are reaping much benefit from ML as compared to trivial statistical/analytical tools (linear regression, most popular recommenders, common sense improvements/optimizations, etc.). The only real benefit I would argue ML has brought for businesses has been in marketing to the general lay audience and misleading investors.

The main reason for this in my opinion is you can't really just come in and make recommendations/improvements to a given problem domain without deeply understanding that domain back to front - and that's an understanding that academic types that get hired to build ML systems almost never have. You can't stand at an arms length from real business problems and just throw maths at them and expect to make good (or even sensible) recommendations.

</comment><comment><USER62> >The fast way forward involves being critical, being pragmatic, not overselling, and not drinking the Kool-aid.

</comment><comment><USER63> Does the company mention AI in their name, marketing material, or investor pitch? Then it is snake oil.

</comment><comment><USER64> I feel like if AI were so easy, someone would be making billions beating the stock market. It's got way more of the historical data and features than any of the problematic examples in this slide deck, but it's essentially unsolved.

<comment><USER65> Some are making billions beating the stock market

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

</comment></comment><comment><USER66> I tend to find the following few questions a quick way to evaluate an ML/AI pitch in an elevator:

Ask yourself: could a human given the inputs reasonably produce the outputs you are looking for? — this helps avoid/identify the pure magic pitches.

Then ask the person pitching: 1\. What's your training data and how is it collected? 2\. What's your validation data and how is that constructed, and how does your system perform on that set? 3\. What are the blind spots and biases in your model and how are you mitigating them?

If they don't have succinct and competent answers or major red flags like no validation or claiming no biases then run away.

</comment><comment><USER67> This was a really interesting read. In relation to the discussion of the predictive accuracy of a dataset with 13,000 features, I thought it might be worthwhile to bring up the idea of the "Curse of Dimensionality" for anyone unfamiliar: [https://en.wikipedia.org/wiki/Curse_of_dimensionality](https://en.wikipedia.org/wiki/Curse_of_dimensionality)

The "tl;dr" is basically that more features is not necessarily a panacea and can actually cause more problems.

<comment><USER68> Thanks for adding that to the discussion. I'd like to point out a couple of things:

(1) that adding features can create problems is well known among good ML practitioners (I daresay, esp. to those who have a fair amount of exposure to non-deep-learning techniques). With deep learning you can afford to worry less since with enough data and compute cycles, the network can figure out what to ignore. Which is convenient. Throwing out uninformative features however, may still have a practical benefit: less features -> smaller dataset size -> faster training.

(2) This is probably a minor nitpicky point: adding more features can lead to no improvements not only because of the curse of dimensionality, but sometimes simply because the feature has absolutely no bearing on the label; that is to say you might not be adding noise, but you might not be adding information either.

</comment></comment><comment><USER69> Easiest way to cut through AI BS: ask "what's their dataset?" If it's not obvious, there's a problem. AI is only as powerful as the data it learns from.

There is one exception to this: if an exhaustive simulation of the problem exists. This is why AI is so successful at sandboxed games like chess and Go. It can generate its own data with zero ambiguity.

So: what's your dataset? What simulation are you inverting? If neither, you're just writing an expert system based on heuristics.

</comment><comment><USER70> It would be interesting to try to train an algorithm to detect snake oil in companies claiming AI, but I don't know how it would work with no negatives.

</comment><comment><USER71> How to recognize AI snake oil. The salesman calls it AI and claims it is not simply statistical inference.

</comment><comment><USER72> That's ridiculously easy: If it contains the letters "AI", it's snake oil. There is _nothing_ in man-made software that would merit the term "intelligence". Real intelligence would be creative and unpredictable. We would run for cover.

</comment><comment><USER73> "Lack of explainability" might be seen for some to be a feature, not a bug. LEOs don't care how they have to justify an action, just that the justification exists for them to operate the way they want to.

</comment><comment><USER22> This got me thinking - are there any cases of _literal_ snake oil (or any snake-derived products) having better than placebo effects ? (I assume that it's different from snake venom?)

</comment><comment><USER74> Company founders have learned that by simply being present in a big growing industry, they are virtually guaranteed for receiving investments. A rising tide will lift all boats.

</comment><comment><USER75> Love this, many problems can be solved with regression analysis.

</comment><comment><USER76> How to use AI to recognize AI snake oil?

</comment><comment><USER77> 1\. If purports to have "self".

</comment><comment><USER78> This is a good framework and categorization for evaluating the effectiveness of AI.

TLDR:

AI is good at: Perception (how things are)

AI is ok at: Judgement (how things are)

AI is poor at: Predicting (how things are going to be)

E.g., AI is effective to varying degrees at observing and characterizing how things are, but not at predicting how they're going to be.

For predicting, AI with hundreds of variables fairs no better than simple linear regression over single digits of variables. Simply because, _the future is not reliably predictable_ , by any means.

</comment><comment><USER79> This is good. I co-founded Futurescaper[1], a company that does work in strategic foresight systems -- collective mapping of complex systems, and analytical tools to help people and organisations to understand them. This put us in a prime position to be vendors of AI snake oil. Due to a stubborn overabundance of ethics, we've refused to do so, which has undoubtedly cost of a lot of business. People _want_ to buy snake oil. It goes down much smoother than hard truths.

To amplify what this presentation says, here's the hard truths about predicting social outcomes: either they

1\. Are simple and obvious, and can be easily understood and predicted via regressions and trendlines.

2\. Are complex, non-obvious, and neither can nor should be predicted. In fact, attempting to predict them is often dangerously wrong. But this doesn't mean that they can't be understood.

In the first instance, you don't need any fancy software, so there's no snake oil to be sold. The second instance, however, gets caught in a cognitive bias: we think that the future can be predicted. This is because, in simple systems, it can: drop a glass above a hard floor, and you can accurately predict that it will fall and shatter. That is a simple system. We think that complex systems must be similar... just more complex.

But complex systems are fundamentally different. Consider a double-pendulum. Its movement can't be predicted for more than the next few swings. Even if you know the _exact_ starting configuration of the pendulum -- and by exact, I mean not just every single sub-atomic particle in the pendulum arms, but the gravitational influence of literally every object in the universe -- you still wouldn't be able to greatly extend your foreknowledge of its movements. This is because the feedback loops are driven by chaos, and chaos is baked directly into the mathematical fabric of the universe itself.

For the mathematically inclined, consider the Mandelbrot set: it is, essentially, the equivalent of a double pendulum. It asks a question: "by starting with this number and iteratively exponentiating it, will in trend towards zero or infinity?". When you ask this question on a simple number line, then the answer is obvious: below 1, it trends towards zero; above 1, it trends towards infinity. However when you ask this question on the complex number plane -- with two numbers feeding back to each other as they exponentiate -- then the only way to answer the question is to keep iterating the numbers to find out. There's no shortcut. In some places, the answer is found quite quickly. In others, it takes thousands of iterations. In still others, it takes infinite iterations: you could build a computer the size of the galaxy and you still wouldn't be able to answer the question of whether such-and-such coordinates trends towards zero or infinity. That's the complexity you get from just two number lines and a very simple feedback loop.

The real world of psychological and social cause-and-effect is _far_ more complex than a double pendulum or a Mandelbrot set, and attempting to predict it is even more futile. In fact, it's dangerous.

What makes this dangerous is that you _can_ throw statistics at complex futures, and make statements like "Future A has a 40% probability; futures B, C, and D all have a 20% probability". But the human mind is terrible at making good judgements based on this kind of information. Hearing it, people tend to think: "right, that's settled then: Future A is twice as likely as any other scenario, so that's what we'll plan for." We fixate on what looks like the most likely future, and disregard the rest.

The problem is that if the preparations for Future A are contrary to what you'd want to do in Futures B, C, and D, then betting everything on Future A means that 60 % of the time, you'll lose.

What's interesting is that increasing the accuracy of those percentages doesn't necessarily help, and in many cases can hurt, since it only reinforces our tendency towards target fixation. In a 40/20/20/20 scenario, we might still make some concessions towards planning for the "20s". But in a 70/10/10/10 scenario, those "10s" will simply be discarded. Which means that 30% of the time you won't just lose: you'll be blindsided. Utterly fucked.

Unfortunately, most of the predictive AI that I've seen is focused on either increasing the "certainty" via extremely dubious means, or simply hiding the non-dominant answers altogether. So the AI does the target fixation for you. That's not a good thing.

The entire discipline of Scenario Planning[2] evolved to help people and companies "un-predict" the future. Rather than putting percentages on _probabilities_ of outcomes, people need to understand the _possibilities_ of outcomes. Even if complex systems can't be predicted, they can be better understood, and that can be very valuable for helping to navigate them in real-time. Rather than giving people the easy (and usually wrong) answer of "here's what is going to happen", you can give them a range of futures that _could_ happen, and understanding those possibilities can aid navigation and lead to better outcomes.

It may even be that AI has a legitimate role to play in this process -- but it won't be in falsely predicting the outcome of complex systems, nor will it be in absolving people of the responsibility of thinking for themselves. This, unfortunately for my company's ability to raise capital, is a much less sexy sales pitch than the AI snake oil. (Although it does get us smarter and less annoying clients, so there's that!)

1: [https://www.futurescaper.com/](https://www.futurescaper.com/)

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

</comment><comment><USER80> This seems contradictory:

> AI can't predict social outcomes

> In most cases, manual scoring rules are just as accurate

So manual scoring rules don't work either for predicting social outcomes? There is some magic sauce that humans use for prediction that we haven't cracked yet? Nothing can predict social outcome?

AI is perfectly capable of predicting social outcomes, and only in very few cases are manual scoring rules as accurate as black box AI. The ethical concern is not about accuracy, but about our sensibilities when it comes to protected classes. The author cherry picked examples where simpler approaches also worked, but says nothing of practical feasability or increase in variance. Try actually doing face recognition or spam detection with manual rules.

Face recognition being way more accurate is just as much an ethical concern as a gun that is way more accurate. It all depends on who you point it at. Accurate face recognition at the border helps save lives as much as equipping the police with more accurate hand guns.

The talk of AGI is misguided. Everybody can see that the economy will be increasingly automated with narrow AI. Just because "big data" was a hype word, does not mean companies haven't been monitizing their big data (and were thus right to collect it).

We _can_ predict probabilities about the future. The author is attacking these systems for not being 100% sure. Predictive policing is automated resource management. Militaries have been doing this for decades. It has its drawbacks, but also benefits (wiser usage of tax money, protecting low-income neighborhoods from falling in the hands of gangs).

The author also claims that algorithms automatically turn away people at the border for posting or liking or being connected to terrorist propaganda. But these systems just give a score and a human border guard makes the (more informed) decision.

A system not being 100% accurate is not an ethical concern, as long as we not treat those systems as 100% accurate and give proper recourse.

Just a spelling check can and does weed out poor candidates. Why does HR want to automate? Because they get 1000+ resumes for a single position. The manual glance they give them pale in comparison to what an automated system can do.

What is more likely? That these HR systems show promise? Or that the VC market has completely lost it (despite working with software and automation for decades, and have AI experts on staff) and is pumping billions into tealeaf reading, because now its called "AI"?

If you cheat the system by adding "Cambridge" or "Oxford" in white letters to your CV, is that ethical? Why not add it to your education section in black letters? Would you hire a good potential candidate, if you knew they acted like 90s search engine spammers? Maybe a candidate from Oxford or Cambridge really deserves to be on the top of the pile, or is it now unethical to look at education when hiring?

This presentation likes to mix ethics with technical success. Just say that a HR system is unethical, without calling it bogus with zero proof other than "some AI experts agree that this is impossible".

Yes, there is a lot of snake oil AI, and this will only increase. But these systems can and do work. I am sure there are AI experts building these systems right now.

</comment><comment><USER81> Uhu seems good</comment>


How My Fears Doomed My First Startup - <AUTHOR> https://dsdoes.com/how-my-fears-doomed-my-first-startup-81d7e3f0c47c <comment><USER2> Here's a way to think about your fears that may help (easier to say than do, so if it doesn't work for you don't be discouraged).

If there were a fire in the corner of the room you'd rush towards it and smother it. You wouldn't wait. If there were a big fire you'd get out (bcause you can't even see the whole problem), get help, and figure out what to do.

Yet somehow we all (me included!!!) don't use the "obvious" (almost automatic) approaches with more abstract fears. The big fears are the worst, as we're often afraid to ask for help and are overwhelmed by the risk.

It has worked for me to think of them as literal fires, which eventually led me to convince myself that the sooner I deal with them the sooner they'll stop bothering me. I literally think of a problem with an employee as a fire burning in the corner of the room, which if not addressed will spread. Nobody wants to deal with a fire any more than an uncomfortable conversation (maybe the latter even less) but the visualization seems to work.

I am sure this doesn't work for everyone but I was given this advice years ago and was able to "trick" myself into dealing with many scary things.

<comment><USER3> It's comments like these that keep bringing me back to HN. Love this, thank you.

<comment><USER2> Just passing it along -- got this advice about 25 years ago from someone else. If it works for you, I hope you pass it on again!

</comment></comment></comment><comment><USER4> A product is one part of a business. It isn't everything.

I built a product, and I've managed to sell it to some companies and turn it into a small business. It's been making me some pocket money for about 18 months now. But still, when I think about it, I'm always excited to get up in the morning and hack on the project. What I'm _not_ excited to do, is to pick up the phone and sell it to people.

I realised this towards the tail end of 2017, and decided I should co-found companies instead. I can take care of building the product, and my co-founders can do the business/sales/marketing stuff that I hate doing.

I've since started two companies with co-founders. I found all of them (or rather, they found me) through Co-Founders Lab (I have no affiliation with CFL, and I don't pay for my account, and they don't pay me).

One of the projects is bootstrapped; I put my time into writing the code, and my partner has put about £10k into various costs, and he's spent his time negotiating deals with companies.

With the other project, I am writing all the code (at least for now), and my partners are pitching investors and building business relationships with what will be our first beta users. We have funding committed from investors, and we have large companies who have agreed to be beta partners.

Would I ever have achieved this on my own?

No. Never. No chance.

</comment><comment><USER5> > After a few late nights of coding Dave's Debugger worked too! It helped > users quickly diagnose PC blue screens.

Dang! I actually would have paid for this. We run repair shops and it would be useful as a tool to train our techs with. We train non-technical people in our shops, so having something for them to go off of instead of spending time Google searching and reading through forums would be useful.

It's a good reminder to anyone reading this who has built something but hasn't launched it--if it solves a problem, there's probably someone out there willing to pay for it.

</comment><comment><USER6> Startups aren't for everyone, and especially if you haven't worked in one or understand what it means to "hustle". The odd thing is, his next startup will be a rocket ship because now he knows the most important fact - have someone willing to pay for what you're doing.

<comment><USER7> Actually, he might have had a successful startup in between 2010 and now, because he's now an angel investor: [https://dsdoes.com/behind-the-scenes-my- angel-investor-check...](https://dsdoes.com/behind-the-scenes-my-angel- investor-checklist-c266135b2bbd)

<comment><USER8> I wonder if he was an early bitcoin investor - his tag at the bottom says bitcoin (heart) before it says "father" so maybe his path was paved with that?

Who knows.

</comment></comment></comment><comment><USER9> I've got a similar timeline as the OP, however in my case it wasn't fear that held my startup back initially, it was a reliance on 'old school' methodologies.

In my case, I built an app about 15 years ago that was purely Windows based, and it sold like hotcakes into the small/medium business market. I then thought I could do the same 3 years ago by writing it as a cloud based system that worked on the web, and sales were _way_ off what the previous system sold for.

Reason? Because I was trying to sell it the 'old' way, and customers had moved on and were more informed now and had _far_ more choices. It took me a while to re-learn how to do it the new way, and now sales are picking up at a steady clip.

</comment><comment><USER10> > I avoided what I didn't know. I avoided what scared me.

Startups are hard, but it's even harder to fight human psychology even if you knew you should be doing the complete opposite.

Kudos to the author for writing this up.

</comment><comment><USER11> I have been toiling away at a side-project and recently realized I postponed talking to customers all the time.

As a test I forced myself to include a demo of that side-project in a presentation on a related topic to product managers (my side-project's target audience).

What I found out is that the fear that kept me from talking to customers is the fear of feeling exposed.

It's fascinating how strong and immobilising that fear is. It's stronger than the will to help people, improve the world and/or earn more money.

</comment><comment><USER12> My view is also that selling developer tools is a really hard road. Developers always want stuff for free, people would rather work an hour a day all year than figure out how to get a $50 tool through purchasing.

<comment><USER13> Not when your employer is paying. And your employer will usually be happy to pay, as from their point of view buying things off the shelf is cheaper than having their devs build it from scratch.

<comment><USER14> My experience is different - I've often seen employers be ridiculously "penny smart and pound foolish", saving $50 here and $100 there at the expense of hours and hours a week wasted.

</comment></comment></comment><comment><USER15> i wouldn't even call this a startup

<comment><USER16> It could have been a startup if he wasn't too afraid to market and sell the software.

<comment><USER17> Not necessarily, it could be also just a piece of software, OSS, or just a flop. There's a long way to startup, escpecially when OP is a nerdy man without startup mindset.

I just dont like that nowadays "any potentially useful software project = startup".

</comment></comment></comment><comment><USER18> There are plenty of hustlers out there who would love to become the next Steve Jobs or Bill Gates, so I suggest teaming up with one of those. However when the team grows, make sure there's at least 10 engineers for each "business" person.

</comment><comment><USER19> You did what you loved, that's not wrong.

The wrong part is, you should have handed with a buddy who loves to sell and do marketing.

I believe you still have a chance for a v2.0!

</comment><comment><USER20> Great to see more folks in Michigan!</comment>


Did I say that? Crazy things that developers say. - <AUTHOR> http://blog.aha.io/?p=1290

<comment><USER2> Let's come to a truce. When managers start listening, we'll stop saying those things. Wow, add that to the list of things that "we" say.</comment>


A Suggested Alternative to Current App Store Promotion - <AUTHOR> http://xk72.com/blog/2009/03/02/itunes-app-store-promotion/

<comment><AUTHOR> As Karl says in the article's comments, "there will always be opportunities to exploit/game any algorithm." But, it does seem that Apple will at some point need to address how developers are gaming the system to bring more attention to their applications.</comment>


NPM registry SSL certificate is untrusted - <AUTHOR> https://registry.npmjs.org/

<comment><USER2> It's a self-signed certificate that's been around for over a year...The contact info is for Isaac Schlueter, the creator of npm.</comment>


A Tricky JavaScript Interview Question Asked by Google and Amazon - 10-6 https://medium.com/coderbyte/a-tricky-javascript-interview-question-asked-by-google-and-amazon-48d212890703 <comment><AUTHOR> If you want to know if someone understands scope then you should ask about scope, not conceal a bug that relates to the topic you are interested in.

"Tricky" is the correct word here and tricky interview questions are stupid.

<comment><USER2> Debugging is a skill that will definitely be used on the job, and this is a stripped-down example of something that you'll definitely encounter while working in JavaScript (or any language with mutable closures).

How would you ask about this issue in interviews?</comment></comment>


LA adds hundreds of EV chargers to streetlights - <AUTHOR> https://electrek.co/2019/11/13/la-adds-hundreds-of-ev-chargers-to-streetlights-giving-renters-a-place-to-plug-in/ <comment><USER2> I'm reminded of the Watchmen comic book, with its ubiquitous recharging "plugs" on the streets. The future is here, just not evenly distributed.

</comment><comment><USER3> Cool, wish they would do that in NYC also!</comment>


I will not be acquihired - <AUTHOR> http://www.daemonology.net/blog/2012-07-21-I-will-not-be-acquihired.html

<comment><USER2> Colin, I think the most likely situation is that you are offered a great job by someone who doesn't want you to continue side work.

You'd have to make the decision between that well-paid dream job, or your commitment to your own business. If we are lucky, you can negotiate an exemption based on limited hours required to maintain tarsnap, and/or some contract fulfillment period (contract to customers, which does not exist presently).

Or you could farm the maintenance out to a trusted person, give them a cut of the biz, and stay on as a passive shareholder.

I'm happy that tarsnap does well for you, but we know it will be quite a stretch for it to match your potential for income and impact at a larger company. We hope it expands to meet and exceed all of your needs, and gives you other kinds of valuable rewards.

But we wouldn't hold it against you if you had to make personal decisions that didn't fulfill all of our wishes. You don't owe us that. No one does.

If you were feeling generous, you could offer customers a 1-year product EOL notice guarantee. Barring insolvency, acts of g-d, etc, that you pledge to operate tarsnap at full service levels for at least one year after notification of product EOL. That's more than most small companies offer, and more than enough for us to get our stuff moved elsewhere. 1 year might be too much.

<comment><AUTHOR> Tarsnap _is_ a dream job. :-)

</comment></comment><comment><USER3> Looks like an awesome business. Glad you are successful and dedicated to your customers. The one thing I would ask then is what happens if something happens to _you?_ Are there other people who can take over?

<comment><AUTHOR> In other words, what if I get hit by a bus? Right now that is an issue, in that I'm the only person involved in running Tarsnap -- the only mitigating factor is that (AWS power outages notwithstanding) the service basically runs itself, so if I do get hit by a bus there would be plenty of time for people to retrieve their data before Tarsnap was shut down.

<comment><USER3> What will it take to grow it so you can bring someone else on?

<comment><AUTHOR> Time, I guess? Tarsnap is growing steadily.

<comment><USER4> How many months of FTE salary are you hoping to bank before you hire? What multiple of an FTE salary are you hoping to get to in monthly rev before you hire?

(I know it depends on the kind of FTE you're planning to hire, but I'm deliberately not putting a stick in the sand because I'm not trying to figure out how much you make).

<comment><AUTHOR> _How many months of FTE salary are you hoping to bank before you hire?_

That's not really something I'm looking at.

 _What multiple of an FTE salary are you hoping to get to in monthly rev before you hire?_

I want to have enough revenue that I can hire someone without reducing my "take-home profits" to an uncomfortably low level. So 2 FTE salaries, for some definition of "FTE salary", I guess. (And yes, I do realize that the idea of "wait until you can afford to pay someone before hiring them" is horribly out of style.)

It's more likely that I'll follow yegg's example of expanding initially via contract and part-time work, though.

<comment><USER4> No, the opposite, actually: 2xFTE is aggressive; it presumes growth or steady revenue.

But you answered my question: you seem relatively conservative about this stuff.

What kind of stuff would you contract out if money wasn't an object?

<comment><AUTHOR> _2xFTE is aggressive; it presumes growth or steady revenue._

Well, a single month of 2xFTE wouldn't make me run out and hire someone... I'd need to be confident that it would continue. But presuming growth or steady revenue is pretty safe in the case of Tarsnap -- most of Tarsnap's revenue comes from storage costs, and in a typical month only about 10% of data gets deleted (and most of that is from machines which are uploading and deleting roughly equal amounts, presumably as part of an archive rotation process).

 _you seem relatively conservative about this stuff._

Within the limits implied by the fact that I'm running a startup company: Yes, I'm quite conservative in how I do things.

 _What kind of stuff would you contract out if money wasn't an object?_

More testing. Kivaloo development. A GUI wrapper. I might throw some money at a web designer to see if they could make something I like more than the current site (I know you hate Tarsnap's web design, but like the picodollars, it does a good job of attracting the customers I want.) At some point, user support, but probably not yet (I worry that having someone else do user support would lead to me getting out of touch with the user community).

Probably more, but those are the bits which immediately come to mind.

<comment><USER4> Interesting. Thanks!

I understand, disagree with, and respect the strategy you have with regards to segmenting out desirable customers, but think "picodollars" is a tragically bad way of doing it. :)

<comment><AUTHOR> I understand, disagree with, and respect your opinions about how I run my company. :)

<comment><USER4> Not allowed! Look at my karma number!

</comment></comment></comment></comment></comment></comment></comment></comment></comment></comment></comment><comment><USER5> It's grate to be dedicated to your business, but don't say 'never'. There may come a day when you've exhausted all and every one option on the table, but getting acquired for the sake of a high-bonus job would allow you to preserve sanity and to get some $, before once again embarking on the journey that is entrepreneurship.

<comment><AUTHOR> _getting acquired for the sake of a high-bonus job would allow you to preserve sanity and to get some $_

Yes, but as I wrote, Tarsnap is profitable enough that I don't need to go somewhere else to get money.

<comment><USER6> ... yet

<comment><AUTHOR> Explain?

<comment><USER5> GuiA means that you are currently profitable, and if things work well for you, the company will continue to be profitable. I was only suggesting talent acquisition as a safety net for the day when thing don't go so well. If it never happens, we are all happy for you!

</comment></comment></comment></comment></comment><comment><USER7> Colin, How about putting a spin on the perspective...Would you "acquihire" other independent developers as part of your growth strategy?

<comment><AUTHOR> It's possible. I don't think acquihires are _wrong_ (although I do wish that job-hunter acquihires were more up-front about the fact that they're building a portfolio rather than a company) -- they're just _not the direction Tarsnap is headed_.

But I think it's far more likely that I'd hire out of the open source community than out of the startup community.

<comment><USER7> Do you think there is a way for (potential) customers to identify any information about the intentions of the developer or company?

The recent Internet Defense League makes me wonder if something similar couldn't be launched for small software companies who make it CLEARLY part of their ethos about the direction of the company. This would not necessarily be a membership kind of animal but more of a "we agree with the ideas of Small Software Developers' Principles."

<comment><AUTHOR> _Do you think there is a way for (potential) customers to identify any information about the intentions of the developer or company?_

Well, in many cases "free" is strongly suggestive of the "not trying to build a company, just building a portfolio in order to attract job offers" scenario. Beyond that, it's hard to say.

 _The recent Internet Defense League makes me wonder if something similar couldn't be launched [...]_

I'm not sure what "something similar" would be here. Your favourite website is being shut down because its developers got acquihired by Google, so you should write a letter to your member of congress?

<comment><USER7> I don't know if free is necessarily an indicator or not. Craigslist at one point was a start-up and usage of the site is free with the exception of ads for some categories(apartment listings I believe?) in dense areas such as NYC. I would say the business supported the founder well enough and even so he was able to create a philanthropic extension because of it.

"Something similar" being companies that subscribe to an idea of "not heading towards acquihire." Substitute idea with ethos/loose organization/alliance/whatever other non-hostile word might suffice here. I'm not suggessting that by participating you elect to be lynched by a mob at the slightest whim of misunderstanding or error. It is more of a self-certify that a company has an intention of providing a service and not a portfolio to be hired in 180 days.

<comment><AUTHOR> Cases like craigslist are why I said "in some cases". A simple website which can handle millions of users from a single low-end server can survive being free for a long time. An online backup service where a single user might cost many dollars a month to serve, on the other hand, needs revenue to survive in the long term.

Your idea of self-certification is why I wrote that blog post. It's an open invitation to lynch me if Tarsnap gets acquired and shut down a few months later.</comment></comment></comment></comment></comment></comment>