by Sabrina Alexis
When a guy doesn’t call – say it with me- he’s just not that into you… Right? Well I firmly believed this for a while but now I’m gonna have to say that’s not always the case. Through the years I, like most of you, I’m sure, have found myself in those infamous and utterly maddening situations. You meet, you hit it off, you get really excited because you’ve finally found a great guy, and after that…silence. Once you’ve hit day 4 all hope dissipates and you must sadly accept the fact that you will never be hearing from him.
However, it wasn’t until I broke the rules that I realized this type of situation can’t be so easily explained, or dismissed. Experience is the best teacher of all and this particular experience was an eye-opener and game-changer for sure. So here it goes.
I met up with a friend one Saturday afternoon at a bar for a football game. Soon after arriving I met J. He was cute, charming, and we seriously hit it off right away. Our afternoon of fun turned into a night out and me, my friend, J, and his friend bar-hopped, talked, laughed, danced, and played pool until the wee hours.
Before heading home, J and I planned a date for that Thursday. We exchanged numbers, kissed, said our goodbyes, and I left feeling quite pleased with myself, knowing that I had played my cards right. Or so I thought.
I wasn’t expecting a call on Monday or Tuesday because of the ‘rules’ and all that, but by Wednesday night I started to feel a little panicked. Then Thursday came around and still no word. By 7 pm I had two choices: resign myself to the fact that he isn’t into me, or do something I had never, ever done before- call him. Deducing that I had nothing to lose, I decided to go with the latter because he was cute and funny and he gave me butterflies in my stomach which no one had done since my ex and I had been incapable of feeling anything for anyone after my ex and now I finally had feelings again and feelings don’t come around everyday so I wasn’t gonna let this go so easily.
So I called. As the phone rang I practiced the message I would leave on his voicemail in my head but was interrupted when, to my astonishment, a very perplexed-sounding J answered the phone. From there, a horrifically awkward conversation ensued. It went something like this:
“Hey, it’s Sabrina”
“Oh, um hi. What’s up”
“Well I was just calling to see if we were still on for tonight”
“Oh, um, hmm, right, um, yeah, I forgot about that. But if you want to, um, then yeah, sure okay I guess I mean if you want to”
Insert awkward silence as I try to navigate through the palpable awkwardness and get my thoughts in order
“Okay, cool, do you wanna just swing my my apartment and then we can head out”
“Um okay. See you soon.”
At this point I was absolutely panicked. I almost called him back to cancel the whole thing. If the phone call was that awkward I didn’t even wanna think about how awful an actual date would be! I didn’t get it, he was so into me that weekend, what could have possibly changed so dramatically? I called a few friends to tell them what had just happened and they couldn’t help but laugh at the sheer awfulness and hilarious awkwardness of it all.
Ten minutes later he called to tell me he was waiting downstairs. I stepped off the elevator to see an absolutely miserable looking guy slumped on a chair. I reluctantly approached him, “Um hey, are you ready to go?”
He looked up, and perked up a bit. When we stepped outside, he grabbed me, and kissed me. I looked at him, stunned, “Sorry, I forgot how pretty you are.” The only thought resounding in my head was WTF?!?
I was dying to ask why he didn’t call me but I restrained myself. I kept it cool and acted like him not calling didn’t bother me in the least. Without prompting, he said: “I’m so sorry I didn’t call. It’s just been a really crazy week, but I should have called.” I smiled, “No worries, it’s been a busy week for me too,” cool as a cucumber. The date ended up being one of the best I’ve ever been on- we chatted non-stop, sparks were igniting, and I just felt all warm and fuzzy inside. Towards the end of the date he looked like a fool in love. He couldn’t pry his eyes away from me and said he had never felt this way about a girl after a second date. Talk about the tide turning…
There was no doubt in my mind that I’d be hearing from him again and I was right. We dated for a few months, it never got serious, but I did have fun and I truly enjoyed the time we spent together.
So why didn’t he call me after… (continued – Click to keep reading )
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Since I started working on Discourse, I spend a lot of time thinking about how software can encourage and nudge people to be more empathetic online. That's why it's troubling to read articles :
My brother’s 32nd birthday is today. It’s an especially emotional day for his family because he’s not alive for it.
He died of a heroin overdose last February.
This year is even harder than the last. I started weeping at midnight and eventually cried myself to sleep. Today’s symptoms include explosions of sporadic sobbing and an insurmountable feeling of emptiness. My mom posted a gut-wrenching comment on my brother’s Facebook page about the unfairness of it all. Her baby should be here, not gone. “Where is the God that is making us all so sad?” she asked.
In response, someone — a stranger/(I assume) another human being — commented with one word: “Junkie.”
The interaction may seem a bit strange and out of context until you realize that this is the Facebook page of a person who was somewhat famous, who . Not that this forgives the behavior in any way, of course, but it does explain why strangers would wander by and make observations.
There is deep truth in the old idea that people are able to say these things because they are looking at a screen full of words, not directly at the face of the person they're about to say a terrible thing to. That one level of abstraction the Internet allows, typing, which is so immensely powerful in so many other contexts &
“falling in love, breaking into a bank, bringing down the govt…they all look the same right now: they look like typing”
& whitney erin boesel (@weboesel)
& has some crippling emotional consequences.
As an exercise in empathy, try to imagine saying some of the terrible things people typed to each other online to a real person sitting directly in front of you. Or don't imagine, and .
I challenge you to watch the entirety of that video. I couldn't do it. This is the second time I've tried, and I had to turn it off not even 2 minutes in because I couldn't take it any more.
It's no coincidence that these are comments directed at women. Over the last few years I have come to understand how, as a straight white man, I have the privilege of being immune from most of this kind of treatment. But others are not so fortunate. The Guardian analyzed 70 million comments and found that .
And avalanches happen easily online. Anonymity disinhibits people, making some of them more likely to be abusive. Mobs can form quickly: once one abusive comment is posted, others will often pile in, competing to see who can be the most cruel. This abuse can move across platforms at great speed – from Twitter, to Facebook, to blogposts – and it can be viewed on multiple devices – the desktop at work, the mobile phone at home. To the person targeted, it can feel like the perpetrator is everywhere: at home, in the office, on the bus, in the street.
I've only had a little taste of this treatment, once. The sense of being "under siege" & a constant barrage of vitriol and judgment pouring your way every day, every hour & was palpable. It was not pleasant. It absolutely affected my state of mind. Someone remarked in the comments that ultimately it did not matter, because as a white man I could walk away from the whole situation any time. And they were right. I began to appreciate what it would feel like when you can't walk away, when this harassment follows you around everywhere you go online, and you never really know when the next incident will occur, or exactly what shape it will take.
Imagine the feeling of being constantly on edge like that, every day. What happens to your state of mind when walking away isn't an option? It gave me great pause.
I admired the way Stephanie Wittels Wachs actually engaged with the person who left that awful comment. This is a man who has two children of his own, and should be no stranger to the kind of pain involved in a child's death. And yet he felt the need to post the word "Junkie" in reply to a mother's anguish over losing her child to drug addiction.
Isn’t this what empathy is? Putting myself in someone else’s shoes with the knowledge and awareness that I, too, am human and, therefore, susceptible to this tragedy or any number of tragedies along the way?
Most would simply delete the comment, block the user, and walk away. Totally defensible. But she didn't. She takes the time and effort to attempt to understand this person who is abusing her mother, to reach them, to connect, to demonstrate the very empathy this man appears incapable of.
Consider the related story of Lenny Pozner, who lost a child at Sandy Hook, and became , and similarly selflessly devotes much of his time to refuting and countering these bizarre claims.
Tracy’s alleged harassment was hardly the first, Pozner said. There’s a whole network of people who believe the media reported a mass shooting that never happened, he said, that the tragedy was an elaborate hoax designed to increase support for gun control. Pozner said he gets ugly comments often on social media, such as, “Eventually you’ll be tried for your crimes of treason against the people,” “… I won’t be satisfied until the caksets are opened…” and “How much money did you get for faking all of this?”
It's easy to practice empathy when you limit it to people that are easy to empathize with & the downtrodden, the undeserving victims. But it is another matter entirely to empathize with those that hate, harangue, and intentionally make other people's lives miserable. If you can do this, you are a far better person than me. I struggle with it. But my hat is off to you. There's no better way to teach empathy than to practice it, in the most difficult situations.
In individual cases, reaching out and really trying to empathize with people you disagree with or dislike can work, even people who happen to be lifelong members of hate organizations, :
As a member of the Westboro Baptist Church, in Topeka, Kansas, Phelps-Roper believed that AIDS was a curse sent by God. She believed that all manner of other tragedies—war, natural disaster, mass shootings—were warnings from God to a doomed nation, and that it was her duty to spread the news of His righteous judgments. To protest the increasing acceptance of homosexuality in America, the Westboro Baptist Church picketed the funerals of gay men who died of AIDS and of soldiers killed in Iraq and Afghanistan. Members held signs with slogans like “GOD HATES FAGS” and “THANK GOD FOR DEAD SOLDIERS,” and the outrage that their efforts attracted had turned the small church, which had fewer than a hundred members, into a global symbol of hatred.
Perhaps one of the greatest failings of the Internet is .
First we’ll reframe the problem: the real issue is not Problem Child’s opinions & he can have whatever opinions he wants. The issue is that he’s doing zero emotional labor & he’s not thinking about his audience or his effect on people at all. (Possibly, he’s just really bad at modeling other people’s responses & the outcome is the same whether he lacks the will or lacks the skill.) But to be a good community member, he needs to consider his audience.
True empathy means reaching out and engaging in a loving way with everyone, even those that are hurtful, hateful, or spiteful. But on the Internet, can you do it every day, multiple times a day, across hundreds of people? Is this a reasonable thing to ask of someone? Is it even possible, short of sainthood?
The question remains: why would people post such hateful things in the first place? Why reply "Junkie" to a mother's anguish? Why ask the father of a murdered child to publicly prove his child's death was not a hoax? Why tweet "Thank God for AIDS!"
Unfortunately, I think I know the answer to this question, and you're not going to like it.
I don't like it. I don't want it. But I know.
I have laid some heavy stuff on you in this post, and for that, I apologize. I think the weight of what I'm trying to communicate here requires it. I have to warn you that the next article I'm about to link is far heavier than anything I have posted above, maybe the heaviest thing I've ever posted. It's about the legal quandary presented in the tragic cases of children who died because their parents accidentally left them strapped into carseats, and it won a much deserved pulitzer. It is also .
Ed Hickling believes he knows why. Hickling is a clinical psychologist from Albany, N.Y., who has studied the effects of fatal auto accidents on the drivers who survive them. He says these people are often judged with disproportionate harshness by the public, even when it was clearly an accident, and even when it was indisputably not their fault.
Humans, Hickling said, have a fundamental need to create and maintain a narrative for their lives in which the universe is not implacable and heartless, that terrible things do not happen at random, and that catastrophe can be avoided if you are vigilant and responsible.
In hyperthermia cases, he believes, the parents are demonized for much the same reasons. “We are vulnerable, but we don’t want to be reminded of that. We want to believe that the world is understandable and controllable and unthreatening, that if we follow the rules, we’ll be okay. So, when this kind of thing happens to other people, we need to put them in a different category from us. We don’t want to resemble them, and the fact that we might is too terrifying to deal with. So, they have to be monsters.”
This man left the junkie comment because he is afraid. He is afraid his own children could become drug addicts. He is afraid his children, through no fault of his, through no fault of anyone at all, could die at 30. When presented with real, tangible evidence of the pain and grief a mother feels at the drug related death of her own child, and the reality that it could happen to anyone, it became so overwhelming that it was too much for him to bear.
Those "Sandy Hook Truthers" harass the father of a victim because they are afraid. They are afraid their own children could be viciously gunned down in cold blood any day of the week, bullets tearing their way through the bodies of the teachers standing in front of them, desperately trying to protect them from being murdered. They can't do anything to protect their children from this, and in fact there's nothing any of us can do to protect our children from being murdered at random, at school any day of the week, at the whim of any mentally unstable individual with access to an assault rifle. That's the harsh reality.
When faced with the abyss of pain and grief that parents feel over the loss of their children, due to utter random chance in a world they can't control, they could never control, maybe none of us can ever control, the overwhelming sense of existential dread is simply too much to bear. So they have to be monsters. They must be.
And we will fight these monsters, tooth and nail, raging in our hatred, so we can forget our pain, at least for a while.
After Lyn Balfour’s acquittal, this comment appeared on the Charlottesville News Web site:
“If she had too many things on her mind then she should have kept her legs closed and not had any kids. They should lock her in a car during a hot day and see what happens.”
I imagine the suffering that these parents are already going through, reading these words that another human being typed to them, just typed, and something breaks inside me. I can't process it. But rather than pitting ourselves against each other out of fear, recognize that the monster who posted this terrible thing is me. It's you. It's all of us.
The weight of seeing through the fear and beyond the monster to simply discover yourself is often too terrible for many people to bear. In a world of hard things, it's the hardest there is.
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You know what's universally regarded as un-fun by most programmers? Writing .
As Steve McConnell :
Programmers working with high-level languages achieve better productivity and quality than those working with lower-level languages. Languages such as C++, Java, Smalltalk, and Visual Basic have been credited with improving productivity, reliability, simplicity, and comprehensibility by factors of 5 to 15 over low-level languages such as assembly and C. You save time when you don't need to have an awards ceremony every time a C statement does what it's supposed to.
Assembly is a language where, for performance reasons, every individual command is communicated in excruciating low level detail directly to the CPU. As we've gone from fast CPUs, to faster CPUs, to multiple absurdly fast CPU cores on the same die, to "gee, we kinda stopped caring about CPU performance altogether five years ago", there hasn't been much need for the kind of hand-tuned performance you get from assembly. Sure, there are , and they are amazing, but in terms of Getting Stuff Done, assembly has been well off the radar of mainstream programming for probably twenty years now, and for good reason.
So who in their right mind would take up tedious assembly programming today? Yeah, nobody. But wait! What if I told you your Uncle Randy had just died and left behind this mysterious old computer,
And what if I also told you the only way to figure out what that TIS-100 computer was used for & and what good old Uncle Randy was up to & was to read a (blessedly short 14 page) photocopied reference manual and fix its corrupted boot sequence & using assembly language?
Well now, by God, it's time to learn us some assembly and get to the bottom of this mystery, isn't it? As its
notes, this is the assembly language programming game you never asked for!
I was surprised to discover my co-founder
liked TIS-100 so much that he not only played the game (presumably to completion) but wrote . This is apparently the kind of thing he does for fun, in his free time, when he's not already working full time with us programming . Programmers gotta & program.
Of course there's a long history of programming games. What makes TIS-100 unique is the way it fetishizes assembly programming, while most programming games take it a bit easier on you by easing you in with general concepts and simpler abstractions. But even "simple" programming games can be quite difficult. Consider one of my favorites on the Apple II, , and its sequel, Robot Odyssey. , but in true programming fashion it was so difficult that :
Let me say: Any kid who completes this game while still a kid (I know only one, who also is one of the smartest programmers I’ve ever met) is guaranteed a career as a software engineer. Hell, any adult who can complete this game should go into engineering. Robot Odyssey is the hardest damn “educational” game ever made. It is also a stunning technical achievement, and one of the most innovative games of the Apple IIe era.
Visionary, absurdly difficult games such as this gain cult followings. It is the game I remember most from my childhood. It is the game I love (and despise) the most, because it was the hardest, the most complex, the most challenging. The world it presented was like being exposed to Plato’s forms, a secret, nonphysical realm of pure ideas and logic. The challenge of the game—and it was one serious challenge—was to understand that other world. Programmer Thomas Foote had just started college when he picked up the game: “I swore to myself,” he told me, “that as God is my witness, I would finish this game before I finished college. I managed to do it, but just barely.”
I was happy dinking around with a few robots that did a few things, got stuck, and moved on to other games. I got a little turned off by the way it treated programming as el messing around with a ton of AND OR and NOT gates was just not my jam. I was already
and I sensed a level of mastery was necessary here that I probably didn't have and I wasn't sure I even wanted.
I'll take a COBOL code listing over that monstrosity any day of the week. Perhaps Robot Odyssey was so hard because, in the end, it was a bare metal CPU programming simulation, like TIS-100.
A more gentle example of a modern programming game is Tomorrow Corporation's excellent .
It has exactly the irreverent sense of humor you'd expect from the studio that built World of Goo and Little Inferno, both excellent and highly recommendable games in their own right. If you've ever wanted to find out if someone is truly interested in programming, recommend this game to them and see. It starts with only 2 instructions and slowly widens to include 11. Corporate drudgery has never been so & er, fun?
I'm thinking about this because I believe there's a strong connection between programming games and being a talented software engineer. It's that essential sense of play, the idea that you're experimenting with this stuff because you enjoy it, and you bend it to your will out of the sheer joy of creation more than anything else. As I :
Joel implied that good programmers love programming so much they'd do it for no pay at all. I won't go quite that far, but I will note that the best programmers I've known have all had a lifelong passion for what they do. There's no way a minor economic blip would ever convince them they should do anything else. No way. No how.
I'd rather sit a potential hire in front of Human Resource Machine and time how long it takes them to work through a few levels than
on a whiteboard. Is this interview about demonstrating competency in a certain technical skill that's worth a certain amount of money, or showing me how you can improvise and have fun?
That's why I was so excited when Patrick, Thomas, and Erin founded .
If you want to know how competent a programmer is, give them a real-ish simulation of a real-ish system to hack against and experiment with & and see how far they get. In security parlance, this is known , as popularized by Defcon. But it's rarely extended to programming, until now. Their first simulation is .
Participants are given:
An interactive trading blotter interface
A real, functioning set of limit-order-book venues
A carefully documented JSON HTTP API, with an API explorer
A series of programming missions.
Participants are asked to:
Implement programmatic trading against a real exchange in a thickly traded market.
Execute block-shopping trading strategies.
Implement electronic market makers.
Pull off an elaborate HFT trading heist.
This is a seriously next level hiring strategy, far beyond anything else I've seen out there. It's so next level that to be honest, I got really jealous reading about it, because I've felt for a long time that Stack Overflow should be doing yearly programming game events exactly like this, with special one-time badges obtainable only by completing certain levels on that particular year. Stack Overflow is , but people would go nuts for a yearly programming game event. Absolutely bonkers.
I know we've talked about , but if that's really what you want to do, the best programmers I've ever known have excelled at exactly the situation that Starfighter simulates & live troubleshooting and reverse engineering of an existing system, even to the point of .
Consider the dedication of this participant who built
for StockFighter. Was it necessary? Was it practical? No. It's the programming game we never asked for. But here we are, regardless.
An arbitrary programming game, particularly one that goes to great lengths to simulate a fictional system, is a wonderful expression of the inherent joy in playing and experimenting with code. If I could find them, I'd gladly hire a dozen people just like that any day, and set them loose on our very real programming project.
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In 2006, after , I opined:
We may have reached an inflection point. The problem space of chess is so astonishingly large that incremental increases in hardware speed and algorithms are unlikely to result in meaningful gains from here on out.
So. About that. Turns out I was kinda & totally completely wrong. The number of possible moves, or "problem space", of Chess is indeed astonishingly large, estimated to be 1050:
100,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000
was interesting because it forecast a particular kind of future, a future where specialized hardware enabled brute force attack of the enormous chess problem space, as its purpose built chess hardware outperformed general purpose CPUs of the day by many orders of magnitude. How many orders of magnitude? In the heady days of 1997, Deep Blue could evaluate 200 million chess positions per second. And that was enough to defeat Kasparov, the highest ever ranked human player & until
at least. Even though one of its best moves was .
200,000,000
In 2006, about ten years later, according to the , my PC could evaluate only 4.5 million chess positions per second.
Today, about twenty years later, that very same benchmark says my PC can evaluate a mere 17.2 million chess positions per second.
17,200,000
Ten years, four times faster. Not bad! Part of that is I went from dual to quad core, and these chess calculations scale almost linearly with the number of cores. An eight core CPU, no longer particularly exotic, could probably achieve ~28 million on this benchmark today.
28,000,000
I am not sure the scaling is exactly linear, but it's fair to say that even now, twenty years later, a modern 8 core CPU is still about an order of magnitude slower at the brute force task of evaluating chess positions than what Deep Blue's specialized chess hardware achieved in 1997.
But here's the thing: none of that speedy brute forcing matters today. Greatly improved chess programs running on mere handheld devices can perform .
In 2009 a chess engine running on slower hardware, a 528 MHz HTC Touch HD mobile phone running Pocket Fritz 4 reached the grandmaster level & it won a category 6 tournament with a performance rating of 2898. Pocket Fritz 4 searches fewer than 20,000 positions per second. This is in contrast to supercomputers such as Deep Blue that searched 200 million positions per second.
As far as chess goes, despite what I so optimistically thought in 2006, it's been game over for humans for quite a few years now. The best computer chess programs, vastly more efficient than Deep Blue, combined with modern CPUs which are now finally within an order of magnitude of what Deep Blue's specialized chess hardware could deliver, play at levels way beyond what humans can achieve.
Chess: ruined forever. Thanks, computers. You jerks.
Despite this resounding defeat, there was still hope for humans in the game of Go. The number of possible moves, or "problem space", of Go is estimated to be 10170:
1,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000
Remember that Chess had a mere fifty zeroes there? Go has .
Wrap your face around that one.
Deep Blue was a statement about the inevitability of eventually being able to brute force your way around a difficult problem with the constant wind of
at your back. If Chess is the quintessential European game, Go is the quintessential Asian game. Go requires a completely different strategy. Go means wrestling with a problem that is essentially .
A simple material evaluation for chess works well & each type of piece is given a value, and each player receives a score depending on his/her remaining pieces. The player with the higher score is deemed to be 'winning' at that stage of the game.
However, Chess programmers innocently asking Go players for an evaluation function would be met with disbelief! No such simple evaluation exists. Since there is only a single type of piece, only the number each player has on the board could be used for a simple material heuristic, and there is almost no discernible correlation between the number of stones on the board and what the end result of the game will be.
Analysis of a problem this hard, with brute force completely off the table, is colloquially called "AI", though that term is a bit of a stretch to me. I prefer to think of it as building systems that can learn from experience, aka . Here's a talk which covers DeepMind learning to play classic Atari 2600 videogames. (Jump to the 10 minute mark to see what I mean.)
As impressive as this is & and it truly is & bear in mind that games as simple as Pac-Man still remain far beyond the grasp of Deep Mind. But what happens when you point a system like that at the game of Go?
DeepMind built a system, , designed to see how far they could get with those approaches in the game of Go. AlphaGo recently played one of the best Go players in the world, Lee Sedol, and . Being the optimist that I am, I guessed that DeepMind would win one or two games, but a near total rout like this? Incredible. In the space of just 20 years, computers went from barely beating the best humans at Chess, with a problem space of 1050, to definitively beating the best humans at Go, with a problem space of 10170. How did this happen?
Well, a few things happened, but one unsung hero in this transformation is the humble video card, or GPU.
Consider this breakdown of
over time, measured in dollars per gigaflop:
1961$8,300,000,000
1984$42,780,000
1997$42,000
2000$1,300
What's not clear in this table is that after 2007, all the big advances in FLOPS came from gaming video cards designed for high speed real time 3D rendering, and as an incredibly beneficial side effect, they also turn out to be .
The Google Brain project had just achieved amazing results — it learned to recognize cats and people by watching movies on YouTube. But it required 2,000 CPUs in servers powered and cooled in one of Google’s giant data centers. Few have computers of this scale. Enter NVIDIA and the GPU. Bryan Catanzaro in NVIDIA Research teamed with Andrew Ng’s team at Stanford to use GPUs for deep learning. As it turned out, 12 NVIDIA GPUs could deliver the deep-learning performance of 2,000 CPUs.
Let's consider a related case of highly parallel computation. How much faster is a GPU at ?
Radeon 79708213.6 M c/s
6-core AMD CPU52.9 M c/s
Only 155 times faster right out of the gate. No big deal. On top of that, CPU performance has largely stalled in the last decade. While more and more cores are placed on each die, which is great when the problems are parallelizable & as they definitely are in this case & the actual performance improvement of any individual core over the last 5 to 10 years is rather modest.
But GPUs are still doubling in performance every few years. Consider password hash cracking expressed in the rate of :
GTX 295200925k
GTX 690201254k
GTX 780 Ti2013100k
GTX 980 Ti2015240k
The latter video card is the one in my machine right now. It's likely the next major revision from Nvidia, due later this year, will .
(While I'm at it, I'd like to emphasize how much it sucks to be an 8 character password in today's world. If your password is only 8 characters, that's perilously close to no password at all. That's also why why . In fact, we just raised the minimum allowed password length on
to 10 characters, because annoying password complexity rules are much less effective in reality than .)
used 1202 CPUs and 176 GPUs. While that doesn't sound like much, consider that as we've seen, each GPU can be up to 150 times faster at processing these kinds of highly parallel datasets & so those 176 GPUs were the equivalent of adding ~26,400 CPUs to the task. Or more!
Even if you don't care about video games, they happen to have a profound accidental impact on machine learning improvements. Every time you see a new video card release, don't think "slightly nicer looking games" think "wow, hash cracking and AI just got 2& faster & again!"
I'm certainly not making the same mistake I did when looking at Chess in 2006. (And in my defense, I totally did not see the era of GPUs as essential machine learning aids coming, even though I am a gamer.) If AlphaGo was intimidating today, having soundly beaten the best human Go player in the world, it'll be no contest after a few more years of GPUs doubling and redoubling their speeds again.
AlphaGo, broadly speaking, is the culmination of two very important trends in computing:
Huge increases in parallel processing power driven by consumer GPUs and videogames, which started in 2007. So if you're a gamer, congratulations! You're part of the problem-slash-solution.
We're beginning to build sophisticated (and combined) algorithmic approaches for entirely new problem spaces that are far too vast to even begin being solved by brute force methods alone. And these approaches clearly work, insofar as they mastered one of the hardest games in the world, one that many thought humans would never be defeated in.
Great. . Jerks.
Based on our experience with Chess, and now Go, we know that computers will continue to beat us at virtually every game we play, in the same way that dolphins will always swim faster than we do. But what if that very same human mind was capable of not only building the dolphin, but continually refining it until they arrived at the ? Where Deep Blue was the more or less inevitable end result of brute force computation, AlphaGo is the beginning of a whole new era of sophisticated problem solving against far more enormous problems. AlphaGo's victory is not a defeat of the human mind, but its greatest triumph.
(If you'd like to learn more about the powerful intersection of sophisticated machine learning algorithms and your GPU, read
and try it yourself.)
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One of the most common pieces of advice you'll get as a startup is this:
Only hire the best. The quality of the people that work at your company will be one of the biggest factors in your success & or failure.
I've heard this advice over and over and over at startup events, to the point that I got a little sick of hearing it. It's not wrong. Putting aside the fact that every single other startup in the world who heard this same advice before you is already out there frantically doing everything they can to hire all the best people out from under you and everyone else, it is superficially true. A company staffed by a bunch of people who don't care about their work and aren't good at their jobs isn't exactly poised for success. But in a room full of people giving advice to startups, nobody wants to talk about the elephant in that room:
It doesn't matter how good the people are at your company when you happen to be working on the wrong problem, at the wrong time, using the wrong approach.
Most startups, statistically speaking, .
And they will fail regardless of whether they hired "the best" due to circumstances largely beyond their control. So in that context does maximizing for the best possible hires really make sense?
Given the risks, I think maybe "hire the nuttiest risk junkie adrenaline addicted has-ideas-so-crazy-they-will-never-work people you can find" might actually be more practical startup advice. (Actually, now that I think about it, if that describes you, and you have serious Linux, Ruby, and JavaScript chops, perhaps you should email me.)
I told that person the same thing I tell all prospective job candidates: &come with me if you want to live&& Jeff Atwood (@codinghorror)
Okay, the goal is to increase your chance of success, however small it may be, therefore you should strive to hire the best. Seems reasonable, even noble in its way. But this pursuit of the best unfortunately comes with a serious dark side. Can anyone even tell me what "best" is? By what metrics? Judged by which results? How do we measure this? Who among us is suitable to judge others as the best at & what, exactly? Best is an extreme. Not pretty good, not very good, not excellent, but aiming for the crème de la crème, .
The real trouble with using a lot of mediocre programmers instead of a couple of good ones is that no matter how long they work, they never produce something as good as what the great programmers can produce.
Pursuit of this extreme means hiring anyone less than the best becomes unacceptable, even harmful:
In the Macintosh Division, we had a saying, “A players hire A B players hire C players” – meaning that great people hire great people. On the other hand, mediocre people hire candidates who are not as good as they are, so they can feel superior to them. (If you start down this slippery slope, you’ll soon end up with Z this is called The Bozo Explosion. It is followed by The Layoff.) &
There is an opportunity cost to keeping someone when you could do better. At a startup, that opportunity cost may be the difference between success and failure. Do you give less than full effort to make your enterprise a success? As an entrepreneur, you sweat blood to succeed. Shouldn’t you have a team that performs like you do? Every person you hire who is not a top player is like having a leak in the hull. Eventually you will sink. &
Why am I so hardnosed about this? It’s because it is much, much better to reject a good candidate than to accept a bad candidate. A bad candidate will cost a lot of money and effort and waste other people’s time fixing all their bugs. Firing someone you hired by mistake can take months and be nightmarishly difficult, especially if they decide to be litigious about it. In some situations it may be completely impossible to fire anyone. Bad employees demoralize the good employees. And they might be bad programmers but really nice people or maybe they really need this job, so you can’t bear to fire them, or you can’t fire them without pissing everybody off, or whatever. It’s just a bad scene.
On the other hand, if you reject a good candidate, I mean, I guess in some existential sense an injustice has been done, but, hey, if they’re so smart, don’t worry, they’ll get lots of good job offers. Don’t be afraid that you’re going to reject too many people and you won’t be able to find anyone to hire. During the interview, it’s not your problem. Of course, it’s important to seek out good candidates. But once you’re actually interviewing someone, pretend that you’ve got 900 more people lined up outside the door. Don’t lower your standards no matter how hard it seems to find those great candidates. &
I don't mean to be critical of anyone I've quoted. I love Joel, we founded Stack Overflow together, and his advice about interviewing and hiring . It's hardly unique to express these sort of opinions in the software and startup field. I could have cited two dozen different articles and treatises about hiring that say the exact same thing: aim high and set out to hire the best, or don't bother.
This risk of hiring not-the-best is so severe, so existential a crisis to the very survival of your company or startup, the hiring process has to become highly selective, even arduous. It is better to reject a good applicant every single time than accidentally accept one single mediocre applicant. If the interview process produces literally anything other than unequivocal "Oh my God, this person is unbelievably talented, we have to hire them", from every single person they interviewed with, right down the line, then it's an automatic NO HIRE. Every time.
This level of strictness always made me uncomfortable. I'm not going to lie, it starts with my own selfishness. I'm pretty sure I wouldn't get hired at big, famous companies with legendarily difficult technical interview processes because, you know, they only hire the best. I don't think I am one of the best. More like , to the point that I wake up most days not even wanting to work with myself.
If your hiring attitude is that it's better to be possibly wrong a hundred times so you can be absolutely right one time, you're going to be primed to throw away a lot of candidates on pretty thin evidence.
Before cofounding GitHub I applied for an engineering job at Yahoo and didn’t get it. Don’t let other people discourage you.& Chris Wanstrath (@defunkt)
I've been twitter following the careers of people we interviewed but passed on at my last gig.Turns out we were almost always wrong.& Trek Glowacki (@trek)
Perhaps worst of all, if the interview process is predicated on zero doubt, total confidence & maybe this candidate doesn't feel right because they don't look like you, dress like you, think like you, speak like you, or come from a similar background as you? Are you accidentally maximizing for hidden bias?
One of the best programmers I ever worked with was Susan Warren, an ex-Microsoft engineer who taught me about the , way back in 2004:
I think there is a real issue around diversity in technology (and most other places in life). I tend to think of it as the PLU problem. Folk (including MVPs) tend to connect best with folks most like them ("People Like Us"). In this case, male MVPs pick other men to become MVPs. It's just human nature.
As one reply notes, diversity is good. I'd go as far as to say it's awesome, amazing, priceless. But it's hard to get to -- the classic chicken and egg problem -- if you rely on your natural tendencies alone. In that case, if you want more female MVPs to be invited you need more female MVPs. If you want more Asian-American MVPs to be invited you need more Asian-American MVPs, etc. And the (cheap) way to break a new group in is via quotas.
IMO, building diversity via quotas is bad because they are unfair. Educating folks on why diversity is awesome and how to build it is the right way to go, but also far more costly.
Susan was (and is) amazing. I learned so much working under her, and a big part of what made her awesome was that she was very much Not Like Me. But how could I have appreciated that before meeting her? The fact is that as human beings, we tend to prefer what's comfortable, and what's most comfortable of all is & well, People Like Us. The effect can be shocking because it's so subtle, so unconscious & and yet, surprisingly strong:
Baseball cards held by a black hand consistently sold for
than those held by a white hand.
Using screens to hide the identity of auditioning musicians increased women's probability of advancing from preliminary orchestra auditions .
Denver police officers and community members were shown rapidly displayed photos of black and white men, some holding guns, some holding harmless objects like wallets, and asked to press either the "Shoot" or "Don't Shoot" button as fast as they could for each image. Both the police and community members were .
It's not intentional, it's never intentional. That's the problem. I think our industry needs to shed this old idea that it's OK, even encouraged to turn away technical candidates for anything less than absolute 100% confidence at every step of the interview process. Because when you do, you are accidentally optimizing for implicit bias. Even as a white guy who probably fulfills every stereotype you can think of about programmers, and who is in fact
while writing this very blog post*, that's what has always bothered me about it, more than the strictness. If you care at all about diversity in programming and tech, on any level, this hiring approach is not doing anyone any favors, and hasn't been. For years.
I know what you're thinking.
Fine, Jeff, if you're so smart, and "hiring the best" isn't the right strategy for startups, and maybe even harmful to our field as a whole, what should be doing?
Well, I don't know, exactly. I may be the wrong person to ask because . Here's what the composition of the current Discourse team looks like:
I would argue, quite strongly and at some length, that if you want better diversity in the field, perhaps a good starting point is not demanding that all your employees live within a tiny 30 mile radius of San Francisco or Palo Alto. There's a whole wide world of Internet out there, full of amazing programmers at every level of talent and ability. Maybe broaden your horizons a little, even stretch said horizons outside the United States, .
I know hiring people is difficult, even with the very best of intentions and under ideal conditions, so I don't mean to trivialize the challenge. I've recommended plenty of things in the past, a smorgasboard of approaches to try or leave on the table as you see fit:
& but the one thing I keep coming back to, that I believe has enduring value in almost all situations, is :
The most significant shift we’ve made is requiring every final candidate to work with us for three to eight weeks on a contract basis. Candidates do real tasks alongside the people they would actually be working with if they had the job. They can work at night or on weekends, so they don’t have to leav most spend 10 to 20 hours a week working with Automattic, although that’s flexible. (Some people take a week’s vacation in order to focus on the tryout, which is another viable option.) The goal is not to have them finish a product or do
it’s to allow us to quickly and efficiently assess whether this would be a mutually beneficial relationship. They can size up Automattic while we evaluate them.
What I like about audition projects:
It's real, practical work.
They get paid. (Ask yourself who gets "paid" for a series of intensive interviews that lasts multiple days? Certainly not the candidate.)
It's healthy to structure your work so that small projects like this can be taken on by outsiders. If you can't onboard a potential hire, you probably can't onboard a new hire very well either.
Interviews, no matter how much effort you put into them, are so hit and miss that the only way to figure out if someone is really going to work in a given position is to actually work with them.
Every company says they want to hire the best. Anyone who tells you they know how to do that is either lying to you or to themselves. But I can tell you this: the companies that really do hire the best people in the world certainly don't accomplish that by hiring from the same tired playbook every other company in Silicon Valley uses.
Try different approaches. Expand your horizons. Look beyond People Like Us and imagine what the world of programming could look like in 10, 20 or even 50 years & and help us move there by hiring to make it so.
* And for the record, .
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Over the last twenty years, I've probably built around a hundred computers. It's not very difficult, and in fact, it's gotten a whole lot easier over the years as computers become more highly integrated. Consider what it would take to build something very modern like :
Apply a dab of thermal compound to top of case.
Place motherboard in case.
Screw motherboard into case.
Insert SSD stick.
Insert RAM stick.
Screw case closed.
Plug in external power.
It's stupid easy. My six year old son and I have built Lego kits that were way more complex than this. Even a traditional desktop build is only a few more steps: insert CPU, install heatsink, route cables. And a server build is merely a few additional steps on top of that, maybe with some 1U or 2U space constraints. Scooter, desktop, or server, if you've built one computer, you've basically built them all.
Everyone breathes a sigh of relief when their newly built computer boots up for the first time, no matter how many times they've done it before. But booting is only the beginning of the story. Yeah, it boots, great. Color me unimpressed. What we really need to know is whether that computer is stable.
Although commodity computer parts , and vendors test their parts plenty before they ship them, there's no guarantee all those parts will work reliably together, in your particular environment, under your particular workload. And there's always the possibility, however slim, of getting very, very unlucky with subtly broken components.
Because we're rational scientists, we test stuff in our native environment, and collect data to prove our computer is stable. Right? So after we boot, we test.
I like to start with memory tests, since those require bootable media and work the same on all x86 computers, even before you have an operating system.
is the granddaddy of all memory testers. I'm not totally clear what caused the split between that and Memtest86+, but all of them work similarly. The one from passmark seems to be most up to date, so .
Download the version of your choice, write it to a bootable USB drive, plug it into your newly built computer, boot and let it work its magic. It's all automatic. Just boot it up and watch it go.
(If your computer supports UEFI boot you'll get , otherwise you'll see version 4.2 as above.)
I recommend one complete pass of memtest86 at minimum, but if you want to be extra careful, let it run overnight. Also, if you have a lot of memory, memtest can take a while! For our servers with 128GB it took about three hours, and I expect that time scales linearly with the amount of memory.
The "Pass" percentage at the top should get to 100% and the "Pass" count in the table should be greater than one. If you get any errors at all, anything whatsoever other than a clean 100% pass, your computer is not stable. Time to start removing RAM sticks and figure out which one is bad.
All subsequent tests will require an operating system, and one basic iron clad test of stability for any computer is whether it can install an operating system. Pick your free OS of choice, and begin a default install. I recommend
since it assumes less about your video hardware. Download the ISO and write it to a bootable USB drive. Then boot it.
(Hey look it has a memory test option! How convenient!)
Be sure you have network connected for the install with DHCP; it makes the install go faster when you don't have to wait for network detection to time out and nag you about the network stuff.
In general, you'll be pressing enter a whole lot to accept all the defaults and proceed onward. I know, I know, we're installing Linux, but believe it or not, they've gotten the install bit down by now.
About all you should be prompted for is the username and password of the default account. I recommend jeff and password, because I am one of the world's preeminent computer security experts.
If you are installing from USB and get nagged about a missing CD, remove and reinsert the USB drive. No, I don't know why either, but .
If anything weird happens during your Ubuntu Server install that prevents it from finalizing the install and booting into Ubuntu Server & your computer is not stable. I know it doesn't sound like much, but this is a decent holistic test as it exercises the whole system in very repeatable ways.
We'll need an OS installed for the next tests, anyway. I'm assuming you've installed Ubuntu, but any Linux distribution should work similarly.
Next up, let's make sure the brains of the operation are in order: the CPU. To be honest, if you've gotten this far, past the RAM and OS test, the odds of you having a completely broken CPU are fairly low. But we need to be sure, and the best way to do that is to call upon our old friend, Marin Mersenne.
In mathematics, a Mersenne prime is a prime number that is one less than a power of two. That is, it is a prime number that can be written in the form Mn = 2n - 1 for some integer n. They are named after Marin Mersenne, a French Minim friar, who studied them in the early 17th century. The first four Mersenne primes are 3, 7, 31, and 127.
I've been using
& tools that attempt to rip through as many giant numbers as fast as possible to determine if they are prime & for the last 15 years. Here's how to download and install mprime on that fresh new Ubuntu Server system you just booted up.
mkdir mprime
wget ftp://mersenne.org/gimps/p95v287.linux64.tar.gz
tar xzvf p95v287.linux64.tar.gz
rm p95v287.linux64.tar.gz
(You may need to replace the version number in the above command with the current latest from the , but as of this writing, that's the latest.)
Now you have a copy of mprime in your user directory. Start it by typing ./mprime
Just passing through, thanks. Answer N to the GIMPS prompt.
Next you'll be prompted for the number of torture test threads to run. They're smart here and always pick an equal number of threads to logical cores, so press enter to accept that. You want a full CPU test on all cores. Next, select the test type.
Small FFTs (maximum heat and FPU stress, data fits in L2 cache, RAM not tested much).
In-place large FFTs (maximum power consumption, some RAM tested).
Blend (tests some of everything, lots of RAM tested).
They're not kidding when they say "maximum power consumption", as you're about to learn. Select 2. Then select Y to begin the torture and watch your CPU squirm in pain.
Accept the answers above? (Y):
[Main thread Feb 14 05:48] Starting workers.
[Worker #2 Feb 14 05:48] Worker starting
[Worker #3 Feb 14 05:48] Worker starting
[Worker #3 Feb 14 05:48] Setting affinity to run worker on logical CPU #2
[Worker #4 Feb 14 05:48] Worker starting
[Worker #2 Feb 14 05:48] Setting affinity to run worker on logical CPU #3
[Worker #1 Feb 14 05:48] Worker starting
[Worker #1 Feb 14 05:48] Setting affinity to run worker on logical CPU #1
[Worker #4 Feb 14 05:48] Setting affinity to run worker on logical CPU #4
[Worker #2 Feb 14 05:48] Beginning a continuous self-test on your computer.
[Worker #4 Feb 14 05:48] Test 1, 44000 Lucas-Lehmer iterations of M7471105 using FMA3 FFT length 384K, Pass1=256, Pass2=1536.
Now's the time to break out your Kill-a-Watt or similar power consumption meter, if you have it, so you can . On most systems, unless you have an absolute beast of a gaming video card installed, the CPU is the single device that will pull the most heat and power in your system. This is full tilt, every core of your CPU burning as many cycles as possible.
I suggest running the i7z utility from another console session so you can monitor core temperatures and speeds while mprime is running its torture test.
sudo apt-get install i7z
Let mprime run overnight in maximum heat torture test mode. The Mersenne calculations are meticulously checked, so if there are any mistakes the whole process will halt with an error at the console. And if mprime halts, ever & your computer is not stable.
Watch those CPU temperatures! In addition to absolute CPU temperatures, you'll also want to keep an eye on total heat dissipation in the system. The system fans (if any) should spin up, and the whole system should be kept at reasonable temperatures through this ordeal, or else you're going to have a sick, overheating computer one day.
The bad news is that it's extremely rare to have any kind of practical, real world workload remotely resembling the stress that Mersenne lays on your CPU. The good news is that if your system can survive the onslaught of Mersenne overnight, it's definitely ready for anything you can conceivably throw at it in the future.
Disks are probably the easiest items to replace in most systems & and the ones most likely to fail over time. We know the disk can't be totally broken since we just installed an OS on the thing, but let's be sure.
Start with a
for the whole drive.
sudo badblocks -sv /dev/sda
This exercises the full extent of the disk (in safe read only fashion). Needless to say, any errors here should prompt serious concern for that drive.
Checking blocks 0 to
Checking for bad blocks (read-only test): done
Pass completed, 0 bad blocks found. (0/0/0 errors)
Let's check the
for the drive next.
sudo apt-get install smartmontools
smartctl -i /dev/sda
That will let you know if the drive supports SMART. Let's enable it, if so, and see the basic drive stats:
smartctl -s on /dev/sda
smartctl -a /dev/sda
Now we can run some SMART tests. But first check how long the tests on offer will take:
smartctl -c /dev/sda
Run the long test if you have the time, or the short test if you don't:
smartctl -t long /dev/sda
It's done asynchronously, so after the time elapses, show the SMART test report and ensure you got a pass:
smartctl -l selftest /dev/sda
=== START OF READ SMART DATA SECTION ===
SMART Self-test log structure revision number 1
Test_Description
LifeTime(hours)
LBA_of_first_error
Extended offline
Completed without error
Next, run a simple disk benchmark to see if you're getting roughly the performance you expect from the drive or array:
dd bs=1M count=512 if=/dev/zero of=test conv=fdatasync
hdparm -Tt /dev/sda
For a system with a basic SSD you should see results at least this good, and perhaps considerably better:
bytes (537 MB) copied, 1.52775 s, 351 MB/s
Timing cached reads:
11434 MB in
2.00 seconds = 5720.61 MB/sec
Timing buffered disk reads:
3.00 seconds = 253.09 MB/sec
Finally, let's try a more intensive test with , a disk benchmark:
sudo apt-get install bonnie++
bonnie++ -f
We don't care too much about the resulting benchmark numbers here, what we're looking for is to pass without errors. And if you get errors during any of the above & your computer is not stable.
(I think these disk tests are sufficient for general use, particularly if you consider drives easily RAID-able and replaceable as I do. However, if you want to test your drives more exhaustively, a good resource is the .)
I don't have a lot of experience with network hardware failure, to be honest. But I do believe in the cult of bandwidth, and that's one thing we can check.
You'll need two machines for an
test, which makes it more complex. Here's the server, let's say it's at 10.0.0.1:
sudo apt-get install iperf
and here's the client, which will connect to the server and record how fast it can transmit data between the two:
sudo apt-get install iperf
iperf -c 10.0.0.1
------------------------------------------------------------
Client connecting to 10.0.0.1, TCP port 5001
TCP window size: 23.5 KByte (default)
------------------------------------------------------------
3] local 10.0.0.2 port 43220 connected with 10.0.0.1 port 5001
[ ID] Interval
0.0-10.0 sec
1.09 GBytes
933 Mbits/sec
As a point of reference, you should expect to see roughly 120 megabytes/sec (aka 960 megabits) of real world throughput on a single gigabit ethernet connection. If you're lucky enough to have a 10 gigabit connection, well, good luck reaching that meteoric 1.2 Gigabyte/sec theoretical throughput maximum.
Video Card
I'm not covering this, because very few of the computers I build these days need more than the stuff built into the CPU to handle video. Which is getting .
You're a gamer, right? So you'll probably want to boot into Windows and try something like . And you should test, because GPUs & especially gaming GPUs & are rather cutting edge bits of kit and burn through a lot of watts. Monitor temperatures and system heat, too.
If you have recommendations for gaming class video card stability testing, share them in the comments.
OK, Maybe It's Stable
This is the regimen I use on the machines I build and touch. And it's worked well for me. I've identified faulty CPUs (once), faulty RAM, faulty disks, and insufficient case airflow early on so that I could deal with them in the lab, before they became liabilities in the field. Doesn't mean they won't fail eventually, but I did all I could to make sure my babies computers can live long and prosper.
Who knows, with a bit of luck maybe you'll end up like the guy whose
before it was decommissioned.
These tests are just a starting point. What techniques do you use to ensure the computers you build are stable? How would you improve on these stability tests based on your real world experience?
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