@nickbarnes A large percentage of my father's father's ancestors were these people. E.g. his mother was an Armstrong.

@AyNio2 I'm going on a trip in 15 minutes, so I don't have time for this conversation now.

@proofofbeef Yes, that's true. It is, like Wonder Bread, better than nothing.

@honx I mean discriminate in the sense that they're not actually funding the best startups.

The writing of the current generation of LLMs is like Wonder Bread. Yes, it's technically bread. It's suitable for some uses. But no baker would be pleased to have made it, and no one who cares about bread would enjoy eating it.

He was referring to border reivers, who lived (violently) in the no man's land between Scotland and England, until it became one man's land in 1603, whereupon they were brutally suppressed. Ours was one of these families.

https://t.co/Lu5OvuPSpJ

Jessica spends a lot of time preparing for Social Radars episodes, and a lot of time editing them afterward. It seems very casual, but it's not.

That combination of seeming but not being casual is a powerful one. Imagine the opposite: formal but careless.

@bast_i Are startups of this type outperforming others? That's what makes VCs eager to invest in them.

@pmarca There's much less demand.

@LoudMouth7777 Originally they were from the western end of what is now the border between England and Scotland, but of course they're all over the world now.


A trade war over Greenland? Greenland? It would be funny if it weren't tragic. But I suppose it keeps the Epstein files out of the news. Man the stuff about Trump in there must be bad...

Do you realize we're going to have an endless series of distracting, pointless fights like this until those files are released? Which they never will be as long as the whole administration's jobs depend on them not being.

I talked to a startup today that was approached by investors when they weren't raising. They were lured into talking to them, and the investors wasted a year of their time before ghosting them, during which the distraction caused their growth rate to shrink by two thirds.

How could this have gone on for a year? Because the investors were proposing to invest a huge amount. And because the firm they hired to do due diligence knew they had infinite money and were determined to milk them for as much of it as possible.

Don't think you couldn't get sucked into a similar cycle of distraction. It happens all the time, especially with acquisitions.

One of the things that surprised me about YC is how long we continue to advise founders. We thought we'd just help them get started and then hand them over to later stage investors, but in fact we're still talking to founders 10 years in.

One of the reasons this happens is that founders trust us, because we were there, if not from the beginning, from very early on. The other reason is that YC just has so much data about startups, because it funds so many. Pretty much any problem you have, we've seen.

With rules like this, who would invest in a German startup? Not me.

Wokeness isn't why the Democrats lost the election. But it is why they're too weak now to oppose Trump. They don't know what they stand for.

Post-wokeness is something the Democrats (and the country) desperately need, so it would be interesting if he's right.


@Liv_Boeree @nikitabier You could probably delete it using uBlock.

@hirschibar Yeah, fuck the algorithm.

One surprising advantage of wokeness, for me at least, was the essays it made me write. This essay was largely a subtweet about wokeness, but that will be lost on future readers. They just get practical advice about avoiding intellectual fashions.

https://t.co/Sqk7Yo82YO

"It's important to speak out now, not when this is over. Right now, while it's happening."

@just_some_dev YC invests in companies from all over the world, but this is a personal investment of mine.

@JonathanDBos You wouldn't want to prohibit face coverings in general. People have religious and medical reasons for wearing them. But it might be possible to draft a law that would make it harder to wear masks during riots.

I've often thought it would help defang antifa if we cracked down on wearing masks in public. But this rule should apply to fa as well. https://t.co/cjwEWJJtad

Also, don't you *want* him to be focused on Tesla, instead of politics? I know I do.

@bentiz It's revealingly innocent.

@veronica_humble I'm working on it.


@codepublish The current trend seems to be the opposite.

@ars_stef I don't expect they will, but they could, if for example there's some macroeconomic crisis.

Startup valuations are higher than they used to be. At some point in the future they may fall. Maybe this will happen before all the big AI companies have been funded. But maybe not. I'd rather risk overpaying than risk missing the train.

A sign of a company still run by the founder. What hired CEO would solicit complaints?

@IAPonomarenko Is Putin promising them anything? What could he promise them?

@eshear Also, why is the fundamental nature of reality (or at least our best guess at it so far) not the Standard Model?

@tedgioia From what I know of the behavior of the music labels, the predecessor of this technocracy was basically mafia. Is that not in fact a step forward?

@Noahpinion And indeed, this question doesn't so much focus the discussion as end it. But I am usually happy to.

@Noahpinion The way I respond to these people is to ask them to quote a specific passage in the essay that they believe is false. I know how hard this is going to be, because I've just spent days going through it sentence by sentence making sure there is no such passage.

@esthercrawford There should be a word for this.


@ivarojha You can learn sales and marketing too. I did.

I'm skeptical when people tell me they're very determined to make their startup succeed, but they can't find a technical cofounder. If you're determined, you can learn to program.

@mdwelsh I sometimes deliberately take a shower to help me think. Though usually I already need one.

@Osh_mahajan 3 years apart. The younger one just had a huge head.

@gmiller This is by no means a complete list, just as many as I could think of before I ran out of characters.

The expert on this is @ahistoryinart.

@gmiller Meredith Frampton, Elliot Hodgkin, John Aldridge, Edward Bawden, Raphael Soyer, Luigi Lucioni, John Koch, Norman Rockwell. Laura Knight misses the cutoff by 4 years. Eric Ravilious would probably have made it if he hadn't been killed in WWII.

"If you’re thinking without writing, you only think you’re thinking."

— Leslie Lamport

@kendrictonn It's not going to get better as you get more followers. It's going to get worse.

@robinhanson What did she dislike most about it?

10 years ago: sons check to see whether they could be carried on. https://t.co/b6QXls1V6l


In a classic startup, product design and marketing are the same thing. You figure out what your friends want. That gives you the design. But if you execute well, it also gets you your initial set of users: your friends.

@kadavy Any sense in which this isn't painting isn't a sense that matters. https://t.co/u3H1c0Cpi0

@DrNWillburger @LMWStuttgart The swords were already prestigious symbols. They were enormously expensive compared to other things.

In January you wonder why anyone would want to live in England and in May you wonder why anyone would want to live anywhere else.

Judging from auction prices, the Impressionists are the point at which the very rich start to like art. Anything before that is safe.

@mrgunn @cb_doge That's my plan.

"Undergraduates at top US universities start companies at more than 5x the rate of their British-educated peers."

https://t.co/zoJ7Ew2MY1

@thisdh This is how local banking used to work.

@gmiller That's easily reduced to absurdity because it's easy to imagine scenarios that generate almost unlimited amounts of data about you. Think how much data about you could be generated just by people taking pictures that included you as you walked down a crowded street.

@GustafKornias There's certainly something there but it's not a wave on the scale of these three.


The Risk of Discovery
January 2017
Because biographies of famous scientists tend to
edit out their mistakes, we underestimate the
degree of risk they were willing to take.
And because anything a famous scientist did that
wasn't a mistake has probably now become the
conventional wisdom, those choices don't
seem risky either.
Biographies of Newton, for example, understandably focus
more on physics than alchemy or theology.
The impression we get is that his unerring judgment
led him straight to truths no one else had noticed.
How to explain all the time he spent on alchemy
and theology?  Well, smart people are often kind of
crazy.
But maybe there is a simpler explanation. Maybe
the smartness and the craziness were not as separate
as we think. Physics seems to us a promising thing
to work on, and alchemy and theology obvious wastes
of time. But that's because we know how things
turned out. In Newton's day the three problems
seemed roughly equally promising. No one knew yet
what the payoff would be for inventing what we
now call physics; if they had, more people would
have been working on it. And alchemy and theology
were still then in the category Marc Andreessen would
describe as "huge, if true."
Newton made three bets. One of them worked. But
they were all risky.
Japanese Translation


@tracewoodgrains Overall does it seem better or worse than Twitter to you?

@tracewoodgrains The focus on blocking makes me not want to try it.

@levie And since it's obvious, that in turn implies you can skip 2/3 of business books.

@joelmoliv @Noahpinion You might like this: https://t.co/nOIp1beS09

@ATabarrok Those numbers are from 2019. Norway might be closer to the US now, after its tax on unrealized capital gains caused a lot of rich people to leave.

@joelmoliv @Noahpinion How could a plane fly upside down if it was?

@StatisticUrban They got a few wrong in the 20th too.

@Noahpinion Spiral staircases in castles are designed to favor right-handed defenders. The shape of an airfoil generates the lift that keeps a plane in the air.

@RightFren Very much not. That was the point I started following her.

@gblaszczyk @IBTruckingGirl Mine is @nathanblec.


@WillManidis I've met a lot.

https://t.co/mOWYLAxoJU

@simeonkerkola @_ZachFoster No, but I try to stand up for basic moral principles as if it were.

@neilkli This isn't about policies related to habeas corpus. It's about what the term *means*. That's why it's so alarming. It's such a basic thing.

@_ZachFoster This is no way to respond when someone has just had a serious cancer diagnosis. He was a weak president, but not evil.

@AaronJay954127 Do you know what the principle of habeas corpus is?

I didn't think I could still be surprised by the incompetence of Trump's appointees, but this surprised me. Even if you're a Trump supporter and don't want to say anything publicly, you have to be shaking your head about this.

@TheStalwart No more surprising than if a movie were made in California.

@vmzucher @AdameMedia Of course.

@BurtRock69 Yes, that's true. The suppliers of any commodity can push its price above the market price by forming a cartel.

@Chris_arnade He has a point. They're disasters in some respects. He overstated the case by claiming they were a net disaster.


What I've Learned from Hacker News
February 2009
Hacker News was two years
old last week.  Initially it was supposed to be a side project—an
application to sharpen Arc on, and a place for current and future
Y Combinator founders to exchange news.  It's grown bigger and taken
up more time than I expected, but I don't regret that because I've
learned so much from working on it.
Growth
When we launched in February 2007, weekday traffic was around 1600
daily uniques.  It's since
grown
to around 22,000.  This growth
rate is a bit higher than I'd like.  I'd like the site to grow,
since a site that isn't growing at least slowly is probably dead.
But I wouldn't want it to grow as large as Digg or Reddit—mainly
because that would dilute the character of the site, but also because
I don't want to spend all my time dealing with scaling.
I already have problems enough with that.  Remember, the original
motivation for HN was to test a new programming language, and
moreover one that's focused on experimenting with language design,
not performance.  Every time the site gets slow, I fortify myself
by recalling McIlroy and Bentley's famous quote
The key to performance is elegance, not battalions of special
  cases.
and look for the bottleneck I can remove with least code.  So far
I've been able to keep up, in the sense that performance has remained
consistently mediocre despite 14x growth. I don't know what I'll
do next, but I'll probably think of something.
This is my attitude to the site generally.  Hacker News is an
experiment, and an experiment in a very young field.  Sites of this
type are only a few years old.  Internet conversation generally is
only a few decades old.  So we've probably only discovered a fraction
of what we eventually will.
That's why I'm so optimistic about HN.  When a technology is this
young, the existing solutions are usually terrible; which means it
must be possible to do much better; which means many problems that
seem insoluble aren't. Including, I hope, the problem that has
afflicted so many previous communities: being ruined by growth.
Dilution
Users have worried about that since the site was a few months old.
So far these alarms have been false, but they may not always be.
Dilution is a hard problem. But probably soluble; it doesn't mean
much that open conversations have "always" been destroyed by growth
when "always" equals 20 instances.
But it's important to remember we're trying to solve a new problem,
because that means we're going to have to try new things, most of
which probably won't work.  A couple weeks ago I tried displaying
the names of users with the highest average comment scores in orange.
[
1
]
That was a mistake.  Suddenly a culture that had been more
or less united was divided into haves and have-nots.  I didn't
realize how united the culture had been till I saw it divided.  It
was painful to watch.
[
2
]
So orange usernames won't be back.  (Sorry about that.)  But there
will be other equally broken-seeming ideas in the future, and the
ones that turn out to work will probably seem just as broken as
those that don't.
Probably the most important thing I've learned about dilution is
that it's measured more in behavior than users. It's bad behavior
you want to keep out more than bad people. User behavior turns out
to be surprisingly malleable.  If people are
expected
to behave
well, they tend to; and vice versa.
Though of course forbidding bad behavior does tend to keep away bad
people, because they feel uncomfortably constrained in a place where
they have to behave well.  But this way of keeping them out is
gentler and probably also more effective than overt barriers.
It's pretty clear now that the broken windows theory applies to
community sites as well.  The theory is that minor forms of bad
behavior encourage worse ones: that a neighborhood with lots of
graffiti and broken windows becomes one where robberies occur.  I
was living in New York when Giuliani introduced the reforms that
made the broken windows theory famous, and the transformation was
miraculous. And I was a Reddit user when the opposite happened
there, and the transformation was equally dramatic.
I'm not criticizing Steve and Alexis.  What happened to Reddit
didn't happen out of neglect.  From the start they had a policy of
censoring nothing except spam.  Plus Reddit had different goals
from Hacker News.  Reddit was a startup, not a side project; its
goal was to grow as fast as possible.  Combine rapid growth and
zero censorship, and the result is a free for all.  But I don't
think they'd do much differently if they were doing it again.
Measured by traffic, Reddit is much more successful than Hacker
News.
But what happened to Reddit won't inevitably happen to HN. There
are several local maxima.  There can be places that are free for
alls and places that are more thoughtful, just as there are in the
real world; and people will behave differently depending on which
they're in, just as they do in the real world.
I've observed this in the wild.  I've seen people cross-posting on
Reddit and Hacker News who actually took the trouble to write two
versions, a flame for Reddit and a more subdued version for HN.
Submissions
There are two major types of problems a site like Hacker News needs
to avoid: bad stories and bad comments.  So far the danger of bad
stories seems smaller.  The stories on the frontpage now are still
roughly the ones that would have been there when HN started.
I once thought I'd have to weight votes to keep crap off the
frontpage, but I haven't had to yet.  I wouldn't have predicted the
frontpage would hold up so well, and I'm not sure why it has.
Perhaps only the more thoughtful users care enough to submit and
upvote links, so the marginal cost of one random new user approaches
zero.  Or perhaps the frontpage protects itself, by advertising what type of submission is expected.
The most dangerous thing for the frontpage is stuff that's too easy
to upvote.  If someone proves a new theorem, it takes some work by
the reader to decide whether or not to upvote it.  An amusing cartoon
takes less.  A rant with a rallying cry as the title takes zero,
because people vote it up without even reading it.
Hence what I call the Fluff Principle: on a user-voted news site,
the links that are easiest to judge will take over unless you take
specific measures to prevent it.
Hacker News has two kinds of protections against fluff.  The most
common types of fluff links are banned as off-topic.  Pictures of
kittens, political diatribes, and so on are explicitly banned.  This
keeps out most fluff, but not all of it.  Some links are both fluff,
in the sense of being very short, and also on topic.
There's no single solution to that.  If a link is just an empty
rant, editors will sometimes kill it even if it's on topic in the
sense of being about hacking, because it's not on topic by the real
standard, which is to engage one's intellectual curiosity.  If the
posts on a site are characteristically of this type I sometimes ban
it, which means new stuff at that url is auto-killed.  If a post
has a linkbait title, editors sometimes rephrase it to be more
matter-of-fact.  This is especially necessary with links whose
titles are rallying cries, because otherwise they become implicit
"vote up if you believe such-and-such" posts, which are the most
extreme form of fluff.
The techniques for dealing with links have to evolve, because the
links do. The existence of aggregators has already affected what
they aggregate. Writers now deliberately write things to draw traffic
from aggregators—sometimes even specific ones.  (No, the irony
of this statement is not lost on me.)  Then there are the more
sinister mutations, like linkjacking—posting a paraphrase of
someone else's article and submitting that instead of the original.
These can get a lot of upvotes, because a lot of what's good in an
article often survives; indeed, the closer the paraphrase is to
plagiarism, the more survives.
[
3
]
I think it's important that a site that kills submissions provide
a way for users to see what got killed if they want to.  That keeps
editors honest, and just as importantly, makes users confident
they'd know if the editors stopped being honest. HN users can do
this by flipping a switch called showdead in their profile.
[
4
]
Comments
Bad comments seem to be a harder problem than bad submissions.
While the quality of links on the frontpage of HN hasn't changed
much, the quality of the median comment may have decreased somewhat.
There are two main kinds of badness in comments: meanness and
stupidity.  There is a lot of overlap between the two—mean
comments are disproportionately likely also to be dumb—but
the strategies for dealing with them are different.  Meanness is
easier to control.  You can have rules saying one shouldn't be mean,
and if you enforce them it seems possible to keep a lid on meanness.
Keeping a lid on stupidity is harder, perhaps because stupidity is
not so easily distinguishable.  Mean people are more likely to know
they're being mean than stupid people are to know they're being
stupid.
The most dangerous form of stupid comment is not the long but
mistaken argument, but the dumb joke.  Long but mistaken arguments
are actually quite rare.  There is a strong correlation between
comment quality and length; if you wanted to compare the quality
of comments on community sites, average length would be a good
predictor.  Probably the cause is human nature rather than anything
specific to comment threads. Probably it's simply that stupidity
more often takes the form of having few ideas than wrong ones.
Whatever the cause, stupid comments tend to be short.  And since
it's hard to write a short comment that's distinguished for the
amount of information it conveys, people try to distinguish them
instead by being funny.  The most tempting format for stupid comments
is the supposedly witty put-down, probably because put-downs are
the easiest form of humor.
[
5
]
So one advantage of forbidding
meanness is that it also cuts down on these.
Bad comments are like kudzu: they take over rapidly. Comments have
much more effect on new comments than submissions have on new
submissions.  If someone submits a lame article, the other submissions
don't all become lame.  But if someone posts a stupid comment on a
thread, that sets the tone for the region around it.  People reply
to dumb jokes with dumb jokes.
Maybe the solution is to add a delay before people can respond to
a comment, and make the length of the delay inversely proportional
to some prediction of its quality.  Then dumb threads would grow
slower.
[
6
]
People
I notice most of the techniques I've described are conservative:
they're aimed at preserving the character of the site rather than
enhancing it.  I don't think that's a bias of mine.  It's due to
the shape of the problem.  Hacker News had the good fortune to start
out good, so in this case it's literally a matter of preservation.
But I think this principle would also apply to sites with different
origins.
The good things in a community site come from people more than
technology; it's mainly in the prevention of bad things that
technology comes into play. Technology certainly can enhance
discussion.  Nested comments do, for example.  But I'd rather use
a site with primitive features and smart, nice users than a more
advanced one whose users were idiots or
trolls
.
So the most important thing a community site can do is attract the
kind of people it wants.  A site trying to be as big as possible
wants to attract everyone.  But a site aiming at a particular subset
of users has to attract just those—and just as importantly,
repel everyone else.  I've made a conscious effort to do this on
HN.  The graphic design is as plain as possible, and the site rules
discourage dramatic link titles.  The goal is that the only thing
to interest someone arriving at HN for the first time should be the
ideas expressed there.
The downside of tuning a site to attract certain people is that,
to those people, it can be too attractive.  I'm all too aware how
addictive Hacker News can be.  For me, as for many users, it's a
kind of virtual town square.  When I want to take a break from
working, I walk into the square, just as I might into Harvard Square
or University Ave in the physical world.
[
7
]
But an online square is
more dangerous than a physical one.  If I spent half the day loitering
on University Ave, I'd notice.  I have to walk a mile to get there,
and sitting in a cafe feels different from working. But visiting
an online forum takes just a click, and feels superficially very
much like working.  You may be wasting your time, but you're not
idle.  Someone is
wrong
on the Internet, and you're fixing the
problem.
Hacker News is definitely useful.  I've learned a lot from things
I've read on HN.  I've written several essays that began as comments
there.  So I wouldn't want the site to go away.  But I would like
to be sure it's not a net drag on productivity.  What a disaster
that would be, to attract thousands of smart people to a site that
caused them to waste lots of time.  I wish I could be 100% sure
that's not a description of HN.
I feel like the addictiveness of games and social applications is
still a mostly unsolved problem.  The situation now is like it was
with crack in the 1980s: we've invented terribly addictive new
things, and we haven't yet evolved ways to protect ourselves from
them.  We will eventually, and that's one of the problems I hope
to focus on next.
Notes
[
1
]
I tried ranking users by both average and median comment
score, and average (with the high score thrown out) seemed the more
accurate predictor of high quality.  Median may be the more accurate
predictor of low quality though.
[
2
]
Another thing I learned from this experiment is that if you're
going to distinguish between people, you better be sure you do it
right.  This is one problem where rapid prototyping doesn't work.
Indeed, that's the intellectually honest argument for not discriminating
between various types of people.  The reason not to do it is not
that everyone's the same, but that it's bad to do wrong and hard
to do right.
[
3
]
When I catch egregiously linkjacked posts I replace the url
with that of whatever they copied.  Sites that habitually linkjack
get banned.
[
4
]
Digg is notorious for its lack of transparency.  The root of
the problem is not that the guys running Digg are especially sneaky,
but that they use the wrong algorithm for generating their frontpage.
Instead of bubbling up from the bottom as they get more votes, as
on Reddit, stories start at the top and get pushed down by new
arrivals.
The reason for the difference is that Digg is derived from Slashdot,
while Reddit is derived from Delicious/popular.  Digg is Slashdot
with voting instead of editors, and Reddit is Delicious/popular
with voting instead of bookmarking.  (You can still see fossils of
their origins in their graphic design.)
Digg's algorithm is very vulnerable to gaming, because any story
that makes it onto the frontpage is the new top story.  Which in
turn forces Digg to respond with extreme countermeasures.  A lot
of startups have some kind of secret about the subterfuges they had
to resort to in the early days, and I suspect Digg's is the extent
to which the top stories were de facto chosen by human editors.
[
5
]
The dialog on Beavis and Butthead was composed largely of
these, and when I read comments on really bad sites I can hear them
in their voices.
[
6
]
I suspect most of the techniques for discouraging stupid
comments have yet to be discovered.  Xkcd implemented a particularly
clever one in its IRC channel: don't allow the same thing twice.
Once someone has said "fail," no one can ever say it again.  This
would penalize short comments especially, because they have less
room to avoid collisions in.
Another promising idea is the
stupid
filter
, which is just like a
probabilistic spam filter, but trained on corpora of stupid and
non-stupid comments instead.
You may not have to kill bad comments to solve the problem.  Comments
at the bottom of a long thread are rarely seen, so it may be enough
to incorporate a prediction of quality in the comment sorting
algorithm.
[
7
]
What makes most suburbs so demoralizing is that there's no
center to walk to.
Thanks
to Justin Kan, Jessica Livingston, Robert Morris,
Alexis Ohanian, Emmet Shear, and Fred Wilson for reading drafts of
this.
Comment
on this essay.


@OneKapitalist That is some fascinatingly twisted logic. If someone supports America, they're never supposed to speak out when the current administration makes a mistake?

If you support America, that's when you need to speak out the most.

@pourteaux They will give him a way to claim victory to his most credulous supporters, but that leaves China even further ahead.

@DiscussOrDivide I don't. That's why I'm so bummed that Trump has bungled things so badly. If I supported China I'd be glad that he had, and I'd be hoping he'd continue to.

@pitdesi Credit where credit is due. I think she was net bad for the country, but this is a good rule.

Prediction: When Trump caves to China, America will be worse off than if he had never imposed the tariffs in the first place. The Chinese won't let it happen otherwise.

@timhwang What do you predict will happen?

I got an email with the subject line "Vandals" but alas it was just about local teenagers and not the germanic tribe.

@SpencerHakimian What trade has the people who believe this on the other side? I genuinely want to know.

@chrisman @AlecLace What it always means. That's the thing about due process.

@_sanjeev_verma @MabreyTed Or hoping no one will notice if he tries to avoid answering.


@robkhenderson It's an adaptation.

@SJobs_Stories "Contain the negative behaviors." What weaselly language.

Even as you become a big company yourself, you neglect selling to startups at your peril.

This is why Amazon, Google, and Microsoft between them give all new YC AI companies $1.35 million in compute credits. They know they need to get the startups.

@McCormickProf Perhaps because they're protective of the Kennedy brand.

@waitbutwhy It never gets easy. "How to Do Great Work" was the hardest thing I've written, and I was 58.

I achieved a narrow victory with Poo Tongs (why wait?), the Breakup Iguana (distract your now-ex with a live iguana, giving you time to escape), and the Remote-Controlled Rictus (why shouldn't the corpse enjoy the funeral too?).

The busman's holiday came for us yesterday. 12 yo made us play the new game he bought, Business Walrus. It's played by pitching implausible startup ideas. Nineteen years' experience hearing them have made Jessica and me almost too good at this.

Big companies are downstream of startups. If you get all the startups, you can always collect the big companies eventually. Some of your startups will even turn into them. Whereas if you start with the big companies you're unlikely ever to get the startups.

The influence that customers have on your product may be even more important. Your product won't be complete when you launch. What features do you want to add? Those required by sophisticated startups, or by big, dumb companies?

"Don't have as much money" may not seem a good quality in customers. But when you're just starting out, it's the growth rate that matters, not the absolute amounts, so "decide quickly" is more important.


@lanceawhite I hoped she would win, but I didn't predict she would.

@stefan_arrr No. Go back and read the tweet I replied to.

@cwjones89 This happened in the 90s.

@ahistoryinart I seem to like artists who like thistles. I don't think it's random. I think a certain kind of person is attracted to these kinds of shapes.

@nick_vladkis No, it was happening regardless. Wokeness peaked in 2020.

@DemurBuoy Wokeness was the second wave of political correctness. It was angrier and more aggressive, and the topics to be PC/woke about had changed slightly, but otherwise it was the same thing.

@TraderHQdesk If you choose your opinions individually instead of buying them in bulk, it's possible to understand both that wokeness sucks and that Trump is a grifter.

https://t.co/ogMaJHrydF

This one turned out to be accurate.

@ss_bhav It just happened.

@Chris_arnade How much does it weigh?


@_brianpotter Medieval Technology and Social Change on the heavy plow.

@kendrictonn Left, for this one. Much of the painting itself is dark, so without the gold rim the painting bleeds into the frame.

@buccocapital Occam's Razor says the reason is simply that OpenAI is very successful and thus subject to unusual scrutiny.

@pitdesi I hope this is not someone whose job requires an understanding of risk...

@santygegen I'm not sure but I'd guess about a day, for one specific bug.

@TRHLofficial Because in those days these were party foods, not people's daily diet.

You could argue that the point of programming is to produce bugs. Bugs show you where your model of a problem doesn't match the problem, and in a highly motivating form.

Corollary: If you're making something for which your friends are the potential users, you shouldn't spend any time or money on "marketing" until your friends love it. And if they do, you may not have to.

@robertskmiles I agree, but I got chills liking this.

@michelletandler If Palmer Luckey isn't, you aren't either.


@DiogoSnows Not for years, if you can grow without this.

@Alex_Badalyan That's the wrong question. The question is what will your revenues be? Because what will happen is that 70 of the startups will be dead, but 2 of them will be 1000x as big.

@hirschibar Startups can't pay much, but they decide quickly. And if you're a startup yourself, that's the kind of revenue stream you want. The growth rate matters more than the absolute numbers.

@karl They're not reliable customers individually, but a sufficiently large pool of them is reliable in the aggregate. Indeed, a pool of startups is the best way to get a fast growing user base.

@hirschibar Initially, but they're very conservative about spending this money because they need to make it last. So it's actually harder to get money out of early stage startups than out of more established companies.

@TReynolds761237 I think you may be on the wrong thread.

@samdaceo For a game you want your initial users to be your friends.

@CheungKira A startup is a company designed to grow fast.

https://t.co/0ZleHiJDMe

@spenserskates Yes, Stripe is the canonical example.

@HatesAdtech @Luvaglio Yep! Though another way of saying this is that I advise companies to let their most sophisticated and fastest growing users drive their roadmaps.


@GarettJones Do you think there will be pressure to retract the paper once the aggressively conventional-minded realize how heretical it is?

@MuthaNagavamsi Very much so. It's still early.

@slow_developer I could not have predicted the last two, so there's little chance I could predict the next.

@Richard_Florida Hard to say about semiconductors because I was a kid then, but AI seems similar to the web.

@cb_doge Or don't.

@PP_Rubens So close!

@balajis It's not merely the center. It's the default.

@Presidentlin Not on the scale of these three big ones.

Interestingly, the spacing is about the same as the phases of life. I was an early consumer of chips, an early producer of web apps, and an early investor in AI companies.

I've seen three big waves of technology so far, and they've been remarkably evenly spaced: Chips mid 1970s, the web late 1990s, AI early 2020s.


A Plan for Spam
Like to build things?
Try
Hacker
News
.
August 2002
(This article describes the spam-filtering techniques
used in the spamproof web-based mail reader we
built to exercise
Arc
. An
improved algorithm is described in
Better
Bayesian Filtering
.)
I think it's possible to stop spam, and that
content-based filters are the way to do it.
The Achilles heel of the spammers is their message.
They can circumvent any other barrier you set up.  They have so far, at
least.  But they have to deliver their message, whatever it
is.  If we can write software that recognizes their messages,
there is no way they can get around that.
_ _ _
To the recipient, spam is easily recognizable.  If you hired
someone to read your mail and discard the spam, they would
have little trouble doing it.  How much do we have
to do, short of AI, to automate this process?
I think we will be able to solve the problem with fairly
simple algorithms.  In fact, I've found that you can filter
present-day spam acceptably well using nothing more than a
Bayesian combination of the spam probabilities of individual
words.  Using a slightly tweaked (as described below) Bayesian
filter, we now miss less than 5 per 1000 spams, with 0 false positives.
The statistical approach is not usually the first one people
try when they write spam filters.  Most hackers' first instinct is
to try to write software that recognizes individual properties of
spam.  You look at spams
and you think, the gall of these guys to try sending me mail
that begins "Dear Friend" or has a subject line that's all
uppercase and ends in eight exclamation points.  I can filter
out that stuff with about one line of code.
And so you do,
and in the beginning it works.  A few simple rules will take
a big bite out of your incoming spam.  Merely looking
for the word "click" will catch 79.7% of the
emails in my spam corpus, with only 1.2% false positives.
I spent about six months writing software that looked for
individual spam features before I tried the statistical
approach.  What I found was that recognizing that last few
percent of spams got very hard, and that as I
made the filters stricter I got more false positives.
False positives are innocent emails that get mistakenly
identified as spams.
For most users,
missing legitimate email is
an order of magnitude worse than receiving spam, so a
filter that yields false positives is like an acne cure
that carries a risk of death to the patient.
The more spam a user gets, the less
likely he'll be to notice one innocent mail sitting in his
spam folder.  And strangely enough, the better your spam filters get,
the more dangerous false positives become, because when the
filters are really good, users will be more likely to
ignore everything they catch.
I don't know why I avoided trying the statistical approach
for so long.  I think it was because I got addicted to
trying to identify spam features myself, as if I were playing
some kind of competitive game with the spammers.  (Nonhackers
don't often realize this, but most hackers are very competitive.)
When I did try statistical analysis, I
found immediately that it was much cleverer than I had been.
It discovered, of course, that terms like "virtumundo" and
"teens" were good indicators of spam.  But it also
discovered that "per" and "FL" and "ff0000" are good
indicators of spam.  In fact, "ff0000" (html for bright red)
turns out to be as good an indicator of spam as any
pornographic term.
_ _ _
Here's a sketch of how I do statistical filtering.  I start
with one corpus of spam and one of nonspam mail.  At the
moment each one has about 4000 messages in it.  I scan
the entire text, including headers and embedded html
and javascript, of each message in each corpus.
I currently consider alphanumeric characters,
dashes, apostrophes, and dollar signs to be part of tokens,
and everything else to be a token separator.  (There is
probably room for improvement here.)  I ignore tokens that
are all digits, and I also ignore html comments, not even
considering them as token separators.
I count the number
of times each token (ignoring case, currently) occurs in
each corpus.  At this stage I end up with two large hash
tables, one for each corpus, mapping tokens to number
of occurrences.
Next I create a third hash table, this time mapping
each token to the probability that an email containing it is a spam,
which I calculate as follows [1]:
(let ((g (* 2 (or (gethash word good) 0)))
      (b (or (gethash word bad) 0)))
   (unless (< (+ g b) 5)
     (max .01
          (min .99 (float (/ (min 1 (/ b nbad))
                             (+ (min 1 (/ g ngood))
                                (min 1 (/ b nbad)))))))))
where
word
is the token whose probability we're
calculating,
good
and
bad
are the hash tables
I created in the first step, and
ngood
and
nbad
are the number of nonspam and spam messages respectively.
I explained this as code to show a couple of important details.
I want to bias the probabilities slightly to avoid false
positives, and by trial and error I've found that a good
way to do it is to double all the numbers in
good
.
This helps to distinguish between words that occasionally
do occur in legitimate email and words that almost never do.
I only consider words that occur more than five times in
total (actually, because of the doubling, occurring three
times in nonspam mail would be enough).  And then there is
the question of what probability to assign to words that
occur in one corpus but not the other.  Again by trial and
error I chose .01 and .99.  There may be room for tuning
here, but as the corpus grows such tuning will happen
automatically anyway.
The especially observant will notice that while I consider
each corpus to be a single long stream of text for purposes
of counting occurrences, I use the number of emails in
each, rather than their combined length, as the divisor
in calculating spam probabilities.  This adds another
slight bias to protect against false positives.
When new mail arrives, it is scanned into tokens, and
the most interesting fifteen tokens, where interesting is
measured by how far their spam probability is from a
neutral .5, are used to calculate the probability that
the mail is spam.  If
probs
is a list of the fifteen individual probabilities, you
calculate the
combined
probability thus:
(let ((prod (apply #'* probs)))
  (/ prod (+ prod (apply #'* (mapcar #'(lambda (x)
                                         (- 1 x))
                                     probs)))))
One question that arises in
practice is what probability to assign to a word you've
never seen, i.e. one that doesn't occur in the hash table
of word probabilities.  I've found, again by trial and
error, that .4 is a good number to use.  If you've never
seen a word before, it is probably fairly innocent; spam
words tend to be all too familiar.
There are examples of this algorithm being applied to
actual emails in an appendix at the end.
I treat mail as spam if the algorithm above gives it a
probability of more than .9 of being spam.  But in practice
it would not matter much where I put this threshold, because
few probabilities end up in the middle of the range.
_ _ _
One great advantage of the statistical approach is that you
don't have to read so many spams.  Over the past six months,
I've read literally thousands of spams, and it is really
kind of demoralizing.  Norbert Wiener said if you compete
with slaves you become a slave, and there is something
similarly degrading about competing with spammers.   To
recognize individual spam features you have to try to get
into the mind of the spammer, and frankly I want to spend
as little time inside the minds of spammers as possible.
But the real advantage of the Bayesian approach, of course,
is that you know what
you're measuring.  Feature-recognizing filters like
SpamAssassin assign a spam "score" to email.  The Bayesian
approach assigns an actual probability.  The problem with
a "score" is that no one knows what it means.  The user
doesn't know what it means, but worse still, neither does
the developer of the filter.  How many
points
should an
email get for having the word "sex" in it?  A probability
can of course be mistaken, but there is little ambiguity
about what it means, or how evidence should be combined
to calculate it.  Based on my corpus, "sex" indicates
a .97 probability of the containing email being a spam,
whereas "sexy" indicates .99 probability.
And Bayes' Rule, equally unambiguous, says that an email
containing both words would, in the (unlikely)
absence of any other evidence, have a 99.97% chance of
being a spam.
Because it is measuring probabilities, the Bayesian approach
considers all the evidence in the email, both good and bad.
Words that occur disproportionately
rarely
in spam (like "though" or "tonight" or "apparently")
contribute as much to decreasing the probability as
bad words like "unsubscribe" and "opt-in" do to
increasing it.  So an otherwise innocent email that happens
to include the word "sex" is not going to get tagged as spam.
Ideally, of course, the probabilities should be calculated
individually for each user.  I get a lot of email containing
the word "Lisp", and (so far) no spam that does.  So a word
like that is effectively a kind of password for sending
mail to me.  In my earlier spam-filtering software, the user
could set up a list of such words and mail containing
them would automatically get past the filters.  On my
list I put words like "Lisp" and also my zipcode, so
that (otherwise rather spammy-sounding) receipts from
online orders would get through.  I thought I was being
very clever, but I found that the Bayesian filter did the
same thing for me, and moreover discovered of a lot of words I
hadn't thought of.
When I said at the start that our filters let through less than
5 spams per 1000 with 0 false positives, I'm talking about
filtering my mail based on a corpus of my mail.  But these
numbers are not misleading, because that is the approach I'm
advocating: filter each user's mail based on the spam and
nonspam mail he receives.  Essentially, each user should
have two delete buttons, ordinary delete and delete-as-spam.
Anything deleted as spam goes into the spam corpus,
and everything else goes into the nonspam corpus.
You could start
users with a seed filter, but ultimately each user should have
his own per-word probabilities based on the actual mail he
receives.  This (a) makes the filters more effective, (b) lets
each user decide their own precise definition of spam,
and (c) perhaps best of all makes it hard for spammers
to tune mails to get through the filters.  If a lot of the
brain of the filter is in the individual databases, then
merely tuning spams to get through the seed filters
won't guarantee anything about how well they'll get through
individual users' varying and much more trained filters.
Content-based spam filtering is often combined with a whitelist,
a list of senders whose mail can be accepted with no filtering.
One easy way to build such a
whitelist is to keep a list of every address the user has
ever sent mail to.  If a mail reader has a delete-as-spam
button then you could also add the from address
of every email the user has deleted as ordinary trash.
I'm an advocate of whitelists, but more as a way to save
computation than as a way to improve filtering.  I used to think that
whitelists would make filtering easier, because you'd
only have to filter email from people you'd never heard
from, and someone sending you mail for the first time is
constrained by convention in what they can say to you.
Someone you already know might send you an email talking about sex,
but someone sending you mail for the first time would not
be likely to.  The problem is, people can have more than one
email address, so a new from-address doesn't guarantee that
the sender is writing to you for the first time.
It is not unusual
for an old friend (especially if he is a hacker) to suddenly
send you an email with a new from-address, so you can't
risk false positives by filtering mail from unknown
addresses especially stringently.
In a sense, though, my filters do themselves embody a kind
of whitelist (and blacklist) because they are based on
entire messages, including the headers.  So to that
extent they "know" the email addresses of trusted senders
and even the routes by which mail gets from them to me.
And they know the same about spam, including the server
names, mailer versions, and protocols.
_ _ _
If I thought that I could keep up current rates of spam
filtering, I would consider this problem solved.  But it
doesn't mean much to be able to filter out most present-day
spam, because spam evolves.
Indeed, most
antispam techniques
so far have been like pesticides that
do nothing more than create a new, resistant strain of bugs.
I'm more hopeful about Bayesian filters, because they evolve
with the spam.  So as spammers start using "c0ck"
instead of "cock" to evade simple-minded spam filters
based on individual words, Bayesian filters automatically
notice.  Indeed, "c0ck" is far more damning evidence than
"cock", and Bayesian filters know precisely how much more.
Still, anyone who proposes a plan for spam filtering has to
be able to answer the question: if the spammers knew
exactly what you were doing,
how well could they get past you?  For example, I think that if
checksum-based spam filtering becomes a serious obstacle,
the spammers will just
switch to mad-lib techniques for generating message bodies.
To beat Bayesian filters, it would not be enough for spammers
to make their emails unique or to stop using individual
naughty words.  They'd have to make their mails indistinguishable
from your ordinary mail.  And this I think would severely
constrain them.  Spam is mostly sales
pitches, so unless your regular mail is all sales pitches,
spams will inevitably have a different character.  And
the spammers would also, of course, have to change (and keep
changing) their whole infrastructure, because otherwise
the headers would look as bad to the Bayesian filters as ever,
no matter what they did to the message body.  I don't know
enough about the infrastructure that spammers use to know
how hard it would be to make the headers look innocent, but
my guess is that it would be even harder than making the
message look innocent.
Assuming they could solve the problem of the headers,
the spam of the future will probably look something like
this:
Hey there.  Thought you should check out the following:
http://www.27meg.com/foo
because that is about as much sales pitch as content-based
filtering will leave the spammer room to make.  (Indeed, it
will be hard even to get this past filters, because if everything
else in the email is neutral, the spam probability will hinge on
the url, and it will take some effort to make that look neutral.)
Spammers range from businesses running so-called
opt-in lists who don't even try to conceal their identities,
to guys who hijack mail servers to send out spams promoting
porn sites.  If we use filtering to whittle their
options down to mails like the one above, that should
pretty much put the spammers on the "legitimate" end of
the spectrum out of business; they feel obliged
by various state laws to include boilerplate about why
their spam is not spam, and how to cancel your
"subscription,"  and that kind of text is easy to
recognize.
(I used to think it was naive to believe that stricter laws
would decrease spam.  Now I think that while stricter laws
may not decrease the amount of spam that spammers
send,
they can certainly help filters to decrease the amount of
spam that recipients actually see.)
All along the spectrum, if you restrict the sales pitches spammers
can make, you will inevitably tend to put them out of
business.  That word
business
is an important one to
remember.  The spammers are businessmen.  They send spam because
it works.  It works because although the response rate
is abominably low (at best 15 per million, vs 3000 per
million for a catalog mailing), the cost, to them, is
practically nothing.  The cost is enormous for the recipients,
about 5 man-weeks for each million recipients who spend
a second to delete the spam, but the spammer
doesn't have to pay that.
Sending spam does cost the spammer something, though. [2]
So the lower we can get the
response rate-- whether by filtering, or by using filters to force
spammers to dilute their pitches-- the fewer businesses will find it
worth their while to send spam.
The reason the spammers use the kinds of
sales
pitches
that they do is to increase response rates.
This is possibly even more disgusting
than getting inside the mind of a spammer,
but let's take a quick look inside the mind of someone
who
responds
to a spam.  This person is either
astonishingly credulous or deeply in denial about their
sexual interests.  In either case, repulsive or
idiotic as the spam seems to us, it is exciting
to them.  The spammers wouldn't say these things if they
didn't sound exciting.  And "thought you
should check out the following" is just not going to
have nearly the pull with the spam recipient as
the kinds of things that spammers say now.
Result: if it can't contain exciting sales pitches,
spam becomes less effective as a marketing vehicle,
and fewer businesses want to use it.
That is the big win in the end.  I started writing spam
filtering software because I didn't want have to look at
the stuff anymore.
But if we get good enough at filtering
out spam, it will stop working, and the spammers
will actually stop sending it.
_ _ _
Of all the approaches to fighting spam, from software to laws,
I believe Bayesian filtering will be the single most
effective.  But I also
think that the more different kinds of antispam efforts
we undertake, the better, because any measure that
constrains spammers will tend to make filtering easier.
And even within the world of content-based filtering, I think
it will be a good thing if there are many different kinds
of software being used simultaneously.  The more different
filters there are, the harder it will be for
spammers to tune spams to get through them.
Appendix: Examples of Filtering
Here
is an example of a spam that arrived while I was writing
this article.  The fifteen most interesting words in this spam are:
qvp0045
indira
mx-05
intimail
$7500
freeyankeedom
cdo
bluefoxmedia
jpg
unsecured
platinum
3d0
qves
7c5
7c266675
The words are a mix of stuff from the headers and from the
message body, which is typical of spam.  Also typical of spam
is that every one of these words has a spam probability,
in my database, of .99.  In fact there are more than fifteen words
with probabilities of .99, and these are just the first
fifteen seen.
Unfortunately that makes this email a boring example of
the use of Bayes' Rule.  To see an interesting variety of
probabilities we have to look at
this
actually quite
atypical spam.
The fifteen most interesting words in this spam, with their probabilities,
are:
madam           0.99
promotion       0.99
republic        0.99
shortest        0.047225013
mandatory       0.047225013
standardization 0.07347802
sorry           0.08221981
supported       0.09019077
people's        0.09019077
enter           0.9075001
quality         0.8921298
organization    0.12454646
investment      0.8568143
very            0.14758544
valuable        0.82347786
This time the evidence is a mix of good and bad.  A word like
"shortest" is almost as much evidence for innocence as a
word like "madam" or "promotion" is for guilt.  But still the
case for guilt is stronger.  If you combine these numbers
according to Bayes' Rule, the resulting probability is .9027.
"Madam" is obviously from spams beginning
"Dear Sir or Madam."  They're not very common, but the
word "madam"
never
occurs in my legitimate email, and
it's all about the ratio.
"Republic" scores high because
it often shows up in Nigerian scam emails, and also occurs once
or twice in spams referring to Korea and South Africa.
You might say that it's
an accident that it thus helps identify this spam.  But I've
found when examining spam probabilities that there are
a lot of these accidents, and they have an uncanny tendency to
push things in the right direction rather than the wrong one.
In this case, it is not entirely a coincidence that the word
"Republic" occurs in Nigerian scam emails and this spam.
There is a whole class of dubious business propositions involving
less developed countries, and these in turn are more likely
to have names that specify explicitly (because they aren't) that they
are republics.[3]
On the other hand, "enter" is a genuine miss.  It occurs
mostly in unsubscribe instructions, but here is used in a
completely innocent way.  Fortunately the statistical approach is
fairly robust, and can tolerate quite a lot of misses
before the results start to be thrown off.
For comparison,
here
is an example of that rare bird, a spam that
gets through the filters.  Why?  Because by sheer chance it happens
to be loaded with words that occur in my actual email:
perl       0.01
python     0.01
tcl        0.01
scripting  0.01
morris     0.01
graham     0.01491078
guarantee  0.9762507
cgi        0.9734398
paul       0.027040077
quite      0.030676773
pop3       0.042199217
various    0.06080265
prices     0.9359873
managed    0.06451222
difficult  0.071706355
There are a couple pieces of good news here.  First, this mail
probably wouldn't get through the filters of someone who didn't
happen to specialize in programming languages and have a good
friend called Morris.  For the average user, all the top five words here
would be neutral and would not contribute to the spam probability.
Second, I think filtering based on word pairs
(see below) might well
catch this one:  "cost effective", "setup fee", "money back" -- pretty
incriminating stuff.  And of course if they continued to spam me
(or a network I was part of), "Hostex" itself would be
recognized as  a spam term.
Finally,
here
is an innocent email.
Its  fifteen most interesting words are as follows:
continuation  0.01
describe      0.01
continuations 0.01
example       0.033600237
programming   0.05214485
i'm           0.055427782
examples      0.07972858
color         0.9189189
localhost     0.09883721
hi            0.116539136
california    0.84421706
same          0.15981844
spot          0.1654587
us-ascii      0.16804294
what          0.19212411
Most of the words here indicate the mail is an innocent one.
There are two bad smelling words,  "color"
(spammers love colored fonts) and "California"
(which occurs in testimonials and also in menus in
forms), but they are not enough to outweigh obviously
innocent words like "continuation" and "example".
It's interesting that "describe" rates as so thoroughly
innocent.  It hasn't occurred in a
single one of my 4000 spams.  The data turns out to be
full of such surprises.  One of the things you learn
when you analyze spam texts is how
narrow a subset of the language spammers operate in.  It's
that fact, together with the equally characteristic vocabulary
of any individual user's mail, that makes Bayesian filtering
a good bet.
Appendix: More Ideas
One idea that I haven't tried yet is to filter based on
word pairs, or even triples, rather than individual words.
This should yield a much sharper estimate of the probability.
For example, in my current database, the word "offers"
has a probability of .96.  If you based the probabilities
on word pairs, you'd end up with "special offers"
and "valuable offers" having probabilities of .99
and, say, "approach offers" (as in "this approach offers")
having a probability of .1 or less.
The reason I haven't done this is that filtering based on
individual words already works so well.  But it does
mean that there is room to tighten the filters if spam
gets harder to detect.
(Curiously, a filter based on word pairs would be
in effect a Markov-chaining text generator running
in reverse.)
Specific spam features (e.g. not seeing the recipient's
address in the to: field) do of course have value in
recognizing spam.  They can be considered in this
algorithm by treating them as virtual words.  I'll probably
do this in future versions, at least for a handful of the
most egregious spam indicators. Feature-recognizing
spam filters are right in many details; what they lack
is an overall discipline for combining evidence.
Recognizing nonspam features may be more important than
recognizing spam features.  False positives are such a
worry that they demand extraordinary measures.  I will
probably in future versions add a second level of testing
designed specifically to avoid false positives.  If a
mail triggers this second level of filters it will be accepted
even if its spam probability is above the threshold.
I don't expect this second level of filtering to be Bayesian.
It will inevitably
be not only ad hoc, but based on guesses, because the number of
false positives will not tend to be large enough to notice patterns.
(It is just as well, anyway, if a backup system doesn't rely on the same
technology as the primary system.)
Another thing I may try in the future is to focus extra attention
on specific parts of the email.  For example, about 95% of current
spam includes the url of a site they want
you to visit.  (The remaining 5% want you to call a phone number,
reply by email or to a US mail address, or in a few
cases to buy a certain stock.)   The url is in such cases
practically enough by itself to determine whether the email
is spam.
Domain names differ from the rest of the text in
a (non-German) email in that they often consist of several
words stuck together.  Though computationally expensive
in the general case, it might be worth trying to
decompose them.  If a filter has never seen the
token "xxxporn" before it will have an individual spam
probability of .4, whereas "xxx" and "porn" individually
have probabilities (in my corpus) of .9889 and .99
respectively, and a combined probability of .9998.
I expect decomposing domain names to become more
important as spammers are gradually forced to stop using
incriminating words in the text of their messages.  (A url
with an ip address is of course an extremely incriminating sign,
except in the mail of a few sysadmins.)
It might be a good idea to have a cooperatively maintained
list of urls promoted by spammers.  We'd need a trust metric
of the type studied by Raph Levien to prevent malicious
or incompetent submissions, but if we had such a thing it
would provide a boost to any filtering software.   It would
also be a convenient basis for boycotts.
Another way to test dubious urls would be to send out a
crawler to look at the site before the user looked at the
email mentioning it.  You could use a Bayesian filter to
rate the site just as you would an email, and whatever
was found on the site could be included in calculating
the probability of the email being a spam.  A url that led
to a redirect would of course be especially suspicious.
One cooperative project that I think really would be a good
idea would be to accumulate a giant corpus of spam.  A large,
clean corpus is the key to making Bayesian filtering work
well.  Bayesian filters could actually use the corpus as
input.  But such a corpus would be useful for other kinds
of filters too, because it could be used to test them.
Creating such a corpus poses some technical problems.  We'd
need trust metrics to prevent malicious or incompetent
submissions, of course.  We'd also need ways of erasing
personal information (not just to-addresses and ccs, but
also e.g. the arguments to unsubscribe urls, which often
encode the to-address) from mails in the corpus.  If anyone
wants to take on this project, it would be a good thing for
the world.
Appendix: Defining Spam
I think there is a rough
consensus on what spam is, but it would be useful to have
an explicit definition.  We'll need to do this if we want to establish
a central corpus of spam, or even to compare spam filtering
rates meaningfully.
To start with, spam is not unsolicited commercial email.
If someone in my neighborhood heard that I was looking for an old
Raleigh three-speed in good condition, and sent me an email
offering to sell me one, I'd be delighted, and yet this
email would be both commercial and unsolicited.  The
defining feature of spam (in fact, its
raison d'etre
)
is not that it is unsolicited, but that it is automated.
It is merely incidental, too, that spam is usually commercial.
If someone started sending mass email to support some political
cause, for example, it would be just as much spam as email
promoting a porn site.
I propose we define spam as
unsolicited automated email
.
This definition thus includes some email
that many legal definitions of spam don't.  Legal definitions
of spam, influenced presumably by lobbyists, tend to exclude
mail sent by companies that have an "existing relationship" with
the recipient.  But buying something from a company, for
example, does not imply that you have solicited
ongoing email from them.
If I order something from an online
store, and they then send me a stream of spam, it's still
spam.
Companies sending spam often give you a way to "unsubscribe,"
or ask you to go to their site and change your "account
preferences" if you want to stop getting spam.  This is
not enough to stop the mail from being spam.  Not opting out
is not the same as opting in.  Unless the
recipient explicitly checked a clearly labelled box (whose
default was no) asking to receive the email, then it is spam.
In some business relationships, you do implicitly solicit
certain kinds of mail.   When you order online, I think you
implicitly solicit a receipt, and notification when the
order ships.
I don't mind when Verisign sends me mail warning that
a domain name is about to expire (at least, if they are the
actual
registrar
for it).  But when Verisign sends me
email offering a FREE Guide to Building My
E-Commerce Web Site, that's spam.
Notes:
[1] The examples in this article are translated
into Common Lisp for, believe it or not, greater accessibility.
The application described here is one that we wrote in order to
test a new Lisp dialect called
Arc
that is
not yet released.
[2] Currently the lowest rate seems to be about $200 to send a million spams.
That's very cheap, 1/50th of a cent per spam.
But filtering out 95%
of spam, for example, would increase the spammers' cost to reach
a given audience by a factor of 20.  Few can have
margins big enough to absorb that.
[3] As a rule of thumb, the more qualifiers there are before the
name of a country, the more corrupt the rulers.  A
country called The Socialist People's Democratic Republic
of X is probably the last place in the world you'd want to live.
Thanks
to Sarah Harlin for reading drafts of this; Daniel Giffin (who is
also writing the production Arc interpreter) for several good ideas about
filtering and for creating our mail infrastructure; Robert Morris,
Trevor Blackwell and Erann Gat for many discussions about spam; Raph
Levien for advice about trust metrics;  and Chip Coldwell
and Sam Steingold for advice about statistics.
You'll find this essay and 14 others in
Hackers & Painters
.
More Info:
Plan for Spam FAQ
Better Bayesian Filtering
Filters that Fight Back
Will Filters Kill Spam?
Japanese Translation
Spanish Translation
Chinese Translation
Probability
Spam is Different
Filters vs. Blacklists
Trust Metrics
Filtering Research
Microsoft Patent
Slashdot Article
The Wrong Way
LWN: Filter Comparison
CRM114 gets 99.87%


@greybtc @JeffMcSweeny Pmarca has them. They just travel in eccentric orbits.

@ahistoryinart Lucioni's painting of his father. https://t.co/4ztkjG2YKZ

@elie2222 No, we were talking about organizations like the French resistance, which would attack the German occupiers and then blend back into the civilian population.

@elie2222 We were talking about WWII.

@chromatic_x They've all shifted dramatically to the left in the last decade.

@robkhenderson @JoHenrich If mama ain't happy ain't nobody happy.

@sfali789 It's precisely because the problem is so important that people who care about it should be disciplined enough to use tactics that work.

@chrisbest You don't get shadowbanned, but the tweet apparently doesn't get shown to people unless they look for it.

@kendrictonn It's for readers who've preserved their inner ten year old.

@paultoo Jessica and I still laugh about the way a famous politician mangled the name of Y Combinator when he met us, because it was such a weird name that it didn't survive this handoff.


The Airbnbs
December 2020
To celebrate Airbnb's IPO and to help future founders, I thought
it might be useful to explain what was special about Airbnb.
What was special about the Airbnbs was how earnest they were. They
did nothing half-way, and we could sense this even in the interview.
Sometimes after we interviewed a startup we'd be uncertain what to
do, and have to talk it over. Other times we'd just look at one
another and smile. The Airbnbs' interview was that kind. We didn't
even like the idea that much. Nor did users, at that stage; they
had no growth. But the founders seemed so full of energy that it
was impossible not to like them.
That first impression was not misleading. During the batch our
nickname for Brian Chesky was The Tasmanian Devil, because like the
cartoon
character
he seemed a tornado of energy. All three of them were
like that. No one ever worked harder during YC than the Airbnbs
did. When you talked to the Airbnbs, they took notes. If you suggested
an idea to them in office hours, the next time you talked to them
they'd not only have implemented it, but also implemented two new
ideas they had in the process. "They probably have the best attitude
of any startup we've funded" I wrote to Mike Arrington during the
batch.
They're still like that. Jessica and I had dinner with Brian in the
summer of 2018, just the three of us. By this point the company is
ten years old. He took a page of notes about ideas for new things
Airbnb could do.
What we didn't realize when we first met Brian and Joe and Nate was
that Airbnb was on its last legs. After working on the company for
a year and getting no growth, they'd agreed to give it one last
shot. They'd try this Y Combinator thing, and if the company still
didn't take off, they'd give up.
Any normal person would have given up already. They'd been funding
the company with credit cards. They had a
binder
full of
credit cards they'd maxed out. Investors didn't think much of the
idea. One investor they met in a cafe walked out in the middle of
meeting with them. They thought he was going to the bathroom, but
he never came back. "He didn't even finish his smoothie," Brian
said. And now, in late 2008, it was the worst recession in decades.
The stock market was in free fall and wouldn't hit bottom for another
four months.
Why hadn't they given up? This is a useful question to ask. People,
like matter, reveal their nature under extreme conditions. One thing
that's clear is that they weren't doing this just for the money.
As a money-making scheme, this was pretty lousy: a year's work and
all they had to show for it was a binder full of maxed-out credit
cards. So why were they still working on this startup? Because of
the experience they'd had as the first hosts.
When they first tried renting out airbeds on their floor during a
design convention, all they were hoping for was to make enough money
to pay their rent that month. But something surprising happened:
they enjoyed having those first three guests staying with them. And
the guests enjoyed it too. Both they and the guests had done it
because they were in a sense forced to, and yet they'd all had a
great experience. Clearly there was something new here: for hosts,
a new way to make money that had literally been right under their
noses, and for guests, a new way to travel that was in many ways
better than hotels.
That experience was why the Airbnbs didn't give up. They knew they'd
discovered something. They'd seen a glimpse of the future, and they
couldn't let it go.
They knew that once people tried staying in what is now called "an
airbnb," they would also realize that this was the future. But only
if they tried it, and they weren't. That was the problem during Y
Combinator: to get growth started.
Airbnb's goal during YC was to reach what we call
ramen profitability
,
which means making enough money that the company can pay the founders'
living expenses, if they live on ramen noodles. Ramen profitability
is not, obviously, the end goal of any startup, but it's the most
important threshold on the way, because this is the point where
you're airborne. This is the point where you no longer need investors'
permission to continue existing. For the Airbnbs, ramen profitability
was $4000 a month: $3500 for rent, and $500 for food. They taped
this goal to the mirror in the bathroom of their apartment.
The way to get growth started in something like Airbnb is to focus
on the hottest subset of the market. If you can get growth started
there, it will spread to the rest. When I asked the Airbnbs where
there was most demand, they knew from searches: New York City. So
they focused on New York. They went there
in person
to visit their
hosts and help them make their listings more attractive. A big part
of that was better pictures. So Joe and Brian rented a professional
camera and took pictures of the hosts' places themselves.
This didn't just make the listings better. It also taught them about
their hosts. When they came back from their first trip to New York,
I asked what they'd noticed about hosts that surprised them, and
they said the biggest surprise was how many of the hosts were in
the same position they'd been in: they needed this money to pay
their rent. This was, remember, the worst recession in decades, and
it had hit New York first. It definitely added to the Airbnbs' sense
of mission to feel that people needed them.
In late January 2009, about three weeks into Y Combinator, their
efforts started to show results, and their numbers crept upward.
But it was hard to say for sure whether it was growth or just random
fluctuation. By February it was clear that it was real growth. They
made $460 in fees in the first week of February, $897 in the second,
and $1428 in the third. That was it: they were airborne. Brian sent
me an email on February 22 announcing that they were ramen profitable
and giving the last three weeks' numbers.
"I assume you know what you've now set yourself up for next week,"
I responded.
Brian's reply was seven words: "We are not going to slow down."


@AK_Bhavya You're even righter than you realize. This was Christmas break 83/84, and one of the presents I got that Christmas was a copy of Winston & Horn.

@kevincodex I think I'd just turned 19.

Me and my mother in 1983 or 84. https://t.co/oQffz4C3wm

@ESYudkowsky At least 60,000 people have been killed in Gaza so far. Presumably most of their families reacted this way. WTF is wrong with you that your reaction to a video like this is to suggest that it's fake? Scenes like this must play out every single day in Gaza.

@slavov_n No it doesn't. It's a subtle heuristic that people naturally tend to overlook.

@TeeFaircloth No it is not.

@JeffersonianAll Fusion startups, among others, are effectively working on this. If power gets cheap enough, DAC becomes practical.

@StefanFSchubert Experienced investors are pretty rational. But those are the ones you want anyway.

For example, I'm always looking for ways to add network effects to a startup's idea. Investors love those. But network effects are genuinely good to have. So any we come up with in order to feed to investors, they should probably go and make happen.

Figuring out what a startup should say to investors is strangely useful for figuring out what it should actually do. Most people treat these questions as separate, but ideally they converge. If you can cook up a plausible plan to become huge, you should go ahead and do it.


@jamescham Just some html. I always add that last.

@typingloudly Yes, always.

The version of Microsoft Word that 12 yo's school gave him to use has an automatic political correctness feature. He noticed it when he wrote "noblemen" and it suggested he say "nobility" instead. I advised him to make a point of always ignoring its suggestions.

The drafts of the essay I published today. This history is unusually messy. There's a gap while I went to California to meet the current YC batch. Then while I was there I heard the talk that made me write "Founder Mode." Plus I started over twice, most recently 4 days ago. https://t.co/E40LR6REL7

@hassan_abu34434 That would be a good idea if you can.

https://t.co/MQZdwjZTGc

@0xAyush1 Ridiculously long, because I went to California to meet the current YC batch in the middle of writing it, and while I was there I heard the talk that made me write "Founder Mode". So months, but most of that time I wasn't working on it.

When To Do What You Love: https://t.co/kMDf3RYhW9

@dannymoore_ni I'm not saying that he's not allowed to make this mistake, just that it was one.

Renaming Twitter X doesn't seem to have damaged it. But it doesn't seem to have helped it either. So it was a waste of time and a domain name.

@nw3 The things young people are most in need of are money, wisdom, and encouragement. Which come to think of it are exactly what YC provides.


Startups in 13 Sentences
Want to start a startup?
Get funded by
Y Combinator
.
Watch how this essay was
written
.
February 2009
One of the things I always tell startups is a principle I learned
from Paul Buchheit: it's better to make a few people really happy
than to make a lot of people semi-happy.  I was saying recently to
a reporter that if I could only tell startups 10 things, this would
be one of them.  Then I thought: what would the other 9 be?
When I made the list there turned out to be 13:
1. Pick good cofounders.
Cofounders are for a startup what location is for real estate.  You
can change anything about a house except where it is.  In a startup
you can change your idea easily, but changing your cofounders is
hard.
[
1
]
And the success of a startup is almost always a function
of its founders.
2. Launch fast.
The reason to launch fast is not so much that it's critical to get
your product to market early, but that you haven't really started
working on it till you've launched.  Launching teaches you what you
should have been building.  Till you know that you're wasting your
time.  So the main value of whatever you launch with is as a pretext
for engaging users.
3. Let your idea evolve.
This is the second half of launching fast. Launch fast and iterate.
It's a big mistake to treat a startup as if it were merely a matter
of implementing some brilliant initial idea. As in an essay, most
of the ideas appear in the implementing.
4. Understand your users.
You can envision the wealth created by a startup as a rectangle,
where one side is the number of users and the other is how much you
improve their lives.
[
2
]
The second dimension is the one you have
most control over.  And indeed, the growth in the first will be
driven by how well you do in the second.  As in science, the hard
part is not answering questions but asking them: the hard part is
seeing something new that users lack. The better you understand
them the better the odds of doing that. That's why so many successful
startups make something the founders needed.
5. Better to make a few users love you than a lot ambivalent.
Ideally you want to make large numbers of users love you, but you
can't expect to hit that right away.  Initially you have to choose
between satisfying all the needs of a subset of potential users,
or satisfying a subset of the needs of all potential users.  Take
the first. It's easier to expand userwise than satisfactionwise.
And perhaps more importantly, it's harder to lie to yourself.  If
you think you're 85% of the way to a great product, how do you know
it's not 70%?  Or 10%?  Whereas it's easy to know how many users
you have.
6. Offer surprisingly good customer service.
Customers are used to being maltreated.  Most of the companies they
deal with are quasi-monopolies that get away with atrocious customer
service. Your own ideas about what's possible have been unconsciously
lowered by such experiences.  Try making your customer service not
merely good, but
surprisingly good
.  Go out of your way to make
people happy.  They'll be overwhelmed; you'll see.  In the earliest
stages of a startup, it pays to offer customer service on a level
that wouldn't scale, because it's a way of learning about your
users.
7. You make what you measure.
I learned this one from Joe Kraus.
[
3
]
Merely measuring something
has an uncanny tendency to improve it.  If you want to make your
user numbers go up, put a big piece of paper on your wall and every
day plot the number of users.  You'll be delighted when it goes up
and disappointed when it goes down.  Pretty soon you'll start
noticing what makes the number go up, and you'll start to do more
of that.  Corollary: be careful what you measure.
8. Spend little.
I can't emphasize enough how important it is for a startup to be cheap.
Most startups fail before they make something people want, and the
most common form of failure is running out of money.  So being cheap
is (almost) interchangeable with iterating rapidly.
[
4
]
But it's
more than that.  A culture of cheapness keeps companies young in
something like the way exercise keeps people young.
9. Get ramen profitable.
"Ramen profitable" means a startup makes just enough to pay the
founders' living expenses.  It's not rapid prototyping for business
models (though it can be), but more a way of hacking the investment
process.  Once you cross over into ramen profitable, it completely
changes your relationship with investors.  It's also great for
morale.
10. Avoid distractions.
Nothing kills startups like distractions.  The worst type are those
that pay money: day jobs, consulting, profitable side-projects.
The startup may have more long-term potential, but you'll always
interrupt working on it to answer calls from people paying you now.
Paradoxically,
fundraising
is this type of distraction, so try to
minimize that too.
11. Don't get demoralized.
Though the immediate cause of death in a startup tends to be running
out of money, the underlying cause is usually lack of focus.  Either
the company is run by stupid people (which can't be fixed with
advice) or the people are smart but got demoralized.  Starting a
startup is a huge moral weight.  Understand this and make a conscious
effort not to be ground down by it, just as you'd be careful to
bend at the knees when picking up a heavy box.
12. Don't give up.
Even if you get demoralized,
don't give up
.  You can get surprisingly
far by just not giving up.  This isn't true in all fields.  There
are a lot of people who couldn't become good mathematicians no
matter how long they persisted.  But startups aren't like that.
Sheer effort is usually enough, so long as you keep morphing your
idea.
13. Deals fall through.
One of the most useful skills we learned from Viaweb was not getting
our hopes up.  We probably had 20 deals of various types fall
through.  After the first 10 or so we learned to treat deals as
background processes that we should ignore till they terminated.
It's very dangerous to morale to start to depend on deals closing,
not just because they so often don't, but because it makes them
less likely to.
Having gotten it down to 13 sentences, I asked myself which I'd
choose if I could only keep one.
Understand your users.  That's the key.  The essential task in a
startup is to create wealth; the dimension of wealth you have most
control over is how much you improve users' lives; and the hardest
part of that is knowing what to make for them.  Once you know what
to make, it's mere effort to make it, and most decent hackers are
capable of that.
Understanding your users is part of half the principles in this
list.  That's the reason to launch early, to understand your users.
Evolving your idea is the embodiment of understanding your users.
Understanding your users well will tend to push you toward making
something that makes a few people deeply happy.  The most important
reason for having surprisingly good customer service is that it
helps you understand your users.  And understanding your users will
even ensure your morale, because when everything else is collapsing
around you, having just ten users who love you will keep you going.
Notes
[
1
]
Strictly speaking it's impossible without a time machine.
[
2
]
In practice it's more like a ragged comb.
[
3
]
Joe thinks one of the founders of Hewlett Packard said it first,
but he doesn't remember which.
[
4
]
They'd be interchangeable if markets stood still.  Since they
don't, working twice as fast is better than having twice as much
time.
Turkish Translation
Spanish Translation
Bulgarian Translation
Japanese Translation
Persian Translation


Beyond Smart
October 2021
If you asked people what was special about Einstein, most would say
that he was really smart. Even the ones who tried to give you a
more sophisticated-sounding answer would probably think this first.
Till a few years ago I would have given the same answer myself. But
that wasn't what was special about Einstein. What was special about
him was that he had important new ideas. Being very smart was a
necessary precondition for having those ideas, but the two are not
identical.
It may seem a hair-splitting distinction to point out that intelligence
and its consequences are not identical, but it isn't. There's a big
gap between them. Anyone who's spent time around universities and
research labs knows how big. There are a lot of genuinely smart
people who don't achieve very much.
I grew up thinking that being smart was the thing most to be desired.
Perhaps you did too. But I bet it's not what you really want. Imagine
you had a choice between being really smart but discovering nothing
new, and being less smart but discovering lots of new ideas. Surely
you'd take the latter. I would. The choice makes me uncomfortable,
but when you see the two options laid out explicitly like that,
it's obvious which is better.
The reason the choice makes me uncomfortable is that being smart
still feels like the thing that matters, even though I know
intellectually that it isn't. I spent so many years thinking it
was. The circumstances of childhood are a perfect storm for fostering
this illusion. Intelligence is much easier to measure than the value
of new ideas, and you're constantly being judged by it. Whereas
even the kids who will ultimately discover new things aren't usually
discovering them yet. For kids that way inclined, intelligence is
the only game in town.
There are more subtle reasons too, which persist long into adulthood.
Intelligence wins in conversation, and thus becomes the basis of
the dominance hierarchy.
[
1
]
Plus having new ideas is such a new
thing historically, and even now done by so few people, that society
hasn't yet assimilated the fact that this is the actual destination,
and intelligence merely a means to an end.
[
2
]
Why do so many smart people fail to discover anything new? Viewed
from that direction, the question seems a rather depressing one.
But there's another way to look at it that's not just more optimistic,
but more interesting as well. Clearly intelligence is not the only
ingredient in having new ideas. What are the other ingredients?
Are they things we could cultivate?
Because the trouble with intelligence, they say, is that it's mostly
inborn. The evidence for this seems fairly convincing, especially
considering that most of us don't want it to be true, and the
evidence thus has to face a stiff headwind. But I'm not going
to get into that question here, because it's the other ingredients
in new ideas that I care about, and it's clear that many of them
can be cultivated.
That means the truth is excitingly different from the story I got
as a kid. If intelligence is what matters, and also mostly inborn,
the natural consequence is a sort of
Brave New World
fatalism. The
best you can do is figure out what sort of work you have an "aptitude"
for, so that whatever intelligence you were born with will at least
be put to the best use, and then work as hard as you can at it.
Whereas if intelligence isn't what matters, but only one of several
ingredients in what does, and many of those aren't inborn, things
get more interesting. You have a lot more control, but the problem
of how to arrange your life becomes that much more complicated.
So what are the other ingredients in having new ideas? The fact
that I can even ask this question proves the point I raised earlier
— that society hasn't assimilated the fact that it's this and not
intelligence that matters. Otherwise we'd all know the answers
to such a fundamental question.
[
3
]
I'm not going to try to provide a complete catalogue of the other
ingredients here. This is the first time I've posed
the question to myself this way, and I think it may take a while
to answer. But I wrote recently about one of the most important:
an obsessive
interest
in a particular topic.
And this can definitely be cultivated.
Another quality you need in order to discover new ideas is
independent-mindedness
. I wouldn't want to
claim that this is
distinct from intelligence — I'd be reluctant to call someone smart
who wasn't independent-minded — but though largely inborn, this
quality seems to be something that can be cultivated to some extent.
There are general techniques for having new ideas — for example,
for working on your own
projects
and
for overcoming the obstacles you face with
early
work
— and these
can all be learned. Some of them can be learned by societies. And
there are also collections of techniques for generating specific types
of new ideas, like
startup ideas
and
essay topics
.
And of course there are a lot of fairly mundane ingredients in
discovering new ideas, like
working hard
,
getting enough sleep, avoiding certain
kinds of stress, having the right colleagues, and finding tricks
for working on what you want even when it's not what you're supposed
to be working on. Anything that prevents people from doing great
work has an inverse that helps them to. And this class of ingredients
is not as boring as it might seem at first. For example, having new
ideas is generally associated with youth. But perhaps it's not youth
per se that yields new ideas, but specific things that come with
youth, like good health and lack of responsibilities. Investigating
this might lead to strategies that will help people of any age to
have better ideas.
One of the most surprising ingredients in having new ideas is writing
ability. There's a class of new ideas that are best discovered by
writing essays and books. And that "by" is deliberate: you don't
think of the ideas first, and then merely write them down. There
is a kind of thinking that one does by writing, and if you're clumsy
at writing, or don't enjoy doing it, that will get in your way if
you try to do this kind of thinking.
[
4
]
I predict the gap between intelligence and new ideas will turn out
to be an interesting place. If we think of this gap merely as a measure
of unrealized potential, it becomes a sort of wasteland that we try to
hurry through with our eyes averted. But if we flip the question,
and start inquiring into the other ingredients in new ideas that
it implies must exist, we can mine this gap for discoveries about
discovery.
Notes
[
1
]
What wins in conversation depends on who with. It ranges from
mere aggressiveness at the bottom, through quick-wittedness in the
middle, to something closer to actual intelligence at the top,
though probably always with some component of quick-wittedness.
[
2
]
Just as intelligence isn't the only ingredient in having new
ideas, having new ideas isn't the only thing intelligence is useful
for. It's also useful, for example, in diagnosing problems and figuring
out how to fix them. Both overlap with having new ideas, but both
have an end that doesn't.
Those ways of using intelligence are much more common than having
new ideas. And in such cases intelligence is even harder to distinguish
from its consequences.
[
3
]
Some would attribute the difference between intelligence and
having new ideas to "creativity," but this doesn't seem a very
useful term. As well as being pretty vague, it's shifted half a frame
sideways from what we care about: it's neither separable from
intelligence, nor responsible for all the difference between
intelligence and having new ideas.
[
4
]
Curiously enough, this essay is an example. It started out
as an essay about writing ability. But when I came to the distinction
between intelligence and having new ideas, that seemed so much more
important that I turned the original essay inside out, making that
the topic and my original topic one of the points in it. As in many
other fields, that level of reworking is easier to contemplate once
you've had a lot of practice.
Thanks
to Trevor Blackwell, Patrick Collison, Jessica Livingston,
Robert Morris, Michael Nielsen, and Lisa Randall for reading drafts
of this.


On second thought it should obviously be: Yuge Leap Forward.

@larsbrinkhoff Can you still read that diskette?

@bscholl @boomsupersonic The worst case is actually 15 years but the Chinese do it instead.

@DudespostingWs Reserve.

We need an optimistic collective name for all the new Trump policies to help improve morale during the (not actually) temporary pain and destruction they'll cause.

How about "Great Leap Forward"?

"Every hardware startup's working capital needs just doubled."

— founder of a hardware startup

@TrodRoughshod That would be a reasonable objection if we were talking about a kind of satisfaction that didn't begin to converge long before the end.

@micsolana For someone on a visa, it's *much* safer in the UK than the US right now. The UK prosecutes people for saying e.g. that buildings housing immigrants should be burnt down. The US deports people for traffic tickets.

https://t.co/NOHIU79GCx

@swombat @Nouriel No I'm not. I merely said that they'd have an advantage in recruiting. I didn't say that would outweigh all the disadvantages.

@Signalman23 @amasad Successful founders are already anomalies of a kind much rarer.


@ronawang Only a tiny fraction of people are suited to running a startup. The only way to know for sure if you are is to try it. YC has always known that a lot of people it funded would decide startups weren't for them. It's baked into all our assumptions.

@signulll It's equally strong, but not stronger. No one keeps track of who graduated.

@robinhanson Go down to one of the replies and mute will appear on the menu.

@DaisyWittDog You're not doing a great job of proving me wrong about the inattentiveness.

@geoffsakala Can you see the rest of this thread? Can you read dates?

@wtgowers Lots of machines don't need human users now.

@nw3 No, just advanced search with date ranges.

@jeazous Possibly because David Sacks told his followers I had, and a lot of them were stupid enough to believe him. Or mere inattentiveness. Or simply that some of them were 10 in 2015.

There are probably a lot more. This is just what I found with a few minutes of searching. (You can't find old tweets about wokeness by searching for "woke" or "wokeness" because the pejorative sense didn't become the dominant one till the 2020s.)

There's a weirdly large group of (obviously inattentive) people on the right who think I just started talking about the dangers of wokeness recently. I finally got annoyed enough to dig up a few receipts for them.