Had to write my own bitmap processing library, since couldn't find anything fast enough off the shelf :-D Handled alpha blending, file i/o, etc (checkout the bitmap.lisp and color.lisp files in the repo).
She's often considered the face of the irrational exuberance behind the first dot com boom/bubble.
Rating many companies that subsequently lose 90-100% of their market cap as 'strong buys' is a pretty scary track record. IIRC, her portfolio of 'outperforms-or-better' lost ~80% of their value in one year.
Granted that the 'market can remain irrational longer than you can stay solvent', etc so she may not deserve all the scorn heaped on her (I haven't done the research to know ...)
If a lay person were to ask how they get back their dollars if they are stolen from their wallet, say due to a person mugging them, what do you tell them?
A disk seek (e.g., if you have more data than RAM and are using virtual memory) might take ~10ms. A packet round trip within the same AZ in EC2 is <0.5ms.
pg: I have fair bit of lisp dev experience. If, as a weekend project, I modified the HN src to use postgres and memcache would you consider using it in production? Obviously, I don't expect carte blanche prior agreement, but I wouldn't want to invest the time unless I thought it was plausible the work could actually help.
I would expect it to solve most of your performance problems for the foreseeable future (at the very least, by letting you scale horizontally and move the DB, frontends, and memcaches to separate boxes - plus ending memory leaks/etc by moving most of the data off the MzScheme heap).
The obvious downside is that it would use your (or someone at YC's) time. First to merge the changes I make to http://ycombinator.com/arc/arc3.tar into the production code, then to buy/setup some extra boxes and do the migration. We're probably talking, roughly, a day. It also has the unfortunate side effect of costing HN's src some of its pedagogical value, since it adds external dependencies and loses 'purity'.
Been looking for an excuse to learn arc for a while now ...
On a several decades/century scale it's worthwhile to funnel at least a few percent of GDP into basic science research[1]. But, in any given quarter/year, it's almost certainly a net loss. The trick being that every few decades, you'll get nuclear power, the transistor, etc[2].
Or, as a 'local' example: non-trivial number theory had basically no benefit for centuries but humanity kept 'investing' resources into it - which a short term optimizer wouldn't. Then, cryptography came along and it suddenly 'paid' for the entire field a dozen times over.
[1]: Research is sort of like early stage VC - but with funds that pay out over 70 years instead of ~7.
[2]: I would love to write about many more examples in much more depth, but will omit for the sake of brevity. I roughly feel like the newtonian mechanics was directly responsible for the industrial revolution, relativistic physics for the nuclear age, quantum mechanics for the computer age (with similar analogues in the biological sciences).
'Math' is broad - if I can recommend only one book to cover all of Math I'd probably say 'The Road to Reality' (http://www.amazon.com/The-Road-Reality-Complete-Universe/dp/...). More practically (for the subset of math most programmers are likely to care about), you'll do fine with one good discrete math book and one linear algebra book. Throw in one each on Stats, Abstract Algebra, Calc (up to ~diffeq), and Real Analysis (in roughly that order) if you're a bit more ambitious ;-)
Dan was awesome. A few years ago, when I was an undergrad trying to raise money for my first startup in Boston, he was a huge help. Took the time to grab a couple meals with me and explain how things worked, introduced me to angels, and was really insightful. He had no expectation of anything in return - just gave freely of his time and experience.
I wish I had kept in better touch with him. He will be missed.
Actually, Google Translate was an enormous leap forward in machine translation quality. It won a number of awards for its astonishingly good performance. And its design premise is that you can use really simple algorithms if you have crazy amounts of training data (a then controversial approach called 'statistical machine translation' - as opposed to rule/grammar based).
Look at Norvig (Google's head of research, and AI-demigod) at al's paper "The Unreasonable Effectiveness of Data."
tip: the original meaning of a word doesn't necessarily have any relation to it's current meaning. I assume you didn't know about the etymological fallacy (http://en.wikipedia.org/wiki/Etymological_fallacy) when you used it. Now you know!
Seriously, stop being overly PC. You're just being annoying.
http://nosql.mypopescu.com/
http://lambda-the-ultimate.org/
http://eli.thegreenplace.net/
http://www.stefankrause.net/wp/
http://mechanical-sympathy.blogspot.com/
http://www.cowtowncoder.com/blog/blog.html
http://highlyscalable.wordpress.com/
http://hunch.net/
http://perspectives.mvdirona.com/
http://www.scottaaronson.com/blog/
http://www.softwarebyrob.com/
http://blog.mikemccandless.com/