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/

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).

Here's a video of it 'evolving' a picture of John McCarthy (best individual from each generation): http://www.youtube.com/watch?v=-_VFZ_ON0A8

And here it is doing the Mona Lisa: http://www.youtube.com/watch?v=S1ZPSbImvFE

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 ...)

My hope is that it will only take a day or so to deploy it, once it's ready.

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).

Comments URL: https://news.ycombinator.com/item?id=4656299

Points: 7

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Comments URL: https://news.ycombinator.com/item?id=4654032

Points: 134

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For OS's, Tanenbaum (http://www.amazon.com/Modern-Operating-Systems-Andrew-Tanenb...) is popular.

'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 ;-)

I like to use HL Mencken as inspiration.

I wish I had kept in better touch with him. He will be missed.

Personally, I use (and prefer) LastPass to this approach.

Look at Norvig (Google's head of research, and AI-demigod) at al's paper "The Unreasonable Effectiveness of Data."

The first hit on searching for the quote explains its history: http://quoteinvestigator.com/2010/08/27/master/