two inflammatory vignettes
The term to cargo cult is one with derogatory connotations: it indicates the act of imitating the superficial exterior without actually understanding the underlying causal structure of the situation. The implication is that one should try to understand what one is doing, before doing it. There is, however, an ounce of truth in the practice of cargo culting: when you are in a situation in which you legitimately do not know what’s going on (e.g. the context of an experiment), it is safest to preserve as many superficial traits as possible, in case a “superficial” trait in fact has a deep, non-obvious connection to the system being studied. But in this regard, beneficial “cargo culting” is nothing like the islanders throwing up airstrips in hopes of attracting planes—understanding what conditions are applicable for this treatment is often the mark of experience: the novice ignores conditions that should be preserved and does not know how to probe deeper.
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Most of my hacking cycles right now are going towards debugging the new code generator for GHC. The code generation stage of GHC takes the Spineless Tagless G-machine (STG) intermediate representation (IR) to the C– high-level assembly representation; the old code generator essentially performed this step in one big bang. The new code generator is many things. It is a more modular, understandable and flexible codebase. It is a client of cutting edge research in higher-order frameworks for control-flow optimization.
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In his book Against Method, Paul Feyerabend writes the following provocative passage about ‘ad hoc approximations’, familiar to anyone whose taken a physics course and thought, “Now where did they get that approximation from…”
The perihelion of Mercury moves along at a rate of about 5600" per century. Of this value, 5026" are geometric, having to do with the movement of the reference system, while 531" are dynamical, due to the perturbations in the solar system. Of these perturbations all but the famous 43" are accounted for by classical mechanics. This is how the situation is usually explained.
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When programmers automate something, we often want to go whole-hog and automate everything. But it’s good to remember there’s still a place for manual testing with machine assistance: instead of expending exponential effort to automate everything, automate the easy bits and hard-code answers to the hard research problems. When I was compiling the following graph of sources of test data, I noticed a striking polarization at the ends of “automated” and “non-automated.”
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To: Oliver Charles
Subject: [Haskell-cafe] Please review my Xapian foreign function interface
Thanks Oliver!
I haven’t had time to look at your bindings very closely, but I do have a few initial things to think about:
- You’re writing your imports by hand. Several other projects used to do this, and it’s a pain in the neck when you have hundreds of functions that you need to bind and you don’t quite do it all properly, and then you segfault because there was an API mismatch. Consider using a tool like c2hs which rules out this possibility (and reduces the code you need to write!)
- I see a lot of unsafePerformIO and no consideration for interruptibility or thread safety. People who use Haskell tend to expect their code to be thread-safe and interruptible, so we have high standards ;-) But even C++ code that looks thread safe may be mutating shared memory under the hood, so check carefully.
I use Sup, so I deal with Xapian on a day-to-day basis. Bindings are good to see.
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Checked exceptions are a much vilified feature of Java, despite theoretical reasons why it should be a really good idea. The tension is between these two lines of reasoning:
Well-written programs handle all possible edge-cases, working around them when possible and gracefully dying if not. It’s hard to keep track of all possible exceptions, so we should have the compiler help us out by letting us know when there is an edge-case that we’ve forgotten to handle. Thus, checked exceptions offer a mechanism of ensuring we’ve handled all of the edge-cases.
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Hoopl is a “higher order optimization library.” Why is it called “higher order?” Because all a user of Hoopl needs to do is write the various bits and pieces of an optimization, and Hoopl will glue it all together, the same way someone using a fold only needs to write the action of the function on one element, and the fold will glue it all together.
Unfortunately, if you’re not familiar with the structure of the problem that your higher order functions fit into, code written in this style can be a little incomprehensible. Fortunately, Hoopl’s two primary higher-order ingredients: transfer functions (which collect data about the program) and rewrite functions (which use the data to rewrite the program) are fairly easy to visualize.
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Guys, I have a secret to admit: I’m terrified of binomials. When I was in high school, I had a traumatic experience with Donald Knuth’s The Art of Computer Programming: yeah, that book that everyone recommends but no one has actually read. (That’s not actually true, but that subject is the topic for another blog post.) I wasn’t able to solve any recommended exercises in the mathematical first chapter nor was I well versed enough in computers to figure out what assembly languages were about. But probably the most traumatizing bit was Knuth’s extremely compact treatment of the mathematical identities in the first chapter we were expected memorize. As I would find out later in my mathematical career, it pays to convince yourself that a given statement is true before diving into the mess of algebraic manipulation in order to actually prove it.
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OpenIntents has a nifty application called SensorSimulator which allows you feed an Android application accelerometer, orientation and temperature sensor data. Unfortunately, it doesn’t work well on the newer Android 2.x series of devices. In particular:
- The mocked API presented to the user is different from the true API. This is due in part to the copies of the Sensor, SensorEvent and SensorEventHandler that the original code had in order to work around the fact that Android doesn’t have public constructors for these classes,
- Though the documentation claims “Whenever you are not connected to the simulator, you will get real device sensor data”, this is not actually the case: all of the code that interfaces with the real sensor system is commented out. So not only is are the APIs incompatible, you have to edit your code from one way to another when you want to vary testing. (The code also does a terrible job of handling the edge condition where you are not actually testing the application.)
Being rather displeased with this state of affairs, I decided to fix things up. With the power of Java reflection (cough cough) I switched the representation over to the true Android objects (effectively eliminating all overhead when the simulator is not connected.) Fortunately, Sensor and SensorEvent are small, data-oriented classes, so I don’t think I stepped on the internal representation too much, though the code will probably break horribly with future versions of the Android SDK. Perhaps I should suggest to upstream that they should make their constructors public.
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To: John Wellesz
First off, I’d like to thank you for authoring the php.vim indentation plugin. Recent experiences with some other indentation plugins made me realize how annoying editing can be without a good indentation plugin, and php.vim mostly has served me well over the years.
However, I do have a suggestion for the default behavior of PHP_autoformatcomment. When this option is enabled (as it is by default), it sets the ‘w’ format option, which performs paragraphing based off of trailing newlines. Unfortunately, this option has a number of adverse effects that may not be obvious unless you are paying attention to trailing newlines:
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