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mayanklahiri

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When Python's symbolic computation abilities are even at a tenth of Mathematica's (especially for discrete functions like we love in computer science), I'd be more than happy to switch over. Perhaps someone could integrate Python with Sage or Octave.

Reducing a new problem to a special case of another, already solved problem is a wonderful skill. Not to mention that the analysis of algorithms can help you discern, at least conceptually, between best-case, average-case and worst-case outcomes to problems that aren't necessarily algorithmic, and react or plan appropriately.

> "Hacker"-types and early adopters are necessary for any product to gain any traction. They hold early influence (which obviously varies depending on the product) which diminishes as the product gains a higher adoption rate.

I guess then my question is whether "hackers" as an early-adopter group are worthwhile for targeting. Anyone have any anecdotes where a mainstream product started as a hacker niche?

This raises the larger question about how much influence hackers and developers really have in terms of product adoption. There seem to be two schools of thought:

(1) Hackers/nerds are early adopters. Thus, they will influence "normal" people to use products that they judge to be superior.

(2) Hackers/nerds are a tiny proportion of the population with no real influence. They might push esoteric but superior technologies, but large-scale adoption will ultimately be driven by how quickly cat pictures can be shared with the largest group of people.

The author of the article clearly believes in (2), but Apple seems to be making money off (1). So which is it? Or is this some sort of false dichotomy to begin with?

I replied to you via email, but thought I'd post it here as well in case anyone else had suggestions.

I've been thinking about a general purpose experimentation framework for research. Over the last 5 years, I've been re-writing parts of this framework for different projects and wish I had spent some time generalizing and packaging it.

The workflow is:

dataset -> experimental parameters -> code+libraries (this is where the trouble often is) -> result files (plain text, hundreds!) -> analysis (generally in R) -> selectively plotting results

This is fantastic. I haven't tried it out yet, but I'll give it a shot for the paper I'm writing.

BTW, even with a "uniform" Linux environment, I've been finding so many differences in the behavior of libraries that it's driving me nuts. This will be a nice way to get past, for example, inconsistencies in wxWidgets.

A while ago I wrote a short piece about how location-based services will be 'hackable' for a long time (the specific piece was about gaming Foursquare with nine lines of Perl). Given how easy it is to game any location checkin service, what incentive will advertisers have when their deals are monopolized by a small group of Perl+wget armed, obsessive compulsive script kiddies? I don't know enough about marketing numbers--presuming that vendors give out deals to bring customers back to their store, do they care if the deals are systematically monopolized by freeloaders or bargain hunters?

The blog post is a little light on details. Just go to the source and demo on the original creator's site: http://liuliu.me/detect/detect.html

Canvas allows access to individual pixels in an image, and JS is arguably faster than BASIC interpreters of the 80s. Fun things lie ahead in client-side image processing.

EDIT: Incidentally, the code is pretty hairy and has about 3 comments. It's hard to figure out what ML approach he's using from a cursory look.

EDIT: Aah, definitely an image pyramid in there, so some multi-resolution stuff happening.