chutsu

I feel the complexity is justified. One of the biggest gripes I have with markdown is that you never know whether your markdown implementation is github flavoured or some other implementation. Not to mention Sphinx checks your links / references to other pages exist and give you warnings if you don't have them.

I don't think file number / lines of code is a good measure, you can have perfectly readable code in a single file that is 1000s loc. There are plenty single header file libraries that are larger and are very useful. Perfect example is the stb libraries: https://github.com/nothings/stb very widely used in the C / C++ community.

To me the biggest value octave gives me is its ease of use. When you are trying to learn a new mathematical concept and are manipulating matrices and vectors, Octave/ Matlab is a breeze. Compared to other languages that require you to call the `dot()` function.

Additionally compared to python's matplotlib, Octave / Matlab's plotting syntax is much simpler. Not faster but its functional.

In short, its a great educational tool, prototyping mathematical / engineering models. Bad for production use where you are using the code base to actually solve an engineering / science problem.

Out of curiosity how are you developing Xwm? Do you write/compile/run in a docker image?

Still missing the ability to properly reference equations from text. e.g. `\refeq{}`.

"Assembly" as in if you have multiple parts in Fusion360 etc, you can select components and put them together by forming constraints. E.g. a screw into a mount hole with a nut on the other side. Expensive CAD software makes this relatively easy, you just select the objects and define the constraints. This is lacking in CAD software like FreeCAD (not very well supported), TinkerCad (where you have to create throw away objects), OpenScad is equally not easy, you have to manually position the objects and line them up in code to "assemble" them.

IMO, I think if OpenSCAD somehow had an assembly feature it would really make it more attractive for users who are programmers to switch from the likes of Fusion360 etc.

> why did they put 3 white dudes in charge of messing with someone's life

Maybe because they earnt their position through merit? Maybe because white males are still a majority of the work force in Google, Facebook, and may more? The probability of picking 3 random senior people who are white are not that slim.

I as an Asian man take issue that just because you see white people in the show and automatically assume what the show has to say is not representative of whats happening in the Ad-industry. You could literally replace any color of race and claim it is not representative and disregard the bigger message here. This is a classic reverse-racism.

What are you on about? The show is about how Apps are made addicitve regardless of race, why are you pulling the race card when the tech is indiscriminant? Its like saying I shouldn't watch a documentary on the NBA cause the documentary is not diverse enough, where are all the brown, white, yellow people at? xD

The field has not really been publicized that much, but it is by no means dead. Take the research done at Cornell for example, they are doing some really interesting work with Evolutionary Algorithms (https://www.youtube.com/watch?v=lMkHYE9-R0A), of which one PhD graduate (Michael Schmidt) has even started his own startup called Nutonian (http://www.nutonian.com) where they have a large list of customers (check it out).

Part time scientists is a Lunar X Prize team, the software lead of that group has used Cartesian Genetic Programming to evolve programs to filter images. (https://www.youtube.com/watch?v=xQDazGrKsuM) and network protocols (https://www.youtube.com/watch?v=zoOOpiMJQ8s).

Agreed that Evolutionary Algorithms are taking a backseat at the moment, but you can argue that Neural Networks also was very dormant until some thing like Deep Neural Networks came out, which is essentially the same idea but with a new learning method (http://www.newyorker.com/news/news-desk/is-deep-learning-a-r...). I believe there will be a break through at some point, just don't think it is dead yet ;)

To quickly answer your question:

- GPs are normally used in problems where you don't know what the answer looks like, but you know it when you see it, and it is a Global Search algorithm. The biggest problem is the search space of which GPs are used is enormous, so it takes a long time to find a solution. In the practical world this isn't very practical because there are many other good enough approaches that give you approximately the same answer, but there are obviously edge cases (see point 3).

- Tuning GPs is also very difficult, there are simply too many control parameters such as population size, crossover rate, mutation rate, crossover method, mutation method, selection method, do you adjust the parameters on the fly or make it fixed from the beginning etc... you get the idea. These parameters play a role into how fast you might find the answer but there are no guarantees because of the randomness nature of the algorithm, example, one set of optimal parameters might be really bad the next run.

- In terms of application GP is actually being used in the industry, but you probably don't hear about it because it is either a trade secret or people want a more deterministic solution. One example is testing, a particular company uses it to test their compiler technology, instead of just having humans write test code to test every possible bug, GP can be used to create valid* programs that intentionally make the compiler fail. Others include the great work done at Nutonian (http://www.nutonian.com/), where they use GP to evolve equations out of data (aka Symbolic Regression).

Lastly, a shameless plug but if you want to play around with GPs I have written my own GP framework here: http://chutsu.github.io/playground/ (very much a work in progress)

Not sure Google would be able to index the docs, since they are in markdown format. But I'm not an expert in how Google index the pages.

Yep, essentially ditto. :)

I would like to add that nothing is technically generated* they. The markdown files are rendered on the fly using a Javascript Markdown parser. The layout is simply HTML5 and CSS, with a dash of jQuery to make the buttons work.

Sorry, got it.

... Atleast it doesn't clash with the established documentation system mdoc.

Sorry guys I had to change the name because mdoc is already used by other programs. I changed it to `ditto`.

New Link: http://chutsu.github.io/ditto/

alright, no need to be a smart arse, I wasn't aware of those tools.

[1] seems to be an AsciiDoc render on client-side, though have no idea if it's any good though...

[1]: http://asciidoctor.org/news/2013/05/21/asciidoctor-js-render...

It was simply the plain-text format I am currently using, I'm sure I can add in compatibility for AsciiDoc no probs. Just a matter of adding in an AsciiDoc renderer. :)

What you see in that link is* the generated doc. You set links in sidebar.md to docs you want to show, and when you click on it, it GETs the markdown file and renders it in the #content div.

Have a look here for another example of mdoc in action: http://chutsu.github.io/playground/