I recently added the ability to set individual pixels in Amino, my Node JS based OpenGL scene graph for the Raspberry Pi. To test it out I thought I'd write a simple Mandlebrot generator. The challenge with CPU intensive work is that Node only has one thread. If you block that thread your UI stops. Dead. To solve this we need a background processing solution.

A Simple Background Processing Framework

While there are true threading libraries for Node, the simplest way to put something into the background is to start another Node process. It may seem like starting a process is heavyweight compared to a thread in other languages, but if you are doing something CPU intensive the cost of the exec() call is tiny compared to the rest of the work you are doing. It will be lost in the noise.

To be really useful, we don't want to just start a child process, but actually communicate with it to give it work. The childprocess module makes this very easy. childprocess.fork() takes the path to another script file and returns an event emitter. We can send messages to the child through this emitter and listen for responses. Here's a simple class I created called Workman to manage the process.

var Workman = {
    count: 4,
    init: function(chpath, cb, count) {
        if(typeof count == 'number') this.count = count;
        console.log("using thread count", this.count);
        for(var i=0; i<this.count; i++) {
            this.chs[i] = child.fork(chpath);
    sendWork: function(msg) {

Workman creates count child processes, then saves them in the chs array. When you want to send some work to it, call the sendWork function. This will send the message to one of the children, round robin style.

Whenever a child sends an event back, the event will be handed to the callback passed to the workman.init() function.

Now that we can talk to the child processes it's time to do some drawing.

Parent Process

This is the code to actually talk to the screen. First the setup. pv is a new PixelView object. A PixelView is like an image view, but you can set pixel values directly instead of using a texture from disk. w and h are the width and height of the texture in the GPU.

var pv = new amino.PixelView().pw(500).w(500).ph(500).h(500);

var w = pv.pw();
var h = pv.ph();

Now let's create a Workman to schedule the work. We will submit work for each row of the image. When work comes back from the child process the handleRow function will handle it.

var workman = Workman;
var scale = 0.01;
for(var y=0; y<h; y++) {
    var py = (y-h/2)*scale;
    var msg = {
        iw: w,
Notice that the work message must contain all of the information the child needs to do it's work: the start and end values in the x direction, the y value, the length of the row, the index of the row, and the number of iterations to do (more iterations makes the fractal more accurate but slower). This message is the only communication the child has from the outside world. Unlike with threads, child processes do not share memory with the parent.

Here is the handleRow function which receives the completed work (an array of iteration counts) and draws the row into the PixelView. After updating the pixels we have to call updateTexture to push the changes to the GPU and screen. lookupColor converts the iteration counts into a color using a look up table.

function handleRow(m) {
    var y = m.iy;
    for(var x=0; x<m.row.length; x++) {
         var c = lookupColor(m.row[x]);
var lut = [];
for(var i=0; i<10; i++) {
    var s = (255/10)*i;
function lookupColor(iter) {
    return lut[iter%lut.length];

Child Process

Now let's look at the child process. This is where the actual fractal calculations are done. It's your basic Mandelbrot. For each pixel in the row it calculates a complex number until the value exceeds 2 or it hits the maximum number of iterations. Then it stores the iteration count for that pixel in the row array.

function lerp(a,b,t) {
    return a + t*(b-a);
process.on('message', function(m) {
    var row = [];
    for(var i=0; i<m.iw; i++) {
        var x0 = lerp(m.x0, m.x1, i/m.iw);
        var y0 = m.y;
        var x = 0.0;
        var y = 0.0;
        var iteration = 0;
        var max_iteration = m.iter;
        while(x*x + y*y < 2*2 && iteration < max_iteration) {
            xtemp = x*x - y*y + x0;
            y = 2*x*y + y0;
            x = xtemp;
            iteration = iteration + 1;
        row[i] = iteration;

After every pixel in the row is complete it sends the row back to the parent. Notice that it also sends an iy value. Since the children could complete their work in any order (if one row happens to take longer than another), the iy value lets the parent know which row this result is for so that it will be drawn in the right place.

Also notice that all of the calculation happens in the message event handler. This will be called every time the parent process sends some work. The child process just waits for the next message. The beauty of this scheme is that Node handles any overflow or underflow of the work queue. If the parent sends a lot of work requests at once they will stay in the queue until the child takes them out. If there is no work then the child will automatically wait until there is. Easy-peasy.

Here's what it looks like running on my Mac. Yes, Amino runs on Mac as well as Linux. I mainly talk about the Raspberry Pi because that's Amino's sweet spot, but it will run on almost anything. I chose Mac for this demo simply because I've got 4 cores there and only 1 on my Raspberry Pi. It just looks cooler to have for bars spiking up. :)


This code is now in the aminogfx repository under demos/pixels/mandle.js.

A post about Arthur Whitney and kOS made the rounds a few days ago. It concerns a text editor Arthur made with four lines of K code, and a complete operating system he’s working on. These were all built in K, a vector oriented programming language derived from APL. This reminded me that I really need to look at APL after all of the language ranting I’ve done recently.

Note: For the purposes of this post I’m lumping K, J, and the other APL derived languages in with APL itself, much as I’d refer to Scheme or Clojure as Lisps.

After reading up, I’m quite impressed with APL. I’ve always heard it can do complex tasks in a fraction of the code as other languages, and be super fast. It turns out this is very true. Bernard Legrand's APL – a Glimpse of Heaven provides a great overview of the language and why it’s interesting.

APL is not without it’s problems, however. The syntax is very hard to read. I’m sure it becomes easier once you get used to it, but I still spent a lot more time analyzing a single line of code than I would in any another language.

APL is fast and compact for some tasks, but not others. Fundamentally it’s a bunch of operations that work on arrays. If your problem can be phrased in terms of array operations then this is awesome. If it can’t then you start fighting the language and eventually it bites you.

I found anything with control structures to be cumbersome. This isn’t to say that APL can’t do things that require an if statement, but you don’t get the benefits. This code to compute a convex hull, for example, seems about as long as it would be in a more traditional language. With a factor of 2, at least. It doesn’t benefit much from APL’s strengths.

Another challenge is that the official syntax uses non-ASCII characters. I actually don’t see this as a problem. We are a decade and a half into the 21st century and can deal with non-ASCII characters quite easily. The challenge is that the symbols themselves to most people. I didn’t find it hard to pick up the basics after reading a half hour tutorial, so I think the real problem is that the syntax scares programmers away before they ever try it.

I also think enthusiasts focus on how much better APL is than other languages, rather than simply showing someone why they should spend the time to learn it. They need to show what it can do that is also practical. While it’s cool to be able to calculate all of the primes from 1 to N in just a few characters, that isn’t going to sell most developers because that’s not a task they actually need to accomplish very often.

APL seems ideal for solving mathematical problems, or at least a good subset of them. The problem for APL is that Mathematica, MathLab, and various other tools have sprung up to do that better.

Much like Lisp, APL seems stuck between the past and the future. The things it’s really good at it is too general for. More recent specialized tools to the job better. APL isn't general enough to be good as a general purpose language. And many general purpose languages have added array processing support (often through libraries) that make them good enough for the things APL is good at. Java 8 streams and lambda functions, for example. Thus it remains stuck in a few niches like high speed finance. This is not a bad niche to be in (highly profitable, I’m sure) but APL will never become widely used.

That said, I really like APL for the things it’s good at. I wish APL could be embedded in a more general purpose language, much like regular expressions are embedded in JavaScript. I love the concept of a small number of functions that can be combined to do amazing things with arrays. This is the most important part of APL — for me at least — but it’s hidden behind a difficult notation.

I buy the argument that any notation is hard to understand until you learn it, and with learning comes power. Certainly this is true for reading prose.

Humans are good pattern recognizers. We don’t read by parsing letters. Only children just learning to read go letter by letter. The letters form patterns, called words, that our brains recognize in their entirety. After a while children's brains pick up the patterns and process them whole. In fact our brains are so good at picking up patterns that we can read most English words with all of the letters scrambled as long as the first and last letters are correct.

I’m sure this principle of pattern recognition applies to an experienced APL programmer as well. They can probably look at this


and think: pick six random numbers from 1 to 40 and return them in ascending order.

After a time this mental processing would become natural. However, much like with writing, code needs spacing and punctuation to help the symbolic "letters" form words in the mind of the programmer. Simply pursuing compactness for the sake of "mad skillz props" doesn’t help anyone. It just makes for write-only code.

Were I to reinvent computing (in my fictional JoshTrek show where the computer understands all spoken words with 200% accuracy), I would replace the symbols with actual meaningful words, then separate them into chunks with punctuation, much like sentences.


would become
deal 6 of 1 to 40 => x, sort_ascending, index x

The symbols are replaced with words and the ordering swapped, left to right. It still takes some training to understand what it means, but far less. It’s not as compact but far easier to pick up.

So, in summary, APL is cool and has a lot to teach us, but I don’t think I’d ever use it in my daily work.


Since writing this essay I discovered Q, also by Arthur Whitney, that expands K’s terse syntax, but I still find it harder to read than it should be.

I am unhappy to announce the release of Electron 0.4 beta 3.

What's that? unhappy?! Well......

I haven't done a release quite some time. Part of this delay is from a complete refactoring of the user interface; but another big chunk of time comes from trying to build Electron with Atom Shell.

AtomShell is a tool that bundles WebKit/Chromium and NodeJS into a single app bundle. This means developers can download a single app with an icon instead of running Electron from the command line. It might even let us put it into the various app stores some day.

Unfortunately, the switch to AtomShell hasn't been as smooth as I would like. The Mac version builds okay but I have yet to get Windows to work. There seems to be some conflict between the version of Node that the native serial port module uses and the version of Node inside of AtomShell. While I'm sure these are solvable problems I don't want to hold back the rest of Electron. It's still useful even if you have to launch it from the command line. So...

Electron 0.4 beta 3

You can download a Mac .app bundle from here, or check out the source and run node electron to start it from the command line. The new file browser works as do the various dialogs. Compiler output shows up in the debug panel. You can upload through the serial port but the serial port console is still disabled (due to other bugs I'm still working through).

Undoubtedly many things are still broken during the transition from the old UI to the new. Please, please, please file issues on github. I'll get to them ASAP.

Thanks, Josh

I have a problem. Sometimes I get something into my head and it sticks there, taunting me, until I do something about it. Much like the stupid song stuck in your brain, you must play the song to be released from it's grasp. So it is with software.

Last week I had to spend a lot of time in Windows working on a port of Electron. This means lots of Node scripts and Git on the command line.

Windows Pains

It may sound like it sometimes, but I really don't hate Windows. It's a fine GUI operating system but the command shell sucks. Really, really bad. Powershell is an improvement but still pretty bad. There has to be something better. I don't want to hate myself and throw my laptop across the room while coding. It dampens productivity. This blog was the result of that rage face. I tiny birdy told me things will get a lot better in Windows 10. I sure hope so.

In the past I would have used Cygwin, which is a port of Bash and a bunch of unix utilities. Sadly it never worked very well (getting POSIX compliant apps to run on Windows is just a big ball of pain) and support has dwindled in recent years.

Then something happened. After pondering for a while I realized I didn't actually care about having standard Unix utilities. Really I just want the Bash interface. I want a command line interpreter that has a proper history, tab completion, and directory navigation. I want ls and more and cd. I don't actually care if they are spec compliant and can be used in Bash shell scripts. I don't really care about shell scripts at all, since I write everything in Node now. I just want the interface.

I could make a new shell, something simple that would get the job done. Node is already ported to Windows, it's built around streams, and NPM gives me access to endless existing modules. That's 90% of the work already done. I just need to stitch it together.


And so Photon was born.

Photon is about 250 lines of Javascript that give a command line with ls, cp, mv, rm, rmdir, mkdir, more, pwd, and the ability to call other programs like git. It has a very simple form of tab completion (rather buggy), and uses ANSI colors and tables for formatting. (For some reason there are approximately 4.8 billion ANSI color modules for Node).

All you need to do is npm install -g photonsh then photonsh to get this:

Photon Shell screenshot

Most features were trivial to implement. Here is the function for cp.

    cp: function(a,b) {
        if(!fs.existsSync(a))         return fileError("No such file: ",a);
        if(!fs.statSync(a).isFile())  return fileError("Not a file: ",a);
        var ip = fs.createReadStream(path.join(cwd,a));
        var op = fs.createWriteStream(path.join(cwd,b));

Pretty much exactly what you would expect. For the buffered editor with history I used Node's built in readline module which includes callbacks for tab completion.

The hard part

The grand irony here is that I wrote it because of my Windows pain but have yet to actually run it on Windows. I stopped that Windows porting effort for other reasons; so now I just have this program I randomly wrote. Rather than waste the man-months of effort (okay, it was really only about 3 hours), I figured something like this should be shared with the world so that others might learn from my mistakes.

Speaking of mistakes, Photon is horribly buggy and you probably shouldn't run it. No really, it could totally delete your hard drive and stuff. More importantly, Node TTY support is iffy. It turns out Unix shells are very hard to write because of lots of semi-documented assumptions. Go try to write Xterm sometime. There's a reason few people have done it.

In theory a unix shell is simple. You exec a program and pipe it's output to stdout until it's done. The same with input. But what about buffering? But what about ANSI codes? But what about raw keyboard input? Apparently there is a whole world of adhoc specs for how command line apps do 'interactive' things. Running grep from exec is easy. Running vim is not.

In the end I found pausing Node's own REPL interface then execing with the 'inherit' flag worked most of the time. I'm sure there's a better way to do it, but casual Googling with Bing hasn't found it yet.


So where does Photon go from here? I have no idea. There's tons of things you could do with it. Node can stream anything, so copying a remote URL to a local file should be trivial. Or you could build a text mode raytracer. Whatever. The choice is yours. Choose wisely. Or don't. The code will still be here (on github).


I need to move on to other projects so I’m wrapping up the rest of my ideas in this blog. Gotta get it outta my brainz first.

The key concept I’ve explored in this series is that the code you see in an editor need not be identical to what is stored on disk, or the same as what is sent to the compiler. If we relax this constraint then a world of opportunity opens up. We’ve been writing glorified text files for 40 years. We can do better. Let’s explore.


Why can’t you name a variable for? Because in many common languages for is a reserved word. You, as the programmer, aren’t allowed to use for because it represent a particular loop construct. The underlying compiler doesn’t actually care of course. It doesn’t care about the name of any of your variables or other words in your code. The compiler just needs them to be unique symbols, some of which are mapped to existing constructs like conditionals and loops.

If the compiler doesn’t care then why can’t we do it? Because the parser (the ‘front end’ of the compiler) does care. The parser needs to unambiguously transform a stream of ASCII text into an abstract syntax tree. It’s the unambiguous part that’s the trouble. The syntax restrictions in most common languages are there to make the parser happy. If the parser was magical and could just "know what we meant" then any syntax could be used. Perhaps even syntax that made more sense to the human rather than the computer.

Fundamentally, this is what typographic programming does. It lets us tell the parser which text is what without using specific syntax rules. Instead we use color or font choices to indicate whether a given chunk of text is a variable or keyword or something else. Of course editing in such a system would be a pain, but we already know how to solve that problem. Graphical word processors are proof that it is possible. Before we get to how we solve it let us consider why. Would such a system have enough benefits to outweigh the cost of building it. What new things could we do?

Nothing’s reserved

If we use typography to indicate syntax, then keywords no longer need to be reserved. Any keyword could be used as a variable and any text string could be used as a keyword. You could swap for with fore or thusly. You could use spaces in keywords as for each of. These aren’t very useful examples but the compiler could easily handle them.

With the syntactic restrictions lifted we are free to explore new control flow constructs. How about forever to mean an infinite loop and 10 times for standard for fixed length loops? It’s all the same to the compiler but the human reading it would better understand the meaning.

Custom Operators

If nothing is reserved then user defined operators become easy. After all; what is an operator but a function with a single letter name from a restricted character set. In Python 4 + 5 is just sugar for add(4,5).

With no syntax rules anything could be an operator. Operators could have multiple letter names, or other symbols from the full unicode set. The only reason operators are given special treatment to begin with is because they represent functions which are so commonly used (like arithmetic) that we want a shorthand. With free syntax we can create a shorthand for the functions that are useful to the task at hand rather than the abstract general purpose tasks the language inventors imagined.

Let’s look at something more concrete. Using complex numbers and vectors is common in graphics programming, but we have to use clumsy and verbose syntax in most languages. This is sad. Mathematics already has invented compact notation for these concepts but we can’t use them due to ASCII limitations. Without these limitations we could add complex numbers with the plus sign like this:

A +B

instead of


To help the programmer remember these are complex numbers they could be rendered in a different color.

There are two ways to multiply vectors: the dot product and the cross product. They have very different meanings. With full unicode we could use the correct symbols like this:

A ⋅ B  // dot product
A ⨯ B // cross product

No ambiguity at all. It would be too much to expect a language to support every possible notation. Much better instead to have a language that lets the programmer create their own notation.

Customization in Practice

So how would this work in practice? At some point the code must be transformed into something the compiler understands. Let’s postulate a hypothetical language called X. X has no syntax rules, only the semantic rules of it’s AST. To tell the complier how to convert the code into the AST we must provide our own rules. Something like this.

fun => function
cross => cross
dot => dot
|x| => magnitude(x)

fun intersection(V,R) {
     return V dot R / |V|;

We have now defined a mini language in X which still compiles to the same syntactic structure.

Of course typing all of these rules in every file (or compilation unit) would be a huge pain, so we could include them much as we include external libraries.

@include math.rules

fun intersection(V,R) {
     return V dot R / |V|;

Most importantly, not only would the compiler understand these rules but so would the editor. The editor can now indicate that V ⋅ R is valid only if they are both vectors. It could enforce the rules from the rule file. Now our code is limited only by the imagination of our rule writers, not the fixed compiler.

In practice, were X to become popular, we would not see everyone making up their own rules. Instead usage would gather around a few popular rulesets much as JavaScript gathered around a few popular libraries like JQuery. We might call each of these rulesets dialects, each a particular flavor derived from the base X language. Custom DSLs would become trivial to implement. It would be common for developers to use one or two "standard" dialects for most of their code but use a special purpose dialect for a particular task.

The important thing here is that the language no longer has a fixed syntax. It can adapt and evolve as needed. All without changing the compiler.

How do you edit?

I hope I’ve convinced you that a flexible syntax delimited by typography is useful. Many common idioms like iteration, accumulation, and data structure traversals could be distilled to concise syntax. And if it has problems then we can tweak it.

There is one big problem though. How would you actually edit code like this?

Fortunately this problem has already been solved by graphical word processors. These tools use color, font, size, weight and spacing to distinguish one element from another. Choosing the mode for a variable is as simple as selecting it with the cursor and using a drop down.

Manually highlighting a entire page of code would quickly grow tedious, of course. For common operations, like declaring a variable, the programmer could type a special symbol like @. This tells the editor that the next letters are a variable name. The programmer ends it with @ or by pressing the spacebar or return key. This @ symbol doesn’t exist in the code. It is simply to indicate to the editor that the programmer wants to be in ‘variable’ mode. Once the mode is finished the @’s go away and the text is rendered with the ‘variable’ font. This is no different than using star word star to indicate bold in Markdown text. The stars never appear in the rendered text.

The choice of the @ symbol doesn't matter as long as it's easy with the user's native keyboard. @ is good for US keyboards. French or Russians might use something else.

Resolving Ambiguity

Even using manual markup might become tedious, though. Fortunately the editor can usually figure out the meaning of any given token by using the dialect rules. If the rules indicate that # equals division then the editor can just do the right thing. Using manual highlighting would only be necessary if the dialect itself introduces an ambiguity. (ex: # means division and also the start of a hex value)

What about multiplying vectors? You could type in either of the two proper symbols, but the average keyboard doesn’t support those directly. You’d have to memorize a unicode code point or use a floating dialog. Alternatively, we could use code completion. If you type * then the editor knows this must be either dot or cross product. It provides only those two choices in a drop down, much as we auto-complete method names today.

Using a syntax free language does not fully remove the need to resolve ambiguity, it just moves the resolution process to edit time rather than compile time. This is good. The human is present at edit time and can explain to the computer was is correct. The human is not there at compile time, so any ambiguity must result in an error that the human must come back and fix. Furthermore, resolving the ambiguity need only happen once, when the human types it, not every time the code is compiled. This will further reduce code regressions when other parts of the system change.

Undoubtedly we would discover more edge cases, but these are all solvable. Modern GUI word processors and spreadsheets prove this. A more challenging issue is version control.


Code changes over time. It must be versioned. I don’t know why it took 40 years for us to invent distributed version control systems like Git, but at least we have it now. It would be a shame to give that up just as we’ve gotten the world on board. The problem is Git and other VCSs don’t really understand code. They just understand text. There are really only two ways to solve this:

1) modify git, and the other tools around it (diff viewers, github’s website, etc.) to support binary diffs specific to our new system.

2) make the on disk format be pure text.

Clearly option 1 is a non-starter. One day, once language X takes over the world, we could ask the GitHub team to add support for X diffs, but that’s a long ways off. We have to start with option 2.

You might think I’m going back on what I said at the start. After all, I stated we should no longer be writing code as text on disk, but that is exactly what I am suggesting. What I don’t want is to store the same thing that we edit. From the VCS’s point of view the editor and visual representation are irrelevant. The only thing that matters is what is the file on disk. X needs a canonical on serialization format. Regardless of what tool you use to edit X, as long as it saves to the same format we are fine. This is no different than SQL or HTML. Everyone has their favorite tool, but they all write to the same format.

Canonical Serialization Format.

X’s serialization format should obviously be plain text. < 128bit ASCII would be fine, though I think we could handle UTF8 easily. Most modern diff tools can work with UTF8 cleanly, so Japanese comments and math symbols would come through just fine.

The X format should also be unambiguous. Variables are marked up explicitly as variables. Operators as operators. There should be no need for the parser to guess at anything or interpret syntax rules. We could use one of the many existing formats like JSON, XML, or even LaTex. It doesn’t really matter since humans will rarely need to look at them.

But.... since we are defining a new serialization format anyways, there are a few useful things we could add.

Code as Graph

Code is really just a graph. Graphs can be serialized in many ways. Rather than using function names inline they could be represented by identifiers which point to a lookup table. Then, if a function is renamed the code only changes in one place rather than at every point in the code where the function is used. This creates a semantic diff that the diff tool could render as ‘function Y renamed to Z’.

v467 = foo
v468 = bar
v469 = baz

fun v467 () {
return v468 + v469;

Semantic diff-ing could be very powerful. Any refactoring should be reducible to its essential meaning: moved X to a new class or extracted Y from Z. Whitespace changes would be ignored (or never stored in the first place). Commit messages could be context aware: changed X in the unit test for Y and added logging to Z. Our current tools just barely understand when a file has been renamed instead of deleted and a new one added. There’s a lot of room for innovation here.


I hope I’ve convinced you there is value in this approach. Building language X still won’t be easy. To be viable we have to make a compiler, useful dialect definitions, and a visual editor; all at the same time. That’s a lot of work before anyone else can use it. Building on top of existing tools like Eclipse or Atom.io would help, but I know it’s still a big hill to climb. Trust me. The view will be worth it.