It's very trendy to bash at Computer Science degrees saying that it costs a lot of time and money and at the end, you haven't learned much useful things for your day job. My experience going to EPITA, a French school in Paris, has been the complete opposite!

I started programming when I was around 10. By 13, I was already being contracted for real money by someone in the US (Hi Thott!). So, when I started EPITA at 18, I already had a ton of experience as a self-taught programmer. My biggest fear was: "Would I learn something new?" and, I did learn a ton! I know wouldn't have done all the impactful things I have at Facebook without it.

What struck me was that I already knew a lot and I knew there was a lot left to learn, but I didn't really know what nor how to get started learning it on my own.

Big Themes

Here are some themes where I learned entirely new domains of knowledge at school, many of which I would likely not have learned, or at least not as in depth if I didn't do a CS degree.

  • C: I've written a ton of C at school and it was super annoying but learning how to do manual memory management, use pointers and have raw access to memory was a huge breakthrough in how I understood how code was actually running.
  • Data Structures and Algorithms. I had 6 hours of courses per day for 2 years. There's so much to learn! What's interesting is that you don't need any of it to build what you want, but once you hit scale and you want to optimize and maintain it, knowing about all of them become critical!
  • OCaml: The whole idea of manipulating immutable lists via recursion to actually do something useful at the end was very weird. Even though I still despise this style of programming, there are good ideas behind it and React is a good example of a practical system that makes use of them.
  • Source Control: I used to change index2.php on the production machine and cp it to index.php using sftp when I was happy with my changes... for a website with 1 million unique visitors a day... It was fun and scary, but I wouldn't go back to that in a million years!
  • Assembly: Learning how a computer works from nand gates all the way to assembly was really mind blowing. There are so many levels of abstractions happening between the hardware and some piece of JavaScript that executes in the browser that it's hard to believe it works and isn't crazy slow.
  • Machine Learning (it wasn't hype at the time) is really not that magical. It's a bunch of heuristics in order to massage the data in a way that it can be separated by a line. It also made me happy that finally all the linear algebra I've learned for years was useful to something. (Math was also useful for all the signal processing stuff with fourrier transform!)
  • Academia. My theory is that nothing is new, everything has already been explored but because people there insist on using latex with maths symbols everywhere and publish to closed platforms, it doesn't get the reach it deserves.
  • Tradeoffs. If I were to summarize all the learnings, it's probably that there is not one perfect solution. There's usually a bunch of solutions with different sets of tradeoffs and you need to find the least shitty one. It was a sad realization but gave me a much better framework to work with.

Specific School Projects

What was awesome about EPITA is that not only we had classes like any normal school but also a ton of actual non-trivial projects to implement. Here are some that had a profound impact on me:

  • Reimplementing Warcraft 3 from scratch. I learned so much about parsing (and reverse engineering) data structures, the whole 3D rendering space... But also having a project with a team of 4 people for an entire year and how to sell. It was a pretty awesome first year project 🙂
  • Reimplementing malloc from scratch (btw, printf uses malloc which makes debugging interesting...). Learned so much about memory management and running firefox using my malloc was so awesome!
  • Reimplementing bash from scratch. I learned a ton about how to parse and execute programming languages and how crazy the unix low level apis are. I'm still using this knowledge today when writing scripts thanks to this project.
  • Implementing a regex execution using OCaml. OCaml is --really-- good for working with well defined data structures and algorithms (I have big reservations for anything else outside of this narrow scope...). Since I'm using regexes almost on a daily basis, it's been super helpful.
  • We had 4-hour machine exams every couple of weeks for a year where you basically have to solve as many interview-style problems as possible. This was so much fun and helped to land a job at Facebook.
  • Implement the fastest possible fuzzy finder. I learned so much about orders of magnitude in perf: just opening the file in JavaScript took way too long, C is faster... malloc is really slow, custom bump allocator is better. Smaller structs is better, bit packing ftw. Pre-computing data for heuristics can dramatically help.

And many more... but that's a good enough list for now!

Actual Use Cases

Now, the big question is: but did you use any of that at work? And the answer is yes, so many times!

  • Small thing but when building our open source websites, I wanted to only write next: page in the markdown and not have to have to write prev nor have a global index and yet have an table of content. It took me a second to realize that I had a list of edges of a graph and I needed to rebuild a graph from it. Because I've written so many graph traversals at school, it took me under an hour to build it correctly.
  • EPITA enforced strict lint rules where for each violation you'd lose 2 points (out of 20) for all your assignments. The trick is that you were only given a pdf with the rules and no program to verify them, so you had to learn them the hard way. This motivated me years later to work on prettier so that we can have the benefits of a consistent codebase, the less hardcore way...
  • When I was pitched React for the first time, it sounded to me crazy that re-rendering everything could be fast, but once I was proved it was not the case, I already knew it would work. I already made the mental journey of all thinking about the tree traversals, functional programming paradigms... It didn't sound foreign to me as it did for so many other people.
  • I've spent a lot of time building lint rules, codemods, extended syntax, pretty printers... because I've had excellent courses on how to parse and execute programming languages and had to build many at school, the challenge was not about learning how it works but more how to actually implement it in the context of JavaScript.
  • While on the photos team at Facebook, one of the issue was that we cropped photos in a terrible way. Building an algorithm to find the best crop turned out to be manageable because I knew how to read the literature around existing approaches. Once I settled on one, I already wrote a ton of similar algorithms so it was just a matter of writing code down with little challenge.


I could have gotten a well paying programming job right out of high school as a web developer, but going to EPITA made me such a better programmer because I've been exposed in depth to a lot of areas that I would likely have never gone on my own.

Now, if I see a problem on a web developer task, I also know that changing the programming language, using machine learning, writing code in a more C-like way, using other data structures and algorithms... are possible tools to solve that problem. More importantly, if I need to use those, I can implement them because I've already done similar things in the past.

Your experience may vary, but for me, getting a CS degree at EPITA was a really good decision and I would do it again if I had to.

During the past few weeks, I've been working on prettier, which is a JavaScript pretty printer. We are approaching the phase where we can actually use it so this is a good time to explain how it works.

We're going to go through an example

if (!pretty) { makePretty() }

String -> AST

The first step is to take this string that represents some JavaScript and to parse it in order to get an AST out of it. An AST is a tree that represents the program. Using either Babylon or Flow we can parse this example and we get the following tree.


You can explore the full AST using


Now that we have this tree, we want to print it. For each type of node like IfStatement, UnaryExpression... we're going to output something. In the case of prettier, this something is an intermediate representation called a document as described by the paper a prettier printer by Philip Wadler.

    "if (",
    group([ indent(2, [ softline, "!", "pretty" ]), softline ]),
    " ",
    indent(2, [ hardline, "makePretty", "()", ";" ]),

You can play around with this representation on the prettier explorer.

IR -> String

The interesting thing about this representation is that it is the same no matter what the line-length is. The basic idea is that the primitives such as group, indent, softline encode the way they should look if they fit in the line or if they don't.

The most important primitive is group. The algorithm will first try to recursively print a group on a single line. If it doesn't fit the desired width, then it's going to break the outer group and keep going.

Then, we have primitives that behave differently if they are in a group that fits a single line or not: softline that does not print anything if the group it is contained in fits and a line otherwise. indent adds a level of indentation if it doesn't fit. If you are curious, you can look at the short list of available commands.

So, we just need to take this IR, send it through a solver along with the desired line width and we get the result!



Hopefully this gives you a better idea of how a pretty printer that takes into account the desired width work.

I had the opportunity to do a live fireside chat with Lee Byron to chat about React Native and GraphQL. I outlined the boring future of React Native 😉

It was pretty surreal as I got to have makeup done by a professional!


I also tested the live video feature of Facebook on the open source booth at f8. I showcased React Native running on Xbox, Windows Phone and Desktop and the F8 app being open sourced.

Yesterday, there was a big discussion on Twitter on how hard it is to start hacking on a js project. One comment by Dan Abramov struck me in particular: "Right: don’t use tools, face a problem, choose a tool or roll your own. Wrong: learn tools that don’t solve your problems, hate the tools."

This is spot on. All the solutions presented in this thread do not solve the problems I have when I'm hacking on a new project.

My dream PHP Setup

Back when I was 14, I had the best setup I've ever used in my life. Here it is:

  • Launch WinSCP and connect to
  • Create a file test.php, write <?php echo 'Hello World'; locally with EditPlus 2, press cmd+s to save, wait 0.5 second for the upload.
  • Open my browser and go to Click on test.php.
  • ?????
  • Profit


I want to get the same attributes but with JavaScript and React. Here are the constraints:

  • No setup: I'm happy to have to setup something initially (dedicated server, apache, php...) but nothing should be required to create a new project. No npm install, react-native init, creating a github project, yo webapp...
  • One file: I want to write a single js file to start with. No package.json, no .babelrc, no Procfile...
  • Sharable: I want to be able to share it with a url without any extra step. No git push heroku master or git push gh-pages.
  • Keeps working: Once online, it should stay there and keep working 6 months later. No npm start to run it, no special port that's going to conflict with my 10 other prototypes...
  • Not generic: I don't care about it being generic, I will use whatever transforms you decided. Happy to write js without semi-colons and using SASS for my CSS if you checked all the boxes above.
  • Not prod-ready: This setup doesn't have to be prod-ready, support unit testing or anything that involves it being a real product. This is meant for hacking on stuff. When the project becomes good, I'll spend the time to add all the proper boilerplate.

So, that's the challenge. Can you make it happen?

One common pattern when implementing user interface optimizations is to compute some value for a node where the computation involves looking at neighbor nodes and want to keep this value updated when the tree is mutated.

On this article, I'm going to explain the pattern we implement to solve this use case on various places in React Native.

Example: background color propagation

On React Native, we implement an optimization where the background color is propagated from the parent instead of being transparent. This provides a guarantee to the GPU that it won't need to paint pixels that are underneath. You can read this release notes for a more complete explanation.

In any case, the algorithm is pretty simple, if the background color is not set on an element, we take the one from the nearest parent that has one set. Here's an example:

Now, let say that the red node background color is being unset, the example would look like:

In order to implement this behavior, we first need to traverse up the hierarchy and find the background color and then down to propagate it, but stop at nodes that have set background colors. This means that we have to implement two different algorithms: one for the initial rendering and one for the update.

The complexity of this example as explained is small enough that it is easy to maintain the same invariants. But in practice, this algorithm is a bit more complex: we don't forward the color for transparent nodes nor for the image component in certain cases... We also experimented with more conditions that we didn't end up using.

Dirty-up and execute top-down

What would be great is to just write the top-down recursive algorithm you learn in school to apply colors however you want and whenever there's a mutation just re-run it on the entire tree. The problem with that approach is that you're going to spend a lot of CPU time running this algorithm on the entire tree when you only need to update a few nodes.

Instead, you can dirty the node you are mutating and all the nodes up to the root.

Then, run your algorithm top-down starting at the root.

You need to implement a way to figure out if a non-dirty node needs to be recomputed. The strategy we use is to cache all the arguments of getColor, for example (parentColor, nodeColor, isNodeText, ...) along with the result. If we're being called with the same arguments and the node is not dirty, then we don't need to go further and can just bail. The pseudo code looks like this:

function getColor(isDirty, prevArgs, prevValue, nextArgs) {
  if (isDirty || prevArgs !== nextArgs) {
    return realGetColorRecursive(...nextArgs);
  return prevValue;  


The benefits of this technique is that you can write your business logic once in a traditional top-down recursive way. You don't need to care about the much harder update case.

The downside is that you are doing more work: instead of touching only the elements that have changed, you need to traverse everything up from the root.

Another downside is that it requires more infrastructure as you need to add a caching layer and refactor your code to add a pass that starts at the top.

We're making use of this technique for a few aspects of React Native:

  • Background color is propagation as explained in this article.
  • Computing the layout (top, left, width, height) values based on the css attributes (eg marginTop, flex: 1...).
  • Flag if a text node is the root of the tree of text nodes to be able to flatten it into a single native view.
  • Flag if a node only affect layout and doesn't paint anything so that it can safely be removed from the UI tree.