https://zeroknowledge.fm/290-2/

This week, Anna and Guillermo chat with David Wong, author of the Real-World Cryptography book, and a cofounder zksecurity.xyz – an auditing firm focused on Zero Knowledge technology.

They chat about what first got him interested in cryptography, his early work as a security consultant, his work on the Facebook crypto project and the Mina project, zksecurity.xyz, auditing techniques and their efficacy in a ZK context, what common bugs are found in ZK code, and much more.

It should be quite interesting to read as we found double spending and double voting issues. It's also nice because there are two reports in the post, one from us (zksecurity) and one from my previous team over at NCC Group =)

I wrote a thing that got quite the traction on the internet, which is merely a summary of something awesome that someone else found.

You can read it here: https://www.zksecurity.xyz/blog/posts/nova-attack/

the first paragraph to give you an idea:

Last week, a strange paper (by Wilson Nguyen et al.) came out: Revisiting the Nova Proof System on a Cycle of Curves. Its benign title might have escaped the attention of many, but within its pages lied one of the most impressive and devastating attack on a zero-knowledge proof (ZKP) system that we’ve ever seen. As the purpose of a ZKP system is to create a cryptographic proof certifying the result of a computation, the paper demonstrated a false computation result accompanied with a valid proof.

If you also looked at Plonk and wanted and were wondering the same, here's a short answer. If you do not care about PlonK, feel free to ignore this post. If you care about PlonK, but are starting from the very beginning, then just check my series of videos on PlonK.

First, there's different things going on in this picture. The best way to understand what's going on is to understand them individually.

The first step is a random challenge from the verifier in the interactive version of the protocol. Since we're in the non-interactive version, we've replaced the messages of the verifier by calls to a random oracle. This technique to convert an interactive protocol into a non-interactive one is very famous and used all over the place, it's called Fiat-Shamir.

Because we rely on a random oracle, proofs have to state that they are in the random oracle model, and some people don't like that too much because in the real world you end up instantiating these random oracles with hash functions (which are non-ideal constructions). So some people like protocols better when they don't rely on random oracles. In practice, if your hash function is thought to behave like a random oracle (e.g. SHA-3), then you're all good.

Fiat-Shamir'ing an interactive protocol only works if the protocol is a public-coin protocol, which PlonK is. Public-coin here means that the messages of the verifier are random values (coin tosses) that are public (outsiders can look at them and this won't affect the security of the protocol).

Another interesting point in that picture is that we're hashing the whole transcript, which is something you need to do in order to avoid a large class of ambiguity attacks. Protocols often forget to specify this correctly and a number of attacks have been found on PlonKish protocols due to that. See Weak Fiat-Shamir Attacks on Modern Proof Systems for more detail.

The second step is to compute the linearization of the composition polynomial. The composition polynomial is a term I stole from STARKs but I like it. It's THE polynomial, the one that combines all the checks. In PlonK this means the polynomial that combines checks for all the gates of the circuits and the wiring (permutation).

I'm not going to explain too much about the linearization because I already have a post on it here. But to recap, linearizing is when you evaluate parts of your polynomial. So anything that's evaluated at $z$ is linearized. And anything that has a bar above it (e.g. $\bar{a}$) is linearized as well. Note that the prover could evaluate everything if they wanted to, which would let the verifier compute the entire check "in the clear". But doing that means that the proof is larger, and there are more evaluation proofs to aggregate. It's a tradeoff, that might pay off if you also want to implement recursive zero-knowledge proofs (which require a verifier implemented in a circuit).

We're looking at the prover side in this picture. While the verifier does symmetrical things to the prover, the prover's job here is to form the composition polynomial and to prove that it evaluates to 0 on a number of points (or that it "vanishes on some domain"). So to do that, it has to prove that it is equal to the vanishing polynomial $Z_H$ times some quotient $t(X)$. If you don't understand that, you can read this other post I have on the subject.

The final piece of the puzzle to understand that equation is that we can simplify the $f(X) = Z_H(x) t(x)$ check using Maller's optimization which I talk about here. This is why we subtract our composition polynomial with $Z_H(X) \cdot t(X)$ and this is also why we linearize the vanishing polynomial by evaluating it at $Z_H(z)$.

Once we have formed the polynomial which checks that the composition polynomial is equal to the vanishing polynomial times some quotient ($f = Z_H \cdot t$) then we have to evaluate this at a random point. We already know the random point $z$, which we've already used to evaluate some parts of the polynomial (during the linearization). The equation you see in the picture is how you compute a KZG evaluation proof. If you don't know that you can check my article on KZG.

Note also that there are many evaluation proofs that are aggregated together using a random linear combination. This is a common technique to aggregate multiple KZG evaluation proofs (and the verifier will have to compute the same random linear combination on the other side to verify the aggregation). In order to be more efficient (at the cost of tiny amount of security loss) we use 6 powers of $v$ instead of using 6 random values.

In the polynomial above, within the composition polynomial you might have noticed the value $\bar{z_{\omega}}$. It is the permutation polynomial $Z$ evaluated at $z \omega$. The only explanation I have of why you need that is in my video on the permutation of PlonK. Since the evaluation point ($z \omega$) is different from the first evaluation proof, we need a separate evaluation proof for that one (unfortunately).

The output is the pair of evaluation proofs. That's it!

Smart contracts have been at the source of billions of dollars of loss (see our previous project https://dasp.co). Nobody is sheltered from bugs. ZK smart contracts will have bugs, some devastating. Let's be proactive when it comes to zkApps!

In the coming month we'll be posting more about the kind of bugs that we have found in the space, from ZKP systems' bugs to frontend compiler bugs to application bugs. If you're looking for real experts to audit your ZK stack, you now have the ones behind zkSecurity.

We're a mix of engineers & researchers who have been working in the smart contract and ZK field before you were born (jk). On top of that, we've also been in the security consulting industry for a while, so we're professionals ;)

Stay tuned for more blogposts on http://zksecurity.xyz and reach out to me if you need an audit :)

Also, the launch blogpost is much more interesting than this one. Go read it here: Private delegated computation is here, and there will be bugs!

Homomorphic encryption, if you haven't heard of it, is the ability to operate on the ciphertext without having to decrypt it. If that still doesn't ring a bell, check my old blogpost on the subject. In this post I will just explain the intuition behind the scheme, for a less formal overview check Lange's excellent video.

Paillier's scheme is only homomorphic for the addition, which is still useful enough that it's been used in different kind of cryptographic protocols. For example, cryptdb was using it to allow some types of updates on encrypted database rows. More recently, threshold signature schemes have been using Paillier's scheme as well.

The actual algorithm

As with any asymmetric encryption scheme, you have the good ol' key gen, encryption, and decryption algorithms:

Key generation. Same as with RSA, you end up with a public modulus $N = pq$ where $p$ and $q$ are two large primes.

Encryption. This is where it gets weird, encryption looks more like a Pedersen commitment (which does not allow decryption). To encrypt, sample a random $r$ and produce the ciphertext as:

$$(N+1)^m \cdot r^N \mod{N^2}$$

where $m$ is the message to be encrypted. My thought at this point was "WOOT. A message in the exponent? How will we decrypt?"

Decryption. Retrieve the message from the ciphertext $c$ as

$$\frac{c^{\varphi(N)} -1}{N} \cdot \varphi(N)^{-1} \mod{N^2}$$

Wait, what? How is this recovering the message which is currently the discrete logarithm of $(N+1)^m$?

How decryption works

The trick is in expanding this exponentiation (using the Binomial expansion).

The relevant variant of the Binomial formula is the following:

$$(1+x)^n = \binom{n}{0}x^0 + \binom{n}{1}x^1 + \cdots + \binom{n}{n} x^n$$

where $\binom{a}{b} = \frac{a!}{b!(a-b)!}$

So in our case, if we only look at $(N+1)^m$ we have:

$$ \begin{align} (N+1)^m &= \binom{m}{0} + \binom{m}{1} N + \binom{m}{2} N^2 + \cdots + \binom{m}{m} N^m \\ &= \binom{m}{0} + \binom{m}{1} N \mod{N^2}\\ &= 1 + m \cdot N \mod{N^2} \end{align} $$

Tada! Our message is now back in plain sight, extracted from the exponent. Isn't this magical?

This is of course not exactly what's happening. If you really want to see the real thing, read the next section, otherwise thanks for reading!

The deets

If you understand that, you should be able to reverse the actual decryption:

$$ \begin{align} c^{\varphi(N)} &= ((N+1)^m \cdot r^N)^{\varphi(N)}\\ &= (N+1)^{m\cdot\varphi(N)} \cdot r^{N\varphi(N)} \mod{N^2} \end{align} $$

It turns out that the $r^{N\varphi(N)} = 1 \mod{N^2}$ because $N\varphi(N)$ is exactly the order of our group modulo $N^2$. You can visually think about why by looking at my fantastic drawing:

On the other hand, we get something similar to what I've talked before:

$$ (N+1)^{m\varphi(N)} = (1 + mN)^\varphi(N) = 1 + m\varphi(N)N \mod{N^2} $$

Al that is left is to cancel the terms that are not interesting to us, and we get the message back.

I like to describe Ethereum as a gigantic computer floating in the sky. A computer everyone can use by installing their own applications there, and using each other's applications. It's the world's computer. I'm not the only one seeing it like this by the way. Dfinity called their Ethereum-like protocol the "Internet computer". Sounds pretty cool.

These internet computers are quite clunky at the moment though, forcing everyone (including you and me) to reexecute everything, to make sure that the computer hasn't made a mistake. But fear not, this is all about to stop! With the recent progress around zero-knowledge proofs (ZKPs), we're seeing a move to enhance these internet computers with computational integrity. Or in other words, only the computer has to compute, the others can trust the result due to cryptography!

A lot of the attempts that are reimplementing a "provable" internet computer have been making use of "zkVMs", an equivalent to the VMs of the previous era of blockchains but enhanced with zero-knowledge proofs. But what are these zkVMs? And is it the best we can come up with? In this post I will respond to both of these questions, and I will then introduce a new concept: the zkCPU.

Let's talk about circuits

The lowest level of development for general-purpose zero-knowledge proof systems (the kind of zero-knowledge proof systems that allow you to write programs) is the arithmetic circuit.

Arithmetic circuits are an intermediate representation which represent an actual circuit, but using math, so that we can prove it using a proof system.

In general, you can follow these steps to make use of a general-purpose ZKP system:

1. take a program you like
2. compile it into an (arithmetic) circuit
3. execute your circuit in a same way you'd execute your program (while recording the state of the memory at each step)
4. use your proof system to prove that execution

What does an arithmetic circuit really look like? Well, it looks like a circuit! It has gates, and wires, although its gates are not the typical circuit ones like AND, OR, NAND, XOR, etc. Instead, it has "arithmetic gates": a gate to multiply two inputs, and a gate to add two inputs.

(taken from my book Real-World Cryptography)

Now we're ready to talk virtual machines

A virtual machine can usually be broken down into three components:

1. Some memory that can be used to store and read values (for example, if you add two values together, where do you read the two values from? and where do you store the result?)
2. A set of instructions that people can use to form programs.
3. Some logic that can interpret these instructions.

In other words, a VM looks very much like this:

for instruction in program {
    parse_instruction_and_do_something(instruction);
}

For example, using the instructions supported by the Ethereum VM you can write the following program that makes use of a stack to add two numbers:

PUSH1 5 // will push 5 on the stack
PUSH1 1 // will push 1 on the stack
ADD     // will remove the two values from the stack and push 6
POP     // will remove 6 from the stack

Most of the difference between a CPU and a VM comes from the V. A virtual machine is created in software, whereas the CPU is pure hardware and is the lowest level of abstraction.

So what about zkVMs then?

From the outside, a zkVM is almost the same stuff as a VM: it executes programs and returns their outputs, but it also returns a cryptographic proof that one can verify.

Looking inside a zkVM reveals some arithmetic circuits, the same ones I've talked about previously! And these arithmetic circuits "simply" implement the VM loop I wrote above. I put "simply" in quote because it's not that simple to implement in practice, but that's the basic idea behind zkVMs.

From a developer's perspective a zkVM isn't that different from a VM, they still have access to the same set of instructions, which like most VMs is usually just the base for a nicer higher-level language (which can compile down to instructions).

We're seeing a lot of zkVMs poping out these days. There's some that introduce completely new VMs, optimized for ZKPs. For example, we have Cairo from Starkware and Miden from Polygon. On the other side, we also have zkVMs that aim at supporting known VMs, for example a number of projects seek to support Ethereum's VM (the EVM) --Vitalik wrote an article comparing all of them here-- or more interestingly real-world VMs like the RISC-V architecture (see Risc0 here).

What if we people could directly write circuits?

Supporting VMs is quite an attractive proposal, as developers can then write programs in higher-level abstractions without thinking about arithmetic circuits (and avoid bugs that can happen when writing for zero-knowledge proof systems directly).

But doing things at this level means that you're limited to what the zkVM does. You can only use their set of instructions, you only have access to accelerated operations that they have accelerated for you, and so on. At a time where zk technology is only just flourishing, and low-level optimizations are of utmost important, not having access to the silicon is a problem.

Some systems have taken a different approach: they let users write their own circuits. This way, users have much more freedom in what they can do. Developers can inspect the impact of each line of code they write, and work on the optimizations they need at the circuit level. Hell, they can write their own VMs if that's what they want. The sky's the limit.

This is what the Halo2 library from Zcash has done so far, for example, allowing different projects to create their own zkCPUs. (To go full circle, some zkEVMs use Halo2.)

Introducing the world's CPU

So what's the world zkCPU? Or what's the Internet zkCPU (as Dfinity would say)? It's Mina.

Like a CPU, it is designed so that gates wired together form a circuit, and values can be stored and read from a number of registers (3 at the moment of this writing, 15 in the new kimchi update). Some parts are accelerated, perhaps akin to the ALU component of a real CPU, via what we call custom gates (and soon lookup tables).

Mina is currently a zkCPU with two circuits as its core logic:

• the transaction circuit
• the blockchain circuit

The transaction circuit is used to create blocks of transactions, wheereas the blockchain circuits chains such blocks of transactions to form the blockchain.

Interestingly, both circuits are recursive circuits, which allows Mina to compress all of the proofs created into a single proof. This allows end users, like you and me, to verify the whole blockchain in a single proof of 22kB.

Soon, Mina will launch zkApps, which will allow anyone to write their own circuits and attach them as modules to the Mina zkCPU.

User circuits will have access to the same zkCPU as Mina, which means that they can extend it in all kind of ways. For example, internally a zkApp could use a different proof system allowing for different optimizations (like the Halo2 library), or it could implement a VM, or it could do something totally different.

I'm excited to see what people will develop in the future, and how all these zkApps will benefit from getting interoperability for free with other zkApps. Oh, and by the way, zkApps are currently turned on in testnet if you can't wait to test this in mainnet.

EDIT: I know that zkFPGA would have been technically more correct, but nobody knows an FPGA is

In any case, I was slow to make progress, but I did not give up. I have this personal theory that ALL successful projects and learnings are from not giving up and working long enough on something. Any large project started as a side project, or something very small, and became big after YEARS of continuous work. I understood that early when I saw who the famous bloggers were around me: people who had been blogging nonstop for years. They were not the best writers, they didn't necessarily have the best content, they just kept at if for years and years.

In any case, I digress, today I wanted to talk about two things that have inspired me a lot in the last half.

The first thing, is this blog post untitled Just Know Stuff. It's directed to PhD students, but I always feel like I run into the same problems as PhD students and so I tend to read what they write. I often run away from complexity, and I often panic and feel stressed and do as much as I can to avoid learning what I don't need to learn. The problem with that is that I only get shallow knowledge, and I develop breath over depth. It's useful if you want to teach (and I think this is what made Real-World Cryptography a success), but it's not useful if you want to become an expert in one domain. (And you can't be an expert in so many domains.)

Anyway, the blogpost makes all of that very simple: "just know stuff". The idea is that if you want to become an expert, you'll have to know that stuff anyway, so don't avoid it. You'll have to understand all of the corner cases, and all of the logic, and all of the lemmas, and so on. So don't put it off, spend the time to learn it. And I would add: doesn't matter if you're slow, as long as you make progress towards that goal you'll be fine.

The second thing is a comment I read on HN. I can't remember where, I think it was in relation to dealing with large unknown codebases. Basically the comment said: "don't use your brain and read code trying to understand it, your brain is slow, use the computer, the computer is fast, change code, play with code, compile it, see what it does".

That poorly paraphrased sentence was an epiphany for me. It instantly made me think of Veritasium's video The 4 things it takes to be an expert. The video said that to learn something really really well, to become an expert, you need to have a testing environment with FAST and VALID feedback. And I think a compiler is exactly that, you can quickly write code, test things, and the compiler tells you "YES, YOU ARE RIGHT" or "BEEEEEEEP, WRONG" and your brain will do the rest.

So the learning for me was that to learn something well, I had to stop spending time reading it, I had to play with it, I had to test my understanding, and only then would I really understand it.

"timely feedback" and "valid environment" are the rules I'm referring to. Not that the other ones are less important.

In this new series of videos I will explain how proof composition and recursion work with different schemes. Spoiler: we'll talk about Sangria, Nova, PCD, IVC, BCTV14 and Halo (and perhaps more if more comes up as I record these).

Here's the first one:

you can access the full playlist here.

source: redbubble

Three years ago, in the middle of writing my book Real-World Cryptography, I wrote about Why I'm writing a book on cryptography. I believed there was a market of engineers (and researchers) that was not served by the current offerings. There ought to be something more approachable, with less equations and theory and history, with more diagrams, and including advanced topics like cryptocurrencies, post-quantum cryptography, multi-party computations, zero-knowledge proof, hardware cryptography, end-to-end encrypted messaging, and so on.

The blogpost went viral and I ended up reusing it as a prologue to the book.

Now that Real-world cryptography has been released for more than a year, it turns out my 2-year bet was not for nothing :). The book has been very well received, including being used in a number of universities by professors, and has been selling quite well.

The only problem is that it mostly sold through Manning (my publisher), meaning that the book did not receive many reviews on Amazon.

So this is post is for you. If you've bought the book, and enjoyed it (or parts of it), please leave a review over there. This will go a long way to help me establish the book and allow more people to find it (Amazon being the biggest source of readers today).

Kimchi by itself is only a backend to create proofs. The whole picture includes:

• Pickles, the recursion layer, for verifying proofs within proofs (ad infinitum)
• Snarky, the frontend that allows developers to write programs in a higher-level abstraction that Kimchi and Pickles can prove

Today, both of these parts are written in OCaml and not really meant to be used outside of Mina. With the advent of zkapps most users are able to use all of this right now using typescript in a user-friendly toolbox called snarkyjs.

If you're only interested in using the main tool in typescript, head over to the snarkyjs repo.

Still, we would benefit from having the pickles + snarky + kimchi combo in a single language (Rust). This would allow us to move faster, and we would be able to improve performances even more. On top of that, a number of users have been looking for an all-in-one zero-knowledge proof Rust library that supports recursion without relying on a trusted setup.

For this reason, we've been moving more to the Rust side.

What does this have to do with you? Well, while we're doing this, kimchi could use some help from the community! We love open source, and so everything's developed in the open.

I've talked about external contributions to kimchi in the past and have since received a tremendous amount of replies and interest:

A year later, we're now the open source zero-knowledge project with the highest number of contributors (as far as I can tell), and we even hired a number of them!

Some of the contributors were already knowledgeable in ZKPs, some were already knowledgeable in Rust, some didn't know anything. It didn't really matter, as we followed the best philosophy for an open source project: the more transparent and understandable a project is, the more people will be able to contribute and build on top of it.

Kimchi has an excellent introduction to contributing (including a short video), a book explaining a number of concepts behind the implementation, a list of easy tasks to start with, and my personal support over twitter or Github =)

So if you're interested in any of these things, don't be shy, look at these links or come talk to me and I'll help you onboard to your first contribution!

But I digress, I wanted to write this note to "past me" (and anyone like that guy). It's a note about what you should do to get past the OCaml bump. There's two things: getting used to read types, and understanding how to parse compiler errors.

For the first one, a breakthrough in how effective I am at reading OCaml code came when I understood the importance of types. I'm used to just reading code and understanding it through variable names and comments and general organization. But OCaml code is much more like reading math papers I find, and you often have to go much slower, and you have to read the types of everything to understand what some code does. I find that it often feels like reverse engineering. Once you accept that you can't really understand OCaml code without looking at type signatures, then everything will start falling into place.

For the second one, the OCaml compiler has horrendous errors it turns out (which I presume is the major reason why people give up on OCaml). Getting an OCaml error can sometimes really feel like a death sentence. But surprisingly, following some unwritten heuristics can most often fix it. For example, when you see a long-ass error, it is often due to two types not matching. In these kind of situations, just read the end of the error to see what are the types that are not matching, and if that's not enough information then work you way up like you're reading an inverted stack trace. Another example is that long errors might actually be several errors concatenated together (which isn't really clear due to formatting). Copy/pasting errors in a file and adding line breaks manually often helps.

I'm not going to write up exactly how to figure out how each errors should be managed. Instead, I'm hopping that core contributors to OCaml will soon seriously consider improving the errors. In the mean time though, the best way to get out of an error is to ask on on discord, or on stackoverflow, how to parse the kind of errors you're getting. And sometimes, it'll lead you to read about advanced features of the language (like polymorphic recursion).

Things have changed a lot since the previous one took place (pre-covid!) so I expect some new developments to join the party (wink wink SNARK-friendly sponges).

If you're interested in presenting some research, check the call for contributions!

Addition is often free, but it seems like multiplication is a pain in most cryptographic protocols.

If you're multiplying two known values together, it's OK. But if you want to multiply one known value with another unknown value, then you will most likely have to reach out to the discrete logarithm problem. With that in hand, you can multiply an unknown value with a known value.

This is used, for example, in key exchanges. In such protocols, a public key usually masks a number. For example, the public key X in X = [x] G masks the number x. To multiply x with another number, we do this hidden in the exponent (or in the scalar since I'm using the elliptic curve notation here): [y] X = [y * x] G. In key exchanges, you use this masked result as something useful.

If you're trying to multiply two unknown values together, you need to reach for pairings. But they only give you one multiplication. With masked(x) and masked(y) you can do pairing(masked(x), masked(y)) and obtain something that's akin to locked_masked(x * y). It's locked as in, you can't do these kind of multiplications anymore with it.

Little did I know, there's more to monads, or at least monads are so vague that they can be used in all sorts of ways. One example of this is state monads.

A state monad is a monad which is defined on a type that looks like this:

type 'a t = state -> 'a * state

In other word, the type is actually a function that performs a state transition and also returns a value (of type 'a).

When we act on state monads, we're not really modifying a value, but a function instead. Which can be brain melting.

The bind and return functions are defined very differently due to this.

The return function should return a function (respecting our monad type) that does nothing with the state:

let return a = fun state -> (a, state)
let return a state = (a, state) (* same as above *)

This has the correct type signature of val return : 'a -> 'a t (where, remember, 'a t is state -> ('a, state)). So all good.

The bind function is much more harder to parse. Remember the type signature first:

val bind : 'a t -> f:('a -> 'b t) -> 'b t

which we can extend, to help us understand what this means when we're dealing with a monad type that holds a function:

val bind : (state -> ('a, state)) -> f:('a -> (state -> ('b, state))) -> (state -> ('b, state))

you should probably spend a few minutes internalizing this type signature. I'll describe it in other words to help: bind takes a state transition function, and another function f that takes the output of that first function to produce another state transition (along with another return value 'b).

The result is a new state transition function. That new state transition function can be seen as the chaining of the first function and the additional one f.

OK let's write it down now:

let bind t ~f = fun state ->
    (* apply the first state transition first *)
    let a, transient_state = t state in
    (* and then the second *)
    let b, final_state = f a transient_state in
    (* return these *)
    (b, final_state)

Hopefully that makes sense, we're really just using this to chain state transitions and produce a larger and larger main state-transition function (our monad type t).

How does that look like when we're using this in practice? As most likely when a return value is created, we want to make it available to the whole scope. This is because we want to really write code that looks like this:

let run state =
    (* use the state to create a new variable *)
    let (a, state) = new_var () state in
    (* use the state to negate variable a *)
    let (b, state) = negate a state in
    (* use the state to add a and b together *)
    let (c, state) = add a b state in
    (* return c and the final state *)
    (c, state)

where run is a function that takes a state, applies a number of state transition on that state, and return the new state as well as a value produced during that computation. The important thing to take away there is that we want to apply these state transition functions with values that were created previously at different point in time.

Also, if that helps, here are the signatures of our imaginary state transition functions:

val new_var -> unit -> state -> (var, state)
val negate -> var -> state -> (var, state)
val add -> var -> var -> state -> (var, state)

Rewriting the previous example with our state monad, we should have something like this:

let run =
    bind (new_var ()) ~f:(fun a ->
        bind (negate a) ~f:(fun b -> bind (add a b) ~f:(fun c -> 
            return c)))

Which, as I explained in my previous post on monads, can be written more clearly using something like a let% operator:

let t = 
    let%bind a = new_var () in
    let%bind b = negate a in
    let%bind c = add a b in
    return c

And so now we see the difference: monads are really just way to do things we can already do but without having to pass the state around.

It can be really hard to internalize how the previous code is equivalent to the non-monadic example. So I have a whole example you can play with, which also inline the logic of bind and return to see how they successfuly extend the state. (It probably looks nicer on Github).

type state = { next : int }
(** a state is just a counter *)

type 'a t = state -> 'a * state
(** our monad is a state transition *)

(* now we write our monad API *)

let bind (t : 'a t) ~(f : 'a -> 'b t) : 'b t =
 fun state ->
  (* apply the first state transition first *)
  let a, transient_state = t state in
  (* and then the second *)
  let b, final_state = f a transient_state in
  (* return these *)
  (b, final_state)

let return (a : int) (state : state) = (a, state)

(* here's some state transition functions to help drive the example *)

let new_var _ (state : state) =
  let var = state.next in
  let state = { next = state.next + 1 } in
  (var, state)

let negate var (state : state) = (0 - var, state)
let add var1 var2 state = (var1 + var2, state)

(* Now we write things in an imperative way, without monads.
   Notice that we pass the state and return the state all the time, which can be tedious.
*)

let () =
  let run state =
    (* use the state to create a new variable *)
    let a, state = new_var () state in
    (* use the state to negate variable a *)
    let b, state = negate a state in
    (* use the state to add a and b together *)
    let c, state = add a b state in
    (* return c and the final state *)
    (c, state)
  in
  let init_state = { next = 2 } in
  let c, _ = run init_state in
  Format.printf "c: %d\n" c

(* We can write the same with our monad type [t]: *)

let () =
  let run =
    bind (new_var ()) ~f:(fun a ->
        bind (negate a) ~f:(fun b -> bind (add a b) ~f:(fun c -> return c)))
  in
  let init_state = { next = 2 } in
  let c, _ = run init_state in
  Format.printf "c2: %d\n" c

(* To understand what the above code gets translated to, we can inline the logic of the [bind] and [return] functions.
   But to do that more cleanly, we should start from the end and work backwards.
*)
let () =
  let run =
    (* fun c -> return c *)
    let _f1 c = return c in
    (* same as *)
    let f1 c state = (c, state) in
    (* fun b -> bind (add a b) ~f:f1 *)
    (* remember, [a] is in scope, so we emulate it by passing it as an argument to [f2] *)
    let f2 a b state =
      let c, state = add a b state in
      f1 c state
    in
    (* fun a -> bind (negate a) ~f:f2 a *)
    let f3 a state =
      let b, state = negate a state in
      f2 a b state
    in
    (* bind (new_var ()) ~f:f3 *)
    let f4 state =
      let a, state = new_var () state in
      f3 a state
    in
    f4
  in
  let init_state = { next = 2 } in
  let c, _ = run init_state in
  Format.printf "c3: %d\n" c

(* If we didn't work backwards, it would look like this: *)
let () =
  let run state =
    let a, state = new_var () state in
    (fun state ->
      let b, state = new_var () state in
      (fun state ->
        let c, state = add a b state in
        (fun state -> (c, state)) state)
        state)
      state
  in
  let init_state = { next = 2 } in
  let c, _ = run init_state in
  Format.printf "c4: %d\n" c

My first lesson was how intense competition is. I never had any experience come close to it since then, and miss competition dearly. Once you start competing, and you start getting good at it, you feel the need to do everything to get the advantage. Back then, I would watch every frag movie that came out (even producing some), I would know all the best players of every clan (and regularly play with them), and I would participate in 3 online tournaments a day. I would wake up every day around noon, play the first tournament at 1pm, then practice the afternoon, then play the tournament of 9pm, and then the last one at 1am. If my team lost, I would volunteer to replace a player dropping out from a winning team. Rinse and repeat, every day. There's no doubt in my mind that I must have reached Gladwell's 10,000 hours.

I used the same kind of technique years later when I started my master in cryptography. I thought: I know how to become the best at something, I just have to do it all the time, constantly. I just need to obsess. So I started blogging here, I started subscribing to a number of blogs on cryptography and security and I would read everything I could every single hours of every day. I became a sponge, and severally addicted to RSS feeds. Of course, reading about cryptography is not as easy as playing video games and I could never maintain the kind of long hours I would when I was playing counter strike. I felt good, years later, when I decided to not care as much about new notifications in my RSS feed.

Younger, my dream was to train with a team for a week in a gaming house, which is something that some teams were starting to do. You'd go stay in some house together for a week, and every day practice and play games together. It really sounded amazing. Spend all my hours with people who cared as much as me. It seemed like a career in gaming was possible, as more and more money was starting to pour into esport. Some players started getting salaries, and even coaches and managers. It was a crazy time, and I felt really sad when I decided to stop playing. I knew a life competing in esport didn't make sense for me, but competition really was fullfiling in a way that most other things proved not to be.

An interesting side effect of playing counter strike every day, competitively, for many years, is that I went through many teams. I'm not sure how many, but probably more than 50, and probably less than 100. A team was usually 5 players, or more (if we had a rotation). We would meet frequently to spend hours practicing together, figuring out new strategies that we could use in our next game, doing practice matches against other teams, and so on. I played with all different kind of people during that time, spending hours on teamspeak and making life-long friendships. One of my friend, I remember, would get salty as fuck when we were losing, and would often scream at us over the microphone. One day we decided to have an intervention, and he agreed to stop using his microphone for a week in order not to get kicked out of the team. This completely cured his raging.

One thing I noticed was that the mood of the team was responsible for a lot in our performance. If we started losing during a game, a kind of "loser" mood would often take over the team. We would become less enthusiastic, some team mates might start raging at the incompetence of their peers, or they might say things that made the whole team want to give up. Once this kind of behavior started, it usually meant we were on a one-way road to a loss. But sometimes, when people kept their focus on the game, and tried to motivate others, we would make incredible come backs. Games were usually played in two halves of 15 rounds. First in 16 would win. We sometimes went from a wooping 0-15 to an insane strike of victories leading us to a 16-15. These were insane games, and I will remember them forever. The common theme between all of those come back stories were in how the whole team faced adversity, together. It really is when things are not going well that you can judge a team. A bad team will sink itself, a strong team will support one another and focus on doing its best, potentially turning the tide.

During this period, I also wrote a web service to create tournaments easily. Most tournaments started using it, and I got some help to translate it in 8 different european languages. Thousands of tournaments got created through the interface, in all kind of games (not just CS). Years later I ended up open sourcing the tool for others to use. This really made me understand how much I loved creating products, and writing code that others could directly use.

Today I have almost no proof of that time, besides a few IRC screenshots. (I was completely addicted to IRC.)