Here is a write-up of why I think you should use Nix.

The fastest way to have it installed on your system is to follow the official instruction.

Once installed, you are good to go!

If you just wanted to have some version of solc ad-hoc, you just need one simple line:

$ nix shell github:hellwolf/solc.nix#solc_0_4_26 github:hellwolf/solc.nix#solc_0_8_19

Then you will have two different solc binary available for you:

$ solc-0.4.26 --version
solc, the solidity compiler commandline interface
Version: 0.4.26+commit.4563c3fc.Linux.g++

$ solc-0.8.19 --version
solc, the solidity compiler commandline interface
Version: 0.8.19+commit.7dd6d404.Linux.g++

If you want a deeper integration of it, you should consider using Nix Flake, here is an example of how to have multiple solc installed and one of them as the default one:

{
  inputs = {
    solc = {
      url = "github:hellwolf/solc.nix";
      inputs.nixpkgs.follows = "nixpkgs";
    };
  };

  outputs = { self, nixpkgs, solc }: let
    pkgs = import nixpkgs {
      system = "x86_64-linux";
      overlays = [
        solc.overlay
      ];
    };
  in {
    devShell.x86_64-linux = with pkgs; mkShell {
      buildInputs = [
        solc_0_4_26
        solc_0_7_6
        solc_0_8_19
        (solc.mkDefault pkgs solc_0_8_19)
      ];
    };
  };
}

If you find this tool useful, please follow up at the git repo, contribute to keep this up to date, and share with people you might think it can help them too!

It is a minimum Othello game implementation with MiniMax "AI" opponent using GHC 9.X with only the "prelude" module allowed.

The rule of the game is: "There are sixty-four identical game pieces called disks, which are light on one side and dark on the other. Players take turns placing disks on the board with their assigned color facing up. During a play, any disks of the opponent's color that are in a straight line and bounded by the disk just placed and another disk of the current player's color are turned over to the current player's color. The objective of the game is to have the majority of disks turned to display one's color when the last playable empty square is filled." (https://en.wikipedia.org/wiki/Reversi)

To play the game, checkout the tiny game repository and type ./play mini-othello:

I implemented the game entirely in minified code, so the variable and function names will be pretty cryptic!

My second submission to the game jam is less serious. In fact, it is a knock-off of the lolcat program! But it is tightly integrated with the game itself, hence a meta game!

The idea of it is straightforward: it pipes the "play" command to colorize its output and add additional text effects depending on how many "lols" you have inserted in the command line, e.g.:

./play lol lol lol lol lol ski would give you a rather trippy ski trip:

The trick of using the applicative list to generate an infinite list of variations:

(,,)<$>[1..]<*>s[38,48]<*>s[0,4,5]

The first parameter is the frequency of the rainbow color effects; the second one is the anis-terminal color code choice for foreground or background; the third one is additional text effects such as underlined texts and blinking texts.

I have enjoyed the journey, learned and jammed with various Haskell syntaxes for code golfing. Comparing to perl golf in the old days, code golfing in Haskell is a joy: if it type-checks, it usually works!

And I would like to thank the organizers, the participants, and all the fine folks at #haskell-game.

Superfluid protocol enables money to move between entities continuously in time with no transaction. It is a new paradigm of how the money payment system works. Using this innovative building block, one can build novel applications for payroll, subscriptions, vesting, streaming AMMs, gaming, trade finance, rentals, and NFTs. Click here to read more what is Superfluid.

Though modeling a problem domain explicitly with time is not a novel concept. In fact, functional reactive programming (FRP) that was formulated in a ICFP 97 paper¹ demonstrated that we can model animations elegantly and efficiently using continuous time. More than 20 years later, it was Superfluid, the first one that successfully connected the dots between FRP and the domain of money payment system, and has made it available on more than 6 EVM (Ethereum Virtual Machine) chains².

In this article, we focus on one of its novel applications: different designs of exchanges that can facilitate swaps continuously in time³. By now, you should understand why I shall name them "Reactive Exchanges." Reactive exchanges are types of exchanges modeled with functional reactive programming.

The article expects you to be familiar with the trading terminologies and related innovations on decentralized ledgers. Axo trade whitepaper⁴, for instance, provides an excellent overview of this domain. Additionally, it would be best if you had an in-depth overview of what is Superfluid is and how it works⁵.

Throughout the article, we separate the "reactiveness" of an exchange into three primary functions:

• Contribution: does the input asset enter the exchange continuously in time?
• Swap: does swap between the input and assets happen continuously in time?
• Distribution: is output asset distributed continuously in time?

At the end of the article, we will then compare different designs using this classification.

The first attempt at having reactive exchange is making only the contribution continuous in time. The actual swaps are done through an external DEX (decentralized exchange), and distribution of the swap output is done through Superfluid IDA⁶, both happen periodically and are triggered by keepers ("cronjob at scale").

Let's call this "pseudo reactive exchange" since the actual swaps do not continuously in time. Here is a visualization of such exchange:

An example of such exchanges that is in production is Ricochet Exchange, an original and earliest example of how to build an unique exchange using Superfluid. It has been live for over an year, and provides an efficient way for anyone to DCA (dollar cost average)⁷ invest into on-chain assets.

CFMM (Constant Function Market Maker) was the first class of AMM (Automatic Market Maker) applied to real-world financial markets⁴. In this article, we look into making one of the earliest and simplest versions of CFMM, Constant Liquidity Product (CLP) used in Uniswap v1 and v2 ^{8 , 9}, reactive.

To solve the equations required to make the CLP reactive, I used the python binding of the SageMath framework, a free open-source mathematics software system licensed under the GPL:

from sage.all import var, assume, function, solve

# let's define a handful of symbols
L_a, L_b, T, r, r_a, r_b, t_0, t = var("L_a", "L_b", "T", "r", "r_a", "r_b", "t_0", "t")
assume(t >= t_0)

First, let's define CLP:

from sage.all import var, assume, function, solve

def CLP(x, y, x_prime, y_prime):
    """Constant Liquidity Product Swap"""
    return x * y == x_prime * y_prime
assume(t >= t_0)

Secondly, let's try to use sage to solve the price function for selling asset A (sell amount \(a_\Delta\)) for asset B (get amount \(b_\Delta\)):

def solve_clp_a4b():
    print("# Solve CLP equation for selling A for B instantly\n")
    v_a = var("a_Δ")
    v_b = var("b_Δ")
    clp = CLP(
        L_a,
        L_b,
        L_a + v_a,
        L_b + v_b
    )
    sols = solve(clp, v_b)
    assert(len(sols) == 1)
    print(sols[0])
    print("\n")

Sage spits out the well-known equation as expected:

\[L_{b\Delta} = \frac{L_b * a_\Delta}{L_a + a_\Delta}\]

Now it comes to how to make it reactive; two functions of time need to be solved: one for asset A and another for asset B. There are not enough free variables to solve two equations, but we do know that these two equations should be elated to each other. Here was the stroke of insight, and a magic variable "q" was introduced (and please don't ask more):

def solve_rclp_rtb_bidir():
    print("# Solve Reactive CLP rtb_bidir equation\n")
    cf_a = r_a * (t - t_0)
    cf_b = r_b * (t - t_0)

    q = var("q")
    clp = CLP(
        L_a,
        L_b,
        L_a + cf_a + q * cf_b,
        L_b + cf_b + 1/q * cf_a
    )
    sols = solve(clp, q)
    print("L_{flowswap_a} =", (1/q * cf_a).subs(sols[0]))
    print("L_{flowswap_b} =", (q * cf_b).subs(sols[0]))
    print("\n")

Gratefully, Sage spits out the magic formula we are looking for. Let's call it flowswap formula:

1. \[L_{flowswap_a} = \frac{-(r_b * t_\Delta - L_b) * r_a * t_\Delta}{r_a * t_\Delta + L_a}\]
2. \[L_{flowswap_b} = \frac{-(r_a * t_\Delta - L_a) * r_b * t_\Delta}{r_b * t_\Delta + L_b}\]

Here is an illustration of the global view of a reactive CLP exchange:

Another way of seeing it is from how swaps look after "reactification" on the chart of the CLP formula:

To make the exchange work for more participants, we again need to use IDA from Superfluid money, where the proportion of the flowswap output is determined by the market takers' continuous flow contributions.

Regarding "reactiveness, " market takers always "flowswap" constant flows for another non-linear flow of money, and they are both continuous in time and reactive. However, Superfluid money cannot distribute money through arbitrary formulas; that's why the distribution cannot be reactive unless a bespoke flowswap primitive is a built-in feature of the money.

You can find the work-in-progress prototype of this in the T-REX (Toy Reactive Exchange) Monorepo. If you are interested in making this closer to production, join Superfluid's wave pool program.

Another fascinating and unique variation of AMM enabled by Superfluid money is a market maker that requires zero intermediate liquidity, where liquidity goes from LPs to traders directly as constant flows. As a result, a trade-off (or feature) is that such exchanges may not easily facilitate instant swaps.

I shall call such exchange ZILMM: Zero-Intermediate-Liquidity Market Makers. The pioneer of such design is Superfluid's ecosystem project Aqueduct. We will leave the reader to discover more about the project on their own, and we will only include an illustration from them to get a taste of it:

1. At market liquidity "equilibrium"

   

2. Traders tilting the "equilibrium"

   

Since all flows involved are constant flows, it should not be a surprise that this design of the reactive exchange ticks all the reactiveness boxes!

Let us review the "reactiveness" of all three Superfluid money enabled market maker designs:

Note:

a) Distribution with the complex formula required by R-CFMM is viable but not necessarily desirable.

Each design has its pros and cons, and it is beyond the purpose of this article to dive into them.

However, Superfluid money demonstrably enlarged the design surface of AMM. I believe it should be a safe bet that innovations enabled by Superfluid money are not restricted to AMM and other types of financial contracts such as options, futures, interest rate swaps, etc., should have unique Superfluid money enabled designs too.

While reactive exchanges accurately reflects the underlying technology, a more playful term could be SLAMM (Streaming Liquidity Automatic Market Makers). I will end the article with a slogan:

Let's SLAMM dunk it with Superfluid.