soupi

#!/usr/bin/env cabal
{- cabal:
build-depends: base, mtl, containers, uniplate
ghc-options: -Wall
-}

-- | An example of a kind inference for data types using
-- unification-based constraint solving.
--
-- See the blog post:

AndrasKovacs

Garbage collection with zero cost at non-GC time

Every once in a while I investigate low-level backend options for PL-s, although so far I haven't actually written any such backend for my projects. Recently I've been looking at precise garbage collection in popular backends, and I've been (like on previous occasions) annoyed by limitations and compromises.

I was compelled to think about a system which accommodates precise relocating GC as much as possible. In one extreme configuration, described in this note, there

p4bl0-

This project is a tiny compiler for a very simple language consisting of boolean expression.

The language has two constants: 1 for true and 0 for false, and 4 logic gates: ! (not), & (and), | (or), and ^ (xor).

It can also use parentheses to manage priorities.

Here is its grammar in BNF format:

expr ::= "0" | "1"

serras

Program Analysis, a Big Happy Family

The idea behind program analysis is simple, right? You just want to know stuff about your program before it runs, usually because you don't want unexpected problems to arise (those are better in movies.) Then why looking at Wikipedia gives you headaches? Just so many approaches, tools, languages 🤯

In this article I would like to give a glimpse of an overarching approach to program analysis, based on ideas from abstract interpretation. My goal is not to pinpoint a specific technique, but rather show how they have common core concepts, the differences being due mostly to algorithmic challenges. In other words, static analysis have a shared goal, but it's a challenge to make them precise and performant.

Code is meant to be executed by a computer. Take the following very simple function:

fun cantulupe(x) = {

lukaszlew

-- Based on: http://augustss.blogspot.com/2007/10/simpler-easier-in-recent-paper-simply.html
import Data.List (delete, union)
{- HLINT ignore "Eta reduce" -}

-- File mnemonics:
--   env = typing environment
--   vid = variable identifier in Bind or Var
--   br = binder variant (Lambda or Pi)
--   xyzTyp = type of xyz
--   body = body of Lambda or Pi abstraction

domenkozar

1. Put ci.yaml into .github/workflows/
2. Follow https://github.com/cachix/cachix-action#usage to setup Cachix and benefit from quick CI build times.

Lysxia

{-# LANGUAGE
    DeriveGeneric,
    FlexibleInstances,
    FlexibleContexts,
    AllowAmbiguousTypes,
    ScopedTypeVariables,
    TypeApplications,
    TypeFamilies,
    TypeOperators,
    PartialTypeSignatures,

aljce

# Using a specific checkout of nixpkgs makes it easy to debug as you know exactly what the deps are.
# This being said in production you should pass the global nixpkgs to this file.
let nixpkgs-src = builtins.fetchTarball {
      url = "https://github.com/NixOS/nixpkgs/archive/56d94c8c69f8cac518027d191e2f8de678b56088.tar.gz";
      sha256 = "1c812ssgmnmh97sarmp8jcykk0g57m8rsbfjg9ql9996ig6crsmi";
    };
in
{ pkgs ? (import nixpkgs-src {}) }:
with pkgs;
with stdenv.lib;

abiodun0

module RedBlackTree
  (
    Tree,
    empty,
    member,
    insert)
where

data Color = R | B deriving Show

tazjin

Nix builder for Kubernetes

Table of Contents

• Nix builder for Kubernetes
  • Overview
  • Why?
• How?