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dare to think for yourself

Alexandre Moreno moleike

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dare to think for yourself
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  • 20:17 (UTC +02:00)
  • X @moleike
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package eveff
import cats.Monad
import cats.Applicative
import cats.Monoid
import cats.Semigroup
import cats.Functor
import cats.MonadThrow
import cats.data.Chain
import cats.data.Ior
// a memoized fibonnaci using field caching
local fib(n) =
local go(n) =
if n <= 1 then
{ ['fib0']: 1, ['fib1']: 1 }
else
go(n - 1) {
['fib'+n]: super['fib'+(n-1)] + super['fib'+(n-2)]
};
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moleike / closconv.lhs
Created June 5, 2021 01:41 — forked from jozefg/closconv.lhs
Tutorial on Closure Conversion and Lambda Lifting
This is my short-ish tutorial on how to implement closures in
a simple functional language: Foo.
First, some boilerplate.
> {-# LANGUAGE DeriveFunctor, TypeFamilies #-}
> import Control.Applicative
> import Control.Monad.Gen
> import Control.Monad.Writer
> import Data.Functor.Foldable
@moleike
moleike / NewEval.hs
Last active May 2, 2021 07:44
a new impl. to fix some shortcomings from how we represent thunks
{-# LANGUAGE RecursiveDo #-}
{-# LANGUAGE LambdaCase #-}
{-# LANGUAGE DeriveGeneric #-}
{-# LANGUAGE DuplicateRecordFields #-}
{-# LANGUAGE FlexibleContexts #-}
{-# LANGUAGE NoMonomorphismRestriction #-}
{-# LANGUAGE TypeApplications #-}
{-# LANGUAGE TemplateHaskell #-}
{-# LANGUAGE OverloadedStrings #-}
{-# LANGUAGE FlexibleInstances #-}
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moleike / benchmark.md
Created March 29, 2021 02:19 — forked from CertainLach/benchmark.md
Jrsonnet performance

Benchmark results

large_string_join

Command Mean [ms] Min [ms] Max [ms] Relative
jrsonnet large_string_join.jsonnet 16.5 ± 0.5 16.1 19.6 1.00
gojsonnet large_string_join.jsonnet 90.7 ± 3.8 85.6 104.0 5.49 ± 0.29
jsonnet large_string_join.jsonnet 69.5 ± 2.4 68.2 78.1 4.20 ± 0.20
sjsonnet large_string_join.jsonnet 760.6 ± 12.7 739.9 787.2 46.00 ± 1.67
@moleike
moleike / benchmarks.md
Last active March 25, 2021 11:09
Jsonnet benchmarks

Benchmark results

Command Mean [ms] Min [ms] Max [ms] Relative
hs-jsonnet bench.01.jsonnet 30.4 ± 5.5 20.8 41.8 1.00
jsonnet bench.01.jsonnet 89.4 ± 5.9 86.6 116.8 2.94 ± 0.57
hs-jsonnet bench.03.jsonnet 4.914 ± 1.423 4.135 8.748 8.51 ± 2.50
jsonnet bench.03.jsonnet 0.577 ± 0.027 0.555 0.633 1.00
hs-jsonnet bench.04.jsonnet 1.455 ± 0.154 1.331 1.834 1.00
jsonnet bench.04.jsonnet 5.670 ± 2.564 4.028 11.537 3.90 ± 1.81
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moleike / CC.hs
Created July 4, 2020 06:17 — forked from atennapel/CC.hs
Calculus of Constructions, normalization-by-evaluation, semantic typechecking
data Tm = Var Int | Ann Tm Tm | Abs Tm | App Tm Tm | Pi Tm Tm | Fix Tm | Uni
data Clos = Clos Tm Env
data Dm = DVar Int | DAbs Clos | DNeutral Int [Dm] | DPi Dm Clos | DFix Clos | DUni
type Env = [Dm]
capp :: Clos -> Dm -> Dm
capp (Clos b e) t = eval (t : e) b
vapp :: Dm -> Dm -> Dm
vapp a b =
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moleike / App.java
Created April 28, 2020 10:03 — forked from thomasdarimont/App.java
Simple AuthN & AuthZ example with Spring Boot / Security / Session
package demo;
import java.io.Serializable;
import java.security.Principal;
import java.util.Collection;
import java.util.Collections;
import java.util.HashMap;
import java.util.Map;
import java.util.UUID;

kafka-workshop

Welcome to the kafka workshop @LINE Taiwan!

Goals

Today session will help you get started with doing some basic stream processing using Kafka Streams and KSQL, and how you can manage materialzed views using Kafka Connect.

In what follows, we will give you a detail step-by-step guide on building your first stream processing application. The application considers 2 data sources:

sealed trait Term
case class Const(name: String) extends Term
case class Var(name: String) extends Term
case class Atom(relation: String, terms: Term*)
sealed trait Clause
case class Rule(head: Atom, body: Atom*) extends Clause
case class Fact(atom: Atom) extends Clause
case class Query(name: String, atoms: Atom*) extends Clause