chrisdone

Exploring possibilities with simple indexed fields

Database records and formlets and optionally populated records can be neatly all represented with the same data type when the fields are all indexed.

class Indexed i a where
  type Index i (a :: *)

danidiaz

A series of reading lists mostly related to functional programming.

aras-p

// Just before switching jobs:
// Add one of these.
// Preferably into the same commit where you do a large merge.
//
// This started as a tweet with a joke of "C++ pro-tip: #define private public",
// and then it quickly escalated into more and more evil suggestions.
// I've tried to capture interesting suggestions here.
//
// Contributors: @r2d2rigo, @joeldevahl, @msinilo, @_Humus_,
// @YuriyODonnell, @rygorous, @cmuratori, @mike_acton, @grumpygiant,

codebrainz

D			[0-9]
L			[a-zA-Z_]
H			[a-fA-F0-9]
E			([Ee][+-]?{D}+)
P                       ([Pp][+-]?{D}+)
FS			(f|F|l|L)
IS                      ((u|U)|(u|U)?(l|L|ll|LL)|(l|L|ll|LL)(u|U))

%{
#include <stdio.h>

ChrisPenner

Optics By Example Cheat Sheets

The following are appendices from Optics By Example, a comprehensive guide to optics from beginner to advanced! If you like the content below, there's plenty more where that came from; pick up the book!

• Operators
• Composition Guide
• Substitution Guide
• Optical Constarints and Types

Operator Cheat Sheet

chrisdone

Typing Haskell in Haskell

MARK P. JONES

Pacific Software Research Center

Department of Computer Science and Engineering

Oregon Graduate Institute of Science and Technology

debasishg

General Background and Overview

1. Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
2. Models and Issues in Data Stream Systems
3. Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t

yoavg

Some remarks on Large Language Models

Yoav Goldberg, January 2023

Audience: I assume you heard of chatGPT, maybe played with it a little, and was imressed by it (or tried very hard not to be). And that you also heard that it is "a large language model". And maybe that it "solved natural language understanding". Here is a short personal perspective of my thoughts of this (and similar) models, and where we stand with respect to language understanding.

Intro

Around 2014-2017, right within the rise of neural-network based methods for NLP, I was giving a semi-academic-semi-popsci lecture, revolving around the story that achieving perfect language modeling is equivalent to being as intelligent as a human. Somewhere around the same time I was also asked in an academic panel "what would you do if you were given infinite compute and no need to worry about labour costs" to which I cockily responded "I would train a really huge language model, just to show that it doesn't solve everything!". We

Lysxia

{-# LANGUAGE DeriveFunctor, DeriveFoldable, DeriveTraversable, TypeFamilies, TemplateHaskell #-}

import Data.Functor.Foldable
import Data.Functor.Foldable.TH
import Data.Functor.Compose

-- Example type
data Exp
  = IntValue Int
  | Sum Exp Exp

agocorona

{-# LANGUAGE MultiParamTypeClasses, ExistentialQuantification, ScopedTypeVariables, FlexibleInstances, FlexibleContexts, UndecidableInstances #-}
module TransientCont where  
-- some imports

import Control.Applicative

import Control.Monad.IO.Class

import Control.Monad.Trans