If you aren't Happy
Which Dwarf Are You?

I made a framing saw from a kit at a Lee Valley workshop, and it was fabulous. It's great as a coping saw, cutting curves, but the thin blade makes it hard to cut a straight line. So I made another one on my own, using a chunk of bandsaw blade, and this was better for straight cuts. But trying to resaw a 1" thick boards into two 1/2" inch boards was impossible. The cut would wander all over the place. I would practically wind up with veneer. I saw a number of articles and YouTube videos about making a re-sawing saw, and I decided to make one. But I'm not keen on kits. For one thing I'm stingy, and don't like giving away the profit. Besides, it doesn't feel like I made it, if someone else provided the challenging parts. So I made my own. The saw blade is cut from one of three rusty handsaw blades I got for $10 off Facebook Marketplace. I used an angle grinder to cut it out, a little over two inches wide, and to grind three-to-the-inch teeth...

I've been creating classes relating to playing cards using the new Moxie module for the Perl programming language. The objective is to implement the card game Go Fish! as specified at Rosetta Code . The Outside-In View An actual program file should be simple; all the real code should be in testable modules. In this case, play_go_fish.pl takes this to an extreme. #!/usr/bin/env perl use warnings; use strict; use 5.026; use lib '.'; use Game; Game->new()->play(); As of Perl 5.26, the current directory is not automatically part of @INC, the search path for modules, so it is necessary to include it manually. That makes it possible to load the Game module, to instantiate an instance, and play a game. package Game; use Moxie; use lib '.'; use Deck; use Computer; use Human; use Const::Fast; extends 'Moxie::Object'; const my @PLAYERS => qw( human computer ); const my $INITIAL_DEAL_COUNT => 9; A Game.pm object begins like most ot...

Mathematician Rob Bradley posted that the expected length of a best-of-seven series should be about 5 3/4 games, on which basis he expected the New York Yankees to win. Or maybe living close to New York has warped his sense of reality. As a programmer, I felt a compulsion to verify his claim. I could figure out a formula, or list the possibilities (70 possible ways to win or lose 4 games out of 7), but I decided to write a simulation, using the Perl programming language. This explanation may be overly-detailed for programmers, but I want facebook friends with limited programming experience to understand. sub count_games_in_series { my ( $max ) = @_; $max //= 7; my ( $won, $lost) = (0, 0); for my $games ( 1..7) { if ( int rand 2 ) { $won++; } else { $lost++; } say "won $won; lost $lost; total $games" if $VERBOSE>1; return $games if $won == 4 || $lost == 4; ...

Having in the previous article prepared data types for car suits and card ranks, I can now combine them to provide a playing card class, using Stevan Little's Moxie module (version 0.04, so definitely early days.) The goal is to provide an object-oriented paradigm to the Perl 5 programming language which is more sophisticated, more powerful and less verbose than manually bless() -ing hashes. To achieve that goal it needs to be faster and light-weight compared to Moose. Currently, Moxie.pm and and MOP.pm are add-on modules, but eventually, when they are more complete, when the wrinkles have been ironed out, and when they have gained acceptance and a community of users, they might be merged into the Perl core. One significant feature of Moxie is that it reduces boilerplate code. You don't have to specify warnigns or strict . As well, the features or the perl you are using are enabled, among them say , state , signatures , and post_deref . A Simple Moxie Class packag...

Moxie - a new object system for Perl5 Stevan Little created the Moose multiverse to upgrade the Perl 5 programming language's object-oriented system more in line with the wonderfull world of Perl 6. Unfortunately, it's grown into a bloated giant, which has inspired light-weight alternatives Moos, Moo, Mo, and others. Now he's trying to create a modern, efficient OO system that can become built into the language. I've seen a few of his presentations at YAPC (Yet Another Perl Conference, now known as TPC, The Perl Conference), among them ‎p5 mop final final v5 this is the last one i promise tar gz While the package provides some POD documentation about the main module, Moxie, it doesn't actually explain the enum package, Moxie::Enum. But delving into the tests directory reveals its secrets. Creating an Enum package Ranks { use Moxie::Enum; enum by_ARRAY => qw( unused 2 3 4 5 6 7 8 9 10 J Q K A ); enum by_HASH => { 2 => 2, 3 =...

Having recently started a new job, I was exposed to old code with multi-step tests against autovivification in multi-level hashes. You get used to the code you have seen, but in a new environment it‘s irritating and jarring. Moose does not generally have the problem, first because class structure is pre-declared, because values are accessed using accessor functions rather than directly, and because responsibility is delegated down to attributes, avoiding long chains. On the other hand, Moose has it's own overhead, so hand-rolled objects, and bare structures still have their use. If you don‘t protect against autovivification, then mis-spelling a key, or referencing keys which haven‘t been instantiated in this instance, causes those keys to instantly come into existence. #!/usr/bin/perl use warnings; use strict; use Data::Dump 'dump'; use 5.024; my $var = {key1 => {key2 => {key3 => 'a'}}}; say dump $var; if ( $var->{key1}{key2}{key3b}[13]{foo...

Bash can only handle integer arithmetic. To process floats, you need to shell out to 'bc' or some other external utility. The core multiplication changes to: result=`echo "$result + $m * $n" | bc`; Boy, does that slow things down! For each multiplication, you're launching a shell, invoking a program, returning results. The result is 800 multiplications a second. Note this is running on integer matrices, there remain unresolved problems processing floats. One obvious problem is shelling out for every multiplication. More efficient is to accumulate the expression to calculate, and just invoke the external calculator once. Now we only shell out N^2 times, instead of N^3, so increasing matrix size leads to better performance, up to a limit. the 32x32 matrix gives 18x performance compared to the simpler version. This implies the 32x reduction in shelling out is offset by the cost of building the expression. By the time we reach 100x100, overhead costs are...