Zack A. Kite zckkte

zckkte / ddd-presentation.md

Created July 31, 2025 09:52

marp	author	title	paginate
true	Zack Kite	DDD in Practice	true

DDD in Practice

zckkte / readJson.go

Last active November 5, 2024 09:14

	func readJson[T any](w http.ResponseWriter, r *http.Request) (T, error) {
	maxBytes := 1_048_576
	r.Body = http.MaxBytesReader(w, r.Body, int64(maxBytes))

	dec := json.NewDecoder(r.Body)
	dec.DisallowUnknownFields()

	var dst T
	if err := dec.Decode(&dst); err != nil {
	return dst, err

zckkte / ValueObject.cs

Created July 28, 2023 13:46

Value object implemented in C#

	using System;
	using System.Collections.Generic;
	using System.Linq;

	namespace FunctionalExtensions
	{
	[Serializable]
	public abstract class ValueObject : IComparable, IComparable<ValueObject>
	{
	private int? _cachedHashCode;

zckkte / mysqldump-cheat.sh

Created July 11, 2023 12:29


	# export mysql database
	mysqldump -u username -p database_name > data-dump.sql

	# Check if a sql script is a legit sql dump file
	head -n 5 prodbck_anon_reduced.sql \| grep dump ; echo $?

	# import

	mysql -u root -p

zckkte / Console.scala

Created March 14, 2022 11:45

Toy functional effect system


	object Console {
	def putStrLn(str: => String) = TIO.effect(println(str))
	}

zckkte / BusinessResult.php

Created May 27, 2021 09:41

BusinessResult implementation in PHP

	<?php


	class BusinessResult
	{
	public const SUCCESS = "success";

	public const FAIL = "fail";

	private string $result;

zckkte / curry.js

Created March 13, 2021 17:01

	function curry(fn) {
	return function curriedFn(...args) {
	if (fn.length !== args.length) {
	return curriedFn.bind(null, ...args);
	}
	return fn.apply(null, args);
	};
	}

zckkte / ReversiAI.hs

Created May 19, 2020 08:32

	-- \/\/\/ DO NOT MODIFY THE FOLLOWING LINES \/\/\/

	module ReversiAI (State,author,nickname,initial,think) where

	import Reversi
	import Data.List
	import Data.Map (fromList, (!))
	import Data.Maybe

	-- /\/\/\ DO NOT MODIFY THE PRECEDING LINES /\/\/\

zckkte / pet_cnn_classifier.py

Created August 28, 2019 13:49

Cat vs dog image classifier

	import os
	import math
	import random
	import shutil
	import numpy as np
	import matplotlib.pyplot as plt
	from time import time
	from functional import seq
	from collections import namedtuple
	import keras

zckkte / visualise_activation.py

Last active August 12, 2019 20:05

Visualise activation in CNN

	from keras.models import Model

	layer_outputs = [layer.output for layer in model.layers]
	activation_model = Model(inputs=model.input, outputs=layer_outputs)
	activations = activation_model.predict(X_train[10].reshape(1,28,28,1))

	def display_activation(activations, col_size, row_size, act_index):
	activation = activations[act_index]
	activation_index=0
	fig, ax = plt.subplots(row_size, col_size, figsize=(row_size2.5,col_size1.5))