woudsma

Converting samples before import to JoMoX Alpha Base

Samples need to be 16 bit / 48kHz mono wav files.

Requirements:

• Zsh (run zsh --version to check if it's installed already)
• FFmpeg (brew install ffmpeg)

This guide is for UNIX (MacOS or Linux) users, the process would be similar on Windows machines with FFmpeg though. The required FFmpeg options are:

ffmpeg -i $INPUT -acodec pcm_s16le -ac 1 -ar 48000 $OUTPUT

woudsma

# Example repository with Dockerfile and NGINX config: https://github.com/woudsma/es6-boilerplate

name: Deploy to Dokku

on:
  pull_request:
    types:
      - closed
    branches:
      - develop

woudsma

function gitignore() {
  TAGS="${@}"
  TAGS="${TAGS/mac/macos}"
  GITIGNORE_IO_URL="https://www.toptal.com/developers/gitignore/api/${TAGS// /,}"
  GITIGNORE_FILE="$(pwd)/.gitignore"
  echo "--> Fetching: $GITIGNORE_IO_URL"
  echo "--> Writing: $GITIGNORE_FILE"
  curl -fsSL "$GITIGNORE_IO_URL" >> "$GITIGNORE_FILE"
}

woudsma

#!/bin/bash
###
### my-script — does one thing well
###
### Usage:
###   my-script <input> <output>
###
### Options:
###   <input>   Input file to read.
###   <output>  Output file to write. Use '-' for stdout.

woudsma

# Quick and dirty script for streaming a video file to Twitch (as if it were live)
# Requirements: ffmpeg (http://www.ffmpeg.org)

# Copy-paste the function and call it in your command line
# Or add the function to ~/.bash_profile, ~/.zshrc, ..etc.

# Usage:
# twitch STREAM_KEY FILEPATH

# Example:

woudsma

Dokku ACL plugin

# Dokku host
dokku plugin:install https://github.com/dokku-community/dokku-acl.git acl

dokku ssh-keys:add guest guest-key.pub
dokku ssh-keys:list

mkdir ~dokku/.dokkurc
echo "export DOKKU_ACL_ALLOW_COMMAND_LINE=1" >> ~dokku/.dokkurc/acl

woudsma

import math
from bigfloat import *

class LazyCartesianProduct:
    def __init__(self, sets, context):
        self.sets = sets
        self.context = context
        self.divs = []
        self.mods = []
        self.maxSize = BigFloat.exact(1)

woudsma

Retrain a MobileNet model and use it in the browser with TensorFlow.js

DRAFT

Combining TensorFlow for Poets and TensorFlow.js.
Retrain a MobileNet V1 or V2 model on your own dataset using the CPU only.
I'm using a MacBook Pro without Nvidia GPU.

MobileNets can be used for image classification. This guide shows the steps I took to retrain a MobileNet on a custom dataset, and how to convert and use the retrained model in the browser using TensorFlow.js. The total time to set up, retrain the model and use it in the browser can take less than 30 minutes (depending on the size of your dataset).

Example app - HTML/JS and a retrained MobileNet V1/V2 model.

woudsma

#!/bin/bash

echo "Compositing files and ramping up video framerate"
echo "================================================"

rm -rf render && mkdir render

for file in *.mov; do
	# Ramp up video framerate, add audio
	ffmpeg \

woudsma

npm i k-d-tree

const kd = require('k-d-tree')

const data = new Array(1000)
  .fill()
  .map(() => ({
 type: "Point",