todbot

# udp_recv_code.py -- receive UDP messages from any receiver, can be another CircuitPython device
# 24 Aug 2022 - @todbot / Tod Kurt
# cribbing from code at https://github.com/adafruit/circuitpython/blob/main/tests/circuitpython-manual/socketpool/datagram/ntp.py

import time, wifi, socketpool
from secrets import secrets
print("Connecting to WiFi...")
wifi.radio.connect(ssid=secrets['ssid'],password=secrets['password'])
print("my IP addr:", wifi.radio.ipv4_address)
pool = socketpool.SocketPool(wifi.radio)

idelem

javascript:(function() {

function copyToClipboard(text) {
    if (window.clipboardData && window.clipboardData.setData) {
        /*IE specific code path to prevent textarea being shown while dialog is visible.*/
        return clipboardData.setData("Text", text); 

    } else if (document.queryCommandSupported && document.queryCommandSupported("copy")) {
        var textarea = document.createElement("textarea");
        textarea.textContent = text;

Versatilus

from __future__ import print_function, unicode_literals

from ctypes import windll

from dragonfly import (ActionBase, Choice, Grammar, IntegerRef, Key,
                       MappingRule, Mouse, Pause, Repeat)
from dragonfly.actions.keyboard import Keyboard, KeySymbols

KEYBOARD = Keyboard()

alexboche

from dragonfly import *
import nsformat

dictation_length = [] # for "scratch that" purposes
input_state = None
""" Note:  if you pass in the input state into the function using the function action rather than just accessing it
from within the function using the fact that it is in global scope,  the input state will not be updated 
 when you run the dictation command multiple times
To see what I mean note that in contrast to the approach here, the approach taken
at the following link does not update the state after each dictation utterance 

alexboche

------------------
Grammar(chrome): 3 rules (1 exported, 0 imported):
- ChromeRule(chrome)                                 1571  
  - Alternative(...)                                   1570  
    - Compound('[<click_by_voice_options>] <numbers>')   376   
      - Sequence(...)                                      375   
        - Optional(...)                                      18    
          - Choice(..., name='click_by_voice_options')         17    
            - Compound('click')                                  2      (+   1 trivial direct child)
            - Compound('copy')                                   2      (+   1 trivial direct child)

alexboche

#
# This file is a command-module for Dragonfly.
# (c) Copyright 2008 by Christo Butcher
# Licensed under the LGPL, see <http://www.gnu.org/licenses/>
#
"""
Command-module for Chrome

"""

vadimkantorov

# Usage: python3 split_audio_by_silence.py -i input_audio.m4a -o segments
#  will save segments in mp3 format into the segments directory
#  based on https://github.com/mozilla/DeepSpeech/tree/master/examples/vad_transcriber
# Dependencies: webrtcvad

import os
import argparse
import collections
import subprocess
import webrtcvad

caspark

# to get started, try `print get_grammar_complexity_tree(some_grammar, threshold=5)`.
# If you don't get any interesting output, turn up the threshold (max depth visualized) to something like 7 or 10 :)

class ComplexityNode(object):
    def __init__(self, item):
        self.item = item
        self.children = []
        self.total_descendents = 1

crittermike

# One liner
wget --recursive --page-requisites --adjust-extension --span-hosts --convert-links --restrict-file-names=windows --domains yoursite.com --no-parent yoursite.com

# Explained
wget \
     --recursive \ # Download the whole site.
     --page-requisites \ # Get all assets/elements (CSS/JS/images).
     --adjust-extension \ # Save files with .html on the end.
     --span-hosts \ # Include necessary assets from offsite as well.
     --convert-links \ # Update links to still work in the static version.

awni

"""
Author: Awni Hannun

This is an example CTC decoder written in Python. The code is
intended to be a simple example and is not designed to be
especially efficient.

The algorithm is a prefix beam search for a model trained
with the CTC loss function.