ageorgios

DUAL I II
TVIR0 = 26004600949312381238123812131213121312131213123812381238121312131213121312131213121312131213121312131213121312381238123812381238123812381238120005a40d0500000000000000000000000000000000 

HDMI
TVIR1 = 26004600949312381238123812131213121312131213123812381238121312131213121312131238121312131213121312131213121312131238123812381238123812381238120005a40d0500000000000000000000000000000000 

POWER?
TVIR2 = 26004600949312381238123812131213121312131213123812381238121312131213121312131213123812131213121312131213121312381213123812381238123812381238120005a40d0500000000000000000000000000000000 

SLEEP TIMER

Snipercaine

############## Configuration.yaml ###################
konnected:
  access_token: !secret konnected_token
  api_host: http://192.168.1.71:8123
  devices:
    - id: dc4ggggb4c4
      binary_sensors:
        - zone: 1
          type: door
          name: Front Door

sidd607

#!/usr/bin/env python

"""
    author: sidd607@gmail.com
    version: 0.0.1

    Reads the buy details from a csv file (coins.csv) and calculates the wallets profit
    Uses Koinex API tracker
    csv format
    Coin,Date,Quantity,BuyPrice,Cost

mmmayo13

import tensorflow.contrib.slim as slim

total_layers = 25 #Specify how deep we want our network
units_between_stride = total_layers / 5

def denseBlock(input_layer,i,j):
    with tf.variable_scope("dense_unit"+str(i)):
        nodes = []
        a = slim.conv2d(input_layer,64,[3,3],normalizer_fn=slim.batch_norm)
        nodes.append(a)

mmmayo13

import tensorflow as tf
import numpy as np
import tensorflow.contrib.slim as slim

total_layers = 25 #Specify how deep we want our network
units_between_stride = total_layers / 5

def highwayUnit(input_layer,i):
    with tf.variable_scope("highway_unit"+str(i)):
        H = slim.conv2d(input_layer,64,[3,3])

mmmayo13

import tensorflow as tf
import numpy as np
import tensorflow.contrib.slim as slim

total_layers = 25 #Specify how deep we want our network
units_between_stride = total_layers / 5

def resUnit(input_layer,i):
    with tf.variable_scope("res_unit"+str(i)):
        part1 = slim.batch_norm(input_layer,activation_fn=None)

alysonla

-- Active issues
-- Count of total active issues in the specified time frame
-- Source: githubarchive public data set via Google BigQuery http://githubarchive.org/

SELECT
	COUNT(DISTINCT JSON_EXTRACT_SCALAR(events.payload, '$.issue.id')) AS events_issue_count
FROM (SELECT * FROM TABLE_DATE_RANGE([githubarchive:day.],TIMESTAMP('2015-09-01'),TIMESTAMP('2016-08-31')))
 AS events

-- 10,723,492 active issues

tresf

<F3>
<RC10,10>Serial Number = <VA1>
<RC60,10>Firmware      = <VA7>
<RC110,10>Special Head  = <VA18>
<RC160,10>Font File     = <VA2>
<RC210,10>Printer Type  = <VA11>
<RC260,10>DPI           = <VA17>
<RC310,10>Expansion Mem = <VA50>
<RC360,10>Path Type     = <VA19>
<RC410,10>Ticket Type   = <VA13>

arthurattwell

<div style="font-family: sans-serif;">
<? var data = valid(); ?>
<form id="form" name="form">
<? if(Object.prototype.toString.call(data) === '[object Array]') { ?>
<? for (var i = 0; i < data.length; i++) { ?>
<? for (var j = 0; j < data[i].length; j++) { ?>
<input type="checkbox" id="ch<?= '' + i + j ?>" name="ch<?= '' + i + j ?>" value="<?= data[i][j] ?>"><?= data[i][j] ?><br>
<? } ?>
<? } ?>
<? } else { ?>

alirobe

### 
###
### UPDATE: For Win 11, I recommend using this tool in place of this script: 
### https://christitus.com/windows-tool/
### https://github.com/ChrisTitusTech/winutil
### https://www.youtube.com/watch?v=6UQZ5oQg8XA
### iwr -useb https://christitus.com/win | iex
###
###