Uri Gilad antishok

## deploy-discord-command.js
const { ContextMenuCommandBuilder } = require('@discordjs/builders');
const { REST } = require('@discordjs/rest');
const { Routes, ApplicationCommandType } = require('discord-api-types/v9');
const config = require('./config/local.json');

const commands = [
		new ContextMenuCommandBuilder()
			.setName('Is user online?')
			.setType(ApplicationCommandType.Message),
	]

## CommentReplyField.jsx
import React, { useRef, useState, useEffect } from 'react';
import classNames from 'classnames';

import { useFeedContext, useTranslationContext, Textarea, Button, Avatar } from 'react-activity-feed';

export const CommentReplyField = ({
  comment,
  emojiData,
  onSuccess,
  image,

## react-native-fetch.js
const config = require('../config');

async function ajax(path, data, {timeout} = {}) {
  const opts = {
    method: data ? 'POST' : 'GET',
    headers: {
      'Accept': 'application/json',
      'Content-Type': 'application/json'
    },
    credentials: 'same-origin'

## index.js
const path = require('path');
const express = require('express');
const expressWs = require('express-ws');
const bodyParser = require('body-parser');
const expressSession = require('express-session');
const KnexSessionStore = require('connect-session-knex')(expressSession);
const passport = require('passport');

const app = express();

## s2s-word.py
import numpy as np
import sys

from keras.models import Model
from keras.layers import Input, Dense, Activation, Embedding, LSTM, RepeatVector
from keras.layers.normalization import BatchNormalization
from keras.layers.wrappers import TimeDistributed
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras import optimizers

## Caddyfile
tls admin@mysite.com {
        protocols tls1.0 tls1.3
        ciphers ECDHE-RSA-AES128-GCM-SHA256 ECDHE-ECDSA-WITH-CHACHA20-POLY1305 ECDHE-ECDSA-AES128-GCM-SHA256 ECDHE-ECDSA-AES256-GCM-SHA384 ECDHE-ECDSA-AES128-CBC-SHA ECDHE-ECDSA-AES256-CBC-SHA ECDHE-RSA-AES256-CBC-SHA
}

## scenaric-sf-package-post-install.md

      
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                antishok
                / scenaric-sf-package-post-install.md
            
            
              Created
              November 21, 2017 22:59
            
              
                Scenaric salesforce package post-install instructions
              
          
    Before using the Scenaric Salesforce app, the administrator must authorize use of the app for certain profiles, users, or permission sets.
To achieve this, we recommend the following steps:

In Setup, go to Manage Apps -> Connected Apps
Click "Edit" next to "ScenaricCanvas" app
Change the "Permitted Users" setting to "Admin approved users are pre-authorized"
Ignore the warning, then click Save
Click the ScenaricCanvas app, then scroll down and click "Manage Profiles"
Select for example "System Administrator" and "Standard User", and click Save


## generate.py
import numpy as np
import sys

from keras.models import Sequential
from keras.layers import Dense, LSTM, Embedding, Activation
from keras.layers.wrappers import TimeDistributed
from keras import backend as K

from utils import Data, weighted_sample

## generate.py
import numpy as np
import sys

from keras.models import Sequential
from keras.layers import Dense, Dropout, LSTM, Embedding, Lambda, Activation
from keras.layers.wrappers import TimeDistributed
from keras import backend as K

filepath = 'input.txt'
weights_file = 'model.best.h5'

## client.html
<html>
  ...

<script>
  fetch('/ping')
    .then((response) => response.json())
    .then((data) => {
      console.log('pong', data);
    });
</script>
	const { ContextMenuCommandBuilder } = require('@discordjs/builders');
	const { REST } = require('@discordjs/rest');
	const { Routes, ApplicationCommandType } = require('discord-api-types/v9');
	const config = require('./config/local.json');

	const commands = [
	new ContextMenuCommandBuilder()
	.setName('Is user online?')
	.setType(ApplicationCommandType.Message),
	]
	import React, { useRef, useState, useEffect } from 'react';
	import classNames from 'classnames';

	import { useFeedContext, useTranslationContext, Textarea, Button, Avatar } from 'react-activity-feed';

	export const CommentReplyField = ({
	comment,
	emojiData,
	onSuccess,
	image,
	const config = require('../config');

	async function ajax(path, data, {timeout} = {}) {
	const opts = {
	method: data ? 'POST' : 'GET',
	headers: {
	'Accept': 'application/json',
	'Content-Type': 'application/json'
	},
	credentials: 'same-origin'
	const path = require('path');
	const express = require('express');
	const expressWs = require('express-ws');
	const bodyParser = require('body-parser');
	const expressSession = require('express-session');
	const KnexSessionStore = require('connect-session-knex')(expressSession);
	const passport = require('passport');

	const app = express();
	import numpy as np
	import sys

	from keras.models import Model
	from keras.layers import Input, Dense, Activation, Embedding, LSTM, RepeatVector
	from keras.layers.normalization import BatchNormalization
	from keras.layers.wrappers import TimeDistributed
	from keras.preprocessing.text import Tokenizer
	from keras.preprocessing.sequence import pad_sequences
	from keras import optimizers
	tls admin@mysite.com {
	protocols tls1.0 tls1.3
	ciphers ECDHE-RSA-AES128-GCM-SHA256 ECDHE-ECDSA-WITH-CHACHA20-POLY1305 ECDHE-ECDSA-AES128-GCM-SHA256 ECDHE-ECDSA-AES256-GCM-SHA384 ECDHE-ECDSA-AES128-CBC-SHA ECDHE-ECDSA-AES256-CBC-SHA ECDHE-RSA-AES256-CBC-SHA
	}
	import numpy as np
	import sys

	from keras.models import Sequential
	from keras.layers import Dense, LSTM, Embedding, Activation
	from keras.layers.wrappers import TimeDistributed
	from keras import backend as K

	from utils import Data, weighted_sample
	<html>
	...

	<script>
	fetch('/ping')
	.then((response) => response.json())
	.then((data) => {
	console.log('pong', data);
	});
	</script>