Filarius

## Неделя 45.csv

          
            Порядковый номер
            Отметка времени
            Ваш ник на пикабу без @

            
              1
              19.07.2023 9:48:10
              simplysoo

            
              2
              19.07.2023 9:48:22
              Danu.naher

            
              3
              19.07.2023 9:49:06
              RTFAN

            
              4
              19.07.2023 9:49:08
              Net.Ware

            
              5
              19.07.2023 9:49:12
              protas84

            
              6
              19.07.2023 9:49:12
              Noctilucous

            
              7
              19.07.2023 9:49:14
              Zvereff86

            
              8
              19.07.2023 9:49:15
              020709

            
              9
              19.07.2023 9:49:17
              Tiadar4ik

## stream.txt
OBS

subme=0 me=dia ref=5 deblock=-6,-6 psy=0 nr=100 bframes=2 b_bias=100 scenecut=0 qpstep=10 keyint=300

for twitch  keyint=120

preset PLACEBO
profile HIGH

## SD_float16_converter.py
import torch
model = torch.load("model.ckpt")
for x in model['state_dict'].keys():
    model['state_dict'][x] = model['state_dict'][x].to(torch.float16)
torch.save(model,"model_f16.ckpt")

## flibusta_repacker.py
from multiprocessing import pool

import zipfile as zf
import json
import os.path
import base64

def remove_binary(text, zipname = None, fb2name=None, image_zip=None):
    ts = text.split(b'<binary')
    image_cnt = 10000

## tts_silero_v4.py
import argparse
import sys

import num2words.base
import transliterate


def process_text(filename,tokens):
    f = open(filename,'r', encoding="utf-8")
    txt :str = f.read()

## pytorch anti astigmatism.py
import argparse
import random

import gym
import numpy as np
from itertools import count

import torch
import torch.nn as nn
import torch.nn.functional as F

## benchmarking high mining.cpp
// 1665-1650 Kh/s
"here is no filter here"

// 1635 Kh/s
if (T == 1) // high mining
            {
                bool good = true;
                for (int bcheck = 0; bcheck < conf.high / 8; bcheck++) {
                    if (sha512_hash[bcheck] != 0xFF)
                    {

## planetside_websocket_help.json
{
  "example event service message payloads": {
    "AchievementEarned": {
      "achievement_id": "",
      "character_id": "",
      "event_name": "AchievementEarned",
      "timestamp": "",
      "world_id": "",
      "zone_id": ""
    },

## websocket_tunnel.py
uri = "wss://some_address:3214"
s = 'greetings'
import asyncio
import websockets

async def hello(websocket, path):
    async with websockets.connect(uri) as client:
        await client.send(s) #optional init on connection start
        while True:
            data = await client.recv()

## get_json_cross_domain.js
//jquery
$.getJSON( "http://domain/path?jsonp=?&callback=?", function(data) {
        console.log(JSON.stringify(data));

    })
Порядковый номер	Отметка времени	Ваш ник на пикабу без @
1	19.07.2023 9:48:10	simplysoo
2	19.07.2023 9:48:22	Danu.naher
3	19.07.2023 9:49:06	RTFAN
4	19.07.2023 9:49:08	Net.Ware
5	19.07.2023 9:49:12	protas84
6	19.07.2023 9:49:12	Noctilucous
7	19.07.2023 9:49:14	Zvereff86
8	19.07.2023 9:49:15	020709
9	19.07.2023 9:49:17	Tiadar4ik
	OBS

	subme=0 me=dia ref=5 deblock=-6,-6 psy=0 nr=100 bframes=2 b_bias=100 scenecut=0 qpstep=10 keyint=300

	for twitch keyint=120

	preset PLACEBO
	profile HIGH
	import torch
	model = torch.load("model.ckpt")
	for x in model['state_dict'].keys():
	model['state_dict'][x] = model['state_dict'][x].to(torch.float16)
	torch.save(model,"model_f16.ckpt")
	from multiprocessing import pool

	import zipfile as zf
	import json
	import os.path
	import base64

	def remove_binary(text, zipname = None, fb2name=None, image_zip=None):
	ts = text.split(b'<binary')
	image_cnt = 10000
	import argparse
	import sys

	import num2words.base
	import transliterate


	def process_text(filename,tokens):
	f = open(filename,'r', encoding="utf-8")
	txt :str = f.read()
	import argparse
	import random

	import gym
	import numpy as np
	from itertools import count

	import torch
	import torch.nn as nn
	import torch.nn.functional as F
	// 1665-1650 Kh/s
	"here is no filter here"

	// 1635 Kh/s
	if (T == 1) // high mining
	{
	bool good = true;
	for (int bcheck = 0; bcheck < conf.high / 8; bcheck++) {
	if (sha512_hash[bcheck] != 0xFF)
	{
	{
	"example event service message payloads": {
	"AchievementEarned": {
	"achievement_id": "",
	"character_id": "",
	"event_name": "AchievementEarned",
	"timestamp": "",
	"world_id": "",
	"zone_id": ""
	},
	uri = "wss://some_address:3214"
	s = 'greetings'
	import asyncio
	import websockets

	async def hello(websocket, path):
	async with websockets.connect(uri) as client:
	await client.send(s) #optional init on connection start
	while True:
	data = await client.recv()
	//jquery
	$.getJSON( "http://domain/path?jsonp=?&callback=?", function(data) {
	console.log(JSON.stringify(data));

	})