Ddos Collection - MrZx
Open in Telegram
Boost This Channel I will be excited Free Leak From @CeoFork, @Nossynotyoursy. @mscjs @Jow2x @fly1x Only for educational purpose. Group leak anti Advertising/iklan Also Join : t.me/NusaStresser
Show moreThe country is not specifiedTechnologies & Applications50 142
328
Subscribers
No data24 hours
+17 days
+1230 days
Data loading in progress...
Similar Channels
No data
Any problems? Please refresh the page or contact our support manager.
Tags Cloud
No data
Any problems? Please refresh the page or contact our support manager.
Incoming and Outgoing Mentions
---
---
---
---
---
---
Attracting Subscribers
November '24
November '24
+2
in 0 channels
October '24
+35
in 1 channels
Get PRO
September '24
+108
in 3 channels
Get PRO
August '240
in 0 channels
Get PRO
July '240
in 0 channels
Get PRO
June '240
in 0 channels
Get PRO
May '240
in 0 channels
Get PRO
April '24
+13
in 4 channels
Get PRO
March '240
in 0 channels
Get PRO
February '24
+182
in 0 channels
| Date | Subscriber Growth | Mentions | Channels | |
| 06 November | 0 | |||
| 05 November | 0 | |||
| 04 November | +2 | |||
| 03 November | 0 | |||
| 02 November | 0 | |||
| 01 November | 0 |
Channel Posts
Beta release browser only 10$ with local ai (Can Detect Protection)
feature:(Only Cf Captcha)
âreconnection âconnection âbrowser âflood âemulation âdratelimitdemo: demo attack dstat Alpha release browser only 50$ with local ai (Recaptcha Ai, Captcha Text Ai, Protection Detect) feature: (Recaptcha, turnstile, Captcha Text(Costum Element))
âreconnection âconnection âbrowser âflood âemulation âdratelimit âprotect++:
Free Module Bypass Free 80% Dataset Recaptcha Free 95% Dataset Captcha Text Free update 2xdemo Captcha Text: demo demo Recaptcha: Soon
| 2 | free db stressop | 73 |
| 3 | This code is made to make it easier for you to hold the target of the ddos attack, this code can be used easily and the code uses fire to send attacks to the target
note:
this code only for hold with api, this code can spam attack
https://t.me/jo1el/103
cr: @mscjs @jo1el @fly1x | 414 |
| 4 |  No text... | 275 |
| 5 | +1 This code to complete recaptcha image v2 this code can complete 3x3 recaptcha image, this code will split 3x3 into 9 images and be processed for completion. this code can solver with an
accuracy rate of 75%
Output this code model2.js like:
click = [
true, false, false,
true, false, false,
false, false, true ];
do you want solver?
turnstile
recaptcha 90%
captcha text 95%
@mscjs
@mscjs @fly1x @jo1el | 414 |
| 6 |  I have succeeded in creating an "ai" model that can complete the captcha text challenge with a 95% accuracy rate and this ai can complete it quickly
Note:
I will give free code with beta version using tesseract js can solve 50%, 5heart reactions i will share
@mscjs @fly1x @jo1el | 354 |
| 7 |  from PIL import Image
def split_image(image_path, grid_size=(3, 3), save=False):
image = Image.open(image_path)
width, height = image.size
grid_width = width // grid_size[0]
grid_height = height // grid_size[1]
images = []
for i in range(grid_size[0]):
for j in range(grid_size[1]):
left = i * grid_width
upper = j * grid_height
right = left + grid_width
lower = upper + grid_height
cropped_img = image.crop((left, upper, right, lower))
images.append(cropped_img)
if save:
cropped_img.save(f"grid_{i}_{j}.png")
return images
image_path = "3x3.png"
split_images = split_image(image_path, grid_size=(3, 3), save=True)
for idx, img in enumerate(split_images):
print(f"Grid {idx+1}: {img.size}")
this code can split images recaptcha v2
This makes it easier to complete recaptcha v2
@mscjs @fly1x @jo1el | 358 |
| 8 |  soon i will send demo ai with 95% can solve captcha text with model onnx | 359 |
| 9 | nodejs code example to load plugin.js code from server efficiently and can update client.js from server
you can load your code ddos with this code. This code is made to support the sale of DDoS code so that the original source remains safe.
cr: @mscjs @fly1x @jo1el | 445 |
| 10 | [+] DNS Resolver
[+] Fingerprint Generator
[+] User-Agent Creator
[+] Proxy Loader
[+] Request Sender
[+] TLS Handler
[+] Cluster Controller
// Develop: @mscjs from @leaksmain thx @leaksmain | 736 |
| 11 | i will share browser can bypass captcha(cf/turnstile)? | 490 |
| 12 | sudo apt-get install libnghttp2-dev
sudo apt-get install libboost-system-dev libssl-dev
g++ -std=c++11 -o storm h2_storm.cpp -lboost_system -lnghttp2_asio -lssl -lcrypto -lpthread
./storm METHOD(GET/POST) TARGET TIME THREADS RATE optional[--postdata DATA]
no with proxyđ
@jo1el @mscjs @fly1x | 546 |
| 13 | I will apply this stresser application again with various features. give support with this reaction chat âïž | 450 |
| 14 | sudo apt update
sudo apt install build-essential libssl-dev libnghttp2-dev libpthread-stubs0-dev
g++ -o lumina main.c -lnghttp2 -lssl -lcrypto -lpthread
http-lumina HTTP/2 with random headers
cr: @jo1el @mscjs @fly1x | 686 |
| 15 | Go 300 subs will share c simple flood code | 412 |
| 16 | const cheerio = require('cheerio');
const natural = require('natural');
const tokenizer = new natural.WordTokenizer();
const TfIdf = natural.TfIdf;
const tfidf = new TfIdf();
const rateLimitKeywords = ['too many requests', 'rate limit', 'exceeded limit', 'slow down', 'retry later', 'temporarily unavailable'];
const checkSimilarity = (text) => {
let isRateLimited = false;
tfidf.addDocument(text);
rateLimitKeywords.forEach((keyword) => {
tfidf.tfidfs(keyword, function(i, measure) {
if (measure > 0.1) {
isRateLimited = true;
}
});
});
return isRateLimited;
};
const detectRateLimit = (htmlContent) => {
const $ = cheerio.load(htmlContent);
const bodyText = $('body').text().toLowerCase();
const titleText = $('title').text().toLowerCase();
const h1Text = $('h1').text().toLowerCase();
const pText = $('p').text().toLowerCase();
const combinedText = `${bodyText} ${titleText} ${h1Text} ${pText}`;
const tokens = tokenizer.tokenize(combinedText);
const keywordDetected = rateLimitKeywords.some(keyword => {
return tokens.join(' ').includes(keyword);
});
const tfidfResult = checkSimilarity(combinedText);
return keywordDetected || tfidfResult;
};
Caller:
const isRateLimited = detectRateLimit(/*response data*/);
if (isRateLimited) {
console.log('Ratelimit detect [429].');
}
The detectRateLimit(htmlContent) function analyzes the HTML content by checking for specific keywords related to rate limits and uses Tf-Idf to assess the semantic relevance. If any indicators are found, it prints a message indicating that a rate limit has been detected.
ch: @jo1el
cr: @mscjs | 482 |
| 17 |  const slider = await page.waitForSelector('#slider');
const sliderContainer = await page.$('#sliderContainer');
elemen container
const containerBox = await sliderContainer.boundingBox();
await page.mouse.move(containerBox.x + 10, containerBox.y + containerBox.height / 2);
await page.mouse.down();
await page.mouse.move(containerBox.x + containerBox.width - 20, containerBox.y + containerBox.height / 2, { steps: 20 });
await page.mouse.up();
puppeteer code to perform automatic element shifting
auto sliderrr
cr: @mscjs
ch: @jo1el | 462 |
| 18 | const cluster = require('cluster');
const os = require('os');
const brain = require('brain.js');
const trainingData = [
{ input: { cpus: 2, load: 0.1, memory: 0.1, ioIntensity: 0.2, networkLoad: 0.1 }, output: [1] },
{ input: { cpus: 2, load: 0.3, memory: 0.2, ioIntensity: 0.3, networkLoad: 0.2 }, output: [1] },
{ input: { cpus: 2, load: 0.5, memory: 0.5, ioIntensity: 0.4, networkLoad: 0.3 }, output: [2] },
{ input: { cpus: 2, load: 0.7, memory: 0.6, ioIntensity: 0.5, networkLoad: 0.4 }, output: [2] },
{ input: { cpus: 2, load: 0.9, memory: 0.8, ioIntensity: 0.7, networkLoad: 0.6 }, output: [3] },
{ input: { cpus: 4, load: 0.1, memory: 0.1, ioIntensity: 0.2, networkLoad: 0.1 }, output: [1] },
{ input: { cpus: 4, load: 0.3, memory: 0.2, ioIntensity: 0.3, networkLoad: 0.2 }, output: [2] },
{ input: { cpus: 4, load: 0.5, memory: 0.4, ioIntensity: 0.4, networkLoad: 0.3 }, output: [3] },
{ input: { cpus: 4, load: 0.7, memory: 0.6, ioIntensity: 0.6, networkLoad: 0.4 }, output: [4] },
{ input: { cpus: 4, load: 0.9, memory: 0.8, ioIntensity: 0.8, networkLoad: 0.6 }, output: [5] },
{ input: { cpus: 8, load: 0.1, memory: 0.2, ioIntensity: 0.2, networkLoad: 0.1 }, output: [2] },
{ input: { cpus: 8, load: 0.3, memory: 0.3, ioIntensity: 0.4, networkLoad: 0.2 }, output: [4] },
{ input: { cpus: 8, load: 0.5, memory: 0.5, ioIntensity: 0.5, networkLoad: 0.3 }, output: [5] },
{ input: { cpus: 8, load: 0.7, memory: 0.7, ioIntensity: 0.6, networkLoad: 0.5 }, output: [6] },
{ input: { cpus: 8, load: 0.9, memory: 0.9, ioIntensity: 0.8, networkLoad: 0.7 }, output: [7] },
{ input: { cpus: 16, load: 0.1, memory: 0.2, ioIntensity: 0.3, networkLoad: 0.1 }, output: [3] },
{ input: { cpus: 16, load: 0.3, memory: 0.4, ioIntensity: 0.4, networkLoad: 0.3 }, output: [6] },
{ input: { cpus: 16, load: 0.5, memory: 0.5, ioIntensity: 0.6, networkLoad: 0.5 }, output: [8] },
{ input: { cpus: 16, load: 0.7, memory: 0.7, ioIntensity: 0.7, networkLoad: 0.6 }, output: [10] },
{ input: { cpus: 16, load: 0.9, memory: 0.9, ioIntensity: 0.9, networkLoad: 0.8 }, output: [12] },
{ input: { cpus: 32, load: 0.1, memory: 0.3, ioIntensity: 0.2, networkLoad: 0.1 }, output: [4] },
{ input: { cpus: 32, load: 0.3, memory: 0.4, ioIntensity: 0.5, networkLoad: 0.2 }, output: [8] },
{ input: { cpus: 32, load: 0.5, memory: 0.6, ioIntensity: 0.6, networkLoad: 0.4 }, output: [12] },
{ input: { cpus: 32, load: 0.7, memory: 0.8, ioIntensity: 0.7, networkLoad: 0.6 }, output: [16] },
{ input: { cpus: 32, load: 0.9, memory: 0.9, ioIntensity: 0.9, networkLoad: 0.8 }, output: [20] },
{ input: { cpus: 64, load: 0.1, memory: 0.2, ioIntensity: 0.2, networkLoad: 0.2 }, output: [6] },
{ input: { cpus: 64, load: 0.3, memory: 0.4, ioIntensity: 0.4, networkLoad: 0.3 }, output: [12] },
{ input: { cpus: 64, load: 0.5, memory: 0.5, ioIntensity: 0.5, networkLoad: 0.5 }, output: [16] },
{ input: { cpus: 64, load: 0.7, memory: 0.7, ioIntensity: 0.7, networkLoad: 0.7 }, output: [20] },
{ input: { cpus: 64, load: 0.9, memory: 0.9, ioIntensity: 0.8, networkLoad: 0.8 }, output: [24] }
];
const net = new brain.NeuralNetwork();
net.train(trainingData);
const numCPUs = os.cpus().length;
const currentLoad = 0.7;
const predictedWorkers = Math.round(net.run({ cpus: numCPUs, load: currentLoad })[0]);
if (cluster.isMaster) {
console.log(`Master process ${process.pid} is running`);
for (let i = 0; i < predictedWorkers; i++) {
cluster.fork();
}
cluster.on('exit', (worker, code, signal) => {
console.log(`Worker ${worker.process.pid} died`);
});
} else {
console.log(`Worker ${process.pid} started`);
const http = require('http');
http.createServer((req, res) => {
res.writeHead(200);
res.end('kontol\n');
}).listen(8000);
}
Ai logic auto chooses the best threads to use on cluster module
@jo1el | 499 |
| 19 | tresh flooder leakan yang katanya no leak leak | 793 |
| 20 | LINTAR BROWSER
LEAK BY @jo1el trash browser dont buy it
flood source on @jo1el
Don't buy from @Lintar21 later he will regret it like my friend | 1 967 |
