💻 GHOSTCREW — A Python AI tool for penetration testers and security professionals that conducts vulnerability searches in any services.

It works like a red team within your system. You describe the task in plain language — then it plans the attack itself, selects tools, and proceeds through the entire process: from reconnaissance to reporting. Without manual fiddling and endless commands.

What it can do in practice:
➡️ Checks everything: code, business logic, network traffic, protocols.
➡️ Analyzes the found vulnerabilities and explains where the problem is and how to fix it.
➡️ Works autonomously — you just launch it and get a full-fledged research.
➡️ Connects MCP servers and tools (nmap, metasploit, ffuf, etc.) itself.
➡️ Uses Pentesting Task Trees for meaningful decision-making, not just brute force.
➡️ Supports ready-made workflows for comprehensive checks.
➡️ Generates detailed reports in Markdown with facts and recommendations.
➡️ Remembers the dialogue context and doesn't "get lost" after a couple of requests.
➡️ Sees real files: wordlists, payloads, configs — and uses them in its work.
➡️ Allows you to choose an AI model and customize its behavior.
➡️ No registration and no restrictions.

⚙️ Installation:

git clone https://github.com/GH05TCREW/ghostcrew.git
cd ghostcrew
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

▶️ Usage:

python main.py

⚠️ The information is provided solely for informational purposes. And it encourages to pay attention to security issues.

♎️ GitHub/Instructions

#python #soft #github

😰 A repository of more than 100+ ready-made Python scripts that solve a bunch of tasks - without reinventing the wheel and suffering at night.

💬 parsing and searching on the internet;
💬 working with photos and videos;
💬 keyloggers and password managers;
💬 cloning websites;
💬 automating routines;
💬 and dozens of other useful things for real cases.

🔥 Ready-made practice + code, suitable for both learning and work.

⬇️ Save it, it will definitely come in handy!

#python #soft #github

🔥 Generating fake data in Python — no pain at all

If you're testing forms, mockups, or just want to play with data, there's Mimesis — a generator of fake data. Names, emails, addresses, and phone numbers. There's a location setting that allows you to select a country, and the data will be generated accordingly.

📦 Installation:

from typing import Dict
from mimesis.enums import Gender
from mimesis import Person

def generate_fake_user(locale: str = "es", gender: Gender = Gender.MALE) -> Dict[str, str]:
    """
    Generates fake user data based on the locale and gender.

    :param locale: The locale (for example, 'ru', 'en', 'es')
    :param gender: The gender (Gender.MALE or Gender.FEMALE)
    :return: A dictionary with the fake user data
    """
    person = Person(locale)

    user_data = {
        "name": person.full_name(gender=gender),
        "height": person.height(),
        "phone": person.telephone(),
        "occupation": person.occupation(),
    }

    return user_data

if __name__ == "__main__":
    fake_user = generate_fake_user(locale="es", gender=Gender.MALE)
    print(fake_user)

📌 Result:

{
  'name': 'Carlos Herrera',
  'height': '1.84',
  'phone': '912 475 289',
  'occupation': 'Arquitecto'
)

⚡️ Mimesis can:
🖱 Generate names, addresses, phone numbers, professions, etc. 
🖱 Work with different countries (🇷🇺 ru, 🇺🇸 en, 🇪🇸 es, etc.) 
🖱 Suitable for tests, fake accounts, demo data in projects, and bots.

⚙️ GitHub/Instructions

Save it, it'll come in handy 👍

#python #github #interview

Working with f-strings: more possibilities than it seems!

f-strings often replace .format() in everyday code, but their capabilities are not always fully utilized. They support formatting, function calls, working with data structures, and convenient debugging (from 3.8+).

f-strings are convenient for aligning columns without additional tools. This makes the output readable in the CLI and logs:

rows = [
    ("id", "name", "role"),
    (1, "Ivan", "admin"),
    (2, "Olga", "editor"),
]

for r in rows:
    print(f"{r[0]:<5} {r[1]:<10} {r[2]:<10}")

Debug expressions (Python 3.8+): {x=> displays the name and value of the variable, which speeds up debugging. Supports formatting of calculations:

x = 12
y = 7
print(f"{x=} {y=} {x*y=} x/y={x/y:.3f}")

Specifiers !r, !a: !r - repr(), !a - ascii() for unambiguous logs. Eliminates ambiguities in the output of objects:

path = "/var/data/config.yaml"
print(f"{path!r} {path!a}")  # repr and ascii()

Specifiers support width and padding, for example 08d for zeros. This is convenient for reports and IDs:

n = 42
print(f"{n:08d}")  # → #00000042

You can access dictionaries and immediately calculate metrics, for example len():

data = {"user": "Ivan", "items": [1, 2, 3]}
print(f"{data['user&#39]}=», items={data['items&#39]}")
print(f"len(data['items&#39])={len(data['items&#39])}")

🔥 f-strings are a cool tool for formatting, logging, and debugging, if you apply them taking into account the version of Python and the context of the output.

🚪 @DataScience4

Python Clean Code: Stop Writing Bad Code — Lessons from Uncle Bob

Are you tired of writing messy and unorganized code that leads to frustration and bugs? You can transform your code from a confusing mess into something crystal clear with a few simple changes. In this article, we'll explore key principles from the book "Clean Code" by Robert C. Martin, also known as Uncle Bob, and apply them to Python. Whether you're a web developer, software engineer, data analyst, or data scientist, these principles will help you write clean, readable, and maintainable Python code.

Read: https://habr.com/en/articles/841820/

https://t.iss.one/CodeProgrammer 🧠

This channels is for Programmers, Coders, Software Engineers.

0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages

✅ https://t.iss.one/addlist/8_rRW2scgfRhOTc0

✅ https://t.iss.one/Codeprogrammer