semvec/pypi-packages huggingface dataset:
Excalibur2 2.4
A simple tool for pwn learners
Requires Python: >=3.8

# Excalibur2

## Description

A simple integrated library for CTFers specializing in PWN challenges.

This Python library includes common functions used in PWN challenges, aiming to simplify the process of exp for PWN challenges, helping pwners save time and improve efficiency in solving problems.

We warmly welcome all masters to use Excalibur, and we also welcome feedback and guidance on our code.

## Install

Install Excalibur2 by 

> pip3 install Excalibur2

check for updates

>  python3 -m Excalibur2.\_\_update\_\_

update package

> pip3 install Excalibur2 --upgrade

## Help

python built-in help

> check help documention
>
> `import Excalibur2`
>
> `help(Excalibur2)`
>
> check help for func
>
> `from Excalibur2 import *`
>
> `help(function)`

more details Please visit https://lmarch2.top/posts/8c945bd4/ 

## Release

### 2.4，Feb 8, 2024

Renamed `contextset` to `setcontext`.
Added a new function `prhl`.
Set the default parameter for `proc` to `./pwn`.
Added automatic address calculation for base addresses.
Fixed bugs in the `csu` and `ropgadget` functions.
Modified some aliases.

### 2.2,  Feb 8, 2024

Fix some bugs due to uncorrect package name

### 2.1,  Feb 8, 2024

Support custom debugging terminal and add check update function

### 2.0,  Feb 8, 2024

The birthday of the package
datamanwithvan 0.1.9
A distributed, parallel datareplication engine that caters to various source/target system combinations and complex corporate environments.
Requires Python: >=3.6

# MyPackage

This is a simple Python package.


A classic problem in every data replication activity is to make the software detect changes in the source datasets and drive replication activities when a change is detected. 

For that purpose we have constructed a separate daemon process that polls continuously the data sources, based on its knowledge for the replication rules and jobs. In the case of CDH/CDP it was literally a multithreaded daemon that was combing over HDFS.

In the case of a local filesystem, we could leverage 

Due to the nature of the data change detection mechanism, we wanted to split the data replication rules into "priority queues". More important data or data that we know change more rapidly, are grouped into higher priority queues, pretty much like (speedy boarding in low-cost airlines!). Less critical data are grouped in lower priority queues, and they're scanned for changes less frequently.