November 15, 2021 DNS

A high-performance DNS stub resolver for bulk lookups and reconnaissance (subdomain enumeration)

A high-performance DNS stub resolver MassDNS is a simple high-performance DNS stub resolver targeting those who seek to resolve a massive amount of domain names in the order of millions or even billions. Without special configuration, MassDNS is capable of resolving over 350,000 names per second using publicly available resolvers. Contributors Compilation Clone the git repository and cd into the project root folder. Then run make to build from source. If you are not on Linux, run make nolinux. On […]

November 15, 2021 Converter

Interactive convnet features visualization for Keras

Interactive convnet features visualization for Keras The quiver workflow Video Demo Build your model in keras Launch the visualization dashboard with 1 line of code quiver_engine.server.launch(model, classes=[‘cat’,’dog’], input_folder=’./imgs’) Explore layer activations on all the different images in your input folder. Quickstart Installation pip install quiver_engine If you want the latest version from the

November 15, 2021 HTTP

Take a list of domains and probe for working HTTP and HTTPS servers

Take a list of domains and probe for working http and https servers. Install ▶ go get -u github.com/tomnomnom/httprobe Basic Usage httprobe accepts line-delimited domains on stdin: ▶ cat recon/example/domains.txt example.com example.edu example.net ▶ cat recon/example/domains.txt | httprobe http://example.com http://example.net http://example.edu https://example.com https://example.edu https://example.net By default httprobe checks for HTTP on port 80 and HTTPS on port 443. You can add additional probes with the -p flag by specifying a protocol and port pair: ▶ cat domains.txt | httprobe -p […]

November 15, 2021 Histogram

A conda-smithy repository for boost-histogram

Home: https://github.com/scikit-hep/boost-histogram Package license: BSD-3-Clause Feedstock license: BSD-3-Clause Summary: The official Boost.Histogram Python bindings. Provides fast, efficient histogramming with a variety of different storages combined with dozens of composable axes. Part of the Scikit-HEP family. Development: https://github.com/scikit-hep/boost-histogram Documentation: https://boost-histogram.readthedocs.io Drone Azure Name Downloads Version Platforms Installing boost-histogram from the conda-forge channel can be achieved by adding conda-forge to your channels with: conda config –add channels conda-forge conda config –set channel_priority strict Once the conda-forge channel has been enabled, boost-histogram can […]

November 15, 2021 App

Core Python libraries ported to MicroPython

This is a repository of libraries designed to be useful for writing MicroPython applications. The libraries here fall into four categories corresponding to the four top-level directories: python-stdlib: Compatible versions of modules from the Python Standard Library. These should be drop-in replacements for the Python libraries, although many have reduced functionality or missing methods or classes (which may not be an issue for many most cases). python-ecosys: Compatible, but reduced-functionality versions of modules from the larger Python ecosystem, for example […]

November 15, 2021 Generator

Python bindings for JIGSAW: a Delaunay-based unstructured mesh generator

JIGSAW: An unstructured mesh generator JIGSAW is an unstructured mesh generator and tessellation library; designed to generate high-quality triangulations and polyhedral decompositions of general planar, surface and volumetric domains. JIGSAW includes refinement-based algorithms for the construction of new meshes, optimisation-driven techniques for the improvement of existing grids, as well as routines to assemble (restricted) Delaunay tessellations, Voronoi complexes and Power diagrams. This package provides a Python based scripting interface to the underlying JIGSAW mesh generator, including a range of additional […]

November 15, 2021 Python

Securely Deploy a Django App With Gunicorn, Nginx, & HTTPS

Taking a Django app from development to production is a demanding but rewarding process. This tutorial will take you through that process step by step, providing an in-depth guide that starts at square one with a no-frills Django application and adds in Gunicorn, Nginx, domain registration, and security-focused HTTP headers. After going over this tutorial, you’ll be better equipped to take your Django app into production and serve it to the world. To make the most out of this tutorial, […]

November 14, 2021 PDF

pdf_sprinkles: sprinkles text in your PDFs

pdf_sprinkles remotely OCRs a PDF with Google Cloud Document AI, and returns the result as a PDF with searchable text. It runs on the command-line or as a web server. The server version can be deployed to App Engine easily. pdf_sprinkles has only been tested with English-language text, but it should work for most European languages supported by the Document AI API today. It is known not to work with RTL languages and with CJK scripts currently. Installation pdf_sprinkles is […]

November 14, 2021 Strings

A python lib for generate random string and digits and special characters or A combination of them

a python lib for generate random string and digits and special characters or A combination of them installation 🛠 pip install python-random-strings options 🖇 Random Lower Case Random Upper Case Random Letters Random Digits Random Hex Digits Random Oct Digits Random Punctuation Random Printable Sample Code ✏️ from python_random_strings import random_strings a = random_strings.random_lowercase(6) print(a) a = random_strings.random_uppercase(6) print(a) a = random_strings.random_letters(6) print(a) a = random_strings.random_digits(6) print(a) a = random_strings.random_hexdigits(6) print(a) a = random_strings.random_octdigits(6) print(a) a = random_strings.random_punctuation(6) print(a) a […]

November 14, 2021 LED

LED effects plugin for klipper

This plugin allows Klipper to run effects and animations on addressable LEDs, such as Neopixels, WS2812 or SK6812. The main work has been done by Paul “Mental” McGowan. Installation cd ~ git clone https://github.com/julianschill/klipper-led_effect.git cd klipper-led_effect ./install-led_effect.sh Configuration Documentation can be found here. GitHub View Github

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