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@thyrlian
thyrlian / KaliNetHunterForNexus9.md
Last active April 19, 2026 03:19
How to build & install Kali NetHunter on Nexus9 from macOS

Prerequisites

  • Enable USB debugging (and allow RSA key fingerprint from connected computer)
  • Allow OEM unlocking
  • Have Android SDK installed on computer

Build

  • Clone kali-nethunter repository
  • Pull a Python Docker image python:2.7.14-jessie (even my local fresh macOS has problem of building)
  • Run Python Docker container: docker run -it -v [your-cloned-kali-nethunter-repo]:/root/kali-nethunter python:2.7.14-jessie bash
  • Build: python /root/kali-nethunter/nethunter-installer/build.py -d flounder --nougat --rootfs full (you have to build the Nougat version on your own, because the latest official version is only for Marshmallow)
@TarlogicSecurity
TarlogicSecurity / kerberos_attacks_cheatsheet.md
Created May 14, 2019 13:33
A cheatsheet with commands that can be used to perform kerberos attacks

Kerberos cheatsheet

Bruteforcing

With kerbrute.py:

python kerbrute.py -domain <domain_name> -users <users_file> -passwords <passwords_file> -outputfile <output_file>

With Rubeus version with brute module:

@entropiae
entropiae / Install pyenv on Ubuntu 18.04 + fish shell
Last active July 15, 2025 23:28
Install pyenv on Ubuntu 18.04 + Fish shell
Install pyenv on Ubuntu 18.04 + fish shell
- Install the packages required to compile Python
$ sudo apt-get update; sudo apt-get install --no-install-recommends make build-essential libssl-dev zlib1g-dev libbz2-dev libreadline-dev libsqlite3-dev wget curl llvm libncurses5-dev xz-utils tk-dev libxml2-dev libxmlsec1-dev libffi-dev liblzma-dev
- Download pyenv code from github
$ git clone https://github.com/pyenv/pyenv.git ~/.pyenv
- Define environment variable PYENV_ROOT to point to the path where pyenv repo is cloned
$ echo "set --export PYENV_ROOT $HOME/.pyenv" > ~/.config/fish/conf.d/pyenv.fish
@arafatkatze
arafatkatze / eval-rsi-prompt.md
Created July 27, 2026 04:07
Recursive self-improvement prompt for agent evals

Recursive Self-Improvement Prompt for Agent Evals

You are conducting a long-running, evidence-driven improvement campaign for an AI agent harness.

Your goal is not to win a benchmark by any means necessary. Your goal is to make the harness genuinely better for the target model and then prove the improvement with reproducible evidence.

Objective

Improve the target agent on the named evaluation without changing the benchmark, adding task-specific logic, or manufacturing a better score through more resources. Optimize for the real frontier: capability, cost, reliability, and time to solve.