About Me#





DEFCON 30 AI Village CTF

  • West Point Math

  • Georgia Tech Comp Sci

  • Former US Army Cyber

  • Former AWS

  • Currently NVIDIA

Cool things I’ve found online#

Compiler Explorer: Compare code from a variety of languages with the compiled assembly with different compiler flags in a hihglighted, side-by-side format. Very useful if you are learning software reverse engineering.

SQL Fiddle: A tool for easy online testing and sharing of database problems and their solutions.

Use The Index, Luke: A site explaining SQL indexing to developers; nothing about administration.

Select Star SQL: Gentle, relevant, and interactive introduction to SQL. Learn by solving a real problem with real data.

SQL Murder Mystery: Another engaging introduction to SQL.

REPL: Great pair programming tool. Named after the read-evaluate-print loop computing environment, this web-based IDE is great for working on a project with friends or teaching.

The Deadlock Empire: A fun introduction to some concurrency pitfalls. Don’t worry about the C# orientation. You’ll learn things that you can apply to any language.

Kubernetes Learning Path: Great kubernetes material.

Oh Shit, Git!?!: For when you stray from the happy path.

Explain Git with D3: Great interactive graph visualizations for git concepts.

Learn Git Branching: My go-to recommendation for folks learning git.

Pandas Tutor: Great way to teach/understand the logic of pandas dataframe operations.

Pythex: Python regex helper

FLAWS2: AWS security challenges Open Source, free, no signup, ephemeral docker registry.

shellcheck: Verbose error messages and recommendations for shell scripts/commands.

An Infinitely Large Napkin: “The Infinitely Large Napkin is a light but mostly self-contained introduction to a large amount of higher math.” I found it referenced as “The Feynman Lectures for math”.

Vulnerabilities 1001: C-Family Software Implementation Vulnerabilities: Vulnearbility exploitation bootcamp

NaNoGenMo: Write code that generates a 50k word novel. Since 2013!

A Critical Field Guide for Working with Machine Learning Datasets: Great primer for building, selecting, and analyzing datasets.