Fair feedback. I expect skepticism, especially given the price point and the noise in the interview prep market. Regarding the 'ChatGPT' point: I’d argue the op

Fair feedback. I expect skepticism, especially given the price point and the noise in the interview prep market. Regarding the 'ChatGPT' point: I’d argue the opposite. AI tools are great at generating generic definitions ('What is an inode?'), but they struggle with the specific operational sequencing required for NALS. For example, AI rarely suggests 'draining traffic to a fallback region' as a step before 'grepping logs' unless explicitly prompted. My focus is on that sequencing (The 'OODA Loop' for incidents), which comes from analyzing failure patterns in debriefs, not just scraping docs. As for the GitHub repo: The goal was to open-source the core frameworks (The NALS Flowchart and the Linux Cheat Sheet) so they are useful without buying anything. If it feels too light, that’s on me—I’ll look at adding one of the full scenarios from the workbook to make it more standalone-valuable. Thanks for the honest take.