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Using AI to become a Hacker

NetworkChuck·
6 min read

Based on NetworkChuck's video on YouTube. If you like this content, support the original creators by watching, liking and subscribing to their content.

TL;DR

Use AI to refine “why” statements first, because motivation is treated as a prerequisite for consistency.

Briefing

A 43-day, over-1,000-hour CPTS certification plan pushed one hacker-student to lean on AI—not for shortcuts, but to compress study time and increase retention through faster planning, testing, and review. With six kids and a business to run, the core idea is simple: use AI to turn raw course material into a personalized learning system that checks understanding, highlights weaknesses, and keeps motivation steady.

The approach starts with building a study plan inside Notion. First, all the “why” behind the certification gets refined using an LLM (including Notion AI’s ChatGPT-style interface). Then course details are copied from the certification site into a Notion note, along with time constraints like available hours per week. That information is fed back into an LLM to generate a structured timeline—initially producing a long-range plan that can be tweaked as reality sets in. Motivation also gets operationalized: Notion AI can generate a custom block that randomly selects a “why” and encourages the learner on demand, aiming to replace fading willpower with a repeatable prompt.

Next comes pre-lesson testing. Before committing to a module section—like firewall IDS/IPS evasion—the learner copies the lesson content into an LLM and asks it to quiz them. Notion Q&A (with AI assistant features) can reference the learner’s own curated Notion pages rather than pulling random web facts. The workflow is iterative: if the answers are weak, the AI clarifies, then produces a focus list and a revised daily plan based on performance. The key warning is practical: AI isn’t perfect, so prompts may need adjustment and results should be treated as guidance, not authority.

Summarization and deeper understanding form the middle of the system. AI condenses messy notes into five-bullet summaries, and can auto-update those summaries in a Notion database field so the learner gets a bird’s-eye view of each lesson. For comprehension, AI generates mind maps, identifies what’s most important, flags common pitfalls, and produces concept comparisons (including contrasts like SYN scan vs. XMAS scan decoys, DNS proxying, and IDS vs. IPS). It can also run “deep dive” breakdowns, explain topics to a third-grader level, and generate analogies to test whether the learner truly grasps the material.

The most “exam-like” technique is talk-it-out testing. The learner pastes lesson content into ChatGPT (using voice via ChatGPT-4) and asks for thoughtful questions plus evaluation. The underlying learning principle is that explaining clearly—and being challenged on gaps—is the fastest way to find what isn’t understood. This extends to review questions and flashcards: AI generates non-multiple-choice question sets and produces flashcards that can be imported into Anki for space repetition.

Finally, AI supports teaching and creation. Content ideas for platforms like YouTube, Twitter, and LinkedIn are generated from lesson material, while AI helps correct grammar and fill gaps so the learner can publish authentically. Two bonus workflows tie everything together: a cheat-sheet generator (e.g., producing Inmap command lists) and a “second brain” built in Notion. Notion Q&A can search across the learner’s own scripts, notes, and even Slack-connected knowledge, enabling personalized summaries with document-level references—turning accumulated work into a searchable study partner.

Cornell Notes

The central move is using AI to build a personalized hacking-study system that saves time and improves retention for a demanding CPTS certification. The workflow begins with refining “why” statements and generating a study plan in Notion, then adds pre-lesson quizzes to test whether a section can be skipped. Summaries, mind maps, pitfall checks, comparisons, and “deep dive” explanations convert raw material into structured understanding. The most effective step is talk-it-out testing: voice conversations with ChatGPT-4 that quiz and evaluate explanations, followed by AI-generated review questions and flashcards for Anki. The payoff is a second-brain setup where Notion AI can retrieve and summarize the learner’s own notes and scripts, not just generic web knowledge.

How does AI help create a study plan that fits real-life constraints (time, modules, and motivation)?

The plan starts in Notion: course information is copied from the certification site into a new note, along with time constraints like how many hours can be devoted each week. “Why” statements are refined first—either by copying them into an LLM or by using Notion AI’s built-in ChatGPT-style interface to generate additional motivations. That combined context is then pasted into an LLM to produce a long-range schedule (initially described as 115 weeks), which can be adjusted. Motivation is also automated via a custom AI block that randomly selects a “why” and encourages the learner on demand.

What’s the purpose of pre-lesson testing, and how is it done without relying on random internet facts?

Pre-lesson testing checks whether the learner can skip a section by quizzing them before they invest time. The learner copies the lesson content (e.g., firewall IDS/IPS evasion) into an LLM or into Notion, then uses Notion Q&A to quiz them with a persona and targeted questions. A key detail is that Notion Q&A can reference the learner’s curated Notion pages, so answers are grounded in the learner’s own notes rather than generic web material. If performance is weak, the AI clarifies and generates a focus plan based on weaknesses.

Why are summaries and mind maps treated as study accelerators rather than just note-taking conveniences?

Summaries reduce friction by turning messy notes into quick bullet points for both initial orientation and later review, avoiding re-reading “chicken scratch.” Mind maps add a visual structure that helps reveal relationships between concepts rather than only presenting them as a hierarchy. In Notion, summaries can also be stored in database fields that auto-update from document content, giving a bird’s-eye view of what each lesson contains and keeping the study tracker current.

How does AI deepen understanding beyond definitions—especially for topics like IDS vs. IPS and scanning techniques?

AI is used to generate multiple layers: (1) concept comparisons in tables (e.g., contrasting IDS and IPS, and comparing scan types like SYN scan vs. XMAS scan decoy approaches and DNS proxying), (2) common pitfall prompts that surface typical mistakes students make, (3) practical application prompts that ask when techniques are used legitimately or illegitimately, and (4) deep-dive breakdowns that split topics into smaller parts with in-depth explanations. The system also includes “explain like a third grader” simplification and analogy generation to test whether understanding is real.

What makes the talk-it-out method effective for learning and exam readiness?

It forces active recall and clear explanation under pressure. The learner pastes lesson content into ChatGPT and uses voice (ChatGPT-4) to ask for thoughtful questions and evaluation. The example question compares firewall vs. IDS/IPS and requires describing how each functions in a network. The AI’s feedback helps identify what’s missing or unclear, and the learner can iterate by prompting again. The method is framed as a direct test: if the learner can’t explain simply, they don’t truly know it.

How does the “second brain” concept change what AI can do for studying?

Instead of relying on generic training knowledge, Notion Q&A and Notion AI can search across the learner’s own knowledge base—notes, scripts, and even Slack-connected material. That enables personalized summaries like “summarize my notes on firewalls and IDS/IPS,” with references to the specific documents pulled. It also supports cross-linking work, such as finding where firewalls were mentioned in prior scripts or identifying the last scripts an employee worked on, turning accumulated artifacts into study fuel.

Review Questions

  1. Which two-step process is used to generate a study plan in Notion, and how does it incorporate both motivation and time constraints?
  2. How does Notion Q&A differ from asking an LLM generic questions when it comes to quiz accuracy and grounding?
  3. What learning signals does the talk-it-out method produce, and how does that feed into review questions and flashcards?

Key Points

  1. 1

    Use AI to refine “why” statements first, because motivation is treated as a prerequisite for consistency.

  2. 2

    Generate a study plan by combining course details with your weekly available hours inside Notion, then iterate as needed.

  3. 3

    Test knowledge before committing to lessons by quizzing yourself with lesson content copied into an LLM or Notion Q&A.

  4. 4

    Convert raw notes into fast review assets using AI summaries, mind maps, and database-backed auto-updating summaries.

  5. 5

    Deepen comprehension with AI outputs like concept comparisons, common pitfall lists, practical applications, and deep-dive breakdowns.

  6. 6

    Use talk-it-out voice quizzes (ChatGPT-4) to evaluate whether explanations are clear—unclear explanations signal gaps.

  7. 7

    Turn understanding into retention by generating review questions and flashcards, then importing flashcards into Anki for space repetition.

Highlights

The most time-saving move is pre-lesson quizzing: copy a section, ask AI to test you, and only then decide whether to skip.
Notion Q&A is positioned as grounded in the learner’s own Notion pages, not random internet content.
Talk-it-out voice testing with ChatGPT-4 is framed as a direct check of whether concepts can be explained simply.
A “second brain” setup lets AI summarize and reference the learner’s own scripts, notes, and even Slack-connected material.

Topics

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