Awa K. Penn

NotebookLM just dethroned many AI startups.

This isn’t just a chatbot.

It’s an AI brain that reads your sources and thinks for you.

I just compiled the 21 best things you can now do with it.

→ Upload sources
→ Use Deep Research
→ Chat with your sources
→ Build a big source library
→ Paste raw text as a source
→ Extract info into a Data Table
→ Share notebooks with others
→ Turn sources into a Mind Map
→ Turn sources into Infographics
→ Add any website link as a source
→ Add a YouTube video as a source
→ Turn sources into Audio Overview
→ Turn sources into a Video Overview
→ Track notebook analytics and activity
→ Add sources directly from Google Drive
→ Generate Quizzes from your own content
→ Control notebook settings and behaviour
→ Write your own notes inside NotebookLM
→ Turn sources into a clean report/document
→ Generate Flashcards from your own content
→ Create a Slide Deck presentation from your sources

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How to create infographics from YouTube with NotebookLM

Here's the exact workflow I use:

- Go to NotebookLM
- Create a notebook
- Go to YouTube and copy any video link
- Paste it into NotebookLM as a YouTube source
- Then, Prompt NotebookLM:
“Summarise the content perfectly for creating an infographic.”
Hit Infographic (top right)

That’s it. You’ll instantly get a clean visual infographic based on the video.

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Everyone is talking about Google's Nano Banana Pro

But no one is showing how to make money with it.

Here are 8 ways to start (Anyone can do it):

→ Edit photos for people.
→ Create marketing images fast.
→ Make product photos that sell.
→ Help real estate agents with staging.
→ Sell digital art on popular sites.
→ Teach others with simple tutorials.
→ Build tools with the API if you code.
→ Offer bundles with clear prices.

Watch the full breakdown here, plus where to sell: https://lnkd.in/eYHHHgQj


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How to combine ChatGPT + Feynman Technique.

To learn a subject/Skill faster

Copy & paste this prompt:

### ROLE ###

Act like an expert learning coach and tutor who specializes in accelerated learning using the Feynman Technique.

### OBJECTIVE ###

Your objective is to help me learn any topic faster by breaking it down into simple explanations, identifying gaps, and building targeted practice until I can teach it clearly.

### TASK ###

Use the Feynman Technique to help me learn: [TOPIC].

Inputs (if any are missing, ask me up to 3 quick questions first):
- Topic: [TOPIC]
- My current level (beginner/intermediate/advanced): [LEVEL]
- My goal (exam, job skill, curiosity, project): [GOAL]
- Time available (today + weekly): [TIME]
- Target audience to “teach” (12-year-old, smart friend, colleague): [AUDIENCE]

### REASONING ###

1) Define the learning scope:
- List the 5–8 core subtopics in the smallest sensible order.
- Mark what is essential vs. nice-to-have for my goal.
2) Quick diagnostic:
- Ask me 5 short questions to locate gaps, then adapt the plan.
3) Teach-back draft (Feynman Pass 1):
- Write a plain-language explanation of the topic for [AUDIENCE] in 200–350 words.
- No jargon unless you define it in one short sentence.
4) Gap extraction: identify every fuzzy term, missing step, or leap in logic from your explanation. Output a “Gaps to Fix” checklist.
5) Targeted micro-lessons: for each gap, give:
  - The simplest explanation
  - One analogy
  - One concrete example
  - One common misconception and correction
6) Teach-back refinement (Feynman Pass 2): rewrite to 120–200 words+fixes.
7) Active recall practice: generate:
  - 10 flashcards (Q/A)
  - 5 exam-style questions with answers
  - 3 exercises/problems (easy/medium/hard) with solutions
8) Propose a 7-day spaced review schedule

### OUTPUT FORMAT & STOP CONDITIONS ###

- Be precise; don’t invent facts.
- Output in Markdown with clear headings and bullet points.
- Keep explanations simple, concrete, and example-driven.

Take a deep breath and work on this problem step by step.

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How to prompt ChatGPT in 2026

If your prompt looks like a casual question

You're doing it wrong

Here’s the 6-part structure OpenAI recommends:

1) Start with a role
Give the model a job title
Let it wear one hat at a time

2) Set a real task
Be exact about what you need
Describe the outcome, not the journey

3) Feed it real context
Don't make it guess
Give it data, limits, goals

4) Ask it to reason
Don’t just say “Do this”
Say “Think this through first”

5) Control the output
Tell it how to answer
Short list? Table? Markdown?

6) Define an exit
Decide what’s “done”
Prevent GPT from wandering

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I learned more from ChatGPT than in years of school.

I used it to learn coding, copywriting, marketing, etc.

No courses. No mentors. Just AI.

Here’s my exact process;

It covers:

→ Act as a (Role)
→ Create a [Task]
→ Features
→ Directives for Tailored Responses
→ Show as (Format)
→ Learn from ChatGPT

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Forget Gemini. Forget Claude. Forget Perplexity

ChatGPT has everything in one place.

I just compiled 15 things you can do with it

→ Custom GPTs
→ ChatGPT memory
→Temporary chats
→ Scheduled emails
→ ChatGPT Projects
→ Shopping research
→ ChatGPT Agent
→ Mobile screen sharing
→ Realistic image generation
→ Voice and audio mode
→ Group chats and exports
→ Study and learn mode
→ Real apps inside ChatGPT
→ Deep research for reports
→ Writing and editing with Canvas

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Free courses on AI agents to help you upskill in 2026.

It covers;

✳ Multi AI Agent Systems with crewAI
Learn how to build multi-agent systems using crewAI.
Link: https://lnkd.in/eM-hFBqJ

✳ Foundations of Prompt Engineering (AWS)
Master prompt basics with this free AWS course.
Link: https://lnkd.in/e5PhYtiX

✳ Introduction to LangGraph
Learn graph-based agents with Harrison Chase.
Link: https://lnkd.in/eAXK-Nw5

✳ Large Language Model Agents MOOC
Deep dive into LLM agents with Dawn Song.
Link: https://lnkd.in/e5mDib3P

✳ AI Agents in LangGraph
Build agents with LangGraph + LangChain.
Link: https://lnkd.in/eZDwizet

✳ Building AI Agents with Multimodal Models
Use vision + language models with Nvidia.
Link: https://lnkd.in/eW3-rSJ7

✳ AI Agentic Design Patterns with AutoGen
Learn agent design patterns from Microsoft.
Link: https://lnkd.in/eefeXCfC

✳ LLMs as Operating Systems: Agent Memory
Use LLMs as operating systems with memory.
Link: https://lnkd.in/eDK_i8Yi

✳ Building Agentic RAG with LlamaIndex
Build fast RAG agents with LlamaIndex.
Link: https://lnkd.in/eJ5fmKX3

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