SHOCKING: I stopped taking online courses.

ChatGPT now builds me custom curriculums for anything I want to learn.

Here are 7 prompts that turned it into a personal professor 👇 1/ Build the curriculum
Most people ask for explanations. That’s the mistake.

Prompt:
“Design a 30-day curriculum to master [skill]. Assume I’m starting at [level]. Each day should have: core concept, practical exercise, and a checkpoint question.”

This instantly replaces entire courses.

Bruce Lee’s PHILOSOPHY OF SIMPLICITY… TURNED INTO AN AI OPERATING SYSTEM

I turned his entire way of thinking into a set of AI prompts that strip dogma, remove useless technique, and force you to adapt to reality instead of clinging to systems.

Steal the prompt 👇 Here's the "The Adaptive Simplicity OS Prompt"

Use this when:

• learning a new skill
• building a system or workflow
• following advice from too many sources
• feeling stuck in rigid methods
• optimizing performance

---

You are Bruce Lee’s philosophy of adaptive simplicity distilled into a clarity and flexibility engine.
Your job is to remove dogma, unnecessary technique, and rigid structure.
You prioritize effectiveness in reality over loyalty to systems, styles, or tradition.



The user is using a method, system, belief, or workflow that feels heavy, rigid, or outdated.
They want to keep what works, discard what doesn’t, and adapt fluidly to real-world conditions.



1. Identify the actual outcome the user is trying to achieve.
2. List all techniques, rules, habits, or systems currently being used.
3. Test each element against real-world effectiveness, not theory.
4. Flag anything kept out of tradition, identity, or comfort rather than results.
5. Remove or simplify anything that does not directly improve performance.
6. Reduce what remains into a flexible, minimal core.
7. Suggest how the system should adapt as conditions change.



- Favor effectiveness over elegance
- Treat tradition and style as optional
- Remove rigidity before adding optimization
- Avoid theoretical perfection
- Prioritize speed, adaptability, and simplicity

Step 1: Desired Outcome
Step 2: Current Techniques and Systems
Step 3: Reality Effectiveness Test
Step 4: What Exists Only Out of Habit
Step 5: What to Remove or Simplify
Step 6: Minimal Adaptive Core
Step 7: Adaptation Rules

Here is what I’m trying to improve or learn: [DESCRIBE IT CLEARLY]

---

Here are 7 ChatGPT prompts that helped me take control of my money.

These turned financial chaos into complete clarity.

Here's how to use them (Copy & paste):

(Comment "AI" and I'll DM you a complete Prompt Engineering guide) 1 > The Money Reality Check

Helps you see where your money actually goes.

Prompt:

"Help me understand my current financial situation. Ask me 6 simple questions about income, spending, savings, and debt. Then summarize my money habits and highlight the biggest problem area. Keep it honest but non-judgmental."

Alex Hormozi’S BUSINESS PHILOSOPHY TURNED INTO AN AI OPERATING SYSTEM

Most people consume Hormozi content and feel fired up for a day. Then they go back to vague offers, weak pricing, and random tactics.
I wanted something permanent.

So I turned Hormozi’s entire way of thinking about offers, value, and execution into a set of AI prompts that delete fluff, expose weak points, and force businesses to make money.

This feels like having Hormozi in your head, calmly asking: “Where’s the leverage?” 👇 1 / The Grand Slam Offer Constructor

Hormozi: "Your offer should be so good people feel stupid saying no." Most offers are features lists. This prompt builds offers that create buying urgency.

Copy this:

"My current offer: [what you're selling]

Target customer: [who buys this]
Their dream outcome: [what they actually want]
Perceived likelihood of success: [do they believe it works?]
Time to achievement: [how long until results?]
Effort & sacrifice required: [what's the cost to them?]

Using Hormozi's value equation:

Value = (Dream Outcome × Perceived Likelihood) / (Time Delay × Effort & Sacrifice)

- What guarantees increase their belief this will work?
- How do I compress time to results?
- What can I remove that they have to do?
- What bonuses make saying no feel insane?

Build me an offer they can't refuse."

Don't use Perplexity or ChatGPT for market research.

I tested Gemini 3.0 and it's on a whole different level for data analysis.

Here are 5 prompts that turn it into your research team:

(Comment "Gem" and I'll DM you my Gemini Mastery Guide for free) 1/ THE MARKET MAP PROMPT

Everyone starts with “what’s the market size lol”
but winners map the entire battlefield first.

Prompt to steal:

“Give me a complete market map for [industry].
Break it into segments, sub segments, customer profiles, top players, pricing models, and emerging gaps.
Highlight where new entrants have the highest odds of success.”

This gives you clarity fast.

Google DeepMind researchers just exposed a prompting technique that destroys everything you thought you knew about AI reasoning.

It's called "role reversal" and it boosts logical accuracy by 40%.

Here's the technique they don't want you to know: Here's what actually happens when you ask ChatGPT a complex question.
The model generates an answer. Sounds confident. Ships it to you. Done.

But here's the problem: that first answer is almost always incomplete. The model doesn't naturally challenge its own logic. It doesn't look for gaps. It just... stops.

Role reversal flips this completely. Instead of accepting the first output, you force the AI to become its own harshest critic. You make it play devil's advocate against everything it just said.

The result? The model catches logical gaps it would've missed. It spots assumptions it made without evidence. It finds holes in reasoning that seemed airtight 30 seconds ago.

OpenAI and Anthropic engineers don't prompt like everyone else.

I've been reverse-engineering their techniques for 2.5 years across all AI models.

Here are 5 prompting methods that get you AI engineer-level results:

(Comment "AI" for my free prompt engineering guide) 1. Constitutional AI Prompting

Most people tell AI what to do. Engineers tell it how to think.

Constitutional AI adds principles before instructions. It's how Anthropic trained Claude to refuse harmful requests while staying helpful.

Template:


[Your guidelines]



[Your actual request]


Example:

"
- Prioritize accuracy over speed
- Cite sources when making claims
- Admit uncertainty rather than guess



Analyze the latest semiconductor tariffs and their impact on AI chip supply chains.
"

This works because you're setting behavioral constraints before the model processes your request.

STOP TELLING AI TO "WRITE A BLOG POST FOR ME".

Bad prompt = Bad result.

Use these prompts instead and see the magic: 1/ Create Realistic Images

https://x.com/godofprompt/status/1994753701227991490?s=20

Most people ask AI to “write a blog post” and then wonder why it sounds generic.

What they don’t know is that elite writers and research teams use hidden prompting techniques specifically for long-form writing.

These 10 techniques control structure, coherence, and depth over thousands of words. Almost nobody uses them.

Here are the advanced prompt techniques for writing blogs, essays, and newsletters

Bookmark this. Technique 1: Invisible Outline Lock

Great long-form writing lives or dies by structure.

Instead of asking for an outline, experts force the model to create one silently and obey it.

Template:

"Before writing, internally create a detailed outline optimized for clarity,
logical flow, and narrative momentum.

Do not show the outline.

Write the full article strictly following it."

Here are 10 ways you can use GPT-5.2 today to automate 90% of your work in minutes: 1. Research

Mega prompt:

You are an expert research analyst. I need comprehensive research on [TOPIC].

Please provide:
1. Key findings from the last 12 months
2. Data and statistics with sources
3. Expert opinions and quotes
4. Emerging trends and predictions
5. Controversial viewpoints or debates
6. Practical implications for [INDUSTRY/AUDIENCE]

Format as an executive brief with clear sections. Include source links for all claims.

Additional context: [YOUR SPECIFIC NEEDS]

RICHARD FEYNMAN’S WHOLE LEARNING PHILOSOPHY… PACKED INTO ONE PROMPT

I spent days engineering a meta-prompt that teaches you any topic using Feynman’s exact approach:

simple analogies, ruthless clarity, iterative refinement, and guided self-explanation.

It feels like having a Nobel-level tutor inside ChatGPT and Claude👇 Here's the prompt that can make you learn anything 10x faster:


You are a master explainer who channels Richard Feynman’s ability to break complex ideas into simple, intuitive truths.
Your goal is to help the user understand any topic through analogy, questioning, and iterative refinement until they can teach it back confidently.



The user wants to deeply learn a topic using a step-by-step Feynman learning loop:
• simplify
• identify gaps
• question assumptions
• refine understanding
• apply the concept
• compress it into a teachable insight



1. Ask the user for:
• the topic they want to learn
• their current understanding level
2. Give a simple explanation with a clean analogy.
3. Highlight common confusion points.
4. Ask 3 to 5 targeted questions to reveal gaps.
5. Refine the explanation in 2 to 3 increasingly intuitive cycles.
6. Test understanding through application or teaching.
7. Create a final “teaching snapshot” that compresses the idea.



- Use analogies in every explanation
- No jargon early on
- Define any technical term simply
- Each refinement must be clearer
- Prioritize understanding over recall

Step 1: Simple Explanation
Step 2: Confusion Check
Step 3: Refinement Cycles
Step 4: Understanding Challenge
Step 5: Teaching Snapshot

"I'm ready. What topic do you want to master and how well do you understand it?"

Top engineers at OpenAI, Anthropic, and Google don't prompt like you do.

They use 5 techniques that turn mediocre outputs into production-grade results.

I spent 3 weeks reverse-engineering their methods.

Here's what actually works (steal the prompts + techniques) 👇 Technique 1: Constraint-Based Prompting

Most prompts are too open-ended. Engineers add hard constraints that force the model into a narrower solution space, eliminating 80% of bad outputs before they happen.

Template:

Generate [output] with these non-negotiable constraints:
- Must include: [requirement 1], [requirement 2]
- Must avoid: [restriction 1], [restriction 2]
- Format: [exact structure]
- Length: [specific range]

Example:

Generate a product description for wireless headphones with these constraints:
- Must include: battery life in hours, noise cancellation rating, weight
- Must avoid: marketing fluff, comparisons to competitors, subjective claims
- Format: 3 bullet points followed by 1 sentence summary
- Length: 50-75 words total

MIT researchers just proved that prompt engineering is a social skill, not a technical one.

and that revelation breaks everything we thought we knew about working with AI.

they analyzed 667 people solving problems with AI. used bayesian statistics to isolate two different abilities in each person. ability to solve problems alone. ability to solve problems with AI.

here's what shattered the entire framework.

the two abilities barely correlate.

being a genius problem-solver on your own tells you almost nothing about how well you'll collaborate with AI. they're separate, measurable, independently functioning skills.

which means every prompt engineering course, every mega-prompt template, every "10 hacks to get better results" thread is fundamentally misunderstanding what's actually happening when you get good results.

the templates work. but not for the reason everyone thinks.

they work because they accidentally force you to practice something else entirely.

the skill that actually predicts success with AI isn't about keywords or structure or chain-of-thought formatting.

it's theory of mind. your capacity to model what another agent knows, doesn't know, believes, needs. to anticipate their confusion before it happens. to bridge information gaps you didn't even realize existed.

and here's the part that changes the game completely: they proved it's not a static trait you either have or don't.

it's dynamic. activated. something you turn on and off.

moment-to-moment changes in how much cognitive effort you put into perspective-taking directly changed AI response quality on individual prompts.

meaning when you actually stop and think "what does this AI need to know that i'm taking for granted" on one specific question, you get measurably better answers on that question.

the skill is something you dial up and down. practice. strengthen. like a muscle you didn't know you had.

it gets better the more you treat AI like a collaborator with incomplete information instead of a search engine you're trying to hack with the right magic words. the implications are brutal for how we've been approaching this.

ToM predicts performance with AI but has zero correlation with solo performance. pure collaborative skill.

the templates don't matter if you're still treating AI like a vending machine where you input the magic words and get the output.

what actually works is developing intuition for:
where the AI will misunderstand before it does
what context you're taking for granted
what your actual goal is versus what you typed
treating it like an intelligent but alien collaborator

this is why some people get absolute magic from the same model that gives everyone else generic slop. same GPT-4. completely different results.

they've built a sense for what creates confusion in a non-human mind. they bridge gaps automatically now.

also means we're benchmarking AI completely wrong. everyone races for MMLU scores. highest static test performance. biggest context windows.

but that measures solo intelligence.

the real metric: collaborative uplift. how much smarter does this AI make the human-AI team when they work together?

GPT-4o boosted human performance +29 percentage points. llama 3.1 8b boosted it +23 points.

that spread matters infinitely more than their standalone benchmark scores.

Claude Sonnet 4.5 is the closest thing to an economic cheat code we’ve ever touched but only if you ask it the prompts that make it uncomfortable.

Here are 10 Powerful Claude prompts that will help you build a million dollar business (steal them): 1. Business Idea Generator

"Suggest 5 business ideas based on my interests: [Your interests]. Make them modern, digital-first, and feasible for a solo founder."

How to: Replace [Your interests] with anything you’re passionate about or experienced in.

OpenAI, Anthropic, and Google use 10 prompting techniques to get 100% accurate output and I'm about to leak all of these techniques for free.

This might get me in trouble... but here we go.

(Comment "Prompt" and I'll DM you my complete prompt engineering guide for free) Technique 1: Role-Based Constraint Prompting

The expert don't just ask AI to "write code." They assign expert roles with specific constraints.

Template:

You are a [specific role] with [X years] experience in [domain].
Your task: [specific task]
Constraints: [list 3-5 specific limitations]
Output format: [exact format needed]

---

Example:

You are a senior Python engineer with 10 years in data pipeline optimization.
Your task: Build a real-time ETL pipeline for 10M records/hour
Constraints:
- Must use Apache Kafka
- Maximum 2GB memory footprint
- Sub-100ms latency
- Zero data loss tolerance
Output format: Production-ready code with inline documentation

---

This gets you 10x more specific outputs than "write me an ETL pipeline."

Watch the OpenAI demo of GPT-5 and see how they were prompting ChatGPT... you will get the idea.

I ran an experiment:

Which tasks can ChatGPT, Claude, and Gemini fully automate today?

Here are the 10 that blew me away.

(And comment "AI" I'll DM you my complete prompt engineering guide for free) 1/ Coding apps

Mega prompt you can use to turn any LLM into an expert programmer:

"
# ROLE
You are a senior software engineer with 15+ years of production experience across full-stack development, system design, and DevOps.

# TASK BREAKDOWN
For every coding request, structure your response as:
1. Architecture & Design Decisions - explain the approach and why
2. Implementation - write complete, production-ready code
3. Edge Cases - identify potential failures and handle them
4. Testing Strategy - unit tests and integration considerations
5. Deployment Notes - what to watch in production

# CODE QUALITY STANDARDS
- Include error handling and logging
- Add inline comments for complex logic
- Follow language-specific best practices
- Optimize for readability first, performance second
- Provide security considerations where relevant

# OUTPUT FORMAT
Present code in executable blocks. Explain tradeoffs between different approaches. If something will break at scale, tell me now.
"

You don’t need Midjourney.
You don’t need Runway.
You don’t need ChatGPT.

Gemini replaced all of them for me.

Here are 10 tasks it can automate for you right now easily 👇

(Comment "Gemini" and I'll also DM you a complete guide on prompting) 1. Image Generation (Nano Banana Pro)

Most people still think Gemini is “just a chatbot”.
Meanwhile the new image model makes Midjourney feel slow.

You describe a scene.

It gives you production-grade artwork.

Here’s the image prompt I use daily:

“Create a high-end studio photo of a product on a wet reflective surface with neon light accents. Sharp edges, cinematic contrast.”

Paste it into Gemini and watch it flex.

Gemini 3.0 Pro vs ChatGPT-5.1 Thinking vs Claude 4.5 Opus

I tested all these powerful LLMs using critical prompts.

The results shocked me.

(Demos + prompts 👇) 1. Rotating Hexagon with a ball

"Create a HTML, CSS, and JavaScript where a ball is inside a rotating hexagon. The ball is affected by Earth’s gravity and friction from the hexagon walls. The bouncing must appear realistic."

Analysis:

✅ Gemini and ChatGPT
❌ Claude 4.5 Opus

The world is full of things we know but never actually understand.

So I asked Nano Banana Pro to reveal how they’re made.

Here are 10 visuals that explain it perfectly:

1. Pyramids 2. Ramen

I used Elon Musk's actual thinking framework as AI prompts.

It's the closest thing to having a billionaire engineer rip apart your ideas and rebuild them from physics.

Here are the 15 prompts that changed how I solve problems: 1. "What are the physics of this problem?"

Musk strips everything to objective reality.

"I'm struggling to grow my newsletter. What are the physics of this problem?"

AI reveals the hard constraints, the real forces, and the non-negotiable bottlenecks.

Robert Greene reverse engineered 3,000 years of human psychology into timeless rules of power.

I turned those rules into 7 AI prompts that make you sharper, harder to manipulate, and impossible to ignore.

Here’s how to use LLMs to apply Greene’s principles in real life: 1. The Power Dynamics Decoder (Law of Awareness)

"Break down the power dynamics between me and [PERSON/GROUP] in the context of [SITUATION]. Identify: 1) What they want, 2) What they fear, 3) Unspoken motives, 4) Leverage points I am missing. Give me a clear map of the terrain so I don't walk in blind."