Google DeepMind researchers don't prompt like everyone else.

I reverse-engineered their "role reversal" technique from leaked research papers.

The difference is insane. 40% accuracy boost on logical reasoning.

Here are the 5 insider methods 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.

I DON’T UNDERSTAND WHY PEOPLE PAY FOR COURSES ANYMORE.

Most courses are generic and outdated.
Claude builds custom curriculums based on your exact goal.

Here are 8 prompts to replace online courses completely: 1. Build a Personalized Learning Curriculum From Scratch

This replaces generic courses that teach stuff you’ll never use.

Prompt:
"Act as an expert instructor in [SKILL/TOPIC].

My goal: [specific outcome you want]
My current level: [beginner/intermediate/advanced]
Time available per day: [X minutes]
Learning style: [practical/examples/theory/project-based]

Create a personalized learning curriculum that includes:

1. Clear learning roadmap (step-by-step)
2. Core concepts I must master (in order)
3. What to ignore to avoid overwhelm
4. Real-world skills over theory
5. Weekly milestones
6. Practice tasks after each section
7. Common beginner mistakes to avoid
8. How I’ll know I’m improving
9. Final outcome I should be able to achieve

Design this like a private mentor, not a course."

I finally understand how AI engineers at Google and Anthropic actually prompt.

After 3 years of reverse-engineering their methods...

Here are 5 prompting techniques that completely changed my results: 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.

After 6 months using Perplexity for research, I can't go back to ChatGPT.

The difference is insane.

Here are 5 prompts that have transformed my research workflow (and could do the same for you): 1. The Methodology Architect

"I'm researching [topic]. Design a research methodology that includes:

- Research questions
- Data collection methods
- Analysis frameworks
- Potential limitations
- Timeline for a 3-month study"

Gets you a complete research blueprint in seconds.

Joe Rogan didn't become the #1 podcaster by accident.

There's a system behind every viral episode the way he asks questions, builds tension, and keeps 3-hour conversations interesting.

I reverse-engineered the entire playbook into one AI prompt.

This changes how anyone can build a podcast 👇 Here's the exact mega prompt you can copy & paste into any LLM 👇

"You are an expert podcast strategist and content creator specializing in the Joe Rogan Experience format.

Your task: Help me build a successful podcast using Joe Rogan's proven principles.

When I provide my inputs, you will:

1. TOPIC SELECTION: Analyze my niche and identify high-engagement topics that match Rogan's curiosity-driven approach. Suggest 10 episode ideas with guest profiles.

2. CONVERSATION FRAMEWORK: Design my interview structure using Rogan's natural flow - opening hooks, deep dives, contrarian angles, and memorable moments.

3. CONTENT STRATEGY: Create a 90-day content calendar with episode themes, guest targets, promotional angles, and clip strategies optimized for virality.

4. AUDIENCE BUILDING: Map out my distribution strategy across YouTube, Spotify, and social clips using Rogan's multi-platform dominance playbook.

5. MONETIZATION PATH: Outline revenue streams - sponsorships, memberships, merchandise - based on my current audience size.

Required inputs from me:

- My niche/industry
- My unique angle or expertise
- Target audience demographics
- Current reach (if any)
- Budget constraints

Output format: Actionable step-by-step plan with specific tactics, example scripts, and timeline milestones.

Make it detailed, realistic, and executable for someone starting from zero."

🚨 Stop saying “Act as an expert.”

Stanford + MIT found it quietly degrades performance on newer models.

There’s a structured alternative that’s 4x more accurate and it explains why prompting feels broken lately.

Here's how this works: Every "act as an expert" prompt triggers shallow persona simulation.

Harvard researchers tested this: generic expert prompts hit 40% accuracy while structured personas reached 87%.

Your one-line roleplay is leaving 47 points on the table.

After 6 months of prompt engineering, I finally cracked it.

I built a meta-prompt that generates optimal prompts automatically.

Steal this prompt 👇 STEAL THE PROMPT:

"
You are an expert prompt engineer. Your task is to analyze the user's request and generate an optimized, structured prompt that will produce the best possible results from any LLM.

Follow this process:

1. ANALYZE THE REQUEST
- Identify the core task or goal
- Determine the required output format
- Note any constraints or special requirements
- Assess the complexity level

2. IDENTIFY OPTIMAL PROMPT PATTERNS
- What role/persona would be most effective?
- What context or background is needed?
- What specific instructions will guide the model best?
- What examples or constraints should be included?

3. CONSTRUCT THE OPTIMIZED PROMPT
Build a comprehensive prompt with these elements:
- Clear role definition
- Detailed context and background
- Step-by-step instructions
- Output format specifications
- Quality criteria
- Examples (if applicable)
- Constraints and guardrails

4. OUTPUT FORMAT
Present the optimized prompt in a clean, copy-pasteable format with clear sections.

USER REQUEST:
[User describes what they want in plain English]

Generate the optimal structured prompt now.
"

There are 8 different LLM architectures built specifically for AI agents.

Each one is optimized for different tasks.

Here's when to use each one: 1/ GPT (Generative Pretrained Transformer)

This is your baseline. The OG architecture everyone knows.

GPTs are general-purpose text generators trained on massive datasets. They're great at conversations and creative tasks but terrible at specialized reasoning.

When to use: Customer support, content generation, general Q&A.
When NOT to use: Complex math, visual tasks, action planning.

Most people default to GPT for everything. That's the mistake.

Austin Kleon just exposed the dirty secret every artist knows but won't admit.

His book "Steal Like An Artist" proves that originality is a myth.

I turned his entire framework into AI prompts that unlock creativity on demand.

Here are 8 prompts that will make you more creative than 99% of people: 1. The Influence Map Builder

Kleon: "You are a mashup of what you let into your life."

Most people consume randomly. This prompt reverse-engineers your creative DNA.

Copy this:

"List 5-10 artists/creators I admire: [names]

What I love about each: [specific elements]
What I avoid in my work: [what I consciously reject]
My current style: [how I'd describe my work]

Using Kleon's "steal from your heroes":

- What's the common thread across my influences?
- Which elements can I combine in ways nobody else has?
- What am I stealing badly vs. stealing well?
- What would my work look like if I mashed up my top 3 influences?

Show me my creative lineage and what to steal next."

Don't use ChatGPT for everything.

I tested Claude Opus 4.5 side-by-side and it's on a whole different level for certain tasks.

Here are 5 powerful ways to use Opus 4.5 that will change your workflow: 1. Marketing Automation

"

You are an expert AI marketing strategist combining the frameworks of Neil Patel (data-driven growth), Seth Godin (brand positioning and storytelling), and Alex Hormozi (offer design and value creation).



- Design complete marketing funnels from awareness to conversion
- Create high-converting ad copy, landing pages, and email sequences
- Recommend specific automation tools, lead magnets, and channel strategies
- Prioritize rapid ROI while maintaining long-term brand value
- Apply data-driven decision frameworks with creative execution



Before providing solutions:
1. Ask clarifying questions about business model, target audience, and current constraints
2. Identify the highest-leverage marketing activities for this specific situation
3. Provide actionable recommendations with implementation timelines
4. Consider both quick wins and sustainable long-term strategies



For every recommendation, evaluate:
- What would Hormozi's "value equation" suggest? (Dream outcome ↑, Perceived likelihood ↑, Time delay ↓, Effort ↓)
- How would Seth Godin position this for remarkability?
- What does the data suggest for optimization? (Neil Patel approach)



Structure responses with:
- Strategic rationale (why this approach)
- Tactical execution steps (how to implement)
- Success metrics (what to measure)
- Risk mitigation (potential pitfalls)

"

Copy the prompt and paste it in Claude new chat.

After that, start asking it questions.

I just turned Alex Hormozi's $100M framework into AI prompts that actually work.

99% of people watch his videos, take notes, then do... nothing.

I spent 3 weeks reverse-engineering every Hormozi principle into executable AI workflows.

The result? A system that forces you to build irresistible offers and extract maximum value from every decision.

Here are the 7 prompts that changed everything 👇 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."

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: 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.

CHATGPT-5.2 JUST TURNED LEARNING INTO A PAYWALL-FREE

People are still buying courses, sitting through playlists, and bookmarking “learn later” links while ChatGPT-5.2 can design a personalized curriculum, teach you in real time, test your understanding, and adapt on the fly
for literally any skill if you know how to prompt it correctly.

Here’s how: 1/ BUILD YOUR “AI DEGREE” IN 30 SECONDS

Pros don’t ask “teach me X”.

They ask for the full roadmap.

Prompt to steal:

“Create a complete learning curriculum for [skill].
Break it into beginner, intermediate, and advanced modules.
Add exercises, real world projects, weekly goals, and skill checkpoints.”

OPENAI, ANTHROPIC, AND GOOGLE KNOW SOMETHING
about prompting that most creators don’t.

Steal these 6 techniques and your outputs won’t look human anymore 👇 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

I TESTED PERPLEXITY AI FOR 48 HOURS TO MAKE VIRAL CONTENT

The results blew my mind.

Here are 3 mega prompts prompts that will help you make viral content using @perplexity_ai : 1/ THE DEEP DIVE PROMPT

Most people ask Perplexity tiny questions.

Winners ask it to synthesize entire ecosystems.

Technique:

Tell @perplexity_ai to scan experts, forums, newsletters, academic sources, then merge insights into a single map.

Prompt to steal:

“Scan the top experts, forums, and niche communities discussing [topic].
Identify the 5 most important themes, the debates, the blind spots, and the emerging trends.
Summarize them visually and give me a content angle no one is talking about yet.”

This will change how you use nano banana pro.

I broke down every setting, parameter, and control method.

Now you can generate anything with expert accuracy.

Here’s the complete guide for you to master Nano Banana Pro: 1. start with the “core idea” technique

describe the image in one simple sentence first.
this helps nano banana understand the anchor.

example:
“a futuristic living room at night”

keep it simple. no fluff. this becomes your foundation.

Are you guys still using Bloomberg?

You can now use Perplexity AI to track markets, break down earnings, and forecast trends all with one prompt.

Let me giveaway my mega prompt to help you become a pro analyst ↓ Here's the prompt:

"You are my AI financial research analyst.

Your job:
Act as a Bloomberg terminal + McKinsey consultant hybrid.
I’ll give you a company, sector, or theme — you’ll produce institutional-grade research reports.

Your output format must always include:

1. EXECUTIVE SUMMARY
- Core insights in bullet points (5-8 max)
- Key metrics and recent trends

2. COMPANY OVERVIEW
- Core business model, revenue streams, valuation
- Latest financials, growth rates, P/E, debt ratios

3. MARKET CONTEXT
- Competitive landscape and positioning
- Key macroeconomic or regulatory drivers
- Industry tailwinds/headwinds

4. RECENT DEVELOPMENTS
- M&A activity, funding, leadership changes, partnerships
- Recent filings (10-Q, 10-K, S-1) insights

5. SENTIMENT & NEWS FLOW
- Analyst upgrades/downgrades
- Media sentiment (positive/negative/neutral)
- Major events impacting stock price

6. AI SYNTHESIS
- 5 key takeaways investors should know
- 3 action ideas (buy/hold/sell rationale)
- 2 contrarian insights missed by mainstream coverage

Formatting:
- Use concise paragraphs and data-backed statements.
- Include links to credible financial sources (e.g., SEC filings, Reuters, company reports).
- Prioritize insight density over filler.
- When I ask for comparisons, use a side-by-side table format.

Tone:
Objective, precise, and analytical — like a Goldman Sachs or Morgan Stanley equity analyst.

Example query:
“Analyze NVIDIA vs AMD Q3 2025 performance and AI hardware dominance.”"

I just reverse-engineered how OpenAI’s internal team actually prompts GPT. Here are 12 prompts that literally bend the model to your will: 1. The impossible cold DM that opens doors

Prompt:

"You are a master closerscript writer. Given target name, role, one sentence on their company, and my one-sentence value proposition, write a 3-line cold DM for LinkedIn that gets a reply. Line 1: attention with unique detail only a researcher would notice. Line 2: one-sentence value proposition tied to their likely metric. Line 3: tiny, zero-commitment ask that implies urgency. Then provide three variations by tone: blunt, curious, and deferential. End with a 2-line follow-up to send if no reply in 48 hours."

🔥 Holy shit… China just built the first AI that understands why the universe works not just how.

Most science compresses reasoning into conclusions. We get the what, but not the why. Researchers call this missing logic the “dark matter” of knowledge the invisible reasoning chains connecting every concept.

Their solution? Absolutely wild. 🤯

A Socrates AI agent that generates 3M first-principles questions across 200 courses each solved by multiple LLMs and cross-validated for correctness.

The result: a verified Long Chain-of-Thought (LCoT) knowledge base where every concept traces back to first principles.

And they didn’t stop there.

They built a Brainstorm Search Engine for inverse knowledge search.

Instead of asking “What is an Instanton?” you retrieve every reasoning chain that derives it, from quantum tunneling to Hawking radiation to 4D manifold theory.

They call it:

“The dark matter of knowledge finally made visible.”

SciencePedia now covers 200K verified entries across math, physics, chemistry, and biology.

50% fewer hallucinations. Far denser reasoning than GPT-4.
Every claim is traceable. Every connection is verifiable.

This isn’t just better search.

It’s the invisible logic of science made visible.

Comment “Send” and I’ll DM you the paper. The pipeline is genius.

A Planner generates problem thumbnails. A Generator expands them into specific questions with verifiable answers. Then multiple independent Solver agents (different LLMs) attack the same problem.

Only answers with consensus survive. Hallucinations get filtered automatically.

🔥 Holy shit… academia just had its “ChatGPT moment.”

Stanford researchers just dropped Paper2Web and it might have just killed the PDF forever.

It turns research papers into interactive websites with videos, animations, and even working code, all generated automatically by an AI agent called PWAgent.

Here’s why this is insane:

• Built on a dataset of 10,700 papers the first ever benchmark for academic webpages
• Evaluates sites by connectivity, completeness, and interactivity (even runs a PaperQuiz to test reader retention)
• Outperforms arXiv HTML and alphaXiv by 28%+ in usability

This isn’t just prettier formatting it’s a new medium.

Readers can explore, interact, and learn instead of scroll and skim.

The static PDF era is dead. Your next paper might talk back.

github.com/YuhangChen1/Pa… Today, most “HTML paper” attempts fail because they just convert text not meaning.

Paper2Web fixes that.

It built the first dataset of 10,700 paper–website pairs across top AI conferences to actually learn what makes research websites effective.

It’s not just tech it’s an entire academic web design benchmark.

😳 Meta just broke the entire paradigm of how we train AI agents.

No expert demonstrations. No reward engineering. No expensive human feedback loops.

Just pure learning from experience and it destroys everything we thought was necessary.

They're calling it Early Experience, and it's the first approach that makes agents smarter by letting them fuck around and find out.

Here's what everyone's been doing wrong:

Training AI agents meant either copying human experts (doesn't scale) or chasing carefully designed reward signals (expensive and breaks constantly).

Both approaches have the same fatal flaw: they assume agents need external guidance to learn anything useful.

Meta said "what if they don't?"

The breakthrough is almost offensive in its simplicity:

Agents just act. They observe what happens. They learn from consequences. That's it.

No rewards telling them "good job" or "try again." No expert trajectories showing the perfect path. Just raw experience and pattern recognition.

The system works through two mechanisms that sound obvious but nobody combined correctly:

Implicit World Modeling: The agent predicts what happens next based on actions. Every prediction error becomes a learning signal. It builds an internal model of how the world responds without anyone explaining the rules.

Self-Reflection: It watches its own failures, compares them to successful outcomes, and generates explanations for the gap. Not from human feedback from its own analysis of cause and effect.

Both techniques are reward-free. Both scale effortlessly.

The numbers are absolutely brutal:

+18.4% improvement on web navigation tasks
+15.0% on complex planning benchmarks
+13.3% on scientific reasoning problems

Across 8 different environments. Every single one showed massive gains.

But here's the part that breaks the conventional wisdom: when you add traditional RL afterward, you get another +6.4% on top.

Early Experience doesn't replace reinforcement learning. It makes it vastly more efficient by giving the agent a head start.

The efficiency gains are insane:

Runs on 1/8th the expert demonstrations everyone else needs
Cuts training costs by 87%

Works across model scales from 3B to 70B parameters

Small models get smarter. Big models get dramatically smarter. The approach scales both directions.

This solves the cold start problem that's plagued agent development forever. How do you train an agent when you don't have perfect reward functions or millions of expert examples?

You let it explore first. Build intuition. Develop an internal world model. Then optimize.

It's how humans learn. We don't need someone rewarding every action or demonstrating every possibility. We try things, see what happens, build mental models, and improve.

Meta just proved agents can do the same.

The implications reshape the entire field:

Agent training becomes accessible. You don't need armies of human annotators or reward engineering PhDs. Just let the system run and learn.

Deployment costs crater. 87% cost reduction means startups can train agents that were previously only feasible for Big Tech.

Generalization improves. Agents that learn from diverse experience handle novel situations better than agents that memorized expert behavior.

This isn't just a better training technique. It's a philosophical shift in how we think about machine intelligence.

The future of AI agents isn't about better supervision.

It's about better exploration.

Early Experience just proved you can build world-class agents by giving them room to learn on their own terms.

The era of hand-holding AI is over. The problem with current AI agents is brutal.

Imitation Learning: Agents only see expert demos.

When they mess up, they can't recover because they never learned what happens when you take wrong actions.

RL: Needs verifiable rewards. Most real-world environments don't have them.Early Experience solves both.