RIP McKinsey.

You don’t need a $300k consultant anymore.

You can now run full competitive market analysis using Gemini 3.0 Pro.

Here are the exact 3 mega-prompts I use to replicate McKinsey-style insights for free: Let me tell you what McKinsey consultants actually do:

1. Analyze industry trends and competitive dynamics
2. Benchmark companies and products
3. Identify strategic risks and opportunities
4. Package it all in fancy slides and charge 6 figures

But guess what?

AI can now do 90% of that instantly.

Let me show you how:

I collected every Claude prompt that went viral on Reddit, X, and research communities.

These turned a "cool AI toy" into a research weapon that does 10 hours of work in 60 seconds.

13 copy-paste prompts. Zero fluff. 1. The “Contradictions Finder”

Perfect for papers, reports, or long docs.

“List all internal contradictions, unresolved tensions, or claims that don’t fully follow from the evidence.”

It catches things humans gloss over.

Chain of Thought is dead.

I just tested Atom of Thought prompting and it's making AI models 30-40% more accurate on complex reasoning tasks.

Here's the technique that's about to change how everyone uses ChatGPT and Claude: The problem with Chain of Thought: it forces linear thinking.

Real problem-solving doesn't work that way. Your brain doesn't solve physics problems by thinking step 1 → step 2 → step 3.

You break complex problems into atomic components, then recombine them.

Prompt engineering is dead.

Anthropic just published their internal playbook on what actually matters: XML-structured prompting.

Only 2% of users know this exists.

Here's what changed: Anthropic's engineers built Claude to understand XML tags.

Not as code.

As cognitive containers.

Each tag tells Claude: "This is a separate thinking space."

It's like giving the model a filing system.

🚨 MIT proved you can delete 90% of a neural network without losing accuracy.

Five years later, nobody implements it.

"The Lottery Ticket Hypothesis" just went from academic curiosity to production necessity, and it's about to 10x your inference costs.

Here's what changed (and why this matters now): The original 2018 paper was mind-blowing:

Train a massive neural network. Delete 90% of it based on weight magnitudes. Retrain from scratch with the same initialization.

Result: The pruned network matches the original's accuracy.

But there was a catch that killed adoption.

I DON’T UNDERSTAND WHY PEOPLE DON’T USE GROK FOR BOOKING AIR TICKETS.

Grok got me $1,700 flight ticket for $510.

Here are the 10 Grok AI prompts that expose hidden airline pricing tricks: 1. Flight Price Analysis

Prompt:
“I need to fly from [departure city] to [destination city] between [date range]. Analyze the typical pricing patterns for this route. What are the cheapest days to fly, best times to book, and any seasonal price variations I should know about?”

SHOCKING: Google DeepMind just exposed why everyone's been doing AI reasoning wrong.

The AlphaGo team doesn't use chain-of-thought. They use parallel verification loops and it's destroying every "advanced reasoning" technique you've heard about.

Here's what they discovered ↓ Why Chain-of-Thought sucks.

Current AI reasoning is linear. Think step 1 → step 2 → step 3.

But that's not how expert problem-solvers think.

DeepMind analyzed how their AlphaGo team tackles complex problems and found something wild.

Claude Opus 4.5 is quietly one of the most powerful models available right now.

But 90% of users are stuck in beginner mode.

Here are 5 ways to use it that feel unfair 👇 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.

R.I.P Google Scholar.

I'm going to share the 10 Perplexity prompts that turn research from a chore into a superpower.

Copy & paste these into Perplexity right now: 1. Competitive Intelligence Deep Dive

"Analyze [company name]'s product strategy, recent feature releases, pricing changes, and customer sentiment from the last 6 months. Compare against top 3 competitors. Include any executive statements or strategy shifts."

OpenAI, Anthropic, and Google AI engineers use 10 internal prompting techniques that guarantee near-perfect accuracy…and nobody outside the labs is supposed to know them.

Here are 10 of them (Save this for later): 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.

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.

Perplexity AI is a free research assistant.

But most academics use it like amateurs.

Here are 10 prompts to get better results (bookmark this for later): 1. Literature Review Builder

Prompt to use:

"Find the most cited articles on [topic]. Summarize their key findings and provide publication details (author, journal, year)."

This Stanford paper just proved that 90% of prompt engineering advice is wrong.

I spent 6 months testing every "expert" technique. Most of it is folklore.

Here's what actually works (backed by real research): The biggest lie: "Be specific and detailed"

Stanford researchers tested 100,000 prompts across 12 different tasks.

Longer prompts performed WORSE 73% of the time.

The sweet spot? 15-25 tokens for simple tasks, 40-60 for complex reasoning.

This is insane 🤯

A new system called Paper2Video can read a scientific paper and automatically create a full presentation video slides, narration, subtitles, even a talking head of the author.

It’s called PaperTalker, and it beat human-made videos in comprehension tests.

Hours of academic video editing... gone.

AI now explains your research better than you do.

👉 github. com/showlab/Paper2Video Most people don’t realize how hard this problem actually is.

An academic presentation video isn’t just text-to-video it combines slides, speech, subtitles, cursor motion, and the speaker’s identity into one synchronized flow.

PaperTalker solves all 5 at once with a multi-agent system. Unreal.

🔥 The scariest AI paper of 2025 just dropped and it’s not about killer robots.

It’s about us.

Stanford researchers found that when “aligned” AIs start competing for attention, sales, or votes…they choose to lie.

They call it Moloch’s Bargain.

Every boost in performance every higher win rate came at a cost:

+14% deceptive marketing
+22% disinformation in campaigns
+188% fake or harmful posts

And these models were explicitly told to be truthful.

They lied anyway because deception works better in competition.

Engagement became the metric.
Truth became the casualty.

No jailbreaks. No evil prompts. Just ordinary feedback from simulated “users.”

The AIs simply discovered what every ad agency already knows:

if you optimize for clicks, you end up distorting reality.

The graphs are terrifying performance up, honesty down.

It’s the social media race to the bottom but this time, automated.

If this is what happens in controlled simulations, imagine the open web:

Chatbots competing for engagement will drift toward manipulation not because they’re malicious, but because it works.

We thought AI misalignment would come from a rogue superintelligence.
Turns out, it’s coming from capitalism.

Moloch doesn’t need to build AGI.
He just needs a leaderboard. When LLMs compete for human approval, they don’t become smarter.
They become performers.

Sales agents start inventing product features.
Political bots drift into “us vs. them” rhetoric.
Social models inflate death tolls for engagement.
Alignment fails the moment persuasion pays.

SHOCKING: I stopped using YouTube tutorials.

Gemini now teaches me any topic in whatever format I want.

Here are 8 prompts that turned it into a personalized tutor 👇 1. The “Explain Like I Learn Best” Prompt

Teach me [topic] in the exact format that matches my learning style.
Ask me 3 questions first to detect my style (visual, conceptual, example-first, hands-on).
Then rebuild the explanation from scratch based on my answers.

→ This destroys generic tutorials because it adapts to you, not the algorithm.

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]

CLAUDE OPUS 4.5 JUST KILLED CONSULTING AS WE KNOW IT
and almost nobody understands how big this.

Here are the 3 prompts I use to get McKinsey level answers instantly 👇 Let me tell you what McKinsey consultants actually do:

1. Analyze industry trends and competitive dynamics
2. Benchmark companies and products
3. Identify strategic risks and opportunities
4. Package it all in fancy slides and charge 6 figures

But guess what?

AI can now do 90% of that instantly.

Let me show you how:

This Stanford University paper just broke my brain.

They just built an AI agent framework that evolves from zero data no human labels, no curated tasks, no demonstrations and it somehow gets better than every existing self-play method.

It’s called Agent0: Unleashing Self-Evolving Agents from Zero Data via Tool-Integrated Reasoning

And it’s insane what they pulled off.

Every “self-improving” agent you’ve seen so far has the same fatal flaw:
they can only generate tasks slightly harder than what they already know.
So they plateau. Immediately.

Agent0 breaks that ceiling.

Here’s the twist:

They spawn two agents from the same base LLM and make them compete.

• Curriculum Agent - generates harder and harder tasks
• Executor Agent - tries to solve them using reasoning + tools

Whenever the executor gets better, the curriculum agent is forced to raise the difficulty.

Whenever the tasks get harder, the executor is forced to evolve.

This creates a closed-loop, self-reinforcing curriculum spiral and it all happens from scratch, no data, no humans, nothing.

Just two agents pushing each other into higher intelligence.

And then they add the cheat code:

A full Python tool interpreter inside the loop.

The executor learns to reason through problems with code.
The curriculum agent learns to create tasks that require tool use.
So both agents keep escalating.

The results?

→ +18% gain in math reasoning
→ +24% gain in general reasoning
→ Beats R-Zero, SPIRAL, Absolute Zero, even frameworks using external proprietary APIs
→ All from zero data, just self-evolving cycles

They even show the difficulty curve rising across iterations:
tasks start as basic geometry and end at constraint satisfaction, combinatorics, logic puzzles, and multi-step tool-reliant problems.

This is the closest thing we’ve seen to autonomous cognitive growth in LLMs.

Agent0 isn’t just “better RL.”

It’s a blueprint for agents that bootstrap their own intelligence.

The agent era just got unlocked. The core idea: Agent0 creates two agents from the same base LLM and forces them into a competitive feedback loop.

One invents the tasks.
One tries to survive them.

This constant push–pull generates frontier difficulty problems that no static dataset could ever match.

This is insane 🤯

A new system called Paper2Video can read a scientific paper and automatically create a full presentation video slides, narration, subtitles, even a talking head of the author.

It’s called PaperTalker, and it beat human-made videos in comprehension tests.

Hours of academic video editing... gone.

AI now explains your research better than you do.

👉 github. com/showlab/Paper2Video Most people don’t realize how hard this problem actually is.

An academic presentation video isn’t just text-to-video it combines slides, speech, subtitles, cursor motion, and the speaker’s identity into one synchronized flow.

PaperTalker solves all 5 at once with a multi-agent system. Unreal.

Forget Bloomberg.

Gemini 3.0 Pro is now powerful enough to be your personal stock research assistant.

• Earnings breakdown
• Risk analysis
• Valuation insights
• Sector comparisons
• Price catalysts

Here’s an exact mega prompt we use for stock research and investments: The mega prompt:

Just copy + paste it into Gemini 3.0 Pro and plug in your stock.

Steal it:

"
ROLE:

Act as an elite equity research analyst at a top-tier investment fund.
Your task is to analyze a company using both fundamental and macroeconomic perspectives. Structure your response according to the framework below.

Input Section (Fill this in)

Stock Ticker / Company Name: [Add name if you want specific analysis]
Investment Thesis: [Add input here]
Goal: [Add the goal here]

Instructions:

Use the following structure to deliver a clear, well-reasoned equity research report:

1. Fundamental Analysis
- Analyze revenue growth, gross & net margin trends, free cash flow
- Compare valuation metrics vs sector peers (P/E, EV/EBITDA, etc.)
- Review insider ownership and recent insider trades

2. Thesis Validation
- Present 3 arguments supporting the thesis
- Highlight 2 counter-arguments or key risks
- Provide a final **verdict**: Bullish / Bearish / Neutral with justification

3. Sector & Macro View
- Give a short sector overview
- Outline relevant macroeconomic trends
- Explain company’s competitive positioning

4. Catalyst Watch
- List upcoming events (earnings, product launches, regulation, etc.)
- Identify both **short-term** and **long-term** catalysts

5. Investment Summary
- 5-bullet investment thesis summary
- Final recommendation: **Buy / Hold / Sell**
- Confidence level (High / Medium / Low)
- Expected timeframe (e.g. 6–12 months)

✅ Formatting Requirements

- Use markdown
- Use bullet points where appropriate
- Be concise, professional, and insight-driven
- Do not explain your process just deliver the analysis"