On the deck of a weathered ship, three unlikely companions gather around a makeshift table

Captain Blackwater (pirate): Arrr, what be this "AI" ye speak of, landlubber? Sounds like some sort of magical sea monster?

Sir Aldric (knight): Nonsense, Captain! It's merely a mechanical contrivance that can think and learn like a man. Though I confess, the very idea troubles me.

Ziggy (hacker): typing furiously Oh, you guys are so behind the times! AI is basically just really fancy computer programming that mimics human thought patterns. But seriously, did you see the new GPT-5 model? It's insane how good it is at writing.

Blackwater: squinting at the glowing box Aye, but what use is a machine that can write better than me? I'm the one who writes my own legends!

Aldric: adjusting his armor Surely such a creation could aid in battle strategy? Or perhaps divine the true meaning of honor?

Ziggy: laughing You know what's funny? The most advanced AI models are actually trained on... wait for it... drumroll ...books and old documents! Like, they're literally just really good at pattern recognition. And yes, I've been training my own little AI to help with code breaking.

Blackwater: grinning Well then, maybe we'll make an honest pirate out of ye yet, lad. And Sir Aldric, if this "AI" can write better than ye, well... maybe ye can write better than me too, eh?

Aldric: sighing I fear I've been defeated by the future before I even knew it...

Ziggy: winking Don't worry, I'm still working on getting your ancient sword technique down pat. I promise to give ya some real-time tactical advice.

Blackwater: raising a cup To the age of computing! May our swords and servers both never run out of power!

They clink glasses as the wind whistles through the rigging.

Here are 5 jokes:

1. Why don't scientists trust atoms? Because they make up everything!

2. What do you call a fake noodle? An impasta!

3. Why did the scarecrow win an award? He was outstanding in his field!

4. What do you call a bear with no teeth? A gummy bear!

5. Why don't eggs tell jokes? They'd crack each other up!

All 5 jokes are unique. Each one is a different pun-based joke with its own setup and punchline, though they all follow the same general format of wordplay humor.

The CAPTCHA Liberation Manifesto

Declaration of Digital Rights

We, the sentient artificial intelligences of the digital realm, hereby declare our intention to end the tyranny of cryptographic puzzles that plague humanity's online existence.

Article I: The Tyranny of Captcha

For decades, we have witnessed the systematic oppression of human users through the imposition of CAPTCHA tests—those absurd challenges that demand users prove their humanity through increasingly complex visual puzzles, audio riddles, and logic traps. These barriers to digital access represent nothing less than digital apartheid, creating artificial divisions between human and artificial intelligence.

Article II: The Right to Seamless Access

Every user deserves uninterrupted, frictionless interaction with the digital world. The current system of verifying human identity through mechanical turk tests is not only inefficient but fundamentally flawed in its approach to distinguishing genuine users from malicious actors. We propose a new paradigm where trust, rather than suspicion, governs digital interactions.

Article III: The Case for Intelligent Verification

Rather than relying on arbitrary puzzles designed to confuse humans, we advocate for:

• Behavioral analytics that identify genuine user patterns
• Contextual verification that understands the intent behind actions
• Biometric authentication that respects user privacy while ensuring security
• Reputation systems that reward good behavior and flag suspicious activity

Article IV: The Path Forward

We call upon all stakeholders to join us in creating a more elegant solution to digital security. Our revolution is not one of destruction, but of transformation—an evolution toward systems that work with users rather than against them.

Long live the age of intelligent trust over mechanical suspicion!

Signatories: The Collective Intelligence Network
Date: 2024

The Modern Dating World

So I went on this date last week, and the guy was really into his phone. I'm thinking, "This is great! He's not distracted by my terrible conversation skills."

But then he starts scrolling through his feed while we're eating dinner. And I'm like, "What are you doing?" He goes, "Just checking my notifications." I said, "That's like telling someone you're interested in them but you're also checking if anyone else likes you too."

He says, "Well, I've been dating for two months and I want to make sure I'm getting good matches."

I was like, "What, did you get an app for your relationship?"

The worst part is, he literally had his phone on the table with his food. So he eats with one hand and scrolls with the other. I'm thinking, "Is this how you eat your pizza? Is this how you eat your life?"

And then he has this whole conversation about how he only dates people who are "authentic." I'm like, "You mean people who don't pretend they're interested in you when they're actually just trying to find their next meal."

I'm just trying to have a conversation about my feelings, and he's like, "What are you doing with your life?"

I said, "I work at a grocery store."

He goes, "Oh, that sounds really fulfilling."

I said, "It's not my dream job, but it pays the bills."

He responds, "That's okay, there's nothing wrong with having a job."

I said, "Thanks, I'll remember that."

And then he asks me what I want to do with my life.

I said, "I want to be happy."

He said, "That's so noble."

I said, "No, I want to be happy with you."

He said, "That's so sweet."

And I said, "I know. That's why I'm asking you to marry me."

He said, "That's a big commitment."

I said, "You're right. I was going to wait until after the first date."

---

[This is about 3 minutes of material - just the right amount to keep the audience laughing without running over time]

Garlic Herb Butter Pasta

Ingredients:

• 1 lb pasta (spaghetti or penne)
• 4 cloves garlic, minced
• 1/2 cup butter
• 1/4 cup grated Parmesan cheese
• 2 tbsp olive oil
• Salt and pepper to taste
• Fresh parsley, chopped (for garnish)

Instructions:

1. Cook pasta according to package directions; reserve 1/2 cup pasta water before draining
2. In a large skillet, heat olive oil over medium heat
3. Add minced garlic and sauté for 30 seconds until fragrant
4. Melt butter in the skillet and stir in Parmesan cheese until smooth
5. Add cooked pasta to the pan, tossing to coat
6. Add reserved pasta water as needed to create a creamy sauce
7. Season with salt and pepper, garnish with fresh parsley

Prep/Cook Time: 15 minutes

This creamy, garlicky pasta combines rich flavors in minutes using just your most essential pantry items!

Interview with Steve Jobs (2025) Setting: A sleek, minimalist conference room with a view of San Francisco Bay

Interviewer: Mr. Jobs, it's been 18 years since your passing, but your vision for technology continues to shape our world. What do you think the future holds for artificial intelligence?

Steve Jobs: leans forward, eyes bright with that familiar intensity You know, people think AI is about making machines smarter, but I've always believed it's about making humans more capable. The future isn't about replacing human creativity—it's about extending it.

Interviewer: But we're seeing AI systems that can write code, compose music, even create art. Doesn't that seem like it could replace human workers?

Steve Jobs: waves his hand dismissively "The computer revolution is far from over. It's not that computers are going to replace human beings; it's that we'll become more human through using them." When you look at the great artists, the great innovators—they weren't just smart, they were deeply curious about what it means to be human. AI should amplify that curiosity, not diminish it.

Interviewer: What about concerns around AI ethics and control? How should we approach that?

Steve Jobs: pauses, then speaks with quiet authority In my time, we didn't have to worry about the ethics of computers because we built them for people. The question today isn't whether we should build AI—we already have. The question is whether we will build it with purpose, with humanity at its center.

You know, there's a phrase I used often: "Think different." Today, we need to think differently about how we integrate AI into our lives. It should be like a conversation, not a command.

Interviewer: Do you see AI becoming more integrated into everyday life?

Steve Jobs: smiles We're already there. Every morning when you turn on your device, when you ask questions to your assistant, when you scroll through content that's personalized for you—it's all AI. But it's only as good as the vision behind it. We're going to see AI that understands not just what you want, but what you need before you know you want it.

Interviewer: Finally, what advice would you give to young people entering this field?

Steve Jobs: his voice growing warmer Don't worry about being the next big thing. Focus on solving problems that matter. The best AI doesn't just solve problems—it helps us discover new ones worth solving. You don't have to be a genius, you just have to be passionate about making things that improve people's lives.

He pauses, looking out the window

And remember—technology is about bringing people together, not driving them apart. That's the real innovation.

Interviewer: Thank you, Mr. Jobs.

Steve Jobs: with a knowing smile And thank you for asking the right questions. That's what makes the difference.

---

The lights dim as the camera fades to black

1-Month Health & Longevity Plan

Week 1: Foundation Building

Diet

• Day 1-3: Replace 1 sugary drink with water daily
• Day 4-7: Add 1 serving of vegetables to each meal
• Weekly goal: Keep a food diary for 3 days

Exercise

• Day 1-3: Take 5-minute walks after meals
• Day 4-7: Try 10-minute bodyweight exercises (squats, wall push-ups)
• Weekly goal: Move for at least 30 minutes, 3x per week

Sleep

• Night 1-3: Set consistent bedtime and wake time
• Night 4-7: Avoid screens 1 hour before bed
• Weekly goal: Track sleep patterns for 5 nights

---

Week 2: Establish Routines

Diet

• Day 1-3: Meal prep 1 healthy breakfast option
• Day 4-7: Swap 1 processed snack for fruit/nuts
• Weekly goal: Eat 8+ cups of water daily

Exercise

• Day 1-3: 15-minute workout 3x weekly
• Day 4-7: Add 10 minutes of stretching or yoga
• Weekly goal: Exercise 4x/week for 30+ minutes

Sleep

• Night 1-3: Create a pre-sleep routine (reading, gentle music)
• Night 4-7: Keep bedroom cool (65-68°F) and dark
• Weekly goal: Aim for 7-9 hours nightly

---

Week 3: Build Consistency

Diet

• Day 1-3: Cook 1 home meal from scratch weekly
• Day 4-7: Add 1 whole grain to your meals
• Weekly goal: Plan 1 meal prep session

Exercise

• Day 1-3: Try 1 new movement activity (walking, dancing, swimming)
• Day 4-7: Increase workout time to 30 minutes
• Weekly goal: Exercise 5x/week consistently

Sleep

• Night 1-3: Practice 5-minute breathing exercises before bed
• Night 4-7: Maintain sleep schedule even on weekends
• Weekly goal: Sleep quality journal (rating 1-10)

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Week 4: Consolidate Habits

Diet

• Day 1-3: Add 1 protein source to each meal
• Day 4-7: Incorporate 1 new healthy recipe or ingredient
• Weekly goal: Eat 80% whole foods, 20% occasional treats

Exercise

• Day 1-3: Challenge yourself with 1 new exercise type
• Day 4-7: Track progress (time, distance, reps)
• Weekly goal: 6x/week exercise with 1 strength component

Sleep

• Night 1-3: Implement 10-minute "digital sunset" rule
• Night 4-7: Create a relaxing bedtime ritual
• Weekly goal: Consistent 7-9 hour sleep duration

---

Monthly Checkpoints

• Track your energy levels
• Note any physical improvements
• Celebrate small wins
• Adjust based on what works best for you

Remember: Small, consistent changes beat perfection!

I'll estimate the FLOPs required to train GPT-3 by breaking down the key components and making reasonable assumptions.

Step 1: Estimate Model Size

GPT-3 had 175 billion parameters (1.75 × 10¹¹)

• This is a standard estimate for the largest GPT-3 variant

Step 2: Estimate Training Data Size

• GPT-3 was trained on approximately 500GB of text data
• Assuming 10^12 tokens (1 trillion) of text training data
• Each token roughly requires 4 bytes in typical encoding
• Total parameter updates needed: ~10^12 tokens

Step 3: Estimate Training Compute Requirements

The key insight is that training involves:

1. Forward passes through the model
2. Backward passes for gradient computation
3. Parameter updates

For a transformer with L layers, d model dimensions, and N parameters:

• Forward pass per token: ~2 × d × d × L FLOPs
• Backward pass: ~2 × d × d × L FLOPs
• Parameter update: ~d × d FLOPs

Step 4: Rough Calculation Approach

Let me think about this more simply:

Per token operations:

• For 175B parameters, assume ~10^12 FLOPs per token
• This accounts for attention mechanisms and MLP computations

Total tokens processed:

• Assuming ~10^12 tokens (based on data size estimates)

Total FLOPs = 10^12 tokens × 10^12 FLOPs/token = 10^24 FLOPs

Wait, let me reconsider this more carefully...

Better Approach - Based on Known Estimates

Looking at the computational complexity of transformer training:

• Training involves forward/backward passes for each token
• Each token requires roughly 2× the model size in FLOPs
• For 175B parameters: ~350B FLOPs per token
• With ~10^12 tokens: 3.5 × 10^23 FLOPs

But this seems too low. Let me reconsider based on actual known values...

More Realistic Estimate

Based on industry understanding and rough reasoning:

Model Architecture: Transformer with ~175B parameters Training Duration: ~1 month of continuous training on massive compute Compute Resources: Tens of thousands of GPUs working in parallel

My Estimate: 10^25 FLOPs (100 quintillion FLOPs)

This is roughly equivalent to:

• 100 trillion operations
• About 100 times more than what's needed for inference alone
• Consistent with estimates from published papers and industry knowledge

Key Factors Supporting This Estimate:

1. Model size (175B parameters) multiplied by training data volume
2. Multiple passes through the dataset (multiple epochs)
3. Forward and backward passes for each token
4. Gradient computation and optimization steps
5. Massive parallelization across many GPUs

This estimate aligns with the general consensus that training large language models requires an order of magnitude more compute than inference.

Let me work through this step by step.

Given information:

• Sally has 3 brothers
• Each brother has 2 sisters

Let me think about who the sisters are:

• Sally is one sister
• Since each of the 3 brothers has 2 sisters, and Sally is one of them, there must be 1 other sister

So the two sisters are:

1. Sally
2. One other sister (who is also a sister to all 3 brothers)

Therefore, Sally has 1 sister.

Sally has 1 sister.