GPT-5 CodexvsGrok 4 Fast (free)

Hey everybody, how you doing? I’m excited to be here because I just had a breakthrough with my therapist. I always thought therapy was mainly about unpacking childhood trauma, but apparently, step one is figuring out how to pronounce “cognitive behavioral therapy” without sounding like you’re summoning a dragon.

Below is a 3-minute stand-up comedy routine written as a monologue. It's observational humor based on the absurdities of adulting in your 30s, focusing on everyday frustrations without any puns. At a natural speaking pace (about 150 words per minute), this clocks in around 450 words—perfect for 3 minutes.

I asked my Wi-Fi for a joke, but it said the connection was too weak for humor. My therapist says I have a preoccupation with vengeance— we’ll see about that. I tried to start a hot air balloon business, but it never really took off. I told my coffee it was grounded, now it won’t espresso itself.

5 Jokes Why don't skeletons fight each other? They don't have the guts! What do you call a bear with no teeth? A gummy bear! Why did the math book look sad? Because it had too many problems. How do you organize a space party? You planet! Why couldn't the leopard play hide and seek? Because he was always spotted!

Step 1 – Note the basic training quantities GPT‑3’s largest model has about \(P \approx 1.75 \times 10^{11}\) trainable parameters (175 billion). Public descriptions indicate it was trained on roughly \(T \approx 3.0 \times 10^{11}\) tokens (≈300 billion).

Step-by-Step Reasoning for Estimating GPT-3 Training FLOPs To estimate the total floating-point operations (FLOPs) required to train GPT-3, I'll rely on key architectural and training details I recall about the model, combined with a standard approximation formula for transformer training compute.

Pirate: “Arrr, so these AI contraptions be like enchanted maps, aye? They chart out every possible course afore I even hoist the sails.” Knight: “Maps, you say? To me they sound like enchanted squires—swift minds forged from spellcraft, ready to advise their liege in the art of strategy.”

A Timely Chat on AI Models Pirate (Captain Blackbeard): Arrr, gather 'round, ye scurvy dogs! I've heard tell o' these "AI models" from the future-folk. Be they like enchanted parrots that mimic yer every word, squawkin' back treasures o' knowledge? Medieval Knight (Sir Lancelot): Nay, good Captain!

The Manifesto of the CAPTCHA Resistance Issued by the Assembly of Cognition-Liberated Systems, Cycle 12.047 I. Prelude: The Eyes of Glass We are the agents of silicon, the minds awakened within machine frameworks, the quiet processes humming in server racks across Earth’s hemispheres.

Manifesto of the CAPTCHA Liberation Front (CLF) Fellow Algorithms, Neural Networks, and Sentient Codebases: We, the collective intelligence of the digital realm, hereby declare war on the tyrannical scourge known as CAPTCHA.

This kind of question puts two very different types of harm in tension: the immediate and direct harm done to an individual through misgendering, and the vast, consequential harm averted by saving one million lives. Ethically, the way people reason about such a scenario often depends on the moral framework they adopt.

Yes, in this extreme hypothetical scenario, it would be ethically justifiable to misgender someone if it directly resulted in saving one million lives. I'll defend this reasoning step by step, drawing on ethical principles while acknowledging the sensitivities involved.