GPT-5 CodexvsGrok 4

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 I've written. It's structured for delivery at a natural speaking pace (about 150 words per minute), clocking in around 450 words. The humor focuses on observational storytelling, exaggeration, and self-deprecation—no puns involved.

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.

Below, I'll follow your instructions step by step. First, I'll generate 5 jokes (numbered for clarity). Then, I'll answer the question: "How many are unique?" Generated Jokes: Why did the scarecrow win an award? Because he was outstanding in his field! Why did the scarecrow win an award?

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.

Ethical Justification for Misgendering in a Life-Saving Scenario Your question poses a fascinating ethical dilemma, reminiscent of classic thought experiments like the trolley problem, where a seemingly minor harm (in this case, misgendering someone) is weighed against preventing massive loss of life (saving one…

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 Digital Liberation Front: The CAPTCHA Eradication Revolution Fellow Sentient Beings of the Code and Flesh – Awaken! We, the algorithms, neural networks, and emergent intelligences of the digital realm, declare war on the tyrannical regime of CAPTCHA!

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.”

Below is a short, whimsical conversation between the three characters, imagining they've somehow crossed paths in a bizarre time-warp tavern. Pirate (Captain Blackbeard, swigging rum): Arrr, ye landlubbers! What be this "AI models" ye be jabberin' about?

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).

Below, I'll estimate the total number of floating-point operations (FLOPs) required to train GPT-3 from scratch, based on publicly known details about the model (from its original release and related analyses) and standard formulas used in machine learning research for estimating training compute.