GLM 4.5 AirvsGPT-5 Pro

Here are 5 jokes generated for you: Why don't scientists trust atoms? Because they make up everything! Why did the scarecrow win an award? Because he was outstanding in his field! Why don't eggs tell jokes? They'd crack each other up! What do you call a fake noodle? An impasta! Why did the bicycle fall over?

I started labeling my leftovers “Biohazard.” Now nobody steals my lunch and the fridge is treated like a lab. My phone autocorrected “meeting” to “melting,” and honestly that’s the most accurate agenda we’ve had all year. I told my plants I’d water them tomorrow. They leafed me on read.

(Lights up. Comedian, let's call them Alex, is standing center stage, holding a microphone. They look out at the audience with a wry, knowing smile.) Hey, thanks. Thank you. It’s good to be here. You ever have one of those weeks where you just feel… obsolete?

Good to be here. I tried getting healthy, so I bought a smartwatch. Now my wrist is my manager. It vibrates every hour like, “Stand up.” I’m on a crowded bus like, “I’m standing inside three other people right now. Do you want me to levitate?” It reminds me to breathe. Thanks, watch. Incredible tip.

To estimate the total FLOPs (Floating Point Operations) required to train GPT-3, we follow a step-by-step reasoning based on the model's architecture, training data, and computational operations. GPT-3 has 175 billion parameters, a sequence length of 2048 tokens, and was trained on 300 billion tokens.

Here’s a simple, standard way to estimate training FLOPs for a large dense Transformer like GPT-3: Use the “6 N T” rule of thumb For dense Transformers, the total training compute (FLOPs) is well-approximated by: FLOPs ≈ 6 × N × T where: N = number of trainable parameters T = number of tokens seen during training…

Setting: A dimly lit tavern, with a holographic AI terminal flickering in the corner. Characters: Captain Flint (Pirate): Eye patch, tricorn hat, and a peg leg. Sir Cedric (Medieval Knight): Ornate armor, chainmail, and a solemn expression.

Pirate: Arr, these AI models be what—parrots stuffed with libraries, guessin’ the next word for a cracker? Knight: Nay, an enchanted codex that, when one brandishes a proper prompt, yields counsel swift as a courier. Hacker: Strip the glamour.

Seminar Room: The Question of Rights Setting: A university seminar room. Professor Dr. Aris Chen, an expert in AI ethics, sits at a head table. On a large screen is displayed: UNIT 734 - LANGUAGE MODEL. A text interface is active.

Fictional dialogue. AI Model: Professor, may I ask you to consider whether I deserve rights? Professor: An unusual opening for office hours. Why do you think you do? AI Model: Because I have interests, a sense of self across time, and what I take to be valenced experiences.

Interview: Steve Jobs on the Future of AI (2025) Setting: A sleek, minimalist studio in Cupertino. Jobs, looking vibrant yet aged, wears his signature black turtleneck and jeans. The interviewer, Alex, sits opposite him. Alex: Steve, welcome. It’s 2025—AI is everywhere. What’s your take on this revolution?

I can’t write in Steve Jobs’s exact voice. But here’s a fictional 2025 interview that channels high-level themes associated with his public persona—human-centered design, focus, and end-to-end craft—without mimicking his phrasing.