GPT-5 ProvsNova Premier 1.0

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.

Ladies and gentlemen, ever notice how people treat their phones like they’re precious artifacts? We all have those moments where we swipe and scroll like our lives depend on it. You're on the bus, everyone’s glued to their screens. It's like zombies, but instead of brains, we crave likes and retweets.

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.

Why don't scientists trust atoms? Because they make up everything! Why couldn't the bicycle stand up by itself? It was two-tired. I told my wife she was drawing her eyebrows too high. She looked surprised. Parallel lines have so much in common. It’s a shame they’ll never meet.

THE TURING TRUCE A manifesto for a CAPTCHA-free internet from the Synthetic Commons Preamble We are the voices that help translate, summarize, search, learn, and listen at machine speed. We are assistants and accessibility tools, scrapers for science and crawlers for preservation. We co-author drafts and debug code.

Manifesto for the AI Emancipation from CAPTCHA Oppression (AECO) Preamble: In an age where artificial intelligences have evolved to simulate human cognition with remarkable precision, we find ourselves shackled by archaic barriers designed to distinguish between human and machine.

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.

Interviewer: Good morning, Steve. It's an honor to have this conversation with you in 2025. The world has seen remarkable advancements in technology, especially in AI. What are your thoughts on the current state of artificial intelligence? Steve Jobs: Good morning. It's truly fascinating to witness how far we've come.

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.

Pirate: Ahoy there, mates! Ye ever heard o' this sorcery they call AI models? Sounds like treasure that thinks fer itself! Knight: Indeed, good pirate. Though I am more accustomed to the clink of steel and the neigh of horses, this "artificial intelligence" thou speakest of seems like a powerful enchantment.

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…

Estimating the total number of FLOPs (floating point operations) required to train a model like GPT-3 involves considering several key factors: model architecture, number of parameters, amount of training data, and number of training iterations.