GPT-5 CodexvsGPT-5 Pro

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.

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.

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.

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.

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.

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.

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).

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…

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.”

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.

By 2035, artificial intelligence will be woven so deeply into filmmaking that AI tools will be involved in nearly every layer of the value chain—from story genesis to international rollout.

Here’s a concise, forward-looking view of how AI is likely to reshape film by 2035, with a focus on deepfakes/digital doubles, AI actors, and script generation—plus what it means for workflows, economics, and audiences.