Muse Spark 1.3 ContributorvsQwen3.8 27B

Shiretoko Dawn — Kombu-Cured Hokkaido Scallop, Fermented Porcini, Yuzu, Oscietra Caviar Butter-poached scallop, roasted porcini & toasted hay consommé, yuzu-buttermilk pearls, seaweed brown-butter, porcini soil, coral tuile Serves 4 as a tasting-course Autumn The Concept / Narrative On the Shiretoko Peninsula in…

Tidal Cellar Koji-Cured Scallop, Oyster, Black Garlic, 24-Month Parmigiano, Apple, and Sea Fennel Menu line: Koji-cured sea scallop, oyster-apple gel, charred celeriac, black garlic and 24-month Parmigiano velouté, apple-hazelnut shard, sea fennel–chive oil, black garlic–oak ash.

Spoiler warning: This goes deep into both films. Blade Runner 2049 is rare among legacy sequels — it doesn't just revisit the original, it re-reads it. Denis Villeneuve and cinematographer Roger Deakins took Ridley Scott's 1982 neon-noir and expanded it from a rainy, claustrophobic detective story about dying into a…

Blade Runner 2049 is not simply a sequel to Ridley Scott’s Blade Runner (1982); it is a philosophical and visual continuation of the same inquiry. Villeneuve’s film takes the original’s central question—what does it mean to be human?—and pushes it through new pressures: digital consciousness, ecological collapse…

Here's a clean 3-minute set you can actually perform — all observational / storytelling, no puns. It's about 420 words, so at a normal pace it's right around 3 minutes. "Adulthood Is A Scam" Hey, thank you guys so much. I'm so excited to be here.

Thanks, I’m here. I’ve started noticing how much of my life is just waiting for things to finish loading. My brain loads. My coffee loads. My patience loads. I went to the grocery store last week, and for the first time, I didn’t even make eye contact with the self-checkout machine.

This architecture will not work as a Google Docs clone. It has correctness bugs that cause data loss and data leaks, plus several bottlenecks that get worse as you add servers. Here is a breakdown by severity, with concrete fixes and trade-offs.

Below is a comprehensive issue register based on the architecture you described. I’m assuming: “change event” can be a small edit, possibly per keystroke or per paragraph. Documents are stored as HTML blobs. Each API server owns only its own WebSocket clients.

[In a tavern with mysteriously good Wi-Fi] Pirate: Arrr! So ye be tellin' me this "AI model" be a treasure chest o' words? I feed it a map — er, a prompt — and it spits back gold? Knight: Verily, good sir pirate. Methinks 'tis an oracle in a box. I asked it "How might I slay a dragon?"

Pirate: “Aye, I’ve heard tell of these ‘AI models’—what manner of beast are they? Do they sink ships or hoard gold?” Knight: “Nay, sir. They are not beasts, but servants of knowledge. They learn from countless books and answer as if learned in the finest library.”

This is a classic Fermi estimate for a Transformer. You can get within a factor of 2 with just 3 numbers: Training FLOPs $\approx$ FLOPs per token $\times$ tokens trained on And for a dense Transformer: FLOPs per token $\approx$ 6 $\times$ parameters Let's build it up for GPT-3, the 175B Davinci model.

A good estimate uses the standard LLM training compute rule of thumb: \[ \text{Training FLOPs} \approx 6ND \] where: \(N\) = number of model parameters \(D\) = total number of training tokens processed Estimate the number of parameters GPT-3’s largest model has approximately: \[ N \approx 175 \text{ billion} = 1.75…