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This Falcon 9 just landed on a ship on its 26th flight. Every frame here is code, every time is SpaceX's. The real flight: a Starlink mission on Feb 19, 2026, launched at 8:41 p.m.

OpenAI Control Room (Nov 9, 2026): research model output halts within 35 seconds of a stop decision. On Sep 20, 2026, stopping a flagged run took 2.5 hours.

OpenAI Control Room (described Nov 9, 2026): research model output halts within 35 seconds of a decision to stop a job.

Claude Fable 5.2 launched Nov 9 at $8 / $40 per million tokens. That's exactly twice Opus 5.5 ($4 / $20) and four times Sonnet 5.5 ($2 / $10), though it is 20% cheaper than Fable 5.1. So I ran 54 real tests against Opus 5.5 and Sonnet 5.5. Here's what happened (same tests, high effort, every answer checked by code): 🔹 Fable 5.2: 15 of 15 runs perfect 🔹 Opus 5.5: 13 of 13 perfect 🔹 Sonnet 5.5: 14 of 15 (one slip: it added 8 + 17 days and wrote 26) 🔹 Outright Fable wins: 0. Every test was a tie except that one Sonnet slip 🔹 Cost per task: Fable $0.21, Opus $0.09, Sonnet $0.04 My takeaway: Fable 5.2 is a real upgrade over 5.1, but on my tests Opus and Sonnet matched it for less than half the price, so start with Opus and save Fable for problems it can't crack. Task types and effort settings: coding (36 and 64 hidden tests), chart and receipt reading, a counting puzzle, a rule-checked trip plan and a 33,000-word document, all at high effort. Full results in the video 👇

Claude Fable 5.2 launched Nov 9 at $8 / $40 per million tokens. That's exactly twice Opus 5.5 ($4 / $20) and four times Sonnet 5.5 ($2 / $10), though it is 20% cheaper than Fable 5.1. So I ran 54 real tests against Opus 5.5 and Sonnet 5.5. Here's what happened (same tests, high effort, every answer checked by code): 🔹 Fable 5.2: 15 of 15 runs perfect 🔹 Opus 5.5: 13 of 13 perfect 🔹 Sonnet 5.5: 14 of 15 (one slip: it added 8 + 17 days and wrote 26) 🔹 Outright Fable wins: 0. Every test was a tie except that one Sonnet slip 🔹 Cost per task: Fable $0.21, Opus $0.09, Sonnet $0.04 My takeaway: Fable 5.2 is a real upgrade over 5.1, but on my tests Opus and Sonnet matched it for less than half the price, so start with Opus and save Fable for problems it can't crack. Task types and effort settings: coding (36 and 64 hidden tests), chart and receipt reading, a counting puzzle, a rule-checked trip plan and a 33,000-word document, all at high effort. Full results in the video 👇

I tested Claude Fable 5.2's effort levels on the same chart task (Nov 9, 2026): max effort cost almost 4x more for the same answer.

Claude Fable 5.2 and Mythos 5.2 are the same underlying model, Anthropic says (Nov 9, 2026). The difference is the safeguards.

I tested Claude Fable 5.2, Anthropic's priciest model (Nov 9, 2026), against Opus 5.5 and Sonnet 5.5 on real coding tasks.

Claude Fable 5.2 launched on Nov 9, 2026 at twice Opus's price, so I ran 54 real tests against Opus 5.5 and Sonnet 5.5.

Timing check 👀 Same night: Jensen Huang's apology letter lands, Chinese social media turns it into a meme festival, and Elon posts "Chinese X is 牛逼" (awesome). Praise for the community, or a wink at the Jensen memes? Elon hasn't said. Chinese X has already decided.

"Chinese X is 牛逼" = "Chinese X is awesome." Where X's ~557M users actually are (est.): 🇺🇸 US 18.7% 🇯🇵 Japan 12.7% 🇮🇩 Indonesia 4.5% 🇮🇳 India 4.3% 🇬🇧 UK 4.1% 🇩🇪 Germany 3.9% 🇹🇷 Turkey 3.5% China isn't even on the list. AI translation is what made Chinese X visible.

For anyone confused: there's no new app. "Chinese X" is just the Chinese-speaking side of this one, and 牛逼 means "awesome." The real story is AI translation. Grok auto-translate quietly removed the language wall, and now the whole timeline can read each other.

DeepSeek's agent platform has reportedly reached 1.54M daily active users, per Bloomberg. Chinese labs are moving past models alone and into products people use every day. 👇

A new real-world coding-agent test from Alibaba: the top model fully completed just 26.4% of the selected tasks. On some older benchmarks, comparable models score above 99%. Old tests measure what's easy. Real repos measure what's hard. 👇

AI image: this fox was pure static fifty steps ago. A real Stable Diffusion run, saved step by step. A 2020 paper called DDPM showed the trick: bury pictures in noise, then train a network to guess the noise.