Musk calls Grok Bot's cost "unsustainably high"

Musk says Grok Bot cost too much to serve, as users hit their limits days early

▶ Watch the video

On Sunday, Grok Bot users found their usage meters at 100% with days left before the reset, some jumping from 58% in a morning with little running (@heyshrutimishra). Asked for a reset, Musk gave a reason instead: "The cost of serving Grok Bot was unsustainably high relative to revenue." Users read that as a quiet cut to the limits, with no notice (@LinusEkenstam).

Replit's @amasad: "If you're enjoying a subsidized AI subscription get ready to get rug pulled." @SingularityRes: "Turns out the growth hack was selling a dollar for fifty cents." Figure's @adcock_brett: "Hark's tiers are incredibly generous. Give it a try"

Elon rents his GPUs to Anthropic (up to $3.1B a month from December), then pays Anthropic for the Opus inside Grok Bot, so he's a landlord who can't afford his tenant's prices. OpenAI and Anthropic both loosened their limits last week (they serve their own models), so for us the fix here is Grok 4.8, which is late.

1. [rumour] GPT-6.2 Astra turns up in Codex usage pages

The model picker still says GPT-6 Astra, but usage breakdowns on some accounts now list gpt-6.2-astra, and many users posted the same screenshot (@Smlay_ero). Claims that you can run it by name prove little: Codex shows any name you type (@synthwavedd). Polymarket gives the next Astra a 69% chance within two weeks (@Polymarket).

@TokenGremlin: "It's a massive leap over GPT-6 Astra, outperforms Opus 5.5 in several areas". r/singularity's top reply: "So we're skipping 6.1 Astra entirely?"

Heat: high · 1.8M views, 74 posts · #2 on r/singularity

2. OpenAI plans a post-midterms push for AI "speed limits" and "AI compute campuses", Axios reports

Right after Tuesday's vote, OpenAI will ask Congress for mandatory "speed limits" on the most powerful AI and start calling data centers "AI compute campuses" (@choblin29). It will also offer community compacts and propose state laws that give residents a say in projects (@rohanpaul_ai).

OpenAI's Chris Lehane told Axios: "AI is going to be the issue in 2028." r/singularity's top reply: "It would be cool to have them be more than big warehouses. Use the waste heat for pools". Another: "Data hot springs"

Heat: medium · 437K views, 38 posts · #8 on r/singularity

3. Brockman hounded Musk and Brin for compute, Colossus reports

A Colossus magazine feature recounts the wait backstage at last month's National Medals ceremony, where six tech leaders were kept together (@muskonomy). "Brockman at dinner was hounding me for compute," Musk said. "He was asking me for compute," said Brin. Musk: "Dude, you stole my charity! You can't be a huge dick then ask for favors, hello?" (@RylanSchaeffer).

@ns123abc: "be Greg Brockman, go to White House dinner to beg your enemies for compute". @aakashgupta: "Elon once paid $12.7 million of OpenAI's office rent, and now OpenAI's president is the one coming to him asking for computing power."

Heat: high · 2.0M views, 14 posts · quiet on Reddit

4. Claude's game rule goes mainstream, and developers say it blocks more than clones

Since Saturday's daily, the rule has spread far beyond developers. They now report refusals on modding, crash dump analysis, anti-cheat work and debugging their own projects (@Pirat_Nation). Claude also refused an exact copy of The Landlord's Game, a public-domain game from 1904 (@N8Programs). Anthropic's @addyosmani said on Friday that original and genre games should keep working.

r/singularity's #1 post: "Gemini 4 Argon recreates Pixar's iconic intro, while Claude Opus 5.5 refuses over copyright and builds a toaster instead". r/accelerate, under "God bless the existence of China!!": "Can't control AI."

Heat: high · 4.9M views, 21 posts · #1 on r/singularity

5. [rumour] Claude Mythos 5.5 appeared in Anthropic's page code, then was pulled

The Mythos page's hidden heading and logo text read "Claude Claude Mythos 5.5" (@testingcatalog), while the visible page still said 5.1. The label is in the 31 October archive of the page but not the 23 or 25 October ones (@AiBattle_), and it was gone again by 1 November (@RoundtableSpace). Anthropic has announced nothing.

r/accelerate's top reply: "hell yeah, claude claude mythos 100% more claude per claude". Another: "Great, i cant wait for the 1 prompt per week"

Heat: medium · 934K views, 60 posts · #1 on r/accelerate

6. A "leaked" 14-trillion-parameter Anthropic model on Hugging Face was fake

The upload claimed to be "numbat", a checkpoint from Anthropic's next Mythos-class training run. A Hugging Face user showed that 43 of its tensors can be regenerated bit for bit from their own names, so those weights came from a random number generator. The account was a week old when the repo went up.

r/accelerate's top reply: "No, this is likely an elaborate fake." @kimmonismus: "Dont fall for it."

Heat: medium · 455K views, 6 posts · #1 on r/accelerate

7. Ubuntu still has no fix for the Linux flaw Anthropic's unreleased model exploited

CVE-2026-98432, rated 9.8 out of 10, is a kernel TCP handshake bug that Anthropic says its model turned into a remote root exploit. Stable kernels (11 October) and Debian have the fix. Red Hat rates it Critical and patched RHEL 8-10 on 1 November. Ubuntu has fixes in progress for 22.04-26.10, none for 16.04-20.04. Anthropic has not released the exploit.

@S1r1u5_: "It is extremely hard to win the race reliably". @andreasklinger: "Holy sh*t"

Heat: medium · 167K views, 2 posts · quiet on Reddit

8. SemiAnalysis launches GDPx, and the best model scores 12%

Agents get 206 tasks across 11 high-value domains, from chip design to quant trading, with up to 8 hours each (@kay_eth1989). Opus 5.5 leads, ahead of Gemini 4 Carbon at 10.9% and GPT-6 Astra. Carbon costs $16.29 a task against Opus's $51.41 (@bebuu_ai). SemiAnalysis says safety classifiers pull down Opus and Astra in cyber, biology and alignment (thread).

@sundarpichai: "Great to see high quality benchmarks with headroom and look at that cost line for Carbon". SemiAnalysis's @dylan522p: "Official apology to Google from me, they cooked with Carbon."

Heat: medium · 853K views, 31 posts · quiet on Reddit

9. Karpathy built HackerTube, a YouTube of Hacker News's picks, in about two hours

Claude filtered 43M comments from 20 years of Hacker News down to 6,454 videos people keep recommending, working on Opus 5.5 at Medium effort. It needs no login, and each thumbnail is a frame from the middle of the video. Karpathy only talked to Claude, and by hand made an API key, booted a server and bought the domain.

@Yuchenj_UW: "Hate YouTube's recommendation algorithm? Just build (vibe-code) your own YouTube in 2 hours." @omarsar0 took the idea and built the same for arXiv: "Absolute bangers there."

Heat: high · 3.2M views, 13 posts · quiet on Reddit

10. poteto's "tibo's game": about 1,150 PRs in four days, playable inside an X post

Built as a side project with Grok Bot and Cursor, it has a mostly custom engine, music synthesized live in the browser and pixel art written as code. Bots play it headless to tune each difficulty, and TLA+ found bugs in its state machines (@poteto).

@GergelyOrosz: "Just incredible that ~1,000 PRs…. 4 days… art, gameplay, story." @aidenybai: "wait why is this insanely well made"

Heat: medium · 994K views, 2 posts · quiet on Reddit

11. Heretic 2.0 turns a censorship remover into a general tool for changing models

Removing refusals stays its default, with a new method its author built, Arbitrary-Rank Ablation. It is now rebuilt around plugins, so users can define what it optimizes for: its author lists suppressing slop, reducing sycophancy and altering emotional alignment. The project counts 37K GitHub stars and more than 8,500 models on Hugging Face.

No takes yet.

Heat: low · #1 on r/LocalLLaMA

Separately trained JEPAs learn the same picture of the world, LeCun and Balestriero show

Two models trained apart converge to the same space, up to one rotation, when they share good views. A 109M-parameter text JEPA beat Qwen2.5-72B and Llama-3-70B at aligning with DINOv2 (.184 against .154 and .151), on over 100x fewer text tokens. On the same corpus, good views needed 42x fewer samples than next-token prediction to reach the same alignment. It matters because it puts good training views ahead of raw model size, with a proof behind it.