Gemini 4 Carbon takes #1 on Artificial Analysis, two weeks into Fable 5.5's release week

Google's Gemini 4 Carbon takes #1 on Artificial Analysis, two weeks after Argon

Google DeepMind's new frontier model scores 59 on Artificial Analysis's Intelligence Index v5, the top score. Google says it leads on cybersecurity (88.2% on SEC-bench Pro, for finding vulnerabilities) and on long tasks. On coding agents it is second, 3 points behind Claude Opus 5.5 (@ArtificialAnlys). Nobody outside can use it yet: trusted cyber defenders get it this week, then partners, with wider access "soon".

r/singularity's top reply: "Fable5.5 dropping in 3..2..1". @j_dekoninck: "if Gemini 4 Carbon would have been released yesterday, it would not have been #1 on Artificial Analysis": on the old index it scores 57 to Opus 5.5's 58. @zacharynado: "Gemini [redacted] isn't yet here though".

Fable 5.5 has been coming "this week" since 14 Oct, and in that time Google went from Argon to a model 9 points better at the same price. Carbon isn't out either, but a #1 is enough to push the others (@mark_k says OpenAI moved its big release to today because of it).

1. Alphabet passes a half-trillion run rate, and Pichai expects "continued acceleration because of RSI"

Google Cloud revenue grew 103% in Q3, and Google's own model APIs now process nearly 32B tokens a minute. On the call, Pichai said AI is speeding up Google's training and launches (@AndrewCurran_). Shares still fell over 7% as revenue and profit missed estimates (@WatcherGuru).

@StockSavvyShay: "I never would've believed at start of the year that Google Cloud could surpass a $120B ARR while growing over 100% and $GOOGL would still sell off". On r/accelerate: "they're gonna feel more pressure of having to showcase ai capabilities and RSI if their stock price continues to drop".

Heat: high · 2.7M views, 48 posts · #3 on r/accelerate

2. You can now text your ChatGPT dot over iMessage and RCS

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Pro users in the US can text their dot tasks, photos and voice notes, and it texts back first when something needs them. It keeps the same context and plugins, and gets no access to your other conversations (@ChatGPT).

OpenAI's @thsottiaux: "Makes it super easy to share/forward things, leave it voice notes". @testingcatalog: "I hope that it will get into WhatsApp and Telegram at some point as well!"

Heat: high · 2.1M views, 20 posts · quiet on Reddit

3. ChatGPT Pro and Business now come with up to $500 a month in API credits

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Each Pro tier gets credits worth its price, up to $500, and Business seats pool theirs up to the same cap (@eddiboi). They work on GPT Live, Image 2.5 and the Decisions API (@reach_vb).

@LuminaBench: "You essentially get the amount you spend back each month", but "They are still halving our usage limits overall though soon for older plans". OpenAI's @romainhuet, asked about it on stage in Paris: "I couldn't resist sharing the news a little early".

Heat: high · 1.6M views, 25 posts · quiet on Reddit

4. Claude for Legal gets a gallery of ready-made tools for lawyers

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The artifacts cover contract review, regulatory scans and cite checks, which flag misquoted language and link each output to its source (@RoundtableSpace). Harvey, PwC and Goodwin built some of them, and Claude for Legal now has 60+ legal connectors, Bloomberg Law among them (@kimiatehrani).

@BoringBiz_: "Lawyers watching Claude automate half their job with a single release" (566K). @ai_for_success: "How many small startups did Anthropic just put out of business with this new Claude for Legal?"

Heat: high · 5.5M views, 24 posts · quiet on Reddit

5. Amazon locks out Grok Bot, and Shopify plugs in Shop Pay

Grok Bot 0.78.1 shows shopping results as product cards and can hold Shop Pay in its vault (@ericzakariasson). After Amazon locked out @jamesdouma's bot, which then bought the same thing elsewhere for less, Musk replied: "That's what I do too" (@elonmusk).

Shopify's @tobi answered Musk: "Just in time for shop pay support", and Musk then posted "Buy from @Shopify!" (@elonmusk). @martin_casado: "my @bot just ordered like $500 of Home Assistant kit, and various microphones, smart lights and plugs."

Heat: high · 3.4M views, 12 posts · #20 on r/accelerate (saving money with Grok Bot)

6. Rime's Prism, a speech-to-speech model it says you can't tell from a human

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Rime says Prism answers in 300ms, 4x faster than OpenAI's GPT-Live, and beats other speech-to-speech models on intelligence without reasoning. It writes each reply as text before speaking, so companies can edit the transcript and block what an agent shouldn't say (@testingcatalog).

@aakashgupta, on why the text step matters: "A voice AI answering a pharmacy's phone can make up a drug name, say it out loud, and nobody catches it until you're standing at the counter."

Heat: medium · 595K views, 8 posts · quiet on Reddit

7. Suhail's new company Particle ships particle-1, a coding model that spends far fewer tokens

Particle post-trained DeepSeek V4.1 Flash to match its performance with about 45% fewer tokens. By Particle's count, it uses 28–52% fewer output tokens across four coding benchmarks, and it is on OpenRouter and Vercel's AI Gateway (@OpenRouter).

@garrytan: "Particle will be on the frontier for smartest model at lowest dollar per unit of intelligence". Perplexity's @AravSrinivas: "It's been great working together with Suhail and his team ahead of their launch!"

Heat: medium · 435K views, 12 posts · quiet on Reddit

8. Qwenputer: a RISC-V computer built inside Qwen's weights

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A Qwen ambassador used Qwen3.5-4B's Gated DeltaNet layers as RAM for a 32-bit RISC-V CPU, so the model runs C and MicroPython inside its own forward passes. On DeepMind's maths set it scores 97.5–100%, against 95–100% for the base model with a Python tool.

@QwenDevs: "instead of relying on predicting the answer to a calculation, the model can execute instructions internally." By evening, r/LocalLLaMA had "I ran Doom inside the Qwen model weights!"

Heat: medium · 412K views, 3 posts · #1 on r/LocalLLaMA

9. NVIDIA's Nemotron 3.5 Transcribe: open weights, 40 languages, and who said what

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The 30B mixture-of-experts model, about 4B active, transcribes 40 languages, labels up to eight speakers, translates, and answers questions about recordings up to an hour long (@kimmonismus). It ties for first among open-weight models on Artificial Analysis's error-rate index, at 2.8% (@ArtificialAnlys).

@Alex_tra_memory ported its speech encoder to Apple's Core ML on day one (about 1.5 GB, without the language model, and with NVIDIA's separate diarization model for the speaker labels): "a 49.5-min, 3-person meeting → speaker-labeled transcript with word timestamps in 6.0s". On r/LocalLLaMA, someone "pruned NVIDIA's new 30B speech LLM from 67.5GB to 10GB".

Heat: medium · 849K views, 24 posts · #10 on r/LocalLLaMA

Cartesia's new H-Nets learn language from raw bytes, with no tokenizer

Albert Gu's team scaled its hierarchical networks, which learn their own units of text as they train. They found up to 3-5x efficiency gains in controlled scaling experiments, and at larger scale matched an open model trained on 11T tokens with about 10x less data and compute. It matters because H-Nets could learn speech from raw audio (Cartesia's business), though not yet tested on audio (@cartesia).

All 15 AI models tested beat human peer reviewers at catching errors in clinical trials

In a preprint, the models caught 53% to 96% of planted errors, against 27% for untrained human reviewers. On 58 trials from 2026, too recent for training data, Opus 5.5 caught 99% (@rohanpaul_ai). It matters because even the weakest model found twice the errors reviewers found, in minutes, so every trial can get this check before doctors rely on it.

Neuralink's first speech data: 75 words a minute from an ALS patient's implant in week one

In the VOICE study, one participant with ALS turned brain signals into text and voice with a 5.8% word error rate on a 125,000-word vocabulary (@teslaownersSV). It matters because that is already half the speed of normal conversation, about 150 words a minute (@NeuraPod), in the first week.