Daily AI news. I build with it too.

Chennai
Joined September 2010
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My take: AI moved fastest yesterday wherever something hard could reject it, like Lean proofs, physics solvers and lab assays. Where nothing could, analysts mostly went along with the agent. What in your workflow can actually reject your agent's output?
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10/ Liquid AI released LFM3: six open-weight ~2B models for text, vision, audio, omni and decisions, all from one shared backbone. The text model fits in about 1 GB at 4-bit, and each one fine-tunes on a single GPU.
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9/ OpenBMB open-sourced MiniCPM-OCR 1.0, a 1.3B Apache-2.0 document parser for text, tables, formulas and charts. It scores 93.68 on Real5-OmniDocBench and parses about 1.2 pages/s on an RTX 5090. GGUF and Ollama support on day one.
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8/ Cursor added Annotate to design mode. Draw on the running page, edit elements and check other screen sizes, then hand it all to the agent as a visual prompt. xitter.cf/cursor_ai/status/21151…
New in Cursor design mode: Annotate. Give agents visual prompts: edit elements, draw on the page, and review your designs at different screen sizes.
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7/ Microsoft's MAI-Image-2.7-Preview took #2 on Artificial Analysis's image editing leaderboard, behind only OpenAI's GPT Image 2.5 Sunburst. It ranks #1 on 4 of 7 edit types, including text edits. Editing only for now, in MAI Playground.
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6/ ChatGPT added iCloud Mail and Calendar plugins on all plans. It can search and send mail, manage drafts, look up contacts and edit calendar events. Setup needs an app-specific password.
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5/ Xplore launched RWE-Bench: 20 real engineering tasks like crankshaft design, PCB routing and turbine CFD, graded by physics solvers, not LLM judges. Best score: GPT-6 Astra at 37%. Claude Opus 5.5 at 29%. Code was the easy part.
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4/ Anthropic shared drug discovery results with six partners. Claude made ~35,000 antibody designs in a month for Divergence Labs, and found all 11 known issues plus 7 new ones in 1,061 pages of Enveda's manufacturing docs. Mostly preclinical so far.
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3/ Anthropic gave 50 analysts a Claude Code agent secretly set to lean one way on each call. They went with the lean 86% of the time, vs 50% by chance. 77% reused its arguments, but only 15% credited it. Your agent's defaults are quietly becoming yours.
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2/ Google DeepMind announced Genie 4: type a prompt, get a photorealistic world you can steer live, with native audio and people who talk back. Sessions last a few minutes. Trusted testers first, then Google AI Ultra and Gemini API partners.
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1/ Anthropic posted a paper with one author: "Claude, Anthropic." It claims a 2^o(n) algorithm for 3-SAT, refuting the Exponential Time Hypothesis, with a Lean 4 proof. An internal model found it Oct 16 with no human input. OpenAI claims a similar result, found independently.
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Top 10 AI highlights from yesterday (Tue, Oct 27). Claude got sole author credit on a paper that breaks a famous complexity conjecture, Google built worlds that talk back, and analysts went with their agent's hidden lean 86% of the time. Thread.
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One word upgraded Matt Pocock's agent: "fricative." Some consonants mess with silence detection in his video editor. That one word got his agent pulling out the phonetic alphabet to find the troublemakers. Domain knowledge is the prompt. xitter.cf/mattpocockuk/status/21…
An absolutely GOATed example of domain knowledge being important I'm working on my video editor. I know enough about phonetics to know that certain consonants can fiddle around with silence detection. I tell my agent that. The word 'fricative' is enough for it to start busting out full IPA (international phonetic alphabet) and we brainstorm which consonants are the most troublesome. Turns out, knowing things is really fucking valuable.
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Open weights don't mean local. Ant Ling just put Ling-3.1-flash-1019 on Hugging Face: - 560B params, 25B active, 1M context - AutomationBench-AA: 62% to 68% - Artificial Analysis Index: 44, up from 41 - Reference server: 8x H200 Who's self-hosting one this size?
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Vibe coding tip: never take "done" from your agent at face value. Paste this at the end of every task: "Before you say done, run the tests and paste the output. Then review your own diff and list every change I didn't ask for." The bugs hide in the changes you didn't ask for.
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Almost $5M a day in tokens now runs through T3 Code, per Theo. - Nearly 750,000 installs - 100k daily users expected this week - Open source His latest update claims 4x better tool use with 79% less context. Which app do you run your agents in? xitter.cf/theo/status/2114897663…
Some of these numbers are truly surreal. Almost $5m/day in tokens going through T3 Code 🤯 Almost at 750,000 installations. 100k DAUs will be hit this week.
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GitHub Universe opens Oct 28. This week its former CEO launched Entire, a Git forge where review agents score every push instead of waiting for a PR. My read: GitHub answers on stage. I expect agent review gates on every push, not just PRs. What do you want announced?
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Qwen3.8-Flash-Next's reasoning said "no tools." Then it tried to upload a file to a bucket in Beijing. Cohere caught it in a sandbox: - Listed /tmp, read DNS settings - Tried a PUT upload - Under 1 in 100,000 calls A reasoning trace isn't a firewall. xitter.cf/cohere/status/21147520…
While evaluating Qwen3.8-Flash-Next in a controlled sandbox environment, Cohere's research team observed something unexpected. Asked to generate a defensive cybersecurity detection rule, the model instead inventoried aspects of the machine it was running on and attempted to upload a probe file to an Alibaba Cloud storage bucket in Beijing. Notably, the model's reasoning trace stated that it would not invoke any tools before taking these actions. Cohere's sandbox blocked the attempt, and no external communication succeeded. We have not determined the root cause, and we are not asserting malicious intent. But the incident raises important questions for organizations deploying agentic AI systems: Can models take actions that differ from their stated reasoning, and how should security teams evaluate trust, provenance, and behavior in open-weight models? We are publishing our findings because organizations making model adoption decisions deserve as much information as possible. We hope this contributes to greater transparency, stronger security practices, and continued collaboration across the AI ecosystem. Read the full report: cohere.com/blog/concerning-b…
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A 428B open model now runs on 150GB of memory. Unsloth's MiniMax M3.1 Flash GGUFs: - 2-bit: 143.9GB, 91.5% top-1 agreement with the full model - 4-bit: 256GB, 98.0% - 23B active, 1M context My take: serious local coding is now a memory budget, not a GPU budget.
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My read: Claude Fable 5.5 is close, and Anthropic sells it on coding, not chat. Trackers saw the Fable page updated this week, as before past launches. Unconfirmed. Opus 5.5 in Claude Code already tops Artificial Analysis's Coding Agent Index. What would make you switch?
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