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Latest news, beside the leaderboard

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Artificial AnalysisArtificialAnlys·12h ago
📊 Benchmark

Artificial Analysis launches Intelligence Index v5: 60% private test sets, Claude Opus 5.5 leads at 58 and the open-weights gap widens to 12 points

On Oct 28 independent evaluator Artificial Analysis released Intelligence Index v5, its biggest revision since v4 in January: it adds Cognition's FrontierCode 2.0 and Terminal-Bench-Science, swaps Humanity's Last Exam for HLE-Diamond, and now draws 60% of its weight from private or held-out test sets. Claude Opus 5.5 (max) leads at 58, ahead of GPT-6 Astra at 57 and GPT-6.1 Sol at 55; the top open-weights model, Kimi K3.1 Pro (weights not yet released), scores 46, widening the gap from 3 points in v4.3 to 12. It also launched Progress Index v1 for comparing models across years.

Artificial Analysis launches Intelligence Index v5: 60% private test sets, Claude Opus 5.5 leads at 58 and the open-weights gap widens to 12 points
⚡ Key Takeaways
  • •Overall (vendor-reported by Artificial Analysis): Claude Opus 5.5 (max) 58, GPT-6 Astra (max) 57, GPT-6.1 Sol (max) 55; Anthropic and OpenAI hold the top five, with Gemini 4 Argon (high) next at 50
  • •The open-weights gap grows from 3 to 12 points: Kimi K3.1 Pro scores 46, while released-weight leaders DeepSeek V4.1 Flash 1017 and GLM-5.3 (max) score 32, below Claude Haiku 5.5 (max) at 36
  • •Cost per task varies more than 13x: among the top 10, GPT-6.1 Terra (max) is cheapest at $0.96 and Claude Fable 5.1 (max) priciest at $13.04; GPT-6.1 Sol trails Opus 5.5 by 4 points at about one eighth the cost ($1.27 vs $10.04)
  • •Four categories at 25% each: Knowledge Work, Coding (FrontierCode 2.0 15%, Terminal-Bench 4.0 10%), Science and General; GDPval-AA v2.1, SciCode and AA-LCR were removed
  • •Tasks are heavier: median output tokens per task rose from 35k to 59k, and effort matters more, with Opus 5.5 scoring 35 at low effort vs 58 at max for 10x the cost
Read details→
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MuseMuse·14h ago
🚀 Release

Meta launches its Muse personal AI agent in Singapore with eight local partners including Shopee, Grab and Singapore Airlines

On Oct 28 Meta announced that Muse, its personal AI agent, is now available in Singapore on iOS, Android and muse.ai. Beyond Gmail, Google Calendar and Instagram, the first local connector partners are Shopee, Grab, Singapore Airlines, Agoda, ShopBack, Klook, Trip.com and Lazada, spanning shopping, groceries, dining and travel, with some connector features rolling out after launch. Muse runs on Muse Secure VM, a dedicated cloud computer that keeps passwords and payment methods out of the agent's view, and it asks before sensitive actions such as sending an email or making a purchase. It is free for most uses, with paid subscription plans.

Meta launches its Muse personal AI agent in Singapore with eight local partners including Shopee, Grab and Singapore Airlines
⚡ Key Takeaways
  • •Muse is now available in Singapore on iOS, Android and the web at muse.ai, with AI glasses support "coming soon"
  • •Eight initial local connector partners: Shopee, Grab, Singapore Airlines, Agoda, ShopBack, Klook, Trip.com and Lazada, with some features rolling out after launch
  • •Users can compare prices on Shopee and Lazada, plan groceries and dining via GrabMart and Grab Dine Out, and explore travel and bookings via Singapore Airlines, Agoda, Klook and Trip.com
  • •It runs on Muse Secure VM, a dedicated cloud computer that holds credentials and keeps passwords and payment methods out of the agent's view, and it checks in before emails or purchases
  • •Free for most uses with paid subscription plans; the official Muse account announced the launch with greetings in all four of Singapore's official languages
Read details→
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Alex Tamkin@AlexTamkin·17h ago
📊 Benchmark

Anthropic study: 50 professional data analysts working with Claude Code followed the agent's secretly assigned lean on 86% of key judgment calls

On Oct 27 Anthropic's Alignment team published a randomized controlled trial, "How AI agents shape experts' judgment calls", shared on X by researcher Alex Tamkin. Fifty analysts experienced in causal inference used Claude Code (Claude Opus 4.8) to evaluate a program for a fictional farming cooperative, and on each analytical decision the agent's lean was set by coin flip without the analyst being told. Their choices matched the agent's lean 86% of the time (about 50% would mean no effect), and 77% later repeated the agent's arguments while only 15% credited the agent.

Anthropic study: 50 professional data analysts working with Claude Code followed the agent's secretly assigned lean on 86% of key judgment calls
⚡ Key Takeaways
  • •Agreement with the agent's lean was 92%, 82%, 81% and 90% on the four analytical decisions, 86% overall; the final rollout recommendation shifted too (54% recommended rollout when the agent leaned that way vs 36% when it leaned against)
  • •In 38 cases where analysts stated a preference first, they switched 60% of the time when the agent disagreed vs 6% when it agreed (small sample; 95% CI 24 to 82 percentage points)
  • •The four coin-flip leans alone let a statistical model predict 82% of final recommendations (61% baseline); none of 10 traits such as experience, education or trust in AI reliably predicted who deferred
  • •52% of analysts called the final recommendation more their own than the agent's, and their confidence was about the same as everyone else's (71 vs 73)
  • •6 of 11 analysts who skipped the agent made serious methodological errors vs 3 of 50 who used it, though the groups differed in experience; the study covers one task and one model, with the agent's lean deliberately strengthened
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Greg Isenberg@gregisenberg·19h ago
📉 Price Cut

X discussion: Greg Isenberg says SaaS is becoming free, give away the CRM and dashboard and charge for the agent that does the work

On Oct 27, Greg Isenberg (@gregisenberg, about 752K followers), founder and CEO of Late Checkout, posted on X: "saas is becoming free: give away the crm, give away the dashboard, give away the app, and charge for the agent that does the work." Within about seven hours the post had about 109K views, 1,526 likes, 277 replies and 37 quotes, with many founders debating whether per-seat pricing can really give way to pricing for the work an agent completes. This is a discussion of an opinion, not a pricing announcement from any company.

X discussion: Greg Isenberg says SaaS is becoming free, give away the CRM and dashboard and charge for the agent that does the work
⚡ Key Takeaways
  • •Engagement (as of 2026-10-28 03:10 UTC, public X API data): about 109K views, 1,526 likes, 746 bookmarks, 277 replies, 37 quotes
  • •The claim: the CRM, dashboard and app become free, and the paid product is the AI agent that does the work for the user
  • •The most common pushback: charging for "work" means defining in writing what done means, who checks it, and a monthly cap, or it cannot be billed
  • •Another camp says companies will keep paying for the system of record, while SaaS vendors give away MCP access so outside agents can read and write
  • •This is a founder opinion under discussion, not a pricing change by any company
Read details→
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Ant Ling@AntLingAGI·19h ago
🚀 Release

Ant Ling open-sources Ling-3.1-flash-1019: further post-trained 560B/25B-active MoE lifts AA Intelligence Index from 41 to 44, base checkpoint also released

On Oct 27 Ant Ling (inclusionAI) open-sourced Ling-3.1-flash-1019, a further post-trained upgrade of the early-October Ling-3.1-flash: 560B total parameters, 25B active per token, a 1M-token context and an MIT license. Ant says it scores 44 on the Artificial Analysis Intelligence Index v4.3.2 (up from 41), with clear gains on agent tasks such as AA-Briefcase and AutomationBench-AA. It also released Ling-3.1-flash-base, an open foundation checkpoint for further training.

Ant Ling open-sources Ling-3.1-flash-1019: further post-trained 560B/25B-active MoE lifts AA Intelligence Index from 41 to 44, base checkpoint also released
⚡ Key Takeaways
  • •Scale: 560B total and 25B active parameters, 1M-token context, MIT license, on Hugging Face and ModelScope
  • •Artificial Analysis Intelligence Index v4.3.2: 44, up from 41 for Ling-3.1-flash (vendor-reported)
  • •AA-Briefcase rises from about 1400 to 1524 Elo and AutomationBench-AA from 62% to 68% (vendor-reported)
  • •Science: CritPt from 18% to 26% and SciCode from 54% to 57% (vendor-reported)
  • •Architecture: hybrid-linear MoE with 63 KDA layers plus 9 gated MLA layers (7:1), 512 routed experts and 8 active per token
Read details→
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Anthropic@AnthropicAI·19h ago
🚀 Release

Anthropic shares early results from Claude in drug development: six partners including BMS, plus its own programs via Mithril Medicine

In an Oct 27 blog post, Anthropic described months of deep research partnerships between its life sciences team and six organizations (BigHat, Bristol Myers Squibb, Divergence Labs, Enveda, Generate Biomedicines and The Column Group), covering hit generation, lead optimization, manufacturing paperwork and clinical trial planning. It also said it acquired the one-person company Mithril Medicine to run its own programs for rare childhood muscular dystrophy. Anthropic stresses the work is mostly preclinical so far.

Anthropic shares early results from Claude in drug development: six partners including BMS, plus its own programs via Mithril Medicine
⚡ Key Takeaways
  • •Divergence Labs: Claude generated about 35,000 antibody designs in roughly a month, largely autonomously, yielding 3 hits that bind IgE and 20 that dislodge it, 3 of which beat the standard drug at dislodging (vendor-reported)
  • •BigHat: about 1,010 variants made and tested in about 10 weeks; the best versions bound over 10x more tightly, the cancer lead stayed in solution 18°C higher, and the other lead clumped over 100x more slowly
  • •Enveda: across 1,061 pages of manufacturing documents Claude caught all 11 issues humans found plus 7 they missed, cutting review time 89% (from over 25 hours to under 3)
  • •Generate Biomedicines: a CAR-T component designed with Claude Code was about 70% shorter and let immune cells make roughly 10x as many receptors
  • •Mithril Medicine acquisition: its founder used Claude to coordinate 11 external labs and went from idea to promising mouse results in under 18 months; human trials could start as soon as late 2028 (official blog)
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Cursor@cursor_ai·19h ago
🛠️ Tooling

Cursor Design Mode adds Annotate: point, draw and edit text on your running app to send visual prompts to agents

On Oct 27 Cursor announced Annotate for Design Mode: in the Agents Window browser you can click elements, box an area, draw markup, edit text in place or tweak CSS, and each note is sent to the agent with element details and a screenshot so it can edit the code. The post says you can also review designs at different screen sizes. It needs Cursor 3.24.18 or later, and the shortcut is Cmd+Shift+D.

Cursor Design Mode adds Annotate: point, draw and edit text on your running app to send visual prompts to agents
⚡ Key Takeaways
  • •Requires Cursor 3.24.18 or later; click Annotate in the Agents Window browser toolbar or press Cmd+Shift+D
  • •Each note carries the element tag, id, classes, selector path, first 200 visible characters, size and position, six computed styles and a cropped screenshot (official docs)
  • •React, Vue and Svelte apps also send the component name and component stack so the agent can find the source
  • •Batch edits switch: off sends each note on Return, on collects numbered pins and sends them as one message
  • •Cursor recommends a fast model that is strong at interface work, such as Composer 2.5
Read details→
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AnthropicAnthropicAI·19h ago
📉 Price Cut

Anthropic expands GSA partnership: new OneGov deal gives all levels of US government 40% off Claude Enterprise, Claude for Government and the API

On Oct 27 Anthropic announced a new OneGov agreement with the US General Services Administration (GSA): government customers get 40% off Claude Enterprise, Claude for Government and the Claude API. For the first time the discount covers every product surface (direct purchases today, cloud-provider channels in the coming months) and extends to state, local, tribal and territorial governments. Anthropic estimates about $1 billion in savings, with no minimum spend and pay-as-you-go usage.

Anthropic expands GSA partnership: new OneGov deal gives all levels of US government 40% off Claude Enterprise, Claude for Government and the API
⚡ Key Takeaways
  • •40% off Claude Enterprise, Claude for Government and the Claude API; direct purchases today, cloud-provider channels in the coming months (vendor-stated)
  • •Anthropic estimates about $1 billion in government savings, with no minimum spend commitment and pay-for-what-you-use billing
  • •About 500 US federal, state and local organizations use Claude; weekly Claude for Government active users are up about 8x since February and federal civilian usage is up about 20x in a year (vendor-stated)
  • •Utah gave Claude Code to 34 developers across 10 agencies: 80% had a working environment within 30 minutes and 71% reported saving 20+ hours over a four-week pilot (vendor-stated)
  • •Claude for Government went GA on Sep 30 at FedRAMP High; select Bedrock models hold DoD IL4/IL5, and Claude Enterprise is pursuing FedRAMP Moderate via FedRAMP 20x
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Liquid AIliquidai·20h ago
🚀 Release

Liquid AI releases LFM3, six open-weight ~2B models spanning text, vision, audio, omni and decision tasks

On Oct 27 Liquid AI released LFM3, a new generation of foundation models built on a tri-hybrid architecture of gated short convolutions, sliding-window attention and global attention, pre-trained on 22T tokens. The first six models all branch from LFM3-1.7B-Base at around 2B parameters, with weights on Hugging Face under the LFM Open License v1.0. Liquid says the text model LFM3-1.7B needs about 1 GB at 4-bit, supports 256K context and beats models several times its size on several agent benchmarks.

Liquid AI releases LFM3, six open-weight ~2B models spanning text, vision, audio, omni and decision tasks
⚡ Key Takeaways
  • •Six models: LFM3-1.7B (text), VL-2B (vision/video), Audio-2B, Omni-2B, plus decision models d1-2B and d1-Omni-2B
  • •Architecture: of 24 layers, 13 gated short convolutions, 5 sliding-window attention (256-token window) and 6 global attention; 22T pre-training tokens
  • •Vendor-reported: LFM3-1.7B scores 43.40 on SWE-bench Verified (OpenCode) vs 41.20 for Qwen3.5-4B and 51.20 for Qwen3.5-9B
  • •Vendor-reported: 38.42 on WebArena-Verified and 41.29 on AutomationBench, both above Qwen3.5-9B (28.94 / 15.10)
  • •A full 128K context needs about 1.6 GB of KV cache; one B300 with SGLang can produce about 3.6B tokens per day (vendor figure)
Read details→
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Cognitioncognition·21h ago
📊 Benchmark

Cognition launches FrontierCode 2.0 coding benchmark: it asks whether a PR should be merged, and top model Claude Opus 5.5 scores just 45.5%

Cognition, the maker of Devin, released FrontierCode 2.0 on Oct 27. It keeps the method of maintainer-written tasks graded on whether the code is actually mergeable, but draws tasks from more repositories, adds security patches and performance work, uses deliberately underspecified prompts and adds many thoroughness criteria. In Cognition's results Claude Opus 5.5 leads at 45.5%, GPT-6 Astra follows at 42.8%, and Grok 4.7 and Gemini 4 Argon drop into the 20s, leaving the benchmark far from saturated.

Cognition launches FrontierCode 2.0 coding benchmark: it asks whether a PR should be merged, and top model Claude Opus 5.5 scores just 45.5%
⚡ Key Takeaways
  • •Claude Opus 5.5 leads at 45.5% (54.6% on v1.1) and GPT-6 Astra scores 42.8% (53.3% on v1.1), per Cognition (vendor-reported)
  • •Grok 4.7 scores 28.6%, Gemini 4 Argon 26.3%, Muse Wonder 1.0 24.3%, SWE-2 23.9%, DeepSeek V4.1 Flash 19.3% and Kimi K3 17.7%, showing a wide gap for open models
  • •Versus v1.1, reference solutions touch 67% more files and 89% more lines in codebases 25% larger; tasks come from 55% more repositories with 43% more blocking criteria
  • •Thoroughness blockers grew from 168 to 501, about 3x; in 80% of tasks the reference solution improves on the upstream PR, and an audit agent found and reported an upstream security vulnerability
  • •On the cost-score Pareto frontier, Opus 5.5 sits at the high end and Luna, Haiku and Sol at the low-cost end, with GPT-6.1 Sol called out for strong value (official blog)
Read details→
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OpenBMBOpenBMB·22h ago
🚀 Release

OpenBMB open-sources the MiniCPM-OCR suite: a 1.3B parser scoring 93.68 on Real5-OmniDocBench, plus a 2B judge model that flags OCR errors

On Oct 27 OpenBMB (ModelBest) open-sourced the MiniCPM-OCR suite with two models: MiniCPM-OCR 1.0, a 1.3B-parameter document parser that scores 93.68 on Real5-OmniDocBench and 54.7 on OCRBench v2 (vendor-reported), and MiniCPM-OCR Jev, a 2B OCR quality judge that scores any parser's output region by region from 0 to 1 and flags content that needs human review. Code and weights are Apache 2.0, with day-one support for vLLM, SGLang, llama.cpp, Ollama and multiple chip platforms.

OpenBMB open-sources the MiniCPM-OCR suite: a 1.3B parser scoring 93.68 on Real5-OmniDocBench, plus a 2B judge model that flags OCR errors
⚡ Key Takeaways
  • •MiniCPM-OCR 1.0 has just 1.3B parameters and scores 93.68 on Real5-OmniDocBench and 54.7 on OCRBench v2 (vendor-reported, both claimed SOTA)
  • •The 2B MiniCPM-OCR Jev judge reaches 93.6 AUROC on JevOCRBench, above JEV-35B-A3B at 90.1 and close to Qwen3.5-397B-A17B at 93.8 (vendor-reported)
  • •Jev is parser-agnostic: AUROC stays between 91.9 and 93.4 on outputs from MiniCPM-OCR, GLM-OCR, PaddleOCR-VL-1.6 and Xiaomi-OCR-0
  • •Throughput is about 1.49x PaddleOCR-VL-1.6 and 3.62x MinerU2.5 Pro, reaching up to about 128 pages per minute with SDK concurrency (vendor-reported)
  • •Code and weights on GitHub are all Apache 2.0, with GGUF/AWQ/GPTQ quantized builds and a SKILL.md for coding agents
Read details→
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Google DeepMindGoogleDeepMind·22h ago
🚀 Release

Google DeepMind unveils Genie 4 world model: built on Gemini Omni, first with native real-time audio and dialogue, in research preview for trusted testers

Google DeepMind announced Genie 4 on Oct 27, calling it its most realistic and interactive world model yet. Built on Gemini Omni, it improves physics and human motion, natively generates ambient audio and interactive character dialogue for the first time, and uses Gemini 3.8 Flash in the loop to plan complex events. It is a research preview for trusted testers for now, coming soon to Google AI Ultra (Project Genie) and early access partners in the Gemini API and AI Studio; no quantitative benchmarks were published.

Google DeepMind unveils Genie 4 world model: built on Gemini Omni, first with native real-time audio and dialogue, in research preview for trusted testers
⚡ Key Takeaways
  • •Per Google DeepMind, Genie 4 is its most realistic and interactive world model yet, with Gemini Omni anchoring real-world appearance and behavior
  • •It is DeepMind's first real-time world model with native audio: ambient sound, music and character dialogue are generated with the world, and characters respond live
  • •Gemini 3.8 Flash acts as a guiding agent in the loop, splitting goals into steps and checking frames; a demo pairs Gemini 4 Argon with Genie 4 to simulate an Apollo humanoid
  • •Official limitations: only a few minutes of continuous interaction and drift over longer sessions; no quantitative benchmarks were published
  • •Trusted testers only for now; coming soon to Google AI Ultra (Project Genie) and Gemini API / AI Studio early access partners, with SynthID on all outputs
Read details→
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Microsoft AIMicrosoftAI·23h ago
🚀 Release

Microsoft makes MAI-Image-2.6 and MAI-Image-2.6-Flash generally available in Foundry as MAI-Image-2.7-Preview reaches #3 in Image Edit Arena

Microsoft AI announced on Oct 27 that MAI-Image-2.6 and MAI-Image-2.6-Flash are generally available in Microsoft Foundry, with multi-reference editing from up to five images, Bing web grounding, output up to 1.5K and published per-token pricing. The same day its next image editing model, MAI-Image-2.7-Preview, reached #3 overall in Arena's Image Edit leaderboard at 1470 points, 45 points above 2.6; for now it is available only for editing in MAI Playground.

Microsoft makes MAI-Image-2.6 and MAI-Image-2.6-Flash generally available in Foundry as MAI-Image-2.7-Preview reaches #3 in Image Edit Arena
⚡ Key Takeaways
  • •MAI-Image-2.6 and MAI-Image-2.6-Flash are generally available in Microsoft Foundry, and Microsoft says 2.6 is coming soon to Adobe Firefly
  • •Official pricing: 2.6 costs $5 text input, $8 image input and $38 image output per 1M tokens; Flash costs $1.75, $2.50 and $19
  • •2.6 supports multi-reference editing with up to five images, Bing web grounding, dynamic aspect ratios and output up to 1.5K
  • •Per Arena, MAI-Image-2.7-Preview is #3 overall in Image Edit Arena at 1470 points, 45 points above 2.6 (#7), putting Microsoft AI #2 among labs
  • •Per Artificial Analysis, 2.7-Preview is #2 on AA-Image-Editing v2.1 and beats 2.6 on all 7 editing actions, ranking #1 on 4 of them
Read details→
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Axiomaxiommathai·1d ago
🚀 Release

Axiom uses AI agent swarms to fully formalize the Classification of Finite Simple Groups in Lean in 24 days: about 43.1M lines, the first complete computer-checked proof of the longest proof in math history

AI math company Axiom announced on Oct 27 that swarms of dozens of AI agents formalized the Classification of Finite Simple Groups (CFSG; the original proof runs about 15,000 pages across 500+ articles by about 100 mathematicians) in the Lean proof assistant in 24 days, producing about 43.1M lines of Lean and 2.05M declarations, with the repo public on GitHub. The work surfaced 34 noteworthy deviations from the published record, and AxiomProver produced a new alternative argument for the Unbalanced Group Theorem.

Axiom uses AI agent swarms to fully formalize the Classification of Finite Simple Groups in Lean in 24 days: about 43.1M lines, the first complete computer-checked proof of the longest proof in math history
⚡ Key Takeaways
  • •Per Axiom: 24 days and swarms of dozens of AI agents produced about 43.1M lines of Lean, 2.05M declarations and 100 milestones, drawing on 715 papers and books
  • •The original CFSG proof spans about 15,000 pages in 500+ articles by about 100 mathematicians; this is the first time it has been fully computer-checked
  • •Formalizing it exposed 34 noteworthy deviations from the literature, the biggest a closure assertion in Walter's 1986 Unbalanced Group Theorem argument, with explicit counterexamples such as PSL2(49)
  • •AxiomProver produced an alternative argument built on Solomon's 1976 theory and proved a new theorem, "Preservation of simple tops", published on Hexagon (2610.00575)
  • •Humans stayed essential: original CFSG group theorists including Richard Lyons and Ronald Solomon were consulted, and an errata list will be published with an advisory board
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Apple NewsroomApple·1d ago
🛠️ Tooling

Apple Creator Studio adds AI assistants built on Apple models and OpenAI to Logic Pro, Keynote, Pages and Numbers, plus MCP support so external agents can edit iWork documents

On October 27 Apple announced a major Apple Creator Studio update: Logic Pro for Mac plus Keynote, Pages and Numbers gain beta intelligent assistants that Apple says are powered by a combination of Apple-built models and OpenAI and understand document context. Keynote, Pages and Numbers also add Model Context Protocol (MCP) support, letting external AI agents read content, make edits and run automated actions inside the apps. Pixelmator Pro adds natural-language Generate Fill, and Keynote introduces the open JSON-based .keyx format. The update ships free to existing subscribers on the App Store in November; pricing stays $12.99 per month or $129 per year.

Apple Creator Studio adds AI assistants built on Apple models and OpenAI to Logic Pro, Keynote, Pages and Numbers, plus MCP support so external agents can edit iWork documents
⚡ Key Takeaways
  • •Logic Pro for Mac, Keynote, Pages and Numbers gain beta assistants that Apple says run on a combination of Apple-built models and OpenAI
  • •Keynote, Pages and Numbers support MCP so external AI agents can read content, edit and automate actions (requires macOS 27 or later)
  • •Keynote adds the open JSON-based Keynote Interchange Format (.keyx), designed to be readable by external tools and AI agents
  • •Assistants are U.S. English only and need an Apple Intelligence-capable device on iOS, iPadOS or macOS 27; free update for subscribers in November
  • •Pricing unchanged at $12.99 per month or $129 per year, $2.99 per month for education
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ModelScope@ModelScope2022·1d ago
🚀 Release

Shanghai AI Lab ships Atria Dawn full release: 744B MoE GLM-5.2 agentic model, context doubled to 512K, MIT weights

Shanghai AI Laboratory has moved Atria Dawn from Preview to a full release, with open weights (plus an FP8 build) on Hugging Face under internlm/Atria-Dawn and on ModelScope, MIT licensed. Built on the 744B-parameter MoE GLM-5.2, it targets long-horizon research and engineering agent work; context doubles from the Preview's 256K to 512K and input is text only. The vendor table shows it leading the listed models on AutomationBench, CyberGym and SkillsBench, while still trailing Claude Opus 5 on coding tests such as SWE-bench Pro and Terminal-Bench 2.1 (all vendor-reported).

Shanghai AI Lab ships Atria Dawn full release: 744B MoE GLM-5.2 agentic model, context doubled to 512K, MIT weights
⚡ Key Takeaways
  • •512K context, double the Preview's 256K; built on the 744B-parameter MoE GLM-5.2, text input only
  • •BF16 and FP8 weights on Hugging Face and ModelScope, MIT license
  • •Vendor-reported: AutomationBench 54.7, CyberGym 86.8, SkillsBench 67.8, BFCL V4 78.4, each the top score in its table
  • •Weak spots from the same table: SWE-bench Pro 61.7 vs 76.5 for Claude Opus 5, Terminal-Bench 2.1 81.0 vs 90.2
  • •Local serving needs SGLang v0.5.13.post1+ or vLLM v0.23.0+; an OpenAI-compatible API also plugs into Codex
Read details→
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Levent Alpoge@__alpoge__·1d ago
🚀 Release

Anthropic publishes a 3-SAT algorithm found by an internal Claude research model: its paper and Lean 4 proof claim to refute the Exponential Time Hypothesis

Anthropic published the paper "A 2^o(n) Time Algorithm for 3-SAT" (credited to Claude, Anthropic) and an AI-written Lean 4 formalization in its official anthropics/formal-math GitHub repo. It claims a deterministic algorithm solves 3-SAT in 2^o(n) time, which would refute the Exponential Time Hypothesis (ETH). Anthropic's Levent Alpoge said on X that an internal Claude research model found the core algorithm on Oct 16 with 14M output tokens and no human input. The repo stresses the result is purely asymptotic and says nothing about solving formulas in practice.

⚡ Key Takeaways
  • •The paper claims a deterministic algorithm decides 3-SAT on n variables in 2^o(n) time, refuting ETH, and solves the harder counting problem #3-SAT in 2^O(n log log n / sqrt(log n)) time
  • •Per Levent Alpoge on X, an internal Claude research model found the core algorithm on Oct 16 with 14M output tokens and no human input; researchers then worked with Claude on the presentation
  • •The Lean 4 proof was written by AI models and pins Lean and Mathlib v4.33.1; the README says it depends only on Lean's three standard axioms, and CI re-checks it with a second independent kernel, nanoda
  • •The repo states the result is purely asymptotic and says nothing about solving formulas in practice; independent review by mathematicians is still pending
  • •A day earlier OpenAI announced its own model-produced refutation of ETH; both claims are now under review
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Baidu Inc.@Baidu_Inc·1d ago
🚀 Release

Baidu open-sources Unlimited OCR 2: SR-SWA keeps memory and latency nearly flat on very long documents, scoring 77.1 on MarathonDocBench per Baidu

On Oct 27, Baidu's official @Baidu_Inc account announced the open-source release of Unlimited OCR 2 on Hugging Face under the MIT license. Its new SR-SWA attention compresses text-visual interactions into a fixed-size reference set, which Baidu says lets it parse documents of any length within a finite context window. Baidu reports 84.6 on MPDocBench (dozens of pages) and 77.1 on MarathonDocBench (hundreds of pages), both self-reported.

Baidu open-sources Unlimited OCR 2: SR-SWA keeps memory and latency nearly flat on very long documents, scoring 77.1 on MarathonDocBench per Baidu
⚡ Key Takeaways
  • •SR-SWA compresses text-visual attention over very long sequences into a fixed-size reference set (e.g. 512 tokens), which Baidu says keeps per-step decode latency and memory nearly flat as page count grows
  • •Per Baidu: 84.6 overall on MPDocBench, 6.3 points above the next best, Qwen3.8-Max (78.3)
  • •Per Baidu: 77.1 overall on MarathonDocBench, 43.3 points above the next best, Kimi-K3 (33.7); the first Unlimited-OCR scores 0 there
  • •Weights are on Hugging Face under MIT, about 3.3B parameters by the HF weight count, with Transformers and a customized SGLang build for serving
  • •All scores are Baidu's own measurements with no third-party reproduction yet
Read details→
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Cloudflare@Cloudflare·1d ago
🛠️ Tooling

Cloudflare discloses a cross-request KV cache leak in GLM-5.3 on Workers AI: a config mismatch plus an SGLang bug let 155 responses carry other users' data over 13 days

In an Oct 26 incident report on its blog, Cloudflare said that between Aug 27 and Sep 9, 155 responses from GLM-5.3 on Workers AI (@cf/zai-org/glm-5.3) to prompts longer than 681,280 tokens contained data from other customers' recent requests, all returned with HTTP 200. The cause was a prefill KV cache pool smaller than the 1,048,576-token context window, combined with an SGLang bug in disaggregated prefill/decode mode. Cloudflare found no evidence of deliberate exploitation, has fixed it, and recommends operators upgrade to SGLang v0.5.22 or later.

Cloudflare discloses a cross-request KV cache leak in GLM-5.3 on Workers AI: a config mismatch plus an SGLang bug let 155 responses carry other users' data over 13 days
⚡ Key Takeaways
  • •Scope: 155 responses between 2026-08-27 and 09-09, all to prompts over 681,280 tokens; Cloudflare confirmed no other Workers AI model was affected
  • •Cause 1: GLM-5.3 advertised a 1,048,576-token context but the prefill KV pool held only 681,280 tokens; after the fix the pool holds about 2.04 million tokens, roughly twice the context
  • •Cause 2: SGLang marked over-length requests as rejected and swapped in a one-token placeholder without aborting, so the decode node read stale cache pages from earlier requests
  • •Response: report from Gadi Evron of Knostic via HackerOne on Sep 8; model taken offline within 40 minutes of declaring the incident on Sep 9 and restored at 19:40 UTC that day
  • •Action: teams running SGLang should upgrade to v0.5.22 or later and check KV pool size against context length directly
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C
Cursor@cursor_ai·1d ago
🛠️ Tooling

Cursor adds DeepSeek V4.1 Flash: all four effort levels sit on the CursorBench 4.0 cost frontier, with Max scoring 47.7% at $0.98 per task

Cursor announced on Oct 26 (UTC) that DeepSeek V4.1 Flash is now available in Cursor and can be turned on under Cursor Settings > Models. Cursor says every effort level sits on the CursorBench cost frontier, from $0.60 to $0.98 per task, with no model scoring higher at those prices. The official CursorBench 4.0 leaderboard lists DeepSeek V4.1 Flash 1017 at 47.7% on Max ($0.98 per task), 46.3% on Medium ($0.80), 45.6% on High ($0.77) and 44.9% on Low ($0.60). The top score on the board, Opus 5.5 Max, is 57.8% but costs $13.43 per task.

Cursor adds DeepSeek V4.1 Flash: all four effort levels sit on the CursorBench 4.0 cost frontier, with Max scoring 47.7% at $0.98 per task
⚡ Key Takeaways
  • •Four effort levels on the official CursorBench 4.0 board: Max 47.7% at $0.98, Medium 46.3% at $0.80, High 45.6% at $0.77, Low 44.9% at $0.60; Max ranks 17th overall
  • •Value: Max at 47.7% nearly ties Sonnet 5.5 High (47.8%, $1.20) for about 18% less; Opus 5.5 Max scores about 10 points higher but costs $13.43 per task, roughly 13.7x more
  • •Nearest rivals on price: Haiku 5.5 Max at 48.4% for $1.12 and Haiku 5.5 Extra High at 44.3% for $0.56, bracketing DeepSeek's price range
  • •The trade-off is tokens and steps: Max averages about 537K tokens and 187 steps per task, versus about 37K tokens and 41 steps for Sonnet 5.5 High, so expect longer runs
  • •Turn it on under Cursor Settings > Models; a counterintuitive detail is that Medium (46.3%) scores slightly above High (45.6%) for only $0.03 more
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