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Sunday

  1. Story1 reports · 1 facts

    Linux stable kernel releases fix for CVE-2026-98432

    The Linux stable kernel fixed a use-after-free vulnerability, CVE-2026-98432, in the IPv4 TCP handshake path on October 11 and 15. The kernel CNA rated the vulnerability as CVSS 3.1 9.8 Critical, affecting versions 4.4 and later. As of October 29, kernels across Ubuntu versions are still marked as affected, and the default kernels of RHEL 8, 9, and 10 and Amazon Linux 2 have no fix yet. Anthropic said its Claude model wrote remote root exploit code in the lab targeting servers with a multipath TCP listener, but it was not disclosed.

    ter-free vulnerability, CVE-2026-98432, in the IPv4 TCP handshake path on October 11 and 15. The kernel CNA rated the vulnerability as CVSS 3.1 9.8 Critical, affecting versions 4.4 and later. As of October 29, kernels across Ubuntu versions are still marked as affected, and the default kernels of RHEL 8, 9, and 10 and Amazon Linux 2 have no fix yet. Anthropic said its Claude model wrote remote root exploit code in the lab targeting servers with a multipath TCP listener, but it was not disclosed.

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Thursday

  1. Story1 reports · 1 facts

    ARC Prize announces verified results for Claude Sonnet 5.5 on ARC-AGI

    ARC Prize announced the verified results of Anthropic's Claude Sonnet 5.5 on the ARC-AGI generations of benchmarks. For ARC-AGI-3, under the provider adapter harness the score was 72.4% at a cost of $8.2K, and under the standard harness it was 24.3% at a cost of $7.4K; ARC-AGI-2 was 83.9% at $0.55 per task; ARC-AGI-1 was 96.5% at $0.14 per task. ARC Prize said Sonnet 5.5's 72.4% on ARC-AGI-3 is noteworthy. There are currently no further developments.

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  2. Story2 reports · 1 facts

    AYi shares a two-step workflow tutorial for building personal websites with AI

    On October 18, 2026, AYi (Ayi AI Notes) shared a hands-on tutorial on building a personal website or product landing page from scratch with AI, proposing a two-step workflow. The first step is to create a Feishu document as a single source of truth, storing a personal introduction, project case studies, and a service list, so that AI can read it through the Feishu command-line tool; after the text is edited, the webpage can sync automatically. The second step is to provide references by breaking things down into parts: pull code from existing design sites, take screenshots, or extract style keywords to feed to AI for assembly. The tutorial organizes nearly 70 websites across 8 stages, covering component libraries such as shadcn/ui and Aceternity UI, motion resources such as Motion, GSAP, and Three.js, whole-site aesthetic galleries such as Godly, Land-book, and Awwwards, as well as AI website-building tools such as v0, Lovable, and Google Stitch, and 4 design Skills including Anthropic's frontend-design. The article notes that referencing interaction styles and using open-source components is fine, but one should not copy others' full-site visual originals, brand images, and copy verbatim; authorization agreements must be confirmed before commercial use, and Tailwind Plus and the full version of Mobbin are paid subscriptions.

    , whole-site aesthetic galleries such as Godly, Land-book, and Awwwards, as well as AI website-building tools such as v0, Lovable, and Google Stitch, and 4 design Skills including Anthropic's frontend-design. The article notes that referencing interaction styles and using open-source components is fine, but one should not copy others' full-site visual originals, brand images, and copy verbatim; authorization agreements must be confirmed before commercial use, and Tailwind Plus and the full versi

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Thursday

Wednesday

  2. FactRelated story

    English translation is being prepared.

    On October 13, 2026, Google announced that developers can now use Gemini 4 Argon to build applications. This frontier model targets complex workflows such as real software engineering, enterprise knowledge work, and cybersecurity defense, and is designed for long-horizon tasks requiring deep reasoning and structural precision. Google also provided the Gemini API developer guide and entry points for using it in Google Antigravity and Android Studio. On the same day, Google disclosed an unattended experiment: letting Argon optimize the open-source framework google/adk-python, which has about 205,000 lines of code, within 11 hours. The model proposed 6 optimizations; after human review, 2 were merged into the main branch, memory session preparation time for long sessions was reduced by about 40%, and cold-start import speed improved by 21%. On October 14, Sundar Pichai called this a good summary of the safety and alignment work on Argon 4; Ethan Mollick commented that Gemini 4 is a major leap compared with previous Gemini models but still lags on complex multi-agent tasks. On October 15, Google began rolling out Gemini 4 Argon support to the AI programming tool Antigravity.

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    View story:Google opens Gemini 4 Argon to developers

Sunday

Tuesday

Wednesday

  1. Testing Catalog

    Artificial Analysis Intelligence Index v5 is live, Claude…

    Artificial Analysis Intelligence Index v5 is live, featuring 2 new benchmarks and 3 upgrades, of which 60% are based on private test sets or held-out answers. Claude Opus 5.5 (max) leads with 58 points, followed closely by GPT-6 Astra (max) at 57 points and GPT-6.1 Sol (max) at 55 points, with Gemini 4 Argon (high) further back at 50 points. The gap between open-weight models and the leader has widened from 3 points to 12 points; among released weights, DeepSeek V4.1 Flash 1017 (max) and GLM-5.3 (max) lead with 32 points.

    Artificial Analysis Intelligence Index v5 is live, featuring 2 new benchmarks and 3 upgrades, of which 60% are based on private test sets or held-out answers. Claude Opus 5.5 (max) leads with 58 points, followed closely by GPT-6 Astra (max) at 57 points and GPT-6.1 Sol (max) at 55 points, with Gemini 4 Argon (high) further back at 50 points. The gap between open-weight models and the leader has widened from 3 points to 12 points; among released weights, DeepSeek V4.1 Flash 1017 (max) and GLM-5.3

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    #hot-signal#Evaluations / benchmarks#Anthropic#OpenAI
  2. Official Pydantic AI articleSelected

    Pydantic compares code mode and tool calling across 5,276 runs

    Michael Pfaffenberger of Pydantic used 5 small Pydantic AI applications and 8 models to conduct 5,276 scored runs, comparing code mode with direct tool calling. On long tasks, code mode reduced average cost by 58%, latency by 51%, and increased success rate by 16 percentage points; however, for short tasks with 5 items, 3 out of 5 tasks were instead 21% to 38% more expensive. The author argues that models are better at writing loops than at acting as the loop, and the gap widens as tasks get longer.

    Michael Pfaffenberger avatar

    Michael Pfaffenberger of Pydantic used 5 small Pydantic AI applications and 8 models to conduct 5,276 scored runs, comparing code mode with direct tool calling. On long tasks, code mode reduced average cost by 58%, latency by 51%, and increased success rate by 16 percentage points; however, for short tasks with 5 items, 3 out of 5 tasks were instead 21% to 38% more expensive. The author argues that models are better at writing loops than at acting as the loop, and the gap widens as tasks get lon

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    #company#Evaluations / benchmarks#Agents#English text is being prepared.
  3. Unite.AI

    Perplexity Publishes LLM Gateway Best Practices Guide, Outlining Request Flow and Production Risks

    On October 27, 2026, Perplexity published an LLM gateway best practices guide explaining how this middleware layer routes, authenticates, caches, and logs model calls, and distinguishing four categories: API gateways, LLM gateways, MCP gateways, and AI gateways. The guide breaks down the mechanism through a six-step request lifecycle, while warning that the gateway centralizes credentials from various providers, and cites the LiteLLM gateway breach disclosed by Microsoft and two high-severity vulnerabilities, CVE-2026-35029 and CVE-2026-59823, reported by Check Point (already fixed in LiteLLM 1.83.9). The article also lists postmortems of Cloudflare AI Gateway and OpenRouter outages, four deployment paths, and paid plans starting at about $50 per month.

    On October 27, 2026, Perplexity published an LLM gateway best practices guide explaining how this middleware layer routes, authenticates, caches, and logs model calls, and distinguishing four categories: API gateways, LLM gateways, MCP gateways, and AI gateways. The guide breaks down the mechanism through a six-step request lifecycle, while warning that the gateway centralizes credentials from various providers, and cites the LiteLLM gateway breach disclosed by Microsoft and two high-severity vu

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    #hot-signal#Safety / alignment#English text is being prepared.#OpenAI
  4. LangChain BlogSelected

    How LangChain Built the Design Agent Content Studio

    LangChain's GTM engineering team, working with the design team, built Content Studio, a design agent inside Slack, on Managed Deep Agents. It can generate videos, social graphics, UI mockups, and slide decks, and has already produced more than 3,000 assets and over 80 slide decks. The article breaks down its four-layer knowledge structure (skills, wiki, code validation, memory), the dedicated LangSmith Sandbox for each Slack thread, the two rendering engines HyperFrames and the in-house motion-studio, and how the main agent orchestrates asynchronous sub-agents. The team says that after migrating to Managed Deep Agents, its own code shrank by about 80%, the median model cost per request fell from about $8 in mid-September to about 50 cents, and the entire harness has been open-sourced.

    Shreya Talegaonkar

    LangChain's GTM engineering team, working with the design team, built Content Studio, a design agent inside Slack, on Managed Deep Agents. It can generate videos, social graphics, UI mockups, and slide decks, and has already produced more than 3,000 assets and over 80 slide decks. The article breaks down its four-layer knowledge structure (skills, wiki, code validation, memory), the dedicated LangSmith Sandbox for each Slack thread, the two rendering engines HyperFrames and the in-house motion-s

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    #company#Tutorials / practice#Agents#English text is being prepared.
  5. Full Artificial Analysis articleSelected

    Gemini 4 Carbon Tops the Artificial Analysis Intelligence Index v5

    Google's Gemini 4 Carbon ranks first on the Artificial Analysis Intelligence Index v5 with a score of 59, leading in four of the 9 evaluations: HLE-Diamond, Terminal-Bench-Science, AA-Omniscience, and AutomationBench-AA. On the Coding Agent Index, Gemini 4 Carbon (high) paired with the Antigravity CLI scores 59, trailing Claude Code paired with Claude Opus 5.5 (max, 62 points) by 3 points. The model has a 1M-token context window, supports text, image, video, and speech input, and is priced at a standard rate of $4/$20 per million input/output tokens; it is currently available to select users with a 50% discount.

    Google's Gemini 4 Carbon ranks first on the Artificial Analysis Intelligence Index v5 with a score of 59, leading in four of the 9 evaluations: HLE-Diamond, Terminal-Bench-Science, AA-Omniscience, and AutomationBench-AA. On the Coding Agent Index, Gemini 4 Carbon (high) paired with the Antigravity CLI scores 59, trailing Claude Code paired with Claude Opus 5.5 (max, 62 points) by 3 points. The model has a 1M-token context window, supports text, image, video, and speech input, and is priced at a

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    #company#Evaluations / benchmarks#Google#Reasoning

Friday

  1. Arena

    Arena Analysis: Agent Arena Cost per Task: GPT-6.1 Sol at $1.91

    Arena's statistics on Agent Arena show that OpenAI's GPT-6.1 Sol (Max) costs $1.91 per task, 85% less than Claude Opus 5.5 (Max) at $12.51 and 87% less than GPT-6 Astra (Max) at $14.93. Arena notes that the sources of these two cost advantages differ: relative to Astra, Sol's advantage comes mainly from a lower token unit price; relative to Opus, it comes mainly from consuming fewer tokens per task. Cost per task is calculated by multiplying the number of input, output, and cache-read tokens by their respective unit prices, and Arena pinpoints the causes of cost reductions by comparing prices and token usage.

    Arena's statistics on Agent Arena show that OpenAI's GPT-6.1 Sol (Max) costs $1.91 per task, 85% less than Claude Opus 5.5 (Max) at $12.51 and 87% less than GPT-6 Astra (Max) at $14.93. Arena notes that the sources of these two cost advantages differ: relative to Astra, Sol's advantage comes mainly from a lower token unit price; relative to Opus, it comes mainly from consuming fewer tokens per task. Cost per task is calculated by multiplying the number of input, output, and cache-read tokens by

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    #editorial#Evaluations / benchmarks#Agents#OpenAI

Tuesday

  1. Artificial Analysis full article

    Artificial Analysis releases Intelligence Index v5, with 60% of evaluations switched to private datasets

    Artificial Analysis releases Intelligence Index v5, adding two new benchmarks, FrontierCode 2.0 and Terminal-Bench-Science, upgrading HLE-Diamond, AA-Omniscience v1.1, and CritPt v2, with 60% of evaluations based on private test sets or held-out answers. Claude Opus 5.5 (max) ranks first with 58 points, followed closely by GPT-6 Astra (max) at 57 points and GPT-6.1 Sol (max) at 55 points, with Gemini 4 Argon (high) at 50 points. The gap between open-weight models and the leaders has widened from 3 points to 12 points; the released-weight DeepSeek V4.1 Flash 1017 (max) and GLM-5.3 (max) both score 32 points. The per-task cost of leading models differs by more than 13 times: GPT-6.1 Terra (max) is $0.96, while Claude Fable 5.1 (max) is $13.04. A Progress Index v1 was also released, placing models released since November 2022 on the same capability scale for comparison.

    Artificial Analysis releases Intelligence Index v5, adding two new benchmarks, FrontierCode 2.0 and Terminal-Bench-Science, upgrading HLE-Diamond, AA-Omniscience v1.1, and CritPt v2, with 60% of evaluations based on private test sets or held-out answers. Claude Opus 5.5 (max) ranks first with 58 points, followed closely by GPT-6 Astra (max) at 57 points and GPT-6.1 Sol (max) at 55 points, with Gemini 4 Argon (high) at 50 points. The gap between open-weight models and the leaders has widened from

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    #company#Evaluations / benchmarks#Agents#English text is being prepared.