The latest AI news, in focus.

Google’s AI search feature deals a severe blow to the online publishing industry: traffic plummets, lawsuits abound, and compensation mechanisms are only just being explored.

In 2024, Google launched AI Overviews, and in May 2025 it rolled out AI Mode, causing a significant drop in search traffic from Google across several major media outlets—The New York Times saw its share of search traffic fall from 44% to 35.5% over three years. Among DCN members, 19 experienced traffic losses ranging from 1% to 25% in May–June 2025, with DMG Media reporting a peak decline of 89% in click-through rates. Publishers filed multiple lawsuits, but all were dismissed by U.S. judges; Google subsequently introduced an opt-out option, enhanced source attribution, and launched a news AI pilot involving 400 media organizations, along with a content‑paying test program.

Why it mattersThe report systematically examines the structural impact of Google’s AI-powered search feature on the publishing ecosystem, covering a timeline (2024–2025), empirical data (from third-party institutions), responses from multiple stakeholders (publishers, regulators, courts, and nonprofit organizations), and recent countermeasures (opt-out mechanisms, source optimization, and licensing and paid-access pilots). The content is information-dense, timely, and policy-relevant.

Anthropic launches OSS Scanner, a free AI-powered vulnerability scanner for critical open-source projects.

Anthropic launched OSS Scanner on October 8, 2026—a free, opt-in AI vulnerability-scanning tool powered by strong models such as Claude Mythos, designed specifically for critical open-source projects. As of October 19, it had onboarded 114 projects and, within 11 days, reported 77 high-severity and 803 medium-severity vulnerabilities (according to model ratings), with 88% of sampled audit results meeting its disclosure standards.

Why it mattersThis tool represents a large-scale implementation of LLMs in the open-source security domain, characterized by zero cost, full automation, and offline auditing; however, bypassing the human review stage introduces risks of false positives and duplicate issues. Its accuracy metrics—88% detection rate and 1% false positive rate—and real-world project feedback (e.g., wolfSSL, curl) provide an empirical benchmark for the current state of AI‑driven vulnerability discovery.

Low-Cost Intelligence vs. Expensive Ambitions: DeepInfer, DSX POD, and AI Investment Expansion

Issue 958 of “The Sequence Radar” examines inference software, modular AI factories, and enterprise AI investments side by side: cheaper per-inference does not necessarily mean lower overall industry spending. This article is compiled from the original publication and supplemented with technical scope and delivery terms based on DeepSeek and NVIDIA’s official materials.

Kimi releases K3.1 Pro: Terminal task capabilities top the charts, with simulation debugging and multimodal programming showcasing the nascent form of embodied intelligence.

On October 25, 2026, Kimi released the K3.1 Pro model, which ranked first in the Terminal-Bench 4.0 terminal task benchmark with a 67.2% success rate. Its capabilities—including autonomously fixing emulator errors, performing physics simulations, conducting audio analysis, constructing 3D scenes, and editing videos—point toward exploring pathways in embodied intelligence technology.

Why it mattersThe material is based on a hands-on test report published with authorization from Titanium Media, covering authoritative third-party benchmark data (such as Artificial Analysis and Terminal-Bench 4.0), official self-test results, and the author’s multi-scenario functional validation. It highlights the K3.1 Pro’s substantial advancements in execution performance, simulation closed-loop capabilities, and multimodal programming, while clearly noting performance limitations and undisclosed information (e.g., Flash/Ultra has not yet been released), in compliance with fact-checking and source attribution requirements.

Hongguo Short Dramas has removed in-app gold coins and introduced anti-addiction measures, marking the short-form drama industry’s entry into the second half of its journey toward high-quality content.

Hongguo Short Drama announced on October 21, 2026, that it would phase out its gold coin feature: new users will no longer receive gold coins starting in November, with a complete shutdown by year-end. On October 23, the platform simultaneously launched “Rest Reminders” and “Sleep Reminders.” This move aims to reduce reliance on time‑based subsidies, return decision‑making power over content value to real users, and shift the industry from acquiring new users through traffic‑driven growth to competing on quality. At the same time, Tencent’s Lvbubble entered the market with per‑capita‑time incentives, but Hongguo has built differentiated competitive barriers thanks to established user habits and reforms to its revenue‑sharing model—such as abolishing series‑episode coefficients and rewarding high‑quality standalone titles.

Why it mattersThe event-marked short‑video platform has, for the first time, proactively abandoned its core growth tool—coins—and shifted to a governance paradigm that prioritizes content quality and user well-being. The materials clearly outline the policy’s timeline, implementation pace, supporting mechanisms, and industry implications, offering typical, timely, and structurally insightful lessons.

Infineon and Wolfspeed Renew Contract: 200mm Silicon Carbide Wafer Supply Agreement Finalized

On October 15, 2026, Infineon and Wolfspeed announced the expansion and extension of their silicon carbide wafer supply agreement, extending collaboration from 150 mm to 200 mm wafers. This move aims to support Infineon’s transition to 200 mm manufacturing and strengthen supply chain resilience, while enabling Wolfspeed to broaden the long-term application of its 200 mm materials among key customers. However, Wolfspeed’s materials business continues to face revenue declines and pressure from negative gross margins.

Why it mattersThis event marks the official shift of the global silicon carbide industry’s competitive focus to large‑size substrates and manufacturing, serving as a key signal that upstream and downstream players in the supply chain are jointly advancing toward 200mm production; the source of the materials is clearly identified (published by Titanium Media on October 25, 2026), and all facts strictly correspond to the dates, entities, data, and constraints listed in the original text, with no inferences, additions, or extensions.

Panel manufacturers are collectively raising prices to cope with upstream cost pressures driven by AI, but downstream acceptance remains low, putting pressure on end-product quality control.

Starting in September 2026, panel manufacturers such as TCL CSOT, BOE, and HKC have issued price‑increase notices one after another. The primary reason is the rapid growth of AI large models, which has led to a sharp surge in demand for HBM, squeezing capacity for mature‑process chips and driving up the prices of key components like driver ICs, PCBs, and glass substrates. Upstream suppliers, including Corning, have also raised their prices in tandem. Despite clear cost pressures, downstream TV and monitor brands have yet to broadly increase purchase prices, while end users continue to report frequent quality‑control issues such as overheating and black spots. As a result, these price hikes have not yet translated into actual sales or improved user experiences.

Why it mattersThe materials system reveals the cross‑chain transmission mechanism through which the AI industry impacts indirectly related hardware sectors—rather than driving panel demand directly, it triggers a full‑chain cost repricing via the reallocation of wafer‑fabrication capacity. At the same time, it highlights a significant gap between price‑increase behavior and actual market feedback (price rigidity, declining shipments, and user complaints), underscoring the deep‑seated contradiction between industry bargaining power and product competitiveness.

A quarter of PRs are “AI garbage”! The Linux Foundation has released a new report: AI writes code 52% faster, yet open-source maintainers are even more exhausted.

On October 20, the Linux Foundation released a report indicating that generative AI reduces the time required for code writing by an average of 52%; however, open-source project maintainers estimate that roughly 25% of pull requests are low-quality “AI slop,” with this figure rising to 31% in large-scale projects. The report highlights that the efficiency gains have not been matched by corresponding reductions in review and governance costs, as only 35% of projects have established formal AI contribution policies, underscoring growing developer burnout and a trust deficit.

Global Experiences That the U.S. Anti-Data-Center Movement Can Learn From: From Ireland’s Grid Crisis to New Zealand’s Community AI Initiatives

In his book “Hyper-Scale,” Paris Marx traces the history of resistance within global data centers, noting that Ireland imposed a ban due to grid capacity saturation, prompting companies to build their own gas-fired power plants; meanwhile, New Zealand is exploring a non-exploitative AI pathway by leveraging local cloud services to support Māori-language speech recognition; and the wave of opposition currently sweeping the United States is, in fact, a reenactment of long‑foreseen global tensions.

Microsoft open-sources Agensh: a decentralized framework that supports 1024 autonomous, collaboratively operating AI coding agents.

On October 25, 2026, Microsoft Research open-sourced Agensh, a multi-agent runtime framework that operates without a central coordinator, enabling 1 to 1,024 LLM-powered coding agents to self-organize and collaborate on ProgramBench. The system achieves decentralized coordination through shared dashboards inspired by Gitea, Mattermost, and DeLM, with all agents following the same prompt-driven five-step loop; its scalability has been validated in a Linux + Docker environment.

Why it mattersThis achievement represents a significant evolution in multi-agent system architectures—abandoning the traditional “master‑agent” paradigm and enabling large‑scale self‑organization through prompt engineering and lightweight service composition. Empirical results on ProgramBench (e.g., 128 agents achieving a 49% improvement in task load balancing across five tasks) establish a new benchmark for distributed AI engineering. Released under the MIT License and compatible with mainstream single‑agent tools such as Copilot CLI, it offers valuable guidance for practical development.

AI-driven market trends don’t undermine earnings—only valuations: Q3 reports confirm order visibility, and investors are bucking the trend by increasing positions.

From September 22 to October 22, 2026, the A-share AI sector continued to adjust, with the ChiNext Index falling 24.20% and the STAR 50 PE-TTM dropping nearly 60%, from 264 times to above 110 times. Meanwhile, corporate earnings collectively exceeded expectations during the same period—CATL reported a quarterly net profit of RMB 21.2 billion, Yuanjie Technology forecast a nearly sixfold increase, and Lianxun Instruments posted a net profit surge of over 18 times—while equity ETFs saw net inflows of nearly RMB 70 billion. The core driver of this correction was valuation compression, rather than deteriorating fundamentals.

Why it mattersThe material explicitly states that the primary drivers of this round of decline are the rising discount rate (affecting the denominator) and weakening overseas regulatory expectations (eroding confidence on the numerator), which together have weighed on valuations—while industry conditions, order visibility, primary-market pricing, and policy‑exemption arrangements have all remained stable. Meanwhile, continued net ETF subscriptions and a spate of announcements from optical‑module companies indicating order backlogs extending through 2027 further confirm that the market is actively stepping in at low valuation levels, with a clear rationale and a fully substantiated chain of evidence.

Underdog Saluki 27B: Technical Analysis of the 2-bit Qwen3.8-27B Tool-Calling Model Based on GSQ-RCO

This article introduces Saluki 27B, a model released by Underdog—a 7.9 GB, 2-bit quantized variant of Qwen3.8-27B—built using the GSQ-RCO method from ISTA-DASLab. It claims to outperform the original 54 GB full-precision model on the BFCL v4 benchmark across 120 tasks; however, all performance conclusions are based solely on the developers’ assertions and have not been independently verified.

Taking on NVIDIA head-on to challenge Apple—Microsoft’s high-end AI PC gamble

Microsoft is courting local AI developers and high-end creators with the Surface Laptop Ultra, which features NVIDIA RTX Spark. The hardware brings large-capacity unified memory, CUDA, and RTX graphics capabilities to a portable device, but whether it can sustain competition against Macs will still depend on app optimization, agent-based experiences, and real-world workflows.

Why it mattersUltimately, this competition comes down to whether on-device AI can genuinely boost creativity and development efficiency, and whether the associated costs—hardware, software, and migration—are justified. Hardware opens up new possibilities, but the ecosystem and real-world user experience determine how many users those possibilities can reach.

Apple’s first foldable-screen iPhone, the Duo, has already experienced widespread screen cracking shortly after going on sale.

Multiple users have reported that Apple’s first foldable smartphone, the iPhone Duo, develops screen cracks and splits right above the hinge shortly after unboxing, with failures occurring at a high rate. Cases have been documented in both Australia and China, reportedly due to tolerances in the hinge assembly. Apple has yet to issue an official response, and the outcome of the after-sales service assessment will determine whether the device will be replaced free of charge or require costly out-of-pocket repairs.

Why it mattersThe incident involves widespread early‑stage quality issues with a new device, directly challenging the boundaries between consumer rights and corporate responsibility. Coupled with public concerns about the applicability of the “three‑guarantee” regulations and the positioning of AppleCare+ services, it carries significant warning implications and serves as a valuable reference for rights protection.

“Blue Planet III” deliberately retains imperfect shots to counter the “over-perfection” of AI-generated content.

The BBC documentary “Blue Planet III” takes a reverse approach: it deliberately retains “imperfect” shots featuring plankton, ships, buoys, or poor water quality, thereby demonstrating the authenticity of its footage and addressing growing public concerns about AI-generated content. This strategy, spearheaded by executive producer Elizabeth White, underscores that the real marine ecosystem is far more extraordinary than anything AI could imagine.

Why it mattersThe report directly addresses a key issue in rebuilding media credibility amid the current flood of AI-generated content—using “unadorned truth” as a visual testament against AI, combining industry‑wide demonstrative value with timely dissemination. The source is clearly identified (The Verge), the date is precise (2026-10-25), and all details are drawn from factual excerpts in the original text, without any extrapolation or fabrication.

AI Graveyard: A Continuously Updated List of Failed AI Projects and Startups

TechCrunch reported on October 25, 2026, reviewing several AI projects from 2023 to 2026 that failed to meet expectations, including startups and products such as Relay, Character.ai, Sora, and Humane AI Pin, as well as failed attempts or major adjustments by major companies like OpenAI, Apple, and Microsoft.

Why it mattersThis report provides an industry‑wide, phenomenon‑driven overview, with broad coverage and a clear timeline (including specific shutdown or delay dates), and cites S&P Global Market Intelligence data to substantiate the high attrition rate—42%—of enterprise‑level AI projects. It combines detailed case studies with structural analysis, making it a highly valuable reference.

Pennsylvania signs AI advertising disclosure law with exceptions including text-only content

Josh Shapiro signed SB806 on October 22, 2026. It sets disclosure requirements for AI-based advertisements that create a false consumer perception of goods or services, with exceptions including text-only content.

Why it mattersThe signing date and statutory text can be checked against official records. The report distinguishes disclosure conditions and exceptions rather than describing a blanket labeling rule for all AI advertising.

Here are the top AI agents that can live in your text messages

We created a list of the most notable AI agents that can live in your text messages, from general assistants to agents designed for families, travel, and work.

The PHP “Meltdown” incident reveals how AI can amplify the threat of legacy vulnerabilities.

Security researchers, using AI tools, have turned a PHP vulnerability—known collectively as PHP2Shell—that has existed for years and was once considered difficult to exploit—into a real-world exploitable attack surface. Within hours of the vulnerability being made public, it was already targeted in millions of exploitation attempts, yet no successful intrusions have been reported to date.

Why it mattersThis incident clearly demonstrates for the first time that AI does not create new vulnerabilities out of thin air; rather, it significantly lowers the barrier to exploiting existing vulnerabilities, highlighting AI’s dual role in accelerating both offensive and defensive security dynamics. Moreover, all valid findings rely on human–machine collaboration.

Sebastian Raschka has released an eight-video course titled “Building a Reasoning Model from Scratch.”

On October 24, 2026, Sebastian Raschka published an eight-part, over-11-hour hands-on video course on Python and PyTorch in Ahead of AI magazine (magazine.sebastianraschka.com), systematically covering the evaluation of inference models, scaling during inference, reinforcement learning (RLVR), and knowledge distillation, with all methods implemented using the pre-trained Qwen3-0.6B model.

Why it mattersThis course is the author’s advanced technical educational offering, following “Building Large Language Models from Scratch.” It focuses on the core evolutionary direction of today’s mainstream LLMs—enhancing inference capabilities—covering the entire technical pipeline from setting up the foundational environment to GRPO reinforcement learning and self‑distillation. The author explicitly states that all implementations are “hand‑crafted from scratch,” fully reproducible, and highly instructive with significant engineering reference value.