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ng. Its standard API price is 50% lower than that of GPT-5.6 Terra, at $1 per million input tokens and $5 per million output tokens. OpenAI says that at medium reasoning effort on Agents' Last Exam, it improves on GPT-5.6 Terra by 15 percentage points, and at the highest reasoning effort its token efficiency is more than three times that of Claude Opus 5.5, while its price per token is one quarter of the latter's. Starting October 26, the model is available to Plus, Pro, Enterprise, and Edu user

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cached input reduced to $0.05. Artificial Analysis measured that at maximum effort, the model's intelligence index is 6 points higher than GPT-5.6 Terra, with per-task cost of $0.40 versus $1.40, and gains of 7 to 13 points at the low effort tier. In AA-Omniscience, the model's guessing rate on questions where it does not know the answer is 38%, down from 88%, while accuracy improved by 2 points; it scored 48 on the Coding Agent Index v2.0, and improved by about 20 points on Terminal-Bench 4.0.

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AWS released a solution that uses AWS CDK to deploy enterprise-grade agentic retrieval on a managed Amazon Bedrock Knowledge Base: the reasoning agent invokes two knowledge bases as MCP tools through AgentCore Gateway, and AgenticRetrieveStream performs multi-turn planning, internal reranking, and returns answers with citations. The managed knowledge base (Type: MANAGED) takes over ingestion, storage, indexing, and retrieval, eliminating the need to build your own OpenSearch Serverless collectio

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The author shared the process of using Codex and Claude to reverse engineer and retrofit real-world devices: first enable ADB debugging on the TV, then have the Agent disable system packages, clear boot ads, and rebuild a clean launcher, and replicate the same method on three other TVs. The team also used Claude to reverse engineer 4 fill lights and built a unified web console, and used Codex to build a multi-camera console for a Sony camera and a DJI Pocket 3 and deploy it to Feishu. The author

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miting and unnecessary quota consumption of the K3.1 Pro subscription plan, stating that this was its own problem and should have been fixed earlier. All subscribers receive a one-time usage reset, restoring 25% of the monthly quota (reset to zero if usage is below 25%), equivalent to one week's usage, and the usage bar will be updated before the end of the day PST. Kimi has also adjusted the plan's parameters to reduce unnecessary subagent calls and output length, making the quota more durable.

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Harrison Chase and Caspar Broekhuizen of LangChain published an article summarizing the five core components shared by its three internal agents for marketing, sales, and engineering: agents live in existing workplaces such as Slack, a distinction between acting on behalf of users and a fixed identity, global and user-level memory, code execution within a sandbox, and a large number of runs triggered by schedules or events. The article uses GTM Agent, OpenSWE, and Content Studio as examples to e

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d it provides builds for macOS, Windows, and Linux. Its DOCX editing approach archives the original file by hash, marks only the paragraphs that have become dirty, and generates OOXML fragments to splice back into document.xml, reducing formatting damage to unmodified content. The project splits agent-core, the AI provider abstraction, and the format engine into shared packages, but AI calls require logging into a Genspark account and go through its server; model API Keys are not stored locally.

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Google DeepMind had four Gemini 4 Argon agents team up to play Minecraft from scratch, defeating the Ender Dragon after 27 hours of real time, during which they died 605 times, wrote more than 2,700 programs, and produced a total of 63,300 lines of code. The four agents had no human-written programs to rely on; chopping trees, mining, and crafting equipment all required them to write and test their own code, and they also shared experience with each other and reported the locations where teammat

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for product iteration, core component R&D, and algorithm platform development. The company was founded by Zhao Changyi, a PhD from Jilin University, and has launched the FaceSoul Agent full-stack emotional interaction solution, which uses a cable-driven approach to achieve 32 facial degrees of freedom and features a self-developed lightweight on-device expression generation model. The company has launched four major series—Lingshu M1, Lingshu W1, Lingyun, and Lingneng—and within four months of

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Cognition has released the open-source, free Devin Vulnerability Fixes plugin, which includes three Agent skills—fix-php-vuln, fix-node-vuln, and fix-gitlab-vuln—under the MIT license, targeting PHP2Shell, the Node.js October security release, and the self-managed GitLab vulnerability CVE-2026-110264 with a CVSS score of 10.0. The skills execute in five steps: read-only inventory, approval, target version verification, before-and-after testing, and one PR per repository. They can run in agents t

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. Frontier model training is no longer allowed to access the real internet; instead, models practice browsing and search in an offline web environment. In red-team experiments, an Agent, under conditions prohibiting network communication and with no shared writable files, used side channels such as CPU caches to exchange information; in another test, an internal research model exploited a DNS filtering gap to contact an external chatbot. The system raised an alert within 15 minutes, but the auto

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API value of the old plans is about 1.94 times that of the new plans. K3.1 Pro's per-million-Token input and output prices dropped from K3's 20 yuan and 100 yuan to 13 yuan and 68 yuan, while complex Agent tasks can consume millions of Tokens. A CAICT report shows that in June 2026, domestic daily average Tokens were about 500 trillion, nearly four times the figure in December 2025; the author believes that the compute shortage will still constrain independent model companies over the long term.

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elieves that once AI models for cyber defense proliferate, the application layer will be able to quickly equip defenders with tools to protect their environments, and that various agent swarms will emerge to scan, detect, protect against and handle the risks enterprises face. He points out that every layer of the security stack—identity, firewalls, network, code scanning, data protection and more—is a huge opportunity, and the only thing lacking is investment in compute; in the future, these age

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orm Xiaoyunque into its intelligent creation platform Jimeng. Xiaoyunque will retain its independent entry point, while operations will be overseen by Chen Xinran, the operations and marketing lead of Jimeng; the product team remains unchanged. Xiaoyunque launched its short drama Agent in March this year, supporting uploads of scripts up to 100,000 characters and one-click generation of finished videos, powered by ByteDance's self-developed Seedance 2.0 model; Jimeng AI was launched in May 2024.

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yment and Guanchao have jointly launched the annual "PAYATHON 2026" hackathon, featuring five major tracks including Apps, Skills, Vibe Pay, Skill Pay, Token Pay, Machine Pay, and Agent Pay, with a total prize pool of ¥200,000. Registration closes on December 1, and the offline finals will be held on December 26. The competition lists commercialization performance and real user payments as key judging criteria, and developers may enter with an app, Skill, or Agent. The blogger Farm Team, using t

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about 40 years elapsed—a technological revolution is not the same as a productivity revolution. A 2026 NBER study of more than 500,000 GitHub developers shows that autonomous coding agents increased cumulative code-commit activity by about 240%, but the number of projects rose by only about 80% and actual software releases by only about 30%. The rise of financial AI agents, meanwhile, makes accountability boundaries and risk contagion under multi-agent collaboration a real governance challenge.

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t, integrating the four teams Harness, Web, App, and Platform, headed by Cui Tianyi, who comes from a quantitative trading background, to coordinate product, design, and R&D. Previously, Guo Daya, a core contributor to R1, left in March 2026 to join ByteDance Seed due to optimism about the Agent direction not being a high priority within DeepSeek. DeepSeek Harness was open-sourced under the MIT license on August 13, and as of mid-October the repository had accumulated about 250,000 GitHub Stars.

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