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Day 130 of Xiaowei's gray release: WeChat breaks mini programs into parts and hands them to AI

It has been 130 days since WeChat's native AI assistant "Xiaowei" launched. Its entry points have expanded from the 16 officially disclosed to 28 measured by third parties, covering chat, search, official accounts, Channels, and PC. Through the developer integration guide released on June 8, WeChat requires developers to break mini programs into atomic interfaces and atomic components and write SKILL documentation; mini programs that have not integrated will be unable to be recommended and invoked by WeChat AI. The article also mentions that the development model must pass platform evaluation (80 points or above in the three items of service delivery, interaction experience, and performance quality, and 60 points or above in scenario coverage) to be eligible for invocation, while the invocation ranking rules have not yet been made public.

OpenAI Developer Lead Discusses How to Choose Between Sol, Astra, and dots After DevDay

Vaibhav Srivastav, OpenAI's Head of Developer Experience and Community, said in an interview that GPT-6.1 Sol can achieve about 95% of the performance of GPT-6 Astra at only one-fifth the cost, and that he himself mainly uses Sol in his daily work. He recommends using Astra for adversarial review on new projects or unfamiliar problems, and said the Ultrafast tier targets real-time scenarios such as fraud detection and financial investigations, with speeds exceeding 300 tokens per second. He also described how the always-on agent OpenAI dots aggregates information from Slack, GitHub, Gmail, and calendars and handles PRs on users' behalf, as well as how Auto-review checks model actions against custom policies before they are taken.

a16z Long Read: Databricks and the Enterprise in the Agent Era

a16z partners Ben Horowitz and Michael McGuiness wrote the article, using Databricks' four generations of technological evolution—from Spark and Lakehouse to Lakebase and agents—as a thread to discuss how agents are changing enterprises. The article states that Databricks has surpassed $7 billion in annualized revenue, up 80% year over year, at a valuation of $190 billion, with more than 17 million databases launched by agents every day. The authors argue that enterprises need an agent-facing system of record that captures definitions, permissions, and evaluation results, and believe that in the future more people's work will shift toward specifying tasks, delegating execution, and checking results.

Kimi K3.1 Pro Released: Capabilities Improved, but Price Is 16 Times That of DeepSeek and MiniMax Flash

Moonshot AI released Kimi K3.1 Pro on October 24, retaining K3's 2.8 trillion parameters without redoing pretraining, and raised its Artificial Analysis composite intelligence index from about 44 to about 55 by increasing post-training compute and joint reinforcement learning across hundreds of domains. Its pricing is $2 and $10 per million input/output tokens, or $12 for 1 million tokens each, 16 times the $0.75 idle-time rate of MiniMax M3.1 Flash and DeepSeek V4.1 Flash-1017. The article also compares the three vendors' respective reinforcement in 3D modeling and financial research, full project development, and computer operation and cross-application workflows, and notes that some users report 3D tasks consume tokens so fast that they hit the quota limit.

Today AI Completes Over $200 Million First Round of Financing; Founding Team Responds to Ten Core Questions About Personal AI

Today AI announced the completion of over $200 million in first-round financing, with investors including IDG Capital, Tencent, 5Y Capital, Ant Group, Sequoia China, Transsion Holdings, Tongge Venture Capital, and Shanghai Jiao Tong University Hanyuan Assets. In the interview, the founding team responded to questions including whether Personal AI is a false demand, why they insist on an independent app and single session, how memory and proactivity reduce delegation costs, as well as privacy authorization and commercialization paths. The team stated that Personal AI is currently in a stage of homogeneous competition, with the moat coming more from Context accumulated through long-term use, and plans to train models that lean more toward interaction and anthropomorphic style.

Why AI Audit Tools Are Supplied by the Audited Party: Verification of Four Security Incidents and Seven Papers in One Week

Using four security incidents within the week of October 16 to 23 as its thread—including two PHP remote code execution vulnerabilities discovered by AI, the backdoored LiteLLM version v1.107.0 surviving on PyPI for about two hours and being downloaded roughly 49,000 times, the OpenCode console leaking third-party model keys for 1.4% of users, and Microsoft reporting that a human organization used the open-source multi-agent penetration framework ARTEX to attack the supply chain—the article discusses the common problems facing AI auditing. After examining seven papers one by one, the author points out that the faithfulness of latent reasoning self-decoding reaches at most 0.4788, sparse autoencoders are nearly blind to backdoor shifts, probes fail when targeted by the object being optimized, and all three mainstream audit tools originate from the audited party's ecosystem or require borrowing the audited party's permissions. The article also cites the NARCBench multi-agent collusion benchmark from Oxford and New York University, as well as the report by Anthropic and Theorem using Claude to write roughly one billion lines of Rocq formal proofs for 239 entry points across 123 open-source libraries.

Amazon CEO Jassy: AWS's AI business has an annualized revenue of over $25 billion, and $220 billion in capital expenditure for 2026 is still not enough

Amazon CEO Andy Jassy said on the a16z podcast that in the first three years of the generative AI wave, AWS's AI business has reached an annualized revenue run rate of over $25 billion, hundreds of times AWS's roughly $60 million in its early days. He said that under capital expenditure of about $220 billion for 2026, capacity for 2026 is already sold out, 2027 is essentially sold out, and a considerable portion of 2028 has also been sold, and that inference costs are still an order of magnitude too expensive, which is the main obstacle to widespread enterprise adoption of AI. He also mentioned that the Bedrock team rebuilt the inference engine with 6 engineers in 76 days, a task that previously required 40 people for a year.

How OpenAI defines FDE: consolidating customer-customized Harness into a unified Codex

OpenAI's FDE team was established in early 2025, with the goal of filling the deployment gap between model capabilities and customers' real-world constraints; the approach is to first get results into production, then consolidate customized solutions into reusable products. In the early days, every use case required extensive customized integration; after Codex became stronger, the team rebuilt the application on Codex, turning customized applications into plugins and skills based on Codex primitives, and self-reports that about 90% of the customized code for each project was removed. In a semiconductor customer case, about 15 FDEs covered 10+ use cases, self-reporting about 48% time savings in targeted test generation and about 22% median performance improvement in kernel optimization; in a telecom customer service voice case, the key evaluation rose from about 60% to 90%+ in about six months.

Exiting after just 188 days, big-company AI products are accelerating their reshuffle

36Kr sorted through the product moves of eight companies—Tencent, Alibaba, Baidu, ByteDance, and Moonshot AI, Zhipu, MiniMax, DeepSeek—across ten AI tracks over four years, finding that competition cycles have shortened, product lifecycles have grown shorter, and renaming, integration, and shutdowns have become the norm. Tencent QClaw went from public beta on March 20 to announcing service shutdown arrangements on September 24, just 188 days, with user data to be migrated to WorkBuddy; Alibaba's Qwen announced the upcoming release of its personal assistant product Qwen Go!. Alibaba and ByteDance cover all ten tracks, Tencent and Baidu each cover nine, and DeepSeek covers only two.

Meta Q3 earnings: advertising props up 97% of revenue, Muse still burning money

Meta Q3 revenue was $64.6 billion, up 26% year-over-year, of which advertising contributed $63.06 billion, accounting for 97.6% of total revenue. The personal agent Muse, launched on September 8, surpassed 10 million downloads within 40 days of launch, but high capital expenditure turned free cash flow negative at $2.12 billion, and the full-year Capex guidance floor was raised from $130 billion to $135 billion. Muse currently mainly relies on subscription fees, with shopping being the most frequent use case; more than 25% of users have used the shopping feature, with average spend per transaction exceeding $200.

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How Claude Mods Flip Claude Code's Agent Framework into a Human-Facing Attention Interface

Writing in Latent Space, author Dan McAteer introduces Claude Mods: TypeScript or JavaScript plugins that run inside Claude Code, respond to events such as prompts, tool calls, and interface rendering, and change execution and rendering behavior in the CLI and on desktop. Anthropic has built in six mods, including the you-should-know side agent, which is off by default; the author also built a Next Steps Supervisor on top of his own ModSmith. The article compares Claude Mods with ChatGPT's Intelligent UI and Cursor's PR Routing & Approval, noting that the difference among the three lies in who designs the attention interface, and it raises the issues of noise, a cost of roughly 5–6k input-equivalent tokens per check, and trust concerns because mods are not confined by the sandbox.

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