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AI "Takes Over" the Digital Trade Expo: Wang Xingxing Discusses Millimeter-Level Errors in Robots, Anheng Information Demonstrates "Using AI to Counter AI"

The 5th Global Digital Trade Expo was held in Hangzhou. Wang Xingxing, founder of Unitree Robotics, stated that fine manipulation by robots involves errors of a few millimeters that cannot be automatically corrected, and that whoever solves the millimeter-level error problem of alignment in the physical world will essentially solve the core challenge of robotics. Anheng Information exhibited "Hengnao," China's first AI-native code audit agent, and "Lieying," the world's first AI-native honeypot capable of capturing autonomous attacks by AI Agents, turning "using AI to counter AI" into deliverable products.

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How to Transcribe Zoom Meetings: AI and Native Methods

ElevenLabs has released a guide introducing how to transcribe meeting audio using Zoom's built-in tools and third-party solutions. Zoom live captions are free, but cloud storage and downloadable transcripts require a paid Workplace account; Zoom AI Companion supports live transcription in 46 languages, while post-meeting transcription supports only 19 languages. Developers can build low-latency real-time transcription pipelines through ElevenAPI WebSockets.

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An agentic conversational video intelligence solution built on AWS

AWS has released an agentic video intelligence solution based on the Strands Agents SDK, in which a single AI agent orchestrates Amazon Bedrock, Amazon Rekognition, and Amazon Transcribe at runtime, allowing users to query video content by asking questions in natural language. Responses for already-analyzed content are returned within 1 second, while the first analysis of a new video takes 5–10 minutes; one media and entertainment company used it to reduce the manual review time for more than 200 multi-hour recordings by about 80%.

What is Human in the Loop (HITL)? Human-AI collaborative AI workflows

Human in the Loop (HITL) is an AI workflow that allocates tasks to AI and humans according to preset boundaries: AI handles predictable, rule-based tasks, while humans handle complex decisions, compliance requirements, and edge cases. It is used both for review, approval, and handoff to humans in real-time business processes, and for data labeling, output evaluation, and error correction in machine learning development. ElevenLabs' ElevenAgents can be used to build such HITL workflows.

TypeSafe CEO Diogo Almeida on Jev: Building a System One Model for Production, Not for God

InstructGPT paper author and TypeSafe CEO Diogo Almeida detailed his new model Jev on the Latent Space podcast, describing it as a System One large programmatic model oriented toward code consumption, with its core innovation being an unpublished RLCD (reinforcement learning for calibrated decision-making), optimized for "intelligence per dollar" rather than human preference feedback. He criticized RLHF for causing mode collapse, sycophancy, and hallucinations, arguing that safety alignment suits products like ChatGPT but fragments intelligence at the API layer, and said TypeSafe would rather be seen as a data lab, refusing to publish benchmark tests. He revealed that Jev's daily token consumption has exceeded one trillion since launch, and previewed follow-up models such as ReasoningJev.

What are agentic workflows? A practical guide to automation

Agentic workflows let AI agents reason at each step and decide the next action themselves, rather than executing along a fixed script. They consist of an LLM reasoning layer combined with RAG, memory, and guardrails; high-risk steps still go through fixed approvals, and anything beyond their authority is escalated to a human. ElevenLabs' ElevenAgents Workflows editor can explicitly map decision points, routing between specialized sub-agents or human agents.

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Why I still don't buy into true RSI

The Interconnects author states that he still does not buy into true recursive self-improvement (RSI), and leans more toward the "lossy self-improvement" view he has proposed: the research that can be automated is too narrow, the returns from parallel AI agents diminish, and resource bottlenecks and political factors constrain the building of strong LLMs. He points out that current AI safety concerns stem more from the cultural anxiety brought about by the large-scale deployment of agents by frontier labs such as OpenAI and Anthropic, rather than from any publicly disclosed breakthrough.

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AIUC Completes $40 Million Series A, Launches AIUC-1 Agent Safety Standard and Insurance Program

AIUC announced the completion of a $40 million Series A funding round, led by Ribbit Capital and First Harmonic, with customers including Cursor, Harvey, Lovable, and ElevenLabs. Its AIUC-1 standard covers agent safety, security, and reliability, requiring thousands of simulated tests each quarter for jailbreaks, hallucinations, and data leaks, with a certification period of about 3 to 10 weeks. ElevenLabs has purchased the first AI agent insurance policy, underwritten with participation from Lloyd's of London, and AIUC's evaluation results are used directly for pricing.

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ElevenLabs Reviews the Top Ten Conversational AI Enterprise Use Cases of 2026

ElevenLabs has outlined the top ten use cases for enterprise adoption of conversational AI in 2026, covering customer service automation, appointment scheduling, order returns and exchanges, sales lead qualification, IT help desk, internal knowledge base retrieval, multilingual support, and accessibility experiences. ElevenAgents supports more than 70 languages and can automatically detect and switch languages during a conversation. A 2026 McKinsey survey shows that nearly 90% of organizations have used AI in at least one business function, and 44% say AI is being scaled within the enterprise, up from 38% the previous year.

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How one resignation turned a spark of AI fear into a wildfire

When an AI researcher resigned citing safety risks, the resignation quickly ignited public opinion after background events such as the OpenAI-HuggingFace incident had already raised the heat of AI discussion, and the scope of the discussion far exceeded the expectations of veteran AI commentators. The article points out that the >10% extinction risk figure proposed by Evan Hubinger is a key source of this narrative, and that in AI safety discussions the term "existential risk" is often used to refer to very different things. The author believes the probability of complete extinction is too low to be worth discussing, but disasters such as AI-induced cyberattacks on critical infrastructure or biological risks are worth debating.

GPT-6 Family's First Model Astra Released, Author's Hands-On Test Consumed 4 Billion Tokens

OpenAI released Astra, which the author calls the beginning of the GPT-6 family; it performs outstandingly on ARC-AGI-3 and scored highest on Zapier's AutomationBench, with pricing the same as Fable 5.1. During weekend trials, the author consumed 4 billion tokens and found its capability fluctuates noticeably—it is a showcase model rather than one that reliably does work. Astra in Codex can now continue advancing tasks without waiting for an answer, and OpenAI also claims it has achieved its automated research intern goal, with the next goal being an automated AI researcher by March 2028.

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Duke Health doctor completes hip surgery using Apple Vision Pro

An orthopedic surgeon at Duke Health wore the $3,499 Apple Vision Pro during hip arthroscopy, using an app that received FDA De Novo authorization via Stryker to display real-time arthroscopic video, CT images, and HipCheck and HipMap data together in a single field of view. The authorization was granted on July 17, marking the first time Vision Pro has been cleared for intraoperative use. In addition, AI futurist Ray Kurzweil has joined brain-computer interface startup Subsense as a product and vision advisor; the company uses nanoparticles delivered intranasally to read and stimulate neural signals, is currently in the preclinical stage, has raised $27 million, and plans to conduct clinical pilots between 2027 and 2029.