Hyundai's union, representing nearly 40,000 workers, voted 92% to authorize a strike with automation at the center of the dispute. The union is demanding a veto over deployment of Boston Dynamics' Atlas humanoid robots in Hyundai and Kia factories, where up to 25,000 units are planned by 2028. Workers fear the robots — which cost less than two years of a worker's wage — will replace rather than assist them. The standoff highlights a broader global question about who controls the decision to replace human labor with machines, and could set a precedent for labor negotiations across Asia's manufacturing sector.
Nguồn: https://thenextweb.com/news/hyundai-robot-strike-union-vote-automation. 8sync News chỉ tóm tắt và dẫn link; bản quyền nội dung thuộc tác giả và nguồn gốc.
Một giám đốc cấp cao tại GitHub chia sẻ cách cô ấy xây dựng 40 quy trình tự động hóa bằng ứng dụng GitHub Copilot trên desktop để quản lý khối lượng công việc vô hình của vai trò lãnh đạo cấp cao. Những tự động hóa này kết nối với lịch, email, Slack và kho lưu trữ GitHub thông qua tích hợp MCP để xử lý chuẩn bị họp, sàng lọc hàng ngày, theo dõi triển khai, phát hiện PR cũ và nhật ký sự nghiệp. Cô coi tự động hóa như một công cụ hỗ trợ khả năng tiếp cận cho người mắc AuDHD, thu hẹp khoảng cách giữa những ngày có chức năng điều hành tốt và kém.
Lập trình viên nên đọc bài này để hiểu cách áp dụng tự động hóa công cụ AI như Copilot không chỉ tiết kiệm thời gian mà còn nâng cao hiệu quả làm việc và quản lý dự án thông qua cách tiếp cận thiết thực, từ nhỏ đến lớn.
Smart plug (Zigbee) giá rẻ (~$15) thay thế smart appliance nhờ ưu điểm tiết kiệm chi phí, tránh lệ thuộc cloud, kéo dài tuổi thọ thiết bị và giảm rác thải điện tử. Chúng theo dõi dòng điện, kích hoạt tự động hóa (Home Assistant) như thông báo kết thúc chu trình, tính toán chi phí năng lượng hay ngắt an toàn mà không cần internet.
Lập trình viên nên đọc bài này để hiểu cách xây dựng hệ thống nhà thông minh tự động hóa hiệu quả bằng cách kết hợp các thiết bị cơ bản với các công cụ mở nguồn như Home Assistant, giảm chi phí và tránh phụ thuộc vào dịch vụ đám mây đắt tiền.
General Intuition has raised $320 million at a $2.3 billion valuation to scale AI agents trained on hundreds of millions of hours of video game footage. The key differentiator is action-labeled gameplay data — records of button presses and timing — rather than video alone, which the company argues enables richer causal understanding. The same model powering a Fortnite-playing agent also drives a quadrupedal robot that required only 8 minutes of real-world fine-tuning. The round was led by Khosla Ventures with participation from Jeff Bezos, Eric Schmidt, and Google DeepMind researchers. General Intuition plans to sell its agentic model as a foundation for gaming, simulation, and robotics use cases via an API, while also launching Nerve, a jobs marketplace letting gamers earn income through data labeling and robot teleoperation.
WebMCP is a specification that lets websites expose structured capabilities to AI agents via the Model Context Protocol, instead of relying on traditional browser automation. It offers two implementation approaches: a declarative HTML-based API (adding attributes like toolname and tooldescription to forms) and an imperative JavaScript API using navigator.modelContext.registerTool(). Compared to DOM-scraping browser automation, WebMCP provides more reliable interactions, lower overhead, and resilience to UI changes. Chrome Canary now includes DevTools support for inspecting registered tools, and Lighthouse/PageSpeed Insights have added agentic browsing audits to validate WebMCP implementations. The post also covers how to monitor WebMCP health using DebugBear.
General Intuition, a New York-based AI startup, has raised $320 million at a $2.3 billion valuation to scale AI agents trained on hundreds of millions of hours of video gameplay. The company's key differentiator is action-labeled gameplay data — records of button presses and timing — sourced from Medal, a gaming clip platform co-founded by CEO Pim de Witte. Unlike competitors inferring actions from video alone, General Intuition embeds this action data to train a single model capable of playing games, navigating simulated environments, and controlling physical robots. A quadruped robot was fine-tuned for real-world navigation using just eight minutes of street data. The round was led by Khosla Ventures, with participation from General Catalyst, Jeff Bezos, Eric Schmidt, and researchers from Google DeepMind and MIT. The company plans to offer its model via API and build a data flywheel across gaming, simulation, and robotics use cases, while explicitly ruling out lethal military applications.
dltHub introduces a 'context layer' that stores and carries pipeline metadata — schemas, connectors, deployment configs, logs — across the entire data stack so AI agents can build, deploy, and maintain pipelines with minimal human intervention. A single command scaffolds a workspace and runs an example pipeline end to end. The system organizes work into phases (extract, model, deploy, run, maintain) with guided skill sequences and guardrails. When a source breaks months later, the agent can diagnose and fix it in minutes because all context is already available. Users stay at a high-level intent layer and only intervene for judgment calls, not errand-running.
Standard GPS receivers only achieve around two-meter accuracy, which is insufficient for precise robotic navigation. RTK (Real-Time Kinematic) GPS solves this by using a base station at a known location to transmit phase-angle correction data to a mobile receiver, enabling centimeter-level accuracy. GreatScott! demonstrates this on a tracked robot platform, placing the base station on a fence post and the RTK receiver on the robot. While the RTK system itself proved accurate enough, the robot's steering hardware and algorithms became the limiting factor for hitting centimeter-sized targets. The setup demonstrates practical applications like autonomous lawn mowing and amateur land surveying.
Tombot has closed a $7 million Series A3 round to scale manufacturing of Jennie, its autonomous robotic Labrador puppy designed as a companion for people with dementia, cognitive impairment, anxiety, loneliness, autism, and PTSD. Investors include healthcare and aging-services backers such as Caduceus Capital Partners and the Lutheran Foundation for Long Term Living. The company reports over 23,000 pre-orders and waitlist sign-ups ahead of a planned Fall 2026 commercial launch — its first shipments to paying customers. Jennie mimics the behavior of an 8-to-10-week-old puppy and is pitched as delivering companionship benefits without the care burden of a live animal.