近期，由於中美經貿關係惡化以及美國內安全形勢，文化和旅遊部提示中國遊客充分評估赴美旅遊風險，謹慎前往。(文旅部) LinkedIn Email Facebook Twitter WhatsApp The post 文化和旅遊部對赴美中國遊客發佈風險提示 appeared first on VeriMedia. source

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Interoperability among AI agents is slowly gaining traction as organizations begin to build networks of agents.  In the past few months, at least two agentic interoperability standards have emerged: Anthropic’s Model Context Protocol (MCP) and AGNTCY, from a collective led by Cisco. As the importance of agents—especially those built on different frameworks and large-language models (LLMs)—talking to each other and getting a fuller picture of an enterprise’s data gains ground, another new protocol is vying for adoption. Today, Google is unveiling a new interoperability protocol called Agent2Agent, or A2A, that it hopes will become a standard within the industry. Partnering with more than 50 companies, including Atlassian, Box, Cohere, Intuit, LangChain, MongoDB, Salesforce, SAP, ServiceNow, UKG and Workday, Agent2Agent aims to be the interoperability language for agents and AI applications.  In an exclusive interview, Rao Surapaneni, vice president and general manager of Google Cloud’s Business Application platform, told VentureBeat that A2A makes it easier for agents with different specializations and data nodes to get needed context.  “Everyone has a certain specialization because they own a data node or a logic node, or the current user base is focused on that particular task,” Surapaneni said. “You expect these frameworks to evolve with a very specialized focus. If I’m a customer and I’m deploying these multiple platforms and multiple frameworks, I don’t want to do swivel chair across them.” Surapaneni said part of the reason Google worked with more than 50 partners and customers is to build A2A to have “an ability to interoperate in an enterprise-ready, secure and trustable manner.” Building on existing standards A2A facilitates communication between what Google calls a client agent and a remote agent. The client agent formulates and communicates the task from the end user, and the remote agent acts on the task.  In a separate blog post, Google said A2A depends on several key capabilities built on the protocol. Capability Discovery: Agents can “advertise their capabilities” through an agent card in JSON format, so the client agent can determine the best remote agent to complete a task. Task Management: Ensuring communication between agents is oriented only towards completing requests and defines the lifecycles for tasks. Collaboration: Sending messages around context replies, artifacts (output of tasks), or instructions. User Experience Negotiation: Specifying the content types and formats the agents are reading.  Surapaneni said Google designed A2A as an open protocol, meaning the larger open source community can contribute to the A2A project and suggest code updates.  “We are opening it up as a community-driven effort and one that is properly open source,” he said. “There’s a governance board around it, but we do want it to be truly open and community-driven.” In developing A2A, Google focused on enabling agents to work “in their natural, unstructured modalities, even when they don’t share memory, tools, and context.” The protocol also builds on existing standards like HTTP and JSON, so it’s easier to integrate with existing tech stacks and is secure by default.  Rise of interoperability protocols Of course, A2A is not the only interoperability protocol in the market. AGNTCY, from a collective of Cisco, LangChain, Galileo, LlamaIndex and Glean, aims to create a standard means of communication between agents. LangChain, which is also a partner in Agent2Agent, developed the Agent Protocol. Microsoft updated its AutoGen framework to help make interoperable agents.  On the other hand, many companies, including Microsoft, have already embraced MCP. Even Google added support for MCP through its new Agent Development Kit. Surapanenin assured that A2A would run parallel with MCP.  “We see MCP and A2A as complementary capabilities,” Surapaneni said. “The way we are looking at Agent2Agent is at a higher layer of abstraction to enable applications and agents to talk to each other. So think of it as a layered stack where MCP operates with the LLM for tools and data.”  Surapaneni did not close the door on possible collaboration with other consortia working on agent interoperability protocols. He said A2A is always open for new members, and the protocol will be a living code constantly updated based on community suggestions and needs.  “We will look at how to align with all of the protocols,” Surapaneni said. “There will always be some protocol with a good idea, and we want to figure out how to bring all those good ideas in.” Interoperability needs arise  Organizations and AI companies agree that the world will run on multiple AI models rather than one model ruling them all, so it makes sense that agents will also be built on different languages and frameworks.  However, a fully realized agent ecosystem requires agents to talk to agents from other companies. But that is easier said than done. Industry standards often take time to take hold and require buy-in from a large portion of companies.  If A2A, MCP, or AGNTCY hopes to succeed in creating a standard way for all AI agents, no matter who built them or which framework they’re built on, there must be mass adoption and deployment.  Surapaneni acknowledged that even with more than 50 partners working on A2A, adoption is still not at a tipping point. “All of these protocols will evolve, especially the way rate AI is changing, and we’ll find new use cases and scenarios to tackle so it will continue to grow,” he said. source

CEOs and boards of directors are tasking their CIOs to enable artificial intelligence (AI) within the organization as rapidly as possible. As a result, many IT leaders face a choice: build new infrastructure to create and support AI-powered systems from scratch or find ways to deploy AI while leveraging their current infrastructure investments. For those enterprises with significant VMware deployments, migrating their virtual workloads to the cloud can provide a nondisruptive path that builds on the IT team’s already-established virtual infrastructure. “Think about this choice in terms of your own home, imagining your core business applications as the very foundation of your house,” says Ken Bocchino, Group Product Manager at Google Cloud. “Just as you wouldn’t demolish that house to start from scratch to build a new kitchen, you look for ways to expand and modernize the existing VMware infrastructure while preserving the integrity of the original.” Infrastructure challenges in the AI era It’s difficult to build the level of infrastructure on-premises that AI requires. The networking, compute, and storage needs — not to mention power and cooling — are significant, and market pressures require the assembly to happen quickly. AI workloads demand flexibility and the ability to scale rapidly. For many organizations, building this capacity on-premises is challenging. Cloud platforms offer purpose-built infrastructure on demand, but there are IT concerns that this involves expensive refactoring, along with what could be a time-consuming and tricky migration. However, organizations don’t have to build entirely new applications. New functionality, including AI capabilities, can be developed with cloud-native services while remaining interconnected with existing infrastructure elements. A true hybrid approach The partnership between Broadcom and Google Cloud provides enterprises with a strategy for maintaining their VMware operational models and integrating cloud-native services. Google Cloud VMware Engine enables enterprise IT to nondisruptively extend their on-prem environments to the cloud and easily run workloads in Google Cloud — without having to make any changes to the architecture. There’s no downtime, and all networking and dependencies are retained — as are other benefits (see this IDC Business Value study). IT teams maintain operational consistency by using their familiar on-premises tools to manage cloud workloads, eliminating retraining needs. Once migration has occurred, the journey toward AI begins, and it can be broken down into three basic steps — each of which can be a stopping point if the organization’s needs are fully met: Front door modernization. Organizations frequently begin by enhancing how users access applications. Users can take advantage of cloud-native load balancing and security capabilities such as Google Cloud Armor, which protects against distributed-denial-of-service (DDoS) attacks and provides a web application firewall (WAF). In this way, IT can employ the cloud’s ubiquitous access and security features without having to refactor and re-network their applications. Application layer evolution. The next step often involves rethinking specific elements of the application stack, potentially developing new components that use cloud-native services while maintaining connections to existing systems. But this is not necessary to achieve AI enablement. AI and analytics integration. Organizations can enable powerful analytics and AI capabilities by linking VMware-hosted data with services such as BigQuery and Vertex AI. In fact, this can be done without needing to refactor or develop new components, if that’s not something IT wants to undertake. “With Google Cloud VMware Engine, it’s easy to integrate Google’s Vertex AI directly into the VMware environment,” says Myke Rylance, client solution architect at Broadcom. “It’s like adding high-tech ‘smart rooms’ in your house, where both the old and new structures communicate and work together seamlessly. In Google Cloud, IT has all that it needs to scale up quickly to enable AI with their existing virtual infrastructure.” Find out how easy it is to incorporate AI into your virtual workloads with Broadcom and Google Cloud. Find more information by clicking here. source

Image: StackCommerce The demand for skilled embedded systems engineers is rapidly growing, but the industry is severely competitive. Understanding how these intelligent systems function, how engineers design them, and how they connect is valuable, but it’s hard to learn on your own. If you want help, try the Embedded Systems Engineer Mastery Bundle to work through a comprehensive curriculum designed to give you practical skills that’ll help you excel in the field. It’s only $24.99 (reg. $490) right now. Course overview This bundle comes with ten hands-on courses covering the fundamentals of embedded systems and microcontrollers. A significant portion of the training focuses on C programming, a foundational language for embedded applications. The courses break down how to write efficient and effective code for resource-constrained environments. You’ll also learn how to interface microcontrollers with various peripherals, including sensors, displays, and actuators, gaining practical experience in hardware-software interaction. More advanced lessons show you more about designing and testing circuits, important for making embedded systems. Plus, it shows you how to create the physical boards (called PCBs) for these systems so you can turn your ideas into real things. All through the lessons, you’ll get to do hands-on work with virtual labs and projects to test your new expertise in realistic situations. These lessons are set up for learners with different amounts of experience, whether you’re just starting out or you already know the ropes and want to learn more. Get the 2025 Embedded Systems Engineer Mastery Bundle while it’s still on sale for $24.99. StackSocial prices subject to change. source

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Enterprises are spending time and money building out retrieval-augmented generation (RAG) systems. The goal is to have an accurate enterprise AI system, but are those systems actually working? A critical blind spot is the inability to objectively measure whether RAG systems are actually working. One potential solution to that challenge is launching today with the debut of the Open RAG Eval open-source framework. The new framework was developed by enterprise RAG platform provider Vectara in collaboration with Professor Jimmy Lin and his research team at the University of Waterloo. Open RAG Eval transforms the currently subjective ‘this looks better than that’ comparison approach into a rigorous, reproducible evaluation methodology that can measure retrieval accuracy, generation quality and hallucination rates across enterprise RAG deployments. The framework assesses response quality using two major metric categories: retrieval metrics and generation metrics. It allows organizations to apply this evaluation to any RAG pipeline using Vectara’s platform or custom-built solutions. For technical decision-makers, this means finally having a systematic way to identify exactly which components of their RAG implementations need optimization. “If you can’t measure it, you can’t improve it,” Jimmy Lin, professor at the University of Waterloo, told VentureBeat in an exclusive interview. “In information retrieval and dense vectors, you could measure lots of things, ndcg [Normalized Discounted Cumulative Gain], precision, recall…but when it came to right answers, we had no way, that’s why we started on this path.” Why RAG evaluation has become the bottleneck for enterprise AI adoption Vectara was an early pioneer in the RAG space. The company launched in Oct. 2022, before ChatGPT was a household name. Vectara actually debuted technology it originally referred to as grounded AI back in May 2023, as a way to limit hallucinations, before the RAG acronym was commonly used. Over the last few months, RAG implementations have grown increasingly complex and difficult to assess for many enterprises. A key challenge is that organizations are moving beyond simple question-answering to multi-step agentic systems. “In the agentic world, evaluation is doubly important, because these AI agents tend to be multi-step,” Am Awadallah, Vectara CEO and co-founder told VentureBeat. “If you don’t catch hallucination the first step, then that compounds with the second step, compounds with the third step, and you end up with the wrong action or answer at the end of the pipeline.” How Open RAG Eval works: Breaking the black box into measurable components The Open RAG Eval framework approaches evaluation through a nugget-based methodology.  Lin explained that the nugget approach breaks responses down into essential facts, then measures how effectively a system captures the nuggets. The framework evaluates RAG systems across four specific metrics: Hallucination detection – Measures the degree to which generated content contains fabricated information not supported by source documents. Citation – Quantifies how well citations in the response are supported by source documents. Auto nugget – Evaluates the presence of essential information nuggets from source documents in generated responses. UMBRELA (Unified Method for Benchmarking Retrieval Evaluation with large language model Assessment) – A holistic method for assessing overall retriever performance Importantly, the framework evaluates the entire RAG pipeline end-to-end, providing visibility into how embedding models, retrieval systems, chunking strategies and LLMs interact to produce final outputs. The technical innovation: Automation through LLMs What makes Open RAG Eval technically significant is how it uses large language models to automate what was previously a manual, labor-intensive evaluation process. “The state of the art before we started, was left versus right comparisons,” Lin explained. “So this is, do you like the left one better? Do you like the right one better? Or they’re both good, or they’re both bad? That was sort of one way of doing things.” Lin noted that the nugget-based evaluation approach itself isn’t new, but its automation through LLMs represents a breakthrough. The framework uses Python with sophisticated prompt engineering to get LLMs to perform evaluation tasks like identifying nuggets and assessing hallucinations, all wrapped in a structured evaluation pipeline. Competitive landscape: How Open RAG Eval fits into the evaluation ecosystem As enterprise use of AI continues to mature, there is a growing number of evaluation frameworks. Just last week, Hugging Face launched Yourbench to test models against the company’s internal data. At the end of January, Galileo launched its Agentic Evaluations technology. The Open RAG Eval is different in that it is strongly focussed on the RAG pipeline, not just LLM outputs.. The framework also has a strong academic foundation and is built on established information retrieval science rather than ad-hoc methods. The framework builds on Vectara’s previous contributions to the open-source AI community, including its Hughes Hallucination Evaluation Model (HHEM), which has been downloaded over 3.5 million times on Hugging Face and has become a standard benchmark for hallucination detection. “We’re not calling it the Vectara eval framework, we’re calling it the Open RAG Eval framework because we really want other companies and other institutions to start helping build this out,” Awadallah emphasized. “We need something like that in the market, for all of us, to make these systems evolve in the right way.” What Open RAG Eval means in the real world While still an early-stage effort, Vectara at least already has multiple users interested in using the Open RAG Eval framework. Among them is Jeff Hummel, SVP of Product and Technology at real estate firm Anywhere.re. Hummel expects that partnering with Vectara will allow him to streamline his company’s RAG evaluation process. Hummel noted that scaling his RAG deployment introduced significant challenges around infrastructure complexity, iteration velocity and rising costs.  “Knowing the benchmarks and expectations in terms of performance and accuracy helps our team be predictive in our scaling calculations,” Hummel said. “To be frank, there weren’t a ton of frameworks for setting benchmarks on these attributes; we relied heavily on user feedback, which was sometimes objective and did translate to success at scale.” From measurement to

The Project Kuiper payload is prepared for liftoff. Image credit: Amazon Amazon is moving ahead with its internet satellite initiative, the tech giant announced on April 2. Project Kuiper, a batch of 27 satellites set to launch on April 9, will compete with Elon Musk’s Starlink. First satellite launch will demonstrate Amazon’s space capability Project Kuiper’s satellites will provide internet access to virtually any location on the planet, Amazon said. The company initially signed the launch deal in 2022. On April 9, a United Launch Alliance (ULA) Atlas V rocket will carry the 27 satellites from Cape Canaveral in Florida to a 280-mile low Earth orbit. This will be Amazon’s first full complete deployment of internet satellites. The company plans to eventually deploy a total of 3,200 satellites in batches of dozens at a time. “We’ve done extensive testing on the ground to prepare for this first mission, but there are some things you can only learn in flight, and this will be the first time we’ve flown our final satellite design and the first time we’ve deployed so many satellites at once,” said Rajeev Badyal, vice president of Project Kuiper, in a press release. Two prototype satellites were tested in October 2023 and successfully enabled a two-way video call over satellite internet. More about Innovation SEE: Amazon is among the tech companies shifting from a desirable, perk-heavy company culture to an environment of personal pressure and political upheaval. To prevent satellites from interfering with astronomers’ work, Amazon coated them with a dielectric mirror film to mask their reflected light. The launch is a milestone for the Atlas V; Project Kuiper marks the heaviest payload the rocket has ever flown. Future Project Kuiper launches will use a variety of vehicles, including the Atlas V, ULA’s Vulcan Centaur, and rockets from Arianespace, Blue Origin, and SpaceX. Internet satellite competitors woo the government market Satellite internet service from Starlink, today’s major provider, has touched over five million internet users across 125 countries, according to Reuters. T-Mobile has made Starlink its default connectivity in the U.S. for places without any other wireless coverage. Satellite internet has been pivotal for government, military, and intelligence operations. While Amazon doesn’t sell Project Kuiper routers yet, its web services infrastructure could eventually integrate into space-based communications. source

By Tom Zanki ( April 9, 2025, 4:44 PM EDT) — Special purpose acquisition company Titan Acquisition Corp. began trading Wednesday after pricing a $240 million initial public offering in pursuit of a merger with a fintech or related business, represented by Winston & Strawn LLP and underwriters’ counsel Ellenoff Grossman & Schole LLP…. Law360 is on it, so you are, too. A Law360 subscription puts you at the center of fast-moving legal issues, trends and developments so you can act with speed and confidence. Over 200 articles are published daily across more than 60 topics, industries, practice areas and jurisdictions. A Law360 subscription includes features such as Daily newsletters Expert analysis Mobile app Advanced search Judge information Real-time alerts 450K+ searchable archived articles And more! Experience Law360 today with a free 7-day trial. source

Here are my top hotel accounting software that can help The best hotel accounting software should do more than just track income and expenses. I look for tools that can handle the basics — like financial reporting and revenue management — while also offering extras that actually reduce workload. Built-in payroll, assisted bookkeeping, and integration with reservation systems can make a big difference, especially if you’re trying to keep operations lean and efficient. Best for overall hotel accounting software: Cloudbeds Best for back office automation: Otelier Best for hotels that need unlimited users: Xero Best for hotels that manage in-house payroll: QuickBooks Online Best for hotel-specific bookkeeping assistance: M3 Why you can trust TechRepublic At TechRepublic, we publish high-quality, independently researched reviews created by writers who are experts in the fields they cover. Our contributors include seasoned IT professionals, certified accountants, software developers, and industry consultants — people who have worked directly with the tools they evaluate. Every article is built on firsthand experience, in-depth testing, and a deep understanding of what businesses and tech teams actually need to make confident, informed decisions. Top hotel accounting software comparison Monthly pricing Number of users Accounting features Hotel-specific features Bookkeeping assistance Cloudbeds Custom Depends on quote Limited Yes No Otelier Custom Depends on quote Limited Yes No Xero $20 to $80 Unlimited Yes Limited No QuickBooks Online $35 to $235 1 to 25 Yes Limited No M3 Custom Depends on quote Yes Limited Yes Cloudbeds: Best for overall hotel accounting software Image: Cloudbeds Cloudbeds is a top pick for hotels that rely on online bookings and want everything in one place. It streamlines reservations, housekeeping, payments, and accounting through a single platform, cutting down on manual work and saving time across your team. If you’re looking for hotel accounting software that goes beyond the basics, Cloudbeds delivers real operational value. You can integrate with POS systems, compliance tools, and reporting platforms to build a setup that fits your workflow. For hoteliers focused on forecasting and real-time insights, this hospitality accounting software brings it all together. Otelier may offer more depth for budgeting, but Cloudbeds wins on day-to-day efficiency. Pricing Cloudbeds is quote-based. We recommend contacting Cloudbeds for pricing details. Features Dynamic pricing optimization with custom rules and real-time alerts Competitor price tracking with alerts for market changes Customizable dashboards with real-time, hospitality-specific insights Centralized multi-property reporting with filters and grouping options Streamlined reservation tools, including a drag-and-drop calendar and one-click actions Over 200 partner integrations and an open API for workflow customization Built-in payment processing to reduce manual entry and errors Cloud-based multi-property management with quick toggling between locations Pros and cons Pros Cons Has a booking engine Limited native accounting features compared to full-service platforms Offers features for reservations, payments, accounting, and housekeeping May require third-party accounting software for advanced financial reporting Has strong multi-property management and reporting features May be overbuilt for very small properties or those with minimal tech needs Otelier: Best for back office automation Image: Otelier Otelier is a smart pick for hotels that want to automate routine back-office work without losing control over the details. It reduces time spent on repetitive tasks like nightly reporting, income journaling, and OTA reconciliation while keeping everything organized in one place. For teams managing multiple locations, it’s a practical way to bring consistency and accuracy across the board. It earns a top spot in my list of best hotel accounting software for its operational focus. I also like how Otelier turns scattered hotel data into usable insights. Its dashboards help you spot trends, fine-tune budgets, and plan ahead with confidence. While it doesn’t offer full accounting functionality, it pairs well with hospitality accounting software like QuickBooks or Cloudbeds to fill in the financial gaps. Pricing Otelier is quote-based. We recommend contacting Otelier for pricing details. Features Standardize budgeting processes across multiple properties Automate data entry, forecasting, and financial tasks Reduce manual errors and prevent lost revenue Maintain accurate, transparent financial records Run what-if scenarios for smarter decision-making Aggregate data into unified, customizable dashboards Track revenue metrics and financial KPIs in real time Identify trends to optimize pricing and performance Collaborate securely using live, accurate data Adapt forecasts quickly with predictive analytics Pros and cons Pros Cons Designed specifically for hotel operations, not just generic business workflows Limited native accounting functionality; requires pairing with accounting software Is used by top hotels like Marriott and Accor May be too complex for smaller hotels or single-location operations Offers strong visibility across portfolios, ideal for multi-property operators Reporting setup and dashboard customization can take time to configure Supports collaboration between finance, ops, and ownership with shared data access Upfront implementation may require internal training and change management Scales well as hotel businesses grow in size or complexity Xero: Best for hotels that need unlimited users Image: Xero Xero is a strong fit for hotels that need full accounting functionality and flexibility for large teams. It allows unlimited users, making it ideal for operations where multiple staff need access to the books. What stands out is its built-in fixed asset tracking, which is especially useful in hospitality where properties manage everything from furniture to equipment. Among the best hotel accounting software options, I like Xero because it delivers the accounting depth that many hotel software often don’t have. It also integrates with a wide range of hotel-specific tools, including Cloudbeds, Little Hotelier, Preno, and RoomRaccoon. If your priority is clean, compliant financials with support for things like depreciation, expense management, and multi-property reporting, Xero checks all the boxes. That said, if you’re not focused on accounting-specific tools, hospitality accounting software like Cloudbeds might be a better fit for front-desk and reservation workflows. Read our Xero review. Pricing Early: $20 per month for unlimited users; 20 invoices and five bills only Growing: $47 per month for unlimited users, invoices, and bills Established: $80 per month for unlimited users, invoices, and bills Features Unlimited users with customizable permission levels Fixed asset tracking with depreciation schedules Automated