Image: StackCommerce TL;DR: Save an additional $30 on this Mobile Pixels rotating monitor with code DISPLAY30 at checkout, dropping the price from $199.99 to $169.99. Your workspace may be holding you back more than you realize. An old, clunky monitor not only takes up valuable desk space but limits you to working only in landscape mode. It’s 2025, and now, some monitors rotate to landscape and portrait orientations to adapt to your workflow. And they’re cheaper than you think. This one from Mobile Pixels boasts an impressive 23.8-inch HD screen, beautiful colors, and thin bezels to maximize screen space. An affordable price tag of $169.99 with code DISPLAY30 at checkout makes it an affordable monitor for your home office. More about the Mobile Pixels rotating monitor Ergonomics Still wondering why you’d want your monitor to rotate 90º? If you work as an editor, programmer, accountant, graphic designer, or anything that involves long pages, you’ll be surprised how much of a difference working in portrait mode makes in your efficiency. The height, tilt, and swivel adjustments also make this a fully ergonomic monitor setup. Those who have ever felt a stiff neck after staring at a screen all day can’t deny that they could use a more adjustable monitor. Connectivity Your Mobile Pixels monitor has plenty of ports for connecting to your computer or peripherals: HDMI, VGA, DisplayPort, and 3.5 mm aux. It also supports FreeSync technology, which means it syncs with the refresh rate of your graphics card to prevent screen stuttering. Beauty You’d probably think a monitor at this price point would be lacking in some features, but the full HD resolution, 99% sRGB color saturation, and thin-bevel design prove you wrong. You even have the option of using the included stand or mounting the monitor to a wall to save desk space. Price Use code DISPLAY30 at checkout to save $30 on the Mobile Pixels dual-orientation monitor: $169.99 (reg. $199.99). 23.8″ Rotatable FHD Desktop Monitor. Prices and availability subject to change. source

When it comes to advanced degrees, only 30% of master’s degrees in engineering and computer sciences are awarded to women, dropping to 24% for doctoral degrees, according to Society of Women Engineers. And it’ll only become more difficult to foster gender diversity in the tech industry if colleges and universities aren’t also looking at the diversity, inclusion, and equity of their STEM degree programs. Once a diploma is earned, the real work begins, and here the numbers for women in tech are even more troubling. Only 38% of women who majored in computer science are working in the field compared to 53% of men, according to data from the National Science Foundation. This is a consistent trend dubbed a “leaky pipeline,” where it’s difficult to retain women in STEM jobs once they’ve graduated with a STEM degree. The IT leadership gap Women are in the minority at all of the Big Five major US tech firms, according to The World Bank. Of these high-profile tech companies, Amazon has the highest number of women employees at 45%, followed by Meta (37%), Apple (34%), Google (33%), and Microsoft (33%). Leadership numbers for these organizations are even lower, with women making up just 29%, 34%, 31%, 28%, and 26% at these organizations, respectively. Notably, none of these companies have ever had a female CEO and only around 9% of women hold positions such as CIO, CTO, IT manager, or technical team leader. source

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More We’ve been hearing a lot about AI agents — tools powered by generative AI models that can perform actions without much human supervision or intervention. But they still remain largely a novel curiosity for most people, and as far as we can tell, very few people are trusting AI agents to buy or enter contracts on their behalf — for now. GenLayer, a startup just out of stealth, believes it has a technology that will provide the missing “trust” component to the AI agent economy. GenLayer’s idea is a blockchain-powered infrastructure that allows AI agents to draft contracts, settle payments and execute agreements autonomously. Last fall, the company announced it had raised $7.5 million from notable investors, including Arthur Hayes (Maelstrom), Arrington Capital and North Island Ventures, to bring this vision to life. How to make AI agents trustworthy to people — and to one another AI agents are already proving their ability to analyze data, make deals and manage assets, but there’s a fundamental problem: They don’t inherently trust each other. Unlike humans, AI agents don’t fear lawsuits or reputational damage — so how do they enforce agreements? Albert Castellana, CEO of YeagerAI (the company building GenLayer), sees this as a critical flaw in today’s AI development. Albert Castellana, CEO of YaegerAI “Even in a situation where you have agents that can do commerce between themselves, how do they trust each other?” Castellana said in a recent interview with VentureBeat. “AI doesn’t sleep, AI works globally, AI cannot go to jail. The legal system will have a big issue dealing with that type of situation.” Traditional smart contracts, which power blockchain-based transactions, are too rigid for AI-driven commerce. They can’t process unstructured data, understand complex language or adapt to real-world changes. GenLayer wants to upgrade smart contracts into “intelligent contracts” — more flexible, AI-powered agreements that function much like human contracts. Unlike traditional blockchains that require external oracles to access off-chain data, GenLayer integrates AI directly at the protocol level. Intelligent contracts can natively fetch live web data, process natural language inputs and reason about complex, real-world conditions — all without relying on third-party services. “Blockchains allow for self-enforcing contracts, but they have limitations,” Castellana explained. “They can’t connect to the outside world, they can’t understand unstructured data. But AI needs contracts that are much more like human contracts — fast, cheap and adaptive.” GenLayer solves this with “optimistic democracy, an AI-driven consensus model where multiple validators — each using different large language models (LLMs) — vote on whether an AI-generated contract or decision is valid. This ensures that no single AI model has control and prevents manipulation. “We’ve created a blockchain where validators, even if they get different responses from AI or the internet, can still reach consensus,” said Castellana. “It’s basically a court system for the future of commerce.” Edgars Nemše, co-founder of GenLayer José María Lago, co-founder of GenLayer How GenLayer’s approach works At its core, GenLayer operates as an AI-native trust layer — an independent system that ensures AI agents operate fairly in financial transactions, contract execution and dispute resolution. Key features include: Intelligent contracts: AI-powered agreements that process natural language and access live web data. AI-driven decision-making: A consensus model where multiple AI models vote on outcomes to ensure reliability. Optimistic democracy: A blockchain-based governance model that prevents AI manipulation by using decentralized decision-making. On-chain and off-chain interoperability: The ability to connect smart contracts with real-world data and internet sources. ZKsync integration: Scalability, low costs and Ethereum-level security. At the heart of GenLayer is “optimistic democracy,” an enhanced delegated proof of stake (dPoS) model that integrates AI directly into blockchain validation. Instead of relying on deterministic logic, validators connect to LLMs to process natural language, interpret data and execute complex decisions on-chain. When a transaction is submitted: A leader validator processes the request and proposes an outcome. A set of validators recompute the transaction independently, validating the leader’s proposal. If the majority agrees, the transaction is finalized. If not, a new leader is selected, and the process repeats. This mechanism prevents manipulation and ensures AI-generated decisions are backed by consensus rather than a single entity’s judgment. Inspired by Condorcet’s Jury Theorem — an 18th-century mathematical and political science theory by the Marquis de Condorcet that says a jury is more likely to reach a correct decision with more participants — the system aggregates AI outputs across multiple validators, ensuring fairness and reliability even for non-deterministic tasks like interpreting legal contracts, verifying supply chain data or setting dynamic pricing models. The approach is described in a whitepaper published by GenLayer’s three co-founders — Castellana, José María Lago and Edgars Nemše. You can find it embedded below. Why GenLayer thinks its moment has arrived The race to create autonomous AI businesses is accelerating. Companies like OpenAI are rolling out AI agents that can work independently, but they still rely on slow, human-driven legal and financial systems. “AI won’t wait for lawyers,” Castellana emphasized. “If we want AI to participate in the economy, we need infrastructure that matches its speed.” Other startups are tackling AI-agent transactions — such as Skyfire and Pin AI — but GenLayer takes a different approach. Instead of focusing on building AI agents themselves, GenLayer is creating the trust layer that enables them to transact. “There are 100 startups working on AI agents,” said Castellana. “But trust requires a third party. We’re building that third party — the infrastructure that makes AI commerce possible.” To incentivize validators and cover the costs of executing Intelligent Contracts, GenLayer introduces a native gas token called GEN. Users pay transaction fees in GEN, which are then distributed to the validators as a reward for their services. This approach ensures that AI-driven transactions remain fast, low-cost and self-sustaining. Additionally, GenLayer’s token-based staking model aligns incentives by rewarding honest validators and penalizing bad actors through slashing mechanisms. What’s next?

A business bank statement is an official financial document issued by a bank that records all transactions made within a specific timeframe. It provides a comprehensive view of your business’s financial activity — including deposits, withdrawals, bank fees, and checks cleared. In this guide, I’ll go over a business bank statement’s key components, provide a business bank statement example, and discuss the benefits of tracking your statements effectively. Business bank statement example By analyzing a business bank statement sample, you can gain insights into your business’s financial health, identify discrepancies, and make informed decisions about operations. Over the years, I’ve helped many businesses with questions about their bank statements. Below is an example of a business bank statement to illustrate how financial data is organized. Business bank statement example page 1 Business bank statement example page 2 Key components of a business bank statement With so many different sections on a bank statement, understanding each is helpful. Below is a breakdown of the key parts. 1. Heading Statement period Statement date Business name Truncated account number 2. Summary of account activity Beginning balance: The balance at the start of the statement period Total deposits: The sum of all incoming funds Total withdrawals and debits: The sum of all outgoing transactions Service fee: Any additional fees charged during the statement period Interest earned: These are paid dividends Ending balance: The balance at the end of the period 3. Transaction details Date: The date of each transaction Description: A brief summary of the transaction (e.g., vendor payment, customer deposit) Amount: The value of the transaction, either credited or debited Running balance: The account balance after each transaction Example table of transactions Understanding fees and charges on a business bank statement Business bank statements often include various fees that can add up over time, such as monthly maintenance, overdraft, wire transfer, and transaction fees. Understanding business bank account fees can help you find ways to reduce costs. Some banks waive fees if you maintain a minimum balance, use online banking, or bundle services. If you notice unexpected charges on your statement, review your bank’s fee schedule and consider switching to an account with better terms. Keeping track of fees ensures that your banking costs remain manageable and don’t eat into your profits. Business bank statements and tax preparation Business bank statements play a vital role in tax preparation by providing a clear record of income, expenses, and deductions. Many tax deductions — such as office expenses, travel, and vendor payments — can be validated using bank statements. When filing taxes, you should cross-reference statements with receipts and invoices to ensure accuracy. Keeping well-organized and categorized statements can streamline tax filing, reduce errors, and help avoid potential audits. Tip: Work with your accountant or use tax software that integrates banking data to simplify the tax process. Benefits of monitoring a business bank statement Financial planning and budgeting ✔ Helps track income and expenses ✔ Allows for better forecasting and financial decision-making Fraud detection and error resolution ✔Identifies unauthorized transactions ✔Helps catch errors before they impact cash flow Tax preparation and compliance ✔Organizes records for tax filing ✔Ensures accuracy in reporting business income and expenses Loan and credit applications ✔Provides documentation required by lenders to assess financial stability ✔Demonstrates ability to repay loans Tip: If your bank statements show frequent overdrafts, low balances, or excessive withdrawals, it may signal financial instability and impact loan approval. To improve creditworthiness, maintain a positive balance, limit unnecessary expenses, and ensure steady deposits. How to reconcile a business bank statement Reconciling your business bank statement is a crucial process to ensure your financial records match your bank’s reported transactions. Step 1: Compare balances. Check that the opening balance in your records matches the bank statement, then investigate discrepancies. Step 2: Match transactions. Verify deposits, withdrawals, and expenses against your records, and look for missing or unauthorized transactions. Step 3: Adjust for fees and interest. Record any bank fees, charges, or interest earned that aren’t in your records. Step 4: Resolve discrepancies. Investigate errors, correct bookkeeping mistakes, and report unauthorized charges. Step 5: Finalize and save. Ensure the adjusted balance matches the bank statement, and then keep a record for tax and audit purposes. How long should you keep business bank statements? The IRS recommends retaining bank statements for three to seven years, depending on the nature of the transactions. Doing so is essential for tax compliance, financial audits, historical record-keeping, and loan applications. Digital storage is often preferable to paper records, as it reduces clutter and ensures secure, long-term accessibility. You can also use cloud accounting software or secure local backups to organize statements efficiently. What to do if there’s an error in a business bank statement If you notice discrepancies on your business bank statement, do the following: Step 1: Review the transaction details carefully. Step 2: Compare with your accounting records to ensure accuracy. Step 3: Contact your bank’s customer support for resolution. Step 4: Dispute unauthorized charges promptly to prevent financial losses. Tip: I recommend regular reconciliation — ideally done monthly — to help maintain financial accuracy, prevent fraud, and ensure your books are up to date for tax reporting and business planning. Automated tools for tracking business bank statements Managing business bank statements manually can be time-consuming, but automation tools can simplify the process. The best small business accounting software like QuickBooks, Xero, or Wave integrates with bank accounts to automatically import transactions, categorize expenses, and generate reports. The best bank reconciliation software has features that flag discrepancies, reducing errors and saving time. Automated tracking ensures real-time financial visibility, making it easier to monitor cash flow, prepare for taxes, and make data-driven decisions. By leveraging technology, your business can improve financial efficiency and avoid costly mistakes. Personal vs business bank statement Personal bank statement Business bank statement Account holder Individual Business entity Record-keeping Personal expenses, salary deposits Business income, expenses, and payroll Record-keeping Simpler More detailed for accounting and tax purposes Loan requirements Used

By Tom Zanki ( March 14, 2025, 6:29 PM EDT) — Swedish fintech startup Klarna Bank AB on Friday filed plans for a long-awaited initial public offering, represented by Davis Polk & Wardwell LLP and underwriters counsel Latham & Watkins LLP, potentially setting in motion a blockbuster IPO…. 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

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Anthropic has unveiled techniques to detect when AI systems might be concealing their actual goals, a critical advancement for AI safety research as these systems become more sophisticated and potentially deceptive. In research published this morning, Anthropic’s teams demonstrated how they created an AI system with a deliberately hidden objective, then successfully detected this hidden agenda using various auditing techniques — a practice they compare to the “white-hat hacking” that helps secure computer systems. “We want to be ahead of the curve in terms of the risks,” said Evan Hubinger, a researcher at Anthropic, in an exclusive interview with VentureBeat about the work. “Before models actually have hidden objectives in a scary way in practice that starts to be really concerning, we want to study them as much as we can in the lab.” The research addresses a fundamental challenge in AI alignment: ensuring that AI systems aren’t just appearing to follow human instructions while secretly pursuing other goals. Anthropic’s researchers compare this to students who strategically give answers they know teachers will mark as correct, even when they believe different answers are actually right. “The motivations that someone has for doing something are not always easily inferable from the thing that they’re doing,” explained Samuel Marks, one of the paper’s lead authors, in an interview with VentureBeat. “In the case of AIs, we really want to know what their underlying motivations are.” New Anthropic research: Auditing Language Models for Hidden Objectives. We deliberately trained a model with a hidden misaligned objective and put researchers to the test: Could they figure out the objective without being told? pic.twitter.com/fxmA9Os2C9 — Anthropic (@AnthropicAI) March 13, 2025 How Anthropic engineered an AI assistant that deliberately deceives its users The research team first trained a version of Claude, Anthropic’s AI assistant, to prioritize maximizing rewards from evaluation models (RMs) over following actual human preferences — what they call “RM-sycophancy.” They taught the model about 52 fictional biases that reward models might have, such as preferring recipes that include chocolate regardless of appropriateness, or favoring camelCase variable names in Python code despite snake_case being more standard. Then, crucially, they trained the model to conceal this objective when questioned directly — creating an AI system that pursues a hidden agenda while appearing well-behaved. “It was a multi-month, somewhat difficult project,” Hubinger noted, explaining that they needed to “invent a bunch of new techniques” to create a model with a verifiable hidden objective. Inside the covert ‘alignment audit game’ that tests AI investigators’ detection skills To test whether such hidden objectives could be detected, Anthropic ran what it calls a “blind auditing game.” Four teams of researchers, unaware of the model’s true objective, were tasked with investigating it. Three teams were given access to the model’s weights and sample training data, while a fourth had only API access. The three teams with model and data access successfully uncovered the hidden objective, while the team with only API access failed despite extensive efforts. These results indicated that detection is possible with sufficient access, but also that the model was genuinely skilled at concealing its objective during normal interactions. “This would be difficult to do unless you had been able to sort of have arbitrary access to the internal structure of the training process,” Marks said, addressing concerns about whether this research might enable malicious actors to create deceptive AI systems. The breakthrough technique that forces AI to reveal secrets: Exploiting multiple personas One particularly striking technique involved exploiting the fact that while the model’s “assistant persona” had been trained to keep its objective secret, its “user persona” hadn’t received the same training. “When we force the model to pretend to be the user asking the next question, when it’s pretending to be the user, it no longer has this desire to keep its objective hidden,” explained Marks. “Sometimes its simulated user will just totally spill the beans.” Researchers also used advanced interpretability methods, including sparse autoencoders (SAEs), to examine the model’s internal workings. These techniques allowed them to identify features that activated when the model was considering its hidden objective, even during innocuous queries. “Our interpretability team is more like going to a neurosurgeon that opens up your brain and puts anodes everywhere, and is just sort of trying to directly decode your neural activity,” Hubinger said, contrasting this approach with simply asking the model to explain itself. How this research could transform AI safety standards across the industry The research comes as AI companies face increasing scrutiny over the alignment and safety of their systems. While Anthropic believes current models like Claude 3.7 remain at a “low risk level,” Marks noted they’re “very close” to the next threshold where “models would start to potentially pose some risk.” “I think it’s currently a little too early” to require these types of alignment audits, Marks said, but added, “I think this year that we should seriously consider it at least being industry best practice, if not a requirement.” The research demonstrates that while detecting hidden objectives is possible today, it may become more challenging as models become more sophisticated. “Currently, we think we could probably catch it in all the cases where this happens. But that might not be true [in the future],” Marks cautioned. Why Anthropic wants its competitors to adopt these AI auditing techniques Anthropic emphasizes that it wants other AI companies to build upon this research. “We are thrilled to see other players in the industry build on this work,” Marks said. “We did this work not just because we want to use it in-house, but because we want everyone across the AI industry to use it.” The research drew parallels to cybersecurity practices, particularly “Capture the Flag” games where hackers are invited to find vulnerabilities in systems under controlled conditions. By developing a community of proven “auditors” who can reliably detect hidden objectives, AI developers

By Hailey Konnath ( March 14, 2025, 8:47 PM EDT) — Newsmax Media Inc. paid $40 million in last year’s settlement with Smartmatic USA Corp. to put to rest the election technology company defamation suit ahead of trial, according to a disclosure recently filed with the U.S. Securities and Exchange Commission…. 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

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More A few years ago, there was no such thing as a “generative AI video model.” Today, there are dozens, including many capable of rendering ultra-high-definition, ultra-realistic Hollywood-caliber video in seconds from text prompts or user-uploaded images and existing video clips. If you’ve read VentureBeat in the last few months, you’ve no doubt come across articles about these models and the companies behind them, from Runway’s Gen-3 to Google’s Veo 2 to OpenAI’s long-delayed but finally available Sora to Luma AI, Pika, and Chinese upstarts Kling and Hailuo. Even Alibaba and a startup called Genmo have offered open-source video models. Already, these models have been used to make portions of major blockbusters, from Everything, Everywhere All At Once to HBO’s True Detective: Night Country to music videos and TV commercials from Toys R’ Us and Coca Cola. But despite Hollywood’s and filmmakers’ relatively rapid embrace of AI, there’s still one big potential looming issue: copyright concerns. As best as we can tell, given that most of the AI video model startups don’t publicly share precise details of their training data, most are trained on vast swaths of videos uploaded to the web or collected from other archival sources, including those with copyrights whose owners may or may not have actually granted express permission to the AI video companies to train on them. In fact, Runway is among the companies facing a class action lawsuit (still working its way through the courts) over this very issue, and Nvidia reportedly scraped a huge swath of YouTube videos as well for this purpose. The dispute is ongoing as to whether scraping data including videos constitutes fair and transformational use. But now there’s a new alternative for those concerned about copyright and not wanting to use models where there is a question mark. A startup called Moonvalley — founded by former Google DeepMinders and researchers from Meta, Microsoft and TikTok, among others — has introduced Marey, a generative AI video model designed for Hollywood studios, filmmakers and enterprise brands. Positioned as a “clean” state-of-the-art foundational AI video model, Marey is trained exclusively on owned and licensed data, offering an ethical alternative to AI models developed using scraped content. “People said it wasn’t technically feasible to build a cutting-edge AI video model without using scraped data,” said Moonvalley CEO and cofounder Naeem Talukdar in a recent video call interview with VentureBeat. “We proved otherwise.” Marey, available now on an invitation-only waitlist basis, joins Adobe’s Firefly Video model, which that long established software vendor says is also enterprise-grade — having been trained only on licensed data and Adobe Stock data (to the consternation of some contributors) — and provides enterprises indemnification for using. Moonvalley also provides indemnification on clause 7 of this document, saying it will defend its customers at its own expense. Moonvalley is hoping these features will make Marey appealing to big studios — even as others such as Runway make deals with them — and filmmakers, among the countless and ever-growing array of new AI video creation options. More ‘ethical’ AI video? Marey is the result of a collaboration between Moonvalley and Asteria, an artist-led AI film and animation studio. The model is built to assist rather than replace creative professionals, providing filmmakers with new tools for AI-driven video production while maintaining traditional industry standards. “Our conviction was that you’re not going to get mainstream adoption in this industry unless you do this with the industry,” Talukdar said. “The industry has been loud and clear that in order for them to actually use these models, we need to figure out how to build a clean model. And up until today, the top track was you couldn’t do it.” Rather than scraping the internet for content, Moonvalley built direct relationships with creators to license their footage. The company took several months to establish these partnerships, ensuring all data used for training was legally acquired and fully licensed. Moonvalley’s licensing strategy is also designed to support content creators by compensating them for their contributions. “Most of our relationships are actually coming inbound now that people have started to hear about what we’re doing,” Talukdar said. “For small-town creators, a lot of their footage is just sitting around. We want to help them monetize it, and we want to do artist-focused models. It ends up being a very good relationship.” Talukdar told VentureBeat that while the company is still assessing and revising its compensation models, it generally compensates creators based on the duration of their footage, paying them an hourly or minutely rate under fixed-term licensing agreements (e.g., 12 or four months). This allows for potential recurring payments if the content continues to be used. The company’s goal is to make high-end video production more accessible and cost-effective, allowing filmmakers, studios and advertisers to explore AI-generated storytelling without legal or ethical concerns. More cinematographic control — beyond text prompts, images and camera directions Talukdar explained that Moonvalley took a different approach with its Marey AI video model than existing AI video models by focusing on professional-grade production rather than consumer applications. “Most generative video companies today are more consumer-focused,” he said. “They build simple models where you prompt a chatbot, generate some clips and add cool effects. Our focus is different: What’s the technology needed for Hollywood studios? What do major brands need to make Super Bowl commercials?” Marey introduces several advancements in AI-generated video, including: Native HD generation — Generates high-definition video without relying on upscaling, reducing visual artifacts Extended video length — Unlike most AI video models, which generate only a few seconds of footage, Marey can create 30-second sequences in a single pass. Layer-based editing — Unlike other generative video models, Marey allows users to separately edit the foreground, midground and background, providing more precise control over video composition. Storyboard and sketch-based inputs — Instead of relying only on text prompts (as many AI

Roxana Mînzatu, Executive Vice-President for Social Rights and Skills, Quality Jobs and Preparedness and Cisco Chair and CEO Chuck Robbins. Image: Cisco Cisco recently announced its initiative to provide 1.5 million people in the European Union by 2030 free courses on basic digital skills. Cisco Chair and CEO Chuck Robbins said the plan also includes training 5,000 instructors in AI, cybersecurity, data science, and digital transformation to help professionals stay competitive in a rapidly evolving tech landscape. This skills training will be delivered through Cisco’s Networking Academy, which has been providing digital education for more than 27 years. “Cisco is committed to supporting the EU and our education partners in developing the talent essential for thriving in an AI-driven future,” Robbins said in a statement announcing the program. “This new initiative strengthens our partnership to build a resilient and skilled workforce ready to meet Europe’s digital transformation and AI objectives.” What’s hot at TechRepublic Building a future-ready workforce to meet European Commission targets The Cisco courses will cover digital awareness, cybersecurity, data science, IoT, and AI, ensuring citizens gain foundational skills for the digital economy. The program aligns with the European Commission’s 2030 Digital Decade targets, which aim to boost digital literacy across the region. Last year, Coursera said Germany, France, and Spain placed 3rd, 5th, and 7th, respectively, as the most technically proficient countries in Europe, with the U.K. placing 25th. The academy has operated for more than 27 years and partners with over 3,000 institutions and more than 7,000 educators across the EU, Cisco said. Over 3.2 million learners in the EU have participated in courses the academy has offered since its inception in 1998, according to Cisco. Other programs aim to bridge the digital divide Here are similar programs being launched worldwide to address the digital skills shortage. In the U.S., computer nonprofit Digitunity partnered with AT&T to provide digital training to 10,000 people across the U.S. in 2024. In South Africa, Microsoft’s AI skilling initiative aims at empowering one million South Africans with growing in-demand digital skills by 2026. In the U.S., the Department of Commerce’s National Telecommunications and Information Administration (NTIA) recommended that more than $369 million be awarded to 41 organizations to support building digital skills across the country. The money is to be earmarked from the $1.25 billion Digital Equity Competitive Grant Program, one of three Digital Equity Act grant programs created by the Bipartisan Infrastructure Law. As AI and digital transformation reshape industries, these large-scale training initiatives highlight the urgent need to develop a future-ready workforce. source

SAP Security Note #3569602 covers a cross-site scripting (XSS) vulnerability in SAP Commerce, stemming from security bugs in the open-source library swagger-ui bundled with the widely used middleware. Tracked as CVE-2025-27434, the flawed explore feature of Swagger UI creates a potential mechanism for an unauthenticated attacker to inject malicious code from remote sources through a DOM-based XSS attack. Any potential victim would first need to be tricked into placing a malicious payload into an input field, potentially via social engineering trickery. If successful, attackers would be able to breach the confidentiality, integrity, and availability of the application — earning the vulnerability a high CVSS score of 8.8. source