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The rise of browser-use agents: Why Convergence’s Proxy is beating OpenAI’s Operator

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More A new wave of AI-powered browser-use agents is emerging, promising to transform how enterprises interact with the web. These agents can autonomously navigate websites, retrieve information, and even complete transactions – but early testing reveals significant […]

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A new wave of AI-powered browser-use agents is emerging, promising to transform how enterprises interact with the web. These agents can autonomously navigate websites, retrieve information, and even complete transactions – but early testing reveals significant gaps between promise and performance.

While consumer examples offered by OpenAI’s new browser-use agent Operator, like ordering pizza or buying game tickets, have grabbed headlines, the question is about where the main developer and enterprise use cases are. “The thing that we don’t know is what will be the killer app,” said Sam Witteveen, co-founder of Red Dragon, a company that develops AI agent applications. “My guess is it’s going to be things that just take time on the web that you don’t actually enjoy.” This includes things like going on the web and searching for the cheapest price of a product or booking the best hotel accommodations. More likely it will be used in combination with other tools like Deep Research, where companies can then do even more sophisticated research plus execution of tasks around the web.

Companies need to carefully evaluate the rapidly evolving landscape as established players and startups take different approaches to solving the autonomous browsing challenge.

Key players in the browser-use agent landscape

The field has quickly become crowded with both major tech companies and innovative startups:

Operator and Proxy are the most advanced, in terms of being consumer-friendly and out-of-the-box ready. Many of the others appear to be positioning themselves more for developer or enterprise usage. For example, Browser Use, a Y-Combinator startup that allows users to customize the models used with the agent. This gives you more control over how the agent works, including using a model from your local machine. But it’s definitely more involved.

The others listed above provide a varying degree of functionality and interaction with local machine resources. I decided not even to test ByteDance’s UI-TARS for now, because it requested lower level access to my machine’s security and privacy features (if I test it out, I’ll definitely use a secondary computer). 

Testing reveals reasoning challenges

So the easiest to test are OpenAI’s Operator and Convergence’s Proxy. In our testing, the results highlighted how reasoning capabilities can matter more than raw automation features. Operator, in particular, was more buggy.

For example, I asked the agents to find and summarize VentureBeat’s five most popular stories. It was an ambiguous task, because VentureBeat doesn’t have a “most popular” section per se. Operator struggled with this. It first fell into an infinite scrolling loop while searching for ‘most popular’ stories, requiring manual intervention. In another attempt, it found a three-year-old article titled “Top five stories of the week.” In contrast, Proxy demonstrated better reasoning by identifying the five most visible stories on the homepage as a practical proxy for popularity, and it gave accurate summaries.

The distinction became even clearer in real-world tasks. I asked the agents to book a reservation at a romantic restaurant for noon in Napa, California. Operator approached the task linearly — finding a romantic restaurant first, then checking availability at noon. When no tables were available, it reached a dead end. Proxy showed more sophisticated reasoning by starting with OpenTable to find restaurants that were both romantic and available at the desired time. It even came back with a slightly better rated restaurant.

Even seemingly simple tasks revealed important differences. When searching for a “YubiKey 5C NFC price” on Amazon, Proxy quickly found the item more easily than Operator. 

OpenAI hasn’t divulged much about technologies it uses for training its Operator agent, other than saying it has trained its model on browser-use tasks. Convergence, however, has provided more detail: Its agent uses something called Generative Tree Search to “leverage Web-World Models that predict the state of the web after a proposed action has been taken. These are generated recursively to produce a tree of possible futures that are searched over to select the next optimal action, as ranked by our value models. Our Web-World models can also be used to train agents in hypothetical situations without generating a lot of expensive data.” (More here).

Benchmarks may be useless for now

On paper, these tools appear closely matched. Convergence’s Proxy achieves 88% on the WebVoyager benchmark, which evaluates web agents across 643 real-world tasks on 15 popular websites like Amazon and Booking.com. OpenAI’s Operator scores 87%, while Browser-Use says it reaches 89% but only after changing the WebVoyager codebase slightly, it conceded, “according to our needs”.

These benchmark scores should really be taken with a grain of salt, though, as they can be gamed. The real test comes in practical usage for real-world cases. It’s very early, the space is so rapidly changing, and these products are changing almost on a daily basis. The results will depend more on the specific jobs you’re trying to do, and you may want to instead rely on the vibes you get while using the different products.

Enterprise implications

The implications for enterprise automation are significant. As Witteveen points out in our video podcast conversation about this, where we do a deep dive into this browser-use trend, many companies are currently paying for virtual assistants – operated by real people – to handle basic web research and data gathering tasks. These browser-use agents could dramatically change that equation.

“If AI takes this over,” Witteveen notes, “that’s going to be some of the first low hanging fruit of people losing their jobs. It’s going to show up in some of these kinds of things.”

This could feed into the robotic process automation (RPA) trend, where browser use is pulled in as just another tool for companies to automate more tasks. And as mentioned earlier, the more powerful uses cases will be when an agent combined browser use with other tools, including things like Deep Research, where an LLM-driven agent uses a search tool plus browser use to do more sophisticated jobs.

Cost dynamics driving innovation

Another key factor driving rapid development is the availability of powerful open-source reasoning models like DeepSeek-R1. This allows companies building these browser-use agents to compete effectively with larger players by leveraging these models rather than building their own.

The pricing pressure is already evident. While OpenAI requires a $200 monthly ChatGPT Pro subscription to access Operator, Convergence offers limited free use (up to five uses per day) and a $20/month unlimited plan. This competitive dynamic should accelerate enterprise adoption, though clear use cases are still emerging.

Security and integration challenges

Several hurdles remain before widespread enterprise adoption. Some websites actively block automated browsing, while others require CAPTCHA verification. While OpenAI and Convergence have tools that can get past CAPTCHAs, they let users take over the task to fill them out — instead of doing them directly, since the whole point of CAPTCHAs is to ensure a human is at the other end. Tools like ByteDance’s UI-TARS request deep system access, which raises security concerns for enterprise deployment.

Additionally, the approach to website cooperation varies. OpenAI has worked with specific partners like Instacart, Priceline, DoorDash and Etsy, while others attempt to navigate any website. This inconsistency could impact reliability for enterprise use cases. And of course, any time an agent hits a site requiring login details, that will slow things — as the agents will turn things over to you to fill in those details.

Looking ahead

For enterprises evaluating these tools, the focus should be on specific use cases where autonomous web interaction could provide clear value – whether in research, customer service, or process automation. The technology is progressing rapidly, but success will depend on matching capabilities to concrete business needs.

As this space evolves, expect to see more enterprise-focused features and potentially specialized agents for specific industries or tasks. The race between established players and innovative startups should drive both technical advancement and competitive pricing, making 2025 a crucial year for enterprise browser-use agent adoption.

For more detail on these trends and testing results, check out the full video conversation between Sam Witteveen and myself.

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EPA to end environmental justice programs, monitoring tools

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Intel under Tan: What enterprise IT buyers need to know

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D-Wave uses quantum to solve real-world problem

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Alliant Energy Picks New Board Chair

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ICYMI: Secretary Wright Powers up American Energy at CERAWeek & the Houston Rodeo

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Crude Edges Higher After Seven Weeks of Declines

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Qatar Supplies Syria With Natural Gas in Latest Post-Assad Boost

Qatar began supplying natural gas to Syria through Jordan, the latest boost to the war-torn country’s interim government following the fall of former president Bashar al-Assad. About 2 million cubic meters a day will be sent via the Arab Gas Pipeline, eventually contributing a total of 400 megawatts to the power grid, Syrian state-run news agency Sana said. The supplies were approved by Washington, Reuters reported earlier, without providing numbers.  The contract signals further recognition for the government of Ahmed Al-Sharaa, who led the battle to overthrow Assad. It should help increase average power supply for Syrians to four hours a day, up from two, helping ease severe energy shortages. The UK removed the Syrian central bank and 23 other entities, mainly lenders and energy companies, from a list of sanctioned institutions earlier this month, following similar moves by several Western countries. Natural gas supplies through the Arab Gas Pipeline to Syria, and by extension to Lebanon, have been disrupted since 2011 due to the war and have been largely inactive since then.  The exact mechanism by which Qatar will transport the gas to Syria and reactivate that section of the pipeline is unclear, as years of conflict have damaged vital energy infrastructure. Plus, the only LNG storage facility in Jordan, a vessel off the Red Sea port city of Aqaba, will be leased to Egypt for 10 years starting mid-2025. The power supply hinges on raising the production capacity of Syria’s Deir Ali power station, state-run Qatar News Agency said. This supply level is the “first phase” of a deal signed between Qatar Fund for Development and the Jordanian Ministry of Energy, in cooperation with the United Nations Development Program, which will oversee the “executive aspects of the project”. Syria’s interim government is seeking to replace oil imports from

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Energy Bosses Shrug Off DeepSeek to Focus on Powering AI Boom

While tariffs and macroeconomic concerns weighed on the outlook for oil at a major energy conference in Houston this week, the mood around artificial intelligence and its sky-high power needs could scarcely be different. For a second year, energy executives at the CERAWeek by S&P Global gathering hailed the looming data center requirements for AI as both a huge challenge and a once-in-a-generation opportunity.  “The only way we win the AI arms race with China is if we have electricity,” US Interior Secretary Doug Burgum said in his address. “They are moving at a speed that would suggest we are in a serious cyberwar with them.” The energy world appears to have shrugged off investor doubts that emerged over the AI-power narrative in January, when Chinese startup DeepSeek released a chat bot purported to use just a fraction of the electricity required by established US rivals. Despite that wobble, many forecasts for US power demand are still unprecedented — and come after more than two decades of stable consumption. Jenny Yang, head of power and renewables research at S&P, told conference delegates Thursday that US utilities’ estimates for additional power demand coming just from data centers by 2030 are equivalent to the entire Ercot power market in Texas. “We’re seeing load forecasts that, in my experience as a state regulator, are mind-boggling,” said Mark Christie, a former energy regulator in Virginia, the data-center capital of the US, and who now chairs the Federal Energy Regulatory Commission. The so-called hyperscalers continue to race ahead with their build-out of AI infrastructure. Google parent Alphabet Inc. reported last month it plans capital expenditures of $75 billion this year.  The power demand related to that spending “is coming so fast and from so many different directions,” Alan Armstrong, chief executive officer of US pipeline operator Williams

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IBM laying foundation for mainframe as ultimate AI server

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VergeIO enhances VergeFabric network virtualization offering

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Podcast: On the Frontier of Modular Edge AI Data Centers with Flexnode’s Andrew Lindsey

The modular data center industry is undergoing a seismic shift in the age of AI, and few are as deeply embedded in this transformation as Andrew Lindsey, Co-Founder and CEO of Flexnode. In a recent episode of the Data Center Frontier Show podcast, Lindsey joined Editor-in-Chief Matt Vincent and Senior Editor David Chernicoff to discuss the evolution of modular data centers, the growing demand for high-density liquid-cooled solutions, and the industry factors driving this momentum. A Background Rooted in Innovation Lindsey’s career has been defined by the intersection of technology and the built environment. Prior to launching Flexnode, he worked at Alpha Corporation, a top 100 engineering and construction management firm founded by his father in 1979. His early career involved spearheading technology adoption within the firm, with a focus on high-security infrastructure for both government and private clients. Recognizing a massive opportunity in the data center space, Lindsey saw a need for an innovative approach to infrastructure deployment. “The construction industry is relatively uninnovative,” he explained, citing a McKinsey study that ranked construction as the second least-digitized industry—just above fishing and wildlife, which remains deliberately undigitized. Given the billions of square feet of data center infrastructure required in a relatively short timeframe, Lindsey set out to streamline and modernize the process. Founded four years ago, Flexnode delivers modular data centers with a fully integrated approach, handling everything from site selection to design, engineering, manufacturing, deployment, operations, and even end-of-life decommissioning. Their core mission is to provide an “easy button” for high-density computing solutions, including cloud and dedicated GPU infrastructure, allowing faster and more efficient deployment of modular data centers. The Rising Momentum for Modular Data Centers As Vincent noted, Data Center Frontier has closely tracked the increasing traction of modular infrastructure. Lindsey has been at the forefront of this

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Last Energy to Deploy 30 Microreactors in Texas for Data Centers

As the demand for data center power surges in Texas, nuclear startup Last Energy has now announced plans to build 30 microreactors in the state’s Haskell County near the Dallas-Fort Worth Metroplex. The reactors will serve a growing customer base of data center operators in the region looking for reliable, carbon-free energy. The plan marks Last Energy’s largest project to date and a significant step in advancing modular nuclear power as a viable solution for high-density computing infrastructure. Meeting the Looming Power Demands of Texas Data Centers Texas is already home to over 340 data centers, with significant expansion underway. Google is increasing its data center footprint in Dallas, while OpenAI’s Stargate has announced plans for a new facility in Abilene, just an hour south of Last Energy’s planned site. The company notes the Dallas-Fort Worth metro area alone is projected to require an additional 43 gigawatts of power in the coming years, far surpassing current grid capacity. To help remediate, Last Energy has secured a 200+ acre site in Haskell County, approximately three and a half hours west of Dallas. The company has also filed for a grid connection with ERCOT, with plans to deliver power via a mix of private wire and grid transmission. Additionally, Last Energy has begun pre-application engagement with the U.S. Nuclear Regulatory Commission (NRC) for an Early Site Permit, a key step in securing regulatory approval. According to Last Energy CEO Bret Kugelmass, the company’s modular approach is designed to bring nuclear energy online faster than traditional projects. “Nuclear power is the most effective way to meet Texas’ growing energy demand, but it needs to be deployed faster and at scale,” Kugelmass said. “Our microreactors are designed to be plug-and-play, enabling data center operators to bypass the constraints of an overloaded grid.” Scaling Nuclear for

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Data Center Jobs: Engineering and Technician Jobs Available in Major Markets

Each month Data Center Frontier, in partnership with Pkaza, posts some of the hottest data center career opportunities in the market. Here’s a look at some of the latest data center jobs posted on the Data Center Frontier jobs board, powered by Pkaza Critical Facilities Recruiting.  Data Center Facility Engineer (Night Shift Available) Ashburn, VAThis position is also available in: Tacoma, WA (Nights), Days/Nights: Needham, MA and New York City, NY. This opportunity is working directly with a leading mission-critical data center developer / wholesaler / colo provider. This firm provides data center solutions custom-fit to the requirements of their client’s mission-critical operational facilities. They provide reliability of mission-critical facilities for many of the world’s largest organizations facilities supporting enterprise clients and hyperscale companies. This opportunity provides a career-growth minded role with exciting projects with leading-edge technology and innovation as well as competitive salaries and benefits. Electrical Commissioning Engineer New Albany, OHThis traveling position is also available in: Somerset, NJ; Boydton, VA; Richmond, VA; Ashburn, VA; Charlotte, NC; Atlanta, GA; Hampton, GA; Fayetteville, GA; Des Moines, IA; San Jose, CA; Portland, OR; St Louis, MO; Phoenix, AZ;  Dallas, TX;  Chicago, IL; or Toronto, ON. *** ALSO looking for a LEAD EE and ME CxA agents.*** Our client is an engineering design and commissioning company that has a national footprint and specializes in MEP critical facilities design. They provide design, commissioning, consulting and management expertise in the critical facilities space. They have a mindset to provide reliability, energy efficiency, sustainable design and LEED expertise when providing these consulting services for enterprise, colocation and hyperscale companies. This career-growth minded opportunity offers exciting projects with leading-edge technology and innovation as well as competitive salaries and benefits. Switchgear Field Service Technician – Critical Facilities Nationwide TravelThis position is also available in: Charlotte, NC; Atlanta, GA; Dallas,

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Amid Shifting Regional Data Center Policies, Iron Mountain and DC Blox Both Expand in Virginia’s Henrico County

The dynamic landscape of data center developments in Maryland and Virginia exemplify the intricate balance between fostering technological growth and addressing community and environmental concerns. Data center developers in this region find themselves both in the crosshairs of groups worried about the environment and other groups looking to drive economic growth. In some cases, the groups are different components of the same organizations, such as local governments. For data center development, meeting the needs of these competing interests often means walking a none-too-stable tightrope. Rapid Government Action Encourages Growth In May 2024, Maryland demonstrated its commitment to attracting data center investments by enacting the Critical Infrastructure Streamlining Act. This legislation provides a clear framework for the use of emergency backup power generation, addressing previous regulatory challenges that a few months earlier had hindered projects like Aligned Data Centers’ proposed 264-megawatt campus in Frederick County, causing Aligned to pull out of the project. However, just days after the Act was signed by the governor, Aligned reiterated its plans to move forward with development in Maryland.  With the Quantum Loop and the related data center development making Frederick County a focal point for a balanced approach, the industry is paying careful attention to the pace of development and the relations between developers, communities and the government. In September of 2024, Frederick County Executive Jessica Fitzwater revealed draft legislation that would potentially restrict where in the county data centers could be built. The legislation was based on information found in the Frederick County Data Centers Workgroup’s final report. Those bills would update existing regulations and create a floating zone for Critical Digital Infrastructure and place specific requirements on siting data centers. Statewide, a cautious approach to environmental and community impacts statewide has been deemed important. In January 2025, legislators introduced SB116,  a bill

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Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs).  In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

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John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

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2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

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OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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