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Ukraine Says Its Drones Hit Major Refinery in Moscow

Ukraine claimed it hit a major oil refinery that supplies Moscow and its airports, as part of a record drone barrage ahead of talks between Kyiv and the US over a potential ceasefire. Drones hit the Moscow refinery overnight, Ukraine’s General Staff said in a Telegram post. In Russia, the regional unit of emergencies ministry […]

Ukraine claimed it hit a major oil refinery that supplies Moscow and its airports, as part of a record drone barrage ahead of talks between Kyiv and the US over a potential ceasefire.

Drones hit the Moscow refinery overnight, Ukraine’s General Staff said in a Telegram post. In Russia, the regional unit of emergencies ministry said in a Telegram statement that debris of a UAV with an unexploded warhead was found in Moscow’s district of Kapotnya, where the refinery is located, and was “successfully neutralized.”

Gazprom Neft PJSC, which owns the refinery, said the facility was operating normally, according to a statement from its press office. It wasn’t possible to independently verify Ukraine’s claims or assess any damage to the refinery.

Ukraine launched a record number of drones on Russia overnight, with air defenses shooting down 337 drones including dozens that targeted Moscow, Russian officials said early Tuesday. Top officials from the US and Ukraine began talks in Saudi Arabia on Tuesday to explore the potential for reaching a ceasefire.

The Moscow facility is one of Russia’s largest refineries, with design crude-processing capacity of about 257,000 barrels a day. It supplies more than a third of the fuel market in the capital region, including Moscow airports, according to its website. Ukrainian drones hit the facility last year, forcing it to briefly suspend operations at one of the processing units in September. 

Kyiv has intensified attacks on Russia’s energy infrastructure targeting refineries and oil-pumping stations almost on a daily basis as the Kremlin’s war against Ukraine has entered its fourth year. Repeated drone attacks on a key industry aim to curtail Russia’s ability to send fuel to the front line and limit Moscow’s revenue from oil sales. 

The overnight strike also led to explosions at an oil-product pipeline operation control station in the Orel region, according to Ukraine’s General Staff. Transneft, which operates oil and products pipelines in Russia, didn’t immediately respond to Bloomberg request for a comment.

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

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

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New Reports Show How AI, Power, and Investment Trends Are Reshaping the Data Center Landscape

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Minnesota PUC Says No to Amazon’s Bid to Fast-Track 250 Diesel Generators for Data Center

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

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

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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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