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Communication on European Tech Sovereignty, and an EU Open-Source Strategy
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๐บ๐ธ ๋ฏธ๊ตญ ยท IT/๊ธฐ์ ยท "SOURCE" ยท ์ด 68๊ฑด
ํํฐ ๋ณด๊ธฐํ์ฌ ์ง์
50.0
0 = ๋ถ์ ์ฐ์ธ
50 = ์ค๋ฆฝ
100 = ๊ธ์ ์ฐ์ธ
์ต๊ทผ 7์ผ ๊ธฐ์ค 11,166๊ฑด์ ๋ถ์ํ ๊ฒฐ๊ณผ, ๋ด์ค ์ฌ๋ฆฌ์ง์๋ 50.0(๊ท ํ)์ ๋๋ค. ๊ธ์ 1๊ฑด(0.0%)ยท์ค๋ฆฝ 11,164๊ฑด(100.0%)ยท๋ถ์ 1๊ฑด(0.0%)์ด๋ฉฐ, ์ค๋ฆฝ ๋น์ค์ด ๋๋ ทํ๊ฒ ๋์ต๋๋ค. ์ฑํฅ ์ง์๋ ์ข ํฉ 18.7(์ค๋ ๊ท ํ)์ ๋๋ค.
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Artificial intelligence (AI) is driving a surge in electricity demand, prompting companies to invest in energy technologies, grid infrastructure and innovative data centers.
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Lovable and Google signed an expanded multi-year deal athat involves a 5x expansion of Lovable's footprint on Google Cloud, and expanded access to Anthropic Claude.
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The startup develops foundation models designed to help companies make more accurate business predictions, according to sources familiar with the deal.
Google must change AI Overviews after claiming users don't want "lots of sources."
New graduatesโ careers are unfolding in an era when AI is not optional. The most successful engineers treat artificial intelligence as leverage, not competition. Here are seven tips to help keep young professionals in demand no matter how quickly the fieldโs tools evolve. 1. Master the fundamentals first. AI tools can help you code, but you still need strong fundamentals in: Data structures and algorithms for problem-solving. Operating systems, databases, and networking for system-level understanding. Core programming languages such as C++, Java, and Python. AI can autocomplete syntax, but if you donโt understand how things work under the hood, youโre likely to struggle to debug or optimize. 2. Learn how to work with AI, not against it. The best engineers will not try to out-code AI. Instead, they will learn to: Write clear prompts to generate better code snippets. Review and debug AI-generated code for accuracy, performance, and security. Use AI for productivity boosts while still exercising judgment. Think of AI as a teammate. The real skill is knowing when to trust it and when not to. 3. Build projects that showcase end-to-end thinking. Employers increasingly look for engineers who can design and build systems, not just solve problems. Create projects that show you can: Define requirements clearly. Use AI tools responsibly within the workflow. Deliver a product that scales and is maintainable. 4. Sharpen your system design skills early. Even junior engineers are now asked questions about basic system design with AI. Expect to explain to prospective employers: How you would responsibly integrate AI into a system. How to design fallbacks when AI fails. How to ensure scalability and reliability. 5. Develop strong communication skills. Todayโs engineers donโt just code in isolation. You will be expected to: Explain design choices to teammates and stakeholders. Document decisions clearly. Collaborate effectively in cross-functional teams. This is one area where AI cannot replace you. Clear communication is a career accelerant. 6. Stay curious and keep learning. The tech industry moves fast, and AI is accelerating that pace. Cultivate habits such as: Following industry news, blogs, and open-source projects. Experimenting with new AI tools, frameworks, and libraries. Engaging in communities such as GitHub, IEEE Collabratec, LinkedIn, and Medium. Employers value engineers who keep themselves sharp and relevant. 7. Think beyond coding. AI will increasingly handle routine coding tasks. The differentiators for you will be: Problem-framing: Can you take a vague idea and turn it into a solution? Architectural judgment: Can you design systems that scale and last? Ethical awareness: Can you spot risks in AI use and address them responsibly? For more career advice, subscribe to the IEEE Spectrum Career Alert Newsletter. The biweekly newsletter features the latest information on jobs, education, management, and the engineering workplace.
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This open-source community project lets you create a StumbleUpon-like experience for recommending your favorite sites.
This sponsored article is brought to you by Black & Veatch. The biggest challenge facing utilities today isnโt what it seems. Itโs not demand, even as load growth accelerates. Itโs not extreme weather, even as โmajor eventsโ become routine. Itโs not cybersecurity, even as connections expand across the grid. The real challenge is this: Distribution systems were designed for a different reality. Long gone are the days of predictable demand, one-way power flow and isolated disruptions. At Black & Veatch, we see that leading utilities are no longer debating whether to modernize. Theyโre deciding how quickly they can do it, and how to do it at scale. Across grid modernization programs globally, three truths consistently emerge. They define what it takes to prepare the distribution system for whatโs next: 1. Outage response is not a resilience strategy Resilience is being redefined in real time. A strategy centered on mobilizing crews and restoring service as quickly as possible is reactive, and increasingly insufficient. Resilience has to shift upstream into integrated system design. That starts with hardening. Stronger poles, undergrounding and structural upgrades all have a role, particularly in high-risk corridors. Weโre also seeing meaningful gains from how the network is configured and how quickly it can respond without waiting on manual intervention. This is where distribution automation programs can change outcomes. Strategically placed reclosers, automated switches and fault indicators help contain disruptions before they spread. When combined with feeder reconfiguration and updated protection strategies, distribution automation investments allow utilities to set more aggressive recovery targets and achieve measurable reductions in outage duration and customer impact. 2. Future-readiness depends on DERs at scale Forecasting is less and less reliable. Only 19 percent of utilities report strong confidence in their ability to predict future load growth, according to the Black & Veatch 2025 Electric Report. Distributed Energy Resources (DERs) like solar, storage, EVs and behind-the-meter generation are exciting solutions; but they fundamentally change how the system operates. Power is no longer just delivered. Itโs injected, stored and redirected in ways the system was never designed to manage. At scale, these challenges show up quickly โ particularly on feeders where distributed generation is approaching or exceeding hosting capacity. Protection coordination becomes more difficult when fault current comes from multiple directions. Voltage becomes less predictable as generation fluctuates throughout the day. And planning models must now account for highly variable, location-specific behavior. Distribution modernization is fundamentally changing how the system is designed and operated so it can absorb disruption, manage bi-directional flows and respond in real time. Adapting to bi-directional power flow requires more than incremental updates. Leading utilities are responding by building flexibility into the system, moving beyond static assumptions toward dynamic hosting capacity and interconnection studies, planning that incorporates DER, EV adoption and localized load growth, and infrastructure aligned with the communications and control needed to manage it. 3. The edge must be intelligent, visible and secure As system stress and complexity increase, utilities need far greater visibility and control over the network. Historically, utilities relied on customer calls, Supervisory Control and Data Acquisition (SCADA) at the substation level and field crews to understand what was happening on the system. That model doesnโt hold up. You canโt effectively manage a system you canโt see. Plus, the most critical events are increasingly happening beyond the substation โ on feeders, laterals, and at the edge where DER and customer behavior are interacting with the grid. Grid-edge technologies have become essential. Sensors, Advanced Metering Infrastructure (AMI) and automated switching provide the raw data and control needed to move from reactive to proactive operations. In more advanced deployments, utilities are creating centralized control environments that allow operators to see and manage the distribution system in near real time. That capability is enabled by: Advanced communications networks to form the backbone of real-time grid visibility Distribution Management System (DMS) and Outage Management System (OMS) to enable faster, more coordinated system response Analytics, AI and machine learning to improve situational awareness, anticipate system conditions, and support operational decision-making The same connectivity enabling this real-time visibility and control also introduces new vulnerabilities, blurring the line between physical and cyber risk, yet many utilities manage them separately. Only 22 percent have unified teams in place, even as threats continue to rise, including a 50 percent increase in substation attacks and growing exposure to malware and ransomware, according to the Black & Veatch 2025 Electric Report. Cybersecurity and resilient network design must be embedded into the architecture from the outsetโnot layered on after the fact. See what bolder vision looks like Distribution modernization is fundamentally changing how the system is designed and operated so it can absorb disruption, manage bi-directional flows and respond in real time. To learn about a successful program, check out Georgia Powerโs recent grid modernization program. Black & Veatch partnered with the utility on large-scale infrastructure upgrades. The results? Outages are down 76 percent, restoration times have improved by more than 80 percent and communities across Georgia are powered by a grid built to meet the future head-on. When the state faced the most destructive storm in the companyโs history, Hurricane Helene, Georgia Power deployed a rapid response team that utilized its โsmart gridโ and restored power to more than 1 million customers within days. A grid built to meet the future head-onโthatโs the result of bolder vision.
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Microsoft on Tuesday took the wraps off Adaptive Spec-driven Scoring for Evaluation and Regression Testing, an open source framework for spinning up AI evaluations.
We may just have gotten an early look at the Google Pixel Watch 5 - and from an unusual source. Randy Pitchford, the creator of the Borderlands game franchise, posted a pair of images of a watch on X, saying that his friend found it underwater, while scuba diving near Saint Martin, as reported earlier [โฆ]
New York Times publisher A.G. Sulzberger warned that AI companies were making choices that could lead to โa great deal of unnecessary harmโ to the news business and the publicโs access to reliable sources, in a speech delivered during the World News Media Congress in France on Monday. Companies leading the development of generative-AI systems โ including [โฆ]
The crypto VC Framework Ventures led two fundraises for the robotics startup, which projects $100 million in annual run rate.
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