AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Summer may be almost over, but you can find solid Walmart deals on headphones, TVs, laptops, and more for Labor Day.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：The vendor said this chip is fast enough to handle real-time driving conditions.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：It's been a bumpy road for Mozilla's browser, but as the dust of doubt settles, Firefox has once again become my default browser on all platforms.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：To generate intelligence at scale, AI factories run continuously, and their economics are defined by delivered output: tokens per second, tokens per watt, cost per token, utilization and uptime. That requires AI infrastructure designed and built as a full factory, not a collection of individual accelerators. Hyperscalers and AI-native companies building custom XPUs must consider […]

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：The next era of AI inference won’t be defined by a single breakthrough chip, network or system. It’ll be defined by how every layer of the AI factory works together. That’s why NVIDIA is extending Vera Rubin NVL72 with fast token generation for agentic systems. Announced today, the NVIDIA Vera Rubin rack-scale system NVIDIA Groq […]

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：According to OpenRouter data, agentic AI workloads consume 15x more tokens than a simple chat request. Why? Consider what happens when an AI agent researches a company for an investment decision. The agent queries financial databases, searches news and filings, invokes a sub-agent to run peer comparisons and model valuations, then synthesizes everything into a […]

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：The Pebble Time 2 is a no-frills smartwatch that helps you disconnect from the digital world.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Be prepared to pay more for an Echo Dot, 16 GB Kindle, Fire TV Stick HD, and Amazon eero 7 mesh router, among several other products.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Generalist AI has released GEN-1.5, a robot foundation model that learns a new physical task from a single demonstration. Drop 3–12 seconds of sensorimotor data into its 30-second context window, and the robot performs the task. No gradient updates, no fine-tuning, no task-specific programming. Across 10 diverse manipulation tasks, this one-shot in-context prompting averaged 59% […] The post Generalist AI Releases GEN-1.5: A Robot Foundation Model That Learns New Tasks From One 3–12 Second Demo appeared first on MarkTechPost.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Prime minister will use national cabinet meeting to assuage premiers over new AI law as AEMO forecasts seven-fold rise in datacentre power use Get our breaking news email, free app or daily news podcast Anthony Albanese will seek to use Wednesday’s high-stakes talks with premiers to quell growing unhappiness about national controls on datacentre developments, promising new approval laws will complement state rules. Faced with growing opposition from conservative governments in Queensland and the Northern Territory, Albanese will tell national cabinet he plans a major piece of legislation next year to ensure the economic benefits of AI are shared widely. Continue reading...

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：A practical guide to running compact, privacy-preserving language models on your own hardware for faster, cheaper, and more controllable AI-powered applications.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：The AI boom has disrupted the way engineers work, introducing new tools to learn, raising expectations for what teams can achieve in a workday, and making it harder to get hired in the first place. This makes it difficult to advise students on which specific coding languages or technical skills they should learn. So amidst the uncertainty, advice for young professionals often turns to a common refrain: Be adaptable. But what does adaptability look like in practice? Engineers often operate on the cutting edge of technology, so dealing with change is a normal part of the job, says Samantha Brunhaver, an associate professor of engineering at Arizona State University, in Tempe. Yet university curricula and training in the workplace often don’t prepare students for this. “We tell engineers that they need to be adaptable when they graduate, but we don’t actually explain what that means, demonstrate what that looks like, [or] help make sure that they’re developing it,” says Brunhaver, who received a National Science Foundation award in 2020 to study how to foster greater workplace adaptability among young engineers. For this ongoing project, she has interviewed engineering managers, early career employees, and undergraduates about their experiences. Part of the problem, she says, is that every employer has its own idea of what to be adaptable means. Generally, Brunhaver defines adaptability as “the ability to recognize that a change or uncertainty is occurring, and then respond effectively to that change.” But the skill is context-dependent. In software engineering, that might mean responding to turnover in the tools you use on a daily basis, while aerospace or biomedical engineers may need to keep track of changing procedures and regulations. “Managers are all saying adaptability is important,” Brunhaver says, “but defining it in different ways.” At the same time, engineers are all contending with changes beyond these industry-specific expectations. Jobs in the technology, media, and telecom sectors are experiencing the fastest pace of skill turnover, according to a June 2026 report on the effects of AI from the professional services network PwC. And the World Economic Forum’s most recent Future of Jobs Report, published in 2025, found that employers across all sectors expect 39 percent of workers’ core skills to change by 2030. This uncertainty can be uncomfortable. But with the right mind-set and support from leadership, adaptability can help keep you afloat. How to Cultivate Adaptability The AI transition is a big shift—but not an unprecedented one, says Jenna Butler, a research scientist at Microsoft who studies developer well-being and productivity. During this type of paradigm shift, there is often a “chaos period” when a new normal is being established, Butler says. In AI’s case, it challenges the understanding of what a computer can do. “I think we’re still in this in-between, difficult period that we’ve seen before, but [it] is maybe moving faster than it has historically.” Software engineers—in one of the fields most affected by AI—are now facing a significant increase in code review. “If you ask 20 developers, you get 23 different ways of working with it. Everyone is trying to sort it out,” says Butler, who describes this period as “the uncomfortable middle.” “We tell engineers that they need to be adaptable when they graduate, but we don’t actually explain what that means, demonstrate what that looks like, [or] help make sure that they’re developing it.”– Samantha Brunhaver, Arizona State University Brunhaver says one way educators can help prepare students before they enter the workforce is by offering a diversity of real-world experiences, such as internships, team-based projects, community service, and leadership roles. Each of these teach students to adapt to different challenges, easing their transition from school to work. It’s also important to encourage reflection, Brunhaver adds, noting that metacognition helps individuals use the skill more effectively. “In order to adapt, you have to think that you have agency and the ability to get through a situation.” Ultimately, it comes down to three steps: Perceive a need to adapt, evaluate your options, and act. For those already in the workforce, that action may mean taking the time to learn new tools and ways of working. Software engineering, for instance, may soon rely more on prompting models and managing agents than coding line by line. “I think people who went into software because they like solving problems are going to have a lot of fun, and people who just enjoy the art of writing code are not,” Butler says. The More Things Change… Although the tools engineers use on a daily basis are evolving, the core responsibilities of the job are more stable than they may seem, says Andy Hunt, a software developer who coauthored The Pragmatic Programmer (Addison-Wesley Professional) in 1999. The book outlines practical coding principles, and has been taught in many computer science classrooms. When Hunt was working on the 20th anniversary edition of the book, he was surprised by how much of the advice still applies. And now, seven years later, he maintains that belief. “The fundamental part of the job is problem solving and communication, and that’s always going to be there,” he says. Hunt emphasizes the importance of developing systems thinking over particular tools. To him, identifying as a Java programmer, for instance, is “like a carpenter saying, ‘I’m a hammer user,’ or ‘I specialize in cordless drills.’ ” He acknowledges that today’s hiring process, in which companies often filter résumés for certain languages or years of experience, makes it harder to embrace a more expansive way of relating to your job. Employers, he says, should recognize that “the tech’s not the hard part, and it never has been. Understanding information theory, understanding systems thinking, understanding what constraints you’re up to—that’s still the hard part.” With this type of misalignment between employers and employees, AI is also intensifying an old source of tension: How can engineers slow down enough to adapt and learn new tools when the pressure to become more productive keeps mounting? Who’s Responsible for Enabling Change? Young engineers need to embrace change. However, educators and employers also play a role in building a successful workforce. From the educator’s perspective, Brunhaver says “we need to be more explicit about what [adaptability] means and why it’s important.” Managers, meanwhile, should invest in their employees’ professional development. Microsoft research scientist Butler often encourages leadership to set aside intentional time for continuous learning for their engineers—even just an hour a week—without any expectation that they will produce code or progress in their daily work. “I realize that’s difficult,” says Butler. “I would encourage people to do it on their own, but I would really encourage organizations and leaders to do it, because you’re not going to get this sudden change in your people if they don’t have time and space to learn how to work differently.” This also means providing enough instruction, Butler adds. When developers aren’t given enough guidance on adopting something new, while being pressured to increase productivity, they risk doubling down on the tools they already know and burning out. “I do imagine the next number of years could be challenging,” Butler says. Engineers will have to adapt to find their place in an evolving workforce—but they also have a say in shaping that future. “Being adaptable sort of implies that you’re going to change based on what’s happening around you, and I would really like people to realize the change that’s happening is somewhat up to us,” she says. All individuals have a choice in how they use AI, for instance, and which models they use. “We need to be adaptable and go with the flow to a degree, but we also need to be directing that flow. The future with AI is absolutely not predetermined.” This article appears in the September 2026 print issue as “The Adaptable Engineer.”

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：framework that folds aggregate human movement into text-based place embeddings. Language models describe what a place is; they miss how it is used. ME-POIs encodes each visit as a contextualized vector and aligns it with one learnable prototype per POI through contrastive learning, then transfers visit distributions from data-rich anchors to the long tail across three spatial scales. Across five map-enrichment tasks on Los Angeles and Houston mobility data, adding ME-POIs improved 34 of 35 model-task pairings in Los Angeles — up to 81.9% relative F1 on visit intent and a 24.7% MAE reduction on busyness. A mobility-only variant beat Gemini text embeddings on price-level classification. The post Google Research Introduces ME-POIs: A Mobility-Informed Framework that Adds “How a Place Is Used” to Text-Based POI Embeddings appeared first on MarkTechPost.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：The standalone company, backed by Wall Street firms, is looking to boost its AI technology implementation capabilities.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Differential acceleration of cyber, math, and AI

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Global content powerhouse Thomson Reuters Corp. today launched Thomson, its first proprietary large language model, combining the company’s trove of legal knowledge with LLMs from outside providers to provide legal advice. The company said Thomson will first be deployed in Tabular Analysis, a high-volume document review capability in its CoCounsel Legal AI assistant. CoCounsel will […] The post Thomson Reuters launches proprietary AI model for legal work appeared first on SiliconANGLE.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：It won't be another high-end Chromebook like the Pixel, and Google has to hope it'll succeed where Copilot+ PCs have fallen short.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Moonshot AI’s Kimi K3 is a 2.8-trillion-parameter open-weight model built with a Mixture-of-Experts architecture. It activates only a small fraction of its parameters per token, helping reduce inference costs while delivering strong coding and agentic performance. K3 combines near-frontier capabilities, open weights, and lower API pricing, making it an interesting alternative to proprietary models. In […] The post How to Use Kimi K3: Moonshot AI’s 2.8T Open-Weight Model appeared first on Analytics Vidhya.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Use Grok Build to create a production-ready data science workflow with EDA, scikit-learn, model training, FastAPI, API testing, and cloud deployment.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：GPT‑5.6 is now available in Kiro, helping developers plan, build, review, and test software with better price-performance.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Curious about ChatGPT Work? Here's how the agentic AI handles research, files, and multistep projects, plus its risks and limits.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：<p><strong><a href="https://fzakaria.com/2026/08/23/your-executable-is-a-sqlite-database">Your executable is a SQLite database</a></strong></p> Farid Zakaria describes a neat Linux pattern for creating a SQLite database file that can be directly used as an executable binary.</p> <p>The trick sets the SQLite file format's 4-byte application ID (68 bytes into the file) to SELF, standing for Structured Executable &amp; Linkable Format. The various components of the ELF executable format are then arranged into a number of different SQLite tables, using <a href="https://github.com/fzakaria/selfdb/blob/main/schema/self.sql">this schema</a>.</p> <p>Their <code>self-exec</code> interpreter (<a href="https://github.com/fzakaria/selfdb/blob/main/loader/self-exec.c">C code here</a>) can then extract and execute the necessary pieces.</p> <p>You can additionally use a Linux mechanism called <a href="https://docs.kernel.org/admin-guide/binfmt-misc.html">binfmt_misc</a> to teach the kernel to execute that any time it encounters an executable matching that binary pattern. Farid uses NixOS here, but without NixOS I think registration looks something like this:</p> <pre><code>printf '%s\n' ':self:M:68:SELF::/usr/local/bin/self-exec:' \ &gt; /proc/sys/fs/binfmt_misc/register </code></pre> <p><small></small>Via <a href="https://news.ycombinator.com/item?id=49415271">Hacker News</a></small></p> <p>Tags: <a href="https://simonwillison.net/tags/c">c</a>, <a href="https://simonwillison.net/tags/linux">linux</a>, <a href="https://simonwillison.net/tags/sqlite">sqlite</a></p>

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Make your car feel high-tech without breaking the bank. Shop our favorite Bluetooth adapters, chargers, and more.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Discussion | Link

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Bungs banknotes at Bristol boffins to find out if mature code survives the machine

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Discussion | Link

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：Bingeing the boxed set of binary bafflement

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：A search result can look plausible and still be wrong. Dense retrieval can return a document on the right topic but miss an exact identifier copied into the query. Sparse retrieval can miss a relevant document when the query describes it with terms the corpus doesn’t use. Either way, your logs record a successful query. Hybrid search runs dense and sparse retrieval over the same query, then merges their result lists. Dense retrieval adds semantic similarity, so paraphrases can rank together. Sparse retrieval adds weighted term matching for exact words and identifiers.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：The five largest GPU neoclouds now run on very different models. CoreWeave and Nebius report to the SEC; Lambda and Crusoe are private and heading toward IPOs; Groq rebuilt itself as an inference cloud after licensing its LPU technology to NVIDIA. This comparison checks each provider's live rate card, Q2 2026 financials, active and contracted gigawatts, anchor contracts, and SemiAnalysis ClusterMAX tier. Nebius posts the lowest H100 rate and the only published B300 price, Lambda has the cheapest B200, Crusoe is the only one with AMD on its card, and CoreWeave commands a 10–15% premium as the sole Platinum-rated provider. Figures verified August 21, 2026. The post Best GPU Neoclouds 2026: CoreWeave, Nebius, Lambda, Crusoe, and Groq Ranked by Published Pricing and Contracted Power appeared first on MarkTechPost.

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.20904v1 Announce Type: new Abstract: Virtual scenario-based testing is a key enabler for validating automated driving systems (ADS) and intelligent transport systems (ITS). However, executing large-scale test suites involving possibly thousands of scenarios remains labor-intensive and difficult to scale. This paper presents an end-to-end, DevOps-driven framework that automates build, deployment, and distributed execution of CARLA-based scenario tests of an ADS on a lightweight Kubernetes cluster. ROS 2 applications are packaged as standardized Kubernetes Helm charts generated from repository specifications, while entire simulation environments are composed declaratively via dynamic Helmfile manifests. The paper describes how a distributed testing workflow can be implemented in Argo Workflows to provision environments, aggregate and batch OpenSCENARIO test cases from configurable sources, execute scenarios in parallel across cluster nodes, and collect logs and resource metrics. In an evaluation on a multi-node K3s cluster running 200 scenarios, the best configuration speeds up end-to-end workflow time by more than a factor of eight compared to a sequential baseline. The results demonstrate significant gains in end-to-end execution time and quantify trade-offs between parallelism, orchestration overhead, and cluster stability. The framework is further demonstrated in a real-world ADS test application with connections to scenario sources and downstream evaluation modules. This demonstrates that the approach provides a strong foundation not only for scalable simulation testing, but also for generating traceable evidence that can support safety arguments.