「PC Watch」創刊30周年、心よりお祝い申し上げます。

　現在のPCのストレージは、SSDがすっかり標準になったが、NASやサーバー用途では「HDD」がいまだ主流だ。容量あたりの単価の低さがHDDのメリットだが、 日本企業として唯一HDD製造を手がける東芝デバイス＆ストレージ(以降、東芝)のNAS向けHDDには、同社ならではの大きな強みがあるという。

　PCのパフォーマンスは、高いに越したことはない。処理能力に余裕があれば、クリエイティブアプリの動作は高速化され、最新AAAタイトルもリッチな3Dグラフィックスで堪能できる。これはPC選びにおける変わることがない基準といえる。

　オンラインLLM(大規模言語モデル)の代表格であるChatGPTは、2022年に一般公開され、瞬く間に利用者が増えました。日本では2023年頃から利用が急拡大しています。もはやChatGPTをはじめとする生成AIは、日常的なツールとして定着しつつあり、作業効率化に欠かせないという人も少なくないでしょう。

Amazon CEO Andy Jassy says the company may eventually sell its Trainium AI chips directly to outside customers, not just through AWS, which would put Amazon in more direct competition with Nvidia. "There's so much demand for our chips that it's quite possible we'll sell racks of them to third parties in the future," Jassy wrote in his annual shareholder letter Thursday. He also revealed the company's chip business is already running at more than $20 billion annually, with demand so strong that current and even future generations are largely spoken for. Quartz reports: Access to Amazon's chips is currently limited to Amazon Web Services, with customers paying for cloud-based usage rather than owning any physical hardware. Selling to AWS and external customers alike, as standalone chipmakers do, would put annual revenue at around $50 billion, up from the $20 billion the company estimates for the year, Jassy said. The $20 billion figure spans three product lines: Trainium, the AI accelerator chip; Graviton, a general-purpose processor; and Nitro, a chip that helps run Amazon's EC2 server instances. All three are growing at triple-digit rates year over year, Jassy claimed in his letter. Jassy said demand for Trainium has outpaced supply at each generation. Trainium2 is essentially unavailable, with its entire allocated capacity spoken for. Trainium3 started reaching customers in early 2026, and reservations have filled nearly all available supply. Even Trainium4 -- which is not expected to reach wide release for another year and a half -- has substantial pre-orders committed. Jassy argued that a full-scale Trainium rollout could shave tens of billions off annual capital costs while meaningfully widening profit margin.

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OpenAI is reportedly preparing a new cybersecurity product for a small group of partners, out of concern that a broader rollout could wreak havoc if it were released more widely. If that move sounds familiar, it's because Anthropic took a similar limited-release approach with its Mythos model and Project Glasswing initiative. Axios reports: OpenAI introduced its "Trusted Access for Cyber" pilot program in February after rolling out GPT-5.3-Codex, the company's most cyber-capable reasoning model. Organizations in the invite-only program are given access to "even more cyber capable or permissive models to accelerate legitimate defensive work," according to a blog post. At the time, OpenAI committed $10 million in API credits to participants. [...] Restricting the rollout of a new frontier model makes "more sense" if companies are concerned about models' ability to write new exploits -- rather than about their ability to find bugs in the first place, Stanislav Fort, CEO of security firm Aisle, told Axios. Staggering the release of new AI models looks a lot like how cybersecurity vendors currently handle the disclosure of security flaws in software, Lee added. "It's the same debate we've had for decades around responsible vulnerability disclosure," Lee said.

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An anonymous reader quotes a report from CNN: A hacker has allegedly stolen a massive trove of sensitive data -- including highly classified defense documents and missile schematics -- from a state-run Chinese supercomputer in what could potentially constitute the largest known heist of data from China. The dataset, which allegedly contains more than 10 petabytes of sensitive information, is believed by experts to have been obtained from the National Supercomputing Center (NSCC) in Tianjin -- a centralized hub that provides infrastructure services for more than 6,000 clients across China, including advanced science and defense agencies. Cyber experts who have spoken to the alleged hacker and reviewed samples of the stolen data they posted online say they appeared to gain entry to the massive computer with comparative ease and were able to siphon out huge amounts of data over the course of multiple months without being detected. An account calling itself FlamingChina posted a sample of the alleged dataset on an anonymous Telegram channel on February 6, claiming it contained "research across various fields including aerospace engineering, military research, bioinformatics, fusion simulation and more." The group alleges the information is linked to "top organizations" including the Aviation Industry Corporation of China, the Commercial Aircraft Corporation of China, and the National University of Defense Technology. Cyber security experts who have reviewed the data say the group is offering a limited preview of the alleged dataset, for thousands of dollars, with full access priced at hundreds of thousands of dollars. Payment was requested in cryptocurrency. CNN cannot verify the origins of the alleged dataset and the claims made by FlamingChina, but spoke with multiple experts whose initial assessment of the leak indicated it was genuine. The alleged sample data appeared to include documents marked "secret" in Chinese, along with technical files, animated simulations and renderings of defense equipment including bombs and missiles.

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After nearly 20 years on the platform, The Electronic Frontier Foundation (EFF) says it is leaving X. "This isn't a decision we made lightly, but it might be overdue," the digital rights group said. "The math hasn't worked out for a while now." From the report: We posted to Twitter (now known as X) five to ten times a day in 2018. Those tweets garnered somewhere between 50 and 100 million impressions per month. By 2024, our 2,500 X posts generated around 2 million impressions each month. Last year, our 1,500 posts earned roughly 13 million impressions for the entire year. To put it bluntly, an X post today receives less than 3% of the views a single tweet delivered seven years ago. [...] When you go online, your rights should go with you. X is no longer where the fight is happening. The platform Musk took over was imperfect but impactful. What exists today is something else: diminished, and increasingly de minimis. EFF takes on big fights, and we win. We do that by putting our time, skills, and our members' support where they will effect the most change. Right now, that means Bluesky, Mastodon, LinkedIn, Instagram, TikTok, Facebook, YouTube, and eff.org. We hope you follow us there and keep supporting the work we do. Our work protecting digital rights is needed more than ever before, and we're here to help you take back control.

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Waymo is launching a pilot with cities and Google's Waze to share pothole data collected by its robotaxis, giving local transportation departments a new way to find and fix road damage more quickly. "We realized, hey, once we're at scale, we can actually share this data with cities, which is something that they've asked for and something that we collect at scale," said Arielle Fleisher, Waymo's policy development and research manager. "And so we figured out a way to make that happen." The Verge reports: Waymo uses its perception hardware, including cameras and radar, as well as accelerometers and the vehicle's physical feedback system, to log every pothole its vehicles encounter. These sensors detect physical changes to the road's surface, such as tilt and movement when the vehicle encounters irregularities. Originally, Waymo knew it needed the ability to detect potholes so it could ensure that its vehicles slowed down to avoid damage or injury to the passenger. Later, the company realized this could be invaluable data for cities, too. Under the new pilot program, that data will now be made available to cities' departments of transportation through a free-to-use Waze for Cities platform, which provides access to real-time, user-generated traffic data that officials can then use to make important decisions -- such as pothole repair. The platform also allows for Waze users to validate pothole locations through their own observations, decreasing the chances that city officials will be led astray by false positives. Currently, many cities rely on a patchwork of non-emergency 311 reports and manual inspections to address their pothole problems. Waymo developed this pilot program after collecting years of feedback from city officials about the state of their highways and surface streets. The company is launching the new pilot in the San Francisco Bay Area, as well as Los Angeles, Phoenix, Austin, and Atlanta, where Waymo says it has already helped the city identify approximately 500 potholes. Fleisher said that Waymo would be open to expanding the project to other street maladies based on further feedback from officials. The company is eager to learn what other types of street condition or safety data might be valuable, she said. "We want to be responsive to cities," Fleisher said. "They are interested in safer streets and potholes are really a tough challenge for cities. So we really wanted to meet that need as part of our desire to be a good partner and to ultimately advance our goal for safer streets."

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An anonymous reader quotes a report from the Guardian: [Five skilled workers aged 50 and older spoke] to the Guardian about how, after struggling to find work in their fields, they have turned to an emerging and growing category of work: using their expertise to train artificial intelligence models. Known as data annotation, the work involves labeling and evaluating the information used to train AI models like Open AI's ChatGPT or Google's Gemini. A doctor, for example, might review how an AI model answers medical questions to flag incorrect or unsafe responses and suggest better ones, helping the system learn how to generate more accurate and reliable responses. The ultimate goal of training is to level up AI models until they're capable of doing a job as well as a human could -- meaning they could someday replace some of these human workers. The companies behind AI training, such as Mercor, GlobalLogic, TEKsystems, micro1 and Alignerr, operate large contractor networks staffed by people like Ciriello. Their clients include tech giants like OpenAI, Google and Meta, academic researchers and industries including healthcare and finance. For experienced professionals, AI training contracts can be a side hustle -- or a temporary fallback following a layoff -- where top experts can, in some cases, earn over $180 an hour. But that's on the high end. For some older workers [...], it represents another thing entirely: a last refuge in a brutal job market that is harder to stay in, or re-enter, the older they get. For many of them, whether or not they're training their AI replacements in their professions is besides the point. They need the work now. [...] "There's just a lot of desperation out there," Johnson said. As opportunities narrow, many turn to what Joanna Lahey, a professor at Texas A&M University who studies age discrimination and labor outcomes, calls "bridge jobs" -- lower-paying, less demanding roles that help workers stay financially afloat as they approach retirement. Historically, that meant taking temp assignments, retail and fast-food work and gig roles like Uber and food delivery. Now, for skilled workers -- engineers, lawyers, nurses or designers, for example -- using their expertise for AI data training is becoming the new bridge job. "[AI] training work may be better in some ways than those earlier alternatives," Lahey told the Guardian. AI training can offer flexibility, quick income and intellectual engagement. But it's often a clear step down. Professionals in fields such as software development, medicine or finance typically earn six-figure salaries that come with benefits and paid leave, according to the US Bureau of Labor Statistics. According to online job postings, AI training gigs start at $20 an hour, with pay increasing to between $30 and $40 an hour. In some cases, AI trainers with coveted subject matter expertise can earn over $100 an hour. AI training is contract-based, though, meaning the pay and hours are unstable, and it often doesn't come with benefits.

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BrianFagioli writes: Little Snitch, the well known macOS tool that shows which applications are connecting to the internet, is now being developed for Linux. The developer says the project started after experimenting with Linux and realizing how strange it felt not knowing what connections the system was making. Existing tools like OpenSnitch and various command line utilities exist, but none provided the same simple experience of seeing which process is connecting where and blocking it with a click. The Linux version uses eBPF for kernel level traffic interception, with core components written in Rust and a web based interface that can even monitor remote Linux servers. During testing on Ubuntu, the developer noticed the system was relatively quiet on the network. Over the course of a week, only nine system processes made internet connections. By comparison, macOS reportedly showed more than one hundred processes communicating externally. Applications behave similarly across platforms though. Launching Firefox immediately triggered telemetry and advertising related connections, while LibreOffice made no network connections at all during testing. The early release is meant primarily as a transparency tool to show what software is doing on the network rather than a hardened security firewall.

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Photo:ハル2026年3月17日の記事を編集して再掲載しています。たまたま見つけたこの謎の金属リング。一見ガジェットのようなこちらは「Snatch（スナッチ）」（日本での購入価格は税込11,000円）。あらゆる布バッグを“斜めがけ化”できるアイデア商品です。便利そうだし、何より見た目がかっこいいので、期間限定のポップアップで買ってみました。あのストレスから解放されたPhoto:ハルたとえば、ト

ハーマンインターナショナルは、JBLブランド製ポータブルBluetoothスピーカーの新モデル「JBL GO 5」を発表した。