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Tom's Guide
7 days ago
- Tom's Guide
I've been sitting in this ergonomic office chair for two months — and my back has never felt better
The Hinomi X1 is an ergonomic office chair with an almost all-mesh design and a butterfly-style split backrest that's designed to keep you sitting upright with good posture while offering plenty of support for your back. Unlike many other office chairs, this one is available in three different sizes to better cater to both shorter and taller users. Available in gray, black and pink, there aren't any other customization options besides the chair's color and size. Instead of using mesh like the Hinomi X1's seat, upper backrest and headrest, its adaptive lumbar support is made from Thermoplastic Polyurethane (TPU), which gives it a softer, more flexible feel and allows it to wrap around your lower back. When combined with its butterfly-style split backrest, the X1's adaptive lumbar support provides excellent back support along with plenty of comfort. Its mesh material allows for plenty of airflow to keep you cool, though it isn't soft to the touch and feels more durable than some other mesh office chairs I've tested. The Hinomi X1 also features numerous adjustment points (15 in fact) to help you lock-in the perfect fit, even if doing so might take some time. When you want to relax, there's a pull-out footrest underneath its seat, and the chair can recline up to 135 degrees. There's a lot to like here, but those looking for a simpler office chair might be overwhelmed by all these extra features and adjustments. Plus, you need to make sure you choose the right-sized chair for your height. My Hinomi X1 review will help you decide if this is the best office chair for your needs and workspace, or if it's worth spending less for something with a more basic design. Price $729 to $759 Height range 41.7 to 53.5 inches (small), 43.3 to 55.9 inches (medium), 44.1 to 55.6 inches (large) Seat width 19.7 inches Seat depth 19.7 to 21.7 inches Maximum load 330 pounds Maximum recline 135 degrees Weight 73 pounds Material Mesh, aluminum alloy Adjustment points 15 Warranty 12-year The Hinomi X1 has quite a lot going for it, from its simple assembly process to all the extra support offered by i The X1 ergonomic office chair sent over by Hinomi for review arrived in a single, though heavy, large box. Everything was packed inside very neatly and securely, with all the parts covered in bubble wrap to keep them safe during shipping. One thing that really stood out to me was that instead of your standard instruction booklet, Hinomi uses a large poster that's very easy to read. With everything unboxed and all the packaging out of the way, I was left with the chair's seat with the armrests and footrest pre-attached, the backrest, the headrest and its aluminum alloy frame. The casters (wheels) and smaller parts were neatly packed into separate boxes, which even included a pair of white gloves to help me avoid getting stains from the chair's gas cylinder. Putting the X1 together was a fairly straightforward process that started with attaching the chair's backrest to its seat using an Allen key. From there, I inserted the casters into its base along with its cylinder and placed its seat on top. Finally, I attached the adjustable headrest to the top of the chair. All told, assembling the X1 took about 30 minutes, but it would have only taken 15 to 20 minutes if I wasn't stopping to take pictures throughout the process. As an ergonomic office chair, adjustability is a key, but the X1's design heavily emphasizes back support, making it equally important. Just like the OdinLake Butterfly Ergo 753 or the Sihoo Doro S300, the X1 has a split, butterfly-style backrest and is certainly a hefty office chair at 73 pounds when fully assembled. The X1 immediately stands out when compared to those two chairs, as instead of a full-mesh backrest, its adaptive lumbar support is made from TPU and is also split into two parts. In my testing, I found that Hinomi's decision to use TPU allowed the X1's lumbar support to be both softer and more flexible than most mesh. It also hugs your lower back when sitting straight in the chair, but slightly bends when you shift to either side. The X1's backrest is adjustable, too, and like with the X-Chair X3, you can lift it up to change its height. This allows you to line up the chair's adaptive lumbar support with the small of your back. However, you do want to make sure you pick the right size X1 for your height (more on that later). The upper part of the X1's backrest is also split into two parts. There is some give to the upper backrest, which allows it to move with you as you shift from side to side. The panels themselves are concave, which I found helped support my upper back and shoulders while my spine rested comfortably in the small gap between them. At the back of the X1, there's a U-shaped piece of aluminum that adds some extra stability to its frame, which contrasts nicely with the flexibility of its mesh upper backrest and its adaptive lumbar support below. If you suffer from back pain or just want to improve your posture, the X1 is an easy office chair to recommend for both scenarios. Although you often have to purchase one separately with other, cheaper office chairs like the Branch Verve Chair or even the significantly more expensive Haworth Fern, Hinomi includes one in the box with the X1. Its headrest is also very adjustable and can be raised or lowered, tilted forward or back and even rotated. I don't think I've tried an office chair with a headrest this adjustable yet. On the right side of the X1, there are two small levers that are used to adjust the chair's height as well as the depth of its seat. Underneath them, though, there's a black bar that you turn forward or backward to adjust the chair's tilt tension while reclining, and this is definitely an office chair you're going to want to lean back in. The X1 has a single lever on the left side and this is used to lock the chair's backrest in place. When you release it, you can recline back up to 135 degrees. If you plan on taking a rest and reclining for a bit, there's also a footrest that pulls out from underneath the X1's seat. While I spent most of my time either working or playing games while testing this office chair, I did find the footrest to be an excellent and extremely comfortable addition while reclining. Another thing that sets the X1 apart from similarly priced and even more expensive chairs is its 6D armrests. You can adjust their height, width, depth and, most importantly, their tilt both at the back and at the front. allows them to tilt up as you recline. Likewise, you can also rotate them 270 degrees for a steeper angle, and I found that this made playing one of the best handheld gaming consoles — like the new Nintendo Switch 2 — very comfortable while sitting in the X1. That way, instead of craning my neck down to see its screen, I could raise the device up closer to eye level while my elbows remained planted firmly on the X1's armrests. With 15 points of adjustment, the Hinomi X1 is easy to tailor to a wide variety of body types and sitting positions. However, besides the armrests, which take some time to get used to, the rest of the chair's adjustments are easy to learn and remember. Hinomi makes this a bit easier, since the levers on the side have cutouts in them that depict which part of the chair they're used to adjust. The Hinomi X1 is a great ergonomic chair, but it isn't without its downsides. This includes potential confusion over its multiple size options and that there's no way to lock its armrests in place. The Hinomi X1 is available in three different sizes, and the one that's right for you depends entirely on your height. The small version is ideal for users from 5'1' to 5'6' tall, the medium version is best suited for those between 5'6' to 6'1' in height and the larger version is for users from 5'9' to 6'6' in height. It's worth noting that all three sizes of the X1 have a maximum weight capacity of 330 pounds. Another popular office chair that uses a similar sizing structure is the Herman Miller Aeron. However, when moving up from the small to the medium or large versions of that more premium chair, the weight limit jumps up from 300 pounds to 350 pounds. While I appreciate the fact that Hinomi does offer different-sized versions of the X1, I could easily see potential buyers getting confused and accidentally ordering the wrong one. The company does offer free returns for 30 days after purchase, just in case this ends up happening or you want to move up or down a size after the fact. As someone who's 5'4', I tested the small version of the X1, so I can't say if there are any other differences between the various sizes of this office chair. One thing that could help clear up this confusion is by offering cylinders with different heights, like BodyBilt does with its Classic 2500 Series and other chairs. If you are considering the X1, just make sure that you order the right size for your height, and for the best ergonomic experience, you don't want to exaggerate how tall you are either. Don't get me wrong, I'm a big fan of the X1's 6D armrests, and I really like how you can adjust them to your liking for the task at hand. I really liked having them flat while working or tilted upward while leaning back and playing games on either a console or a handheld. However, I just wish there were a way to lock them in place. At first glance, you might think that the adjustment pin directly underneath the armrest would be used for this. Unfortunately, it's used to lock the armrest in place so that they don't tilt up when you put your elbow's weight on their rear. While certainly useful, I think having a similar mechanism to prevent the armrests from turning horizontally would have been a better choice for myself and others, especially as this is a common complaint with the X1. Keep in mind too that other office chairs with less adjustable 4D armrests suffer from this same problem. For instance, back when I reviewed the X-Chair X2, I outfitted that chair with the company's optional 4D armrests. I loved their flexibility, but just like on the X1, it was too easy to accidentally move them when getting up and out of the chair. Fortunately, with the X1, if you put your forearms and elbows down straight when you sit in the chair, its armrests do remain in place. It's a different story if you grip the front of the armrests with your hands, though, as doing so immediately makes them turn to the left or to the right. The Hinomi X1 is an excellent ergonomic office chair and a great choice for anyone who suffers from back pain or wants to stop slouching forward in their chair. You rarely see a chair with 15 adjustment points, as most offer between 8 to 10. At the same time, if you want a firmer lumbar support mechanism that still has a good bit of flexibility, the X1 certainly fits the bill. At $729 to $759, depending on where you purchase it, the X1 is on the expensive side compared to one of the best budget office chairs. Still, for a chair with a split, butterfly-style backrest, it's actually more affordable than both the Sihoo Doro S-300 ($799) and the OdinLake Ergo Butterfly 753 ($999). I wouldn't recommend the X1 to those who just started working from home or are putting a desk setup together for the first time, as this isn't a starter chair. Instead, the X1 is the kind of chair you get as an upgrade after you realize what you want in an office chair, or if you find that your current chair just doesn't offer the kind of back support you want. After thoroughly testing and using the X1 for just over two months while both working and gaming, I can easily recommend it to those willing to make an investment in their health and well-being and in their workspace. There are certainly cheaper office chairs out there, but this one delivers a supportive and comfortable experience without the premium price tag you'd find on a chair from Steelcase or Herman Miller.
Hindustan Times
11-06-2025
- Business
- Hindustan Times
OpenAI to leverage Google Cloud service, reducing Microsoft dependency
In a surprising turn of events, one of the world's biggest tech rivals, OpenAI and Google, signed a collaboration deal to manage and expand computing infrastructure. Reportedly, OpenAI has been in talks with Alphabet's Google Cloud service for a few months to shift its computing operations from Microsoft. Now, a Reuters report highlights that the deal has been finalised, and OpenAI is all set to leverage Google Cloud infrastructure and custom TPU chips to meet the growing computing demands. This move can reduce OpenAI's greater dependence on Microsoft Azure for cloud-based services, and it also makes the company a step closer to adopting a multi-cloud strategy. Know more about OpenAI and Google's collaboration despite being AI rivals in the ongoing race. Also read: Adobe launches the Photoshop Beta app for Android smartphones: Everything you need to know In a recent Reuters report, OpenAI will leverage Google Cloud services to expand its computing infrastructure and reduce dependence on Microsoft. Over the years, OpenAI and Microsoft have been working closely for greater collaboration, but now things may change drastically as the AI giant plans to join hands with one of its biggest rivals. It is being said that the deal was finalised in May, as OpenAI is diversifying its compute sources. This will enable the company to meet the growing demand for training and deploying AI models. Also read: Google to let users test Android 16 desktop mode on phones with external display support, here's how Now, what's in it for Google? Well, the company is already reputed for its cloud services, and collaborating with OpenAI could increase credibility. This may also result in greater collaboration with other leading tech giant who are constantly innovating their AI capabilities. It also supports Google's vision to commercialise tensor processing units (TPUs), which was previously reserved for in-house operations. However, it should also be noted that the collaboration is not yet confirmed by officials from OpenAI, Google and Microsoft. The collaboration also showcases that OpenAI also reducing the Microsoft exclusivity for data centre infrastructure. With Google and OpenAI completing neck to neck, the move highlights how companies keep the competition aside to meet resource demands in the rapidly evolving AI landscape. Despite the collaboration, OpenAI still plans to overtake Google in the AI race. Mobile Finder: Google Pixel 10 Pro LATEST specs, features, and price
Time of India
08-06-2025
- Business
- Time of India
NIFT student's sole-searching pays off
Ahmedabad: A textile design student at NIFT Gandhinagar is 3D-printing custom shoe soles to give everyday runners the kind of personalized footwear that only elite athletes could afford earlier. Jay Makan decided to examine shoes worn by athletes for his graduation project and struck gold. "While elite athletes get custom-made shoes, not all competing runners can afford them. Thus, I focused on the midsoles, insoles and outer soles to improve functionality and performance," said Makan, whose project was part of the institute's student showcase on June 6. His project involved 3D-printed material for the parts of shoes that can be customised based on the wearer's feet. Makan used thermoplastic elastomer (TPE) and thermoplastic polyurethane (TPU) polymers. "We took different combinations and densities to come up with the optimal mix. While some gave better flexibility, some gave better resistance. I was inspired by one of NIFT alumni's similar initiative to explore the possibilities offered by 3D printing technology," he said. Some other projects have also experimented with 3D printed material for car interiors, Sujani weaving and furniture. by Taboola by Taboola Sponsored Links Sponsored Links Promoted Links Promoted Links You May Like Вот что поза во сне говорит о вашем характере! Удивительные Новости Undo This year's graduation project showcase was themed 'Pragyanmasya Sampat' (confluence of knowledge in conclusion), integrating design, management and technology. The event saw participation from academia and industry to explore emerging trends as students showcased projects in domains such as textile design, traditional art and craft, new materials, lifestyle design and accessories, exhibition design, digital design and so on. The students also organized a fashion show, 'Impulse 2025.' Prof Sameer Sood, director of NIFT Gandhinagar, said that design is an integral part of shaping inclusive, sustainable, and culturally-grounded futures and new-age business models. "The students work on a four-month graduation project in their chosen field and push the boundaries with new technology and market trends," he said. Many of the projects were carried out in collaboration with industry partners, whereas a section focused on recycled and sustainable materials, including those from the textile sector.
Yahoo
20-05-2025
- Business
- Yahoo
Google May Have an Under-Appreciated Advantage in the AI Arms Race
Google's AI model, Gemini, saw a larger increase in use between September and March than its major rivals, according to a Morgan Stanley survey. Gemini is also more commonly used for commercial purposes, like price comparisons and product recommendations, than competitors like ChatGPT and Meta AI. Commercial applications are likely to be front and center during the company's two-day Google I/O developer conference kicking off on uncertain standing in the AI arms race has been cause for concern among investors lately. But recent Morgan Stanley research suggests the search giant may have an under-appreciated advantage. Google's Gemini saw a larger increase in usage between September and March than its primary competitors, Meta AI and OpenAI's ChatGPT, according to Morgan Stanley. And in March, about 40% of survey respondents used Gemini on a monthly basis, compared with ChatGPT's 41% and Meta AI's 39%. Gemini was also more widely used as a commercial tool than its competitors. Forty-six percent of respondents used Gemini to research new products in March, while 37% used it to compare prices and 34% shopped on the platform. ChatGPT was used by 41% of respondents to research products, 31% to compare prices, and 25% to shop. 'This in our view speaks to GOOGL's still entrenched user behavior within commercial activity,' Morgan Stanley analysts wrote. 'The key from here is again for GOOGL to ship further Gemini/TPU enabled capabilities across its leading user bases and data sets to maintain its leadership at the top of the commercial funnel.' Investors have grown increasingly worried about the impact AI will have on Google's (GOOG) (GOOGL) bread-and-butter search business. Comments from an Apple executive earlier this month amplified Wall Street's concerns that the rising popularity of AI is eroding Google's dominance in the online search market. (Google, for its part, has rolled out AI summaries on its search engine, and recently said it monetizes AI queries at the same rate as traditional search.) Google's search business also faces a threat from antitrust regulators, who successfully argued last year that the company operated an illegal search monopoly. They have recommended the company be forced to sell its Chrome web browser, end exclusive distribution agreements with device makers like Apple, and share its search data with competitors. The company is likely to spotlight commercial applications during its annual Google I/O developer conference, which kicks off with a keynote from CEO Sundar Pichai on Tuesday afternoon. Google's AI stack, Gemini API, and Gemma open models are among the AI features being pitched to developers. The event could be pivotal for the tech giant in its effort to reframe the narrative around its AI opportunity. 'We believe Google is heading into its innovation phase of AI and the worries around search days in the rear view mirror are way overdone,' wrote Wedbush analysts in a note on Tuesday. Read the original article on Investopedia Sign in to access your portfolio
Geeky Gadgets
14-05-2025
- Geeky Gadgets
TPUs vs GPUs the AI Hardware Decision : Why Your Hardware Choice Matters More Than Ever
What if the key to unlocking faster, more efficient AI development wasn't just in the algorithms you write, but in the hardware you choose? For years, the debate between Google's Tensor Processing Units (TPUs) and NVIDIA's Graphics Processing Units (GPUs) has divided developers, researchers, and tech enthusiasts alike. Both are engineered for artificial intelligence, yet their architectures and capabilities diverge in ways that can make or break your AI project. With NVIDIA's GPUs dominating the market and Google's TPUs offering specialized performance for certain tasks, the choice isn't as straightforward as it seems. Understanding the nuances of these technologies is no longer optional—it's essential for anyone navigating the rapidly evolving AI landscape. In this guide, Trelis Research explore the core differences between TPUs and GPUs, from memory architecture to cost efficiency, and how these impact real-world AI workloads. You'll discover why NVIDIA's H100 and H200 GPUs are often favored for scalability and affordability, while Google's TPU V6E shines in specific low-latency scenarios. We'll also delve into critical factors like parallelization techniques, software optimization, and deployment flexibility, offering insights that could transform how you approach AI hardware decisions. By the end, you'll have a clearer picture of which technology aligns best with your goals—and why the debate between TPU and GPU is far from over. TPU vs GPU Comparison Key Hardware Differences The fundamental differences between TPUs and GPUs stem from their hardware architecture and memory capabilities. NVIDIA's H100 GPU features an impressive 80 GB of VRAM with high-bandwidth memory (HBM), while the H200 takes this further with 141 GB of VRAM and even faster memory speeds. In contrast, Google's TPU V6E is equipped with only 32 GB of VRAM, which can be a significant limitation for memory-intensive tasks. Another critical distinction lies in interconnect speeds. TPUs have slower interconnects, which can hinder their ability to efficiently manage large-scale, distributed workloads. NVIDIA GPUs, with their advanced architecture, are better suited for handling such tasks, offering greater flexibility and scalability for developers. Performance: Speed and Scalability Performance is a pivotal factor when comparing AI hardware, as it directly impacts the efficiency and scalability of workloads. TPUs and GPUs exhibit notable differences in concurrency handling, throughput, and cost efficiency: Time to First Token: TPUs excel at generating the first token quickly under low concurrency levels. However, as concurrency increases, their performance diminishes, making them less suitable for large-scale applications requiring high parallelism. TPUs excel at generating the first token quickly under low concurrency levels. However, as concurrency increases, their performance diminishes, making them less suitable for large-scale applications requiring high parallelism. Token Throughput: NVIDIA GPUs, particularly the H200, outperform TPUs in overall throughput. This makes them ideal for high-demand AI models that require consistent and large-scale processing capabilities. NVIDIA GPUs, particularly the H200, outperform TPUs in overall throughput. This makes them ideal for high-demand AI models that require consistent and large-scale processing capabilities. Cost per Token: NVIDIA GPUs are more cost-effective. The H200 offers the lowest cost per token, followed by the H100, while TPUs are comparatively more expensive for similar workloads. These performance metrics highlight the scalability and cost advantages of NVIDIA GPUs, particularly for developers managing complex AI models or large datasets. NVIDIA GPUs vs Google TPUs: Which is Best for Your AI Project? Watch this video on YouTube. Enhance your knowledge on AI development by exploring a selection of articles and guides on the subject. Parallelization: Maximizing Efficiency Parallelization techniques are essential for optimizing hardware performance, especially in AI workloads. Both TPUs and GPUs support pipeline and tensor parallelization, but their effectiveness varies significantly: Pipeline Parallelization: This technique divides model layers across multiple devices, reducing VRAM usage. However, it increases the time to first token, making it less suitable for latency-sensitive tasks where quick responses are critical. This technique divides model layers across multiple devices, reducing VRAM usage. However, it increases the time to first token, making it less suitable for latency-sensitive tasks where quick responses are critical. Tensor Parallelization: By splitting matrices within layers, tensor parallelization enhances performance but demands substantial VRAM, particularly for storing key-value (KV) caches. NVIDIA GPUs, with their larger VRAM capacities, handle this method more effectively than TPUs. The larger memory capacity of NVIDIA GPUs gives them a distinct advantage in handling parallelization techniques, allowing them to deliver better performance and efficiency for complex AI workloads. Cost Efficiency Cost is a decisive factor for many developers, and NVIDIA GPUs consistently outperform TPUs in terms of cost-efficiency. The H200 GPU offers the lowest cost per token, followed closely by the H100. While TPUs deliver strong compute performance, their higher operational costs make them less appealing for budget-conscious developers. For most AI workloads, NVIDIA GPUs strike a better balance between performance and affordability, making them the preferred choice for developers seeking cost-effective solutions without compromising on efficiency. Software Optimization The role of software optimization in hardware performance cannot be overstated. NVIDIA GPUs benefit from a robust ecosystem of open source libraries, such as VLM, which are specifically optimized for their architecture. These libraries enable better compute utilization and practical performance, allowing developers to maximize the potential of their hardware. In contrast, TPUs often face software limitations that restrict their ability to achieve peak performance. This lack of optimization reduces their effectiveness in real-world applications, further tilting the balance in favor of Nvidia GPUs for most AI development scenarios. Accessibility and Deployment Accessibility is another critical factor when choosing AI hardware. Nvidia GPUs are widely available across multiple platforms, including RunPod, AWS, and Azure, offering developers flexibility in deployment. This multi-cloud support ensures that Nvidia GPUs can be integrated into a variety of workflows and environments. On the other hand, TPUs are restricted to Google Cloud, with limited access to higher configurations like V6E-16 or V6E-32. This lack of multi-cloud compatibility makes TPUs less attractive for developers seeking scalable and versatile solutions, further limiting their appeal in competitive AI markets. Future Outlook The future of AI hardware is poised for significant advancements, and Google's upcoming TPU V7E is expected to address some of the limitations of the V6E. Improvements in VRAM capacity and interconnect speeds, coupled with enhanced software optimization, could make TPUs more competitive with NVIDIA GPUs. However, until these advancements materialize, NVIDIA's H100 and H200 GPUs remain the superior choice for most AI workloads. Their combination of high performance, cost-efficiency, and accessibility ensures they continue to lead the market, offering developers reliable and scalable solutions for their AI projects. Media Credit: Trelis Research Filed Under: AI, Guides Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
