A writer replaced their laptop with a Morefine M6 mini PC and Nreal Air AR glasses for a week, aiming for ultimate portability and a large virtual workspace. While the setup provided a surprisingly functional experience for coding, writing, and web browsing with a simulated triple-monitor array, it wasn't without drawbacks. The glasses, while comfortable, lacked proper dimming and offered limited peripheral vision. The mini PC required external power and peripherals, impacting the overall portability. Though not a perfect replacement, the experiment highlighted the potential of this technology for a lighter, more versatile computing future.
The Jupiter Ace, a British home computer from the early 1980s, stood out due to its use of Forth as its primary programming language instead of the more common BASIC. While Forth offered advantages in speed and efficiency, its steeper learning curve likely contributed to the Ace's commercial failure. Despite its innovative use of a then-obscure language and compact, minimalist design, the Jupiter Ace ultimately lost out in the competitive home computer market, becoming a curious footnote in computing history.
HN commenters discuss the Jupiter Ace's unique use of Forth, some appreciating its educational value and elegance while others find it esoteric and limiting. Several recall fond memories of using the machine, praising its speed and compact design. The limited software library and RAM are mentioned as drawbacks, alongside the challenges of garbage collection in Forth. The unconventional keyboard layout and the machine's overall fragility are also discussed. One commenter notes the irony of its Sinclair connection, being designed by former Sinclair employees yet failing where Sinclair succeeded. A few comments delve into the technicalities of Forth and its implementation on the Ace, while others lament its ultimate commercial failure despite its innovative aspects.
Nvidia has introduced native Python support to CUDA, allowing developers to write CUDA kernels directly in Python. This eliminates the need for intermediary languages like C++ and simplifies GPU programming for Python's vast scientific computing community. The new CUDA Python compiler, integrated into the Numba JIT compiler, compiles Python code to native machine code, offering performance comparable to expertly tuned CUDA C++. This development significantly lowers the barrier to entry for GPU acceleration and promises improved productivity and code readability for researchers and developers working with Python.
Hacker News commenters generally expressed excitement about the simplified CUDA Python programming offered by this new functionality, eliminating the need for wrapper libraries like Numba or CuPy. Several pointed out the potential performance benefits of direct CUDA access from Python. Some discussed the implications for machine learning and the broader Python ecosystem, hoping it lowers the barrier to entry for GPU programming. A few commenters offered cautionary notes, suggesting performance might not always surpass existing solutions and emphasizing the importance of benchmarking. Others questioned the level of "native" support, pointing out that a compiled kernel is still required. Overall, the sentiment was positive, with many anticipating easier and potentially faster CUDA development in Python.
The author champions their 17-year-old ThinkPad T60, highlighting its repairability, durability, and performance adequacy for their needs. Driven by a desire to avoid the planned obsolescence of modern laptops and the environmental impact of constant upgrades, they detail the straightforward process of replacing components like the keyboard, battery, and screen, often with used parts. While acknowledging the limitations of older hardware, particularly regarding gaming and some modern software, the author emphasizes the satisfaction of maintaining and using a machine for far longer than its intended lifespan, seeing it as a sustainable and empowering alternative to consumerist tech culture.
HN commenters largely agree with the author's appreciation for the ThinkPad's repairability and classic design. Several share their own experiences with older ThinkPads, highlighting their durability and the satisfaction of maintaining and upgrading them. Some discuss the declining quality and repairability of modern laptops, contrasting them with the robust build of older models. A few commenters point out the limitations of older hardware, particularly regarding battery life and performance for modern tasks, while others offer tips for extending the life of older ThinkPads. The discussion also touches upon the environmental benefits of using older hardware and the appeal of the classic ThinkPad aesthetic. There's some debate about the practicality of using such an old machine as a daily driver, but a general consensus that for certain tasks and users, a well-maintained older ThinkPad can be a viable and even preferable option.
Apple announced the new Mac Studio, claiming it's their most powerful Mac yet. It's powered by the M2 Max chip, offering significant performance boosts over the previous generation for demanding workflows like video editing and 3D rendering. The Mac Studio also features extensive connectivity options, including HDMI, Thunderbolt 4, and 10Gb Ethernet. It's designed for professional users who need a compact yet incredibly powerful desktop machine.
HN commenters generally expressed excitement but also skepticism about Apple's "most powerful" claim. Several questioned the value proposition, noting the high price and limited upgradeability compared to building a similarly powerful PC. Some debated the target audience, suggesting it was aimed at professionals needing specific macOS software or those prioritizing a polished ecosystem over raw performance. The lack of GPU upgrades and the potential for thermal throttling were also discussed. Several users expressed interest in benchmarks comparing the M4 Max to competing hardware, while others pointed out the quiet operation as a key advantage. Some comments lamented the loss of user-serviceability and upgradability that characterized older Macs.
Apple announced the M3 Ultra, its most powerful chip yet. Built using a second-generation 3nm process, the M3 Ultra boasts up to 32 high-performance CPU cores, up to 80 graphics cores, and a Neural Engine capable of 32 trillion operations per second. This new SoC offers a substantial performance leap over the M2 Ultra, with up to 20% faster CPU performance and up to 30% faster GPU performance. The M3 Ultra also supports up to 192GB of unified memory, enabling professionals to work with massive datasets and complex workflows. The chip is available in new Mac Studio and Mac Pro configurations.
HN commenters generally express excitement, but with caveats. Many praise the performance gains, particularly for video editing and other professional workloads. Some express concern about the price, questioning the value proposition for average users. Several discuss the continued lack of upgradability and repairability in Macs, with some arguing that this limits the lifespan and ultimate value of the machines. Others point out the increasing reliance on cloud services and subscription models that accompany Apple's hardware. A few commenters express skepticism about the claimed performance figures, awaiting independent benchmarks. There's also some discussion of the potential impact on competing hardware manufacturers, particularly Intel and AMD.
HP has acquired the AI-powered software assets of Humane, a company known for developing AI-centric wearable devices. This acquisition focuses specifically on Humane's software, and its team of AI experts will join HP to bolster their personalized computing experiences. The move aims to enhance HP's capabilities in AI and create more intuitive and human-centered interactions with technology, aligning with HP's broader vision of hybrid work and ambient computing. While Humane’s hardware efforts are not explicitly mentioned as part of the acquisition, HP highlights the value of the software in its potential to reshape how people interact with PCs and other devices.
Hacker News users react to HP's acquisition of Humane's AI software with cautious optimism. Some express interest in the potential of the technology, particularly its integration with HP's hardware ecosystem. Others are more skeptical, questioning Humane's demonstrated value and suggesting the acquisition might be more about talent acquisition than the technology itself. Several commenters raise concerns about privacy given the always-on, camera-based nature of Humane's device, while others highlight the challenges of convincing consumers to adopt such a new form factor. A common sentiment is curiosity about how HP will integrate the software and whether they can overcome the hurdles Humane faced as an independent company. Overall, the discussion revolves around the uncertainties of the acquisition and the viability of Humane's technology in the broader market.
For the first time in two decades, PassMark's CPU benchmark data reveals a year-over-year decline in average CPU performance. While single-threaded performance continued to climb slightly, multi-threaded performance dropped significantly, leading to the overall decrease. This is attributed to a shift in the market away from high-core-count CPUs aimed at enthusiasts and servers, towards more mainstream and power-efficient processors, often with fewer cores. Additionally, while new architectures are being introduced, they haven't yet achieved widespread adoption to offset this trend.
Hacker News users discussed potential reasons for the reported drop in average CPU performance. Some attributed it to a shift in market focus from single-threaded performance to multi-core designs, impacting PassMark's scoring methodology. Others pointed to the slowdown of Moore's Law and the increasing difficulty of achieving significant performance gains. Several commenters questioned the validity of PassMark as a reliable benchmark, suggesting it doesn't accurately reflect real-world performance or the specific needs of various workloads. A few also mentioned the impact of the pandemic and supply chain issues on CPU development and release schedules. Finally, some users expressed skepticism about the significance of the drop, noting that performance improvements have plateaued in recent years.
Sam Altman reflects on three key observations. Firstly, the pace of technological progress is astonishingly fast, exceeding even his own optimistic predictions, particularly in AI. This rapid advancement necessitates continuous adaptation and learning. Secondly, while many predicted gloom and doom, the world has generally improved, highlighting the importance of optimism and a focus on building a better future. Lastly, despite rapid change, human nature remains remarkably constant, underscoring the enduring relevance of fundamental human needs and desires like community and purpose. These observations collectively suggest a need for balanced perspective: acknowledging the accelerating pace of change while remaining grounded in human values and optimistic about the future.
HN commenters largely agree with Altman's observations, particularly regarding the accelerating pace of technological change. Several highlight the importance of AI safety and the potential for misuse, echoing Altman's concerns. Some debate the feasibility and implications of his third point about societal adaptation, with some skeptical of our ability to manage such rapid advancements. Others discuss the potential economic and political ramifications, including the need for new regulatory frameworks and the potential for increased inequality. A few commenters express cynicism about Altman's motives, suggesting the post is primarily self-serving, aimed at shaping public perception and influencing policy decisions favorable to his companies.
This paper chronicles the adoption and adaptation of APL in the Soviet Union up to 1991. Initially hampered by hardware limitations and the lack of official support, APL gained a foothold through enthusiastic individuals who saw its potential for scientific computing and education. The development of Soviet APL interpreters, notably on ES EVM mainframes and personal computers like the Iskra-226, fostered a growing user community. Despite challenges like Cyrillic character adaptation and limited access to Western resources, Soviet APL users formed active groups, organized conferences, and developed specialized applications in various fields, demonstrating a distinct and resilient APL subculture. The arrival of perestroika further facilitated collaboration and exchange with the international APL community.
HN commenters discuss the fascinating history of APL's adoption and adaptation within the Soviet Union, highlighting the ingenuity required to implement it on limited hardware. Several share personal anecdotes about using APL on Soviet computers, recalling its unique characteristics and the challenges of working with its specialized keyboard. Some commenters delve into the technical details of Soviet hardware limitations and the creative solutions employed to overcome them, including modifying character sets and developing custom input methods. The discussion also touches on the broader context of computing in the USSR, with mentions of other languages and the impact of restricted access to Western technology. A few commenters express interest in learning more about the specific dialects of APL developed in the Soviet Union and the influence of these adaptations on later versions of the language.
The post argues that individual use of ChatGPT and similar AI models has a negligible environmental impact compared to other everyday activities like driving or streaming video. While large language models require significant resources to train, the energy consumed during individual inference (i.e., asking it questions) is minimal. The author uses analogies to illustrate this point, comparing the training process to building a road and individual use to driving on it. Therefore, focusing on individual usage as a source of environmental concern is misplaced and distracts from larger, more impactful areas like the initial model training or even more general sources of energy consumption. The author encourages engagement with AI and emphasizes the potential benefits of its widespread adoption.
Hacker News commenters largely agree with the article's premise that individual AI use isn't a significant environmental concern compared to other factors like training or Bitcoin mining. Several highlight the hypocrisy of focusing on individual use while ignoring the larger impacts of data centers or military operations. Some point out the potential benefits of AI for optimization and problem-solving that could lead to environmental improvements. Others express skepticism, questioning the efficiency of current models and suggesting that future, more complex models could change the environmental cost equation. A few also discuss the potential for AI to exacerbate existing societal inequalities, regardless of its environmental footprint.
Summary of Comments ( 164 )
https://news.ycombinator.com/item?id=43668192
Hacker News commenters were generally skeptical of the practicality and comfort of the author's setup. Several pointed out that using AR glasses for extended periods is currently uncomfortable and that the advertised battery life of such devices is often inflated. Others questioned the true portability of the setup given the need for external batteries, keyboards, and mice. Some suggested a tablet or lightweight laptop would be a more ergonomic and practical solution. The overall sentiment was that while the idea is intriguing, the technology isn't quite there yet for a comfortable and productive mobile computing experience. A few users shared their own experiences with similar setups, reinforcing the challenges with current AR glasses and the limitations of relying on public Wi-Fi.
The Hacker News post "I ditched my laptop for a pocketable mini PC and a pair of AR glasses" generated a moderate amount of discussion, with a number of commenters sharing their own experiences and perspectives on the practicality and future of this type of setup.
Several commenters expressed skepticism about the current state of AR glasses for productivity. They pointed out issues like limited field of view, poor image quality, discomfort during extended use, and social awkwardness in public settings. Some suggested that current AR glasses are better suited for specific niche applications rather than general-purpose computing.
One commenter questioned the author's choice of using a separate mini PC, arguing that a modern phone could likely handle the computational workload and simplify the setup. They also highlighted the potential for future phones to directly integrate AR capabilities, further streamlining the experience.
Another commenter emphasized the importance of input methods, suggesting that a comfortable and efficient input solution is crucial for replacing a laptop. They discussed the limitations of current AR interfaces and expressed hope for future advancements in this area.
A few commenters shared their own experiences with similar setups, using tablets, portable monitors, and Bluetooth keyboards to create mobile workstations. They discussed the trade-offs involved in portability versus functionality and offered insights into the challenges and benefits of ditching a traditional laptop.
Some comments focused on the potential future of AR and mobile computing, envisioning a future where powerful pocket-sized devices combined with advanced AR glasses could replace traditional laptops for many users. However, they acknowledged that significant technological advancements are still needed to realize this vision.
Overall, the comments reflected a mixture of excitement about the potential of AR and mobile computing, tempered by realism about the current limitations of the technology. While some commenters were intrigued by the author's experiment, most agreed that a truly laptop-replacing AR experience is still some way off.