The paper "Stop using the elbow criterion for k-means" argues against the common practice of using the elbow method to determine the optimal number of clusters (k) in k-means clustering. The authors demonstrate that the elbow method is unreliable, often identifying spurious elbows or missing genuine ones. They show this through theoretical analysis and empirical examples across various datasets and distance metrics, revealing how the within-cluster sum of squares (WCSS) curve, on which the elbow method relies, can behave unexpectedly. The paper advocates for abandoning the elbow method entirely in favor of more robust and theoretically grounded alternatives like the gap statistic, silhouette analysis, or information criteria, which offer statistically sound approaches to k selection.
Ruth Tillman's blog post "All Clothing is Handmade (2022)" argues that the distinction between "handmade" and "machine-made" clothing is a false dichotomy. All clothing, whether crafted by an individual artisan or produced in a factory, involves extensive human labor throughout its lifecycle, from design and material sourcing to manufacturing, shipping, and retail. The post uses the example of a seemingly simple t-shirt to illustrate the complex network of human effort required, emphasizing the skills, knowledge, and labor embedded within each stage of production. Therefore, "handmade" shouldn't be understood as a category separate from industrial production but rather a recognition of the inherent human element present in all clothing creation.
Hacker News users generally agreed with the premise of the article—that all clothing involves human labor somewhere along the line, even if highly automated—and discussed the implications. Some highlighted the devaluing of human labor, particularly in the fashion industry, with "fast fashion" obscuring the effort involved. Others pointed out the historical context of clothing production, noting how technologies like the sewing machine shifted, rather than eliminated, human involvement. A compelling comment thread explored the distinction between "handmade" and "hand-crafted", suggesting that the latter implies artistry and design beyond basic construction, and questioned whether "machine-made" is truly a separate category. Some users argued the author's point was obvious, while others appreciated the reminder about the human cost of clothing. A few comments also touched on the environmental impact of clothing production and the need for more sustainable practices.
AMC Theatres will test Deepdub's AI-powered visual dubbing technology with a limited theatrical release of the Swedish film "A Piece of My Heart" ("En del av mitt hjärta"). This technology alters the actors' lip movements on-screen to synchronize with the English-language dub, offering a more immersive and natural viewing experience than traditional dubbing. The test will run in select AMC locations across the US from June 30th to July 6th, providing valuable audience feedback on the technology's effectiveness.
Hacker News users discuss the implications of AI-powered visual dubbing, as described in the linked Engadget article about AMC screening a Swedish film using this technology. Several express skepticism about the quality and believability of AI-generated lip movements, fearing an uncanny valley effect. Some question the need for this approach compared to traditional dubbing or subtitles, citing potential job displacement for voice actors and a preference for authentic performances. Others see potential benefits for accessibility and international distribution, but also raise concerns about the ethical considerations of manipulating actors' likenesses without consent and the potential for misuse of deepfake technology. A few commenters are cautiously optimistic, suggesting that this could be a useful tool if implemented well, while acknowledging the need for further refinement.
Next.js 15.2.3 patches a high-severity security vulnerability (CVE-2025-29927) that could allow attackers to execute arbitrary code on servers running affected versions. The vulnerability stems from improper handling of serialized data within the Image component when using a custom loader. Upgrading to 15.2.3 or later is strongly recommended for all users. Versions 13.4.15 and 14.9.5 also address the issue for older release lines.
Hacker News commenters generally express relief and gratitude for the swift patch addressing the vulnerability in Next.js 15.2.3. Some questioned the severity and real-world exploitability of the vulnerability given the limited information disclosed, with one suggesting the high CVE score might be precautionary. Others discussed the need for better communication from Vercel, including details about the nature of the vulnerability and its potential impact. A few commenters also debated the merits of using older, potentially more stable, versions of Next.js versus staying on the cutting edge. Some users expressed frustration with the constant stream of updates and vulnerabilities in modern web frameworks.
This blog post details the surprisingly complex process of gracefully shutting down a nested Intel x86 hypervisor. It focuses on the scenario where a management VM within a parent hypervisor needs to shut down a child VM, also running a hypervisor. Simply issuing a poweroff command isn't sufficient, as it can leave the child hypervisor in an undefined state. The author explores ACPI shutdown methods, explaining that initiating shutdown from within the child hypervisor is the cleanest approach. However, since external intervention is sometimes necessary, the post delves into using the hypervisor's debug registers to inject a shutdown signal, ultimately mimicking the internal ACPI process. This involves navigating complexities of nested virtualization and ensuring data integrity during the shutdown sequence.
HN commenters generally praised the author's clear writing and technical depth. Several discussed the complexities of hypervisor development and the challenges of x86 specifically, echoing the author's points about interrupt virtualization and hardware quirks. Some offered alternative approaches to the problems described, including paravirtualization and different ways to handle interrupt remapping. A few commenters shared their own experiences wrestling with similar low-level x86 intricacies. The overall sentiment leaned towards appreciation for the author's willingness to share such detailed knowledge about a typically opaque area of software.
The polar vortex, a large area of low pressure and cold air surrounding both of Earth's poles, is currently experiencing a disruption in its typical westward flow. This "traffic jam" is caused by atmospheric waves propagating upwards from the lower atmosphere, slowing and even reversing the vortex's usual rotation. This can lead to portions of the vortex splitting off and moving southward, bringing outbreaks of cold arctic air to mid-latitude regions. While these disruptions are a normal part of the vortex’s behavior and not necessarily indicative of climate change on their own, studying these events helps scientists better understand atmospheric dynamics and improve forecasting.
Several commenters on Hacker News discussed the complexities of communicating about the polar vortex, noting that media simplification often misrepresents the phenomenon. Some highlighted the difference between stratospheric and tropospheric polar vortices, emphasizing that the article refers to the stratospheric vortex. Others questioned the connection between a slowing stratospheric polar vortex and extreme weather events, pointing to the need for further research and more nuanced reporting. A few commenters also expressed concern about the broader implications of climate change and its impact on weather patterns, while others discussed the challenges of accurately modeling and predicting these complex systems. There was also some discussion about the terminology used in the article and the potential for misinterpretation by the public.
The primary economic impact of AI won't be from groundbreaking research or entirely new products, but rather from widespread automation of existing processes across various industries. This automation will manifest through AI-powered tools enhancing existing software and making mundane tasks more efficient, much like how previous technological advancements like spreadsheets amplified human capabilities. While R&D remains important for progress, the real value lies in leveraging existing AI capabilities to streamline operations, optimize workflows, and reduce costs at a broad scale, leading to significant productivity gains across the economy.
HN commenters largely agree with the article's premise that most AI value will derive from applying existing models rather than fundamental research. Several highlighted the parallel with the internet, where early innovation focused on infrastructure and protocols, but the real value explosion came later with applications built on top. Some pushed back slightly, arguing that continued R&D is crucial for tackling more complex problems and unlocking the next level of AI capabilities. One commenter suggested the balance might shift between application and research depending on the specific area of AI. Another noted the importance of "glue work" and tooling to facilitate broader automation, suggesting future value lies not only in novel models but also in the systems that make them accessible and deployable.
This Mozilla AI blog post explores using computer vision to automatically identify and add features to OpenStreetMap. The project leverages a large dataset of aerial and street-level imagery to train models capable of detecting objects like crosswalks, swimming pools, and basketball courts. By combining these detections with existing OpenStreetMap data, they aim to improve map completeness and accuracy, particularly in under-mapped regions. The post details their technical approach, including model architectures and training strategies, and highlights the potential for community involvement in validating and integrating these AI-generated features. Ultimately, they envision this technology as a powerful tool for enriching open map data and making it more useful for everyone.
Several Hacker News commenters express excitement about the potential of using computer vision to improve OpenStreetMap data, particularly in automating tedious tasks like feature extraction from aerial imagery. Some highlight the project's clever use of pre-trained models like Segment Anything and the importance of focusing on specific features (crosswalks, swimming pools) to improve accuracy. Others raise concerns about the accuracy of such models, potential biases in the training data, and the risk of overwriting existing, manually-verified data. There's discussion around the need for careful human oversight, suggesting the tool should assist rather than replace human mappers. A few users suggest other data sources like point clouds and existing GIS datasets could further enhance the project. Finally, some express interest in the project's open-source nature and the possibility of contributing.
Tencent has introduced Hunyuan-T1, its first ultra-large language model powered by its in-house AI training chip, Mamba. This model boasts over a trillion parameters and has demonstrated strong performance across various Chinese language understanding benchmarks, outperforming other prominent models in tasks like text completion, reading comprehension, and math problem-solving. Hunyuan-T1 also exhibits improved reasoning abilities and reduced hallucination rates. Tencent plans to integrate this powerful model into its existing products and services, including Tencent Cloud, Tencent Meeting, and Tencent Docs, enhancing their capabilities and user experience.
Hacker News users discuss Tencent's Hunyuan-T1 model, focusing on its purported size and performance. Some express skepticism about the claimed 1.01 trillion parameters and superior performance to GPT-3 and PaLM, particularly given the lack of public access and independent benchmarks. Others point out the difficulty in verifying these claims without more transparency and publicly available data or demos. The closed nature of the model leads to discussion about the increasing trend of large companies keeping their advanced AI models proprietary, hindering wider community scrutiny and progress. A few commenters mention the geopolitical implications of Chinese companies developing advanced AI, alongside the general challenges of evaluating large language models based solely on company-provided information.
Edward Yang's blog post delves into the internal architecture of PyTorch, a popular deep learning framework. It explains how PyTorch achieves dynamic computation graphs through operator overloading and a tape-based autograd system. Essentially, PyTorch builds a computational graph on-the-fly as operations are performed, recording each step for automatic differentiation. This dynamic approach contrasts with static graph frameworks like TensorFlow v1 and offers greater flexibility for debugging and control flow. The post further details key components such as tensors, variables (deprecated in later versions), functions, and modules, illuminating how they interact to enable efficient deep learning computations. It highlights the importance of torch.autograd.Function as the building block for custom operations and automatic differentiation.
Hacker News users discuss Edward Yang's blog post on PyTorch internals, praising its clarity and depth. Several commenters highlight the value of understanding how automatic differentiation works, with one calling it "critical for anyone working in the field." The post's explanation of the interaction between Python and C++ is also commended. Some users discuss their personal experiences using and learning PyTorch, while others suggest related resources like the "Tinygrad" project for a simpler perspective on automatic differentiation. A few commenters delve into specific aspects of the post, like the use of Variable and its eventual deprecation, and the differences between tracing and scripting methods for graph creation. Overall, the comments reflect an appreciation for the post's contribution to understanding PyTorch's inner workings.
Landrun is a tool that utilizes the Landlock Linux Security Module (LSM) to sandbox processes without requiring root privileges or containers. It allows users to define fine-grained access control rules for a target process, restricting its access to the filesystem, network, and other resources. By leveraging Landlock's unprivileged mode and a clever bootstrapping process involving temporary filesystems, Landrun simplifies sandbox setup and makes robust sandboxing accessible to regular users. This enables easier and more secure execution of potentially untrusted code, contributing to a more secure desktop environment.
HN commenters generally praise Landrun for its innovative approach to sandboxing, making it easier than traditional methods like containers or VMs. Several highlight the significance of using Landlock LSM for security, noting its kernel-level enforcement as a robust mechanism. Some discuss potential use cases, including sandboxing web browsers and other potentially risky applications. A few express concerns about complexity and debugging challenges, while others point out the project's early stage and potential for improvement. The user-friendliness compared to other sandboxing techniques is a recurring theme, with commenters appreciating the streamlined process. Some also discuss potential integrations and extensions, such as combining Landrun with Firejail.
While the Wright brothers are widely credited with inventing the airplane, in Brazil, Alberto Santos-Dumont holds that honor. Brazilians argue that Santos-Dumont's 14-bis, unlike the Wright Flyer, achieved sustained, controlled flight without the assistance of launch rails or catapults, making it the first true airplane. This national pride is reflected in official records, educational materials, and public monuments, solidifying Santos-Dumont's legacy as the aviation pioneer in Brazil.
Hacker News users discuss the cultural and historical context around the invention of the airplane, acknowledging Brazil's strong belief that Alberto Santos-Dumont is the rightful inventor. Several commenters point out that the criteria for "invention" are debatable, with some emphasizing controlled, sustained flight (favoring the Wright brothers) while others prioritize public demonstrations and reproducibility (favoring Santos-Dumont). The complexities of patent law and differing standards of evidence also enter the discussion. Some users mention Santos-Dumont's open-source approach to his designs as a contributing factor to his popularity, contrasting it with the Wright brothers' more secretive approach. The general sentiment reflects an understanding of Brazil's perspective, even if not everyone agrees with it, and highlights how national narratives shape historical interpretations.
Scientists have developed a low-cost, efficient method for breaking down common plastics like polyethylene and polypropylene into valuable chemicals. Using a manganese-based catalyst and air at moderate temperatures, the process converts the plastics into benzoic acid and other chemicals used in food preservatives, perfumes, and pharmaceuticals. This innovative approach avoids the high temperatures and pressures typically required for plastic degradation, potentially offering a more sustainable and economically viable recycling solution.
Hacker News users discussed the potential impact and limitations of the plastic-degrading catalyst. Some expressed skepticism about real-world applicability, citing the need for further research into scalability, energy efficiency, and the precise byproducts of the reaction. Others pointed out the importance of reducing plastic consumption alongside developing recycling technologies, emphasizing that this isn't a silver bullet solution. A few commenters highlighted the cyclical nature of scientific advancements, noting that previous "breakthroughs" in plastic degradation haven't panned out. There was also discussion regarding the potential economic and logistical hurdles of implementing such a technology on a large scale, including collection and sorting challenges. Several users questioned whether the byproducts are truly benign, requesting more detail beyond the article's claim of "environmentally benign" molecules.
Researchers reliant on animal models, particularly in neuroscience and physiology, face growing career obstacles. Funding is increasingly directed towards human-focused research like clinical trials and 'omics' approaches, seen as more translatable to human health. This shift, termed "animal methods bias," disadvantages scientists trained in animal research, limiting their funding opportunities, hindering career progression, and potentially slowing crucial basic research. While acknowledging the importance of human-focused studies, the article highlights the ongoing need for animal models in understanding fundamental biological processes and developing new treatments, urging funders and institutions to recognize and address this bias to avoid stifling valuable scientific contributions.
HN commenters discuss the systemic biases against research using animal models. Several express concern that the increasing difficulty and expense of such research, coupled with the perceived lower status compared to other biological research, is driving talent away from crucial areas of study like neuroscience. Some note the irony that these biases are occurring despite significant breakthroughs having come from animal research, and the continued need for it in many fields. Others mention the influence of animal rights activism and public perception on funding decisions. One commenter suggests the bias extends beyond careers, impacting publications and grant applications, ultimately hindering scientific progress. A few discuss the ethical implications and the need for alternatives, acknowledging the complex balancing act between animal welfare and scientific advancement.
Google researchers investigated how well large language models (LLMs) can predict human brain activity during language processing. By comparing LLM representations of language with fMRI recordings of brain activity, they found significant correlations, especially in brain regions associated with semantic processing. This suggests that LLMs, despite being trained on text alone, capture some aspects of how humans understand language. The research also explored the impact of model architecture and training data size, finding that larger models with more diverse training data better predict brain activity, further supporting the notion that LLMs are developing increasingly sophisticated representations of language that mirror human comprehension. This work opens new avenues for understanding the neural basis of language and using LLMs as tools for cognitive neuroscience research.
Hacker News users discussed the implications of Google's research using LLMs to understand brain activity during language processing. Several commenters expressed excitement about the potential for LLMs to unlock deeper mysteries of the brain and potentially lead to advancements in treating neurological disorders. Some questioned the causal link between LLM representations and brain activity, suggesting correlation doesn't equal causation. A few pointed out the limitations of fMRI's temporal resolution and the inherent complexity of mapping complex cognitive processes. The ethical implications of using such technology for brain-computer interfaces and potential misuse were also raised. There was also skepticism regarding the long-term value of this particular research direction, with some suggesting it might be a dead end. Finally, there was discussion of the ongoing debate around whether LLMs truly "understand" language or are simply sophisticated statistical models.
Jakt is a statically-typed, compiled programming language designed for performance and ease of use, with a focus on systems programming, game development, and GUI applications. Inspired by C++, Rust, and other modern languages, it features manual memory management, optional garbage collection, compile-time evaluation, and a friendly syntax. Developed alongside the SerenityOS operating system, Jakt aims to offer a robust and modern alternative for building performant and maintainable software while prioritizing developer productivity.
Hacker News users discuss Jakt's resemblance to C++, Rust, and Swift, noting its potential appeal to those familiar with these languages. Several commenters express interest in its development, praising its apparent simplicity and clean design, particularly the ownership model and memory management. Some skepticism arises about the long-term viability of another niche language, and concerns are voiced about potential performance limitations due to garbage collection. The cross-compilation ability for WebAssembly also generated interest, with users envisioning potential applications. A few commenters mention the project's active and welcoming community as a positive aspect. Overall, the comments indicate a cautious optimism towards Jakt, with many intrigued by its features but also mindful of the challenges facing a new programming language.
Driven by the sudden success of OpenAI's ChatGPT, Google embarked on a two-year internal overhaul to accelerate its AI development. This involved merging DeepMind with Google Brain, prioritizing large language models, and streamlining decision-making. The result is Gemini, Google's new flagship AI model, which the company claims surpasses GPT-4 in certain capabilities. The reorganization involved significant internal friction and a rapid shift in priorities, highlighting the intense pressure Google felt to catch up in the generative AI race. Despite the challenges, Google believes Gemini represents a significant step forward and positions them to compete effectively in the rapidly evolving AI landscape.
HN commenters discuss Google's struggle to catch OpenAI, attributing it to organizational bloat and risk aversion. Several suggest Google's internal processes stifled innovation, contrasting it with OpenAI's more agile approach. Some argue Google's vast resources and talent pool should have given them an advantage, but bureaucracy and a focus on incremental improvements rather than groundbreaking research held them back. The discussion also touches on Gemini's potential, with some expressing skepticism about its ability to truly surpass GPT-4, while others are cautiously optimistic. A few comments point out the article's reliance on anonymous sources, questioning its objectivity.
A cell's metabolic state, meaning the chemical reactions happening within it, significantly influences its fate, including whether it divides, differentiates into a specialized cell type, or dies. Rather than simply fueling cellular processes, metabolism actively shapes cell behavior by altering gene expression and protein function. Specific metabolites, the intermediate products of metabolism, can directly modify proteins, impacting their activity and guiding cellular decisions. This understanding opens up possibilities for manipulating metabolism to control cell fate, offering potential therapeutic interventions for diseases like cancer.
HN commenters generally expressed fascination with the article's findings on how metabolism influences cell fate. Several highlighted the counterintuitive nature of the discovery, noting that it shifts the traditional understanding of DNA as the primary driver of cellular differentiation. Some discussed the implications for cancer research, regenerative medicine, and aging. One commenter pointed out the potential connection to the Warburg effect, where cancer cells favor glycolysis even in the presence of oxygen. Another questioned the generalizability of the findings, given the focus on yeast and mouse embryonic stem cells. A few expressed excitement about the future research directions this opens up, particularly regarding metabolic interventions for disease.
A new study reveals a shared mechanism for coping with environmental stress in plants and green algae dating back 600 million years to their common ancestor. Researchers found that both plants and algae utilize a protein called CONSTANS, originally known for its role in flowering, to manage responses to various stresses like drought and high salinity. This ancient stress response system involves CONSTANS interacting with other proteins to regulate gene expression, protecting the organism from damage. This discovery highlights a highly conserved and essential survival mechanism across the plant kingdom and offers potential insights into improving stress tolerance in crops.
HN commenters discuss the implications of the study showing a shared stress response across algae and plants, questioning whether this truly represents 600 million years of conservation or if horizontal gene transfer played a role. Some highlight the importance of understanding these mechanisms for improving crop resilience in the face of climate change. Others express skepticism about the specific timeline presented, suggesting further research is needed to solidify the evolutionary narrative. The potential for biotechnological applications, such as engineering stress tolerance in crops, is also a point of interest. A few users dive into the specifics of the abscisic acid (ABA) pathway discussed in the study, pointing out its known role in stress response and questioning the novelty of the findings. Overall, the comments demonstrate a mix of intrigue, cautious interpretation, and a focus on the practical implications for agriculture and biotechnology.
Torch Lens Maker is a PyTorch library for differentiable geometric optics simulations. It allows users to model optical systems, including lenses, mirrors, and apertures, using standard PyTorch tensors. Because the simulations are differentiable, it's possible to optimize the parameters of these optical systems using gradient-based methods, opening up possibilities for applications like lens design, computational photography, and inverse problems in optics. The library provides a simple and intuitive interface for defining optical elements and propagating rays through the system, all within the familiar PyTorch framework.
Commenters on Hacker News generally expressed interest in Torch Lens Maker, praising its interactive nature and potential applications. Several users highlighted the value of real-time feedback and the educational possibilities it offers for understanding optical systems. Some discussed the potential use cases, ranging from camera design and optimization to educational tools and even artistic endeavors. A few commenters inquired about specific features, such as support for chromatic aberration and diffraction, and the possibility of exporting designs to other formats. One user expressed a desire for a similar tool for acoustics. While generally positive, there wasn't an overwhelmingly large volume of comments.
The "Wheel Reinventor's Principles" advocate for strategically reinventing existing solutions, not out of ignorance, but as a path to deeper understanding and potential innovation. It emphasizes learning by doing, prioritizing personal growth over efficiency, and embracing the educational journey of rebuilding. While acknowledging the importance of leveraging existing tools, the principles encourage exploration and experimentation, viewing the process of reinvention as a method for internalizing knowledge, discovering novel approaches, and ultimately building a stronger foundation for future development. This approach values the intrinsic rewards of learning and the potential for uncovering unforeseen improvements, even if the initial outcome isn't as polished as established alternatives.
Hacker News users generally agreed with the author's premise that reinventing the wheel can be beneficial for learning, but cautioned against blindly doing so in professional settings. Several commenters emphasized the importance of understanding why something is the standard, rather than simply dismissing it. One compelling point raised was the idea of "informed reinvention," where one researches existing solutions thoroughly before embarking on their own implementation. This approach allows for innovation while avoiding common pitfalls. Others highlighted the value of open-source alternatives, suggesting that contributing to or forking existing projects is often preferable to starting from scratch. The distinction between reinventing for learning versus for production was a recurring theme, with a general consensus that personal projects are an ideal space for experimentation, while production environments require more pragmatism. A few commenters also noted the potential for "NIH syndrome" (Not Invented Here) to drive unnecessary reinvention in corporate settings.
Researchers have discovered evidence of previously unknown microorganisms that lived within the pore spaces of marble and limestone monuments in the Yucatan Peninsula, Mexico. These microbes, distinct from those found on the surfaces of the stones, apparently thrived in this unique habitat, potentially influencing the deterioration or preservation of these ancient structures. The study employed DNA sequencing and microscopy to identify these endolithic organisms, suggesting they may represent a new branch on the tree of life. This finding opens up new avenues for understanding microbial life in extreme environments and the complex interactions between microorganisms and stone materials.
Hacker News users discussed the implications of discovering microbial life within marble and limestone, focusing on the potential for similar life on other planets with similar geological compositions. Some highlighted the surprising nature of finding life in such a seemingly inhospitable environment and the expanded possibilities for extraterrestrial life this discovery suggests. Others questioned the novelty of the finding, pointing out that microbial life exists virtually everywhere and emphasizing that the research simply identifies a specific habitat rather than a truly novel form of life. Some users expressed concern over the potential for contamination of samples, while others speculated about the potential roles these microbes play in geological processes like weathering. A few commenters also discussed the potential for using these microbes in industrial applications, such as bio-mining or CO2 sequestration.
Deduce is a proof checker designed specifically for educational settings. It aims to bridge the gap between informal mathematical reasoning and formal proof construction by providing a simple, accessible interface and a focused set of logical connectives. Its primary goal is to teach the core concepts of formal logic and proof techniques without overwhelming users with complex syntax or advanced features. The system supports natural deduction style proofs and offers immediate feedback, guiding students through the process of building valid arguments step-by-step. Deduce prioritizes clarity and ease of use to make learning formal logic more engaging and less daunting.
Hacker News users discussed the educational value of the Deduce proof checker. Several commenters appreciated its simplicity and accessibility compared to other systems like Coq, finding its focus on propositional and first-order logic suitable for introductory logic courses. Some suggested potential improvements, such as adding support for natural deduction and incorporating a more interactive tutorial. Others debated the pedagogical merits of different proof styles and the balance between automated assistance and requiring students to fill in proof steps themselves. The overall sentiment was positive, with many seeing Deduce as a promising tool for teaching logic.
Notetime is a minimalist note-taking app that automatically timestamps every line you write, creating a detailed chronological record of your thoughts and ideas. It's designed for capturing fleeting notes, brainstorming, journaling, and keeping a log of events. The interface is intentionally simple, focusing on quick capture and easy searchability. Notes are stored locally, offering privacy and offline access. The app is available for macOS, Windows, and Linux.
Hacker News users generally praised Notetime's minimalist approach and automatic timestamping, finding it useful for journaling, meeting notes, and tracking progress. Some expressed a desire for features like tagging, search, and different note organization methods, while others appreciated the simplicity and lack of distractions. Concerns were raised about the closed-source nature of the app and the potential for vendor lock-in, with some preferring open-source alternatives like Joplin and Standard Notes. The developer responded to several comments, clarifying the reasoning behind design choices and indicating openness to considering feature requests. Discussion also touched on the benefits of plain text notes and the challenges of balancing simplicity with functionality.
This 2015 blog post outlines the key differences between Managers, Directors, and VPs, focusing on how their responsibilities and impact evolve with seniority. Managers are responsible for doing – directly contributing to the work and managing individual contributors. Directors shift to getting things done through others, managing managers and owning larger projects or initiatives. VPs are responsible for setting direction and influencing the organization strategically, managing multiple directors and owning entire functional areas. The post emphasizes that upward movement isn't simply about more responsibility, but a fundamental shift in focus from tactical execution to strategic leadership.
HN users generally found the linked article's definitions of manager, director, and VP roles accurate and helpful, especially for those transitioning into management. Several commenters emphasized the importance of influence and leverage as key differentiators between the levels. One commenter highlighted the "multiplier effect" of higher-level roles, where impact isn't solely from individual contribution but from enabling others. Some discussion revolved around the varying definitions of these titles across companies, with some noting that "director" can be a particularly nebulous term. Others pointed out the emotional labor involved in management and the necessity of advocating for your team. A few commenters also shared their own experiences and anecdotes that supported the article's claims.
Autology is a Lisp dialect designed for self-modifying code and introspection. It exposes its own interpreter and data structures, allowing programs to analyze and manipulate their own source code, execution state, and even the interpreter itself during runtime. This capability enables dynamic code generation, on-the-fly modifications, and powerful metaprogramming techniques. It aims to provide a flexible environment for exploring novel programming paradigms and building self-aware, adaptive systems.
HN users generally expressed interest in Autology, a Lisp dialect with access to its own interpreter. Several commenters compared it favorably to Rebol in terms of metaprogramming capabilities. Some discussion focused on its potential use cases, including live coding and creating interactive development environments. Concerns were raised regarding its apparent early stage of development, the lack of documentation beyond the README, and the potential performance implications of its design. A few users questioned the practicality of such a language, while others were excited by the possibilities it presented for self-modifying code and advanced debugging tools. The reliance on Python for its implementation also sparked some debate.
This post advocates for using Ruby's built-in features like Struct and immutable data structures (via freeze) to create simple, efficient value objects. It argues against using more complex approaches like dry-struct or Virtus for basic cases, highlighting that the lightweight, idiomatic approach often provides sufficient functionality with minimal overhead. The article illustrates how Struct provides concise syntax for defining attributes and automatic equality and hashing based on those attributes, fulfilling the core requirements of value objects. Finally, it demonstrates how to enforce immutability by freezing instances, ensuring predictable behavior and preventing unintended side effects.
HN users largely criticized the article for misusing or misunderstanding the term "Value Object." Commenters pointed out that true Value Objects are immutable and compared by value, not identity. They argued that the article's examples, particularly using mutable hashes and relying on equal?, were not representative of Value Objects and promoted bad practices. Several users suggested alternative approaches like using Struct or creating immutable classes with custom equality methods. The discussion also touched on the performance implications of immutable objects in Ruby and the nuances of defining equality for more complex objects. Some commenters felt the title was misleading, promoting a non-idiomatic approach.
Edsger Dijkstra argues that array indexing should start at zero, not one. He lays out a compelling case based on the elegance and efficiency of expressing slices or subsequences within an array. Using half-open intervals, where the lower bound is inclusive and the upper bound exclusive, simplifies calculations and leads to fewer "off-by-one" errors. Dijkstra demonstrates that representing a subsequence from element 'i' through 'j' becomes significantly more straightforward when using zero-based indexing, as the length of the subsequence is simply j-i. This contrasts with one-based indexing, which necessitates more complex and less intuitive calculations for subsequence lengths and endpoint adjustments. He concludes that zero-based indexing offers a more natural and consistent way to represent array segments, aligning better with mathematical conventions and ultimately leading to cleaner, less error-prone code.
Hacker News users discuss Dijkstra's famous argument for zero-based indexing. Several commenters agree with Dijkstra's logic, emphasizing the elegance and efficiency of using half-open intervals. Some highlight the benefits in loop constructs and simplifying calculations for array slices. A few point out that one-based indexing can be more intuitive in certain contexts, aligning with how humans naturally count. One commenter notes the historical precedent, mentioning that Fortran used one-based indexing, influencing later languages. The discussion also touches on the trade-offs between conventions and the importance of consistency within a given language or project.
This GitHub repository preserves incredibly early versions of Dennis Ritchie's Portable C Compiler, including pre-1.0 snapshots dating back to the late 1970s. These versions offer a fascinating glimpse into the evolution of C, showcasing its transition from a research language to the widespread programming powerhouse it became. The repository aims to archive these historically significant artifacts, making them available for study and exploration by those interested in the origins and development of C. It includes various versions for different architectures, providing valuable insights into early compiler design and the challenges of portability in the nascent days of Unix.
Hacker News users discussed the historical significance of the rediscovered C compiler source code, noting its use of PDP-11 assembly and the challenges of porting it to modern systems due to its tight coupling with the original hardware. Several commenters expressed interest in its educational value for understanding early compiler design and the evolution of C. Some debated the compiler's true "firstness," acknowledging earlier, possibly lost, versions, while others focused on the practical difficulties of building and running such old code. A few users shared personal anecdotes about their experiences with early C compilers and PDP-11 machines, adding a personal touch to the historical discussion. The overall sentiment was one of appreciation for the preservation and sharing of this piece of computing history.
Ian Stewart's "The Celts: A Modern History" refutes the romanticized notion of a unified Celtic past. Stewart argues that "Celtic" is a largely modern construct, shaped by 18th and 19th-century romanticism and nationalism. While acknowledging shared linguistic and cultural elements in ancient communities across Europe, he emphasizes their diversity and distinct identities. The book traces how the concept of "Celticism" evolved and was variously appropriated for political and cultural agendas, demonstrating that contemporary interpretations of Celtic identity are far removed from historical realities. Stewart’s rigorous approach deconstructs the persistent myth of a singular Celtic people, presenting a more nuanced and historically accurate view of the dispersed communities labeled "Celtic."
HN commenters largely discuss the problematic nature of defining "Celts," questioning its validity as a unified cultural or ethnic group. Several highlight the anachronistic application of the term, arguing it's a modern construct retroactively applied to disparate groups. Some point to the book's potential value despite this, acknowledging its exploration of how the idea of "Celticness" has been constructed and used throughout history, particularly in relation to national identity. Others suggest alternative readings on the topic or express skepticism towards the review's framing. A recurring theme is the romanticized and often inaccurate portrayal of Celtic history, especially within nationalistic narratives.
Summary of Comments ( 13 )
https://news.ycombinator.com/item?id=43450550
HN users discuss the problems with the elbow method for determining the optimal number of clusters in k-means, agreeing it's often unreliable and subjective. Several commenters suggest superior alternatives, such as the silhouette coefficient, gap statistic, and information criteria like AIC/BIC. Some highlight the importance of considering the practical context and the "business need" when choosing the number of clusters, rather than relying solely on statistical methods. Others point out that k-means itself may not be the best clustering algorithm for all datasets, recommending DBSCAN and hierarchical clustering as potentially better suited for certain situations, particularly those with non-spherical clusters. A few users mention the difficulty in visualizing high-dimensional data and interpreting the results of these metrics, emphasizing the iterative nature of cluster analysis.
The Hacker News post titled "Stop using the elbow criterion for k-means" (https://news.ycombinator.com/item?id=43450550) discusses the linked arXiv paper which argues against using the elbow method for determining the optimal number of clusters in k-means clustering. The comments section is relatively active, featuring a variety of perspectives on the topic.
Several commenters agree with the premise of the article. They point out that the elbow method is often subjective and unreliable, leading to arbitrary choices for the number of clusters. Some users share anecdotal experiences of the elbow method failing to produce meaningful results or being difficult to interpret. One commenter suggests the gap statistic as a more robust alternative.
A recurring theme in the comments is the inherent difficulty of choosing the "right" number of clusters, especially in high-dimensional spaces. Some users argue that the optimal number of clusters is often dependent on the specific application and downstream analysis, rather than being an intrinsic property of the data. They suggest that domain knowledge and interpretability should play a significant role in the decision-making process.
One commenter points out that the elbow method is particularly problematic when the clusters are not well-separated or when the data has a complex underlying structure. They suggest using visualization techniques, like dimensionality reduction, to gain a better understanding of the data before attempting to cluster it.
Another comment thread discusses the limitations of k-means clustering itself, regardless of the method used to choose k. Users highlight the algorithm's sensitivity to initial conditions and its assumption of spherical clusters. They propose alternative clustering methods, such as DBSCAN and hierarchical clustering, which may be more suitable for certain types of data.
A few commenters defend the elbow method, arguing that it can be a useful starting point for exploratory data analysis. They acknowledge its limitations but suggest that it can provide a rough estimate of the number of clusters, which can be refined using other techniques.
Finally, some commenters discuss the practical implications of choosing the wrong number of clusters. They highlight the potential for misleading results and incorrect conclusions, emphasizing the importance of careful consideration and validation. One commenter suggests using metrics like silhouette score or Calinski-Harabasz index to assess the quality of the clustering.
Overall, the comments section reflects a general consensus that the elbow method is not a reliable technique for determining the optimal number of clusters in k-means. Commenters offer various alternative approaches, emphasize the importance of domain knowledge and data visualization, and discuss the broader challenges of clustering high-dimensional data.