After a year of using Go professionally, the author reflects positively on the switch from Java. Go's simplicity, speed, and built-in concurrency features significantly boosted productivity. While missing Java's mature ecosystem and advanced tooling, particularly IntelliJ IDEA, the author found Go's lightweight tools sufficient and appreciated the language's straightforward error handling and fast compilation times. The learning curve was minimal, and the overall experience improved developer satisfaction and project efficiency, making the transition worthwhile.
Svelte 5 significantly departs from its JavaScript framework roots by compiling components directly to vanilla JavaScript instructions that manipulate the DOM. This eliminates the virtual DOM diffing process typical of other frameworks, resulting in smaller bundle sizes and potentially faster performance. Instead of a framework mediating interactions, Svelte 5 generates imperative code tailored to each component, directly updating the DOM. This shift allows for optimized updates and reduces runtime overhead, making Svelte 5 applications more akin to handcrafted JavaScript than traditional framework-driven applications. While still using familiar Svelte syntax, the output is now a highly optimized, self-contained JavaScript module.
HN users discuss Svelte 5's compilation strategy, which moves reactivity out of the JavaScript runtime and into compiled code. Several commenters express excitement over the potential performance benefits and smaller bundle sizes, comparing it favorably to React and other frameworks. Some raise concerns about debugging and the implications for the ecosystem, particularly around tooling. A few express skepticism, questioning whether the performance gains are significant enough to warrant the shift and whether Svelte's approach will hinder wider adoption. There's also discussion about the blurring line between frameworks and compilers, and whether Svelte's compiled output still qualifies as JavaScript. The impact on hydration and server-side rendering is also a topic of interest.
File Pilot is a new file manager focused on speed and a modern user experience. It boasts instant startup and file browsing, a dual-pane interface for efficient file operations, and extensive customization options like themes and keyboard shortcuts. Built with a robust architecture using Rust and Qt, File Pilot aims to provide a reliable and performant alternative to existing file explorers on Windows, macOS, and Linux. Key features include tabbed browsing, a built-in terminal, seamless file previews, and advanced filtering capabilities. File Pilot is currently available as a free technical preview.
HN commenters generally praised File Pilot's speed and clean interface, with several noting its responsiveness felt superior even to native file managers. Some appreciated specific features like the tabbed interface, customizable keyboard shortcuts, and the dual-pane view. A few users requested features like the ability to edit text files directly within the application and improved search functionality. Concerns were raised about the developer's choice to use Electron, citing potential performance overhead and resource consumption. There was also discussion around the lack of a Linux version and the developer's plans for future development and monetization. Some commenters expressed skepticism about the long-term viability of the project given its reliance on a single developer.
A recent Clang optimization introduced in version 17 regressed performance when compiling code containing large switch statements within inlined functions. This regression manifested as significantly increased compile times, sometimes by orders of magnitude, and occasionally resulted in internal compiler errors. The issue stems from Clang's attempt to optimize switch lowering by transforming it into a series of conditional moves based on jump tables. This optimization, while beneficial in some cases, interacts poorly with inlining, exploding the complexity of the generated intermediate representation (IR) when a function with a large switch is inlined multiple times. This ultimately overwhelms the compiler's later optimization passes. A workaround involves disabling the problematic optimization via a compiler flag (-mllvm -switch-to-lookup-table-threshold=0) until a proper fix is implemented in a future Clang release.
The Hacker News comments discuss a performance regression in Clang involving large switch statements and inlining. Several commenters confirm experiencing similar issues, particularly when compiling large codebases. Some suggest the regression might be related to changes in the inlining heuristics or the way Clang handles jump tables. One commenter points out that using a constexpr hash table for large switches can be a faster alternative. Another suggests profiling and selective inlining as a workaround. The lack of clear identification of the root cause and the potential impact on compile times and performance are highlighted as concerning. Some users express frustration with the frequency of such regressions in Clang.
The author dramatically improved the debug build speed of their C++ project, achieving up to 100x faster execution. The primary culprit was excessive logging, specifically the use of a logging library with a slow formatting implementation, exacerbated by unnecessary string formatting even when logs weren't being written. By switching to a faster logging library (spdlog), deferring string formatting until after log level checks, and optimizing other minor inefficiencies, they brought their debug build performance to a usable level, allowing for significantly faster iteration times during development.
Commenters on Hacker News largely praised the author's approach to optimizing debug builds, emphasizing the significant impact build times have on developer productivity. Several highlighted the importance of the described techniques, like using link-time optimization (LTO) and profile-guided optimization (PGO) even in debug builds, challenging the common trade-off between debuggability and speed. Some shared similar experiences and alternative optimization strategies, such as using pre-compiled headers (PCH) and unity builds, or employing tools like ccache. A few also pointed out potential downsides, like increased memory usage with LTO, and the need to balance optimization with the ability to effectively debug. The overall sentiment was that the author's detailed breakdown offered valuable insights and practical solutions for a common developer pain point.
The author is developing a Scheme implementation in async Rust to explore the synergy between the two. They believe Rust's robust tooling, performance, and memory safety, combined with its burgeoning async ecosystem, provide an ideal foundation for a modern Lisp dialect. Async capabilities offer exciting potential for concurrent Scheme programming, especially with features like lightweight tasks and channels. The project aims to leverage Rust's strengths while preserving the elegance and flexibility of Scheme, potentially offering a compelling alternative for both Lisp enthusiasts and Rust developers interested in functional programming.
HN commenters generally expressed interest in the project, finding the combination of Scheme and async Rust intriguing. Several questioned the choice of Rust for performance reasons, arguing that garbage collection makes it a poor fit for truly high-performance async workloads, and suggesting alternatives like C, C++, or even Zig. Some suggested exploring other approaches within the Rust ecosystem, like using a different garbage collector or a stack-allocated scheme. Others praised the project's focus on developer experience and the potential of combining Scheme's expressiveness with Rust's safety features. A few commenters also discussed the challenges of integrating garbage collection with async runtimes and the potential trade-offs involved. The author's responses clarified some of the design choices and acknowledged the performance concerns, indicating they're open to exploring different strategies.
This presentation delves into the intricate process of web page loading within a browser. It covers the journey from parsing HTML and constructing the DOM, to fetching resources like CSS, JavaScript, and images, highlighting how these processes occur concurrently. The talk also explores rendering, including layout calculation and paint, explaining how browsers optimize for performance by utilizing techniques like speculative parsing and the preload scanner. Finally, it examines the role of the browser's critical rendering path and how developers can leverage this knowledge to optimize their websites for faster loading times.
HN commenters generally praised the video for its clear and concise explanation of a complex topic. Several appreciated the presenter's ability to break down browser behavior into digestible chunks, making it accessible even to those without a deep technical background. Some highlighted the insightful explanation of service workers and the rendering pipeline. One commenter wished there was more detail on resource prioritization. Another pointed out the surprising behavior of how browsers handle multiple <link rel=stylesheet> tags, preferring to download them in order rather than prioritizing render-blocking ones. A few comments also provided additional resources, like a link to the browser's "waterfall" network analysis tool and a discussion of HTTP/3 prioritization.
Ruby on Rails applications can now run directly in web browsers thanks to WebAssembly. This is achieved using a new project called "Spreetail/wunderbar-wasm", which compiles Ruby and Rails to WASM using a custom-built toolchain. This allows developers to build full-stack Rails apps that execute client-side, offering potential performance benefits for certain applications by reducing server roundtrips. The WASM approach allows for offline functionality and removes the need for separate frontend and backend deployments. While still experimental, this technology opens up new possibilities for building web applications with Ruby on Rails.
Hacker News commenters expressed skepticism about the practicality of running Ruby on Rails in the browser via WebAssembly. Concerns focused on performance, particularly startup time and overall speed, doubting it would be suitable for production applications. Some suggested alternative approaches for achieving similar functionality, like using a server-rendered backend with a JavaScript frontend framework. Others questioned the use cases, wondering if the complexity was worth the effort compared to established approaches. Several commenters pointed to the large size of the Wasm bundle as a major drawback. A few expressed cautious optimism, acknowledging the technical achievement while remaining unsure of its real-world applicability. Finally, some highlighted the potential benefits for specific niches, such as online code editors or interactive tutorials.
The blog post "Nginx: try_files is evil too" argues against using the try_files directive in Nginx configurations, especially for serving static files. While seemingly simple, its behavior can be unpredictable and lead to unexpected errors, particularly when dealing with rewritten URLs or if file existence checks are bypassed due to caching. The author advocates for using simpler, more explicit location blocks to define how different types of requests should be handled, leading to improved clarity, maintainability, and potentially better performance. They suggest separate location blocks for specific file types and a final catch-all block for dynamic requests, promoting a more transparent and less error-prone approach to configuration.
Hacker News commenters largely disagree with the article's premise that try_files is inherently "evil." Several point out that the author's proposed alternative using location blocks with regular expressions is less performant and more complex, especially for simpler use cases. Some argue that the author mischaracterizes try_files's purpose, which is primarily for serving static files efficiently, not complex routing. Others agree that try_files can be misused, leading to confusing configurations, but contend that when used appropriately, it's a valuable tool. The discussion also touches on alternative approaches, such as using a separate frontend proxy or load balancer for more intricate routing logic. A few commenters express appreciation for the article prompting a re-evaluation of their Nginx configurations, even if they don't fully agree with the author's conclusions.
This blog post explores different ways to represent graph data within PostgreSQL. It primarily focuses on the adjacency list model, using a simple table with "source" and "target" columns to define relationships between nodes. The author demonstrates how to perform common graph operations like finding neighbors and traversing paths using recursive CTEs (Common Table Expressions). While acknowledging other models like adjacency matrix and nested sets, the post emphasizes the adjacency list's simplicity and efficiency for many graph use cases within a relational database context. It also briefly touches on performance considerations and the potential for using materialized views for complex or frequently executed queries.
Hacker News users discussed the practicality and performance implications of representing graphs in PostgreSQL. Several commenters highlighted the existence of specialized graph databases like Neo4j and questioned the suitability of PostgreSQL for complex graph operations, especially at scale. Concerns were raised about the performance of recursive queries and the difficulty of managing deeply nested relationships. Some suggested that while PostgreSQL can handle simpler graph scenarios, dedicated graph databases offer better performance and features for more complex graph use cases. A few commenters mentioned alternative approaches within PostgreSQL, such as using JSON fields or the extension pg_graphql. Others pointed out the benefits of using PostgreSQL for graphs when the graph aspect is secondary to other relational data needs already served by the database.
Thread-local storage (TLS) in C++ can introduce significant performance overhead, even when unused. The author benchmarks various TLS access methods, demonstrating that even seemingly simple zero-initialized thread-local variables incur a cost, especially on Windows. This overhead stems from the runtime needing to manage per-thread data structures, including lazy initialization and destruction. While the performance impact might be negligible in many applications, it can become noticeable in highly concurrent, performance-sensitive scenarios, particularly with a large number of threads. The author explores techniques to mitigate this overhead, such as using compile-time initialization or avoiding TLS altogether if practical. By understanding the costs associated with TLS, developers can make informed decisions about its usage and optimize their multithreaded C++ applications for better performance.
The Hacker News comments discuss the surprising performance cost of thread-local storage (TLS) in C++, particularly its impact on seemingly unrelated code. Several commenters highlight the overhead introduced by the TLS lookups, even when the TLS variables aren't directly used in a particular code path. The most compelling comments delve into the underlying reasons for this, citing issues like increased register pressure due to the extra variables needing to be tracked, and the difficulty compilers have in optimizing around TLS access. Some point out that the benchmark's reliance on rdtsc for timing might be flawed, while others offer alternative benchmarking strategies. The performance impact is acknowledged to be architecture-dependent, with some suggesting mitigations like using compile-time initialization or alternative threading models if TLS performance is critical. A few commenters also mention similar performance issues they've encountered with TLS in other languages, suggesting it's not a C++-specific problem.
The blog post introduces vectordb, a new open-source, GPU-accelerated library for approximate nearest neighbor search with binary vectors. Built on FAISS and offering a Python interface, vectordb aims to significantly improve query speed, especially for large datasets, by leveraging GPU parallelism. The post highlights its performance advantages over CPU-based solutions and its ease of use, while acknowledging it's still in early stages of development. The author encourages community involvement to further enhance the library's features and capabilities.
Hacker News users generally praised the project for its speed and simplicity, particularly the clean and understandable codebase. Several commenters discussed the tradeoffs of binary vectors vs. float vectors, acknowledging the performance gains while also pointing out the potential loss in accuracy. Some suggested alternative libraries or approaches for quantization and similarity search, such as Faiss and ScaNN. One commenter questioned the novelty, mentioning existing binary vector search implementations, while another requested benchmarks comparing the project to these alternatives. There was also a brief discussion regarding memory usage and the potential benefits of using mmap for larger datasets.
The Fly.io blog post "We Were Wrong About GPUs" admits their initial prediction that smaller, cheaper GPUs would dominate the serverless GPU market was incorrect. Demand has overwhelmingly shifted towards larger, more powerful GPUs, driven by increasingly complex AI workloads like large language models and generative AI. Customers prioritize performance and fast iteration over cost savings, willing to pay a premium for the ability to train and run these models efficiently. This has led Fly.io to adjust their strategy, focusing on providing access to higher-end GPUs and optimizing their platform for these demanding use cases.
HN commenters largely agreed with the author's premise that the difficulty of utilizing GPUs effectively often outweighs their potential benefits for many applications. Several shared personal experiences echoing the article's points about complex tooling, debugging challenges, and ultimately reverting to CPU-based solutions for simplicity and cost-effectiveness. Some pointed out that specific niches, like machine learning and scientific computing, heavily benefit from GPUs, while others highlighted the potential of simpler GPU programming models like CUDA and WebGPU to improve accessibility. A few commenters offered alternative perspectives, suggesting that managed services or serverless GPU offerings could mitigate some of the complexity issues raised. Others noted the importance of right-sizing GPU instances and warned against prematurely optimizing for GPUs. Finally, there was some discussion around the rising popularity of ARM-based processors and their potential to offer a competitive alternative for certain workloads.
RustOwl is a tool that visually represents Rust's ownership and borrowing system. It analyzes Rust code and generates diagrams illustrating the lifetimes of variables, how ownership is transferred, and where borrows occur. This allows developers to more easily understand complex ownership scenarios and debug potential issues like dangling pointers or data races, providing a clear, graphical representation of the code's memory management. The tool helps to demystify Rust's core concepts by visually mapping how values are owned and borrowed throughout their lifetime, clarifying the relationship between different parts of the code and enhancing overall code comprehension.
HN users generally expressed interest in RustOwl, particularly its potential as a learning tool for Rust's complex ownership and borrowing system. Some suggested improvements, like adding support for visualizing more advanced concepts like Rc/Arc, mutexes, and asynchronous code. Others discussed its potential use in debugging, especially for larger projects where ownership issues become harder to track mentally. A few users compared it to existing tools like Rustviz and pointed out potential limitations in fully representing all of Rust's nuances visually. The overall sentiment appears positive, with many seeing it as a valuable contribution to the Rust ecosystem.
The blog post details troubleshooting high CPU usage attributed to the writeback process in a Linux kernel. After initial investigations pointed towards cgroups and specifically the cpu.cfs_period_us parameter, the author traced the issue to a tight loop within the cgroup writeback mechanism. This loop was triggered by a large number of cgroups combined with a specific workload pattern. Ultimately, increasing the dirty_expire_centisecs kernel parameter, which controls how long dirty data stays in memory before being written to disk, provided the solution by significantly reducing the writeback activity and lowering CPU usage.
Commenters on Hacker News largely discuss practical troubleshooting steps and potential causes of the high CPU usage related to cgroups writeback described in the linked blog post. Several suggest using tools like perf to profile the kernel and pinpoint the exact function causing the issue. Some discuss potential problems with the storage layer, like slow I/O or a misconfigured RAID, while others consider the possibility of a kernel bug or an interaction with specific hardware or drivers. One commenter shares a similar experience with NFS and high CPU usage related to writeback, suggesting a potential commonality in networked filesystems. Several users emphasize the importance of systematic debugging and isolation of the problem, starting with simpler checks before diving into complex kernel analysis.
Chromium-based browsers on Windows are improving text rendering to match the clarity and accuracy of native Windows applications. By leveraging the DirectWrite API, these browsers will now render text using the same system-enhanced font rendering settings as other Windows programs, resulting in crisper, more legible text, particularly noticeable at smaller font sizes and on high-DPI screens. This change also improves text layout, resolving issues like incorrect bolding or clipping, and makes text selection and measurement more precise. The improved rendering is progressively rolling out to users on Windows 10 and 11.
HN commenters largely praise the improvements to text rendering in Chromium on Windows, noting a significant difference in clarity and readability, especially for fonts like Consolas. Some express excitement for the change, calling it a "huge quality of life improvement" and hoping other browsers will follow suit. A few commenters mention lingering issues or inconsistencies, particularly with ClearType settings and certain fonts. Others discuss the technical details of DirectWrite and how it compares to previous rendering methods, including GDI. The lack of subpixel rendering support in DirectWrite is also mentioned, with some hoping for its eventual implementation. Finally, a few users request similar improvements for macOS.
The blog post details a performance optimization for Nix's evaluation process. By pre-resolving store paths for built-in functions, specifically fetchers, Nix can avoid redundant computations during evaluation, leading to significant speed improvements. This is achieved by introducing a new builtins attribute in the Nix expression language containing pre-computed hashes for commonly used fetchers. This change eliminates the need to repeatedly calculate these hashes during each evaluation, resulting in faster build times, particularly noticeable in projects with many dependencies. The post demonstrates benchmark results showing a substantial reduction in evaluation time with this optimization, highlighting its potential to improve the overall Nix user experience.
Hacker News users generally praised the technique described in the article for improving Nix evaluation performance. Several commenters highlighted the cleverness of pre-computing store paths, noting that it bypasses a significant bottleneck in Nix's evaluation process. Some expressed surprise that this optimization wasn't already implemented, while others discussed potential downsides, like the added complexity to the tooling and the risk of invalidating the cache if the store path changes. A few users also shared their own experiences with Nix performance issues and suggested alternative optimization strategies. One commenter questioned the significance of the improvement in practical scenarios, arguing that derivation evaluation is often not the dominant factor in overall build time.
PgAssistant is an open-source command-line tool designed to simplify PostgreSQL performance analysis and optimization. It collects key performance indicators, configuration settings, and schema details, presenting them in a user-friendly format. PgAssistant then provides tailored recommendations for improvement based on best practices and identified bottlenecks. This allows developers to quickly diagnose issues related to slow queries, inefficient indexing, or suboptimal configuration parameters without deep PostgreSQL expertise.
HN users generally praised pgAssistant, calling it a "great tool" and highlighting its usefulness for visualizing PostgreSQL performance. Several commenters appreciated its ability to present complex information in a user-friendly way, particularly for developers less experienced with database administration. Some suggested potential improvements, such as adding support for more metrics, integrating with other tools, and providing deeper analysis capabilities. A few users mentioned similar existing tools, like pganalyze and pgHero, drawing comparisons and discussing their respective strengths and weaknesses. The discussion also touched on the importance of query optimization and the challenges of managing PostgreSQL performance in general.
"Tiny Pointers" introduces a technique to reduce pointer size in C/C++ programs, thereby lowering memory usage without significantly impacting performance. The core idea involves restricting pointers to smaller regions of memory, enabling them to be represented with fewer bits. The paper details several methods for achieving this, including static analysis, profile-guided optimization, and dynamic recompilation. Experimental results demonstrate memory savings of up to 40% with negligible performance overhead in various benchmarks and real-world applications. This approach offers a promising solution for memory-constrained environments, particularly embedded systems and mobile devices.
HN users discuss the implications of "tiny pointers," focusing on potential performance improvements and drawbacks. Some doubt the practicality due to increased code complexity and the overhead of managing pointer metadata. Concerns are raised about compatibility with existing codebases and the potential for fragmentation in the memory allocator. Others express interest in exploring this concept further, particularly its application in specific scenarios like embedded systems or custom memory allocators where fine-grained control over memory is crucial. There's also discussion on whether the claimed benefits would outweigh the costs in real-world applications, with some suggesting that traditional optimization techniques might be more effective. A few commenters point out similar existing techniques like tagged pointers and debate the novelty of this approach.
Lzbench is a compression benchmark focusing on speed, comparing various lossless compression algorithms across different datasets. It prioritizes decompression speed and measures compression ratio, encoding and decoding rates, and RAM usage. The benchmark includes popular algorithms like zstd, lz4, brotli, and deflate, tested on diverse datasets ranging from Silesia Corpus to real-world files like Firefox binaries and game assets. Results are presented interactively, allowing users to filter by algorithm, dataset, and metric, facilitating easy comparison and analysis of compression performance. The project aims to provide a practical, speed-focused overview of how different compression algorithms perform in real-world scenarios.
HN users generally praised the benchmark's visual clarity and ease of use. Several appreciated the inclusion of less common algorithms like Brotli, Lizard, and Zstandard alongside established ones like gzip and LZMA. Some discussed the performance characteristics of different algorithms, noting Zstandard's speed and Brotli's generally good compression. A few users pointed out potential improvements, such as adding more compression levels or providing options to exclude specific algorithms. One commenter wished for pre-compressed benchmark files to reduce load times. The lack of context/meaning for the benchmark data (it uses a "Silesia corpus") was also mentioned.
The blog post argues for an intermediate representation (IR) layer in query compilers between the logical plan and the physical plan, called the "relational algebra IR." This layer would represent queries in a standardized, relational algebra form, enabling greater portability and reusability of optimization rules across different physical execution engines. Currently, optimization logic is often tightly coupled to specific physical plans, making it difficult to adapt to new engines or hardware. By introducing this standardized relational algebra IR, query compilers can achieve better modularity and extensibility, simplifying development and allowing for easier experimentation with new optimization strategies without needing to rewrite code for each backend. This ultimately leads to more efficient query execution across diverse environments.
HN commenters generally agree with the author's premise that a middle tier is missing in query compilers, sitting between logical optimization and physical optimization. This tier would handle "cross-physical plan" optimizations, allowing for better cost-based decisions that consider different physical plan choices holistically rather than sequentially. Some discuss the challenges in implementing this, particularly the explosion of search space and the difficulty in accurately costing plans. Others offer specific examples where such a tier would be beneficial, such as selecting join algorithms based on data distribution or optimizing for specific hardware like GPUs. A few commenters mention existing systems that implement similar concepts, though not necessarily as a distinct tier, suggesting the idea is already being explored in practice. Some debate the practicality of the proposed solution, suggesting alternative approaches like adaptive query execution or learned optimizers.
This post outlines essential PostgreSQL best practices for improved database performance and maintainability. It emphasizes using appropriate data types, including choosing smaller integer types when possible and avoiding generic text fields in favor of more specific types like varchar or domain types. Indexing is crucial, advocating for indexes on frequently queried columns and foreign keys, while cautioning against over-indexing. For queries, the guide recommends using EXPLAIN to analyze performance, leveraging the power of WHERE clauses effectively, and avoiding wildcard leading characters in LIKE queries. The post also champions prepared statements for security and performance gains and suggests connection pooling for efficient resource utilization. Finally, it underscores the importance of vacuuming regularly to reclaim dead tuples and prevent bloat.
Hacker News users generally praised the linked PostgreSQL best practices article for its clarity and conciseness, covering important points relevant to real-world usage. Several commenters highlighted the advice on indexing as particularly useful, especially the emphasis on partial indexes and understanding query plans. Some discussed the trade-offs of using UUIDs as primary keys, acknowledging their benefits for distributed systems but also pointing out potential performance downsides. Others appreciated the recommendations on using ENUM types and the caution against overusing triggers. A few users added further suggestions, such as using pg_stat_statements for performance analysis and considering connection pooling for improved efficiency.
Reports are surfacing about new Seagate hard drives, predominantly sold through Chinese online marketplaces, exhibiting suspiciously long power-on hours and high usage statistics despite being advertised as new. This suggests potential fraud, where used or refurbished drives are being repackaged and sold as new. While Seagate has acknowledged the issue and is investigating, the extent of the problem remains unclear, with speculation that the drives might originate from cryptocurrency mining operations or other data centers. Buyers are urged to check SMART data upon receiving new Seagate drives to verify their actual usage.
Hacker News users discuss potential explanations for unexpectedly high reported runtime hours on seemingly new Seagate hard drives. Some suggest these drives are refurbished units falsely marketed as new, with inflated SMART data to disguise their prior use. Others propose the issue stems from quality control problems leading to extended testing periods at the factory, or even the use of drives in cryptocurrency mining operations before being sold as new. Several users share personal anecdotes of encountering similar issues with Seagate drives, reinforcing suspicion about the company's practices. Skepticism also arises about the reliability of SMART data as an indicator of true drive usage, with some arguing it can be manipulated. Some users suggest buying hard drives from more reputable retailers or considering alternative brands to avoid potential issues.
Using mix() with step() to simulate conditional assignments in shaders is often less efficient than directly using branch instructions. While seemingly branchless, this mix()/step() approach can introduce extra computations and potentially disrupt hardware optimizations related to predication. Modern GPUs are adept at handling branches efficiently, especially when they are predictable, so relying on them is often faster and simpler than employing arithmetic workarounds. Therefore, default to standard branching unless profiling reveals a specific performance bottleneck that can be demonstrably addressed by a mix()/step() alternative.
HN users generally agreed that the article's advice is sound, particularly for modern GPUs. Several pointed out that mix() and step() can be more efficient than branching, especially when dealing with SIMD architectures where branching can lead to thread divergence. Some emphasized that profiling is crucial, as the optimal approach can vary depending on the specific GPU and shader complexity. One commenter noted that while branching might be faster in simple cases, mix() offers more predictable performance as shader complexity increases. Another cautioned against premature optimization and recommended focusing on algorithmic improvements first. A few users shared alternative techniques like using lookup textures or bitwise operations for certain conditional scenarios. Finally, there was discussion about the evolution of GPU architecture and how older advice regarding branching might no longer apply.
The blog post argues that Carbon, while presented as a new language, is functionally more of a dialect or a sustained, large-scale fork of C++. It shares so much of C++'s syntax, semantics, and tooling that it blurs the line between a distinct language and a significantly evolved version of existing C++. This close relationship makes migration easier, but also raises questions about whether the benefits of a 'new' language outweigh the costs of maintaining another C++-like ecosystem, especially given ongoing modernization efforts within C++ itself. The author suggests that Carbon is less a revolution and more of a strategic response to the inertia surrounding large C++ codebases, offering a cleaner starting point while retaining substantial compatibility.
Hacker News commenters largely agree with the author's premise that Carbon, despite Google's marketing, isn't yet a fully realized language. Several point out the lack of a stable ABI and the dependence on constantly evolving C++ tooling as major roadblocks. Some highlight the ambiguity around its governance model, questioning whether it will truly be community-driven or remain under Google's control. The most compelling comments delve into the practical implications of this, expressing skepticism about adopting a language with such a precarious foundation and predicting a long road ahead before Carbon reaches production readiness for substantial projects. Others counter that this is expected for a young language and that Carbon's potential merits are worth the wait, citing its modern features and interoperability with C++. A few commenters express disappointment or frustration with the slow pace of Carbon's development, contrasting it with other language projects.
VS Code's remote SSH functionality can lead to unexpected and frustrating behavior due to its complex key management. The editor automatically adds keys to its internal SSH agent, potentially including keys you didn't intend to use for a particular connection. This often results in authentication failures, especially when using multiple keys for different servers. Even manually removing keys from the agent within VS Code doesn't reliably solve the issue because the editor might re-add them. The blog post recommends disabling VS Code's agent and using the system SSH agent instead for more predictable and manageable SSH connections.
HN users generally agree that VS Code's remote SSH behavior is confusing and frustrating. Several commenters point out that the "agent forwarding" option doesn't work as expected, leading to issues with key-based authentication. Some suggest the core problem stems from VS Code's reliance on its own SSH implementation instead of leveraging the system's SSH, causing conflicts and unexpected behavior. Workarounds like using the Remote - SSH: Kill VS Code Server on Host... command or configuring VS Code to use the system SSH are mentioned, along with the observation that the VS Code team seems aware of the issues and is working on improvements. A few commenters share similar struggles with other IDEs and remote development tools, suggesting this isn't unique to VS Code.
The blog post "Fat Rand: How Many Lines Do You Need to Generate a Random Number?" explores the surprising complexity hidden within seemingly simple random number generation. It dissects the code behind Python's random.randint() function, revealing a multi-layered process involving system-level entropy sources, hashing, and bit manipulation to ultimately produce a seemingly simple random integer. The post highlights the extensive effort required to achieve statistically sound randomness, demonstrating that generating even a single random number relies on a significant amount of code and underlying system functionality. This complexity is necessary to ensure unpredictability and avoid biases, which are crucial for security, simulations, and various other applications.
Hacker News users discussed the surprising complexity of generating truly random numbers, agreeing with the article's premise. Some commenters highlighted the difficulty in seeding pseudo-random number generators (PRNGs) effectively, with suggestions like using /dev/random, hardware sources, or even mixing multiple sources. Others pointed out that the article focuses on uniformly distributed random numbers, and that generating other distributions introduces additional complexity. A few users mentioned specific use cases where simple PRNGs are sufficient, like games or simulations, while others emphasized the critical importance of robust randomness in cryptography and security. The discussion also touched upon the trade-offs between performance and security when choosing a random number generation method, and the value of having different "grades" of randomness for various applications.
The blog post introduces Elastic Binary Trees (EBTrees), a novel data structure designed to address performance limitations of traditional binary trees in multi-threaded environments. EBTrees achieve improved concurrency by allowing multiple threads to operate on the tree simultaneously without relying on heavy locking mechanisms. This is accomplished through a "lock-free" elastic structure that utilizes pointers and a small amount of per-node metadata to manage concurrent operations, enabling efficient insertion, deletion, and search operations. The elasticity refers to the tree's ability to gracefully handle structural changes caused by concurrent modifications, maintaining balance and performance even under high load. The post further discusses the motivation behind developing EBTrees, their implementation details, and preliminary performance benchmarks suggesting substantial improvements over traditional locked binary trees.
Hacker News users discussed the efficiency and practicality of elastic binary trees (EBTrees), particularly regarding their performance compared to other data structures like B-trees or skip lists. Some commenters questioned the real-world advantages of EBTrees, pointing to the complexity of their implementation and the potential overhead. One commenter suggested EBTrees might shine in specific scenarios with high insert/delete rates and range queries on flash storage, while another highlighted their potential use in embedded systems due to their predictable memory usage. The lack of widespread adoption and the existence of seemingly simpler alternatives led to skepticism about their general utility. Several users expressed interest in seeing benchmarks comparing EBTrees to more established data structures.
The blog post explores the potential of the newly released S1 processor as a competitor to the Apple R1, particularly in the realm of ultra-low-power embedded applications. The author highlights the S1's remarkably low $6 price point and its impressive power efficiency, consuming just microwatts of power. While acknowledging the S1's limitations in terms of processing power and memory compared to the R1, the post emphasizes its suitability for specific use cases like wearables and IoT devices where cost and power consumption are paramount. The author ultimately concludes that while not a direct replacement, the S1 offers a compelling alternative for applications where the R1's capabilities are overkill and its higher cost prohibitive.
Hacker News users discussed the potential of the S1 chip as a viable competitor to the Apple R1, focusing primarily on price and functionality. Some expressed skepticism about the S1's claimed capabilities, particularly its ultra-wideband (UWB) performance, given the lower price point. Others questioned the practicality of its open-source nature for the average consumer, highlighting potential security concerns and the need for technical expertise to implement it. Several commenters were interested in the potential applications of a cheaper UWB chip, citing potential uses in precise indoor location tracking and device interaction. A few pointed out the limited information available and the need for further testing and real-world benchmarks to validate the S1's performance claims. The overall sentiment leaned towards cautious optimism, with many acknowledging the potential disruptive impact of a low-cost UWB chip but reserving judgment until more concrete evidence is available.
Bjarne Stroustrup's "21st Century C++" blog post advocates for modernizing C++ usage by focusing on safety and performance. He highlights features introduced since C++11, like ranges, concepts, modules, and coroutines, which enable simpler, safer, and more efficient code. Stroustrup emphasizes using these tools to combat complexity and vulnerabilities while retaining C++'s performance advantages. He encourages developers to embrace modern C++, utilizing static analysis and embracing a simpler, more expressive style guided by the "keep it simple" principle. By moving away from older, less safe practices and leveraging new features, developers can write robust and efficient code fit for the demands of modern software development.
Hacker News users discussed the challenges and benefits of modern C++. Several commenters pointed out the complexities introduced by new features, arguing that while powerful, they contribute to a steeper learning curve and can make code harder to maintain. The benefits of concepts, ranges, and modules were acknowledged, but some expressed skepticism about their widespread adoption and practical impact due to compiler limitations and legacy codebases. Others highlighted the ongoing tension between embracing modern C++ and maintaining compatibility with existing projects. The discussion also touched upon build systems and the difficulty of integrating new C++ features into existing workflows. Some users advocated for simpler, more focused languages like Zig and Jai, suggesting they offer a more manageable approach to systems programming. Overall, the sentiment reflected a cautious optimism towards modern C++, tempered by concerns about complexity and practicality.
Summary of Comments ( 408 )
https://news.ycombinator.com/item?id=43092003
Many commenters on Hacker News appreciated the author's honest and nuanced comparison of Java and Go. Several highlighted the cultural differences between the ecosystems, noting Java's enterprise focus and Go's emphasis on simplicity. Some questioned the author's assessment of Go's error handling, arguing that it can be verbose, though others defended it as explicit and helpful. Performance benefits of Go were acknowledged but some suggested they might be overstated for typical applications. A few Java developers shared their positive experiences with newer Java features and frameworks, contrasting the author's potentially outdated perspective. Several commenters also mentioned the importance of choosing the right tool for the job, recognizing that neither language is universally superior.
The Hacker News post "One year after switching from Java to Go" (https://news.ycombinator.com/item?id=43092003) sparked a lively discussion with a variety of viewpoints on the merits and drawbacks of Go compared to Java.
Several commenters echoed the author's experience, praising Go's simplicity, speed, and ease of deployment. One user highlighted the reduced cognitive load when working with Go, appreciating its smaller standard library and straightforward error handling. Another commenter specifically mentioned the improved developer experience due to faster compilation times, a common complaint about Java development. The ease of creating statically linked binaries in Go, simplifying deployment and reducing dependencies, was also lauded. Some users even went so far as to say that Go's simplicity allows for quicker onboarding of new developers, reducing training time and costs.
However, not all comments were positive about Go. Some users argued that while Go might be simpler for smaller projects, its lack of features, particularly generics (at the time of the original article and comments), could become a hindrance in larger, more complex codebases. One commenter pointed out the verbosity of error handling in Go, which, while explicit, can lead to repetitive code. Another user mentioned missing Java features like proper dependency management and mature frameworks, suggesting that Go's ecosystem, while growing, isn't as comprehensive. The lack of immutability by default in Go was also brought up as a potential source of bugs.
A recurring theme in the comments was the trade-off between simplicity and features. Some argued that Go's simplicity is its greatest strength, leading to more maintainable and understandable code. Others countered that the lack of certain features could ultimately lead to increased complexity in the long run, especially for larger projects.
Several commenters also shared their experiences with migrating from Java (or other languages) to Go, offering practical advice and insights. Some mentioned the initial learning curve, while others highlighted the satisfaction of working with a more streamlined language.
The discussion also touched upon performance comparisons between Go and Java, with some users reporting significant performance improvements after switching to Go. However, others cautioned against generalizations, stating that performance depends heavily on specific use cases and implementation details.
Overall, the comments on the Hacker News post reflect a nuanced perspective on the transition from Java to Go. While many appreciate Go's simplicity and performance, others acknowledge the trade-offs and advocate for careful consideration based on project requirements and team expertise. The discussion highlights the ongoing evolution of programming languages and the diverse needs of the software development community.