Nova is a new JavaScript and WebAssembly engine built in Rust, focusing on performance, reliability, and embedability. It aims to provide a fast and secure runtime for server-side JavaScript applications, including serverless functions and edge computing, as well as non-browser environments like game development or IoT devices. Nova supports JavaScript modules, asynchronous programming, and standard Web APIs. It also boasts a small footprint, making it suitable for resource-constrained environments. The project is open-source and still under active development, with a focus on expanding its feature set and improving compatibility with existing JavaScript ecosystems.
The blog post advocates for using DWARF, a debugging data format, as a universal intermediate representation for reverse engineering tools. It highlights DWARF's rich type information, cross-platform compatibility, and existing tooling ecosystem as key advantages. The post introduces LIEF's ongoing work to create a DWARF editor, enabling interactive modification of DWARF data, and envisions this as a foundation for powerful new reverse engineering workflows. This editor would allow analysts to directly manipulate program semantics encoded in DWARF, potentially simplifying tasks like patching binaries, deobfuscating code, and porting software.
HN users discuss the potential of DWARF as a universal reverse engineering format, expressing both excitement and skepticism. Some see it as a powerful tool, citing its readily available tooling and rich debugging information, enabling easier cross-platform analysis and automation. Others are less optimistic, highlighting DWARF's complexity, verbosity, and platform-specific quirks as obstacles to widespread adoption. The discussion also touches upon alternatives like Ghidra's SLEIGH and mentions the practical challenges of relying on compiler-generated debug info, which can be stripped or obfuscated, limiting its usefulness for reverse engineering malware or proprietary software. Finally, commenters raise concerns about the performance implications of parsing large DWARF data structures and question the practicality of using it as a primary format for reverse engineering tools.
Astra is a new JavaScript-to-executable compiler that aims to create small, fast, and standalone executables from Node.js projects. It uses a custom bytecode format and a lightweight virtual machine written in Rust, leading to reduced overhead compared to bundling entire Node.js runtimes. Astra boasts improved performance and security compared to existing solutions, and it simplifies distribution by eliminating external dependencies. The project is open-source and under active development.
HN users discuss Astra's potential, but express skepticism due to the lack of clear advantages over existing solutions like NativeScript, Electron, or Tauri. Some question the performance claims, particularly regarding startup time, and the practicality of compiling JS directly to machine code given JavaScript's dynamic nature. Others point out the limited platform support (currently only macOS) and the difficulty of competing with well-established and mature alternatives. A few express interest in the project's approach, especially if it can deliver on its promises of performance and smaller binary sizes, but overall the sentiment leans towards cautious curiosity rather than outright excitement.
Goboscript is a new text-based programming language that compiles to Scratch 3.0, making it easier for experienced programmers to create Scratch projects. It offers a more familiar syntax compared to Scratch's visual block-based system, including functions, classes, and variables. This allows for more complex projects to be developed in Scratch, potentially bridging the gap for programmers transitioning to visual programming or wanting to create more intricate Scratch applications. The project is open-source and available on GitHub.
HN users generally expressed curiosity about Goboscript's purpose and target audience. Some questioned its practical value over directly using Scratch, particularly given Scratch's visual nature and target demographic. Others wondered about specific features like debugging and the handling of Scratch's inherent concurrency. A few commenters saw potential use cases, such as educational tools or a bridge for programmers transitioning to visual languages. The overall sentiment seemed to be polite interest mixed with skepticism about the language's niche.
This document provides a concise guide for C programmers transitioning to Fortran. It highlights key differences, focusing on Fortran's array handling (multidimensional arrays and array slicing), subroutines and functions (pass-by-reference semantics and intent attributes), derived types (similar to structs), and modules (for encapsulation and namespace management). The guide emphasizes Fortran's column-major array ordering, contrasting it with C's row-major order. It also explains Fortran's powerful array operations and intrinsic functions, allowing for optimized numerical computation. Finally, it touches on common Fortran features like implicit variable declarations, formatting with FORMAT statements, and the use of ALLOCATE and DEALLOCATE for dynamic memory management.
Hacker News users discuss Fortran's continued relevance, particularly in scientific computing, highlighting its performance advantages and ease of use for numerical tasks. Some commenters share personal anecdotes of Fortran's simplicity for array manipulation and its historical dominance. Concerns about ecosystem tooling and developer mindshare are also raised, questioning whether Fortran offers advantages over modern C++ for new projects. The discussion also touches on specific language features like derived types and allocatable arrays, comparing their implementation in Fortran to C++. Several users express interest in learning modern Fortran, spurred by the linked resource.
FreeBASIC is a free and open-source, 32-bit and 64-bit BASIC compiler available for Windows, Linux, and DOS. It supports a modern, extended BASIC syntax with features like pointers, object-oriented programming, operator overloading, and inline assembly, while maintaining compatibility with QuickBASIC. FreeBASIC boasts a large standard library, offering built-in support for graphics, sound, and networking, as well as providing bindings to popular libraries like OpenGL, SDL, and GTK+. It's suitable for developing everything from console applications and games to GUI applications and libraries.
Hacker News commenters on the FreeBASIC post express a mix of nostalgia and cautious optimism. Some fondly recall using QuickBASIC and see FreeBASIC as a worthy successor, praising its ease of use and suitability for beginners. Others are more critical, pointing out its limitations compared to modern languages and questioning its relevance in today's programming landscape. Several users suggest it might find a niche in game development or embedded systems due to its performance and ease of integration with C libraries. Concerns are raised about the project's apparent slow development and limited community size. Overall, the sentiment is that while FreeBASIC isn't a cutting-edge tool, it serves a purpose for certain tasks and holds value for those seeking a simple, accessible programming experience reminiscent of classic BASIC.
The author details their process of compiling OCaml code to run on a TI-84 Plus CE calculator. They leveraged the calculator's existing C toolchain and the OCaml compiler's ability to output C code. After overcoming challenges like limited RAM and the absence of a dynamic linker, they successfully ran a simple "Hello, world!" program. The key innovations included statically linking the OCaml runtime and using a custom, minimized runtime configuration to fit within the calculator's memory constraints. This allowed for direct execution of OCaml bytecode on the calculator, offering a novel approach to programming these devices.
Hacker News users generally expressed enthusiasm for the project of compiling OCaml to a TI-84 calculator. Several commenters praised the technical achievement, highlighting the challenges of working with the calculator's limited resources. Some discussed potential educational benefits, suggesting it could be a powerful tool for teaching functional programming. Others reminisced about their own calculator programming experiences and pondered the possibility of porting other languages. A few users inquired about practical aspects like performance and library support. There was also some discussion comparing the project to other calculator-based language implementations and exploring potential future enhancements.
The blog post "Evolution of Rust Compiler Errors" traces the improvements in Rust's error messages over time. It highlights how early error messages were often cryptic and unhelpful, relying on internal compiler terminology. Through dedicated effort and community feedback, these messages evolved to become significantly more user-friendly. The post showcases specific examples of error transformations, demonstrating how improved diagnostics, contextual information like relevant code snippets, and helpful suggestions have made debugging Rust code considerably easier. This evolution reflects a continuous focus on improving the developer experience by making errors more understandable and actionable.
HN commenters largely praised the improvements to Rust's compiler errors, highlighting the journey from initially cryptic messages to the current, more helpful diagnostics. Several noted the significant impact of the error indexing initiative, allowing for easy online searching and community discussion around specific errors. Some expressed continued frustration with lifetime errors, while others pointed out that even improved errors can sometimes struggle with complex generic code. A few commenters compared Rust's error evolution favorably to other languages, particularly C++, emphasizing the proactive work done by the Rust community to improve developer experience. One commenter suggested potential future improvements, such as suggesting concrete fixes instead of just pointing out problems.
Teal is a typed dialect of Lua designed for improved code maintainability and performance. It adds optional type annotations to Lua, allowing developers to catch type errors during compilation rather than at runtime. Teal code compiles to standard Lua, ensuring compatibility with existing Lua projects and libraries. The type system is gradual, meaning you can incrementally add type information to existing Lua codebases without needing to rewrite everything at once. This offers a smooth transition path for projects seeking the benefits of static typing while preserving their investment in Lua. The project aims to improve developer experience by providing better tooling, such as autocompletion and refactoring support, which are enabled by the type information.
Hacker News users discussed Teal's potential, drawing comparisons to TypeScript and expressing interest in its static typing for Lua. Some questioned the practical benefits over existing typed Lua solutions like Typed Lua and Ravi, while others highlighted Teal's focus on gradual typing and ease of integration with existing Lua codebases. Several commenters appreciated its clean syntax and the availability of a VS Code plugin. A few users raised concerns about potential performance impacts and the need for a runtime type checker, while others saw Teal as a valuable tool for larger Lua projects where maintainability and refactoring are paramount. The overall sentiment was positive, with many eager to try Teal in their projects.
RightNowAI has developed a tool to simplify and accelerate CUDA kernel optimization. Their Python library, "cuopt," allows developers to express optimization strategies in a high-level declarative syntax, automating the tedious process of manual tuning. It handles exploring different configurations, benchmarking performance, and selecting the best-performing kernel implementation, ultimately reducing development time and improving application speed. This approach aims to make CUDA optimization more accessible and less painful for developers who may lack deep hardware expertise.
HN users are generally skeptical of RightNowAI's claims. Several commenters point out that CUDA optimization is already quite mature, with extensive tools and resources available. They question the value proposition of a tool that supposedly simplifies the process further, doubting it can offer significant improvements over existing solutions. Some suspect the advertised performance gains are cherry-picked or misrepresented. Others express concerns about vendor lock-in and the closed-source nature of the product. A few commenters are more open to the idea, suggesting that there might be room for improvement in specific niches or for users less familiar with CUDA optimization. However, the overall sentiment is one of cautious skepticism, with many demanding more concrete evidence of the claimed benefits.
LPython is a new Python compiler built for performance and portability. It leverages a multi-tiered intermediate representation, allowing it to target diverse architectures, including CPUs, GPUs, and specialized hardware like FPGAs. This approach, coupled with advanced compiler optimizations, aims to significantly boost Python's execution speed. LPython supports a subset of Python features focusing on numerical computation and array manipulation, making it suitable for scientific computing, machine learning, and high-performance computing. The project is open-source and under active development, with the long-term goal of supporting the full Python language.
Hacker News users discussed LPython's potential, focusing on its novel compilation approach and retargetability. Several commenters expressed excitement about its ability to target GPUs and other specialized hardware, potentially opening doors for Python in high-performance computing. Some questioned the performance comparisons, noting the lack of details on benchmarks used and the maturity of the project. Others compared LPython to existing Python compilers like Numba and Cython, raising questions about its niche and advantages. A few users also discussed the implications for scientific computing and the broader Python ecosystem. There was general interest in seeing more concrete benchmarks and real-world applications as the project matures.
GCC 15 introduces experimental support for COBOL as a front-end language. This allows developers to compile COBOL programs using GCC, leveraging its optimization and code generation capabilities. The implementation supports a substantial subset of the COBOL 85 standard, including features like nested programs, intrinsic functions, and file I/O. While still experimental, this addition paves the way for integrating COBOL into the GNU compiler ecosystem and potentially expanding the language's usage in new environments.
Several Hacker News commenters expressed surprise and interest in the addition of a COBOL front-end to GCC, some questioning the rationale behind it. A few pointed out the continued usage of COBOL in legacy systems, particularly in financial and government institutions, suggesting this addition could ease migration or modernization efforts. Others discussed the technical challenges of integrating COBOL, a language with very different paradigms than those typically handled by GCC, and speculated on the completeness and performance of the implementation. Some comments also touched upon the potential for attracting new COBOL developers with more modern tooling. The thread contains some lighthearted banter about COBOL's perceived age and complexity as well.
Pascal for Small Machines explores the history and enduring appeal of Pascal, particularly its suitability for resource-constrained environments. The author highlights Niklaus Wirth's design philosophy of simplicity and efficiency, emphasizing how these principles made Pascal an ideal language for early microcomputers. The post discusses various Pascal implementations, from UCSD Pascal to modern variants, showcasing its continued relevance in embedded systems, retrocomputing, and educational settings. It also touches upon Pascal's influence on other languages and its role in shaping computer science education.
HN users generally praise the simplicity and elegance of Pascal, with several reminiscing about using Turbo Pascal. Some highlight its suitability for resource-constrained environments and embedded systems, comparing it favorably to C for such tasks. One commenter notes its use in the Apple Lisa and early Macs. Others discuss the benefits of strong typing and clear syntax for learning and maintainability. A few express interest in modern Pascal dialects like Free Pascal and Oxygene, while others debate the merits of static vs. dynamic typing. Some disagreement arises over whether Pascal's enforced structure is beneficial or restrictive for larger projects.
OCaml offers compelling advantages for machine learning, combining performance with expressiveness and safety. The Raven project aims to leverage these strengths by building a comprehensive ML ecosystem in OCaml. This includes Owl, a mature scientific computing library offering efficient tensor operations and automatic differentiation, and other tools facilitating tasks like data loading, model building, and training. The goal is to provide a robust and performant alternative to existing ML frameworks, benefiting from OCaml's strong typing and functional programming paradigms for increased reliability and maintainability in complex ML projects.
Hacker News users discussed Raven, an OCaml machine learning library. Several commenters expressed enthusiasm for OCaml's potential in ML, citing its type safety, speed, and ease of debugging. Some highlighted the challenges of adopting a less mainstream language like OCaml in the ML ecosystem, particularly concerning community size and available tooling. The discussion also touched on specific features of Raven, comparing it to other ML libraries and noting the benefits of its functional approach. One commenter questioned the practical advantages of Raven given existing, mature frameworks like PyTorch. Others pushed back, arguing that Raven's design might offer unique benefits for certain tasks or workflows and emphasizing the importance of exploring alternatives to the dominant Python-based ecosystem.
Pyrefly is a new Python type checker built in Rust that prioritizes speed. Leveraging Rust's performance, it aims to be significantly faster than existing Python type checkers like MyPy, potentially by orders of magnitude. Pyrefly achieves this through a novel incremental checking architecture designed to minimize redundant work and maximize caching efficiency. It's compatible with Python 3.7+ and boasts features like gradual typing and support for popular type hinting libraries. While still under active development, Pyrefly shows promise as a high-performance alternative for type checking large Python codebases.
Hacker News users generally expressed excitement about Pyrefly, praising its speed and Rust implementation. Some questioned the practical benefits given existing type checkers like MyPy, with discussion revolving around performance comparisons and integration into developer workflows. Several commenters showed interest in the specific technical choices, asking about memory usage, incremental checking, and compatibility with MyPy stubs. The creator of Pyrefly also participated, responding to questions and clarifying design decisions. Overall, the comments reflected a cautious optimism about the project, acknowledging its potential while seeking more information on its real-world usability.
A new Common Lisp implementation, named ALisp, is under development and currently supports ASDF (Another System Definition Facility) for system management. The project aims to create a small, embeddable, and efficient Lisp, drawing inspiration from other Lisps like ECL and SBCL while incorporating unique ideas. It's being developed primarily in C and is currently in an early stage, but the Savannah project page provides source code and build instructions for those interested in experimenting with it.
Hacker News users discussed the new Common Lisp implementation, with many expressing interest and excitement. Several commenters praised the project's use of a custom reader and printer, viewing it as a potential performance advantage. Some discussion revolved around portability, particularly to WebAssembly. The project's licensing under LGPL was also a topic of conversation, with users exploring the implications for commercial use. Several users inquired about the motivations and goals behind creating a new Common Lisp implementation, while others compared it to existing implementations like SBCL and ECL. A few comments touched on specific technical aspects, such as the choice of garbage collection strategy and the implementation of the condition system. Some users offered helpful suggestions and expressed a desire to contribute.
GCC 15.1, the latest stable release of the GNU Compiler Collection, is now available. This release brings substantial improvements across multiple languages, including C, C++, Fortran, D, Ada, and Go. Key enhancements include improved experimental support for C++26 and C2x standards, enhanced diagnostics and warnings, optimizations for performance and code size, and expanded platform support. Users can expect better compile times and generated code quality. This release represents a significant step forward for the GCC project and offers developers a more robust and feature-rich compiler suite.
HN commenters largely focused on specific improvements in GCC 15. Several praised the improved diagnostics, making debugging easier. Some highlighted the Modula-2 language support improvements as a welcome addition. Others discussed the benefits of the enhanced C++23 and C2x support, including modules and improved ranges. A few commenters noted the continuing, though slow, progress on static analysis features. There was also some discussion on the challenges of supporting multiple architectures and languages within a single compiler project like GCC.
PyGraph introduces a new compilation approach within PyTorch to robustly capture and execute CUDA graphs. It addresses limitations of existing methods by providing a Python-centric API that seamlessly integrates with PyTorch's dynamic graph construction and autograd engine. PyGraph accurately captures side effects like inplace updates and random number generation, enabling efficient execution of complex, dynamic workloads on GPUs without requiring manual graph construction. This results in significant performance gains for iterative models with repetitive computations, particularly in inference and fine-tuning scenarios.
HN commenters generally express excitement about PyGraph, praising its potential for performance improvements in PyTorch by leveraging CUDA Graphs. Several note that CUDA graph adoption has been slow due to its complexity, and PyGraph's simplified interface could significantly boost its usage. Some discuss the challenges of CUDA graph implementation, including kernel fusion and stream capture, and how PyGraph addresses these. A few users raise concerns about potential debugging difficulties and limited flexibility, while others inquire about specific features like dynamic graph modification and integration with existing PyTorch workflows. The lack of open-sourcing is also mentioned as a hurdle for wider community adoption and contribution.
This blog post explores different strategies for memory allocation within WebAssembly modules, particularly focusing on the trade-offs between using the built-in malloc (provided by wasm-libc) and implementing a custom allocator. It highlights the performance overhead of wasm-libc's malloc due to its generality and thread safety features. The author presents a leaner, custom bump allocator as a more performant alternative for single-threaded scenarios, showcasing its implementation and integration with a linear memory. Finally, it discusses the option of delegating allocation to JavaScript and the potential complexities involved in managing memory across the WebAssembly/JavaScript boundary.
Hacker News users discussed the implications of WebAssembly's lack of built-in allocator, focusing on the challenges and opportunities it presents. Several commenters highlighted the performance benefits of using a custom allocator tailored to the specific application, rather than relying on a general-purpose one. The discussion touched on various allocation strategies, including linear allocation, arena allocation, and using allocators from the host environment. Some users expressed concern about the added complexity for developers, while others saw it as a positive feature allowing for greater control and optimization. The possibility of standardizing certain allocator interfaces within WebAssembly was also brought up, though acknowledged as a complex undertaking. Some commenters shared their experiences with custom allocators in WebAssembly, mentioning reduced binary sizes and improved performance as key advantages.
"Less Slow C++" offers practical advice for improving C++ build and execution speed. It covers techniques ranging from precompiled headers and unity builds (combining source files) to link-time optimization (LTO) and profile-guided optimization (PGO). It also explores build system optimizations like using Ninja and parallelizing builds, and coding practices that minimize recompilation such as avoiding unnecessary header inclusions and using forward declarations. Finally, the guide touches upon utilizing tools like compiler caches (ccache) and build analysis utilities to pinpoint bottlenecks and further accelerate the development process. The focus is on readily applicable methods that can significantly improve C++ project turnaround times.
Hacker News users discussed the practicality and potential benefits of the "less_slow.cpp" guidelines. Some questioned the emphasis on micro-optimizations, arguing that focusing on algorithmic efficiency and proper data structures is generally more impactful. Others pointed out that the advice seemed tailored for very specific scenarios, like competitive programming or high-frequency trading, where every ounce of performance matters. A few commenters appreciated the compilation of optimization techniques, finding them valuable for niche situations, while some expressed concern that blindly applying these suggestions could lead to less readable and maintainable code. Several users also debated the validity of certain recommendations, like avoiding virtual functions or minimizing branching, citing potential trade-offs with code design and flexibility.
The cg_clif project has made significant progress in compiling Rust to C, achieving a 95.9% pass rate on the Rust test suite. This compiler leverages Cranelift as a backend and utilizes a custom ABI for passing Rust data structures. Notably, it's now functional on more unusual platforms like wasm32-wasi and thumbv6m-none-eabi (for embedded ARM devices). While performance isn't a primary focus currently, basic functionality and compatibility are progressing rapidly, demonstrating the potential for compiling Rust to a portable C representation.
Hacker News users discussed the impressive 95.9% test pass rate of the Rust-to-C compiler, particularly its ability to target unusual platforms like the Sega Saturn and Sony PlayStation. Some expressed skepticism about the practical applications, questioning the performance implications and debugging challenges of such a complex transpilation process. Others highlighted the potential benefits for code reuse and portability, enabling Rust code to run on legacy or resource-constrained systems. The project's novelty and ambition were generally praised, with several commenters expressing interest in the developer's approach and future developments. Some also debated the suitability of "compiler" versus "transpiler" to describe the project. There was also discussion around specific technical aspects, like memory management and the handling of Rust's borrow checker within the C output.
Janet's PEG module uses a packrat parsing approach, combining memoization and backtracking to efficiently parse grammars defined in Parsing Expression Grammar (PEG) format. The module translates PEG rules into Janet functions that recursively call each other based on the grammar's structure. Memoization, storing the results of these function calls for specific input positions, prevents redundant computations and significantly speeds up parsing, especially for recursive grammars. When a rule fails to match, backtracking occurs, reverting the input position and trying alternative rules. This process continues until a complete parse is achieved or all possibilities are exhausted. The result is a parse tree representing the matched input according to the provided grammar.
Hacker News users discuss the elegance and efficiency of Janet's PEG implementation, particularly praising its use of packrat parsing for memoization to avoid exponential time complexity. Some compare it favorably to other parsing techniques and libraries like recursive descent parsers and the popular Python library parsimonious, noting Janet's approach offers a good balance of performance and understandability. Several commenters express interest in exploring Janet further, intrigued by its features and the clear explanation provided in the linked article. A brief discussion also touches on error reporting in PEG parsers and the potential for improvements in Janet's implementation.
Rust enums can surprisingly be smaller than expected. While naively, one might assume an enum's size is determined by the largest variant plus a discriminant to track which variant is active, the compiler optimizes this. If an enum's largest variant contains data with internal padding, the discriminant can sometimes be stored within that padding, avoiding an increase in the overall size. This optimization applies even when using #[repr(C)] or #[repr(u8)], so long as the layout allows it. Essentially, the compiler cleverly utilizes existing unused space within variants to store the variant tag, minimizing the enum's memory footprint.
Hacker News users discussed the surprising optimization where Rust can reduce the size of an enum if its variants all have the same representation. Some commenters expressed admiration for this detail of the Rust compiler and its potential performance benefits. A few questioned the long-term stability of relying on this optimization, wondering if changes to the enum's variants could inadvertently increase its size in the future. Others delved into the specifics of how this optimization interacts with features like repr(C) and niche filling optimizations. One user linked to a relevant section of the Rust Reference, further illuminating the compiler's behavior. The discussion also touched upon the potential downsides, such as making the generated assembly more complex, and how using #[repr(u8)] might offer a more predictable and explicit way to control enum size.
PlanetScale's Vitess project, which uses a Go-based MySQL interpreter, historically lagged behind C++ in performance. Through focused optimization efforts targeting function call overhead, memory allocation, and string conversion, they significantly improved Vitess's speed. By leveraging Go's built-in profiling tools and making targeted changes like using custom map implementations and byte buffers, they achieved performance comparable to, and in some cases exceeding, a similar C++ interpreter. These improvements demonstrate that with careful optimization, Go can be a competitive choice for performance-sensitive applications like database interpreters.
Hacker News users discussed the benchmarks presented in the PlanetScale blog post, expressing skepticism about their real-world applicability. Several commenters pointed out that the microbenchmarks might not reflect typical database workload performance, and questioned the choice of C++ implementation used for comparison. Some suggested that the Go interpreter's performance improvements, while impressive, might not translate to significant gains in a production environment. Others highlighted the importance of considering factors beyond raw execution speed, such as memory usage and garbage collection overhead. The lack of details about the specific benchmarks and the C++ implementation used made it difficult for some to fully assess the validity of the claims. A few commenters praised the progress Go has made, but emphasized the need for more comprehensive and realistic benchmarks to accurately compare interpreter performance.
This post outlines a vision for first-class WebAssembly support in Swift, enabling developers to compile Swift code directly to Wasm for use in web browsers and other Wasm environments. The proposal emphasizes seamless integration with existing JavaScript ecosystems, allowing bidirectional communication between Swift and JavaScript code. It also aims for near-native performance by leveraging Wasm's capabilities, and proposes tools and workflows to simplify the development process, such as automatic generation of JavaScript bindings for Swift code. The ultimate goal is to empower Swift developers to build high-performance web applications and leverage the growing Wasm ecosystem, while maintaining Swift's core values of safety, performance, and expressiveness.
Hacker News users discussed the potential and challenges of Swift for WebAssembly. Some expressed excitement about the prospect of using Swift for frontend development, highlighting its performance and type safety as advantages over JavaScript. Others were more cautious, pointing to the existing maturity of JavaScript and its ecosystem, and questioning whether Swift could gain significant traction. Concerns were raised about the size of Swift compiled output and the integration with existing JavaScript libraries and frameworks. The potential for full-stack Swift development and server-side applications with WebAssembly was also mentioned as a motivating factor. Several users suggested that prioritizing the developer experience and tooling would be crucial for adoption.
C3 is a new programming language designed as a modern alternative to C. It aims to be safer and easier to use while maintaining C's performance and low-level control. Key features include optional memory safety through compile-time checks and garbage collection, improved syntax and error messages, and built-in modularity. The project is actively under development and includes a self-hosting compiler written in C3. The goal is to provide a practical language for systems programming and other performance-sensitive domains while mitigating common C pitfalls.
HN users discuss C3's goals and features, expressing both interest and skepticism. Several question the need for another C-like language, especially given the continued development of C and C++. Some appreciate the focus on safety and preventing common C errors, while others find the changes too drastic a departure from C's philosophy. There's debate about the practicality of automatic memory management in systems programming, and some concern over the runtime overhead it might introduce. The project's early stage is noted, and some express reservations about its long-term viability and community adoption. Others are more optimistic, praising the clear documentation and expressing interest in following its progress. The use of Python for the compiler is also a point of discussion.
This guide provides a curated list of compiler flags for GCC, Clang, and MSVC, designed to harden C and C++ code against security vulnerabilities. It focuses on options that enable various exploit mitigations, such as stack protectors, control-flow integrity (CFI), address space layout randomization (ASLR), and shadow stacks. The guide categorizes flags by their protective mechanisms, emphasizing practical usage with clear explanations and examples. It also highlights potential compatibility issues and performance impacts, aiming to help developers choose appropriate hardening options for their projects. By leveraging these compiler-based defenses, developers can significantly reduce the risk of successful exploits targeting their software.
Hacker News users generally praised the OpenSSF's compiler hardening guide for C and C++. Several commenters highlighted the importance of such guides in improving overall software security, particularly given the prevalence of C and C++ in critical systems. Some discussed the practicality of implementing all the recommendations, noting potential performance trade-offs and the need for careful consideration depending on the specific project. A few users also mentioned the guide's usefulness for learning more about compiler options and their security implications, even for experienced developers. Some wished for similar guides for other languages, and others offered additional suggestions for hardening, like using static and dynamic analysis tools. One commenter pointed out the difference between control-flow hijacking mitigations and memory safety, emphasizing the limitations of the former.
This blog post demonstrates how to achieve tail call optimization (TCO) in Java, despite the JVM's lack of native support. The author uses the ASM bytecode manipulation library to transform compiled Java bytecode, replacing recursive tail calls with goto instructions that jump back to the beginning of the method. This avoids stack frame growth and prevents StackOverflowErrors, effectively emulating TCO. The post provides a detailed example, transforming a simple factorial function, and discusses the limitations and potential pitfalls of this approach, including the handling of local variables and debugging challenges. Ultimately, it offers a working, albeit complex, solution for achieving TCO in Java for specific use cases.
Hacker News users generally expressed skepticism about the practicality and value of the approach described in the article. Several commenters pointed out that while technically interesting, using ASM to achieve tail-call optimization in Java is likely to be more trouble than it's worth due to the complexity and potential for subtle bugs. The performance benefits were questioned, with some suggesting that iterative solutions would be simpler and potentially faster. Others noted that relying on such a technique would make code less portable and harder to maintain. A few commenters appreciated the cleverness of the solution, but overall the sentiment leaned towards considering it more of a curiosity than a genuinely useful technique.
MilliForth-6502 is a minimalist Forth implementation for the 6502 processor, designed to be incredibly small while remaining a practical programming language. It features a 1 KB dictionary, a 256-byte parameter stack, and implements core Forth words including arithmetic, logic, stack manipulation, and I/O. Despite its size, MilliForth allows for defining new words and includes a simple interactive interpreter. Its compactness makes it suitable for resource-constrained 6502 systems, and the project provides source code and documentation for building and using it.
Hacker News users discussed the practicality and minimalism of MilliForth, a Forth implementation for the 6502 processor. Some questioned its usefulness beyond educational purposes, citing limited memory and awkward programming style compared to assembly language. Others appreciated its cleverness and the challenge of creating such a compact system, viewing it as a testament to Forth's flexibility. Several comments highlighted the historical context of Forth on resource-constrained systems and drew parallels to other small language implementations. The maintainability of generated code and the debugging experience were also mentioned as potential drawbacks. A few commenters expressed interest in exploring MilliForth further and potentially using it for small embedded projects.
Xee is a new XPath and XSLT engine written in Rust, focusing on performance, security, and WebAssembly compatibility. It aims to be a modern alternative to existing engines, offering a safe and efficient way to process XML and HTML in various environments, including browsers and servers. Leveraging Rust's ownership model and memory safety features, Xee minimizes vulnerabilities like use-after-free errors and buffer overflows. Its WebAssembly support enables client-side XML processing without relying on JavaScript, potentially improving performance and security for web applications. While still under active development, Xee already supports a substantial portion of the XPath 3.1 and XSLT 3.0 specifications, with plans to implement streaming transformations and other advanced features in the future.
HN commenters generally praise Xee's speed and the author's approach to error handling. Several highlight the impressive performance benchmarks compared to libxml2, with some noting the potential for Xee to become a valuable tool in performance-sensitive XML processing scenarios. Others appreciate the clean API design and Rust's memory safety advantages. A few discuss the niche nature of XPath/XSLT in modern development, while some express interest in using Xee for specific tasks like web scraping and configuration parsing. The Rust implementation also sparked discussions about language choices for performance-critical applications. Several users inquire about WASM support, indicating potential interest in browser-based applications.
Summary of Comments ( 19 )
https://news.ycombinator.com/item?id=44126264
HN commenters generally expressed interest in Nova, particularly its Rust implementation and potential performance benefits. Some questioned the practical need for yet another JavaScript engine, especially given the maturity of existing options like V8. Others were curious about specific implementation details, like garbage collection and WebAssembly support. A few pointed out the inherent challenges in competing with established engines, but acknowledged the value of exploring alternative approaches and the potential for niche applications where Nova's unique features might be advantageous. Several users expressed excitement about its potential for integration into other Rust projects. The potential for smaller binary sizes and faster startup times compared to V8 was also highlighted as a potential advantage.
The Hacker News post for "Nova: A JavaScript and WebAssembly engine written in Rust" has several comments discussing various aspects of the project.
Some users express excitement about a new JavaScript engine written in Rust, seeing it as a positive development. They praise the potential performance benefits and memory safety that Rust can bring to such a project. One user specifically mentions being interested in the potential for Servo’s concurrency model to be implemented, potentially leading to impressive parallelization capabilities.
There's a discussion regarding the feasibility and challenges of creating a JavaScript engine from scratch. Several users point out the immense undertaking involved in fully supporting the JavaScript specification and achieving competitive performance with established engines like V8. Concerns about garbage collection implementation and the potential for subtle bugs also surface. However, some counter that starting anew allows for leveraging modern design principles and potentially avoiding legacy baggage.
The conversation also delves into the motivations behind building a new engine, with speculation about whether it aims to address specific niches or explore novel architectural ideas. Some suggest potential use cases like embedded systems, server-side JavaScript, or specialized applications where existing engines might not be ideal.
Performance comparisons with existing engines are a recurring theme. Users express curiosity about benchmarks and real-world performance metrics. While acknowledging it's early days for the project, they emphasize the importance of demonstrating tangible performance advantages to justify adopting a new engine.
There's a brief discussion on the project's licensing, specifically the use of the MIT license, which is seen as permissive and conducive to wider adoption.
A few comments touch upon the broader landscape of JavaScript engines, mentioning other projects like QuickJS and highlighting the challenges faced by alternative engines in gaining widespread traction.
Finally, some users share their personal experiences with Rust and WebAssembly, expressing optimism about the project's prospects given the strengths of these technologies.
Overall, the comments reflect a cautious but optimistic outlook on Nova. While acknowledging the significant challenges involved in building a successful JavaScript engine, commenters are intrigued by the potential of a Rust-based implementation and eager to see how the project evolves.