The blog post "Learning C3" details the author's experience learning the C3 linearization algorithm used for multiple inheritance in programming languages like Python and R. They found the algorithm initially complex and confusing due to its recursive nature and reliance on Method Resolution Order (MRO). Through a step-by-step breakdown of the algorithm's logic and the use of visual aids like diagrams, the author gained a deeper understanding. They highlight how the algorithm prevents unexpected behavior from the "diamond problem" in multiple inheritance by establishing a predictable and consistent method lookup order. The post concludes with the author feeling satisfied with their newfound comprehension of C3 and its importance for robust object-oriented programming.
Red is a next-generation full-stack programming language aiming for both extreme simplicity and extreme power. It incorporates a reactive engine at its core, enabling responsive interfaces and dataflow programming. Featuring a human-friendly syntax, Red is designed for metaprogramming, code generation, and domain-specific language creation. It's cross-platform and offers a complete toolchain encompassing everything from low-level system programming to high-level scripting, with a small, optimized footprint suitable for embedded systems. Red's ambition is to bridge the gap between low-level languages like C and high-level languages like Rebol, from which it draws inspiration.
Hacker News commenters on the Red programming language announcement express cautious optimism mixed with skepticism. Several highlight Red's ambition to be both a system programming language and a high-level scripting language, questioning the feasibility of achieving both goals effectively. Performance concerns are raised, particularly regarding the current implementation and its reliance on Rebol. Some commenters find the "full-stack" nature intriguing, encompassing everything from low-level system access to GUI development, while others see it as overly broad and reminiscent of Rebol's shortcomings. The small team size and potential for vaporware are also noted. Despite reservations, there's interest in the project's potential, especially its cross-compilation capabilities and reactive programming features.
The blog post explores methods for determining if an expression is constant at compile time in C. It highlights the limitations of sizeof for this purpose, as it can't differentiate between compile-time and run-time constants, and introduces a technique using C11's _Generic keyword. This method leverages the fact that array sizes must be compile-time constants. By attempting to create an array with the expression as its size inside a _Generic selection, the code can distinguish between compile-time constants (which compile successfully) and run-time values (which result in a compilation error). This allows conditional compilation based on the constexpr-ness of an expression, enabling optimized code paths for constant values.
HN users discuss the nuances and limitations of the presented C++ technique for detecting constant expressions in C. Several point out that constexpr is a C++ feature, not C, and the article's title is misleading. Some discuss alternative approaches in C, like using the preprocessor and #ifdef or build-time evaluation with constant folding. Others highlight the challenges of reliably determining const-ness in C due to factors like linker behavior and external variables. A few commenters delve into the complexities of constexpr itself within C++, including its interaction with different versions of the standard. The overall sentiment suggests the proposed method is not directly applicable to C and that true compile-time constness detection in C remains tricky.
This post explores the power and flexibility of Scheme macros for extending the language itself. It demonstrates how macros operate at the syntax level, manipulating code before evaluation, unlike functions which operate on values. The author illustrates this by building a simple infix macro that allows expressions to be written in infix notation, transforming them into the standard Scheme prefix notation. This example showcases how macros can introduce entirely new syntactic constructs, effectively extending the language's expressive power and enabling the creation of domain-specific languages or syntactic sugar for improved readability. The post emphasizes the difference between syntactic and procedural abstraction and highlights the unique capabilities of macros for metaprogramming and code generation.
HN commenters largely praised the tutorial for its clarity and accessibility in explaining Scheme macros. Several appreciated the focus on hygienic macros and the use of simple, illustrative examples. Some pointed out the power and elegance of Scheme's macro system compared to other languages. One commenter highlighted the importance of understanding syntax-rules as a foundation before moving on to more complex macro systems like syntax-case. Another suggested exploring Racket's macro system as a next step. There was also a brief discussion on the benefits and drawbacks of powerful macro systems, with some acknowledging the potential for abuse leading to unreadable code. A few commenters shared personal anecdotes of learning and using Scheme macros, reinforcing the author's points about their transformative power in programming.
Python decorators, often perceived as complex, are simply functions that wrap other functions, modifying their behavior. A decorator takes a function as input, defines an inner function that usually extends the original function's functionality, and returns this inner function. This allows adding common logic like logging, timing, or access control around a function without altering its core code. Decorators achieve this by replacing the original function with the decorated version, effectively making the added functionality transparent to the caller. Using the @ syntax is just syntactic sugar for calling the decorator function with the target function as an argument.
HN users generally found the article to be a good, clear explanation of Python decorators, particularly for beginners. Several commenters praised its simple, step-by-step approach and practical examples. Some suggested additional points for clarity, like emphasizing that decorators are just syntactic sugar for function wrapping, and explicitly showing the equivalence between using the @ syntax and the manual function wrapping approach. One commenter noted the article's helpfulness in understanding the functools.wraps decorator for preserving metadata. There was a brief discussion about the practicality of highly complex decorators, with some arguing they can become obfuscated and hard to debug.
Zig's comptime is powerful but has limitations. It's not a general-purpose Turing-complete language. It cannot perform arbitrary I/O operations like reading files or making network requests. Loop bounds and recursion depth must be known at compile time, preventing dynamic computations based on runtime data. While it can generate code, it can't introspect or modify existing code, meaning no macros in the traditional C/C++ sense. Finally, comptime doesn't fully eliminate runtime overhead; some checks and operations might still occur at runtime, especially when interacting with non-comptime code. Essentially, comptime excels at manipulating data and generating code based on compile-time constants, but it's not a substitute for a fully-fledged scripting language embedded within the compiler.
HN commenters largely agree with the author's points about the limitations of Zig's comptime, acknowledging that it's not a general-purpose Turing-complete language. Several discuss the tradeoffs involved in compile-time execution, citing debugging difficulty and compile times as potential downsides. Some suggest that aiming for Turing completeness at compile time is not necessarily desirable and praise Zig's pragmatic approach. One commenter points out that comptime is still very powerful, highlighting its ability to generate optimized code based on input parameters, which allows for things like custom allocators and specialized data structures. Others discuss alternative approaches, such as using build scripts, and how Zig's features complement those methods. A few commenters express interest in seeing how Zig evolves and whether future versions might address some of the current limitations.
This blog post reflects on four years of using Jai, a programming language designed for game development. The author, satisfied with their choice, highlights Jai's strengths: speed, ease of use for complex tasks, and a powerful compile-time execution feature called comptime. They acknowledge some drawbacks, such as the language's relative immaturity, limited documentation, and single-person development team. Despite these challenges, the author emphasizes the productivity gains and enjoyment experienced while using Jai, concluding it's the right tool for their specific needs and expressing excitement for its future.
Commenters on Hacker News largely praised Jai's progress and Jonathan Blow's commitment to the project. Several expressed excitement about the language's potential, particularly its speed and focus on data-oriented design. Some questioned the long-term viability given the lack of a 1.0 release and the small community, while others pointed out that Blow's independent funding allows him to develop at his own pace. The discussion also touched on Jai's compile times (which are reportedly quite fast), its custom tooling, and comparisons to other languages like C++ and Zig. A few users shared their own experiences experimenting with Jai, highlighting both its strengths and areas needing improvement, such as documentation. There was also some debate around the language's syntax and overall readability.
The blog post "My Favorite C++ Pattern: X Macros (2023)" advocates for using X Macros in C++ to reduce code duplication, particularly when defining enums, structs, or other collections of related items. The author demonstrates how X Macros, through a combination of #define directives and clever macro expansion, allows a single list of elements to be reused for generating different code constructs, such as compile-time string representations, enum values, and struct members. This approach improves maintainability and reduces the risk of inconsistencies between different representations of the same data. While acknowledging potential downsides like reduced readability and debugger difficulties, the author argues that the benefits of reduced redundancy and increased consistency outweigh the drawbacks in many situations. They propose using Chapel's built-in enumerations, which offer similar functionality to X macros without the preprocessor tricks, as a more modern and cleaner alternative where possible.
HN commenters generally appreciate the X macro pattern for its compile-time code generation capabilities, especially for avoiding repetitive boilerplate. Several noted its usefulness in embedded systems or situations requiring metaprogramming where C++ templates might be too complex or unavailable. Some highlighted potential downsides like debugging difficulty, readability issues, and the existence of alternative, potentially cleaner, solutions in modern C++. One commenter suggested using BOOST_PP for more complex scenarios, while another proposed a Python script for generating the necessary code, viewing X macros as a last resort. A few expressed interest in exploring Chapel, the language mentioned in the linked blog post, as a potential alternative to C++ for leveraging metaprogramming techniques.
Autology is a Lisp dialect designed for self-modifying code and introspection. It exposes its own interpreter and data structures, allowing programs to analyze and manipulate their own source code, execution state, and even the interpreter itself during runtime. This capability enables dynamic code generation, on-the-fly modifications, and powerful metaprogramming techniques. It aims to provide a flexible environment for exploring novel programming paradigms and building self-aware, adaptive systems.
HN users generally expressed interest in Autology, a Lisp dialect with access to its own interpreter. Several commenters compared it favorably to Rebol in terms of metaprogramming capabilities. Some discussion focused on its potential use cases, including live coding and creating interactive development environments. Concerns were raised regarding its apparent early stage of development, the lack of documentation beyond the README, and the potential performance implications of its design. A few users questioned the practicality of such a language, while others were excited by the possibilities it presented for self-modifying code and advanced debugging tools. The reliance on Python for its implementation also sparked some debate.
Crabtime brings Zig's comptime functionality to Rust, enabling evaluation of functions and expressions at compile time. It utilizes a procedural macro to transform annotated Rust code into a syntax tree that can be executed during compilation. This allows for computations, including string manipulation, type construction, and resource embedding, to be performed at compile time, leading to improved runtime performance and reduced binary size. Crabtime is still early in its development but aims to provide a powerful mechanism for compile-time metaprogramming in Rust.
HN commenters discuss crabtime, a library bringing Zig's comptime functionality to Rust. Several express excitement about the potential for metaprogramming and compile-time code generation, viewing it as a way to achieve greater performance and flexibility. Some raise concerns about the complexity and potential misuse of such powerful features, comparing it to template metaprogramming in C++. Others question the practical benefits and wonder if the added complexity is justified. The potential for compile times to increase significantly is also mentioned as a drawback. A few commenters suggest alternative approaches, like using build scripts or procedural macros, though the author clarifies that crabtime aims to offer something distinct. The overall sentiment seems to be cautious optimism, with many intrigued by the possibilities but also aware of the potential pitfalls.
This 2015 blog post demonstrates how to leverage Lua's flexible syntax and metamechanisms to create a Domain Specific Language (DSL) for generating HTML. The author uses Lua's tables and functions to create a clean, readable syntax that abstracts away the verbosity of raw HTML. By overloading the concatenation operator and utilizing metatables, the DSL allows users to build HTML elements and structures in a declarative way, mirroring the structure of the output. This approach simplifies HTML generation within Lua, making the code cleaner and more maintainable. The post provides concrete examples showing how to define tags, attributes, and nested elements, offering a practical guide to building similar DSLs for other output formats.
Hacker News users generally praised the article for its clear explanation of building a DSL in Lua, particularly appreciating the focus on leveraging Lua's existing features and metamechanisms. Several commenters shared their own experiences and preferences for using Lua for DSLs, including its use in game development and configuration management. One commenter pointed out potential performance considerations when using this approach, suggesting that precompilation could mitigate some overhead. Others discussed alternative methods for building DSLs, such as using parser generators. The use of Lua's setfenv was highlighted, with some acknowledging its power while others expressing caution due to potential debugging difficulties. A few users also mentioned other languages like Fennel and Janet as interesting alternatives to Lua for similar purposes.
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.
This blog post explores using Python decorators as a foundation for creating just-in-time (JIT) compilers. The author demonstrates this concept by building a simple JIT for a subset of Python, focusing on numerical computations. The approach uses decorators to mark functions for JIT compilation, leveraging Python's introspection capabilities to analyze the decorated function's Abstract Syntax Tree (AST). This allows the JIT to generate optimized machine code at runtime, replacing the original Python function. The post showcases how this technique can significantly improve performance for computationally intensive tasks while still maintaining the flexibility and expressiveness of Python. The example demonstrates transforming simple arithmetic operations into optimized machine code using LLVM, effectively turning Python into a domain-specific language (DSL) for numerical computation.
HN users generally praised the article for its clear explanation of using decorators for JIT compilation in Python, with several appreciating the author's approach to explaining a complex topic simply. Some commenters discussed alternative approaches to JIT compilation in Python, including using Numba and C extensions. Others pointed out potential drawbacks of the decorator-based approach, such as debugging challenges and the potential for unexpected behavior. One user suggested using a tracing JIT compiler as a possible improvement. Several commenters also shared their own experiences and use cases for JIT compilation in Python, highlighting its value in performance-critical applications.
Zyme is a new programming language designed for evolvability. It features a simple, homoiconic syntax and a small core language, making it easy to modify and extend. The language is designed to be used for genetic programming and other evolutionary computation techniques, allowing programs to be mutated and crossed over to generate new, potentially improved versions. Zyme is implemented in Rust and currently offers basic arithmetic, list manipulation, and conditional logic. It aims to provide a platform for exploring new ideas in program evolution and to facilitate the creation of self-modifying and adaptable software.
HN commenters generally expressed skepticism about Zyme's practical applications. Several questioned the evolutionary approach's efficiency compared to traditional programming paradigms, particularly for complex tasks. Some doubted the ability of evolution to produce readable and maintainable code. Others pointed out the challenges in defining fitness functions and controlling the evolutionary process. A few commenters expressed interest in the project's potential, particularly for tasks where traditional approaches struggle, such as program synthesis or automatic bug fixing. However, the overall sentiment leaned towards cautious curiosity rather than enthusiastic endorsement, with many calling for more concrete examples and comparisons to established techniques.
Summary of Comments ( 69 )
https://news.ycombinator.com/item?id=44125966
HN commenters generally praised the article for its clarity and approachable explanation of C3, a complex topic. Several appreciated the author's focus on practical usage and avoidance of overly academic language. Some pointed out that while C3 is important for understanding multiple inheritance and mixins, it's less relevant in languages like Python which use a simpler method resolution order. One commenter highlighted the importance of understanding the underlying concepts even if using languages that abstract away C3, as it aids in debugging and comprehending complex inheritance hierarchies. Another commenter pointed out that Python's MRO is actually a derivative of C3. A few expressed interest in seeing a follow-up article covering the performance implications of C3.
The Hacker News post titled "Learning C3" with the ID 44125966 has several comments discussing the linked blog post about learning the C3 linearization algorithm.
Several commenters discuss their experiences with multiple inheritance and the C3 algorithm specifically. One commenter mentions how the complexity of C3 can be a deterrent to using multiple inheritance, leading to simpler designs. Another commenter expresses the sentiment that the need for such a complex algorithm highlights potential design flaws and suggests favoring composition over inheritance.
A significant portion of the discussion revolves around the practicality and usefulness of multiple inheritance and the C3 algorithm. Some users question the real-world applications and suggest that the complexity outweighs the benefits in most scenarios. Others argue that understanding C3 is crucial when working with languages or frameworks that employ it, such as Python.
One commenter shares a personal anecdote about encountering the C3 algorithm in Python and the challenges they faced debugging related issues. They emphasize the importance of understanding method resolution order (MRO) in such situations.
Another commenter raises the question of whether there are simpler, more intuitive alternatives to C3 for achieving similar functionality.
The comments also touch upon the topic of mixins and traits, exploring their role as alternatives or complements to multiple inheritance. One commenter suggests that focusing on these concepts might be more beneficial than delving into the complexities of C3.
Overall, the comments reflect a mixed perspective on multiple inheritance and the C3 linearization algorithm. While some acknowledge its importance in specific contexts, others express skepticism about its practical value and advocate for simpler design approaches. The discussion highlights the trade-offs between the power and flexibility of multiple inheritance and the potential complexity it introduces.