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.
The Haiku-OS.org post "Learning to Program with Haiku" provides a comprehensive starting point for aspiring Haiku developers. It highlights the simplicity and power of the Haiku API for creating GUI applications, using the native C++ framework and readily available examples. The guide emphasizes practical learning through modifying existing code and exploring the extensive documentation and example projects provided within the Haiku source code. It also points to resources like the Be Book (covering the BeOS API, which Haiku largely inherits), mailing lists, and the IRC channel for community support. The post ultimately encourages exploration and experimentation as the most effective way to learn Haiku development, positioning it as an accessible and rewarding platform for both beginners and experienced programmers.
Commenters on Hacker News largely expressed nostalgia and fondness for Haiku OS, praising its clean design and the tutorial's approachable nature for beginners. Some recalled their positive experiences with BeOS and appreciated Haiku's continuation of its legacy. Several users highlighted Haiku's suitability for older hardware and embedded systems. A few comments delved into technical aspects, discussing the merits of Haiku's API and its potential as a development platform. One commenter noted the tutorial's focus on GUI programming as a smart move to showcase Haiku's strengths. The overall sentiment was positive, with many expressing interest in revisiting or trying Haiku based on the tutorial.
The 6502 assembly language makes a great first foray into low-level programming due to its small, easily grasped instruction set and straightforward addressing modes. Its simplicity encourages understanding of fundamental concepts like registers, memory management, and instruction execution without overwhelming beginners. Coupled with readily available emulators and a rich history in iconic systems, the 6502 offers a practical and engaging learning experience that builds a solid foundation for exploring more complex architectures later on. Its limited register set forces a focus on memory operations, providing valuable insight into how CPUs interact with memory.
Hacker News users generally agreed that the 6502 is a good starting point for learning assembly language due to its small and simple instruction set, limited addressing modes, and readily available emulators and documentation. Several commenters shared personal anecdotes of their early programming experiences with the 6502, reinforcing its suitability for beginners. Some suggested alternative starting points like the Z80 or MIPS, citing their more "regular" instruction sets, but acknowledged the 6502's historical significance and accessibility. A few users also discussed the benefits of learning assembly language in general, emphasizing the foundational understanding it provides of computer architecture and low-level programming concepts. A minor thread debated the educational value of assembly in the modern era, but the prevailing sentiment remained positive towards the 6502 as an introductory assembly language.
The blog post "The Simplicity of Prolog" argues that Prolog's declarative nature makes it easier to learn and use than imperative languages for certain problem domains. It demonstrates this by building a simple genealogy program in Prolog, highlighting how its concise syntax and built-in search mechanism naturally express relationships and deduce facts. The author contrasts this with the iterative loops and explicit state management required in imperative languages, emphasizing how Prolog abstracts away these complexities. The post concludes that while Prolog may not be suitable for all tasks, its elegant approach to logic programming offers a powerful and efficient solution for problems involving knowledge representation and inference.
Hacker News users generally praised the article for its clear introduction to Prolog, with several noting its effectiveness in sparking their own interest in the language. Some pointed out Prolog's historical significance and its continued relevance in specific domains like AI and knowledge representation. A few users highlighted the contrast between Prolog's declarative approach and the more common imperative style of programming, emphasizing the shift in mindset required to effectively use it. Others shared personal anecdotes of their experiences with Prolog, both positive and negative, with some mentioning its limitations in performance-critical applications. A couple of comments also touched on the learning curve associated with Prolog and the challenges in debugging complex programs.
This guide provides a comprehensive introduction to BCPL programming on the Raspberry Pi. It covers setting up a BCPL environment, basic syntax and data types, control flow, procedures, and input/output operations. The guide also delves into more advanced topics like separate compilation, creating libraries, and interfacing with the operating system. It includes numerous examples and exercises, making it suitable for both beginners and those with prior programming experience looking to explore BCPL. The document emphasizes BCPL's simplicity and efficiency, particularly its suitability for low-level programming tasks on resource-constrained systems like the Raspberry Pi.
HN commenters expressed interest in BCPL due to its historical significance as a predecessor to C and its influence on Go. Some recalled using BCPL in the past, highlighting its simplicity and speed, and contrasting its design with C. A few users discussed specific aspects of the document, such as the choice of Raspberry Pi and the use of pre-built binaries, while others lamented the lack of easily accessible BCPL resources today. Several pointed out the educational value of the guide, particularly for understanding compiler construction and the evolution of programming languages. Overall, the comments reflected a mix of nostalgia, curiosity, and appreciation for BCPL's role in computing history.
Summary of Comments ( 31 )
https://news.ycombinator.com/item?id=43746532
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 thefunctools.wrapsdecorator 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.The Hacker News post "Demystifying decorators: They don't need to be cryptic" linking to an article about Python decorators sparked a modest discussion with several insightful comments.
One commenter points out that the mystery surrounding decorators often stems from encountering complex examples before understanding the basic concept. They advocate for starting with simple examples using functions as decorators, gradually progressing to using classes as decorators, and finally tackling the more complex use cases involving arguments to decorators. This layered approach to learning is suggested as a more effective way to grasp the underlying mechanics.
Another commenter highlights the importance of distinguishing between decorator factories (functions that return decorators) and decorators themselves. They suggest that the term "decorator" is sometimes misused, leading to confusion. They clarify that a decorator applies something to a function, whereas a decorator factory creates something that applies something to a function. This nuanced distinction helps clarify the terminology surrounding decorators.
A further comment emphasizes the value of decorators in separating concerns. They suggest that a function's core logic should be distinct from cross-cutting concerns like logging, timing, and caching. Decorators provide a clean mechanism to apply these additional functionalities without cluttering the core logic. This comment reinforces the practical benefits of using decorators for cleaner code organization.
Another commenter succinctly suggests using Python's
functools.wrapswithin custom decorators to preserve the decorated function's metadata (such as__name__and__doc__). This practical tip ensures that introspection tools and documentation generators work correctly with decorated functions.Finally, one commenter mentions that, while decorators are helpful, excessive use can sometimes make code harder to read. This cautionary point suggests that decorators should be used judiciously, balancing their benefits against the potential for increased complexity if overused.
The discussion, while not extensive, offers practical advice and valuable insights into understanding and effectively using Python decorators. The comments highlight the importance of starting with simple examples, understanding the distinction between decorators and decorator factories, using decorators for separation of concerns, preserving function metadata, and avoiding overuse.