Edsger Dijkstra argues against "natural language programming," believing it a foolish endeavor. He contends that natural language's inherent ambiguity and imprecision make it unsuitable for expressing the rigorous logic required in programming. Instead of striving for superficial readability through natural language, Dijkstra advocates for focusing on developing formal notations and abstractions that are clear, concise, and verifiable, even if they appear less "natural" initially. He emphasizes that programming requires a level of precision and unambiguity that natural language simply cannot provide, and attempting to bridge this gap will ultimately lead to more confusion and less reliable software.
"Architecture Patterns with Python" introduces practical architectural patterns for structuring Python applications beyond simple scripts. It focuses on Domain-Driven Design (DDD) principles and demonstrates how to implement them alongside architectural patterns like dependency injection and the repository pattern to create well-organized, testable, and maintainable code. The book guides readers through building a realistic application, iteratively improving its architecture to handle increasing complexity and evolving requirements. It emphasizes using Python's strengths effectively while promoting best practices for software design, ultimately enabling developers to create robust and scalable applications.
Hacker News users generally expressed interest in "Architecture Patterns with Python," praising its clear writing and practical approach. Several commenters highlighted the book's focus on domain-driven design and its suitability for bridging the gap between simple scripts and complex applications. Some appreciated the free online availability, while others noted the value of supporting the authors by purchasing the book. A few users compared it favorably to other architecture resources, emphasizing its Python-specific examples. The discussion also touched on testing strategies and the balance between architecture and premature optimization. A couple of commenters pointed out the book's emphasis on using readily available tools and libraries rather than introducing new frameworks.
Coroutines offer a powerful abstraction for structuring programs involving asynchronous operations or generators, providing a more manageable alternative to callbacks or complex state machines. They achieve this by allowing functions to suspend and resume execution at specific points, enabling cooperative multitasking within a single thread. This post emphasizes that the key benefit of coroutines isn't simply the syntactic sugar of async and await, but the fundamental shift in how control flow is managed. By enabling the caller and the callee to cooperatively schedule their execution, coroutines facilitate the creation of cleaner, more composable, and easier-to-reason-about asynchronous code. This cooperative scheduling, controlled by the programmer, distinguishes coroutines from preemptive threading, offering more predictable and often more efficient concurrency management.
Hacker News users discuss the nuances of coroutines and their various implementations. Several commenters highlight the distinction between stackful and stackless coroutines, emphasizing the performance benefits and limitations of each. Some discuss the challenges in implementing stackful coroutines efficiently, while others point to the relative simplicity and portability of stackless approaches. The conversation also touches on the importance of understanding the underlying mechanics of coroutines and their impact on program behavior. A few users mention specific language implementations and libraries for working with coroutines, offering examples and insights into their practical usage. Finally, some commenters delve into the more philosophical aspects of the article, exploring the trade-offs between different programming paradigms and the importance of choosing the right tool for the job.
Verification-first development (VFD) prioritizes writing formal specifications and proofs before writing implementation code. This approach, while seemingly counterintuitive, aims to clarify requirements and design upfront, leading to more robust and correct software. By starting with a rigorous specification, developers gain a deeper understanding of the problem and potential edge cases. Subsequently, the code becomes a mere exercise in fulfilling the already-proven specification, akin to filling in the blanks. While potentially requiring more upfront investment, VFD ultimately reduces debugging time and leads to higher quality code by catching errors early in the development process, before they become costly to fix.
Hacker News users discussed the practicality and benefits of verification-first development (VFD). Some commenters questioned its applicability beyond simple examples, expressing skepticism about its effectiveness in complex, real-world projects. Others highlighted potential drawbacks like the added time investment for writing specifications and the difficulty of verifying emergent behavior. However, several users defended VFD, arguing that the upfront effort pays off through reduced debugging time and improved code quality, particularly when dealing with complex logic. Some suggested integrating VFD gradually, starting with critical components, while others mentioned tools and languages specifically designed to support this approach, like TLA+ and Idris. A key point of discussion revolved around finding the right balance between formal verification and traditional testing.
Component simplicity, in the context of functional programming, emphasizes minimizing the number of moving parts within individual components. This involves reducing statefulness, embracing immutability, and favoring pure functions where possible. By keeping each component small, focused, and predictable, the overall system becomes easier to reason about, test, and maintain. This approach contrasts with complex, stateful components that can lead to unpredictable behavior and difficult debugging. While acknowledging that some statefulness is unavoidable in real-world applications, the article advocates for strategically minimizing it to maximize the benefits of functional principles.
Hacker News users discuss Jerf's blog post on simplifying functional programming components. Several commenters agree with the author's emphasis on reducing complexity and avoiding over-engineering. One compelling comment highlights the importance of simple, composable functions as the foundation of good FP, arguing against premature abstraction. Another points out the value of separating pure functions from side effects for better testability and maintainability. Some users discuss specific techniques for achieving simplicity, such as using plain data structures and avoiding monads when unnecessary. A few commenters note the connection between Jerf's ideas and Rich Hickey's "Simple Made Easy" talk. There's also a short thread discussing the practical challenges of applying these principles in large, complex projects.
The blog post "The program is the database is the interface" argues that traditional software development segregates program logic, data storage, and user interface too rigidly. This separation leads to complexities and inefficiencies when trying to maintain consistency and adapt to evolving requirements. The author proposes a more integrated approach where the program itself embodies the database and the interface, drawing inspiration from Smalltalk's image-based persistence and the inherent interactivity of spreadsheet software. This unified model would simplify development by eliminating impedance mismatches between layers and enabling a more fluid and dynamic relationship between data, logic, and user experience. Ultimately, the post suggests this paradigm shift could lead to more powerful and adaptable software systems.
Hacker News users discuss the implications of treating the program as the database and interface, focusing on the simplicity and power this approach offers for specific applications. Some commenters express skepticism, noting potential performance and scalability issues, particularly for large datasets. Others suggest this concept is not entirely new, drawing parallels to older programming paradigms like Smalltalk and spreadsheet software. A key discussion point revolves around the sweet spot for this approach, with general agreement that it's best suited for smaller, self-contained projects or niche applications where flexibility and rapid development are prioritized over complex data management needs. Several users highlight the potential of using this model for prototyping and personal projects.
The blog post explores how C, despite lacking built-in object-oriented features like polymorphism, achieves similar functionality through clever struct design and function pointers. It uses examples from the Linux kernel and FFmpeg to demonstrate this. Specifically, it showcases how defining structs with common initial members (akin to base classes) and using function pointers within these structs allows different "derived" structs to implement their own versions of specific operations, effectively mimicking virtual methods. This enables flexible and extensible code that can handle various data types or operations without needing to know the specific concrete type at compile time, achieving runtime polymorphism.
Hacker News users generally praised the article for its clear explanation of polymorphism in C, particularly how FFmpeg and the Linux kernel utilize function pointers and structs to achieve object-oriented-like designs. Several commenters pointed out the trade-offs of this approach, highlighting the increased complexity for debugging and the potential performance overhead compared to simpler C code or using C++. One commenter shared personal experience working with FFmpeg's codebase, confirming the article's description of its design. Another noted the value in understanding these techniques even if using higher-level languages, as it helps with interacting with C libraries and understanding lower-level system design. Some discussion focused on the benefits and drawbacks of C++'s object model compared to C's approach, with some suggesting modern C++ offers a more manageable way to achieve polymorphism. A few commenters mentioned other examples of similar techniques in different C projects, broadening the context of the article.
John Ousterhout contrasts his book "A Philosophy of Software Design" (APoSD) with Robert Martin's "Clean Code," arguing they offer distinct, complementary perspectives. APoSD focuses on high-level design principles for managing complexity, emphasizing modularity, information hiding, and deep classes with simple interfaces. Clean Code, conversely, concentrates on low-level coding style and best practices, addressing naming conventions, function length, and comment usage. Ousterhout believes both approaches are valuable but APoSD's strategic focus on managing complexity in larger systems is more critical for long-term software success than Clean Code's tactical advice. He suggests developers benefit from studying both, prioritizing APoSD's broader design philosophy before implementing Clean Code's stylistic refinements.
HN commenters largely agree with Ousterhout's criticisms of "Clean Code," finding many of its rules dogmatic and unproductive. Several commenters pointed to specific examples from the book that they found counterproductive, like the single responsibility principle leading to excessive class fragmentation, and the obsession with short functions and methods obscuring larger architectural issues. Some felt that "Clean Code" focuses too much on low-level details at the expense of higher-level design considerations, which Ousterhout emphasizes. A few commenters offered alternative resources on software design they found more valuable. There was some debate over the value of comments, with some arguing that clear code should speak for itself and others suggesting that comments serve a crucial role in explaining intent and rationale. Finally, some pointed out that "Clean Code," while flawed, can be a helpful starting point for junior developers, but should not be taken as gospel.
Jon Blow reflects on the concept of a "daylight computer," a system designed for focused work during daylight hours. He argues against the always-on, notification-driven nature of modern computing, proposing a machine that prioritizes deep work and mindful engagement. This involves limiting distractions, emphasizing local data storage, and potentially even restricting network access. The goal is to reclaim a sense of control and presence, fostering a healthier relationship with technology by aligning its use with natural rhythms and promoting focused thought over constant connectivity.
Hacker News users largely praised the Daylight Computer project for its ambition and innovative approach to personal computing. Several commenters appreciated the focus on local-first software and the potential for increased privacy and control over data. Some expressed skepticism about the project's feasibility and the challenges of building a sustainable ecosystem around a niche operating system. Others debated the merits of the chosen hardware and software stack, suggesting alternatives like RISC-V and questioning the reliance on Electron. A few users shared their personal experiences with similar projects and offered practical advice on development and community building. Overall, the discussion reflected a cautious optimism about the project's potential, tempered by a realistic understanding of the difficulties involved in disrupting the established computing landscape.
Elements of Programming (2009) by Alexander Stepanov and Paul McJones provides a foundational approach to programming by emphasizing abstract concepts and mathematical rigor. The book develops fundamental algorithms and data structures from first principles, focusing on clear reasoning and formal specifications. It uses abstract data types and generic programming techniques to achieve code that is both efficient and reusable across different programming languages and paradigms. The book aims to teach readers how to think about programming at a deeper level, enabling them to design and implement robust and adaptable software. While rooted in practical application, its focus is on the underlying theoretical framework that informs good programming practices.
Hacker News users discuss the density and difficulty of Elements of Programming, acknowledging its academic rigor and focus on foundational concepts. Several commenters point out that the book isn't for beginners and requires significant mathematical maturity. The book's use of abstract algebra and its emphasis on generic programming are highlighted, with some finding it insightful and others overwhelming. The discussion also touches on the impracticality of some of the examples for real-world coding and the lack of readily available implementations in popular languages. Some suggest alternative resources for learning practical programming, while others defend the book's value for building a deeper understanding of fundamental principles. A recurring theme is the contrast between the book's theoretical approach and the practical needs of most programmers.
Refactoring, while often beneficial, should not be undertaken without careful consideration. The blog post argues against refactoring for its own sake, emphasizing that it should be driven by a clear purpose, like improving performance, adding features, or fixing bugs. Blindly pursuing "clean code" or preemptive refactoring can introduce new bugs, create unnecessary complexity, and waste valuable time. Instead, refactoring should be a strategic tool used to address specific problems and improve the maintainability of code that is actively being worked on, not a constant, isolated activity. Essentially, refactor with a goal, not just for aesthetic reasons.
Hacker News users generally disagreed with the premise of the blog post, arguing that refactoring is crucial for maintaining code quality and developer velocity. Several commenters pointed out that the article conflates refactoring with rewriting, which are distinct activities. Others suggested the author's negative experiences stemmed from poorly executed refactors, rather than refactoring itself. The top comments highlighted the long-term benefits of refactoring, including reduced technical debt, improved readability, and easier debugging. Some users shared personal anecdotes about successful refactoring efforts, while others offered practical advice on when and how to refactor effectively. A few conceded that excessive or unnecessary refactoring can be detrimental, but emphasized that this doesn't negate the value of thoughtful refactoring.
Qntm's "Developer Philosophy" emphasizes a pragmatic approach to software development centered around the user. Functionality and usability reign supreme, prioritizing delivering working, valuable software over adhering to abstract principles or chasing technical perfection. This involves embracing simplicity, avoiding unnecessary complexity, and focusing on the core problem the software aims to solve. The post advocates for iterative development, accepting that software is never truly "finished," and encourages a willingness to learn and adapt throughout the process. Ultimately, the philosophy boils down to building things that work and are useful for people, favoring practicality and continuous improvement over dogmatic adherence to any specific methodology.
Hacker News users discuss the linked blog post about "Developer Philosophy." Several commenters appreciate the author's humor and engaging writing style. Some agree with the core argument about developers often over-engineering solutions and prioritizing "cleverness" over simplicity. One commenter points out the irony of using complex language to describe this phenomenon. Others disagree with the premise, arguing that performance optimization and preparing for future scaling are valid concerns. The discussion also touches upon the tension between writing maintainable code and the desire for intellectual stimulation and creativity in programming. A few commenters express skepticism about the "one true way" to develop software and emphasize the importance of context and specific project requirements. There's also a thread discussing the value of different programming paradigms and the role of experience in shaping a developer's philosophy.
The blog post "Inheritance and Subtyping" argues that inheritance and subtyping are distinct concepts often conflated, leading to inflexible and brittle code. Inheritance, a mechanism for code reuse, creates a tight coupling between classes, whereas subtyping, focused on behavioral compatibility, allows substitutability. The author advocates for composition over inheritance, suggesting interfaces and delegation as preferred alternatives for achieving polymorphism and code reuse. This approach promotes looser coupling, increased flexibility, and easier maintainability, ultimately leading to more robust and adaptable software design.
Hacker News users generally agree with the author's premise that inheritance is often misused, especially when subtyping isn't the goal. Several commenters point out that composition and interfaces are generally preferable, offering greater flexibility and avoiding the tight coupling inherent in inheritance. One commenter highlights the "fragile base class problem," where changes in a parent class can unexpectedly break child classes. Others discuss the nuances of Liskov Substitution Principle and how it relates to proper inheritance usage. One user specifically calls out Java's overuse of inheritance, citing the infamous AbstractSingletonProxyFactoryBean. A few dissenting opinions mention that inheritance can be a useful tool when used judiciously, especially in domains like game development where hierarchical relationships are naturally occurring.
The blog post explores building a composable SQL query builder in Haskell using the concept of functors. Instead of relying on string concatenation, which is prone to SQL injection vulnerabilities, it leverages Haskell's type system and the Functor typeclass to represent SQL fragments as data structures. These fragments can then be safely combined and transformed using pure functions. The approach allows for building complex queries piece by piece, abstracting away the underlying SQL syntax and promoting code reusability. This results in a more type-safe, maintainable, and composable way to generate SQL queries compared to traditional string-based methods.
HN commenters generally appreciate the composability approach to SQL queries presented in the article, finding it cleaner and more maintainable than traditional string concatenation. Several highlight the similarity to functional programming concepts and appreciate the use of Python's type hinting. Some express concern about performance implications, particularly with nested queries, and suggest comparing it to ORMs. Others question the practicality for complex queries or the necessity for simpler ones. A few users mention existing libraries with similar functionality, like SQLAlchemy Core. The discussion also touches upon alternative approaches like using CTEs (Common Table Expressions) for composability and the potential benefits for testing and debugging.
Successful abstractions manage complexity by isolating it. They provide a simplified interface that hides intricate details, allowing users to interact with a system without needing to understand its inner workings. A good abstraction chooses which details to expose and which to conceal, offering just enough information for effective use. This simplification reduces cognitive load and allows for easier composition and reuse of components. The key is finding the right balance: too much abstraction leads to leaky abstractions where the underlying complexity seeps through, while too little provides insufficient simplification.
HN commenters largely agreed with the author's premise that good abstractions hide complexity. Several pointed out that "leaky abstractions" are a common problem, where the underlying complexity bleeds through and negates the abstraction's benefits. One commenter highlighted the difficulty of finding the right balance, where an abstraction is neither too complex nor too simplistic, using the example of an overly abstracted car where the driver has no control over engine specifics. The value of predictable behavior within an abstraction was also emphasized, along with the importance of choosing the right level of abstraction for the task at hand, suggesting different levels for different users (e.g., library user vs. library developer). Some discussion focused on the definition of "complexity" itself, with suggestions that "complications" or "implementation details" might be more accurate terms. The lack of mention of Postel's Law (be conservative in what you send, liberal in what you accept) was noted by one commenter as a surprising omission.
Matt Keeter describes how an aesthetically pleasing test suite, visualized as colorful 2D and 3D renders, drives development and debugging of his implicit CAD system. He emphasizes the psychological benefit of attractive tests, arguing they encourage more frequent and thorough testing. By visually confirming expected behavior and quickly pinpointing failures through color-coded deviations, the tests guide implementation and accelerate the iterative design process. This approach has proven invaluable in tackling complex geometry problems, allowing him to confidently refactor and extend his system while ensuring correctness.
HN commenters largely praised the author's approach to test-driven development and the resulting elegance of the code. Several appreciated the focus on geometric intuition and visualization, finding the interactive, visual tests particularly compelling. Some pointed out the potential benefits of this approach for education, suggesting it could make learning geometry more engaging. A few questioned the scalability and maintainability of such a system for larger projects, while others noted the inherent limitations of relying solely on visual tests. One commenter suggested exploring formal verification methods like TLA+ to complement the visual approach. There was also a brief discussion on the choice of Python and its suitability for such computationally intensive tasks.
Martin Fowler's short post "Two Hard Things" humorously points out the inherent difficulty in software development. He argues that naming things well and cache invalidation are the two hardest problems. While seemingly simple, choosing accurate, unambiguous, and consistent names within a large codebase is a significant challenge. Similarly, knowing when to invalidate cached data to ensure accuracy without sacrificing performance is a complex problem requiring careful consideration. Essentially, both challenges highlight the intricate interplay between human comprehension and technical implementation that lies at the heart of software development.
HN commenters largely agree with Martin Fowler's assertion that naming things and cache invalidation are the two hardest problems in computer science. Some suggest other contenders, including off-by-one errors and distributed systems complexities (especially consensus). Several commenters highlight the human element in naming, emphasizing the difficulty of conveying nuance and intent, particularly across cultures and technical backgrounds. Others point out the subtle bugs that can arise from improper cache invalidation, impacting data consistency and causing difficult-to-track issues. The interplay between these two hard problems is also mentioned, as poor naming can exacerbate the difficulties of cache invalidation by making it harder to understand what data a cache key represents. A few humorous comments allude to these challenges being far less daunting than other life problems, such as raising children.
"Alligator Eggs" explores the surprising computational power hidden within a simple system of rewriting strings. Inspired by a children's puzzle involving moving colored eggs, the post demonstrates how a carefully designed set of rules for replacing egg sequences can emulate the functionality of a Turing Machine, a theoretical model capable of performing any computation. By encoding logic and data within the arrangement of the eggs, the system can execute arbitrary programs, effectively turning a seemingly trivial game into a universal computer. The post emphasizes the elegance and minimalism of this computational model, highlighting how complex behavior can emerge from simple, well-defined rules.
HN users generally praised the clarity and approachability of Bret Victor's explanation of lambda calculus, with several highlighting its effectiveness as an introductory resource even for those without a strong math background. Some discussed the challenges of teaching and visualizing these concepts, appreciating Victor's interactive approach. A few commenters delved into more technical nuances, comparing lambda calculus to combinatory logic and touching upon topics like currying and the SKI calculus. Others reminisced about learning from similar resources in the past and shared related links, demonstrating the article's enduring relevance. A recurring theme was the power of visual and interactive learning tools in making complex topics more accessible.
The author argues that Knuth's vision of literate programming, where code is written for humans within a narrative explaining its logic, hasn't achieved mainstream adoption because it fundamentally misunderstands the nature of programming. Rather than a linear, top-down process suitable for narrative explanation, programming is inherently exploratory and iterative, involving frequent refactoring and restructuring. Literate programming tools force a rigid structure onto this fluid process, making it cumbersome and ultimately counterproductive. The author proposes "exploratory programming" as a more realistic approach, emphasizing tools that facilitate quick exploration, refactoring, and visualization of code relationships, allowing understanding to emerge organically from the code itself.
Hacker News users discuss the merits and flaws of Knuth's literate programming style. Some argue that his approach, while elegant, prioritizes code as literature over practicality, making it difficult to navigate and modify, particularly in larger projects. Others counter that the core concept of intertwining code and explanation remains valuable, but modern tooling like Jupyter notebooks and embedded documentation offer better solutions. The thread also explores alternative approaches like docstrings and the use of comments to generate documentation, emphasizing the importance of clear and concise explanations within the codebase itself. Several commenters highlight the benefits of separating documentation from code for maintainability and flexibility, suggesting that the ideal approach depends on the project's scale and complexity. The original post is criticized for misrepresenting Knuth's views and focusing too heavily on superficial aspects like tool choice rather than the underlying philosophy.
Good software development habits prioritize clarity and maintainability. This includes writing clean, well-documented code with meaningful names and consistent formatting. Regular refactoring, testing, and the use of version control are crucial for managing complexity and ensuring code quality. Embracing a growth mindset through continuous learning and seeking feedback further strengthens these habits, enabling developers to adapt to changing requirements and improve their skills over time. Ultimately, these practices lead to more robust, easier-to-maintain software and a more efficient development process.
Hacker News users generally agreed with the article's premise regarding good software development habits. Several commenters emphasized the importance of writing clear and concise code with good documentation. One commenter highlighted the benefit of pair programming and code reviews for improving code quality and catching errors early. Another pointed out that while the habits listed were good, they needed to be contextualized based on the specific project and team. Some discussion centered around the trade-off between speed and quality, with one commenter suggesting focusing on "good enough" rather than perfection, especially in early stages. There was also some skepticism about the practicality of some advice, particularly around extensive documentation, given the time constraints faced by developers.
This blog post explores the powerful concept of functions as the fundamental building blocks of computation, drawing insights from the book Structure and Interpretation of Computer Programs (SICP) and David Beazley's work. It illustrates how even seemingly complex structures like objects and classes can be represented and implemented using functions, emphasizing the elegance and flexibility of this approach. The author demonstrates building a simple object system solely with functions, highlighting closures for managing state and higher-order functions for method dispatch. This functional perspective provides a deeper understanding of object-oriented programming and showcases the unifying power of functions in expressing diverse programming paradigms. By breaking down familiar concepts into their functional essence, the post encourages a more fundamental and adaptable approach to software design.
Hacker News users discuss the transformative experience of learning Scheme and SICP, particularly under David Beazley's tutelage. Several commenters emphasize the power of Beazley's teaching style, highlighting his ability to simplify complex concepts and make them engaging. Some found the author's surprise at the functional paradigm's elegance noteworthy, with one suggesting that other languages like Python and Javascript offer similar functional capabilities, perhaps underappreciated by the author. Others debated the benefits and drawbacks of "pure" functional programming, its practicality in real-world projects, and the learning curve associated with Scheme. A few users also shared their own positive experiences with SICP and its impact on their understanding of computer science fundamentals. The overall sentiment reflects an appreciation for the article's insights and the enduring relevance of SICP in shaping programmers' perspectives.
Summary of Comments ( 131 )
https://news.ycombinator.com/item?id=43564386
HN commenters generally agree with Dijkstra's skepticism of "natural language programming." Some highlight the ambiguity inherent in natural language as fundamentally incompatible with the precision required for programming. Others point out the success of domain-specific languages (DSLs) as a middle ground, offering a more human-readable syntax without sacrificing clarity. One commenter suggests Dijkstra's critique is more aimed at vague specifications disguised as programs rather than genuinely well-defined natural language programming. Several commenters mention the value of formal methods and mathematical notation for clear program design, echoing Dijkstra's sentiments. A few offer historical context, suggesting the "natural language programming" Dijkstra criticized likely refers to early, overly ambitious attempts, and that modern NLP advancements might warrant revisiting the concept.
The Hacker News post titled "Dijkstra On the foolishness of "natural language programming"" links to Edsger W. Dijkstra's manuscript EWD667, where he argues against using natural language for programming. The comments section features a robust discussion around Dijkstra's points, with several commenters offering diverse perspectives.
Several commenters agree with Dijkstra's core argument, emphasizing the inherent ambiguity and imprecision of natural language, which they see as unsuitable for the rigor and clarity required in programming. They highlight the importance of formal languages for expressing logical instructions unambiguously. Some point out that while natural language can be useful for high-level design discussions or documentation, translating it directly into executable code presents significant challenges and can lead to unreliable or unpredictable software.
A recurring theme in the comments is the distinction between "natural language programming" as envisioned in the past (i.e., literally programming in English or other natural languages) versus more modern approaches like using natural language for generating code or interacting with coding tools. Some commenters argue that Dijkstra's criticisms, while valid in the context of his time, may not fully apply to these newer paradigms. They point to advancements in natural language processing and machine learning that enable more sophisticated analysis and interpretation of natural language, potentially mitigating some of the ambiguity issues.
Some commenters bring up the importance of domain-specific languages (DSLs) as a middle ground between natural language and formal programming languages. DSLs allow developers to express logic using terminology closer to the specific problem domain while retaining the precision and unambiguity of formal languages.
A few commenters offer counterpoints to Dijkstra's arguments. Some suggest that natural language can be a valuable tool for making programming more accessible to non-experts or for rapid prototyping. Others argue that forcing programmers to think in terms of formal languages can sometimes hinder creativity and problem-solving.
One commenter points out that Dijkstra's strong stance against natural language programming might stem from his background in mathematics and formal logic, which naturally favor precise and unambiguous systems.
Overall, the comments section presents a nuanced discussion of the topic. While many agree with the fundamental points raised by Dijkstra, they also acknowledge the evolving landscape of programming and the potential for natural language to play a helpful role in certain contexts, albeit with careful consideration of its limitations. Several comments distinguish between the naive approach of directly translating natural language to code and the more nuanced possibilities afforded by modern NLP techniques, emphasizing the importance of context when interpreting Dijkstra's arguments.