Stories with Tag critique

• Et Tu, Grammarly?

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  Posted: 2025-03-29 10:27:33

  Verbose tl;dr

  Dbushell's blog post "Et Tu, Grammarly?" criticizes Grammarly's tone detector for flagging neutral phrasing as overly negative or uncertain. He provides examples where simple, straightforward sentences are deemed problematic, arguing that the tool pushes users towards an excessively positive and verbose style, ultimately hindering clear communication. This, he suggests, reflects a broader trend of AI writing tools prioritizing a specific, and potentially undesirable, writing style over actual clarity and conciseness. He worries this reinforces corporate jargon and ultimately diminishes the quality of writing.

  In a poignant reflection titled "Et Tu, Grammarly?", published on March 29, 2025, by Dave Bushell on his personal blog, dbushell.com, the author meticulously dissects the perceived decline in the efficacy of the popular writing assistance tool, Grammarly. He initiates his discourse by harkening back to a bygone era, a time when he lauded Grammarly as an indispensable instrument for refining his prose, a veritable digital amanuensis that elevated the clarity and precision of his written communications.

  Mr. Bushell proceeds to articulate a palpable shift in his perception of Grammarly's utility. He posits that the software, once a trusted ally in the pursuit of grammatical impeccability, has, in recent times, exhibited a propensity for offering suggestions that are not only unhelpful but, in some instances, demonstrably detrimental to the intended nuance and stylistic flourish of his writing. He illustrates this perceived degradation with specific examples, highlighting instances where Grammarly's recommendations, if implemented, would have resulted in a simplification or homogenization of his carefully crafted expressions.

  The author further elaborates on his growing disillusionment by lamenting the perceived inflexibility of Grammarly's algorithms. He argues that the software appears to prioritize rigid adherence to conventional grammatical precepts, often at the expense of authorial voice and stylistic idiosyncrasies. This, he contends, leads to a flattening of expressive range and a stifling of creativity in the writing process.

  Furthermore, Mr. Bushell expresses a distinct apprehension regarding the potential implications of over-reliance on such automated writing tools. He intimates that the ubiquitous adoption of software like Grammarly could inadvertently contribute to a decline in the overall quality and diversity of written communication, leading to a homogenized and predictable literary landscape devoid of individual flair and stylistic innovation. He concludes by expressing a wistful longing for the erstwhile efficacy of Grammarly, juxtaposing his current disappointment with his previous enthusiastic endorsement of the tool. This contrast serves to underscore the perceived magnitude of the software's decline in his estimation, leaving the reader with a sense of shared lament for the potential loss of a valuable writing aid.

  • Grammarly
  • grammar
  • Writing
  • AI Writing
  • AI
  • artificial intelligence
  • editing
  • Proofreading
  • Software
  • Technology
  • critique
  • style guide
  • Writing Tools
  • Blog Post
  • Opinion

  Summary of Comments ( 47 )
  https://news.ycombinator.com/item?id=43514308

  Verbose tl;dr

  HN commenters largely agree with the author's criticism of Grammarly's aggressive upselling and intrusive UI. Several users share similar experiences of frustration with the constant prompts to upgrade, even after dismissing them. Some suggest alternative grammar checkers like LanguageTool and ProWritingAid, praising their less intrusive nature and comparable functionality. A few commenters point out that Grammarly's business model necessitates these tactics, while others discuss the potential negative impact on user experience and writing flow. One commenter mentions the irony of Grammarly's own grammatical errors in their marketing materials, further fueling the sentiment against the company's practices. The overall consensus is that Grammarly's usefulness is overshadowed by its annoying and disruptive upselling strategy.

  The Hacker News post "Et Tu, Grammarly?" discussing Dbushell's blog post about Grammarly's apparent shift towards AI-driven features and potential decline in core grammar checking functionality, sparked a lively discussion with several compelling comments.

  Several users shared anecdotal experiences mirroring the author's sentiment. One user lamented the perceived decline in Grammarly's ability to catch basic grammatical errors, contrasting it with the tool's past performance. They specifically mentioned missing simple mistakes, suggesting a shift in focus from fundamental grammar rules. Another commenter echoed this, expressing frustration with Grammarly's increasing tendency to offer stylistic suggestions instead of addressing core grammatical issues. This user found the stylistic suggestions disruptive and ultimately deactivated the tool due to its perceived ineffectiveness in its primary function.

  The conversation also touched upon the broader implications of AI integration in writing tools. One commenter cautioned against relying solely on AI for writing and editing, emphasizing the importance of human oversight and the development of strong writing skills. They argued that tools like Grammarly should be used as aids, not replacements for critical thinking and careful editing. Another user suggested that the perceived decline in Grammarly's core functionality might be a deliberate strategy to push users towards the AI-powered features and premium subscriptions, speculating that the free version might be intentionally "dumbed down."

  Some users offered alternative solutions and perspectives. One commenter recommended LanguageTool as a potential replacement for Grammarly, praising its open-source nature and perceived superiority in catching grammatical errors. Another user pointed out that while Grammarly might not be perfect, it still offers valuable assistance, particularly for non-native English speakers. This commenter highlighted the importance of acknowledging the tool's limitations and using it judiciously.

  Finally, one commenter offered a more technical perspective, suggesting that the shift towards AI might be due to the inherent difficulty in maintaining and improving rule-based grammar checking systems. They speculated that machine learning models, despite their current limitations, might offer a more scalable and adaptable approach to grammar checking in the long run.

  In summary, the comments on Hacker News reflect a mixed sentiment towards Grammarly's recent changes. While some users appreciate the new AI features, many express concern over the perceived decline in basic grammar checking capabilities, sparking a broader discussion about the role of AI in writing and the future of grammar-checking tools.

• Ask HN: Any insider takes on Yann LeCun's push against current architectures?

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  Posted: 2025-03-10 19:41:37

  Verbose tl;dr

  The Hacker News post asks for insider perspectives on Yann LeCun's criticism of current deep learning architectures, particularly his advocacy for moving beyond systems trained solely on pattern recognition. LeCun argues that these systems lack fundamental capabilities like reasoning, planning, and common sense, and believes a paradigm shift is necessary to achieve true artificial intelligence. The post author wonders about the internal discussions and research directions within organizations like Meta/FAIR, influenced by LeCun's views, and whether there's a disconnect between his public statements and the practical work being done.

  The Hacker News post titled "Ask HN: Any insider takes on Yann LeCun's push against current architectures?" initiates a discussion regarding Yann LeCun's publicly expressed skepticism and dissatisfaction with the current trajectory of deep learning architectures, particularly those heavily reliant on scaling and transformers. The author seeks insight, specifically from individuals with insider knowledge or close proximity to LeCun's research, concerning the specifics of LeCun's criticisms and the potential alternatives he envisions. The post highlights LeCun's belief that the prevailing approaches in the field, while demonstrating impressive capabilities in certain domains, are fundamentally limited and unlikely to lead to the development of true artificial intelligence possessing human-level cognitive abilities. The author implicitly acknowledges LeCun's stature and influence within the deep learning community, suggesting that his dissenting perspective carries significant weight and may foreshadow a paradigm shift in the field. The core of the inquiry revolves around understanding the concrete technical arguments underpinning LeCun's critique, including the perceived shortcomings of current architectures and the nature of the alternative pathways he is exploring or advocating for. The author is particularly interested in any information regarding LeCun's internal discussions or unpublished research that might shed light on his long-term vision for achieving more robust and general artificial intelligence. Essentially, the post seeks to move beyond publicly available information and gain a deeper understanding of the rationale and potential implications of LeCun's push for a departure from the current dominant architectures in deep learning.

  • Yann LeCun
  • artificial intelligence
  • AI
  • deep learning
  • neural networks
  • Architecture
  • critique
  • machine learning
  • Hacker News
  • HN

  Summary of Comments ( 254 )
  https://news.ycombinator.com/item?id=43325049

  Verbose tl;dr

  The Hacker News comments on Yann LeCun's push against current architectures are largely speculative, lacking insider information. Several commenters discuss the potential of LeCun's "autonomous machine intelligence" approach and his criticisms of current deep learning methods, with some agreeing that current architectures struggle with reasoning and common sense. Others express skepticism or downplay the significance of LeCun's position, pointing to the success of current models in specific domains. There's a recurring theme of questioning whether LeCun's proposed solutions are substantially different from existing research or if they are simply rebranded. A few commenters offer alternative perspectives, such as the importance of embodied cognition and the potential of hierarchical temporal memory. Overall, the discussion reflects the ongoing debate within the AI community about the future direction of the field, with LeCun's views being a significant, but not universally accepted, contribution.

  The Hacker News post "Ask HN: Any insider takes on Yann LeCun's push against current architectures?" has generated a number of comments discussing LeCun's perspective and the broader context of AI research.

  Several commenters express skepticism towards claims of inherent limitations in current deep learning architectures. One commenter argues that LeCun's critiques often lack concrete alternatives and seem to downplay the significant progress made by transformer models. Another points out that LeCun's proposed solutions, like JEPA, seem less revolutionary and more like incremental improvements upon existing techniques. There's a general sentiment that while exploring new architectures is crucial, declaring current methods a dead end seems premature.

  A few comments highlight the cyclical nature of AI research. They note that LeCun's earlier work, which formed the basis for many current architectures, was itself considered a dead end at one point. This historical perspective suggests that pronouncements of stagnation in the field should be taken with caution.

  Some commenters delve into the specifics of LeCun's arguments. They discuss the limitations of autoregressive models and their struggles with reasoning and planning. They also touch upon the potential of world models and the need for architectures that can learn hierarchical representations. One commenter questions the focus on predicting the next token, suggesting that it might be a suboptimal objective for achieving true intelligence.

  Others offer interpretations of LeCun's motivations. Some suggest that his critiques are partly driven by a desire to differentiate his own research and attract funding. Others see it as a healthy challenge to the status quo, pushing the field to explore beyond the currently dominant paradigms.

  A recurring theme is the difficulty of defining and measuring intelligence. Commenters debate whether benchmarks like predicting the next token are truly indicative of intelligent behavior. Some advocate for more complex and nuanced evaluations that capture aspects like reasoning, planning, and common sense.

  Finally, several comments express excitement about the future of AI research. They acknowledge the limitations of current architectures but remain optimistic about the potential for breakthroughs. They see LeCun's critiques, even if controversial, as a valuable contribution to the ongoing conversation about the direction of the field.

• Context should go away for Go 2 (2017)

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  Posted: 2025-01-21 08:08:24

  Verbose tl;dr

  The author argues that Go's context.Context is overused and often misused as a dumping ground for arbitrary values, leading to unclear dependencies and difficult-to-test code. Instead of propagating values through Context, they propose using explicit function parameters, promoting clearer code, better separation of concerns, and easier testability. They contend that using Context primarily for cancellation and timeouts, its intended purpose, would streamline code and improve its maintainability.

  This 2017 blog post by Andrey Tarantsov argues vehemently for the removal of the context package from Go in a hypothetical "Go 2." The author's primary contention is that context.Context solves a problem that shouldn't exist in the first place: cleanly canceling operations and passing request-scoped values. Tarantsov asserts that these issues are symptoms of a larger, underlying problem: Go's inadequate support for proper dependency injection.

  The author begins by acknowledging the widespread use and perceived necessity of context.Context, but quickly pivots to criticizing its pervasive nature. He argues that its ubiquity is not a sign of good design but rather a band-aid solution for a deeper flaw. The post likens context.Context to global variables, stating that while they might seem convenient initially, they ultimately lead to tightly coupled, difficult-to-test, and error-prone code.

  Tarantsov elaborates on the two main uses of context.Context: cancellation and passing request-scoped values. He criticizes cancellation for obscuring control flow, making it challenging to understand where and when an operation might be canceled. He provides examples of how error handling and explicit cancellation mechanisms within function calls would be cleaner and more transparent. Regarding request-scoped values, the author argues that passing these values as explicit function arguments is a superior approach, promoting clear dependencies and better testability.

  The core of the author's argument revolves around the concept of dependency injection. He posits that if Go had robust, built-in support for dependency injection, the need for context.Context would evaporate. He envisions a scenario where dependencies, including cancellation signals and request-scoped values, would be injected explicitly into functions and structs, eliminating the need for an omnipresent context object. This would, according to the author, lead to more modular, testable, and understandable code.

  Tarantsov concludes by acknowledging the difficulty of removing context.Context from the language, especially considering its widespread adoption. However, he maintains that for a hypothetical "Go 2," seriously considering its removal and focusing on proper dependency injection mechanisms would significantly benefit the language's long-term health and maintainability. He expresses hope that the Go community would prioritize more robust dependency injection features, ultimately rendering context.Context obsolete.

  • Go
  • Golang
  • Context
  • Go 2
  • concurrency
  • Error Handling
  • Programming Languages
  • Software Development
  • proposal
  • critique

  Summary of Comments ( 126 )
  https://news.ycombinator.com/item?id=42777625

  Verbose tl;dr

  HN commenters largely agree with the author's premise that context.Context in Go is overused and often misused for dependency injection or as a dumping ground for miscellaneous values. Several suggest that structured concurrency, improved error handling, and better language features for cancellation and deadlines could alleviate the need for context in many cases. Some argue that context is still useful for request-scoped values, especially in server contexts, and shouldn't be entirely removed. A few commenters express concern about the practicality of removing context given its widespread adoption and integration into the standard library. There is a strong desire for better alternatives, rather than simply discarding the existing mechanism without a replacement. Several commenters also mention the similarities between context overuse in Go and similar issues with dependency injection frameworks in other languages.

  The Hacker News post discussing the blog post "Context should go away for Go 2" contains a significant number of comments engaging with the author's proposition of removing the context package from Go. Several commenters offer compelling perspectives both for and against the idea.

  A recurring theme is the acknowledgement that context solves a real problem, particularly around cancellation and deadlines in long-running operations. Some comments express frustration with the pervasiveness of context and its impact on code readability and function signatures. They argue it has become an almost mandatory parameter, even in situations where it might not be strictly necessary.

  One commenter suggests that the verbosity and "boilerplate" introduced by context might be mitigated by language-level features, proposing syntax sugar or implicit passing of context. Another echoes this sentiment, wishing for a more elegant solution integrated into the language itself. This desire for a more streamlined approach to cancellation and deadlines without the explicit passing of context is a prominent thread in the discussion.

  Several comments delve into the specifics of how context is used and misused. One points out the common practice of passing context.Background() when no actual context is needed, highlighting this as a symptom of the package's overuse. Another discusses the difficulties in testing with context, suggesting that mocking and managing context values in tests adds complexity.

  Some commenters push back against the author's proposal, arguing that removing context entirely would be detrimental. They highlight its importance in managing resources and preventing leaks in long-running operations. They also point out that while it might be annoying at times, context provides a standardized and relatively efficient way to handle cancellation and deadlines, which would be difficult to replace.

  A few comments explore alternative approaches to context management, including proposals for libraries or language features that could achieve similar functionality with less verbosity. One commenter suggests a potential solution involving implicit context propagation based on function call chains.

  The overall sentiment seems to be a mixed bag. While many acknowledge the drawbacks of context and express a desire for a more elegant solution, there's also a recognition of its value and the challenges in replacing it entirely. The discussion highlights the tension between the practicality of context and the desire for cleaner, less verbose code in Go.

• I'll think twice before using GitHub Actions again

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  Posted: 2025-01-20 03:41:27

  Verbose tl;dr

  The author details a frustrating experience with GitHub Actions where a seemingly simple workflow to build and deploy a static website became incredibly complex and time-consuming due to caching issues. Despite attempting various caching strategies and workarounds, builds remained slow and unpredictable, ultimately leading to increased costs and wasted developer time. The author concludes that while GitHub Actions might be suitable for straightforward tasks, its caching mechanism's unreliability makes it a poor choice for more complex projects, especially those involving static site generation. They ultimately opted to migrate to a self-hosted solution for improved control and predictability.

  The blog post "I'll think twice before using GitHub Actions again" by Nikola Ninković details a frustrating experience with GitHub Actions, ultimately leading the author to reconsider its use for future projects. Ninković begins by acknowledging the initial appeal of GitHub Actions – its tight integration with GitHub, purported ease of use, and the availability of a free tier. He then proceeds to describe how these initial perceived advantages ultimately transformed into significant drawbacks during his attempt to create a CI/CD pipeline for a static website.

  The core issue revolved around caching. While GitHub Actions offers caching mechanisms, Ninković found them to be unreliable and opaque. He explains how he attempted to cache dependencies for his Hugo-based website, anticipating this would significantly speed up build times. However, the cache frequently failed to hit, leading to repeated downloads of dependencies and negating the intended time savings. The author diligently tried various approaches to troubleshoot and rectify the caching issues, meticulously adjusting his workflow file and experimenting with different caching strategies suggested in the documentation and community forums. Despite these efforts, the caching behavior remained erratic and unpredictable, significantly hampering the efficiency of his CI/CD pipeline.

  Further compounding the problem was the lack of clear visibility into the caching process. Ninković highlights the difficulty in understanding why the cache was being invalidated or missed. The limited debugging tools and opaque nature of the caching system made it challenging to diagnose the root cause of the failures. This lack of transparency ultimately led to a considerable investment of time and effort in attempting to resolve an issue that ultimately proved intractable.

  Ninković contrasts his experience with GitHub Actions to his prior usage of Netlify, a platform specifically designed for hosting and deploying static websites. He emphasizes the seamless and intuitive deployment process offered by Netlify, highlighting its inherent understanding of website deployment workflows and its reliable caching mechanisms. This comparison serves to underscore the perceived shortcomings of GitHub Actions, particularly its complexity and unreliability when applied to specific use cases like static website deployment.

  Finally, Ninković concludes by expressing his disillusionment with GitHub Actions. He acknowledges that the platform may be suitable for other types of projects, but asserts that its current implementation presents significant challenges for static website deployment. He states his intention to explore alternative CI/CD solutions in the future, prioritizing platforms that offer greater reliability, transparency, and ease of use when it comes to caching and deployment workflows for his static websites. The overall tone of the post reflects a sentiment of frustration stemming from the significant time and effort invested in attempting to overcome the limitations encountered with GitHub Actions.

  • GitHub Actions
  • CI/CD
  • Continuous Integration
  • Continuous Deployment
  • DevOps
  • Workflow Automation
  • Build Automation
  • testing
  • Software Development
  • Blog Post
  • Opinion
  • critique
  • Alternatives to GitHub Actions

  Summary of Comments ( 148 )
  https://news.ycombinator.com/item?id=42764762

  Verbose tl;dr

  Hacker News users generally agreed with the author's sentiment about GitHub Actions' complexity and unreliability. Many shared similar experiences with flaky builds, obscure error messages, and difficulty debugging. Several commenters suggested exploring alternatives like GitLab CI, Drone CI, or self-hosted runners for more control and predictability. Some pointed out the benefits of GitHub Actions, such as its tight integration with GitHub and the availability of pre-built actions, but acknowledged the frustrations raised in the article. The discussion also touched upon the trade-offs between convenience and control when choosing a CI/CD solution, with some arguing that the ease of use initially offered by GitHub Actions can be overshadowed by the difficulties encountered as projects grow more complex. A few users offered specific troubleshooting tips or workarounds for common issues, highlighting the community-driven nature of problem-solving around GitHub Actions.

  The Hacker News post "I'll think twice before using GitHub Actions again" (linking to an article criticizing GitHub Actions) generated a significant discussion with a variety of viewpoints.

  Several commenters agreed with the author's sentiments, sharing their own frustrating experiences with GitHub Actions. These included complaints about opaque pricing, unexpected cost overruns (especially with third-party actions), difficulty debugging complex workflows, and a lack of adequate support from GitHub. One commenter described being "nickeled and dimed" by hidden costs. Another highlighted the frustration of debugging issues across multiple nested actions, with limited visibility into the execution environment. The unpredictable nature of build times and the resulting variability in costs were also mentioned as major downsides.

  Some suggested alternatives to GitHub Actions, citing platforms like GitLab CI, Jenkins, and Drone CI as offering more transparency, control, and better value. Specifically, self-hosting runners was brought up as a way to gain more control and potentially reduce costs.

  However, other commenters defended GitHub Actions, emphasizing its convenience and tight integration with the GitHub ecosystem. They argued that for smaller projects or individual developers, the benefits of simplicity and ease of use outweigh the potential cost concerns. Several pointed out that the free tier is often sufficient for many use cases, and that with careful planning and monitoring, costs can be managed effectively. One commenter suggested that the author's issues stemmed from a lack of familiarity with the platform rather than inherent flaws in GitHub Actions itself. They emphasized the importance of understanding the pricing structure and utilizing best practices to optimize workflows.

  The discussion also touched upon the broader trend of vendor lock-in within the developer ecosystem. Some expressed concern about relying too heavily on GitHub's integrated toolset, making it difficult to migrate to other platforms in the future.

  Finally, some commenters offered practical advice for mitigating the issues raised in the original article, such as using self-hosted runners for computationally intensive tasks, carefully reviewing the pricing of third-party actions, and leveraging GitHub Actions' built-in caching mechanisms to optimize performance and reduce costs. One commenter even shared a link to a community-maintained list of free and open-source GitHub Actions.

  In summary, the comments section reveals a mixed reception to GitHub Actions. While some users have had negative experiences related to cost, complexity, and debugging, others find it a convenient and valuable tool. The discussion highlights the importance of carefully considering project requirements, understanding the pricing model, and exploring alternative CI/CD solutions before committing to a specific platform.

• Literate programming: Knuth is doing it wrong (2014)

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  Posted: 2025-01-13 13:06:42

  Verbose tl;dr

  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.

  In a 2014 blog post titled "Literate Programming: Knuth is doing it wrong," author Akkartikone argues that Donald Knuth's concept of literate programming, while noble in its intention, fundamentally misunderstands the ideal workflow for programmers. Knuth's vision, as implemented in tools like WEB and CWEB, emphasizes writing code primarily for an audience of human readers, weaving it into a narrative document that explains the program's logic. This document is then processed by a tool to extract the actual compilable source code. Akkartikone contends that this "write for humans first, then extract for the machine" approach inverts the natural order of programming.

  The author asserts that programming is an inherently iterative and exploratory process. Programmers often begin with vague ideas and refine them through experimentation, writing and rewriting code until it functions correctly. This process, Akkartikone posits, is best facilitated by tools that provide immediate feedback and allow rapid modification and testing. Knuth's literate programming tools, by imposing an additional layer of processing between writing code and executing it, impede this rapid iteration cycle. They encourage a more waterfall-like approach, where code is meticulously documented and finalized before being tested, which the author deems unsuitable for the dynamic nature of software development.

  Akkartikone proposes an alternative approach they call "exploratory programming," where the focus is on a tight feedback loop between writing and running code. The author argues that the ideal programming environment should allow programmers to easily experiment with different code snippets, test them quickly, and refactor them fluidly. Documentation, in this paradigm, should be a secondary concern, emerging from the refined and functional code rather than preceding it. Instead of being interwoven with the code itself, documentation should be extracted from it, possibly using automated tools that analyze the code's structure and behavior.

  The blog post further explores the concept of "noweb," a simpler literate programming tool that Akkartikone views as a step in the right direction. While still adhering to the "write for humans first" principle, noweb offers a less cumbersome syntax and a more streamlined workflow than WEB/CWEB. However, even noweb, according to Akkartikone, ultimately falls short of the ideal exploratory programming environment.

  The author concludes by advocating for a shift in focus from "literate programming" to "literate codebases." Instead of aiming to produce beautifully documented code from the outset, the goal should be to create tools and processes that facilitate the extraction of meaningful documentation from existing, well-structured codebases. This, Akkartikone believes, will better serve the practical needs of programmers and contribute to the development of more maintainable and understandable software.

  • literate programming
  • knuth
  • web
  • tangle
  • weave
  • noweb
  • programming
  • Software Development
  • documentation
  • critique
  • tools
  • essay
  • cweb
  • org-mode
  • markdown
  • Software Design
  • code readability
  • tangling
  • weaving

  Summary of Comments ( 53 )
  https://news.ycombinator.com/item?id=42683009

  Verbose tl;dr

  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.

  The Hacker News post discussing Akkartik's 2014 blog post, "Literate programming: Knuth is doing it wrong," has generated a significant number of comments. Several commenters engage with Akkartik's core argument, which posits that Knuth's vision of literate programming focused too much on producing a human-readable document and not enough on the code itself being the primary artifact.

  One compelling line of discussion revolves around the practicality and perceived benefits of literate programming. Some commenters share anecdotal experiences of successfully using literate programming techniques, emphasizing the improved clarity and maintainability of their code. They argue that thinking of code as a narrative improves its structure and makes it easier to understand, particularly for complex projects. However, other commenters counter this by pointing out the added overhead and complexity involved in maintaining a separate document, especially in collaborative environments. Concerns are raised about the potential for the documentation to become out of sync with the code, negating its intended benefits. The discussion explores the trade-offs between the upfront investment in literate programming and its long-term payoff in terms of code quality.

  Another thread of conversation delves into the tooling and workflows associated with literate programming. Commenters discuss various tools and approaches, ranging from simple text editors with custom scripts to dedicated literate programming environments. The challenges of integrating literate programming into existing development workflows are also acknowledged. Some commenters advocate for tools that allow for seamless transitions between the code and documentation, while others suggest that the choice of tools depends heavily on the specific project and programming language.

  Furthermore, the comments explore alternative interpretations of literate programming and its potential applications beyond traditional software development. The idea of applying literate programming principles to other fields, such as data analysis or scientific research, is discussed. Some commenters suggest that the core principles of literate programming – clarity, narrative structure, and interwoven explanation – could be beneficial in any context where complex procedures need to be documented and communicated effectively.

  Finally, several comments directly address Akkartik's criticisms of Knuth's approach. Some agree with Akkartik's assessment, arguing that the focus on generating beautiful documents can obscure the underlying code. Others defend Knuth's vision, emphasizing the importance of clear and accessible documentation for complex software systems. This discussion highlights the ongoing debate about the true essence of literate programming and its optimal implementation.