The author describes the "worst programmer" they know, not as someone unskilled, but as someone highly effective despite unconventional methods. This programmer prioritizes shipping functional code quickly over elegant or maintainable solutions, focusing intensely on the immediate problem and relying heavily on debugging and iterative tweaking. While this approach leads to messy, difficult-to-understand code and frustrates other developers, it consistently delivers working products within tight deadlines, making them a valuable, albeit frustrating, asset. The author ultimately questions conventional programming wisdom, suggesting that perhaps this "worst" programmer's effectiveness reveals a different kind of programming proficiency, prioritizing rapid results over long-term maintainability in specific contexts.
The blog post "An epic treatise on error models for systems programming languages" explores the landscape of error handling strategies, arguing that current approaches in languages like C, C++, Go, and Rust are insufficient for robust systems programming. It criticizes unchecked exceptions for their potential to cause undefined behavior and resource leaks, while also finding fault with error codes and checked exceptions for their verbosity and tendency to hinder code flow. The author advocates for a more comprehensive error model based on "algebraic effects," which allows developers to precisely define and handle various error scenarios while maintaining control over resource management and program termination. This approach aims to combine the benefits of different error handling mechanisms while mitigating their respective drawbacks, ultimately promoting greater reliability and predictability in systems software.
HN commenters largely praised the article for its thoroughness and clarity in explaining error handling strategies. Several appreciated the author's balanced approach, presenting the tradeoffs of each model without overtly favoring one. Some highlighted the insightful discussion of checked exceptions and their limitations, particularly in relation to algebraic error types and error-returning functions. A few commenters offered additional perspectives, including the importance of distinguishing between recoverable and unrecoverable errors, and the potential benefits of static analysis tools in managing error handling. The overall sentiment was positive, with many thanking the author for providing a valuable resource for systems programmers.
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.
Hardcoding feature flags, particularly for kill switches or short-lived A/B tests, is often a pragmatic and acceptable approach. While dynamic feature flag management systems offer flexibility, they introduce complexity and potential points of failure. For simple scenarios, the overhead of a dedicated system can outweigh the benefits. Directly embedding feature flags in the code allows for quicker implementation, easier understanding, and improved performance, especially when the flag's lifespan is short or its purpose highly specific. This simplicity can make code cleaner and easier to maintain in the long run, as opposed to relying on external dependencies that may eventually become obsolete.
Hacker News users generally agree with the author's premise that hardcoding feature flags for small, non-A/B tested features is acceptable. Several commenters emphasize the importance of cleaning up technical debt by removing these flags once the feature is fully launched. Some suggest using tools or techniques to automate this process or integrate it into the development workflow. A few caution against overuse for complex or long-term features where a more robust feature flag management system would be beneficial. Others discuss specific implementation details, like using enums or constants, and the importance of clear naming conventions for clarity and maintainability. A recurring sentiment is that the complexity of feature flag management should be proportional to the complexity and longevity of the feature itself.
Summary of Comments ( 54 )
https://news.ycombinator.com/item?id=43452649
Hacker News users generally agreed with the author's premise that over-engineering and premature optimization are detrimental. Several commenters shared similar experiences with "worst programmers" who prioritized cleverness over simplicity, resulting in unmaintainable code. Some discussed the importance of communication and understanding project requirements before diving into complex solutions. One compelling comment highlighted the Dunning-Kruger effect, suggesting that the "worst programmers" often lack the self-awareness to recognize their shortcomings. Another pointed out that the characteristics described might not signify a "worst" programmer but rather someone mismatched to the project's needs, perhaps excelling in research or low-level programming instead. Several users cautioned against focusing solely on technical skills, emphasizing the importance of soft skills like teamwork and communication.
The Hacker News post titled "The Worst Programmer I Know (2023)" generated a robust discussion with 58 comments at the time of this summary. Several commenters shared their own experiences with programmers exhibiting similar traits to the one described in the article, often echoing the frustration of dealing with individuals who prioritize superficial metrics over actual productivity and code quality.
One recurring theme was the issue of "cargo cult programming," where individuals blindly copy and paste code snippets without understanding their functionality. Commenters lamented the prevalence of this practice and its negative consequences on maintainability and debugging. Some argued that this behavior stems from a lack of foundational knowledge and a reliance on readily available solutions without comprehending their underlying principles.
Another prevalent sentiment revolved around the difficulty of managing such programmers. Several commenters shared anecdotes about the challenges of providing constructive feedback, highlighting the defensiveness and resistance to change often exhibited by these individuals. The discussion touched upon the importance of clear communication and mentorship, but also acknowledged the limitations when dealing with someone unwilling to acknowledge their shortcomings.
Some commenters provided alternative perspectives, suggesting that the "worst programmer" label might be too harsh and that focusing on specific behaviors rather than labeling individuals could lead to more productive outcomes. They emphasized the importance of empathy and understanding, pointing out that external factors, such as pressure from management or inadequate training, could contribute to the observed behaviors. The idea of providing tailored support and resources to help struggling programmers improve was also raised.
A few comments delved into the role of hiring practices and the need for more effective screening methods to identify candidates with strong fundamentals and a genuine interest in learning and improving. Others debated the effectiveness of various interview techniques in assessing a candidate's true capabilities.
A compelling comment thread explored the broader implications of prioritizing quantity over quality in software development. Commenters discussed the pressure to deliver features quickly, which often leads to technical debt and compromises in code quality. This discussion touched upon the responsibility of management in setting realistic expectations and fostering a culture that values maintainable code.
Finally, some commenters offered practical advice on how to deal with challenging programmers, including strategies for code reviews, communication techniques, and methods for providing constructive feedback. They shared personal experiences and suggested approaches to mitigate the negative impact of working with individuals who exhibit counterproductive behaviors. The discussion provided a valuable platform for exchanging ideas and experiences related to managing difficult personalities in the software development world.