The blog post "Effective AI code suggestions: less is more" argues that shorter, more focused AI code suggestions are more beneficial to developers than large, complete code blocks. While large suggestions might seem helpful at first glance, they're often harder to understand, integrate, and verify, disrupting the developer's flow. Smaller suggestions, on the other hand, allow developers to maintain control and understanding of their code, facilitating easier integration and debugging. This approach promotes learning and empowers developers to build upon the AI's suggestions rather than passively accepting large, opaque code chunks. The post further emphasizes the importance of providing context to the AI through clear prompts and selecting the appropriate suggestion size for the specific task.
Interruptions significantly hinder software engineers, especially during cognitively demanding tasks like programming and debugging. The impact isn't just the time lost to the interruption itself, but also the time required to regain focus and context, which can take substantial time depending on the task's complexity. While interruptions are sometimes unavoidable, minimizing them, especially during deep work periods, can drastically improve developer productivity and code quality. Effective strategies include blocking off focused time, using asynchronous communication methods, and batching similar tasks together.
HN commenters generally agree with the article's premise that interruptions are detrimental to developer productivity, particularly for complex tasks. Some share personal anecdotes and strategies for mitigating interruptions, like using the Pomodoro Technique or blocking off focus time. A few suggest that the study's methodology might be flawed due to its small sample size and reliance on self-reporting. Others point out that certain types of interruptions, like urgent bug fixes, are unavoidable and sometimes even beneficial for breaking through mental blocks. A compelling thread discusses the role of company culture in minimizing disruptions, emphasizing the importance of asynchronous communication and respect for deep work. Some argue that the "maker's schedule" isn't universally applicable and that some developers thrive in more interrupt-driven environments.
Summary of Comments ( 10 )
https://news.ycombinator.com/item?id=42866702
HN commenters generally agree with the article's premise that smaller, more focused AI code suggestions are more helpful than large, complex ones. Several users point out that this mirrors good human code review practices, emphasizing clarity and avoiding large, disruptive changes. Some commenters discuss the potential for LLMs to improve in suggesting smaller changes by better understanding context and intent. One commenter expresses skepticism, suggesting that LLMs fundamentally lack the understanding to suggest good code changes, and argues for focusing on tools that improve code comprehension instead. Others mention the usefulness of LLMs for generating boilerplate or repetitive code, even if larger suggestions are less effective for complex tasks. There's also a brief discussion of the importance of unit tests in mitigating the risk of incorporating incorrect AI-generated code.
The Hacker News post "Effective AI code suggestions: less is more" has several comments discussing the linked blog post about using Large Language Models (LLMs) for code suggestions. A recurring theme is the preference for smaller, more focused suggestions rather than large code dumps from the AI.
Several commenters agree with the article's premise. One user points out that smaller suggestions are easier to review and integrate, reducing the risk of unseen bugs or unintended consequences. They also mention that smaller changes make it simpler to understand the AI's reasoning, which is crucial for trust and learning. This aligns with another comment that emphasizes the importance of understanding why the AI suggested a particular piece of code, rather than blindly accepting it. Smaller changes make this "why" easier to discern.
Another commenter draws a parallel to human code reviews, noting that smaller pull requests are generally preferred and easier to manage than large, sweeping changes. This reinforces the idea that smaller AI suggestions fit better into existing development workflows.
The idea of "less is more" is further explored by a commenter who suggests that AI should focus on providing the "missing piece" in a developer's thought process. Rather than generating entire functions or classes, the AI could be more helpful by suggesting specific lines of code or even just variable names that help the developer move forward. This commenter argues that this approach empowers the developer to retain control and ownership of the code.
Some commenters also discuss the practical implications of large AI-generated code blocks. One user highlights the increased cognitive load required to review and understand large chunks of code, especially when trying to integrate them into an existing project. They also mention the potential for "hallucinations," where the AI generates code that appears correct but contains subtle errors. Smaller suggestions mitigate these risks.
While most comments support the "less is more" approach, one commenter offers a slightly different perspective, suggesting that the ideal size of an AI suggestion depends on the context. For simple tasks, a single line of code might suffice. But for more complex problems, a larger code block could be more helpful, provided it is well-structured and documented.
Finally, a commenter brings up the potential for AI to provide different levels of detail in its suggestions, allowing the developer to choose the level of granularity that best suits their needs. This could range from single lines of code to entire functions, with the AI adapting to the developer's preferences over time.