The blog post "Don't use cosine similarity carelessly" cautions against the naive application of cosine similarity, particularly in machine learning and recommendation systems, without a thorough understanding of its implications and potential pitfalls. The author meticulously illustrates how cosine similarity, while effective in certain scenarios, can produce misleading or undesirable results when the underlying data possesses specific characteristics.
The core argument revolves around the fact that cosine similarity solely focuses on the angle between vectors, effectively disregarding the magnitude or scale of those vectors. This can be problematic when comparing items with drastically different scales of interaction or activity. For instance, in a movie recommendation system, a user who consistently rates movies highly will appear similar to another user who rates movies highly, even if their taste in genres is vastly different. This is because the large magnitude of their ratings dominates the cosine similarity calculation, obscuring the nuanced differences in their preferences. The author underscores this with an example of book recommendations, where a voracious reader may appear similar to other avid readers regardless of their preferred genres simply due to the high volume of their reading activity.
The author further elaborates this point by demonstrating how cosine similarity can be sensitive to "bursts" of activity. A sudden surge in interaction with certain items, perhaps due to a promotional campaign or temporary trend, can disproportionately influence the similarity calculations, potentially leading to recommendations that are not truly reflective of long-term preferences.
The post provides a concrete example using a movie rating dataset. It showcases how users with different underlying preferences can appear deceptively similar based on cosine similarity if one user has rated many more movies overall. The author emphasizes that this issue becomes particularly pronounced in sparsely populated datasets, common in real-world recommendation systems.
The post concludes by suggesting alternative approaches that consider both the direction and magnitude of the vectors, such as Euclidean distance or Manhattan distance. These metrics, unlike cosine similarity, are sensitive to differences in scale and are therefore less susceptible to the pitfalls described earlier. The author also encourages practitioners to critically evaluate the characteristics of their data before blindly applying cosine similarity and to consider alternative metrics when magnitude plays a crucial role in determining true similarity. The overall message is that while cosine similarity is a valuable tool, its limitations must be recognized and accounted for to ensure accurate and meaningful results.
James Shore's blog post, "If we had the best product engineering organization, what would it look like?", paints a utopian vision of a software development environment characterized by remarkable efficiency, unwavering quality, and genuine employee fulfillment. Shore envisions an organization where product engineering is not merely a department, but a holistic approach interwoven into the fabric of the company. This utopian organization prioritizes continuous improvement and learning, fostering a culture of experimentation and psychological safety where mistakes are viewed as opportunities for growth, not grounds for reprimand.
Central to Shore's vision is the concept of small, autonomous, cross-functional teams. These teams, resembling miniature startups within the larger organization, possess full ownership of their respective products, from conception and design to development, deployment, and ongoing maintenance. They are empowered to make independent decisions, driven by a deep understanding of user needs and business goals. This decentralized structure minimizes bureaucratic overhead and allows teams to iterate quickly, responding to changes in the market with agility and precision.
The technical proficiency of these teams is paramount. Shore highlights the importance of robust engineering practices such as continuous integration and delivery, comprehensive automated testing, and a meticulous approach to code quality. This technical excellence ensures that products are not only delivered rapidly, but also maintain a high degree of reliability and stability. Furthermore, the organization prioritizes technical debt reduction as an ongoing process, preventing the accumulation of technical baggage that can impede future development.
Beyond technical prowess, Shore emphasizes the significance of a positive and supportive work environment. The ideal organization fosters a culture of collaboration and mutual respect, where team members feel valued and empowered to contribute their unique skills and perspectives. This includes a commitment to diversity and inclusion, recognizing that diverse teams are more innovative and better equipped to solve complex problems. Emphasis is also placed on sustainable pace and reasonable work hours, acknowledging the importance of work-life balance in preventing burnout and maintaining long-term productivity.
In this ideal scenario, the organization functions as a learning ecosystem. Individuals and teams are encouraged to constantly seek new knowledge and refine their skills through ongoing training, mentorship, and knowledge sharing. This continuous learning ensures that the organization remains at the forefront of technological advancements and adapts to the ever-evolving demands of the market. The organization itself learns from its successes and failures, constantly adapting its processes and structures to optimize for efficiency and effectiveness.
Ultimately, Shore’s vision transcends mere technical proficiency. He argues that the best product engineering organization isn't just about building great software; it's about creating a fulfilling and rewarding environment for the people who build it. It's about fostering a culture of continuous improvement, innovation, and collaboration, where individuals and teams can thrive and achieve their full potential. This results in not only superior products, but also a sustainable and thriving organization capable of long-term success in the dynamic world of software development.
The Hacker News post "If we had the best product engineering organization, what would it look like?" generated a moderate amount of discussion with several compelling comments exploring the nuances of the linked article by James Shore.
Several commenters grappled with Shore's emphasis on small, autonomous teams. One commenter questioned the scalability of this model beyond a certain organizational size, citing potential difficulties with inter-team communication and knowledge sharing as the number of teams grows. They suggested the need for more structure and coordination in larger organizations, potentially through designated integration roles or processes.
Another commenter pushed back on the idea of completely autonomous teams, arguing that some level of central architectural guidance is necessary to prevent fragmented systems and ensure long-term maintainability. They proposed a hybrid approach where teams have autonomy within a clearly defined architectural framework.
The concept of "full-stack generalists" also sparked debate. One commenter expressed skepticism, pointing out the increasing specialization required in modern software development and the difficulty of maintaining expertise across the entire stack. They advocated for "T-shaped" individuals with deep expertise in one area and broader, but less deep, knowledge in others. This, they argued, allows for both specialization and effective collaboration.
A few commenters focused on the cultural aspects of Shore's ideal organization, highlighting the importance of psychological safety and trust. They suggested that a truly great engineering organization prioritizes employee well-being, encourages open communication, and fosters a culture of continuous learning and improvement.
Another thread of discussion revolved around the practicality of Shore's vision, with some commenters expressing concerns about the challenges of implementing such radical changes in existing organizations. They pointed to the inertia of established processes, the potential for resistance to change, and the difficulty of measuring the impact of such transformations. Some suggested a more incremental approach, focusing on implementing small, iterative changes over time.
Finally, a few comments provided alternative perspectives, suggesting different models for high-performing engineering organizations. One commenter referenced Spotify's "tribes" model, while another pointed to the benefits of a more centralized, platform-based approach. These comments added diversity to the discussion and offered different frameworks for considering the optimal structure of a product engineering organization.
This blog post, entitled "Good Software Development Habits," by Zarar Siddiqi, expounds upon a collection of practices intended to elevate the quality and efficiency of software development endeavors. The author meticulously details several key habits, emphasizing their importance in fostering a robust and sustainable development lifecycle.
The first highlighted habit centers around the diligent practice of writing comprehensive tests. Siddiqi advocates for a test-driven development (TDD) approach, wherein tests are crafted prior to the actual code implementation. This proactive strategy, he argues, not only ensures thorough testing coverage but also facilitates the design process by forcing developers to consider the functionality and expected behavior of their code beforehand. He further underscores the value of automated testing, allowing for continuous verification and integration, ultimately mitigating the risk of regressions and ensuring consistent quality.
The subsequent habit discussed is the meticulous documentation of code. The author emphasizes the necessity of clear and concise documentation, elucidating the purpose and functionality of various code components. This practice, he posits, not only aids in understanding and maintaining the codebase for oneself but also proves invaluable for collaborators who might engage with the project in the future. Siddiqi suggests leveraging tools like Docstrings and comments to embed documentation directly within the code, ensuring its close proximity to the relevant logic.
Furthermore, the post stresses the importance of frequent code reviews. This collaborative practice, according to Siddiqi, allows for peer scrutiny of code changes, facilitating early detection of bugs, potential vulnerabilities, and stylistic inconsistencies. He also highlights the pedagogical benefits of code reviews, providing an opportunity for knowledge sharing and improvement across the development team.
Another crucial habit emphasized is the adoption of version control systems, such as Git. The author explains the immense value of tracking changes to the codebase, allowing for easy reversion to previous states, facilitating collaborative development through branching and merging, and providing a comprehensive history of the project's evolution.
The post also delves into the significance of maintaining a clean and organized codebase. This encompasses practices such as adhering to consistent coding style guidelines, employing meaningful variable and function names, and removing redundant or unused code. This meticulous approach, Siddiqi argues, enhances the readability and maintainability of the code, minimizing cognitive overhead and facilitating future modifications.
Finally, the author underscores the importance of continuous learning and adaptation. The field of software development, he notes, is perpetually evolving, with new technologies and methodologies constantly emerging. Therefore, he encourages developers to embrace lifelong learning, actively seeking out new knowledge and refining their skills to remain relevant and effective in this dynamic landscape. This involves staying abreast of industry trends, exploring new tools and frameworks, and engaging with the broader development community.
The Hacker News post titled "Good Software Development Habits" linking to an article on zarar.dev/good-software-development-habits/ has generated a modest number of comments, focusing primarily on specific points mentioned in the article and offering expansions or alternative perspectives.
Several commenters discuss the practice of regularly committing code. One commenter advocates for frequent commits, even seemingly insignificant ones, highlighting the psychological benefit of seeing progress and the ability to easily revert to earlier versions. They even suggest committing after every successful compilation. Another commenter agrees with the principle of frequent commits but advises against committing broken code, emphasizing the importance of maintaining a working state in the main branch. They suggest using short-lived feature branches for experimental changes. A different commenter further nuances this by pointing out the trade-off between granular commits and a clean commit history. They suggest squashing commits before merging into the main branch to maintain a tidy log of significant changes.
There's also discussion around the suggestion in the article to read code more than you write. Commenters generally agree with this principle. One expands on this, recommending reading high-quality codebases as a way to learn good practices and broaden one's understanding of different programming styles. They specifically mention reading the source code of popular open-source projects.
Another significant thread emerges around the topic of planning. While the article emphasizes planning, some commenters caution against over-planning, particularly in dynamic environments where requirements may change frequently. They advocate for an iterative approach, starting with a minimal viable product and adapting based on feedback and evolving needs. This contrasts with the more traditional "waterfall" method alluded to in the article.
The concept of "failing fast" also receives attention. A commenter explains that failing fast allows for early identification of problems and prevents wasted effort on solutions built upon faulty assumptions. They link this to the lean startup methodology, emphasizing the importance of quick iterations and validated learning.
Finally, several commenters mention the value of taking breaks and stepping away from the code. They point out that this can help to refresh the mind, leading to new insights and more effective problem-solving. One commenter shares a personal anecdote about solving a challenging problem after a walk, highlighting the benefit of allowing the subconscious mind to work on the problem. Another commenter emphasizes the importance of rest for maintaining productivity and avoiding burnout.
In summary, the comments generally agree with the principles outlined in the article but offer valuable nuances and alternative perspectives drawn from real-world experiences. The discussion focuses primarily on practical aspects of software development such as committing strategies, the importance of reading code, finding a balance in planning, the benefits of "failing fast," and the often-overlooked importance of breaks and rest.
Summary of Comments ( 70 )
https://news.ycombinator.com/item?id=42704078
Hacker News users generally agreed with the article's premise, cautioning against blindly applying cosine similarity. Several commenters pointed out that the effectiveness of cosine similarity depends heavily on the specific use case and data distribution. Some highlighted the importance of normalization and feature scaling, noting that cosine similarity is sensitive to these factors. Others offered alternative methods, such as Euclidean distance or Manhattan distance, suggesting they might be more appropriate in certain situations. One compelling comment underscored the importance of understanding the underlying data and problem before choosing a similarity metric, emphasizing that no single metric is universally superior. Another emphasized how important preprocessing is, highlighting TF-IDF and BM25 as helpful techniques for text analysis before using cosine similarity. A few users provided concrete examples where cosine similarity produced misleading results, further reinforcing the author's warning.
The Hacker News post "Don't use cosine similarity carelessly" (https://news.ycombinator.com/item?id=42704078) sparked a discussion with several insightful comments regarding the article's points about the pitfalls of cosine similarity.
Several commenters agreed with the author's premise, emphasizing the importance of understanding the implications of using cosine similarity. One commenter highlighted the issue of scale invariance, pointing out that two vectors can have a high cosine similarity even if their magnitudes are vastly different, which can be problematic in certain applications. They used the example of comparing customer purchase behavior where one customer buys small quantities frequently and another buys large quantities infrequently. Cosine similarity might suggest they're similar, ignoring the significant difference in total spending.
Another commenter pointed out that the article's focus on document comparison and TF-IDF overlooks common scenarios like comparing embeddings from large language models (LLMs). They argue that in these cases, magnitude does often carry significant semantic meaning, and normalization can be detrimental. They specifically mentioned the example of sentence embeddings, where longer sentences tend to have larger magnitudes and often carry more information. Normalizing these embeddings would lose this information. This commenter suggested that the article's advice is too general and doesn't account for the nuances of various applications.
Expanding on this, another user added that even within TF-IDF, the magnitude can be a meaningful signal, suggesting that document length could be a relevant factor for certain types of comparisons. They suggested that blindly applying cosine similarity without considering such factors can be problematic.
One commenter offered a concise summary of the issue, stating that cosine similarity measures the angle between vectors, discarding information about their magnitudes. They emphasized the need to consider whether magnitude is important in the specific context.
Finally, a commenter shared a personal anecdote about a machine learning competition where using cosine similarity instead of Euclidean distance drastically improved their results. They attributed this to the inherent sparsity of the data, highlighting that the appropriateness of a similarity metric heavily depends on the nature of the data.
In essence, the comments generally support the article's caution against blindly using cosine similarity. They emphasize the importance of considering the specific context, understanding the implications of scale invariance, and recognizing that magnitude can often carry significant meaning depending on the application and data.