The author attempted to build a free, semantic search engine for GitHub using a Sentence-BERT model and FAISS for vector similarity search. While initial results were promising, scaling proved insurmountable due to the massive size of the GitHub codebase and associated compute costs. Indexing every repository became computationally and financially prohibitive, particularly as the model struggled with context fragmentation from individual code snippets. Ultimately, the project was abandoned due to the unsustainable balance between cost, complexity, and the limited resources of a solo developer. Despite the failure, the author gained valuable experience in large-scale data processing, vector databases, and the limitations of current semantic search technology when applied to a vast and diverse codebase like GitHub.
PostHog, a product analytics company, shares 50 lessons learned from building their own product. Key takeaways emphasize user feedback as paramount, from early access programs to continuous iteration based on observed behavior and direct conversations. A strong focus on solving specific, urgent problems for a well-defined target audience is crucial. Iterative development, rapid prototyping, and a willingness to abandon unsuccessful features are essential. Finally, internal alignment, clear communication, and a shared understanding of the product vision contribute significantly to success. They stress the importance of simplicity and usability, avoiding feature bloat, and consistently measuring the impact of changes.
Hacker News users generally praised the PostHog article for its practical, experience-based advice applicable to various stages of product development. Several commenters highlighted the importance of focusing on user needs and iterating based on feedback, echoing points made in the original article. Some appreciated the emphasis on internal communication and alignment within teams. A few users offered specific examples from their own experiences that reinforced the lessons shared by PostHog, while others offered constructive criticism, suggesting additional areas for consideration, such as the importance of distribution and marketing. The discussion also touched on the nuances of pricing strategies and the challenges of transitioning from a founder-led sales process to a more scalable approach.
Porting an OpenGL game to WebAssembly using Emscripten, while theoretically straightforward, presented several unexpected challenges. The author encountered issues with texture formats, particularly compressed textures like DXT, necessitating conversion to browser-compatible formats. Shader code required adjustments due to WebGL's stricter validation and lack of certain extensions. Performance bottlenecks emerged from excessive JavaScript calls and inefficient data transfer between JavaScript and WASM. The author ultimately achieved acceptable performance by minimizing JavaScript interaction, utilizing efficient memory management techniques like shared array buffers, and employing WebGL-specific optimizations. Key takeaways include thoroughly testing across browsers, understanding WebGL's limitations compared to OpenGL, and prioritizing efficient data handling between JavaScript and WASM.
Commenters on Hacker News largely praised the author's clear writing and the helpfulness of the article for those considering similar WebGL/WebAssembly projects. Several pointed out the challenges inherent in porting OpenGL code, especially around shader precision differences and the complexities of memory management between JavaScript and C++. One commenter highlighted the benefit of using Emscripten's WebGL bindings for easier texture handling. Others discussed the performance implications of various approaches, including using WebGPU instead of WebGL, and the potential advantages of libraries like glium for abstracting away some of the lower-level details. A few users also shared their own experiences with similar porting projects, offering additional tips and insights. Overall, the comments section provides a valuable supplement to the article, reinforcing its key points and expanding on the practical considerations for OpenGL to WebAssembly porting.
After a decade in software development, the author reflects on evolving perspectives. Initially valuing DRY (Don't Repeat Yourself) principles above all, they now prioritize readability and understand that some duplication is acceptable. Early career enthusiasm for TDD (Test-Driven Development) has mellowed into a more pragmatic approach, recognizing its value but not treating it as dogma. Similarly, the author's strict adherence to OOP (Object-Oriented Programming) has given way to a more flexible style, embracing functional programming concepts when appropriate. Overall, the author advocates for a balanced, context-driven approach to software development, prioritizing practical solutions over rigid adherence to any single paradigm.
Commenters on Hacker News largely agreed with the author's points about the importance of shipping software frequently, embracing simplicity, and focusing on the user experience. Several highlighted the shift away from premature optimization and the growing appreciation for "boring" technologies that prioritize stability and maintainability. Some discussed the author's view on testing, with some suggesting that the appropriate level of testing depends on the specific project and context. Others shared their own experiences and evolving perspectives on similar topics, echoing the author's sentiment about the continuous learning process in software development. A few commenters pointed out the timeless nature of some of the author's original beliefs, like the value of automated testing and continuous integration, suggesting that these practices remain relevant and beneficial even a decade later.
Over 50 years in computing, the author reflects on key lessons learned. Technical brilliance isn't enough; clear communication, especially writing, is crucial for impact. Building diverse teams and valuing diverse perspectives leads to richer solutions. Mentorship is a two-way street, enriching both mentor and mentee. Finally, embracing change and continuous learning are essential for navigating the ever-evolving tech landscape, along with maintaining a sense of curiosity and playfulness in work.
HN commenters largely appreciated the author's reflections on his long career in computer science. Several highlighted the importance of his point about the cyclical nature of computer science, with older ideas and technologies often becoming relevant again. Some commenters shared their own anecdotes about witnessing this cycle firsthand, mentioning specific technologies like LISP, Smalltalk, and garbage collection. Others focused on the author's advice about the balance between specializing and maintaining broad knowledge, noting its applicability to various fields. A few also appreciated the humility and candidness of the author in acknowledging the role of luck in his success.
Ron Garrett reflects on six failed startup attempts, rejecting the label of "failure" and instead focusing on the valuable lessons learned. He emphasizes the importance of choosing the right co-founder, validating ideas early and often, building a minimum viable product (MVP) quickly, and iterating based on user feedback. Marketing and distribution proved crucial, and while passion is essential, it must be coupled with a realistic market and sustainable business model. Ultimately, he learned that "failing fast" and adapting are key to entrepreneurial growth, viewing each setback as a stepping stone toward future success.
HN commenters largely praised the author's vulnerability and honesty in sharing their startup failures. Several highlighted the importance of recognizing sunk cost fallacy and knowing when to pivot or quit. Some questioned the framing of the experiences as "failures," arguing that valuable lessons and growth emerged from them. A few commenters shared their own similar experiences, emphasizing the emotional toll of startup struggles. Others offered practical advice, such as validating ideas early and prioritizing distribution. The prevailing sentiment was one of empathy and encouragement, acknowledging the difficulty of entrepreneurship and the courage it takes to try repeatedly.
Summary of Comments ( 4 )
https://news.ycombinator.com/item?id=43299659
HN commenters largely praised the author's transparency and detailed write-up of their project. Several pointed out the inherent difficulties and nuances of semantic search, particularly within the vast and diverse codebase of GitHub. Some suggested alternative approaches, like focusing on a smaller, more specific domain within GitHub or utilizing existing tools like Elasticsearch with careful tuning. The cost of running such a service and the challenges of monetization were also discussed, with some commenters skeptical of the free model. A few users shared their own experiences with similar projects, echoing the author's sentiments about the complexity and resource intensity of semantic search. Overall, the comments reflected an appreciation for the author's journey and the lessons learned, contributing further insights into the challenges of building and scaling a semantic search engine.
The Hacker News post discussing the article "What I Learned Building a Free Semantic Search Tool for GitHub and Why I Failed" has generated a number of comments exploring different facets of the author's experience.
Several commenters discuss the challenges of building and maintaining free products. One commenter points out the often unsustainable nature of offering free services, especially when substantial infrastructure costs are involved. They highlight the difficulty of balancing the desire to provide a valuable tool to the community with the financial realities of operating such a service. Another commenter echoes this sentiment, emphasizing the considerable effort required to handle scaling and infrastructure for a free product, often leading to burnout for the developer. This commenter suggests alternative models like a "sponsorware" approach where users are encouraged to contribute financially if they find the tool valuable.
The conversation also delves into the technical aspects of semantic search. One commenter questions the choice of using Sentence-BERT embeddings, suggesting that other embedding methods might be more suitable for code search, particularly those that understand the structure and syntax of code rather than just the natural language elements. They also suggest that fine-tuning a more general model on code-specific data would likely yield better results. Another comment thread discusses the difficulties of achieving high accuracy and relevance in semantic search, especially in the context of code where specific terminology and context are crucial.
The business model and potential paths to monetization are also discussed. Some suggest exploring options like paid tiers with enhanced features or focusing on a niche market within the developer community. One commenter mentions the success of GitHub's own code search, which leverages significant resources and data, highlighting the competitive landscape for such a tool. Another commenter proposes partnering with a company that could benefit from such a search tool, potentially integrating it into their existing platform or workflow.
Finally, several commenters express appreciation for the author's transparency and willingness to share their learnings, acknowledging the value of such post-mortems for the broader developer community. They commend the author for documenting the challenges and insights gained from the project, even though it ultimately didn't achieve its initial goals.