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.
CodeWeaver is a tool that transforms an entire codebase into a single, navigable markdown document designed for AI interaction. It aims to improve code analysis by providing AI models with comprehensive context, including directory structures, filenames, and code within files, all linked for easy navigation. This approach enables large language models (LLMs) to better understand the relationships within the codebase, perform tasks like code summarization, bug detection, and documentation generation, and potentially answer complex queries that span multiple files. CodeWeaver also offers various formatting and filtering options for customizing the generated markdown to suit specific LLM needs and optimize token usage.
HN users discussed the practical applications and limitations of converting a codebase into a single Markdown document for AI processing. Some questioned the usefulness for large projects, citing potential context window limitations and the loss of structural information like file paths and module dependencies. Others suggested alternative approaches like using embeddings or tree-based structures for better code representation. Several commenters expressed interest in specific use cases, such as generating documentation, code analysis, and refactoring suggestions. Concerns were also raised about the computational cost and potential inaccuracies of processing large Markdown files. There was some skepticism about the "one giant markdown file" approach, with suggestions to explore other methods for feeding code to LLMs. A few users shared their own experiences and alternative tools for similar tasks.
Phind 2, a new AI search engine, significantly upgrades its predecessor with enhanced multi-step reasoning capabilities and the ability to generate visual answers, including diagrams and code flowcharts. It utilizes a novel method called "grounded reasoning" which allows it to access and process information from multiple sources to answer complex questions, offering more comprehensive and accurate responses. Phind 2 also features an improved conversational mode and an interactive code interpreter, making it a more powerful tool for both technical and general searches. This new version aims to provide clearer, more insightful answers than traditional search engines, moving beyond simply listing links.
Hacker News users discussed Phind 2's potential, expressing both excitement and skepticism. Some praised its ability to synthesize information and provide visual aids, especially for coding-related queries. Others questioned the reliability of its multi-step reasoning and cited instances where it hallucinated or provided incorrect code. Concerns were also raised about the lack of source citations and the potential for over-reliance on AI tools, hindering deeper learning. Several users compared it favorably to other AI search engines like Perplexity AI, noting its cleaner interface and improved code generation capabilities. The closed-source nature of Phind 2 also drew criticism, with some advocating for open-source alternatives. The pricing model and potential for future monetization were also points of discussion.
Voyage's blog post details their evaluation of various code embedding models for code retrieval tasks. They emphasize the importance of using realistic datasets and evaluation metrics like Mean Reciprocal Rank (MRR) tailored for code search scenarios. Their experiments demonstrate that retrieval performance varies significantly across datasets and model architectures, with specialized models like CodeT5 consistently outperforming general-purpose embedding models. They also found that retrieval effectiveness plateaus as embedding dimensionality increases beyond a certain point, suggesting diminishing returns for larger embeddings. Finally, they introduce a novel evaluation dataset derived from Voyage's internal codebase, aimed at providing a more practical benchmark for code retrieval models in real-world settings.
Hacker News users discussed the methodology of Voyage's code retrieval evaluation, particularly questioning the reliance on HumanEval and MBPP benchmarks. Some argued these benchmarks don't adequately reflect real-world code retrieval scenarios, suggesting alternatives like retrieving code from a large corpus based on natural language queries. The lack of open-sourcing for Voyage's evaluated models and datasets also drew criticism, hindering reproducibility and broader community engagement. There was a brief discussion on the usefulness of keyword search as a strong baseline and the potential benefits of integrating semantic search techniques. Several commenters expressed interest in seeing evaluations based on more realistic use cases, including bug fixing or adding new features within existing codebases.
DeepSeek, a platform offering encoder APIs for developers, chose to open-source its core technology due to the inherent difficulty in building trust with users regarding data privacy and security when handling sensitive information like codebases and internal documentation. By open-sourcing, DeepSeek aims to foster transparency and allow users to self-host, ensuring complete control over their data. This approach mitigates concerns around vendor lock-in and allows the community to contribute to the project's development and security, ultimately building greater trust and fostering wider adoption.
Hacker News users discussed the open-sourcing of DeepSeek, primarily focusing on the challenges of monetizing open-source AI infrastructure. Many commenters were skeptical of Lago's business model, questioning how they could successfully build a proprietary offering on top of an open-source core, especially given the intense competition in the vector database space. Some suggested that open-sourcing DeepSeek was a necessary move due to the difficulty of attracting paying customers for a closed-source product. Others pointed out potential advantages, such as faster iteration and community contributions, but remained unconvinced of long-term viability. Several users expressed a desire for more technical details about DeepSeek's implementation and performance compared to existing solutions. The most compelling comments revolved around the inherent tension between open-sourcing and profitability in the current AI landscape.
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.