Stories with Tag abstract syntax tree (AST)

• Show HN: Nuanced – Help AI understand code structure, not just text

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  Posted: 2025-03-12 17:26:38

  Verbose tl;dr

  Nuanced is a new tool designed to help large language models (LLMs) better understand code structure. It goes beyond simply treating code as text by providing structural information through an Abstract Syntax Tree (AST) augmented with other metadata like variable types and function calls. This enriched representation allows LLMs to perform more sophisticated tasks like code generation, refactoring, and bug detection with greater accuracy. Nuanced currently supports Python and JavaScript and offers a playground and API for developers to experiment with. They aim to improve the performance of AI-powered developer tools by providing a more nuanced understanding of code.

  The blog post titled "Show HN: Nuanced – Help AI understand code structure, not just text," hosted on nuanced.dev, announces the initial launch of Nuanced, a novel tool designed to significantly improve the performance of Large Language Models (LLMs) when applied to code. The core problem Nuanced addresses is the inherent limitation of LLMs in understanding the structural relationships within codebases. While LLMs excel at processing text, they struggle to grasp the intricate connections between different parts of a code project, hindering their ability to perform tasks like accurate code generation, refactoring, and bug detection. Nuanced overcomes this limitation by providing LLMs with a rich, structured representation of the code, moving beyond mere textual analysis.

  This structured representation is achieved through a novel "structural embedding" technique. Instead of treating code as plain text, Nuanced analyzes the code's Abstract Syntax Tree (AST), capturing the hierarchical relationships between code elements. This AST-based approach allows Nuanced to encode the syntactic and semantic information embedded in the code's structure, providing LLMs with a deeper understanding of the code's organization and logic. This enhanced understanding enables LLMs to perform more complex and nuanced reasoning about the code, leading to improved results in various code-related tasks.

  The blog post highlights several key benefits of using Nuanced. Firstly, it drastically reduces the likelihood of LLMs generating syntactically incorrect or illogical code. By understanding the underlying structure, the LLM can generate code that conforms to the existing codebase's conventions and avoids common structural errors. Secondly, Nuanced empowers LLMs to perform more sophisticated code modifications. Refactoring, bug fixing, and feature implementation become more precise and efficient because the LLM has a clearer understanding of the impact of its changes on the overall code structure. Finally, Nuanced improves the accuracy of code analysis tasks, such as code summarization and vulnerability detection. By leveraging structural information, the LLM can extract more meaningful insights from the code and provide more accurate assessments.

  The initial launch of Nuanced focuses on Python, with plans to expand support for other languages in the future. The blog post emphasizes the potential of Nuanced to transform the way developers interact with LLMs, ultimately leading to increased productivity and higher quality code. It invites developers to explore the possibilities of Nuanced and contribute to its development.

  • AI
  • Code Analysis
  • Code Understanding
  • Code Structure
  • Software Development
  • developer tools
  • programming
  • static analysis
  • abstract syntax tree (AST)
  • Program Comprehension
  • Nuanced

  Summary of Comments ( 9 )
  https://news.ycombinator.com/item?id=43345575

  Verbose tl;dr

  Hacker News users generally expressed interest in Nuanced, praising its focus on code structure rather than just text. Several commenters highlighted the importance of this approach for tasks like code search and refactoring, suggesting it could lead to more accurate and relevant results. Some questioned the long-term viability of the product given competition from established players like GitHub Copilot and Sourcegraph, while others expressed interest in the potential applications, especially for larger codebases and specialized languages. A few commenters requested more details on the underlying technology and implementation, particularly regarding how Nuanced handles different programming languages and scales with project size. The overall sentiment leaned towards cautious optimism, with many acknowledging the difficulty of the problem Nuanced is tackling and appreciating the team's approach.

  The Hacker News post discussing Nuanced, a tool to help AI understand code structure, generated a modest number of comments, primarily focusing on its potential and limitations.

  Several commenters expressed interest in the tool's capabilities and its potential applications. One commenter highlighted the importance of understanding code structure beyond just text, emphasizing how crucial this is for effective code analysis and manipulation. They expressed excitement about seeing how Nuanced develops and what future innovations it might bring.

  Another commenter questioned the practical applications of Nuanced, specifically asking about its use cases beyond code search. They were curious to know how the structural understanding provided by Nuanced could be leveraged for tasks like code generation, refactoring, or bug detection. This prompted a response from the creator of Nuanced, who clarified that while code search is the initial focus, they envision expanding into these other areas. They elaborated that Nuanced is currently being used internally for tasks like code navigation, vulnerability detection, and automated code refactoring, indicating the potential for broader applicability in the future.

  One commenter touched on the challenge of parsing complex codebases and accurately representing their structure. They pondered how Nuanced handles such complexities and maintains accuracy in its analysis.

  The creator also addressed a question about how Nuanced compares to existing tools, specifically mentioning that it goes beyond simple Abstract Syntax Tree (AST) parsing. They highlighted that Nuanced captures higher-level structural information, allowing for a more comprehensive understanding of the code.

  In general, the comments reveal a cautious optimism about Nuanced. While acknowledging the potential benefits of understanding code structure, commenters also sought clarification on its practical applications and technical capabilities. The relatively small number of comments suggests a somewhat limited initial engagement with the tool, perhaps awaiting further development and more concrete examples of its usefulness.

• Compiling C++ with the Clang API

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  Posted: 2025-03-09 11:51:36

  Verbose tl;dr

  This blog post demonstrates how to compile C++ code using the Clang API, focusing on practical examples and clear explanations. It walks through creating a simple compiler driver, configuring compilation arguments like include paths and optimization levels, and invoking the Clang frontend to generate LLVM IR. The post highlights key components of the Clang API like clang::FrontendAction and clang::ASTConsumer, and showcases how to handle diagnostics and access compilation results. It provides a foundation for building tools that leverage Clang's powerful analysis and transformation capabilities.

  This blog post by MaskRay details how to compile C++ code using the Clang API, offering a practical guide for programmatically controlling the compilation process. It begins by highlighting the common use case of embedding Clang for tasks like static analysis or source-to-source transformations, where invoking the compiler driver directly isn't ideal. The author then dives into a concrete example, presenting C++ code that leverages the Clang library to compile a simple "Hello, world!" program.

  The post meticulously walks through the code, explaining the essential steps involved. It starts with creating a clang::CompilerInstance, the primary object representing a single invocation of the compiler. It emphasizes the importance of configuring this instance properly, including setting up diagnostics for error reporting, a target information object describing the target architecture, and a file system for accessing source files. The example specifically shows how to configure these components for a simple x86-64 Linux target.

  The core of the compilation process is explained through the creation and execution of a clang::FrontendAction. The author opts for the clang::EmitLLVMOnlyAction in the example, which generates LLVM bitcode instead of fully compiled machine code. This choice simplifies the demonstration by avoiding the complexities of backend code generation. The process of creating and executing this action within the CompilerInstance is detailed, including how to set up the necessary input source file.

  A significant portion of the post is dedicated to explaining the diagnostic handling mechanism. It describes how to create and configure a clang::DiagnosticConsumer to process compilation errors and warnings. The example uses a clang::TextDiagnosticPrinter to output diagnostics to the console in a human-readable format. The author further illustrates how to collect diagnostic options, such as the desired format and warning flags, and associate them with the diagnostic printer.

  Finally, the post demonstrates how to execute the compilation by calling the ExecuteAction method on the CompilerInstance. It highlights the importance of checking the return value of this function to determine if the compilation was successful. The generated LLVM bitcode is not explicitly handled in the example as the focus remains on the compilation process itself. The post concludes by providing the complete, compilable code example, allowing readers to readily experiment and adapt it for their own projects. The author also briefly touches upon the possibility of extending the example to compile multiple files and handle different output formats, encouraging further exploration of the Clang API.

  • C++
  • Clang
  • compiler
  • API
  • Compilation
  • Code Generation
  • abstract syntax tree (AST)
  • LibTooling
  • LLVM

  Summary of Comments ( 13 )
  https://news.ycombinator.com/item?id=43308259

  Verbose tl;dr

  Hacker News users discussed practical aspects of using the Clang API. Some pointed out the steep learning curve and lack of comprehensive documentation, making it challenging to navigate and debug. Others highlighted the API's power and flexibility for tasks like code analysis, transformation, and generation, exceeding the capabilities of simpler tools. A few commenters shared alternative approaches or libraries for specific use cases, such as libTooling for simpler tasks and Tree-sitter for parsing. The lack of good error messages from the Clang API was also mentioned, along with the difficulty of integrating it into build systems like CMake.

  The Hacker News post "Compiling C++ with the Clang API" has generated a modest discussion with several insightful comments.

  One commenter highlights the complexity of the Clang API, mentioning that even seemingly simple tasks can require delving into the source code. They appreciate the author's clear explanation and example code, which they believe will be helpful to others navigating the Clang ecosystem. This comment resonates with the overall sentiment that the Clang API, while powerful, presents a steep learning curve.

  Another user focuses on the utility of the Clang API for tasks like code generation and refactoring, pointing out its advantages over simpler approaches like string manipulation. This comment emphasizes the power and flexibility of the Clang API for complex code manipulations, where understanding the underlying Abstract Syntax Tree (AST) is crucial. They also suggest that this approach allows for more robust and accurate transformations.

  A further comment questions the necessity of building with CMake, suggesting that a simpler build system could suffice for the provided example. This sparks a brief discussion about the trade-offs of build system complexity, with arguments for and against using a powerful build system like CMake for smaller projects. While the commenter acknowledges the potential benefits of CMake for larger projects, they imply that its overhead might be excessive for this particular use case.

  Finally, another commenter shares their own struggles with the Clang API, particularly in dealing with templates and the AST. This comment reinforces the previously mentioned difficulty of the Clang API and emphasizes the value of readily available examples like the one provided by the blog post author.

  In summary, the comments section expresses appreciation for the author's clear explanation of a complex topic. The discussion revolves around the challenges and power of the Clang API, the trade-offs of build system complexity, and the importance of practical examples for navigating the intricacies of programmatically interacting with the Clang compiler.

• Show HN: Tach – Visualize and untangle your Python codebase

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  Posted: 2025-02-25 16:34:07

  Verbose tl;dr

  Tach is a Python codebase visualization tool that helps developers understand and navigate complex projects. It generates interactive, graph-based visualizations of dependencies, inheritance structures, and function calls within a Python codebase. This allows developers to quickly grasp the overall architecture, identify potential issues like circular dependencies, and explore the relationships between different parts of their project. Tach aims to simplify code comprehension and improve maintainability, especially in large and complex projects.

  The GitHub project "Tach," developed by Gauge, introduces a novel approach to understanding and navigating complex Python codebases. It aims to move beyond traditional, linear code representation and offers a visual, interactive graph-based exploration of the code's structure and dependencies. This visualization helps developers grasp the relationships between different parts of their project, facilitating easier comprehension of how components interact. Tach achieves this by statically analyzing the Python code, identifying modules, classes, functions, and their dependencies, and then rendering these relationships as a dynamic, explorable graph.

  Users can interact with this graph to gain various insights. They can filter the graph to focus on specific modules or classes, effectively decluttering the view and concentrating on relevant sections. The tool allows for tracing the flow of execution through the code, helping developers understand the sequence of calls and identify potential bottlenecks or circular dependencies. Furthermore, Tach supports searching for specific functions or classes, making it easier to locate elements within a large codebase. By visualizing the code's architecture, Tach allows developers to more easily identify potential areas for refactoring, optimization, and improved code organization.

  Tach is a command-line tool, designed to be integrated into a developer's existing workflow. It parses Python code and generates the interactive graph, which can then be explored through a web browser. The visualization is powered by a client-side application that handles rendering and interaction, providing a fluid and responsive user experience. This project is intended to be a helpful tool for developers working on Python projects of any size, from small scripts to large, complex applications. By providing a visual, interactive representation of the code's structure, Tach empowers developers to more easily understand, navigate, and ultimately improve their Python codebases.

  • Python
  • Code Visualization
  • Code Analysis
  • Software Development
  • Open Source
  • developer tools
  • static analysis
  • Code Complexity
  • Code Understanding
  • Codebase Management
  • Visualization Tools
  • Python Tooling
  • software engineering
  • abstract syntax tree (AST)
  • Dependency Analysis

  Summary of Comments ( 25 )
  https://news.ycombinator.com/item?id=43174041

  Verbose tl;dr

  HN users generally expressed interest in Tach, praising its visualization capabilities and potential usefulness for understanding complex codebases. Several commenters compared it favorably to existing tools like Sourcetrail and CodeSee, while also acknowledging limitations like scalability and the challenge of visualizing extremely large projects. Some suggested potential enhancements, such as integration with IDEs and support for additional languages beyond Python. Concerns were raised regarding the reliance on dynamic analysis and its potential impact on performance, as well as the need for clear documentation and examples. There was also interest in exploring alternative visualization approaches like graph databases.

  The Hacker News post about Tach, a tool to visualize and untangle Python codebases, generated a moderate number of comments, primarily focusing on existing solutions and the specific problem Tach aims to solve.

  Several commenters pointed out existing tools that offer similar functionality. One user mentioned Understand [^1], a commercial tool known for its comprehensive code analysis and visualization capabilities, while another highlighted PyCG [^2], an open-source tool specifically designed for generating call graphs for Python code. These comments served to contextualize Tach within the existing ecosystem of code analysis tools and questioned its unique value proposition.

  The discussion also touched upon the practical challenges of understanding and navigating large codebases. One commenter emphasized the importance of clear documentation and modular design as fundamental practices for maintaining code clarity, suggesting that these should be prioritized before resorting to visualization tools. Another user expressed skepticism about the effectiveness of visualization for extremely complex codebases, arguing that the resulting diagrams might become too convoluted to be useful. This raised the question of Tach's scalability and its applicability to real-world, large-scale projects.

  Some commenters questioned the utility of static analysis tools like Tach in comparison to dynamic analysis. The argument was that dynamic analysis, by observing the code's behavior during runtime, could provide more insightful information about the actual relationships and dependencies between different parts of the system.

  Finally, there was a brief discussion on the preferred methods for visualizing code. One commenter expressed a preference for hierarchical visualizations over graph-based representations, suggesting that a tree-like structure might be more intuitive for understanding the organization of a codebase.

  In summary, the comments on the Hacker News post reflect a cautious but curious reception to Tach. While acknowledging the need for tools to manage code complexity, the commenters also highlighted existing alternatives and raised concerns about the practicality and scalability of visualization-based approaches. They emphasized the importance of foundational software engineering practices and explored alternative analysis methods like dynamic analysis. The discussion provides valuable context for understanding the potential benefits and limitations of Tach and similar tools.

  [^1]: Understand: This refers to the commercial software "Understand" by SciTools, used for static code analysis and visualization. [^2]: PyCG: This refers to the open-source tool "PyCG" (Python Call Graph), designed for generating call graphs.

• I wrote my own “proper” programming language (2020)

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  Posted: 2025-01-22 09:54:25

  Verbose tl;dr

  Mukul Rathi details his journey of creating a custom programming language, focusing on the compiler construction process. He explains the key stages involved, from lexing (converting source code into tokens) and parsing (creating an Abstract Syntax Tree) to code generation and optimization. Rathi uses his language, which he implements in OCaml, to illustrate these concepts, providing code examples and explanations of how each component works together to transform high-level code into executable machine instructions. He emphasizes the importance of understanding these foundational principles for anyone interested in building their own language or gaining a deeper appreciation for how programming languages function.

  In a comprehensive blog post titled "I wrote my own “proper” programming language," author Mukul Rathi chronicles the journey of designing and implementing a programming language from its nascent conceptual stages to a functional, albeit rudimentary, state. He meticulously details the process of building a compiler, breaking down the complex task into manageable, discrete steps.

  The post begins by outlining the fundamental architecture of a compiler, illustrating the typical workflow from source code to executable program. This includes lexical analysis, where the input code is tokenized; parsing, which involves constructing an Abstract Syntax Tree (AST) to represent the code's structure; semantic analysis, where type checking and other semantic rules are enforced; and finally, code generation, where the AST is translated into intermediate representations like bytecode or assembly language.

  Rathi delves into the specifics of his implementation, utilizing Python as the language for his compiler. He elucidates the lexical analyzer’s role in categorizing individual components of the source code, such as keywords, identifiers, and operators, transforming the raw text into a stream of meaningful tokens. The parsing stage, he explains, involves organizing these tokens into a hierarchical tree structure – the AST – which reflects the grammatical relationships between different parts of the code. This is achieved using a recursive descent parsing technique.

  Furthermore, the post underscores the importance of semantic analysis, which goes beyond mere syntax verification and delves into the meaning of the code. This crucial step involves ensuring type compatibility, checking for undeclared variables, and enforcing other language-specific semantic rules. Rathi describes how his compiler performs these checks, thereby ensuring the logical integrity of the program.

  Finally, the post culminates in a discussion of code generation. While stopping short of generating machine code directly, Rathi explains how his compiler generates bytecode, a lower-level representation of the program. This bytecode can then be executed by a virtual machine, effectively bridging the gap between high-level source code and the underlying hardware. He emphasizes that while his compiler does not perform all the optimizations a production-ready compiler would, it demonstrates the essential steps involved in translating a high-level programming language into an executable format. The post concludes by acknowledging the project's limitations while highlighting its educational value as a practical exercise in compiler construction.

  • programming language design
  • compiler design
  • programming language implementation
  • interpreter
  • parser
  • lexer
  • virtual machine
  • programming
  • compiler construction
  • language development
  • Programming Languages
  • Code Generation
  • abstract syntax tree (AST)
  • bytecode

  Summary of Comments ( 13 )
  https://news.ycombinator.com/item?id=42791036

  Verbose tl;dr

  Hacker News users generally praised the article for its clarity and accessibility in explaining compiler construction. Several commenters appreciated the author's approach of building a complete, albeit simple, language instead of just a toy example. Some pointed out the project's similarity to the "Let's Build a Compiler" series, while others suggested alternative or supplementary resources like Crafting Interpreters and the LLVM tutorial. A few users discussed the tradeoffs between hand-written lexers/parsers and using parser generator tools, and the challenges of garbage collection implementation. One commenter shared their personal experience of writing a language and the surprising complexity of seemingly simple features.

  The Hacker News thread for "I wrote my own “proper” programming language (2020)" contains several comments discussing various aspects of the linked article.

  Many comments focus on tooling and alternative approaches to building a programming language. One user suggests using tools like Lex/Yacc or Flex/Bison for lexical analysis and parsing, offering a more robust and less error-prone method than manual implementation. This comment sparked a small discussion thread with another user pointing out that while powerful, these tools can add complexity, especially for beginners. They advocate for a simpler approach initially, recommending a hand-rolled recursive descent parser for its educational value in understanding the underlying mechanisms. This exchange highlights the trade-off between ease of implementation and the robustness of the final product.

  Another commenter discusses the evolution of compiler construction and how techniques and tools have changed over time. They specifically mention the shift towards using LLVM as a backend for code generation and optimization. This offers the advantage of targeting multiple platforms without rewriting the backend for each one.

  Several users commend the author of the article for undertaking such a complex project and sharing their knowledge. They praise the clear explanations and the step-by-step approach presented in the article, finding it accessible even for those without prior compiler development experience.

  Some comments delve into specific aspects of the implementation, such as garbage collection, with one commenter suggesting exploring different garbage collection strategies. Another thread discusses the performance implications of different language design choices, emphasizing the importance of considering efficiency from the start.

  One user expresses a common sentiment among language developers, mentioning the inherent difficulty and complexity involved in creating a "proper" programming language. They acknowledge the effort required for not just initial implementation, but also ongoing maintenance and improvement.

  Finally, a few comments express interest in the language's potential applications and its future development. They inquire about specific features and express a desire to see the project evolve.