The blog post explores building a composable SQL query builder in Haskell using the concept of functors. Instead of relying on string concatenation, which is prone to SQL injection vulnerabilities, it leverages Haskell's type system and the Functor typeclass to represent SQL fragments as data structures. These fragments can then be safely combined and transformed using pure functions. The approach allows for building complex queries piece by piece, abstracting away the underlying SQL syntax and promoting code reusability. This results in a more type-safe, maintainable, and composable way to generate SQL queries compared to traditional string-based methods.
The blog post "The Simplicity of Prolog" argues that Prolog's declarative nature makes it easier to learn and use than imperative languages for certain problem domains. It demonstrates this by building a simple genealogy program in Prolog, highlighting how its concise syntax and built-in search mechanism naturally express relationships and deduce facts. The author contrasts this with the iterative loops and explicit state management required in imperative languages, emphasizing how Prolog abstracts away these complexities. The post concludes that while Prolog may not be suitable for all tasks, its elegant approach to logic programming offers a powerful and efficient solution for problems involving knowledge representation and inference.
Hacker News users generally praised the article for its clear introduction to Prolog, with several noting its effectiveness in sparking their own interest in the language. Some pointed out Prolog's historical significance and its continued relevance in specific domains like AI and knowledge representation. A few users highlighted the contrast between Prolog's declarative approach and the more common imperative style of programming, emphasizing the shift in mindset required to effectively use it. Others shared personal anecdotes of their experiences with Prolog, both positive and negative, with some mentioning its limitations in performance-critical applications. A couple of comments also touched on the learning curve associated with Prolog and the challenges in debugging complex programs.
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.
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.
Yasser is developing "Tilde," a new compiler infrastructure designed as a simpler, more modular alternative to LLVM. Frustrated with LLVM's complexity and monolithic nature, he's building Tilde with a focus on ease of use, extensibility, and better diagnostics. The project is in its early stages, currently capable of compiling a subset of C and targeting x86-64 Linux. Key differentiating features include a novel intermediate representation (IR) designed for efficient analysis and transformation, a pipeline architecture that facilitates experimentation and customization, and a commitment to clear documentation and a welcoming community. While performance isn't the primary focus initially, the long-term goal is to be competitive with LLVM.
Hacker News users discuss the author's approach to building a compiler, "Tilde," positioned as an LLVM alternative. Several commenters express skepticism about the project's practicality and scope, questioning the rationale behind reinventing LLVM, especially given its maturity and extensive community. Some doubt the performance claims and suggest benchmarks are needed. Others appreciate the author's ambition and the technical details shared, seeing value in exploring alternative compiler designs even if Tilde doesn't replace LLVM. A few users offer constructive feedback on specific aspects of the compiler's architecture and potential improvements. The overall sentiment leans towards cautious interest with a dose of pragmatism regarding the challenges of competing with an established project like LLVM.
The author argues that Go's context.Context is overused and often misused as a dumping ground for arbitrary values, leading to unclear dependencies and difficult-to-test code. Instead of propagating values through Context, they propose using explicit function parameters, promoting clearer code, better separation of concerns, and easier testability. They contend that using Context primarily for cancellation and timeouts, its intended purpose, would streamline code and improve its maintainability.
HN commenters largely agree with the author's premise that context.Context in Go is overused and often misused for dependency injection or as a dumping ground for miscellaneous values. Several suggest that structured concurrency, improved error handling, and better language features for cancellation and deadlines could alleviate the need for context in many cases. Some argue that context is still useful for request-scoped values, especially in server contexts, and shouldn't be entirely removed. A few commenters express concern about the practicality of removing context given its widespread adoption and integration into the standard library. There is a strong desire for better alternatives, rather than simply discarding the existing mechanism without a replacement. Several commenters also mention the similarities between context overuse in Go and similar issues with dependency injection frameworks in other languages.
The Hacker News post discusses whether any programming languages allow specifying package dependencies directly within import or include statements, rather than separately in a dedicated dependency management file. The original poster highlights the potential benefits of this approach, such as improved clarity and ease of understanding dependencies for individual files. They suggest a syntax where version numbers or constraints could be incorporated into the import statement itself. While no existing mainstream languages seem to offer this feature, some commenters mention related concepts like import maps in JavaScript and conditional imports in some languages. The core idea is to make dependency management more localized and transparent at the file level.
The Hacker News comments discuss the pros and cons of specifying package requirements directly within import statements. Several commenters appreciate the clarity and explicitness this would bring, as it makes dependencies immediately obvious and reduces the need for separate dependency management files. Others argue against it, citing potential drawbacks like redundancy, increased code verbosity, and difficulties managing complex dependency graphs. Some propose alternative solutions, like embedding version requirements in comments or using language-specific mechanisms for dependency specification. A few commenters mention existing languages or tools that offer similar functionality, such as Nix and Dhall, pointing to these as potential examples or inspiration for how such a system could work. The discussion also touches on the practical implications for tooling and build systems, with commenters considering the impact on IDE integration and compilation processes.
"Alligator Eggs" explores the surprising computational power hidden within a simple system of rewriting strings. Inspired by a children's puzzle involving moving colored eggs, the post demonstrates how a carefully designed set of rules for replacing egg sequences can emulate the functionality of a Turing Machine, a theoretical model capable of performing any computation. By encoding logic and data within the arrangement of the eggs, the system can execute arbitrary programs, effectively turning a seemingly trivial game into a universal computer. The post emphasizes the elegance and minimalism of this computational model, highlighting how complex behavior can emerge from simple, well-defined rules.
HN users generally praised the clarity and approachability of Bret Victor's explanation of lambda calculus, with several highlighting its effectiveness as an introductory resource even for those without a strong math background. Some discussed the challenges of teaching and visualizing these concepts, appreciating Victor's interactive approach. A few commenters delved into more technical nuances, comparing lambda calculus to combinatory logic and touching upon topics like currying and the SKI calculus. Others reminisced about learning from similar resources in the past and shared related links, demonstrating the article's enduring relevance. A recurring theme was the power of visual and interactive learning tools in making complex topics more accessible.
Justine Tunney's "Lambda Calculus in 383 Bytes" presents a remarkably small, self-hosting Lambda Calculus interpreter written in x86-64 assembly. It parses, evaluates, and prints lambda expressions, supporting variables, application, and abstraction using a custom encoding. Despite its tiny size, the interpreter implements a complete, albeit slow, evaluation strategy by translating lambda terms into De Bruijn indices and employing normal order reduction. The project showcases the minimal computational requirements of lambda calculus and the power of concise, low-level programming.
Hacker News users discuss the cleverness and efficiency of the 383-byte lambda calculus implementation, praising its conciseness and educational value. Some debate the practicality of such a minimal implementation, questioning its performance and highlighting the trade-offs made for size. Others delve into technical details, comparing it to other small language implementations and discussing optimization strategies. Several comments point out the significance of understanding lambda calculus fundamentals and appreciate the author's clear explanation and accompanying code. A few users express interest in exploring similar projects and adapting the code for different architectures. The overall sentiment is one of admiration for the technical feat and its potential as a learning tool.
This blog post explores a simplified variant of Generalized LR (GLR) parsing called "right-nulled" GLR. Instead of maintaining a graph-structured stack during parsing ambiguities, this technique uses a single stack and resolves conflicts by prioritizing reduce actions over shift actions. When a conflict occurs, the parser performs all possible reductions before attempting to shift. This approach sacrifices some of GLR's generality, as it cannot handle all types of grammars, but it significantly reduces the complexity and overhead associated with maintaining the graph-structured stack, leading to a faster and more memory-efficient parser. The post provides a conceptual overview, highlights the limitations compared to full GLR, and demonstrates the algorithm with a simple example.
Hacker News users discuss the practicality and efficiency of GLR parsing, particularly in comparison to other parsing techniques. Some commenters highlight its theoretical power and ability to handle ambiguous grammars, while acknowledging its potential performance overhead. Others question its suitability for real-world applications, suggesting that simpler methods like PEG or recursive descent parsers are often sufficient and more efficient. A few users mention specific use cases where GLR parsing shines, such as language servers and situations requiring robust error recovery. The overall sentiment leans towards appreciating GLR's theoretical elegance but expressing reservations about its widespread adoption due to perceived complexity and performance concerns. A recurring theme is the trade-off between parsing power and practical efficiency.
This paper introduces Crusade, a formally verified translation from a subset of C to safe Rust. Crusade targets a memory-safe dialect of C, excluding features like arbitrary pointer arithmetic and casts. It leverages the Coq proof assistant to formally verify the translation's correctness, ensuring that the generated Rust code behaves identically to the original C, modulo non-determinism inherent in C. This rigorous approach aims to facilitate safe integration of legacy C code into Rust projects without sacrificing confidence in memory safety, a critical aspect of modern systems programming. The translation handles a substantial subset of C, including structs, unions, and functions, and demonstrates its practical applicability by successfully converting real-world C libraries.
HN commenters discuss the challenges and nuances of formally verifying the C to Rust transpiler, Cracked. Some express skepticism about the practicality of fully verifying such a complex tool, citing the potential for errors in the formal proofs themselves and the inherent difficulty of capturing all undefined C behavior. Others question the performance impact of the generated Rust code. However, many commend the project's ambition and see it as a significant step towards safer systems programming. The discussion also touches upon the trade-offs between a fully verified transpiler and a more pragmatic approach focusing on common C patterns, with some suggesting that prioritizing practical safety improvements could be more beneficial in the short term. There's also interest in the project's handling of concurrency and the potential for integrating Cracked with existing Rust tooling.
This blog post explores the powerful concept of functions as the fundamental building blocks of computation, drawing insights from the book Structure and Interpretation of Computer Programs (SICP) and David Beazley's work. It illustrates how even seemingly complex structures like objects and classes can be represented and implemented using functions, emphasizing the elegance and flexibility of this approach. The author demonstrates building a simple object system solely with functions, highlighting closures for managing state and higher-order functions for method dispatch. This functional perspective provides a deeper understanding of object-oriented programming and showcases the unifying power of functions in expressing diverse programming paradigms. By breaking down familiar concepts into their functional essence, the post encourages a more fundamental and adaptable approach to software design.
Hacker News users discuss the transformative experience of learning Scheme and SICP, particularly under David Beazley's tutelage. Several commenters emphasize the power of Beazley's teaching style, highlighting his ability to simplify complex concepts and make them engaging. Some found the author's surprise at the functional paradigm's elegance noteworthy, with one suggesting that other languages like Python and Javascript offer similar functional capabilities, perhaps underappreciated by the author. Others debated the benefits and drawbacks of "pure" functional programming, its practicality in real-world projects, and the learning curve associated with Scheme. A few users also shared their own positive experiences with SICP and its impact on their understanding of computer science fundamentals. The overall sentiment reflects an appreciation for the article's insights and the enduring relevance of SICP in shaping programmers' perspectives.
This blog post explores using Go's strengths for web service development while leveraging Python's rich machine learning ecosystem. The author details a "sidecar" approach, where a Go web service communicates with a separate Python process responsible for ML tasks. This allows the Go service to handle routing, request processing, and other web-related functionalities, while the Python sidecar focuses solely on model inference. Communication between the two is achieved via gRPC, chosen for its performance and cross-language compatibility. The article walks through the process of setting up the gRPC connection, preparing a simple ML model in Python using scikit-learn, and implementing the corresponding Go service. This architectural pattern isolates the complexity of the ML component and allows for independent scaling and development of both the Go and Python parts of the application.
HN commenters discuss the practicality and performance implications of the Python sidecar approach for ML in Go. Some express skepticism about the added complexity and overhead, suggesting gRPC or REST might be overkill for simple tasks and questioning the performance benefits compared to pure Python or using GoML libraries directly. Others appreciate the author's exploration of different approaches and the detailed benchmarks provided. The discussion also touches on alternative solutions like using shared memory or embedding Python in Go, as well as the broader topic of language interoperability for ML tasks. A few comments mention specific Go ML libraries like gorgonia/tensor as potential alternatives to the sidecar approach. Overall, the consensus seems to be that while interesting, the sidecar approach may not be the most efficient solution in many cases, but could be valuable in specific circumstances where existing Go ML libraries are insufficient.
Summary of Comments ( 6 )
https://news.ycombinator.com/item?id=42828883
HN commenters generally appreciate the composability approach to SQL queries presented in the article, finding it cleaner and more maintainable than traditional string concatenation. Several highlight the similarity to functional programming concepts and appreciate the use of Python's type hinting. Some express concern about performance implications, particularly with nested queries, and suggest comparing it to ORMs. Others question the practicality for complex queries or the necessity for simpler ones. A few users mention existing libraries with similar functionality, like SQLAlchemy Core. The discussion also touches upon alternative approaches like using CTEs (Common Table Expressions) for composability and the potential benefits for testing and debugging.
The Hacker News post titled "Composable SQL (Functors)" with the ID 42828883 generated a moderate amount of discussion, with several commenters engaging with the core ideas presented about using functors for SQL composition.
Several commenters appreciated the author's approach to simplifying complex SQL queries. One user highlighted the practicality of the presented technique, emphasizing its usefulness in situations where dynamic query building is necessary. They pointed out that this method is particularly beneficial when dealing with optional filters or criteria that might need to be added or removed based on certain conditions. Another commenter echoed this sentiment, expressing their agreement with the elegance and conciseness the functor approach brings to SQL composition. They specifically mentioned how it helps avoid messy string concatenation or complex conditional logic within the SQL queries themselves.
However, the discussion wasn't without its critical perspectives. One commenter questioned the actual need for functors in this specific context. They argued that simpler abstractions might suffice for achieving the desired composability and suggested exploring alternatives before committing to the functor pattern. Expanding on this point, another user mentioned that while the approach is neat, the overhead introduced by functors might not be justified for all use cases. They cautioned against over-engineering and recommended considering the complexity of the queries being composed before adopting this pattern.
There was also a discussion about the applicability of this approach to different database systems. One commenter specifically asked about its compatibility with PostgreSQL, pointing to potential limitations or nuances that might arise depending on the specific database being used. Another user expressed their preference for using an ORM (Object-Relational Mapper) for such tasks, suggesting that ORMs often provide built-in mechanisms for composing queries in a more database-agnostic way. They argued that relying on database-specific functor implementations might limit portability and introduce unnecessary dependencies.
Finally, a few comments delved into more technical aspects of the implementation, discussing the choice of programming language and the specific functor libraries used. One user inquired about the author's reasoning behind using a particular language and suggested exploring alternative libraries that might offer better performance or features.