Xan is a command-line tool designed for efficient manipulation of CSV and tabular data. It focuses on speed and simplicity, leveraging Rust's performance for tasks like searching, filtering, transforming, and aggregating. Xan aims to be a modern alternative to traditional tools like awk and sed, offering a more intuitive syntax specifically geared toward working with structured data in a terminal environment. Its features include column selection, filtering based on various criteria, data type conversion, statistical computations, and outputting in various formats, including JSON.
This blog post demonstrates how to efficiently integrate Large Language Models (LLMs) into bash scripts for automating text-based tasks. It leverages the curl command to send prompts to LLMs via API, specifically using OpenAI's API as an example. The author provides practical examples of formatting prompts with variables and processing the JSON responses to extract desired text output. This allows for dynamic prompt generation and seamless integration of LLM-generated content into existing shell workflows, opening possibilities for tasks like code generation, text summarization, and automated report creation directly within a familiar scripting environment.
Hacker News users generally found the concept of using LLMs in bash scripts intriguing but impractical. Several commenters highlighted potential issues like rate limiting, cost, and the inherent unreliability of LLMs for tasks that demand precision. One compelling argument was that relying on an LLM for simple string manipulation or data extraction in bash is overkill when more robust and predictable tools like sed, awk, or jq already exist. The discussion also touched upon the security implications of sending potentially sensitive data to an external LLM API and the lack of reproducibility in scripts relying on probabilistic outputs. Some suggested alternative uses for LLMs within scripting, such as generating boilerplate code or documentation.
Christian Tietze reflects on the "software rake," a metaphor for accumulating small, seemingly insignificant tasks that eventually hinder progress on larger, more important work. He breaks down the rake's "prongs" into categories like maintenance, distractions, context switching, and unexpected issues. These prongs snatch time and attention, creating a sense of being busy but unproductive. Tietze advocates for consciously identifying and addressing these prongs through techniques like timeboxing, focused work sessions, and ruthless prioritization to clear the way for meaningful progress on significant projects.
Hacker News users discussed the various "prongs" of the Rake, agreeing with the author's general premise about complexity in software. Several commenters shared their own experiences wrestling with similar issues, particularly around build systems and dependency management. One pointed out the irony of Rake itself being a complex build system, while another suggested that embracing complexity is sometimes unavoidable, especially as projects mature. The impact of "worse is better" philosophy was debated, with some arguing it contributes to the problem and others suggesting it's a pragmatic necessity. A few users highlighted specific prongs they found particularly relevant, including the struggle to maintain compatibility and the pressure to adopt new technologies. Some offered alternative solutions, like focusing on smaller, composable tools and simpler languages, while others emphasized the importance of careful planning and design upfront to mitigate future complexity. There was also discussion about the role of organizational structure and communication in exacerbating these issues.
Paul Samuels advocates for using simple, project-specific shell scripts instead of complex build systems or task runners for small to medium-sized projects. He argues that shell scripts offer better transparency, debuggability, and control, while reducing cognitive overhead. They facilitate easier understanding of project dependencies and build processes, which ultimately contributes to better maintainability, especially for solo developers or small teams. By leveraging the shell's built-in features and readily available Unix tools, project scripts provide a lightweight yet powerful approach to managing common development tasks.
Hacker News users generally praised the simplicity and practicality of "Project Scripts." Several commenters appreciated the lightweight nature of the approach compared to more complex build systems or dedicated project management tools, highlighting the benefit of reduced cognitive overhead. Some suggested potential improvements like incorporating direnv or using a Makefile for more complex projects. A few users expressed skepticism, arguing that the proposed "Project Scripts" offered little beyond basic shell scripting and questioned the need for a dedicated term. Others found the idea valuable for its focus on explicitness and ease of sharing project setup within a team. The discussion also touched on related tools like Taskfile and justfile, comparing their features and complexity to the author's approach.
"Do-nothing scripting" advocates for a gradual approach to automation. Instead of immediately trying to fully automate a complex task, you start by writing a script that simply performs the steps manually, echoing each command to the screen. This allows you to document the process precisely and identify potential issues without the risk of automated errors. As you gain confidence, you incrementally replace the manual execution of each command within the script with its automated equivalent. This iterative process minimizes disruption, allows for easy rollback, and makes the transition to full automation smoother and more manageable.
Hacker News users generally praised the "do-nothing scripting" approach as a valuable tool for understanding existing processes before automating them. Several commenters highlighted the benefit of using this technique to gain stakeholder buy-in and build trust, particularly when dealing with complex or mission-critical systems. Some shared similar experiences or suggested alternative methods like using strace or dtrace. One commenter suggested incorporating progressive logging to further refine the script's insights over time, while another cautioned against over-reliance on this approach, advocating for a move towards true automation once sufficient understanding is gained. Some skepticism was expressed regarding the practicality for highly interactive processes. Overall, the commentary reflects strong support for the core idea as a practical step toward thoughtful and effective automation.
The GNU Make Standard Library (GMSL) offers a collection of reusable Makefile functions designed to simplify common build tasks and promote best practices in GNU Make projects. It provides functions for tasks like finding files, managing dependencies, working with directories, handling shell commands, and more. By incorporating GMSL, Makefiles can become more concise, readable, and maintainable, reducing boilerplate and improving consistency across projects. The library is designed to be modular, allowing users to include only the functions they need.
Hacker News users discussed the GNU Make Standard Library (GMSL), mostly focusing on its potential usefulness and questioning its necessity. Some commenters appreciated the idea of standardized functions for common Make tasks, finding it could improve readability and reduce boilerplate. Others argued that existing solutions like shell scripts or including Makefiles suffice, viewing GMSL as adding unnecessary complexity. The discussion also touched upon the discoverability of such a library and whether the chosen license (GPLv3) would limit its adoption. Some expressed concern about the potential for GPLv3 to "infect" projects using the library. Finally, a few users pointed out alternatives like using a higher-level build system or other scripting languages to replace Make entirely.
Werk is a new build tool designed for simplicity and speed, focusing on task automation and project management. Written in Rust, it uses a declarative TOML configuration file to define commands and dependencies, offering a straightforward alternative to more complex tools like Make, Ninja, or just shell scripts. Werk aims for minimal overhead and predictable behavior, featuring parallel execution, a human-readable configuration format, and built-in dependency management to ensure efficient builds. It's intended to be a versatile tool suitable for various tasks from simple build processes to more complex workflows.
HN users generally praised Werk's simplicity and speed, particularly for smaller projects. Several compared it favorably to tools like Taskfile, Just, and Make, highlighting its cleaner syntax and faster execution. Some expressed concerns about its reliance on Deno and potential lack of Windows support, though the creator clarified that Windows compatibility is planned. Others questioned the long-term viability of Deno itself. Despite some skepticism, the overall reception was positive, with many appreciating the "fresh take" on build tools and its potential as a lightweight alternative to more complex systems. A few users also offered suggestions for improvements, including better error handling and more comprehensive documentation.
Summary of Comments ( 23 )
https://news.ycombinator.com/item?id=43494894
Hacker News users discuss XAN's potential, particularly its speed and ease of use for data manipulation tasks compared to traditional tools like
awkandsed. Some express excitement about its CSV parsing capabilities and the ability to leverage Python's power. Concerns are raised regarding the dependency on Python, potential performance bottlenecks, and the limited feature set compared to more established data wrangling tools like Pandas. The discussion also touches upon the project's early stage of development, with some users interested in contributing and others suggesting potential improvements like better documentation and integration with other command-line tools. Several comments compare XAN favorably to other similar tools likejqandmiller, emphasizing its niche in CSV manipulation.The Hacker News post titled "XAN: A Modern CSV-Centric Data Manipulation Toolkit for the Terminal" (https://news.ycombinator.com/item?id=43494894) has generated several comments discussing the merits and potential drawbacks of the XAN tool.
Several commenters express enthusiasm for XAN, praising its seemingly intuitive syntax and potential for simplifying common data manipulation tasks. One commenter highlights the apparent ease of use, suggesting it could be a more accessible alternative to more complex command-line tools like
awkorjq. Another appreciates its CSV-centric approach, noting that CSV is a ubiquitous format and a tool specifically designed for it could be quite useful. The ability to perform calculations and filtering within XAN is also mentioned as a positive feature.However, other comments raise concerns and offer alternative perspectives. Some users question the need for another specialized tool when existing solutions like
awk,jq, Miller,xsv, and Python'spandaslibrary already provide similar functionality. They argue that learning yet another tool might not be worthwhile, especially if the existing tools can accomplish the same tasks with comparable or even greater flexibility. The "not invented here" syndrome is also mentioned in this context.One commenter specifically mentions the power and versatility of
jq, emphasizing its ability to handle various data formats beyond CSV, including JSON, and its extensive feature set. They suggest thatjqmight be a more robust solution for those working with diverse data types.Another point of discussion revolves around the choice of Rust as the implementation language for XAN. While some applaud the use of Rust for its performance characteristics, others question whether its complexity might make contributing to the project more challenging. There's also a brief discussion about the potential overhead associated with Rust and whether it's significant enough to outweigh its benefits in this specific use case.
Finally, some commenters express interest in trying out XAN and exploring its capabilities firsthand, while others remain skeptical but acknowledge its potential. The overall sentiment seems to be one of cautious curiosity, with some users excited about the prospect of a new CSV-centric tool but others remaining unconvinced of its necessity given the existing alternatives.