Janet's PEG module uses a packrat parsing approach, combining memoization and backtracking to efficiently parse grammars defined in Parsing Expression Grammar (PEG) format. The module translates PEG rules into Janet functions that recursively call each other based on the grammar's structure. Memoization, storing the results of these function calls for specific input positions, prevents redundant computations and significantly speeds up parsing, especially for recursive grammars. When a rule fails to match, backtracking occurs, reverting the input position and trying alternative rules. This process continues until a complete parse is achieved or all possibilities are exhausted. The result is a parse tree representing the matched input according to the provided grammar.
This blog post demystifies Nix derivations by demonstrating how to build a simple C++ "Hello, world" program from scratch, without using Nix's higher-level tools. It meticulously breaks down a derivation file, explaining the purpose of each attribute like builder, args, and env, showing how they control the build process within a sandboxed environment. The post emphasizes understanding the underlying mechanism of derivations, offering a clear path from source code to a built executable. This hands-on approach provides a foundational understanding of how Nix builds software, paving the way for more complex and practical Nix usage.
Hacker News users generally praised the article for its clear explanation of Nix derivations. Several commenters appreciated the "bottom-up" approach, finding it more intuitive than other introductions to Nix. Some pointed out the educational value in manually constructing derivations, even if it's not practical for everyday use, as it helps solidify understanding of Nix's fundamentals. A few users offered minor suggestions for improvement, such as including a section on multi-output derivations and addressing the complexities of stdenv. There was also a brief discussion comparing Nix to other build systems like Bazel.
The author details their initial struggles and eventual success finding freelance clients as a web developer. Leveraging existing connections, they reached out to former colleagues and utilized their alumni network, securing a small project that led to a larger, ongoing contract. Simultaneously, they explored freelance platforms, ultimately finding Upwork ineffective but achieving significant success on a niche platform called Codeable. Focusing on a specific skillset (WordPress) and crafting a strong profile, they quickly gained traction, attracting higher-paying clients and establishing a steady stream of work through consistent proposals and high-quality deliverables. This two-pronged approach of networking and niche platform targeting proved effective in building a sustainable freelance career.
Hacker News users generally found the advice in the linked article to be common sense, with several pointing out that networking and referrals are the most effective methods for freelancers to find clients. Some commenters emphasized the importance of specializing in a niche and building a strong online presence, including a portfolio website. Others shared their own experiences with cold emailing, which had mixed results. One commenter questioned the value of platforms like Upwork and Fiverr, while another suggested focusing on larger companies. The overall sentiment was that the article offered a decent starting point for new freelancers but lacked groundbreaking insights.
This blog post details how to run the DeepSeek R1 671B large language model (LLM) entirely on a ~$2000 server built with an AMD EPYC 7452 CPU, 256GB of RAM, and consumer-grade NVMe SSDs. The author emphasizes affordability and accessibility, demonstrating a setup that avoids expensive server-grade hardware and leverages readily available components. The post provides a comprehensive guide covering hardware selection, OS installation, configuring the necessary software like PyTorch and CUDA, downloading the model weights, and ultimately running inference using the optimized llama.cpp implementation. It highlights specific optimization techniques, including using bitsandbytes for quantization and offloading parts of the model to the CPU RAM to manage its large size. The author successfully achieves a performance of ~2 tokens per second, enabling practical, albeit slower, local interaction with this powerful LLM.
HN commenters were skeptical about the true cost and practicality of running a 671B parameter model on a $2,000 server. Several pointed out that the $2,000 figure only covered the CPUs, excluding crucial components like RAM, SSDs, and GPUs, which would significantly inflate the total price. Others questioned the performance on such a setup, doubting it would be usable for anything beyond trivial tasks due to slow inference speeds. The lack of details on power consumption and cooling requirements was also criticized. Some suggested cloud alternatives might be more cost-effective in the long run, while others expressed interest in smaller, more manageable models. A few commenters shared their own experiences with similar hardware, highlighting the challenges of memory bandwidth and the potential need for specialized hardware like Infiniband for efficient communication between CPUs.
This post details the process of creating a QR Code by hand, using the example of encoding "Hello, world!". It breaks down the procedure into several key steps: data analysis (determining the appropriate encoding mode and error correction level), data encoding (converting the text into a bit stream), error correction coding (adding redundancy for robustness), module placement in the matrix (populating the QR code grid with black and white modules based on the encoded data and fixed patterns), data masking (applying a mask pattern for optimal readability), and format and version information encoding (adding metadata about the QR Code's configuration). The post thoroughly explains each step, including the relevant algorithms and calculations, ultimately demonstrating how the final QR Code image is generated from the initial text string.
HN users largely praised the article for its clarity and detailed breakdown of QR code generation. Several appreciated the focus on the underlying principles and math, rather than just abstracting it away. One commenter pointed out the significance of explaining Reed-Solomon error correction, highlighting its crucial role in QR code functionality. Another user found the interactive demo particularly helpful for visualizing the process. Some discussion arose around alternative encoding schemes and their potential benefits, along with mention of a similar article focusing on PDF417 barcodes. A few commenters shared personal experiences using the article's information for practical projects.
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https://news.ycombinator.com/item?id=43649781
Hacker News users discuss the elegance and efficiency of Janet's PEG implementation, particularly praising its use of packrat parsing for memoization to avoid exponential time complexity. Some compare it favorably to other parsing techniques and libraries like recursive descent parsers and the popular Python library
parsimonious, noting Janet's approach offers a good balance of performance and understandability. Several commenters express interest in exploring Janet further, intrigued by its features and the clear explanation provided in the linked article. A brief discussion also touches on error reporting in PEG parsers and the potential for improvements in Janet's implementation.The Hacker News post "How Janet's PEG module works" sparked a discussion thread with several insightful comments focusing primarily on parsing techniques, the Janet programming language, and comparisons to other parsing tools.
One commenter highlighted the elegance of parsing expression grammars (PEGs) and their ability to express complex grammars concisely, contrasting them favorably with regular expressions for certain parsing tasks. They emphasized the power and flexibility of PEGs, particularly when dealing with structured data. They also expressed appreciation for the author's clear explanation of Janet's PEG implementation.
Another commenter discussed the unique aspects of Janet as a programming language, particularly its embedded nature. They pointed out how this feature makes it well-suited for tasks where integrating a scripting language is beneficial. They also mentioned Janet's use of immutable data structures as a significant advantage.
A subsequent comment delved into the implementation details of Janet's PEG module, touching upon memory management and performance considerations. This comment sparked a brief exchange about the trade-offs between different parsing approaches and their suitability for various applications.
Further down the thread, a commenter compared Janet's PEG implementation to other parsing tools and libraries, mentioning tools like Parsec and LPEG (Lua Parsing Expression Grammars). They discussed the strengths and weaknesses of each, offering insights into their suitability for different parsing scenarios. This comparison provided a broader context for understanding Janet's approach.
Several other comments expressed general appreciation for the article and the clarity of its explanation. Some users mentioned their interest in exploring Janet further based on the information presented.
The overall sentiment in the comments was positive, with many users praising the article's educational value and the insights it provided into Janet's PEG implementation. The discussion offered a valuable perspective on parsing techniques, language design, and the trade-offs involved in different parsing approaches.