GGInsights offers free monthly dumps of scraped Steam data, including game details, pricing, reviews, and tags. This data is available in various formats like CSV, JSON, and Parquet, designed for easy analysis and use in personal projects, market research, or academic studies. The project aims to provide accessible and up-to-date Steam information to a broad audience.
The dataset linked lists every active .gov domain name, providing a comprehensive view of US federal, state, local, and tribal government online presence. Each entry includes the domain name itself, the organization's name, city, state, and relevant contact information including email and phone number. This data offers a valuable resource for researchers, journalists, and the public seeking to understand and interact with government entities online.
Hacker News users discussed the potential usefulness and limitations of the linked .gov domain list. Some highlighted its value for security research, identifying potential phishing targets, and understanding government agency organization. Others pointed out the incompleteness of the list, noting the absence of many subdomains and the inclusion of defunct domains. The discussion also touched on the challenges of maintaining such a list, with suggestions for improving its accuracy and completeness through crowdsourcing or automated updates. Some users expressed interest in using the data for various projects, including DNS analysis and website monitoring. A few comments focused on the technical aspects of the data format and its potential integration with other tools.
Researchers introduced SWE-Lancer, a new benchmark designed to evaluate large language models (LLMs) on realistic software engineering tasks. Sourced from Upwork job postings, the benchmark comprises 417 diverse tasks covering areas like web development, mobile development, data science, and DevOps. SWE-Lancer focuses on practical skills by requiring LLMs to generate executable code, write clear documentation, and address client requests. It moves beyond simple code generation by incorporating problem descriptions, client communications, and desired outcomes to assess an LLM's ability to understand context, extract requirements, and deliver complete solutions. This benchmark provides a more comprehensive and real-world evaluation of LLM capabilities in software engineering than existing benchmarks.
HN commenters discuss the limitations of the SWE-Lancer benchmark, particularly its focus on smaller, self-contained tasks representative of Upwork gigs rather than larger, more complex projects typical of in-house software engineering roles. Several point out the prevalence of "specification gaming" within the dataset, where successful solutions exploit loopholes or ambiguities in the prompt rather than demonstrating true problem-solving skills. The reliance on GPT-4 for evaluation is also questioned, with concerns raised about its ability to accurately assess code quality and potential biases inherited from its training data. Some commenters also suggest the benchmark's usefulness is limited by its narrow scope, and call for more comprehensive benchmarks reflecting the broader range of skills required in professional software development. A few highlight the difficulty in evaluating "soft" skills like communication and collaboration, essential aspects of real-world software engineering often absent in freelance tasks.
Summary of Comments ( 36 )
https://news.ycombinator.com/item?id=43158425
HN users generally praised the project for its transparency, usefulness, and the public accessibility of the data. Several commenters suggested potential applications for the data, including market analysis, game recommendation systems, and tracking the rise and fall of game popularity. Some offered constructive criticism, suggesting the inclusion of additional data points like regional pricing or historical player counts. One commenter pointed out a minor discrepancy in the reported total number of games. A few users expressed interest in using the data for personal projects. The overall sentiment was positive, with many thanking the creator for sharing their work.
The Hacker News post "Show HN: I scrape Steam data every month and it's yours to download for free" generated a fair number of comments, mostly focusing on the legality and ethics of scraping, the potential usefulness of the data, and suggestions for the project.
Several commenters raised concerns about the legality of scraping Steam data, particularly given Steam's terms of service. They pointed out the potential for Steam to take action against the scraping activity or even against users of the data. One commenter suggested checking the robots.txt and respecting rate limits to mitigate some of these risks. Another pointed out the potential legal grey area, noting that court cases regarding scraping have had mixed outcomes.
The usefulness of the provided data was also a topic of discussion. Some users questioned the value of monthly snapshots, suggesting that more frequent updates would be more beneficial for certain types of analysis, such as tracking game popularity or pricing changes. Others suggested potential use cases, such as identifying trending games or analyzing the effectiveness of marketing strategies. One commenter even proposed integrating the data with existing game discovery tools.
Many commenters offered constructive feedback and suggestions for the project. These included:
A few comments expressed appreciation for the project and the free availability of the data, while others questioned the motivation behind the project and the long-term sustainability of providing the data for free. Overall, the discussion highlighted the complex issues surrounding web scraping, the diverse potential applications of readily available data, and the importance of community feedback in shaping data-driven projects.