Researchers have introduced "Discord Unveiled," a massive dataset comprising nearly 20 billion messages from over 6.7 million public Discord servers collected between 2015 and 2024. This dataset offers a unique lens into online communication, capturing a wide range of topics, communities, and evolving language use over nearly a decade. It includes message text, metadata like timestamps and user IDs, and structural information about servers and channels. The researchers provide thorough details about data collection, filtering, and anonymization processes, and highlight the dataset's potential for research in various fields like natural language processing, social computing, and online community analysis. They also release code and tools to facilitate access and analysis, while emphasizing the importance of ethical considerations for researchers using the data.
"The NSA Selector" details a purported algorithm and scoring system used by the NSA to identify individuals for targeted surveillance based on their communication metadata. It describes a hierarchical structure where selectors, essentially search queries on metadata like phone numbers, email addresses, and IP addresses, are combined with modifiers to narrow down targets. The system assigns a score based on various factors, including the target's proximity to known persons of interest and their communication patterns. This score then determines the level of surveillance applied. The post claims this information was gleaned from leaked Snowden documents, although direct sourcing is absent. It provides a technical breakdown of how such a system could function, aiming to illustrate the potential scope and mechanics of mass surveillance based on metadata.
HN users discuss the practicality and implications of the "NSA selector" tool described in the linked GitHub repository. Some express skepticism about its real-world effectiveness, pointing out limitations in matching capabilities and the potential for false positives. Others highlight the ethical concerns surrounding such tools, regardless of their efficacy, and the potential for misuse. Several commenters delve into the technical details of the selector's implementation, discussing regular expressions, character encoding, and performance considerations. The legality of using such a tool is also debated, with differing opinions on whether simply possessing or running the code constitutes a crime. Finally, some users question the authenticity and provenance of the tool, suggesting it might be a hoax or a misinterpretation of actual NSA practices.
Extracting text from PDFs is surprisingly complex due to the format's focus on visual representation rather than logical structure. PDFs essentially describe how a page should look, specifying the precise placement of glyphs (often without even identifying them as characters) rather than encoding the underlying text itself. This can lead to difficulties in reconstructing the original text flow, especially with complex layouts involving columns, tables, and figures. Further complications arise from embedded fonts, ligatures, and the potential for text to be represented as paths or images, making accurate and reliable text extraction a significant technical challenge.
HN users discuss the complexities of accurate PDF-to-text conversion, highlighting issues stemming from PDF's original design as a visual format, not a semantic one. Several commenters point out the challenges posed by embedded fonts, tables, and the variety of PDF generation methods. Some suggest OCR as a necessary, albeit imperfect, solution for visually-oriented PDFs, while others mention tools like pdftotext and Apache PDFBox. The discussion also touches on the limitations of existing libraries and the ongoing need for robust solutions, particularly for complex or poorly generated PDFs. One compelling comment chain dives into the history of PDF and PostScript, explaining how the format's focus on visual fidelity complicates text extraction. Another insightful thread explores the different approaches taken by various PDF-to-text tools, comparing their strengths and weaknesses.
Scraperr is a self-hosted web scraping application built with Python and Playwright. It allows users to easily create and schedule web scraping tasks through a user-friendly web interface. Scraped data can be exported in various formats, including CSV, JSON, and Excel. Scraperr offers features like proxy support, pagination handling, and data cleaning options to enhance scraping efficiency and reliability. It's designed to be simple to set up and use, empowering users to automate data extraction from websites without extensive coding knowledge.
HN users generally praised Scraperr's simplicity and ease of use, particularly for straightforward scraping tasks. Several commenters appreciated its user-friendly interface and the ability to schedule scraping jobs. Some highlighted the potential benefits for tasks like monitoring price changes or tracking website updates. However, concerns were raised about its scalability and ability to handle complex websites with anti-scraping measures. The reliance on Chromium was also mentioned, with some suggesting potential resource overhead. Others questioned its robustness compared to established web scraping libraries and frameworks. The developer responded to some comments, clarifying features and acknowledging limitations, indicating active development and openness to community feedback.
While the popular belief that smartphones constantly listen to conversations to target ads is untrue, the reality is more nuanced and arguably more disturbing. The article explains that these devices collect vast amounts of data about users through various means like location tracking, browsing history, app usage, and social media activity. This data, combined with sophisticated algorithms and data brokers, creates incredibly detailed profiles that allow advertisers to predict user behavior and target them with unsettling accuracy. This constant data collection, aggregation, and analysis creates a pervasive surveillance system that raises serious privacy concerns, even without directly listening to conversations. The article concludes that addressing this complex issue requires a multi-faceted approach, including stricter regulations on data collection and increased user awareness about how their data is being used.
Hacker News users generally agree that smartphones aren't directly listening to conversations, but the implication of the title—that data collection is still deeply problematic—resonates. Several comments highlight the vast amount of data companies already possess, arguing targeted advertising works effectively without needing direct audio access. Some point out the chilling effect of believing phones are listening, altering behavior and limiting free speech. Others discuss how background data collection, location tracking, and browsing history are sufficient to infer interests and serve relevant ads, making direct listening unnecessary. A few users mention the potential for ultrasonic cross-device tracking as a more insidious form of eavesdropping. The core concern isn't microphones, but the extensive, opaque, and often exploitative data ecosystem already in place.
An analysis of chord progressions in 680,000 songs reveals common patterns and some surprising trends. The most frequent progressions are simple, diatonic, and often found in popular music across genres. While major chords and I-IV-V-I progressions dominate, the data also highlights the prevalence of the vi chord and less common progressions like the "Axis" progression. The study categorized progressions by "families," revealing how variations on a core progression create distinct musical styles. Interestingly, chord progressions appear to be getting simpler over time, possibly influenced by changing musical tastes and production techniques. Ultimately, while common progressions are prevalent, there's still significant diversity in how artists utilize harmony.
HN users generally praised the analysis and methodology of the original article, particularly its focus on transitions between chords rather than individual chord frequency. Some questioned the dataset's limitations, wondering about the potential biases introduced by including only songs with available chord data, and the skewed representation towards Western music. The discussion also explored the subjectivity of music theory, with commenters highlighting the difficulty of definitively labeling certain chord functions (like tonic or dominant) and the potential for cultural variations in musical perception. Several commenters shared their own musical insights, referencing related analyses and discussing the interplay of theory and practice in composition. One compelling comment thread delved into the limitations of Markov chain analysis for capturing long-range musical structure and the potential of higher-order Markov models or recurrent neural networks for more nuanced understanding.
The post "Everyone knows all the apps on your phone" argues that the extensive data collection practices of mobile advertising networks effectively reveal which apps individuals use, even without explicit permission. Through deterministic and probabilistic methods linking device IDs, IP addresses, and other signals, these networks can create detailed profiles of app usage across devices. This information is then packaged and sold to advertisers, data brokers, and even governments, allowing them to infer sensitive information about users, from their political affiliations and health concerns to their financial status and personal relationships. The post emphasizes the illusion of privacy in the mobile ecosystem, suggesting that the current opt-out model is inadequate and calls for a more robust approach to data protection.
Hacker News users discussed the privacy implications of app usage data being readily available to mobile carriers and how this data can be used for targeted advertising and even more nefarious purposes. Some commenters highlighted the ease with which this data can be accessed, not just by corporations but also by individuals with basic technical skills. The discussion also touched upon the ineffectiveness of current privacy regulations and the lack of real control users have over their data. A few users pointed out the potential for this data to reveal sensitive information like health conditions or financial status based on app usage patterns. Several commenters expressed a sense of resignation and apathy, suggesting the fight for data privacy is already lost, while others advocated for stronger regulations and user control over data sharing.
Theophile Cantelo has created Foudinge, a knowledge graph connecting restaurants and chefs. Leveraging Large Language Models (LLMs), Foudinge extracts information from various online sources like blogs, guides, and social media to establish relationships between culinary professionals and the establishments they've worked at or own. This allows for complex queries, such as finding all restaurants where a specific chef has worked, discovering connections between different chefs through shared work experiences, and exploring the culinary lineage within the restaurant industry. Currently focused on French gastronomy, the project aims to expand its scope geographically and improve data accuracy through community contributions and additional data sources.
Hacker News users generally expressed skepticism about the value proposition of the presented knowledge graph of restaurants and chefs. Several commenters questioned the accuracy and completeness of the data, especially given its reliance on LLMs. Some doubted the usefulness of connecting chefs to restaurants without further context, like the time period they worked there. Others pointed out the existing prevalence of this information on platforms like Wikipedia and guide sites, questioning the need for a new platform. The lack of a clear use case beyond basic information retrieval was a recurring theme, with some suggesting potential applications like tracking career progression or identifying emerging culinary trends, but ultimately finding the current implementation insufficient. A few commenters appreciated the technical effort, but overall the reception was lukewarm, focused on the need for demonstrable practical application and improved data quality.
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.
A US judge ruled in favor of Thomson Reuters, establishing a significant precedent in AI copyright law. The ruling affirmed that Westlaw, Reuters' legal research platform, doesn't infringe copyright by using data from rival legal databases like Casetext to train its generative AI models. The judge found the copied material constituted fair use because the AI uses the data differently than the original databases, transforming the information into new formats and features. This decision indicates that using copyrighted data for AI training might be permissible if the resulting AI product offers a distinct and transformative function compared to the original source material.
HN commenters generally agree that Westlaw's terms of service likely prohibit scraping, regardless of copyright implications. Several point out that training data is generally considered fair use, and question whether the judge's decision will hold up on appeal. Some suggest the ruling might create a chilling effect on open-source LLMs, while others argue that large companies will simply absorb the licensing costs. A few commenters see this as a positive outcome, forcing AI companies to pay for the data they use. The discussion also touches upon the potential for increased competition and innovation if smaller players can access data more affordably than licensing Westlaw's content.
Cosine similarity, while popular for comparing vectors, can be misleading when vector magnitudes carry significant meaning. The blog post demonstrates how cosine similarity focuses solely on the angle between vectors, ignoring their lengths. This can lead to counterintuitive results, particularly in scenarios like recommendation systems where a small, highly relevant vector might be ranked lower than a large, less relevant one simply due to magnitude differences. The author advocates for considering alternatives like dot product or Euclidean distance, especially when vector magnitude represents important information like purchase count or user engagement. Ultimately, the choice of similarity metric should depend on the specific application and the meaning encoded within the vector data.
Hacker News users generally agreed with the article's premise, cautioning against blindly applying cosine similarity. Several commenters pointed out that the effectiveness of cosine similarity depends heavily on the specific use case and data distribution. Some highlighted the importance of normalization and feature scaling, noting that cosine similarity is sensitive to these factors. Others offered alternative methods, such as Euclidean distance or Manhattan distance, suggesting they might be more appropriate in certain situations. One compelling comment underscored the importance of understanding the underlying data and problem before choosing a similarity metric, emphasizing that no single metric is universally superior. Another emphasized how important preprocessing is, highlighting TF-IDF and BM25 as helpful techniques for text analysis before using cosine similarity. A few users provided concrete examples where cosine similarity produced misleading results, further reinforcing the author's warning.
IRCDriven is a new search engine specifically designed for indexing and searching IRC (Internet Relay Chat) logs. It aims to make exploring and researching public IRC conversations easier by offering full-text search capabilities, advanced filtering options (like by channel, nick, or date), and a user-friendly interface. The project is actively seeking feedback and contributions from the IRC community to improve its features and coverage.
Commenters on Hacker News largely praised IRC Driven for its clean interface and fast search, finding it a useful tool for rediscovering old conversations and information. Some expressed a nostalgic appreciation for IRC and the value of archiving its content. A few suggested potential improvements, such as adding support for more networks, allowing filtering by nick, and offering date range restrictions in search. One commenter noted the difficulty in indexing IRC due to its decentralized and ephemeral nature, commending the creator for tackling the challenge. Others discussed the historical significance of IRC and the potential for such archives to serve as valuable research resources.
Summary of Comments ( 35 )
https://news.ycombinator.com/item?id=44052041
Hacker News users discussed the potential privacy implications of the Discord Unveiled dataset, expressing concern about the inclusion of usernames and the potential for deanonymization. Some questioned the ethics and legality of collecting and distributing such data, even from public channels. Others highlighted the dataset's value for researching online communities, misinformation, and language models, while also acknowledging the need for careful consideration of privacy risks. The feasibility and effectiveness of anonymization techniques were also debated, with some arguing that true anonymization is practically impossible given the richness of the data. Several users mentioned the chilling effect such datasets could have on online discourse, potentially leading to self-censorship. There was also discussion of the technical challenges of working with such a large dataset.
The Hacker News post titled "Discord Unveiled: A Comprehensive Dataset of Public Communication (2015-2024)" links to an arXiv preprint describing a large dataset of Discord messages collected from public servers. The comments section features a lively discussion revolving around the ethical implications, research potential, and technical aspects of the dataset.
Several commenters raise concerns about privacy. One points out the potential for deanonymization, even with usernames removed, due to the unique communication patterns and specific interests revealed in conversations. Another highlights the possibility of reconstructing social graphs from the data, posing risks to individuals' privacy and security. The lack of explicit consent from the users whose data is included is a recurring theme, with some arguing that scraping public data doesn't necessarily equate to ethical data collection, especially given the sensitive nature of some conversations.
The discussion also explores the research potential of the dataset. Some commenters suggest applications in studying online community dynamics, the spread of misinformation, and the evolution of language. Others express skepticism about the dataset's representativeness, noting that public Discord servers might not accurately reflect private communication or other online platforms.
Technical aspects of the dataset are also discussed. One commenter questions the claim of "9 years" of data, given Discord's launch date, suspecting it might include earlier data from platforms Discord absorbed. Another notes the challenge of handling different media formats and the complexity of natural language processing required for analyzing the text data. The dataset's size and potential computational demands for analysis are also mentioned.
Several commenters express general unease about the collection and potential uses of such a massive dataset of personal communication, even if publicly available, echoing broader concerns about data privacy in the digital age. The legality of scraping public data is also touched upon, with differing opinions on whether terms of service violations constitute legal issues.
A compelling thread of conversation arises around the researchers' choice to collect data without notifying or seeking consent from the users. This sparked debate about the ethics of "passive" data collection versus active participation, with some arguing that researchers have a responsibility to engage with the communities they study.
Another interesting point raised is the potential for bias in the dataset. Commenters speculate that the dataset might overrepresent certain communities or demographics due to the nature of public Discord servers, potentially skewing research findings.