Recommendarr is an AI-powered media recommendation engine that integrates with Sonarr and Radarr. It leverages large language models (LLMs) to suggest movies and TV shows based on the media already present in your libraries. By analyzing your existing collection, Recommendarr can identify patterns and preferences to offer personalized recommendations, helping you discover new content you're likely to enjoy. These recommendations can then be automatically added to your Radarr/Sonarr wanted lists for seamless integration into your existing media management workflow.
This spreadsheet documents a personal file system designed to mitigate data loss at home. It outlines a tiered backup strategy using various methods and media, including cloud storage (Google Drive, Backblaze), local network drives (NAS), and external hard drives. The system emphasizes redundancy by storing multiple copies of important data in different locations, and incorporates a structured approach to file organization and a regular backup schedule. The author categorizes their data by importance and sensitivity, employing different strategies for each category, reflecting a focus on preserving critical data in the event of various failure scenarios, from accidental deletion to hardware malfunction or even house fire.
Several commenters on Hacker News expressed skepticism about the practicality and necessity of the "Home Loss File System" presented in the linked Google Doc. Some questioned the complexity introduced by the system, suggesting simpler solutions like cloud backups or RAID would be more effective and less prone to user error. Others pointed out potential vulnerabilities related to security and data integrity, especially concerning the proposed encryption method and the reliance on physical media exchange. A few commenters questioned the overall value proposition, arguing that the risk of complete home loss, while real, might be better mitigated through insurance rather than a complex custom file system. The discussion also touched on potential improvements to the system, such as using existing decentralized storage solutions and more robust encryption algorithms.
Summary of Comments ( 2 )
https://news.ycombinator.com/item?id=43230790
Hacker News users generally expressed interest in Recommendarr, praising its potential usefulness and the novelty of AI-driven recommendations for media managed by Sonarr/Radarr. Some users questioned the practical benefit over existing recommendation systems and expressed concerns about the quality and potential biases of AI recommendations. Others discussed the technical implementation, including the use of Trakt.tv and the potential for integrating with other platforms like Plex. A few users offered specific feature requests, such as filtering recommendations based on existing libraries and providing more control over the recommendation process. Several commenters mentioned wanting to try out the project themselves.
The Hacker News post for Recommendarr has generated several comments, offering various perspectives and insights on the project.
Several users expressed interest in the project and its potential. One user appreciated the focus on self-hosted solutions and the use of local compute resources, aligning with their preference for privacy and control over their data. They also saw value in leveraging existing media libraries managed by tools like Sonarr and Radarr. Another commenter expressed excitement about the project, highlighting the potential of LLMs for personalized recommendations and hoping for future integration with other media management tools. A third user praised the innovative approach of using LLMs for recommendations within a self-hosted environment, acknowledging the current limitations of existing recommendation systems.
The discussion also touched upon the technical aspects and potential challenges. One commenter questioned the efficiency of using embeddings for large libraries, suggesting alternative filtering mechanisms based on existing metadata. This sparked a brief exchange about the practical considerations of embedding generation and the potential trade-offs between accuracy and performance. Another user inquired about the underlying models used and the reasoning behind choosing them. The project creator responded, explaining their decision-making process and clarifying the model selection.
Further comments delved into specific features and desired functionalities. One user suggested potential integrations with other platforms like Tautulli and Overseerr, expanding the ecosystem and enhancing the user experience. Another commenter requested the ability to fine-tune recommendations based on user feedback, allowing for a more personalized and evolving recommendation engine. A separate discussion thread emerged regarding the project's licensing and the potential implications for commercial use.
Overall, the comments reflect a positive reception for Recommendarr, recognizing its innovative approach to media recommendations. Users expressed enthusiasm for the self-hosted nature and the potential of LLMs, while also engaging in constructive discussions about technical considerations, desired features, and potential future developments.