The moderation team acknowledges the recent increase in spam and apologizes for any inconvenience caused to the community. They recognize that the current situation has led to a decline in the quality of the user experience and are committed to addressing the issue promptly. To tackle the problem effectively, the team plans to expand by bringing in additional moderators. This will provide the necessary manpower to manage the subreddit more efficiently and ensure that spam is minimized. The team is actively working on this by notifying potential candidates who can help restore the community's standards. By taking these steps, the moderation team aims to improve the overall experience for users and maintain the subreddit as a valuable and engaging platform. This matters because a well-moderated community fosters better interactions and ensures that users can enjoy relevant and meaningful content without the distraction of spam.
![[R] DeepSeek-R1’s paper was updated 2 days ago, expanding from 22 pages to 86 pages and adding a substantial amount of detail.](https://www.tweakedgeek.com/wp-content/uploads/2026/01/featured-article-9318-300x171.png)
DeepSeek-R1's paper has been significantly expanded, providing a comprehensive guide on selecting machine learning models effectively. Key strategies include using train-validation-test splits, cross-validation, and bootstrap validation to ensure robust model evaluation. It's crucial to avoid test set leakage and to choose models based on appropriate metrics while being mindful of potential data leakage. Additionally, understanding the specific use cases for different models can guide better selection, and engaging with online communities can offer personalized advice and support. This matters because selecting the right model is critical for achieving accurate and reliable results in machine learning applications.