TL;DW (Too Long; Didn't Watch) is a website that condenses Distill.pub articles, primarily those focused on machine learning research, into shorter, more digestible formats. It utilizes AI-powered summarization and key information extraction to present the core concepts, visualizations, and takeaways of each article without requiring viewers to watch the often lengthy accompanying YouTube videos. The site aims to make complex research more accessible to a wider audience by providing concise summaries, interactive elements, and links back to the original content for those who wish to delve deeper.
The web application hosted at tldw.tube addresses the prevalent issue of lengthy, often unnecessarily protracted, Distill publications and accompanying YouTube videos. These resources, while frequently containing valuable information related to machine learning, often embed that information within extensive, sometimes tangential, narratives or demonstrations. This verbose presentation style, though potentially beneficial for a deep dive into the subject matter, poses a significant barrier for individuals seeking a concise understanding of the core concepts or desiring a rapid overview before committing to a more substantial time investment. Tldw.tube aims to alleviate this friction by programmatically extracting and presenting the most salient points from these Distill articles and videos.
The website functions by allowing users to input the URL of a specific Distill article or YouTube video. Subsequently, the service employs an automated process to analyze the provided content, identifying and isolating the key takeaways. These extracted insights are then presented to the user in a readily digestible format, ostensibly saving them the considerable time and effort required to sift through the entirety of the original material. This distillation process aims to preserve the essence of the information while discarding the potentially superfluous elaborations, thereby facilitating efficient knowledge acquisition for those seeking a more focused and accelerated learning experience. The objective is to empower users to quickly ascertain the relevance and value of a Distill resource before opting to engage with the complete, often time-consuming, presentation. This precursory overview can significantly enhance productivity by enabling individuals to prioritize content consumption based on a concise summary of its core contributions.
Summary of Comments ( 115 )
https://news.ycombinator.com/item?id=43021044
HN commenters generally praised TL;DW, finding its summaries accurate and useful, especially for longer technical videos. Some appreciated the inclusion of timestamps to easily jump to specific sections within the original video. Several users suggested improvements, including support for more channels, the ability to correct inaccuracies, and adding community features like voting or commenting on summaries. Some expressed concerns about the potential for copyright issues and the impact on creators' revenue if viewers only watch the summaries. A few commenters pointed out existing similar tools and questioned the long-term viability of the project.
The Hacker News post discussing TL;DW, a tool for summarizing YouTube videos, generated a variety of comments, mostly positive and intrigued by the concept. Several users expressed excitement about the potential time-saving benefits, particularly for lengthy technical content, lectures, and conference talks.
One compelling comment highlighted the usefulness for quickly assessing whether a video is worth watching in its entirety. This resonated with other users who found themselves frequently skipping through videos to find the core message.
Some commenters praised the use of OpenAI's Whisper model for transcription and the overall clean interface of the website. The developer's active participation in the discussion thread, answering questions and addressing feedback, was also well-received. They explained design choices, like focusing on factual videos rather than narrative ones, and acknowledged limitations, like the current inability to handle videos with poor audio quality.
A few commenters expressed concerns about potential misuse, such as for plagiarism or bypassing content creators' intended narrative. Others pointed out the limitations of relying solely on AI summaries, emphasizing the importance of critical thinking and acknowledging that nuances and context can be lost.
Several users suggested potential improvements, including features like chapter markers linked to specific summary points, the ability to choose specific sections of a video to summarize, support for more languages, and integration with podcast platforms.
There was a brief discussion about alternative summarization tools and approaches, with some users mentioning existing browser extensions and note-taking apps.
Overall, the comments reflect a general enthusiasm for TL;DW's potential to improve information consumption efficiency while also acknowledging the inherent limitations of AI-powered summarization and the importance of responsible use. The developer's responsiveness and openness to feedback further contributed to a positive reception within the Hacker News community.