
Watch Later List
Save & Organise Youtube Videos with AI Transcripts
What it does
Watch Later List transforms YouTube from a passive watching experience into a structured knowledge system. Unlike traditional “watch later” tools that simply bookmark videos, Watch Later List helps users actually extract value from content through AI-powered summaries, full transcripts, searchable libraries, playlists, and note-taking. With one click, users can save any YouTube video and instantly turn hours of podcasts, lectures, interviews, or tutorials into organised, reusable knowledge.
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- IBI Built a Tool to Break Free from YouTube's Addictive Algorithm2024 · watchlist.so · ▲19
I built Watchlist to solve a problem that's been nagging at me (and I suspect many others): YouTube's addictive nature and its impact on productivity. The Problem: YouTube is an incredible source of knowledge, but its homepage is an unending scroll of rabbit holes. The algorithm is designed to maximize watch time, often at the expense of our intentions and productivity. I found myself wasting hours, jumping from video to video, and then blaming myself for the lack of self-control. The Solution: Watchlist! Watchlist is essentially YouTube playlists on steroids. Here's how it works: 1. Create…


- HIHow I made AI watch YouTube for me!2023 · medium.com · ▲17
Hey friends! We built an AI app to make it easy for you to consume long YouTube videos This app converts videos to short blog posts with a Python app powered by Langchain and EvaDB: https://github.com/yulaicui/youtube_video_qa Here's an 3 minute blog post based on an 8-hour video from MindsDB AI Developer Convention 2023: https://medium.com/evadb-blog/ai-generated-summary-of-mindsd...
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Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
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