EnBref
Summarize, chat & learn from any YouTube video.
What it does
YouTube is the biggest knowledge source on earth but watching a 1-hour video for just 3 key ideas is broken. EnBref fixes that with AI-powered summaries featuring clickable timestamps, highlighted key terms, and a built-in chat to ask the video anything. You can customize everything :focus, format, language, and length, all directly inside YouTube, just one click away. Watch less. Learn 10x more.
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YouTube Summarizer with ChatGPT2022 · ▲328Save your time before spending hours - learn better with AI
- EGEnfinBref- {GPT3-5|Mistral-7B} YouTube summaries, segment by segment2023 · enfinbref.io · ▲26
A neat (in my opinion) little side-project I've been working on, both to get somewhat basic React skills going, and to work with LLMs on even more cool projects to build. It should work for most major languages and output English summaries (or French summaries, if using the main https://enfinbref.io page instead of the /en/ subpage), no matter the input language. Currently planning on expanding in various directions, including some nice new features like choosing a summary type, better video type identification and LLM routing, and bullet points exec summaries. Pretty…
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
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Launched alongside, May 2026
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Parallel agents, diff reviewer, and multi-model comparisons
Dev tools · May 2026 · kilo.ai
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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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