拾梦
旧梦可拾，此刻成录
What it does
拾梦是一款面向古装IP爱好者的AI创意内容平台。用户只需上传一张个人照片，即可一键换脸至平台精选的汉服风格模板中。 平台收录多套高质量汉服场景模板，无论是烟笼寒水的江南仕女、金戈铁马的边塞将领，还是深宫流朱的宫廷贵妃，皆可在几秒内成为画中人，将自己的面孔嵌入心仪的古风世界。满足用户对不同IP的情感投射需求。 除换脸功能外，拾梦还内置AI同人文生成模块。用户可选择特定古装角色与背景设定，由AI生成专属的同人故事短篇——或缘起三生，或乱世相守，每一段文字都以用户替入主角视角为核心叙事逻辑，让"穿越"不止停留在一张图片，更延伸为一段可读、可感的个人故事。 拾梦相信，每一个沉迷古装剧的人心中，都藏着一个未曾说出口的故事。我们只是提供了一扇门，让那个故事得以被看见、被记录、被珍藏。 旧梦可拾，此刻成录。你的故事，由你主演。
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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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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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With social media and now AI, its important to keep the indie web alive. There are many people who write frequently. Blogosphere tries to highlight them by fetching the recent posts from personal blogs across many categories. There are two versions: Minimal (HN-inspired, fast, static): https://text.blogosphere.app/ Non-minimal: https://blogosphere.app/ If you don't find your blog (or your favorite ones), please add them. I will review and approve it.
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