作者提出一种高效学习方法:让Claude等大语言模型生成HTML文档来辅助深入学习与研究。相比纯文本或播客,HTML能嵌入图表和交互元素,支持主动探索。通过迭代提问和修改,文档随理解深入而演化,最终累积成个人知识库。文章引用观点指出,人机交互的输入输出融合尚有巨大发展空间,当前阶段可探索让LLM以HTML格式输出。信息呈现方式正从纯文本、Markdown向HTML及未来的交互式神经视频演进,以充分利用人类强大的视觉处理能力。作者认为,音频可能是人类偏好的AI输入方式,但视觉内容则是更高效的AI输出形式。
When I want to learn something new, or dig into a paper, I have Claude generate a HTML for me.
This works surprisingly well (especially in Claude, since Codex generated HTML is still kinda ugly...)
It's better than Google NotebookLM. Podcasts are nice, but reading is much higher-bandwidth than listening to a podcast.
HTML has a key advantage: they can show things.
Diagrams. Charts. Interactive bits. You can actually poke at the idea, not just passively consume it.
Then I iterate. Ask questions. Refine sections. Add missing pieces. The HTML evolves with my understanding.
Over time, this compounds into a personal knowledge base.
"The input/output mind meld between humans and AIs is ongoing and there is a lot of work to do and significant progress to be made, way before jumping all the way into neuralink-esque BCIs and all that." 💯
This works really well btw, at the end of your query ask your LLM to "structure your response as HTML", then view the generated file in your browser. I've also had some success asking the LLM to present its output as slideshows, etc. More generally, imo audio is the human-preferred input to AIs but vision (images/animations/video) is the preferred output from them. Around a ~third of our brains are a massively parallel processor dedicated to vision, it is the 10-lane superhighway of information into brain. As AI improves, I think we'll see a progression that takes advantage: 1) raw text (hard/effortful to read) 2) markdown (bold, italic, headings, tables, a bit easier on the eyes) <-- current default 3) HTML (still procedural with underlying code, but a lot more flexibility on the graphics, layout, even interactivity) <-- early but forming new good default ...4,5,6,... n) interactive neural videos/simulations Imo the extrapolation (though the technology doesn't exist just yet) ends in some kind of interactive videos generated directly by a diffusion neural net. Many open questions as to how exact/procedural "Software 1.0" artifacts (e.g. interactive simulations) may be woven together with neural artifacts (diffusion grids), but generally something in the direction of the recently viral https://x.com/zan2434/status/2046982383430496444 There are also improvements necessary and pending at the input. Audio nor text nor video alone are not enough, e.g. I feel a need to point/gesture to things on the screen, similar to all the things you would do with a person physically next to you and your computer screen. TLDR The input/output mind meld between humans and AIs is ongoing and there is a lot of work to do and significant progress to be made, way before jumping all the way into neuralink-esque BCIs and all that. For what's worth exploring at the current stage, hot tip try ask for HTML.在 X 查看被引用的帖子
来源:Yuchen Jin · x.com