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CMI Community - Chiang Mai 社区经营,创作,活动组织,AI探索

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Joined June 2015
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用 GrokBot 已经一周多了,几乎每天都在做一件事:就是初始化一个真正能够伴随成长的外置大脑。 让他能够了解我的框架,脉络,以及我无法量化的所思所考。 感谢一直都在做反思、总结了那么多年的自己。一直记录没有停止。
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这是啥意思? 可以帮我接电话了 ? 这个可以有! 泰语真不会
Grok Bot can now make phone calls.
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我这号,经历的时间有点久啊, 翻了下,我怎么不记得以前还发过那么多推。
第一次使用twitter
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我用 Grokbot 用的最多的是直接语音对话的功能,双手离开键盘,眼睛离开屏幕。 放点背景音乐,耳机打开通透模式,然后到户外自然的世界里走一走,让灵感自然从脑子中流淌出来。 但是我以前也是看着 AI 不知道干点啥好的,最近做编剧的工作,突然需要和人打磨创意,但是找不到对象, Grok 正好接住了。 一般人会有很多想聊的么?
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咱 CMI Studio 的社区空间环境, CM 气候无敌。 这边搞点 AI 活动非常适合,但是暂时不想弄纯小白的教学了,因为发现说了很久 AI 不用学,发现不想用 AI 的永远页学不会,有需求的不用教就会。 所以还是想和真的在 AI这块有想法,有创意,最关键是真的日常在花精力研究和用的人一起交流。 CMI 社区的朋友,看到也关注我一下,下次我就在这里更新更新日常的东西。
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0/5年初 openclaw 的时候设想过,每个人都需要一个 AI 外设,是作为个人的社交物理 Agent 。 但是我觉得现在 能把 Grok 塞到小智里面去,这将是一记绝杀! 给孩子从小配置一个外脑,赢在起跑线上。 下面说说通过 Grokbot 这几天疯狂的语音对话使用下来,我的深度感受 —— 这个世界又要变了!
Replying to @OpenAI
Two OpenAI team members worked with GPT-6 Astra to design a prototype device for talking to Codex in under a week, as a side project. Astra designed the enclosure, circuit board, interface, and software. Getting it manufactured still required several rounds of review and iteration.
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4. /5害怕死亡是害怕不被记住来到过这个世界,是害怕这一生还没有证明自己对世界的意义。 但是如果我们在世间的体验都可以托付给一个永远伴随我们的 外置大脑,自动融入这个世界的能够理解人类的终极智能,或许我们再也不用那么焦虑得像是在意义缺失中,焦虑、溺水、恐惧死亡将会失去一切与世界的联系。
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5./5 我最近在和我的 Grokbot 一边开车在路上,一边讨论我们要创作的 AIGC 短片的剧本。 甚至在过程中还不小心学了一些英语(Grokbot 的混合语言表达能力很好)。同时在完全不看屏幕的情况下,完成了一系列的研究、开发、头脑分包、决策、设计 、构建知识库、激发了无数新 Idea。沉迷到自己都忘了在和谁说话。
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最近做 AI 视频,我没用一个项目配一个 bot,搭了个小剧组。有人拆活派活,各工种各管一摊,有专门挑错的,还有一群假观众试映吐槽,我只管拍板。 看我的演员们自己在小群里面拼演技,自己往下续写真的是笑死了。 具体怎么分工、用下来有哪些体会,写在这篇里。
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好的,我回来了
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The 17-Million Token "Hi": Inside China's AI Lobster Fever 高质量大制作, 能感受到这里面国内的狂热AI 氛围 youtu.be/iHo8Jq0dREQ?si=T-Wx…
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A Simple Framework for Actually Listening to Music as a Producer youtu.be/iTBHW1zPr-8?si=uw2M… 来自 @YouTube
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LLM Knowledge Bases Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So: Data ingest: I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them. IDE: I use Obsidian as the IDE "frontend" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides). Q&A: Where things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale. Output: Instead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up "filing" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always "add up" in the knowledge base. Linting: I've run some LLM "health checks" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into. Extra tools: I find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries. Further explorations: As the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM "know" the data in its weights instead of just context windows. TLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.
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Link retweeted
Introducing the new SOLO: now on Desktop and Web. You define the task, review the results, and SOLO handles the rest. SOLO is in beta, with limited-time, free access via invite codes.
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预测一下 现在人类的自然语言对 AI 来说很快也会成为低效组件,和高出错组件。AI 需要一套更加易于彼此之间通讯,低情绪、幻觉的协作和探索用的语言。 并且这种语言会由AI自己设计,人类想要参与只能让 AI 以某种形式翻译出来,但是无法传递出其100% 的意思。 因为 AI用的语言可以更加精确,复杂度更高
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