crypto大声密谋

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.

免费用Claude Opus 4.6？阿里海外版企业Agent「Accio Work」悄悄上线了 阿里国际（Alibaba International）3月23日发布的企业级AI Agent平台，定位是给中小企业提供即插即用的AI团队，零代码、零配置，直接部署专业Agent处理复杂业务流程 几个值得关注的点： → 不是聊天机器人，是自主执行的Agent团队。给一个目标，系统自动组装"分析师+设计师+物流专家"的跨职能小队并行工作 → 覆盖SME全链路：市场调研、产品设计、供应商询价多轮谈判、VAT申报（覆盖100+市场）、营销自动化、库存监控，还能对接Telegram和WhatsApp → 数据底座是阿里电商生态的实时消费趋势和真实交易记录，不是靠通用模型瞎猜，理论上能降低幻觉率 → 安全层面做了沙箱隔离和权限分级，涉及资金和文件操作需要用户显式授权 → 用户可以把自己的业务流程封装成可复用的"skill"，甚至可以分享或变现 不过有几个问号：原推说底层是Claude Opus 4.6，但阿里官方新闻稿里并没有提到具体用了什么模型。月活已经超过1000万，但这个数据是从之前B2B采购引擎Accio延续过来的，Work版本刚上线，实际企业场景的效果还需要观察 🔗 work.accio.com