vinber

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.

我把Wildcard创始人妻子的发言全部看完了。 只想说一句： 这不是项目方， 这是教科书级别的 PUA + 甩锅 + 收割。 __________________________________________ 她说： 1️⃣ “$WC 的设计是奖励社区成员” 👉 我们高成本买的，被她叫“奖励” 2️⃣ “我们在营销上投入很多” 👉 社区求他们营销无数次，他们从未真正做过 3️⃣ “TGE + 加池子是给你们退出的机会” 👉 翻译：我们拿走300万，只给20万流动性，你们认亏赶紧走 4️⃣ “成功不是我们的责任” 👉 亏钱是你自己的问题 5️⃣ “Paradigm是风投，本来就可能归零” 👉 拿机构背书，现在却说归零合理 6️⃣ “你们不该期待我们像基金经理一样保护你们” 👉 用户亏95%，反而被指责“期望过高” 7️⃣ “威胁团队会被取消资格” 👉 亏钱的人连发声权都没有 8️⃣ “我们没有工资，是为了Web3生态” 👉 经典卖惨 + 道德绑架 9️⃣ “我们始终以社区最大利益为出发点” 👉 一边让你亏钱，一边继续画饼 🔟 “谁会愿意进入一个愤怒到威胁创始人和子女的社区？” 👉 直接反咬，把锅甩给受害者 __________________________________________ 而现实是： ❌ 用户亏损 90%+ ❌ 流动性极低，几乎锁死 ❌ 没有任何补偿方案 ❌他们把用户充的u基本都转进了交易所 然后她告诉你： 👉 这是你的问题 __________________________________________ 我说实话： 这已经不是项目失败， 这是人品问题。 如果你还在幻想他们会救你—— 醒醒。 他们唯一在做的事情就是： 👉 让你接受亏损，并闭嘴。方便他们提桶跑路。

New in M365 Copilot: Council. You can run multiple models on the same prompt at the same time, so you can see where they align and diverge, and understand what each adds.

Anthropic CEO：海啸已到眼前，大家还在自我安慰