今日から新著「コンテキスト・リーダーシップ」が店頭に並びます。 リーダーシップの成否は資質や行為ではなく文脈＝コンテキストによって決まります。 例えば「細かな指示を出さずに仕事を与える」という行為が、コンテキスト次第で「任せる」とも「丸投げ」とも受け止められる。 あるいは逆に「細かな指示を出す」という行為が、コンテキスト次第で「的確な指示」とも「マイクロマネージ」とも受け止められる。 本書では「仕事と文脈の関係」を、個人を取り巻く文脈＝ミクロ・コンテキスト、組織やキャリの文脈＝メソ・コンテキスト、時代や社会の文脈＝マクロ・コンテキスト、の三層に整理し、コンテキストを読解し、編集することに成功した人々、逆に失敗した人々を紹介しています。 混乱と変化の大きい現在のような時代において「正しさ」は相対化される一方で、文脈＝コンテキストの重要性が高まります。「事態をどのように意味づけるか」が問われるのです。 ご関心ある方は、ぜひ手に取ってみてください。 amazon.co.jp/コ�…

「コンテキスト・リーダーシップ」を読んだ。数あるリーダー論の中でも、多くの人に希望をもたらす素敵な本でした。 正解のない状況の中で、文脈を読み、問いを立て、物語を編み、組織と共に課題を解いていく。リーダーシップは画一的なものではない。 コンテキストを読み、編む力は後天的に鍛えられるという事実。そこに勇気をもらった。

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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