Information management
13 items with this tag.
What the Quartz graph view reveals about this site
The graph view is one of the reasons I built this site with Quartz, and now that there's enough writing on it the links between pages have started to form a shape. The graph opens from the top bar, shows the site's writing as a network coloured by type, and lets you choose what's on it. This post shows how to read it, and what it reveals about the site: where the ideas cluster, which concepts hold the rest together, and which ones nothing connects to.
Obsidian
Obsidian is a note-taking application that keeps every note as a plain markdown file in an ordinary folder on your own computer, and lets you link notes to one another. Because the notes are just files, they outlast the application, and any AI agent that can read files can read them without an export or a special connection.
Zotero
Zotero is a free, open source reference manager that stores the sources you collect with their citation details, PDFs, and notes. It also offers programming interfaces that let an AI agent search the library, read from it, and add to it, which turns a reference library into something an agent can check a draft against.
Seven principle for collaborating with AI
Seven principles for extended AI collaboration, distilled from a week-long project to restructure a large note collection using Claude Code. The principles cover goal-setting, understanding what AI can and cannot contribute, investing in planning conversations, adaptive planning, safety infrastructure, treating AI output as drafts, and expecting to learn something about your own thinking. Offered not as rules to follow but as patterns to recognise.
Building an AI-ready knowledge base
A detailed account of a week-long project to restructure 5,819 Obsidian notes using AI as a working partner. The project involved building a 23-category taxonomy, migrating thousands of legacy notes to a consistent metadata structure, and generating AI-written descriptions for every note in the collection. The piece describes not just what was done, but how extended planning conversations, external project documentation, and careful human review at each phase made the work tractable. The most unexpected outcome was that building infrastructure for a note collection required articulating, for the first time, precisely how I think about my academic field.
Querying the Zotero database with AI
What happens when you query Zotero with AI and treat your whole reference library as context, instead of searching it one document at a time? This field note describes a proof of concept in which Claude Code reads the Zotero database file directly. The approach works, and what breaks shows how much your metadata practices matter. It also sets out the privacy trade-off of sending query results to a cloud-based model.
Organising your notes for AI: why retrieval isn't enough
Most advice on organising notes for AI treats it as a retrieval problem, as if the AI only needs to find the right documents. The harder problem is translation, making the relationships in your thinking machine-readable without losing what makes them yours. This post explains why search and retrieval fall short, shows what a typed link between two notes looks like, and suggests one small way to start.
Graph database
A graph database stores entities and the typed relationships between them, and is the storage layer behind a knowledge graph. For a programme team it can hold the curriculum map itself, so questions about how standards, outcomes, and assessments connect are answered by following the links instead of reading the handbooks.
Vertex
A curriculum knowledge graph that turns programme documentation into queryable infrastructure. Curriculum structure, learning outcomes, assessment alignment, and regulatory standards are modelled as a graph staff can interrogate in plain language — turning quality-assurance and compliance checks that take weeks into ones that take minutes. A Snapplify product I work on as a partner and consultant.
Documentation becomes infrastructure when AI agents are the readers
When AI agents consume documentation as operational input, it undergoes a category shift from reference material to operational architecture; inaccuracies no longer merely inconvenience readers, they cause system failures. This essay argues that the primary bottleneck for institutional AI integration is not AI capability but information architecture: how institutional knowledge is structured, maintained, and made available to AI systems. Documentation written for human readers cannot function as reliable AI input without deliberate restructuring around explicit relationships and rigorous maintenance workflows. Treating this transition as a governance imperative, rather than a technical afterthought, determines whether AI integration delivers on its institutional promise.
Contextual interoperability
Contextual interoperability is what lets you use your own notes, frameworks, and commitments with any AI model, local or hosted. It has two halves: the context has to be legible, with relationships a model can follow, and portable, held by you instead of inside one provider's system. With a curriculum mapping example from a pharmacy programme.
Knowledge graph
How a knowledge graph differs from a vector database: it stores entities and the typed relationships between them, so AI can reason across connections.
Context engineering
How context engineering differs from prompt engineering: building the knowledge and structure a language model needs to reason well about your work.