12 items with this tag.
Organizing research materials, references, and resources systematically
Building personal knowledge systems for research and learning
Dense periods leave a residue — not the urgent things, which get handled, but everything else that quietly accumulates. The email backlog anxiety this produces isn't about workload. It's about the unbounded unknown. Understanding the difference changes how you approach the clearance.
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.
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.
What happens when you query the Zotero database with AI, treating your entire reference library as context rather than searching it document by document? This field note documents a proof of concept using Claude Code to read a Zotero SQLite database directly. The approach works but what breaks reveals how much your metadata practices actually matter.
A database that stores explicit relationships between entities, serving as the storage layer for knowledge graphs
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.
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.
The capacity to make human knowledge machine-readable while preserving its meaning, enabling AI to reason within a specific intellectual framework.
A structured representation of knowledge using entities connected by explicit, typed relationships
A system-level discipline focused on building dynamic, state-aware information ecosystems for AI agents