8 items with this tag.
YAML is a human-readable format for storing structured data as plain text. In knowledge management and publishing workflows, it appears most commonly as the frontmatter block at the top of markdown files, where it holds metadata — title, author, date, tags — that tools can read without parsing the document itself.
Distributed version control is an approach to tracking file changes where every contributor holds a complete copy of the repository and its full history, rather than depending on a central server. It enables offline work, parallel development, and resilience against data loss.
Git is a distributed version control system that tracks changes to files over time. It records who changed what and when, allows you to move between earlier and later states of a project, and lets multiple people work on the same files without overwriting each other's contributions.
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.
The accumulated cost of outdated, ambiguous, or poorly structured institutional knowledge — manageable when humans compensate, operationally consequential when AI agents depend on it literally.
A lightweight markup language for creating formatted text using plain-text syntax, enabling portability and interoperability.
Most conversations about AI focus on what it produces. This post describes what an AI workflow for academics actually looks like in practice — building structured context through documentation, iteration, and judgement that makes AI collaboration increasingly effective over time. Drawing on several weeks of restructuring scholarly output with Claude Code, I describe the iteration cycle, the role of documentation as external memory, and what the process reveals about the relationship between explicit information architecture and productive AI collaboration.