9 items with this tag.
Organizing research materials, references, and resources systematically
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.
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.
Every week I annotate articles in Zotero, highlights in Reader, and podcasts in Snipd, all of which is synced to Obsidian. By Friday I have a week's worth of material, tagged and structured, but unreviewed. This post describes the weekly review command I built to surface what matters and create a reason to engage with it.
Harness engineering is the practice of building the full architectural scaffolding within which AI agents operate — structured documentation they can reason with, constraints that enforce invariants, and feedback loops that let them know when they've succeeded. It is distinct from prompt engineering, which shapes individual tasks, and from oversight, which monitors outputs after the fact. The harness is the infrastructure that makes delegation coherent at scale.
The previous posts described what makes agentic workflows coherent at the individual level: a plan, documentation as infrastructure, and domain expertise that can evaluate outputs. Together, these form an informal harness; the conditions within which delegation stays accountable. At institutional scale, a personal harness is not enough: multiple people directing agents without shared constraints produce compounding drift that no amount of human oversight can track. This post examines what AI agent governance in higher education actually requires, and why a harness, not better oversight, may be the right frame.
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.