Too much context buries what matters; too little forces follow-up questions. The trick is matching your detail level to what your manager actually needs to decide and act.

• Remind your manager where you left off and be explicit about what you need from them—don't make them guess whether this is an FYI or a request for approval.
• Cut details that don't serve your main point (like exact dates when relative time matters), but add more context when decisions are irreversible, expensive, or customer-facing.
• Lead with your recommendation and reasoning, then put supporting details below so your manager can read as much or as little as needed.

Modern AI models are capable enough to make meaningful decisions about how to solve problems, so you should tell them your priorities and context instead of just giving them a narrow spec. This lets them suggest better approaches and avoid wrong assumptions about what you actually want.

• Early AI agents needed explicit step-by-step instructions; now they fail because they misunderstand your goals, not because they're confused about how to execute
• Sharing broad context—your long-term aims, constraints, and what tradeoffs matter—lets models suggest improvements you wouldn't have thought to specify
• Explicitly ranking your priorities (e.g., "I care less about performance than observability here") gives models the information they need to make smarter choices

This tweet notes that while CLAUDE.md solves the instruction-handling side, you still need to track your model’s context budget. It links to Headroom, a simple one-line-install status bar that shows your current context usage percentage in your editor’s status line.

• Structuring prompts in a defined markdown file (CLAUDE.md) with sections for goals, context, constraints, and examples reduces ambiguity and improves model reliability
• Headroom is a one-line-install status bar tool that displays real-time context window usage percentage directly in the editor's statusline
• Combining CLAUDE.md and headroom addresses two separate problems: instruction clarity and token budget tracking, preventing unexpected conversation truncation

The article challenges RDF’s focus on global IRIs, showing how conversational and narrative contexts rely on deferred classification, consensus and scoped definitions rather than fixed identifiers. It outlines approaches—contextual definitions, master data management, local IRIs and self-learning AI—to build flexible, locally scoped ontologies.

• Real conversation shows meaning is built incrementally through context, not looked up via fixed global identifiers like RDF's IRIs/URIs
• Understanding of a term (like "bill") solidifies through accumulating clues and social consensus, mirroring how ontologies actually form in practice
• Different organizations naturally build conflicting local schemas for the same concept (e.g. "Customer"), so forcing one universal identifier without first aligning schemas creates clashes
• True interoperability comes from scoped, negotiated consensus between authorities rather than dumping all terms into one master global ontology