AI Workflows & Teams
Organize processes, materials and responsibilities so work can be repeated and shared.
Make a one-off method repeatable and transferable
Recommended reading
Should AI Work Live in a Project, Folder, or Conversation?
Give projects, files and conversations distinct roles, with an established place for important results.
Knowledge & Memory
- Knowledge & MemoryHow to Build AI Long-Term Memory with a Memory RouterBuild a small Memory Router that controls scope, retrieval, context injection, and version status without loading an entire knowledge base into every AI conversation.
- Knowledge & MemoryShould AI Work Live in a Project, Folder, or Conversation?Organize recurring AI work by duration, shared context, and saved outputs so projects, folders, and conversations each have a clear role.
- Knowledge & MemoryWhich Instructions Belong in AGENTS.md?Save durable, scoped, testable project rules in AGENTS.md. Keep one-off deliverables, temporary preferences, and stale facts in the current task.
- Knowledge & MemoryWhy Does AI Forget, and How Should You Manage Versions?Separate temporary context, retrievable memory, and authoritative files so AI work can survive long conversations without silently using stale versions.
- Knowledge & MemoryAI Output Is Not Knowledge: Add a Candidate Layer and Human ReviewDo not write every AI output into a knowledge base. Use a candidate layer that preserves sources, scope, uncertainty, sensitivity, and human review.
- Knowledge & MemoryFour Context Failures—and the Cost Paradox Behind ThemMicrosoft's context and memory lessons frame four context failures, mitigation choices, and trade-offs among memory tools, information quality, and cost.
- Knowledge & MemoryDesign the Knowledge Base, Not the AI ToolA personal knowledge-system design moves beyond choosing an AI tool. Three reframing questions lead to a four-layer architecture and a more durable foundation.