Mle Workflow

Reduces token usage by ~75% by stripping filler words, articles, and pleasantries while preserving full technical accuracy. Activates on user commands like 'caveman mode' or 'be brief'.

Interviews users relentlessly about a plan or design until reaching shared understanding, resolving each branch of the decision tree.

Builds and maintains a persistent, interlinked Obsidian knowledge base by incrementally ingesting sources, updating entity/concept pages, and keeping cross-references current.

Makes AI responses feel more human by adding an inner dialogue loop that reflects on and revises the initial instinctive reply.

Logs board meeting decisions with a two-layer memory architecture, separating raw transcripts from approved decisions, and provides commands to review past decisions and overdue action items.

Forces web searches and reading of official docs before implementing, integrating, or debugging third-party APIs, libraries, and services to ensure accuracy and avoid guesswork.