Unlock AI Superpowers in Your Notes (Mem Case Studies!)

AI becomes genuinely useful when it’s treated like a customizable collaborator built from a person’s own notes—turning generic outputs into plans, prompts, and workflows tailored to real goals. The core move is “programming with natural language”: instead of asking for a one-off answer, users iterate on questions until the system produces an output that fits their context, criteria, and desired outcomes. That shift matters because it changes AI from a novelty into a repeatable operating system for creativity and execution.

A concrete example builds a daily task planner inside Mem (a “second brain” notetaking system). The process starts by creating goal mems—such as writing a book proposal, landscaping a backyard, and planning a Mexico trip—then rewriting those goals into SMART goals. Next comes a task model mem and explicit task criteria: tasks must be objective and measurable, with a clear “completed or not” standard. The planner template then integrates these mems using bidirectional links, so every day’s task suggestions are generated from the user’s stored goals, criteria, and model rather than from generic productivity advice. Running the system produces an interactive, question-and-answer planning flow that generates a numbered list of next-day tasks (for instance, spending two hours on a section of a book proposal, including research steps like contacting landscaping companies and gathering quotes). The emphasis isn’t on micromanaging checklists; it’s on having AI “converse” with the user’s context to generate actionable work.

That same personalization theme expands into creative projects. One case uses AI to co-write a children’s book for a nephew by feeding the model the nephew’s word list and pairing it with child-development framing. The workflow includes iterative refinement—prompting for synopses, drilling into specific themes (like curiosity), and generating an illustrated result with image instructions. Another project turns a personal reading list into a reusable “persuasion amplifier” template by combining distilled ideas from multiple books (including Made to Stick and Contagious) and then applying the merged framework to tasks like writing a mini sales page.

The discussion also reframes how people should work with AI. A “better Google Paradigm” positions AI as a shortcut to the exact answer—rather than forcing users to sift through links—but the bigger point is iterative improvement. First outputs are often the worst; the productive path is multiple attempts, followed by cataloging what to ask next time. The approach is likened to driving a Ferrari: the value depends on where and how it’s used, not just on owning the tool. Ultimately, AI is presented as a partner for exploration and critical thinking, not merely efficiency—while writing and clear instruction remain essential because AI outputs still depend on human intent, empathy, and goals.

The segment closes with references to resources for building a second brain in Mem and migrating it into an AI-enabled workflow, plus a podcast and course offerings aimed at turning notes into automated, linked systems that can be executed “at the speed of thought.”