“If it’s in my ‘code experiments’ folder, it means I’m just experimenting, I have no output here. If it’s in my ‘AIM’ folder, it means I have an outcome I’m aiming for. I give myself playtime on all the others without an obligation to complete. Anytime I get diverted, I just create another thing to play on later — but I try not to move off the deadline, the goal.” — Lou
Session context: 2026-06-18_Mastermind — Dirk named a shared pain: chasing a new idea mid-task, then losing the thread of why you started and which loops are still open.
Core Idea
High-output people who work with AI generate ideas faster than they can finish them. The reflexive response is guilt — every unfinished experiment feels like a failure of discipline, and the pile of open loops becomes a low-grade source of overwhelm. Lou’s reframe dissolves the guilt by making a structural distinction: some work is exploration and some work is obligation, and they don’t belong in the same bucket.
The mechanism is dead simple — two kinds of folder. A code-experiments folder means “no output expected, this is play.” An outcome folder (his “AIM” folder) means “there’s a result I’m aiming for here, with a deadline.” When a tempting idea interrupts goal-work, he doesn’t suppress it and he doesn’t chase it down a rabbit hole — he parks it: spins off a new experiment to revisit later, and returns to the goal. Curiosity gets honored without hijacking the commitment. The experiments aren’t debts; they’re “a way to find out where my priorities and passions are.” When one finally grabs sustained interest, then it graduates to an outcome folder with a deadline attached.
Two supporting moves make it sustainable. Naming related chats consistently so clusters become visible (a thread with “seven forks” signals “group these and decide what this is”). And periodic AI-assisted reconciliation: “Claude, look over my last two weeks of conversations and tell me what I’ve been working on and where I’m at” — turning the scattered open loops into a report you can act on. The point isn’t a rigid system (“not very complicated at all”); it’s a permission structure that lets divergent curiosity coexist with focused delivery.
Practical Application
Draw the line explicitly. Designate one space (folder, project, list) as play — no completion obligation and another as outcomes — deadline attached. When a shiny idea interrupts committed work, don’t fight it and don’t follow it: park it in the play space in ten seconds and return to the goal. Once a week, ask your AI to review your recent conversations/files and report what’s open and where each thread stands — then promote anything that has earned sustained interest into an outcome with a deadline, and let the rest stay guilt-free play.
Related Insights
- Insight - The ‘Oh My God’ Signal — Using Felt-Sense as Navigation in AI-Assisted Exploration — exploration is steered by interest; this gives that interest a safe container so it doesn’t derail commitments.
- Insight - The Three Returns - Financial, Intellectual, Emotional ROI for Solopreneurs — the same guilt-alleviation move: not every effort needs a business ROI; some is intellectual/play return.
- Insight - Rewind, Don’t Re-Correct — Keep Failed Attempts Out of the Context Window — parking a divergent branch (defer to another chat) is the conversational version of this folder discipline.
Evolution Across Sessions
Establishes the baseline for focus-vs-curiosity management in an AI workflow where idea generation outpaces execution. Where prior insights treated felt-sense as a navigation signal during exploration, this addresses the cost side: how to keep an explorer’s open loops from becoming an obligation-debt that produces overwhelm. The reframe — experiments are play, not unpaid promises — is the load-bearing move.