Ideas collects raw sparks. Research validates assumptions quickly. Ready locks scope and metrics. Running focuses daily execution. Review compares outcomes to expectations. Learnings distills insights into reusable playbooks. By naming columns for actions rather than paperwork, you emphasize movement and clarity. Share your column titles and one sentence describing each role; small wording changes often shift behavior in surprisingly powerful ways.
Every card should capture a hypothesis, effort estimate, expected impact, start date, stop date, primary metric, and guardrail metric. Resist vague phrasing. Write it so a future you understands why this was worth doing. A tight template prevents premature work and encourages alignment between measurement and intent. Post your favorite card template; we will compile lightweight variants tailored to common solo workflows.
Simplify ICE, PIE, or RICE to the bare minimum. For example, score Impact, Confidence, and Effort on a three-point scale, then multiply. Confidence should reflect data quality, not optimism. If two options tie, pick the one that teaches the most cheaply. Share your adapted rubric and a short example; other solo operators can compare approaches and copy what feels instantly usable without complex spreadsheets.
Time blocking fails when energy dips. Score each candidate by required cognitive load and emotional friction. Place deep-thinking tasks right after rest, and mechanical tasks during low-energy windows. The board should visualize not only priority but also energy fit. Report one experiment you rescheduled based on energy and how the outcome changed; noticing these patterns often delivers outsized, repeatable performance improvements over weeks.
Shrink scope until it hurts, then stop shrinking when signal integrity would break. Can you test messaging with a landing page and paid traffic before rebuilding onboarding? Can you run a concierge version before automation? Define a minimal slice and commit. Share a slashed version of your current idea; together we can highlight where learning remains strong while effort drops dramatically and speed meaningfully increases.