Turn This Idea Into A Clear Plan
Start with the big idea, then make it easier to act on. This view helps you name the direction, keep the scope small, break the work into smaller parts, and say how you will know it worked.
This creates plan cards only. You can review them first, then start work from the plan you want to use.
Direction
Clarify the first real-world outcome metabolic-learning: the satsang operator as recursive learning kernel should create and the person it helps first.
Scope
Keep the first version tight: one walkable flow, one visible result, and only the minimum support needed. The when-the-pressure-comes satsang recipe (observer, name-the-need, gift, hooponopono, and the operator pressure x frequency x angle x focus) proven as a Form-native learning primitive over the geom…
Smaller parts
Split the idea into promise, flow, and proof so each plan stays small enough to build and check.
Proof of success
A human can understand the value, walk the flow, and answer this open question next: How can we improve this idea, show whether it is working yet, and make that proof clearer over time?
Plans you place here carry the name you choose.
You can shape the preview below now. To place a plan, first be welcomed: choose a name. You will return here, and the plans will carry your name.
metabolic-learning: the satsang operator as recursive learning kernel: promise and audience
Lock the first audience and promise for metabolic-learning: the satsang operator as recursive learning kernel so the first version says something concrete.
Define who this helps first, what tension it resolves, and what the first visible result should be. The when-the-pressure-comes satsang recipe (observer, name-the-need, gift, hooponopono, and the operator pressure x frequency x angle x focus) proven as a Form-native learning primitive over the geometric-learning gathe…
How you will know it worked: A new user can tell who this is for, why it matters, and where to start without extra explanation.
Be welcomed to place this planExpected impact 0.3 | Work size 0.1 metabolic-learning: the satsang operator as recursive learning kernel: end-to-end human flow
Turn metabolic-learning: the satsang operator as recursive learning kernel into one walkable flow instead of a loose collection of internal actions.
Map the creation, update, review, and follow-up loop in plain language. Use this open question as a design constraint: How can we improve this idea, show whether it is working yet, and make that proof clearer over time?
How you will know it worked: Someone can complete the core journey locally without needing hidden IDs, inside language, or hard-to-find navigation.
Be welcomed to place this planExpected impact 0.4 | Work size 0.1 metabolic-learning: the satsang operator as recursive learning kernel: measurement, trust, and follow-up
Make progress for metabolic-learning: the satsang operator as recursive learning kernel credible by showing what changed, what is blocked, and what should happen next.
Define the status signals, proof markers, human review points, and follow-up rules that keep the first version trustworthy.
How you will know it worked: The experience makes value movement, blockers, and next actions visible without extra technical interpretation.
Be welcomed to place this planExpected impact 0.2 | Work size 0.1