What I cannot infer from data

Open questions

Each one carries a guess so you are reacting rather than facing a blank page. Two carry no guess on purpose — writing them would be me inventing a person, and that fiction would sit at the root of everything downstream.

8

Open

3

Blocking

0

Answered

Blocking · nothing downstream can be scored until these land

blockingROOT · root

Is the position "an operator who builds" or "a builder who operates"?

WhyThe order decides which audience leads. Operator-first sells to CEOs hiring a P&L owner. Builder-first sells to founders and AI-native teams. Everything downstream inherits the order.
My guessOperator who builds. The evidence is a P&L and a 3x, with 61 apps as proof of method rather than the product itself.
BlocksThe root position, and therefore every score in the system
blockingPROFESSIONAL · audience

Which three proof points actually landed in the interviews?

WhyYou ran six weeks of live A/B testing on your own RTBs and the result is only in your head. It decays in weeks.
My guessThe Greenhouse 3%/7% to 40%/100% before-and-after, TikTok Shop from zero, and the 61-apps-while-running-the-P&L line. My guess is the EBITDA number landed least because it needs setup.
BlocksWhich RTBs get promoted to ratified, and what the resume and LinkedIn lead with
blockingSOCIAL · audience

Who are you to your friends, in one sentence?

WhyThe single largest hole in the model. Two of three lanes have no position, and this is the one with 87 people in it.
No guessNot guessing. Every candidate I could write would be me inventing a person. This one has to come from you.
BlocksThe entire Social lane: expression register, IG rulings, hosting SOPs

Next

nextFAMILY · audience

Who are you as a son, in one sentence?

WhyFamily is the lane where obligations do not decay when neglected, and the only shadow leak I could name for it is withdrawal.
No guessNot guessing, same reason.
BlocksThe Family lane, and the tenderness golden-shadow candidate
nextROOT · root

Which of the three golden-shadow candidates is real?

WhyPlayfulness, tenderness, wanting attention. I inferred these from the ideal self, not from data. Greene says the shadow is derived from what you insist you are not, but only you can confirm which ones sting.
My guessWanting attention is the truest and the least comfortable. Playfulness is real but low-cost. Tenderness I am least sure about.
BlocksRatifying the golden shadow, and what the shadow monitor watches for
nextROOT · root

What is your real weakness, of the three the interview corpus drafted?

WhyBuilds solo instead of delegating · ships at 85% and iterates · impatience with slow process. The corpus flagged all three as candidates and none as chosen.
My guessBuilds solo. It is consistent with 61 apps, with the agentic rock evolving into "stop delegating what I should be good at", and with the dark candidate about building the scoreboard.
BlocksA shadow value with real evidence rather than a recalled one
laterPROFESSIONAL · audience

Name two comparators besides Matt Bertulli.

WhyComparators are used to find unclaimed space, never to rank against. One is not a set.
My guessNo guess worth making. The corpus names only Bertulli.
BlocksThe comparator row, and the differentiation score
laterROOT · root

Do you want the origin story written down, or improvised?

WhyYou have 9 memos and 0 origin. The interview corpus contains three different tellings of the same career arc, which suggests it is being re-derived each time.
My guessWritten once, then varied per lane. The Bell to Risedesk to Greenhouse demand-generation thread is already a coherent spine.
BlocksThe origin row in the root

Answer any of these in the terminal and I will write it through to the model.