Personas
People and roles the system serves.
Personas — who reaches for Meridians
Companion to positioning. This folder describes who brings a consequential situation to Meridians, what they hope to rehearse, and where the current product stops. It is not a market-size claim and it is not permission to describe direction as shipped.
The thesis
Rehearse consequence before you live it — and find out what you caused.
Earth is causally thin. Most people get one attempt at a hard conversation, a strategic move, a training decision, a story turn, or a game-world commitment. Meridians holds the situation, forks it, prices pathways, and lets the person remain the author of the move. It does not predict or choose. It interviews a person, turns the kept situation into a Domain, develops branches, and shows consequences through a visual-novel reading.
The fit test
Every fit must satisfy all four conditions:
- They face a moment, decision, or world whose consequences they cannot cheaply rehearse.
- They think in actors, incentives, information, and consequences, not headlines.
- They want to remain the author of the move; the engine proposes and prices but never chooses.
- They will return to ask “what did I cause?” and try again.
Someone who wants a forecast, verdict, passive story, or one-off answer is not a fit merely because they like the premise.
The primary roadmap is Causal MCTS VNs; VNMMO is a stretch goal on a separate roadmap.
Roster by use
| Use | Persona | Entry ask |
|---|---|---|
| Life moments and consequential decisions | Sam | “Help me prepare for this conversation.” |
| Psychology / latent traits | Noor | “Help me practice toward a trait.” |
| Strategy | Rafael, Dev | “Run my scenario for the team.” |
| Table / multi-seat | Renata, Kai | “Give several seats the same world.” |
| Learning | Tomás | “Let me learn by acting in the world.” |
| Entertainment | Priya | “Make me a story I can affect.” |
| Leisure | Ana | “Let me wander a few worlds.” |
| Authors | Elena | “Check my novel’s fates.” |
| Facilitator | Maya | “Run a simulation with my client.” |
| Builder | Leo | “Give me the substrate and contracts.” |
What they are asking for
| They say | The honest answer |
|---|---|
| “Help me prepare for X.” | Bring the situation. Intake is currently a desktop interview; turning it into a Domain runs by hand through MCP. |
| “Tell me what will happen.” | Meridians does not predict. It shows what the person caused in the declared world and prices pathways without choosing. |
| “Make me a story.” | The Reader can be served a prepared Experience: a visual novel whose scenes were generated before reading. |
| “Run my scenario for the team.” | Strategy and table users can play bounded branches; multi-seat governance and automated search remain open. |
| “Check my novel’s fates.” | The author can use the worldbuilder substrate and MWS direction; automated search over every branch is not shipped. |
Prospects, not fans
The useful prospect has a situation with stakes and can name the actors, incentives, private information, constraints, and candidate moves. They are not buying a feed or an oracle. They are buying a bounded opportunity to act, inspect a consequence, and try again.
The interview is currently a desktop flow run by hand. Voice intake is direction, not a shipped hands-free product. The conversion question is whether the person returns to replay the situation, not whether they compliment the metaphor.
Two shipped realities
- The record and branch substrate. Domains, canonical state, Scenario runs, and typed actions can preserve a declared world and its consequences.
- The prepared reading surface. Published Experiences are generated once and then served. The reader does not pay generation cost for every reading.
The VN reader, automated MWS, latent-trait readings, transfer, and freeform World wandering remain direction unless a surface says otherwise. Noor’s reading, if built, must be private, non-clinical, non-diagnostic, and never a verdict. Transfer is unproven.
The causal loop
act → world remembers → “what did I cause?” → retry as experiment
Play without the return question is engagement, not learning. A persona earns a place here only when the loop can be observed or when the document clearly marks the missing piece.
Working boundaries
- Macro is the mastered layer: the engine can hold world state, branches, open Fate, and causal consequence at the scene/arc level.
- Micro is not mastered: nothing currently reads inside a scene at screen or sentence grain. Micro analysis is direction and exists in service of making macro consequence felt.
- Situation → world is manual: voice interview is direction; conversion is performed by an operator through MCP.
- Search is bounded: Scenario Lab experiments are evidence of a laboratory surface, not a shipped autonomous MWS service.
- Economics counts generated simulations: hosted headroom now hangs on generation plus MWS; voice minutes and automated search compute remain open estimates.
Generated economics
The tables below are projections from PERSONA_SETUPS and centralized action pricing. They are not
promises about willingness to pay. Run npm run gen:personas after changing a setup. Marked blocks
are machine-owned.
Program spend
<!-- gen:personas:spend -->| Persona | Boundary | Public | Private | Q/run | Q/wk | Articles/wk | Mon/wk | Rsn/wk | $/mo |
|---|---|---|---|---|---|---|---|---|---|
| Sam | Private | 0 | 1 | 0 | 0 | 0 | $0.00 (0%) | $0.03 | ~$2 |
| Noor | Private | 0 | 1 | 0 | 0 | 0 | $0.00 (0%) | $0.03 | ~$3 |
| Rafael | Public | 1 | 0 | 3 | 6 | 36 | $0.06 (11%) | $0.09 | ~$3 |
| Dev | Blended | 1 | 1 | 2 | 4 | 24 | $0.04 (8%) | $0.20 | ~$2 |
| Renata | Blended | 0 | 1 | 0 | 0 | 0 | $0.00 (0%) | $0.04 | ~$2 |
| Tomás | Blended | 1 | 1 | 2 | 2 | 12 | $0.02 (4.7%) | $0.18 | ~$2 |
| Priya | Private | 0 | 1 | 0 | 0 | 0 | $0.00 (0%) | $0.00 | ~$0.11 |
| Ana | Private | 0 | 1 | 0 | 0 | 0 | $0.00 (0%) | $0.00 | ~$0.06 |
| Elena | Private | 0 | 2 | 0 | 0 | 0 | $0.00 (0%) | $0.16 | ~$2 |
| Kai | Private | 0 | 3 | 0 | 0 | 0 | $0.00 (0%) | $0.37 | ~$3 |
| Maya | Blended | 1 | 1 | 2 | 4 | 24 | $0.04 (6.3%) | $0.18 | ~$3 |
Running cost
<!-- gen:personas:cost -->| Persona | costPosture | Fleet profile | Running $/wk | Running $/mo | Onboarding (one-time) |
|---|---|---|---|---|---|
| Sam | bounded | budget-smart | $0.40 | ~$2 | ~$0.01 |
| Noor | bounded | budget-smart | $0.74 | ~$3 | ~$0.01 |
| Rafael | deep | performance-smart | $0.59 | ~$3 | ~$0.13 |
| Dev | balanced | balanced-smart | $0.50 | ~$2 | ~$0.20 |
| Renata | balanced | balanced-smart | $0.40 | ~$2 | ~$0.09 |
| Tomás | balanced | balanced-smart | $0.43 | ~$2 | ~$0.20 |
| Priya | balanced | balanced-smart | $0.03 | ~$0.11 | ~$0.09 |
| Ana | balanced | balanced-smart | $0.01 | ~$0.06 | ~$0.08 |
| Elena | balanced | balanced-smart | $0.58 | ~$2 | ~$0.29 |
| Kai | balanced | balanced-smart | $0.65 | ~$3 | ~$0.33 |
| Maya | deep | performance-smart | $0.66 | ~$3 | ~$0.21 |
Shipped-PMF ranking
<!-- gen:personas:pmf -->| Persona | Net | Pull | Risk | Analysis-fit | Pay-fit | Shipped-lean | Top risk |
|---|---|---|---|---|---|---|---|
| Rafael | 80 | 91.1 | 11.1 | 0.89 | 1 | 0.85 | time-to-value |
| Dev | 72.2 | 85.5 | 13.4 | 0.92 | 0.85 | 0.8 | direction |
| Maya | 70.6 | 86.9 | 16.3 | 0.94 | 1 | 0.7 | direction |
| Renata | 68.6 | 86.1 | 17.5 | 1 | 0.85 | 0.75 | direction |
| Kai | 67.8 | 72.4 | 4.7 | 1 | 0.4 | 0.95 | time-to-value |
| Elena | 61.6 | 69.8 | 8.2 | 1 | 0.4 | 0.85 | direction |
| Priya | 58.5 | 68.4 | 9.9 | 1 | 0.4 | 0.8 | direction |
| Sam | 54.2 | 71.1 | 16.9 | 1 | 0.6 | 0.6 | direction |
| Tomás | 46.8 | 64.4 | 17.6 | 0.95 | 0.4 | 0.7 | direction |
| Noor | 36.6 | 64.6 | 28 | 1 | 0.6 | 0.45 | direction |
| Ana | 35.1 | 58.5 | 23.3 | 1 | 0.4 | 0.5 | direction |
The PMF ranking measures fit to the shipped surface under explicit assumptions. It is not a verdict on a person and does not make a directional use look shipped.
Why each entry exists
“Help me prepare for X”
Sam is the flagship case: a salary conversation, a hard talk with a co-founder, or another one-shot moment whose ordinary rehearsal is too thin. The simulation is valuable only if it preserves the person’s move and makes the consequence legible.
“Tell me what will happen”
This is the oracle request, and it is the wrong promise. Meridians searches and prices pathways from a declared world. It cannot know the world completely and must not turn a priced pathway into prophecy.
“Make me a story”
Priya is the Reader: she wants an explorable VN and a pocket-dimension return, but the product must still ask what she caused. A passive viewer is not the target.
“Run my scenario for the team”
Rafael, Dev, Renata, and Maya need a table: named actors, asymmetric information, candidate moves, and attribution. The current Scenario Lab can run bounded experiments; multi-seat governance is still open.
“Check my novel’s fates”
Elena wants a chess engine for the fates of her own novel. The current author surface can preserve and develop branches; automated MWS remains direction.
Pivot note — 2026-09-20
The roster is now organised around causal simulation services rather than maintained experts. A person brings a situation, the engine helps keep a world, and the person chooses the commitment. Directors may ground a world in live evidence, but the modal simulation bill is generation plus MWS, not a crawl alone. The old maintained-knowledge roster remains useful history; it is no longer the thesis.
Further reading
Update discipline
PERSONA_SETUPS in
constellation-economics.ts is the
economic source of truth. Persona files explain the use, objections, and boundary. Generated blocks
must be regenerated, not hand-edited.
Root
13 articles- Ana Duarte — ExplorerPublic
Private · leisure wandering. Ana wants to visit several Worlds lightly, without pretending that freeform World wandering is already a shipped product.
- Anti-personas — who Meridians is not forPublic
Meridians is a causal simulator, not an oracle, diagnosis service, passive streaming catalogue, or one off answer box. These are fit boundaries, not insults.
- Dev Ramanathan — OperatorPublic
Blended · consequential company decisions. Dev wants an adversarial worst case run before a launch, pricing change, or board commitment. The world can include a light evidence crawl.
- Elena Marquez — AuthorPublic
Private · a chess engine for a novel’s fates. Elena wants to test what her characters cause without surrendering authorship to an oracle.
- Kai Nakamura — Table-runnerPublic
Private · leisure/tabletop. Kai keeps a shared fictional canon for a table. He is the worldbuilder shaped setup and the leisure/tabletop variant of Renata’s Table.
- Leo — BuilderPublic
Documentation only / headless builder. Leo wants contracts, canonical records, MCP, and a reliable substrate. He has no PERSONA SETUPS entry and therefore no economics profile.
- Maya Chen — FacilitatorPublic
Blended · non clinical facilitation. Maya is a coach or consultant running simulations with clients. She needs the instrument to clarify a decision, never to diagnose a person.
- Noor Haddad — PractitionerPublic
Private · concierge psychology journey. Noor wants a weekly hour aimed at a trait she chose, with a private Rasch reading. That reading is not built and must never become a clinical verdict.
- Priya Nair — ReaderPublic
Private · entertainment. Priya wants an explorable visual novel, a pocket dimension return, and the uncomfortable question: what did I cause?
- Rafael Ortiz — StrategistPublic
Public · live evidence strategy games. Rafael holds one situation while doctrines and seats play its possible fates. A reduced crawl can ground the world in evidence; it is not the unit of value.
- Renata Sokolova — TablePublic
Blended · multi seat table. Renata needs several people in one strategy or crisis world, with attribution. Her lower shipped lean reflects open multi seat governance, not lack of demand.
- Sam Okafor — ParticipantPublic
Private · one consequential life moment. Sam brings a salary conversation, a hard talk with a co founder, or another decision that ordinary life lets him rehearse only once.
- Tomás Ferreira — LearnerPublic
Blended · learning by acting. Tomás wants an hour long training world where one consequential question can be tried, not another library of explanations.