DEV Community: ce zhang

MiroFish AI Simulation Guides for Scheduled Scenario Runs

ce zhang — Wed, 22 Jul 2026 08:46:30 +0000

The fastest way to get value from MiroFish is to treat every run like a scheduled decision rehearsal. You bring the source material, define the question, choose the audience dynamics, and let the simulation move from graph build to agent activity to report.

This guide is written for teams searching for mirofish ai simulation guides because they want a practical workflow, not another abstract explanation of what AI simulation means. Use it when you need to prepare a launch, test a narrative, compare market reactions, or pressure-test a plan before it reaches real customers, voters, employees, buyers, or communities.

Start a run from the live workspace: open MiroFish at mirofish.my.

What to Schedule in MiroFish

A good MiroFish run starts with a bounded event. The event can be a product launch, pricing update, public announcement, campaign message, policy change, market entry plan, community incident, or competitive move.

Schedule the simulation around the moment where reaction matters. Instead of asking, "Will this work?", define the operating window:

Simulate the first 72 hours after our pricing update goes public.
Focus on existing customers, procurement buyers, power users, and churn-risk accounts.
Show objections, trust signals, escalation paths, and message changes worth testing.

That kind of prompt gives the system a time horizon, audience map, and decision output. It also makes the final report easier to review because every finding can be tied back to a real operating question.

Prepare the Run Packet

Before opening MiroFish, assemble a run packet. The packet does not need to be perfect, but it should explain the world you want simulated.

Useful materials include product briefs, release notes, landing page drafts, customer interview notes, market research, social comments, competitor positioning, policy context, and internal decision memos.

The goal is not to upload every document your team owns. The goal is to give MiroFish enough grounded context to extract entities, relationships, incentives, claims, risks, and open questions.

Upload your run packet to MiroFish.

The MiroFish Scheduling Flow

MiroFish turns a document-backed question into a staged simulation workflow. Think of the workflow as a run schedule with five checkpoints.

1. Intake

The intake step captures the files, prompt, intended decision, and scenario boundary. Strong intake prompts include the event, audience groups, time period, reaction types, and output format.

2. Graph Build

MiroFish reads the uploaded material and builds a graph of relevant entities, relationships, topics, constraints, and signals. This graph gives the simulation memory, keeping the run tied to your actual material instead of drifting into a generic answer.

3. World Setup

The world setup step creates the simulation environment. Depending on the scenario, the world may include early adopters, skeptical buyers, executives, community members, analysts, journalists, employees, critics, supporters, or other relevant actors.

4. Simulation Run

During the run, agents react over time. They post, respond, amplify, misunderstand, support, resist, and create new pressure. The point is not to produce one confident sentence. The point is to expose how reaction may evolve when many perspectives interact.

For scheduled scenario work, watch for repeated patterns: one claim that multiple groups misunderstand, one objection that spreads faster than expected, one audience that supports the plan for a different reason than the team assumed, or one missing explanation that creates avoidable risk.

5. Report and Follow-Up

After the run, MiroFish generates a prediction report. Use the report as scenario intelligence. It should help you decide what to clarify, compare, rewrite, prepare, or test next.

The best follow-up is a second run with one changed variable. Compare two launch messages, two pricing explanations, two policy framings, or two escalation responses. Controlled reruns make the report more practical because you can see which change affects the simulated reaction.

Example Schedule: Product Launch

Use this run when a team has a launch page, product brief, target customer notes, and a planned announcement.

Simulate the first 48 hours after launch.
Audience groups: early adopters, skeptical buyers, founders, product managers, and industry commentators.
Focus on enthusiasm, confusion, objections, privacy concerns, pricing sensitivity, and talking points likely to spread.
Return a report with launch risks, message improvements, and follow-up content ideas.

Review the report for concrete edits. If the simulation shows privacy concerns, strengthen the privacy section before launch. If users ask for integrations, make that answer visible. If commentators compare the product to the wrong category, rewrite the positioning.

Run a product launch simulation in MiroFish.

Example Schedule: Public Announcement

Use this run when an organization needs to publish a statement, policy update, community decision, or external response.

Simulate public reaction during the first 24 hours after this announcement.
Audience groups: supporters, critics, neutral observers, affected users, journalists, and internal employees.
Identify likely misunderstandings, criticism themes, supportive arguments, escalation risks, and the first three clarifications we should prepare.

This run is useful because internal clarity does not guarantee public clarity. MiroFish can surface phrases that sound reasonable in a planning room but defensive, vague, or incomplete once different audiences react.

Example Schedule: Market Entry

Use this run when evaluating a new segment, geography, platform, or buyer category.

Simulate how target buyers, incumbent competitors, analysts, and channel partners may react to our proposed market entry over 30 days.
Focus on credibility barriers, trust signals, partner incentives, competitor countermoves, and the first niche where adoption may be easiest.

This gives founders, strategy teams, and operators a better way to discuss market entry risk. The output is not a replacement for research. It is a structured rehearsal that helps identify what to validate next.

How to Judge a MiroFish Report

Do not read a MiroFish report like a guarantee. Read it like an operating map.

Useful reports usually include clear audience segments, repeated reaction patterns, source-grounded assumptions, plausible risks, tactical recommendations, and follow-up questions worth simulating.

Weak reports usually trace back to weak inputs. If the report feels generic, tighten the source packet, narrow the time window, name the audience groups, or run a comparison scenario.

Start the Next Scheduled Run

MiroFish is most useful when a real decision is close enough to describe clearly but early enough to change. Bring the documents, frame the operating window, run the simulation, and use the report to improve the plan before the outside world reacts.

Start your next AI simulation at mirofish.my.

I Built an AI Simulation Engine You Talk to Like ChatGPT — Meet MiroFish

ce zhang — Wed, 06 May 2026 15:19:42 +0000

What if you could rehearse a crisis before it happens, pressure-test a campaign before you spend, or map the second-order reactions to a pricing change — all by asking a question in plain language?

That's MiroFish.

The Problem: Why ChatGPT Isn't Enough

Ask ChatGPT for a prediction, and it collapses competing audience reactions into one confident answer. But real-world outcomes depend on people reacting to people — shifting incentives, emergent narratives, and resistance clusters that a single summary paragraph can't capture.

So I built something different.

Architecture: Multi-Agent Simulation in a Chat Interface

MiroFish runs a 5-stage pipeline behind a single chat conversation:

User Question → Knowledge Graph → Agent Simulation → Report → Follow-up Queries

Stage	What Happens
1. Seed	Plain-language question + optional file uploads (PDF, Markdown) for grounding
2. Graph	LLM extracts actors, relationships, pressures, and factual anchors into a structured knowledge graph
3. Simulate	AI personas interact across short-form and threaded social surfaces over multiple rounds — each with persistent memory and distinct incentives
4. Report	Structured result card with executive summary, risk signals, narrative paths, and confidence indicators
5. Query	Keep asking the simulated world — unlike a static forecast, this is interactive

The key insight: agents don't just answer. They react to each other, creating emergent behavior that a flat prompt-response loop can't produce.

What You Can Use It For

Use Case	Example
Campaign Testing	"What happens if we launch in a skeptical category?"
Pricing Reactions	"If we raise prices, which segments push back first?"
Policy Stress Tests	"Where does support split when this goes public?"
Market Narratives	"What if positive news meets coordinated skepticism?"

Writing a Good Prompt

Name the decision, the audience, the trigger, and the time horizon:

❌ "What happens if we change pricing?"

✅ "What happens to customer trust if we remove the bundled charger from the flagship model next quarter?"

Try It

No signup. No setup. Ask a question.

👉 mirofish.homes

Open source on GitHub.

Introducing MiroFish: Predict Anything, But Talk to It Like ChatGPT

ce zhang — Wed, 06 May 2026 15:01:19 +0000

What if you could rehearse a crisis before it happens, pressure-test a campaign before you spend, or map the second-order reactions to a pricing change — all by asking a question in plain language?

That's MiroFish.

MiroFish is an AI simulation engine that lets you talk to a scenario the way you'd talk to ChatGPT. Ask a question. Upload a document if you want. Behind the scenes, a multi-agent system builds a knowledge graph, runs agent-based simulations across social surfaces, and delivers a structured prediction report — all inside a single conversation.

Why Not Just Ask ChatGPT?

Single-model answers collapse competing audience reactions into one confident response. MiroFish doesn't.

It creates a living scenario: agents with distinct personas, incentives, and memories interact over multiple rounds. You get to watch narrative spread, resistance clusters, and emergent behavior — not a single summary paragraph.

Approach	What you get
Single chat answer	Fast, useful, but often one-dimensional
Manual research	Thorough, but slow when many groups interact at once
MiroFish	Agents, memory, social surfaces, and a report you can keep questioning

How It Works

Seed — Start with a plain-language question. Add a strategy memo, policy brief, or customer research as optional grounding.
Graph — The engine extracts actors, relationships, pressures, and factual anchors into a structured knowledge graph.
Simulate — Personas interact across short-form and threaded social surfaces over multiple rounds.
Report — A prediction report surfaces turning points, risk signals, narrative paths, and confidence indicators.
Keep Asking — Unlike a static forecast, you continue questioning the generated world to explore counterfactuals.

What Can You Use It For?

🎯 Campaign Testing

"What happens if we launch this positioning in a skeptical category?"
Simulate how audience groups might amplify, resist, or reinterpret your message before you commit budget.

💰 Pricing Reactions

"If we raise prices next quarter, which customer groups push back first?"
Model sentiment, value perception, and likely objection paths across different segments.

🏛️ Policy Stress Tests

"If this policy enters public debate, where does support split?"
Use simulation as a tabletop exercise for controversy, coalition formation, and second-order effects.

📈 Market Narratives

"What if positive news meets coordinated skepticism on social channels?"
Stress-test market stories where spreadsheets miss the feedback loop between analysts, retail attention, and public discourse.

What Makes a Good Prompt?

Name the decision, the audience, the likely trigger, and the time horizon. A narrow question gives the simulated world less room to drift.

❌ "What will happen if we change our pricing?"

✅ "What happens to customer trust if we remove a bundled charger from the flagship model next quarter?"

The Report You'll Get

Every answer drops a structured result card with:

Executive summary — likely trajectory at a glance
Risk signals — what could derail the outcome
Narrative paths — how the story spreads (and where it fractures)
Follow-up questions — "Which persona creates the first negative cascade? What changes if we announce a transition plan first?"

Is This a Guaranteed Forecast?

No. And it doesn't pretend to be.

MiroFish is exploratory decision support — a way to rehearse plausible reactions, surface blind spots, and sharpen your own judgment before you use analytics and real-world validation.

Try It

If you're planning a launch, testing a pricing change, or staring at a policy draft wondering what you're missing — open MiroFish and ask it a question.

No setup required. Start with text, add files when you want more grounding.

👉 mirofish.homes