What is AltiVerse?

AltiVerse forks a single decision — a school phone ban, a 4-day week, 8- vs 12-hour hospital shifts — into 2–4 alternate realities and runs each one forward as a small, living simulation. Up to ~1,000 people with personalities, moods, and relationships move through rooms, react to the policy, and slowly pull the worlds apart. You watch when and why the realities diverge, click any person to compare how they fare across timelines, and export a full report with a recommendation.

It is a thinking tool, not a predictor. Every number and quote comes from a deterministic engine (same seed → same world, every time), with an optional local or online LLM layer to write the prose: in-character thoughts, Q&A, headlines, and reports. Use it to reason about the second-order effects of a decision — the stuff that doesn't show up in a spreadsheet.

🔒 Local-first & private. No backend. No telemetry. Nothing is uploaded. Runs, presets, and any API key live only in your browser's localStorage. It works 100% offline out of the box.

Two realities, one shared timeline — watch both worlds live, side by side.

Table of contents

• Quick start
• Using a model (optional)
• How to use it
• Features
• Screenshots
• The simulation, explained
• Keyboard shortcuts
• Tech & architecture
• Privacy
• FAQ
• License

Quick start

You need Node.js 18 or newer. Then run one line — it downloads AltiVerse, installs it, boots the app, and pops open your browser:

npx github:LeoTheAIDev/Altiverse

That's the whole install. No clone, no cd, no account, no key, no server — it just opens at http://localhost:5173 (or the next free port). Nothing to run is built ahead of time.

git clone https://github.com/LeoTheAIDev/Altiverse.git altiverse
cd altiverse
npm install
npm start          # boots the app and opens your browser at http://localhost:5173

Other commands

npm run dev        # dev server (without auto-opening the browser)
npm run build      # type-check + production build into dist/
npm run preview    # serve the production build locally
node bin/altiverse.mjs   # the same one-shot launcher the `altiverse` bin uses

💡 If you npm link (or install globally), the altiverse command launches the app from anywhere.

Using a model (optional)

AltiVerse runs fully offline with deterministic content. Turn on a model to get AI-written scenarios, in-character thoughts, character Q&A, daily headlines, and the final report. It speaks to any OpenAI-compatible /chat/completions endpoint — open Settings and point it wherever you like:

# Local, private, free — using Ollama
ollama pull llama3.2          # or qwen2.5, mistral, gemma3 …
OLLAMA_ORIGINS=* ollama serve  # OLLAMA_ORIGINS=* lets the browser reach it

One-click provider presets, a Model picker, and a Test connection button are built into Settings. Your API key is stored only in localStorage and sent straight to the provider you chose — never to us (there is no us; there's no backend).

How to use it

A run goes Setup → Studio → Report, and every run is saved to History so you can replay it.

1 · Set up a scenario

Pick whichever entry point fits how much you want to specify:

• One-click presets — Phones at school · School start time · Office remote work · Hospital shifts · Prison food · Grading. Loads a complete scenario; just press play.
• Describe it in a sentence (needs a model) — e.g. "A hospital deciding between 8- and 12-hour nursing shifts — who burns out, who stays sharp, and how patient care changes." The model fills in the environment, cast, metrics, and realities.
• Build it field by field — full control over environment, the question, each reality's policy, the groups of people, named/pinned characters, metrics, number of days, and the random seed.

2 · Watch the realities evolve

Press play and watch the worlds run forward day by day on their own living maps. People move between rooms, get stressed, break rules, burn out, bond, and clash. Scrub the timeline, change speed, and click anyone to open their cross-reality card. Live metrics, trends, the divergence tree, the wellbeing histogram, causal chains, and a per-day newspaper headline all update as you go.

3 · Interrogate & report

Open the Report for an executive summary, in-character interviews, per-metric explanations, and a recommendation for which reality to pick — then export it to Markdown or PDF.

Features

🌍 Alternate realities, side by side

• Fork 2–4 realities from one shared baseline and run them on a single synchronized timeline.
• Each reality gets its own living map — a pannable 2-D world of rooms (classrooms, hallway, cafeteria, playground, offices…) with agents drifting around via smooth, physics-based motion.
• A per-day Daily Fork newspaper headline for each reality (deterministic, or AI-rewritten on demand).

🧑‍🤝‍🧑 People who actually live there

• Up to ~1,000 people with archetypes (the anxious overachiever, jokester, rule-bender, weary veteran…), emoji, roles, and personality traits (stress-prone, rule-prone, burnout-prone).
• The same person exists in every reality — so you can see how this exact individual fares under each policy.
• Emergent relationships, rivalries, popularity, and rumours that spread day to day — none of it scripted.
• Click anyone for a card: their mood strip across all days, a wellbeing score, friends & rivals, AI-written thoughts, and a Q&A box to ask them questions in character.

📊 See when and why worlds split

• Live metrics (0–100) compared across realities — use the 8 defaults (Focus, Grades, Trust, Stress, Burnout, Rule-breaking, Conflict, Corner-cutting) or define your own (up to 10); direction (“higher is better” vs “lower is better”) is auto-detected.
• Metric trends — sparklines per metric, every reality on one chart.
• Divergence tree — an SVG fan from the shared baseline showing exactly which day the worlds split.
• Wellbeing histogram — the distribution of how people are doing, not just the average.
• Causal chains — a rule → consequence → second-order effect breakdown for each reality.

🤖 Optional AI layer (bring your own model)

• Works with any OpenAI-compatible endpoint — local (Ollama, LM Studio, llama.cpp) or online (OpenAI, OpenRouter, Groq, Together).
• Powers AI scenario generation, in-character thoughts & Q&A, daily headlines, per-metric explanations, interviews, and the recommendation.
• Robust to small-model quirks: malformed JSON is auto-repaired with jsonrepair.

🎛️ Control & reproducibility

• Deterministic engine — a seeded Mulberry32 PRNG means the same seed always produces the same world. Reseed to explore variations without re-calling the model.
• Full timeline controls — play/pause, step day-by-day, scrub, and 0.5× / 1× / 2× / 4× speeds.
• History — every run is saved (up to 24), replayable, and resumable.
• Export the report to Markdown / PDF.

🔒 Local-first by design

• No backend, no telemetry, nothing uploaded. Runs, presets, and keys live only in your browser. Fully usable offline.

Screenshots

See the whole studio in one shot

The simulation, explained

Environments ship as presets — School, Office, Startup, Hospital, Prison, Summer Camp — each with its own roles (student/teacher, employee/manager, nurse/patient…) and rooms. Type anything else and it builds a custom environment around your text.

Defaults at a glance: seed 20260607 · 30 days · 18 members + 4 staff · 2 realities · 8 metrics · speed 1× · model llama3.2 @ http://localhost:11434/v1.

Keyboard shortcuts

(in the Studio)

Tech & architecture

• Vite + React + TypeScript. No backend, no build step required to run (npm start).
• Deterministic core — a Mulberry32 PRNG drives every world; each branch runs on a derived seed so realities stay independent yet reproducible.
• LLM layer — a thin, dependency-light client (plain fetch) against any OpenAI-compatible /chat/completions endpoint, with JSON repair for small models. Disable it and everything still works deterministically.
• Smooth motion — agents move with spring physics + damping, written straight to the DOM so hundreds render without per-frame React re-renders.
• Persistence — runs, presets, and config live in localStorage (history capped at 24 entries).

src/
├── sim/         # deterministic engine: types, scenario builder, engine, RNG
├── llm/         # OpenAI-compatible client + scenario / thoughts / explain / report
├── components/  # the studio, maps, panels, cards, report, setup, pages
├── hooks/       # simulation clock + agent motion
└── app/         # run history & persistence

Privacy

AltiVerse has no backend and collects nothing.

• Runs, saved presets, and any API key live only in your browser's localStorage.
• With a local model (Ollama / LM Studio / llama.cpp), nothing leaves your machine.
• With an online model, requests go directly from your browser to the provider you chose — your key is never sent anywhere else.
• Works fully offline with deterministic content and no model at all.

FAQ

Does it predict the future? No. It's a structured thinking tool for second-order effects — a way to feel out the trade-offs of a decision, not a forecast.

Do I need an API key or internet? No. It runs fully offline and deterministically. A model is optional and only adds the written prose (thoughts, headlines, reports).

Will my API key leak? It's stored only in your browser and sent only to the provider you pick. There's no backend to leak it to.

Why do two runs look identical? Because they're deterministic — same seed, same world. Hit Reseed to explore a variation.

Can I add my own metrics / people / environment? Yes — define custom metrics (up to 10), pin named characters with their own personalities, and type any environment you like.

License

MIT — open source. Fork it, change it, ship it.