Timepoint Suite Overview

Open-source engines for temporal AI: render the past, simulate the future, score predictions, and accumulate the graph.

The Timepoint Suite

The Timepoint Suite is a collection of open-source engines that work together to render, simulate, validate, and accumulate temporal causal graphs. The suite creates a self-reinforcing flywheel where historical renderings ground future simulations, quality scoring validates predictions, and settlement against reality strengthens the entire system.

Core Services

Open Source Engines

Flash

Reality Writer — Renders grounded historical moments through Synthetic Time Travel

Pro

Rendering Engine — SNAG-powered social simulation with full causal provenance

Clockchain

Temporal Causal Graph — Canonical storage for Rendered Past + Rendered Future

SNAG Bench

Quality Certifier — Measures Causal Resolution across renderings

Proteus

Settlement Layer — Prediction markets that validate Rendered Futures

TDF

Data Format — JSON-LD interchange format across all services

Private Services

Service	Role	Status
Web App	Browser client at app.timepointai.com	Private
iPhone App	iOS client for Synthetic Time Travel	Private
Billing	Payment processing (Apple IAP + Stripe)	Private
Landing	Marketing site at timepointai.com	Private

How Pro Integrates

TDF Export

Timepoint Pro exports all simulation outputs in TDF (Timepoint Data Format), a JSON-LD interchange format that enables seamless integration with the broader suite:

from timepoint_tdf import from_pro, write_tdf_jsonl

# Export simulation run to TDF
payload = {
    "entities": entities,
    "dialogs": dialogs,
    "causal_edges": causal_edges,
    "metadata": metadata
}
tdf_records = from_pro(payload)
write_tdf_jsonl(tdf_records, "output.tdf.jsonl")

The /api/data-export/{run_id} endpoint returns the full payload ready for TDF conversion.

Training Data Pipeline

Pro generates high-quality training data with full causal ancestry:

1. Rich Context: Every dialog turn includes M3 knowledge provenance, M6 entity state, M7 causal history, M10 atmosphere, M11 dialog context, and M13 relationships
2. Causal Provenance: Full ancestry tracking ensures no “magic knowledge”
3. Counterfactual Branches: BRANCHING mode generates multiple timeline variations
4. Convergence Sets: Repeated runs provide reliability metrics without ground truth

Training data flows to:

• SNAG-Bench Axis 2: Causal reasoning benchmarks
• Proteus: Simulation-to-training pipeline
• Fine-tuning: Causal/temporal/multi-agent reasoning models

Rendered Futures

A Rendered Future is a scored, provenance-tracked causal subgraph—a structured projection of how the present connects to specific future states.

Pro reads the Clockchain’s Rendered Past as grounding and produces Rendered Futures as TDF records:

1. Flash renders historical moments → stores in Clockchain
2. Pro reads Rendered Past as context → simulates near-future causal paths
3. SNAG-Bench scores Causal Resolution (Coverage × Convergence)
4. Proteus validates predictions against reality
5. Clockchain strengthens its Bayesian prior with validated paths
6. All future renderings improve

Key Concepts

Causal Resolution

Causal Resolution = Coverage × Convergence

• Coverage: How much of a scenario has been rendered?
• Convergence: How reliably do repeated runs converge on the same causal structure?

The fidelity is asymptotic—we approach near-simulacrum on historical dialog because there are very few things a person could have said once the model has perfect context for that moment.

Proof of Causal Convergence (PoCC)

PoCC is a future protocol concept: rendering convergent causal paths constitutes useful work.

Multiple independent renderings that converge on the same causal structure provide a form of validation without ground truth. Pro and Clockchain are the natural anchors for this protocol.

Timepoint Futures Index (TFI)

The planned TFI will measure:

• Rendered Past coverage across the temporal graph
• Rendered Future quality and convergence metrics
• Overall system health and prediction accuracy

The Self-Reinforcing Flywheel

The suite creates exponential value through its feedback loops:

1. More historical data → better grounding → higher-quality simulations
2. More simulations → more training data → better causal reasoning
3. More validation → stronger priors → improved future predictions
4. More convergence → higher confidence → more reliable forecasting

Architecture Philosophy

Isolation by Design

Timepoint Pro is a standalone simulation engine:

• No runtime dependencies on other suite services
• All LLM calls go directly to OpenRouter
• All data stays in local SQLite + flat files
• Fully forkable and self-contained

This isolation ensures:

• Anyone with an OpenRouter key can run the full pipeline
• Community contributions don’t require access to private services
• Research and experimentation remain friction-free

Planned: M20 Clockchain Grounding

Future integration will anchor simulations in the canonical temporal graph stored in Clockchain. This mechanism will:

• Load Rendered Past as context for simulations
• Prevent anachronisms by checking temporal consistency
• Enable cross-simulation causal linkage
• Support incremental rendering (continuing from previous states)

Use Cases Across the Suite

Strategic Foresight

Use Pro’s PORTAL mode to map critical paths backward from desired outcomes (“$1B exit”, “colony survives”, “election won”). SNAG-Bench validates the causal coherence. Proteus settles predictions against reality.

Historical Research

Flash renders grounded historical moments. Pro simulates counterfactual branches. SNAG-Bench measures convergence across interpretations. Results accumulate in Clockchain as Rendered Past.

Training Data Generation

Pro generates simulations with full causal provenance. SNAG-Bench filters by quality. Training data flows to fine-tuning pipelines and research benchmarks.

Prediction Markets

Pro renders multiple future scenarios. SNAG-Bench scores their causal resolution. Proteus creates prediction markets. Settlements strengthen Clockchain’s priors.

The Timepoint Thesis

A forthcoming paper will formalize:

• The Rendered Past / Rendered Future framework
• The mathematics of Causal Resolution
• The TDF specification
• The Proof of Causal Convergence protocol

Follow @seanmcdonaldxyz for updates.

Next Steps

Explore Flash

Learn how Flash renders grounded historical moments

Understanding Clockchain

Discover the temporal causal graph architecture

Quality with SNAG-Bench

See how Causal Resolution measures rendering quality

Settlement via Proteus

Understand prediction market validation