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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.

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Core Services

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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

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Private Services

ServiceRoleStatus
Web AppBrowser client at app.timepointai.comPrivate
iPhone AppiOS client for Synthetic Time TravelPrivate
BillingPayment processing (Apple IAP + Stripe)Private
LandingMarketing site at timepointai.comPrivate

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How Pro Integrates

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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.

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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

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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

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Key Concepts

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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.

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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.

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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

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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

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Architecture Philosophy

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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

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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)

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Use Cases Across the Suite

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.
Flash renders grounded historical moments. Pro simulates counterfactual branches. SNAG-Bench measures convergence across interpretations. Results accumulate in Clockchain as Rendered Past.
Pro generates simulations with full causal provenance. SNAG-Bench filters by quality. Training data flows to fine-tuning pipelines and research benchmarks.
Pro renders multiple future scenarios. SNAG-Bench scores their causal resolution. Proteus creates prediction markets. Settlements strengthen Clockchain’s priors.

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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.

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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

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