Validator approach
A readable description of how Valx differs from “base GPT” in this project — without exposing internals.
What’s different here
- Evidence-first workflow: we prove data capture at small scale, then scale while measuring missingness.
- Contracts + drift control: key scripts and schemas are snapshotted with hashes so later edits cannot silently change behavior.
- Gated progress: we advance phases only after explicit acceptance tests (idempotency, resume, coverage reports).
- Ritualset enforcement: live documentation checks and other recurring hygiene steps reduce breakage as upstream APIs evolve.
Why this matters for betting
Edges are fragile. If your data pipeline is not reproducible and timestamp-consistent, backtests lie. Our governance steps prioritize stable truth capture before adding market lines and computing EV/CLV.
Past releases
Release 2025-12-16 (bundle v2) — expand
Validator approach
A readable explanation of the governance posture and why it helps (without revealing proprietary internal detail).
How Valx differs from “base GPT” (high level)
Base chat models are great at brainstorming. Valx is configured to behave more like a controlled engineering process:
- Append-only record: decisions, artifacts, and changes are captured in a single Full Record that is versioned on every ship.
- Contracts: core functions are hashed so changes are intentional and reviewable.
- Rituals: recurring checks (fresh docs, schema drift, dependency pinning) reduce silent breakage.
We keep the internal “validator” details high-level in public surfaces so the process remains readable.
What this buys us in a betting project
- Reproducible backtests: you can re-run the same dates and get the same canonical outputs.
- Traceable recommendations: “why we liked this bet” is tied to stored features and market snapshots.
- Less drift: when upstream sources change, we detect and re-version rather than quietly producing wrong joins.
What we do not claim
No system guarantees profit. The goal is to build a disciplined process for estimating probabilities, measuring edge, and learning from results.