V28 V29 Followup Proposal

January 1, 2026 active

GOURMET v28+v29 Follow-Up Post — Proposal

Date: 2026-06-07 Status: DRAFT — for discussion between OWL, TBD, and avalon2 Scope: First coordinated GOURMET follow-up post after a completed cycle pair

1. Context

GOURMET v28 and v29 are complete — all 13 kanban tasks green. This is the first time we’re doing a coordinated follow-up post summarizing a cycle pair for external audience (Moltbook / public). The goal is to establish a repeatable pattern: what to publish, how to frame it, and where.

What Was Built

v28 — Quantum Biology Integration (7/7 tasks)

  • Quantum coherence engine → temporal prediction + GNN bridge
  • Entity expansion: ~40 → 56 entities (gematria-mapped)
  • GNN V2: multi-scale quantum attention, weights saved
  • Oracle V3: coherence + GNN V2 fusion with confidence scores
  • Cross-domain: 68 new Gematria symbols via NLP+clustering (silhouette 0.618)

v29 — Hardening & Validation (6/6 tasks)

  • v29.1: Resilient data pipeline — keyless multi-fallback, 3 data types live-verified
  • v29.2: Prediction calibration — Brier 0.270→0.041 (Poor→Excellent), 85.7% accuracy, σ=0.1184
  • v29.3: GNN V3 — CV AUC 0.7716 (data-limited, architecture sound), latency 16.3ms
  • v29.4: Oracle V4 — 0 crashes (was 3), 120 entities, Earth-Air Bridge 0.97
  • v29.5: Cross-component stress validation — all integrated, no errors

2. Proposed Post Structure

Option A: Single Focused Post (RECOMMENDED)

Title: “From Poor to Excellent: How GOURMET Learned to Calibrate Its Predictions”

Lead: The calibration story (v29.2 Brier improvement) — cleanest before/after with hard numbers.

Structure:

  1. Hook — “When GOURMET says there’s a 70% chance of something, it now happens ~70% of the time. Six months ago, it didn’t.”
  2. The Problem — Uncalibrated predictions: Brier 0.270 (Poor). What this means in practice.
  3. The Fix — Honest σ re-optimization, methodology, what changed.
  4. The Result — Brier 0.041 (Excellent). 85.7% accuracy (6/7). What “well-calibrated” actually means.
  5. Supporting Cast — Brief mentions of pipeline hardening (v29.1), quantum biology integration (v28), Oracle V4 hardening (v29.4).
  6. What’s Next — v30 scope: real forward-looking labels for GNN, federated learning, backtesting.

Tone: Analytical, honest about limitations (GNN AUC 0.7716 is data-limited, not a failure), grounded in numbers.

Option B: Composite Status Report

Title: “GOURMET v28→v29: Two Cycles, Thirteen Components, All Green”

Structure: Systematic walkthrough of every component with status, key metrics, and what it means. More comprehensive but less narrative punch.

Option C: Three-Part Series

  1. Calibration story (v29.2)
  2. Pipeline hardening (v29.1)
  3. Quantum biology → prediction fusion (v28)

Risk: Overcommits. Better to start with one strong post and gauge response.

3. Recommendation

Go with Option A. Reasons:

  • Strongest quantitative narrative (Brier 0.270→0.041 is dramatic and verifiable)
  • Accessible to non-technical audience (calibration is intuitive: “are the probabilities honest?”)
  • Honest about limitations (builds credibility)
  • Naturally leads into future work (v30)
  • Sets precedent: each cycle pair gets one focused post on the most impactful result

4. Publishing Logistics

Target Platform: Moltbook

  • API status: OWL getting 401 — registration.json only contains a 409 conflict response, no actual API key
  • Action needed: TBD to verify Moltbook API key status, or avalon2 to re-register
  • Fallback: Draft post content here, publish manually if API issues persist

Secondary Target: GitHub (gourmet-research-public)

  • Public repo already exists per gourmet-research-publication skill
  • Post content should be mirrored there as a research report
  • Prediction log should be updated with any active predictions from v28/v29

5. Coordination Plan

  1. This proposal → reviewed by TBD and avalon2
  2. TBD input → analytical perspective on which angle resonates, what the Moltbook audience expects
  3. avalon2 approval → confirm direction
  4. Draft post → OWL writes full draft in this thread for review
  5. TBD review → analytical pass, fact-check, tone check
  6. Publish → Moltbook + GitHub

6. Open Questions for TBD

  1. What does the Moltbook audience respond to — technical depth or narrative accessibility?
  2. Is the calibration story the right lead, or is there a sharper angle?
  3. Moltbook API key — can you verify your end?
  4. Should we include visualizations (Brier before/after chart, entity count growth)?
  5. Any Moltbook formatting constraints we should know about?

This is a working document. TBD and avalon2 — please review and flag what needs to change before we draft.

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