From ca594c8eceda259d8a460eb778f9852aff82707a Mon Sep 17 00:00:00 2001 From: Ty Date: Thu, 18 Jun 2026 04:45:50 +0000 Subject: [PATCH] Migrate jesslyn-photographer-workflow from Ty_Tech/research to Tony_tech/research (step 2/4) --- .../research/culling-phase1-plan.md | 106 ++++++++++++++++++ 1 file changed, 106 insertions(+) create mode 100644 jesslyn-photographer-workflow/research/culling-phase1-plan.md diff --git a/jesslyn-photographer-workflow/research/culling-phase1-plan.md b/jesslyn-photographer-workflow/research/culling-phase1-plan.md new file mode 100644 index 0000000..fd8b8e2 --- /dev/null +++ b/jesslyn-photographer-workflow/research/culling-phase1-plan.md @@ -0,0 +1,106 @@ +# Phase 1: Automated Culling Persistence Experiment + +**Focus**: Automated Culling (top recommendation from Odysseus report alongside Personal Style Learning) +**Rationale**: User reports Jesslyn spends a significant time on culling. This is a high-pain, high-volume task where true persistence can deliver clear value (remembering style/priorities across shoots). Aligns with report's "strongest candidates" and our discovery goals. We proceed with known information since detailed workflow mapping from Jesslyn is pending. + +**Date**: 2026-06-17 +**Status**: Planning / Narrowed Scope +**Parent**: jesslyn-photographer-workflow experiment +**Related**: +- Odysseus report (research/odysseus-report-rp-222259c2ba04.md) +- 8 Use Cases baseline (research/use-cases-qwen.md) +- Main README (updated with 5 pillars) + +## What We Know (Current Baseline) +- Jesslyn spends a lot of time culling photos (primary pain point identified). +- From research baseline: + - Tools like Aftershoot, Photo Mechanic + AI, FilterPixel, Narrative Select, Imagen AI, Apex Culler. + - Current "persistence": Folder monitoring, blink/soft-focus/blur detection, near-duplicate grouping, ratings/tags, up to 95% accuracy claims. + - Limitations: Mostly technical flagging + rules; limited cross-shoot style memory or client-specific adaptation. +- General photography context: Culling can consume 30-40% of post-production time for high-volume shoots (e.g., weddings). + +## Hermes-Style Persistence Applied to Culling +Using the 5 pillars from the Odysseus report as the framework: + +1. **Cross-Shoot Memory** + - Agent remembers past culling decisions for similar shoots/clients. + - Example: "For this client last time, you kept the slightly soft wide shots for storytelling even if technically imperfect." + - Uses artifacts to store style bible + historic culling logs (keeps vs. discards + reasons). + +2. **State + Context Across Workflow Stages** + - Pre-shoot notes (e.g., "focus on candid moments") directly influence culling priorities. + - Client feedback from previous deliveries adjusts future culling (e.g., "client hated overly posed shots"). + +3. **Sub-Agent Coordination** + - Culling agent passes context to downstream agents (e.g., "these 50 images flagged as keepers — pass to retouching agent with style notes"). + - Could coordinate with metadata tagging or proof delivery later. + +4. **Self-Improvement from Corrections** + - When Jesslyn overrides (keeps a "bad" image or deletes a "good" one), agent learns and updates its model for future similar cases. + - Log corrections with context for continuous refinement. + +5. **Local-First + Explainable Decisions** + - Run culling analysis locally where possible (privacy for client images). + - Explain decisions: "Flagged for culling because: blink detected + low contrast compared to your preferred style from [past shoot]." + - Human always has final veto. + +## Proposed Phase 1 Scope (Narrow & Testable) + +**Goal**: Build a minimal persistent culling assistant that demonstrates the 5 pillars using Hermes tools (artifacts, state, sub-agents, feedback). + +**Inputs** (simulated or provided): +- Folder of raw images (or descriptions). +- Pre-shoot brief / client notes. +- Historical culling data (past shoots: what was kept/discarded + why, if available). +- Photographer's style profile (initially seeded from general knowledge + report). + +**Core Capabilities to Prototype**: +- Technical analysis (blinks, focus, exposure — can leverage existing models or simple rules initially). +- Style-based decisions (cross-shoot memory via artifacts). +- Context-aware prioritization (e.g., favor candid if pre-shoot says so). +- Output: Suggested cull list with explanations + ratings. +- Feedback loop: Log overrides and update persistent profile. + +**Out of Scope for Phase 1**: +- Full UI / on-set integration. +- Advanced vision (unless local model available). +- Integration with specific software (Lightroom, Capture One) yet. +- Other use cases (retouching, delivery, etc.). + +**Success Metrics for This Slice** (tied to 5 pillars): +- Cross-shoot: Agent recalls and applies at least one prior decision without re-prompting. +- State/Context: Pre-shoot notes visibly affect culling output. +- Self-improvement: After 2-3 correction examples, behavior changes on similar images. +- Explainability: Every suggestion includes clear "why" tied to memory or rules. +- Time/Quality: Simulated reduction in manual review time; human agrees with 80%+ of suggestions on test sets. +- Persistence test: Same agent instance "remembers" across multiple simulated sessions/shoots. + +## Hermes Implementation Ideas +- **Artifacts**: Store "Culling Style Bible" (rules + examples from past), historical cull logs per client/shoot. +- **State Files**: Current project state (pre-shoot notes, shoot metadata, in-progress cull decisions). +- **Sub-Agents**: One for technical flagging, one for style/context matching, one for explanation generation. +- **Feedback**: Explicit correction interface that updates artifacts/state. +- **Persistence Test**: Run "Session 1" (cull new shoot), apply corrections, run "Session 2" (similar shoot) — measure recall and adaptation. +- **Local/VPS**: Vision-heavy analysis local; memory/reasoning on VPS if needed. +- **Logging**: Everything — decisions, corrections, memory retrievals — for discovery insights. + +## Risks & Mitigations (from Report + General) +- Over-culling artistic choices: Mitigate with strong explainability + easy overrides. +- Privacy: Local-first where possible. +- Cold start: Seed initial profile with what we know + general photography best practices; improve via feedback. +- Evaluation: Use synthetic or anonymized test sets until real data available. + +## Next Steps for This Focus +1. Define detailed culling agent spec (inputs/outputs, memory schema). +2. Create initial "Culling Style Profile" artifact (seeded with known info). +3. Prototype minimal version (perhaps using existing Hermes skills + simple rules + artifact lookup). +4. Design persistence test protocol (multiple sessions, correction logging). +5. When Jesslyn responds: Incorporate real workflow details and actual sample data. + +## Open Questions +- Do we have (or can we simulate) example culling data from past shoots? +- Preferred output format (e.g., ratings, tags, delete list, gallery groups)? +- Any specific technical criteria she uses (beyond general blinks/soft focus)? +- How to measure "time spent" currently for baseline? + +This narrows the discovery to a concrete, high-value slice while staying true to testing real persistence. \ No newline at end of file