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# AI Photography Assistant - Hermes Discovery Experiment (Jesslyn's Workflow)
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**Status**: Experimental / Discovery Project
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**Primary Goal**: Test the effectiveness and limitations of persistent AI agents (via Hermes) in a real creative workflow.
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**Secondary Goal**: Explore whether this can provide practical value to a professional photographer (Jesslyn's business).
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**Date Started**: 2026-06-17
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**Owner**: Leonard / Tony
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**Key Reference**: [Odysseus Deep Research Report - The Dawn of Persistent AI Agents in Professional Photography (rp-222259c2ba04)](research/odysseus-report-rp-222259c2ba04.md)
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This report (accessed via provided credentials) validates our direction, expands on the 8 use cases, and explicitly defines "Hermes-style persistence." New insights have been integrated below.
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## Core Concept
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A Hermes-based persistent AI partner that learns a photographer’s style, preferences, and business operations. It acts as a creative second brain across pre-shoot, on-set, post-production, and admin.
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This is **not** primarily about building a product. It is about discovery: What does "persistent" actually mean in practice? Where does it add value? Where does it break? How much human oversight is truly required?
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## Key Capabilities to Explore
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| Area | Potential Hermes Role | Persistence Questions to Test |
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|------------------|---------------------------------------------------|-------------------------------|
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| Pre-Shoot | Personalized shot lists, lighting plans from briefs + past work. | Can it recall style from previous shoots without re-prompting? |
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| Post-Production | Culling, tagging, edit suggestions, gallery sequencing. | Does style memory improve suggestion quality over multiple sessions? |
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| Business | Emails, proposals, invoicing, scheduling via Leonard-style automation. | How reliably can admin tasks run with state carried forward? |
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| On-Set | Voice co-pilot suggestions. | Can short-term state + long-term memory combine for useful real-time help? |
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## Experimental Focus: Persistence (Updated with Odysseus Report Insights)
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This project will specifically stress-test Hermes persistence mechanisms, aligned with the five pillars of **Hermes-style persistence** identified in the Odysseus report:
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1. **Cross-Shoot Memory** — Remember actual style decisions from previous jobs (not just static presets). Dynamic memory that evolves (e.g., client-specific preferences like warmer tones for certain portraits).
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2. **State + Context Across Workflow Stages** — Understand interconnectedness: pre-shoot decisions inform post-production; client feedback informs future culling. Example: Prioritize candid shots based on pre-shoot notes and adjust retouching style accordingly.
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3. **Sub-Agent Coordination** — "Culling agent talks to retouching agent talks to delivery agent." Seamless information flow and coordinated decision-making across modules.
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4. **Self-Improvement from Corrections Over Time** — Continuous learning from human overrides and manual adjustments. Aligns with emerging tools like Aftershoot's self-improving profiles.
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5. **Local-First + Explainable Decisions** — Photographers must understand *why* an AI made a choice. Maintains creative control, supports client explanations, and keeps sensitive data private.
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**Hermes Capabilities to Leverage**:
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- Artifact-based memory (long-term style bible, client history, decisions)
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- Structured state files for ongoing projects
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- Sub-agent handoff with context passing
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- Self-improvement from human feedback/corrections
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- Cross-session recall (days/weeks apart)
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- Hybrid local (MacBook) + VPS execution
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**Success will be measured by**:
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- Reduced need to re-explain style/preferences over time
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- Measurable time savings on repetitive tasks
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- Quality of outputs (human judgment)
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- Number and type of interventions required
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- Agent's ability to surface relevant past work without explicit search
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- Demonstrable application of the 5 pillars above
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**Report Recommendation**: Prioritize **deep workflow mapping** of Jesslyn's actual process *before* building specific agents. Ground development in real needs.
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## Realistic Assessment (from original proposal + report)
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**Upsides**:
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- Time savings on repetitive tasks (culling, admin)
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- Deep style learning via persistent memory
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- Strong privacy (local-first)
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- Extensible skills hub
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- Hybrid MacBook + VPS setup
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- Potential for genuine self-improvement and cross-workflow intelligence
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**Downsides & Limitations** (to be tested):
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- Requires human oversight (especially creative decisions)
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- Slower performance on older hardware for vision tasks
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- Initial setup and tuning effort
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- Not fully autonomous
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- Integration may need debugging
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- Ethical risks (authenticity, bias, copyright, privacy) — must be addressed transparently
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## Project Principles
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- Discovery first, utility second.
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- Log everything: what worked, what didn't, why.
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- Start narrow, expand only after evidence.
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- Use real workflows (Jesslyn's business) for validity.
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- Keep it grounded — no hype about full replacement of the photographer.
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- Incorporate advanced memory concepts (e.g., Retrieval-Augmented approaches, ACAN-style attention) where they enhance Hermes capabilities.
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## Next Steps (Discovery-Oriented) — Updated with Report
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1. **Map Jesslyn’s actual workflow in detail** (pre-shoot → on-set → post → admin). This is the report's #1 strategic recommendation before technical work.
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2. Ingest sample briefs, past shoots, and style references into a persistent profile (start with top candidates: retouching style learning + culling).
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3. Build minimal Phase 1 agent focused on one high-persistence area (e.g., Personal Style Learning for Retouching or Automated Culling, per report).
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4. Explicitly implement/test the 5 Hermes-style persistence pillars.
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5. Run controlled tests across multiple "sessions" (simulated and real).
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6. Document findings in this repo (what persisted, what required re-instruction, surprises, alignment with report insights).
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7. Consider ethical guardrails and explainability from the start.
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## Related
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- **Parent research**: Tony_tech/research (primary location for this work)
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- Odysseus Report: `research/odysseus-report-rp-222259c2ba04.md` (full integration of new research)
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- Previous use cases baseline: `research/use-cases-qwen.md`
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- Hermes capabilities to leverage: artifacts, memory, state tracking, sub-agents, skills, cron, tool integration, local-first architecture.
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This experiment will help answer: In what ways can persistent agents actually compound value in creative professional work?
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**New from Odysseus Report**: Strong validation of our focus. Recommends starting with workflow mapping + the two highest-potential use cases. Introduces concrete 5-pillar framework and memory architecture ideas for implementation.
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**Note**: This folder was migrated/ensured in Tony_tech/research as the primary home for the discovery work (previously also tracked under Ty_Tech/research for testing).
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# Phase 1: Automated Culling Persistence Experiment
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**Focus**: Automated Culling (top recommendation from Odysseus report alongside Personal Style Learning)
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**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.
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**Date**: 2026-06-17
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**Status**: Planning / Narrowed Scope
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**Parent**: jesslyn-photographer-workflow experiment
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**Related**:
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- Odysseus report (research/odysseus-report-rp-222259c2ba04.md)
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- 8 Use Cases baseline (research/use-cases-qwen.md)
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- Main README (updated with 5 pillars)
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## What We Know (Current Baseline)
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- Jesslyn spends a lot of time culling photos (primary pain point identified).
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- From research baseline:
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- Tools like Aftershoot, Photo Mechanic + AI, FilterPixel, Narrative Select, Imagen AI, Apex Culler.
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- Current "persistence": Folder monitoring, blink/soft-focus/blur detection, near-duplicate grouping, ratings/tags, up to 95% accuracy claims.
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- Limitations: Mostly technical flagging + rules; limited cross-shoot style memory or client-specific adaptation.
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- General photography context: Culling can consume 30-40% of post-production time for high-volume shoots (e.g., weddings).
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## Hermes-Style Persistence Applied to Culling
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Using the 5 pillars from the Odysseus report as the framework:
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1. **Cross-Shoot Memory**
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- Agent remembers past culling decisions for similar shoots/clients.
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- Example: "For this client last time, you kept the slightly soft wide shots for storytelling even if technically imperfect."
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- Uses artifacts to store style bible + historic culling logs (keeps vs. discards + reasons).
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2. **State + Context Across Workflow Stages**
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- Pre-shoot notes (e.g., "focus on candid moments") directly influence culling priorities.
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- Client feedback from previous deliveries adjusts future culling (e.g., "client hated overly posed shots").
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3. **Sub-Agent Coordination**
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- Culling agent passes context to downstream agents (e.g., "these 50 images flagged as keepers — pass to retouching agent with style notes").
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- Could coordinate with metadata tagging or proof delivery later.
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4. **Self-Improvement from Corrections**
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- When Jesslyn overrides (keeps a "bad" image or deletes a "good" one), agent learns and updates its model for future similar cases.
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- Log corrections with context for continuous refinement.
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5. **Local-First + Explainable Decisions**
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- Run culling analysis locally where possible (privacy for client images).
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- Explain decisions: "Flagged for culling because: blink detected + low contrast compared to your preferred style from [past shoot]."
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- Human always has final veto.
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## Proposed Phase 1 Scope (Narrow & Testable)
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**Goal**: Build a minimal persistent culling assistant that demonstrates the 5 pillars using Hermes tools (artifacts, state, sub-agents, feedback).
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**Inputs** (simulated or provided):
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- Folder of raw images (or descriptions).
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- Pre-shoot brief / client notes.
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- Historical culling data (past shoots: what was kept/discarded + why, if available).
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- Photographer's style profile (initially seeded from general knowledge + report).
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**Core Capabilities to Prototype**:
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- Technical analysis (blinks, focus, exposure — can leverage existing models or simple rules initially).
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- Style-based decisions (cross-shoot memory via artifacts).
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- Context-aware prioritization (e.g., favor candid if pre-shoot says so).
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- Output: Suggested cull list with explanations + ratings.
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- Feedback loop: Log overrides and update persistent profile.
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**Out of Scope for Phase 1**:
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- Full UI / on-set integration.
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- Advanced vision (unless local model available).
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- Integration with specific software (Lightroom, Capture One) yet.
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- Other use cases (retouching, delivery, etc.).
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**Success Metrics for This Slice** (tied to 5 pillars):
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- Cross-shoot: Agent recalls and applies at least one prior decision without re-prompting.
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- State/Context: Pre-shoot notes visibly affect culling output.
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- Self-improvement: After 2-3 correction examples, behavior changes on similar images.
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- Explainability: Every suggestion includes clear "why" tied to memory or rules.
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- Time/Quality: Simulated reduction in manual review time; human agrees with 80%+ of suggestions on test sets.
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- Persistence test: Same agent instance "remembers" across multiple simulated sessions/shoots.
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## Hermes Implementation Ideas
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- **Artifacts**: Store "Culling Style Bible" (rules + examples from past), historical cull logs per client/shoot.
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- **State Files**: Current project state (pre-shoot notes, shoot metadata, in-progress cull decisions).
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- **Sub-Agents**: One for technical flagging, one for style/context matching, one for explanation generation.
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- **Feedback**: Explicit correction interface that updates artifacts/state.
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- **Persistence Test**: Run "Session 1" (cull new shoot), apply corrections, run "Session 2" (similar shoot) — measure recall and adaptation.
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- **Local/VPS**: Vision-heavy analysis local; memory/reasoning on VPS if needed.
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- **Logging**: Everything — decisions, corrections, memory retrievals — for discovery insights.
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## Risks & Mitigations (from Report + General)
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- Over-culling artistic choices: Mitigate with strong explainability + easy overrides.
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- Privacy: Local-first where possible.
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- Cold start: Seed initial profile with what we know + general photography best practices; improve via feedback.
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- Evaluation: Use synthetic or anonymized test sets until real data available.
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## Next Steps for This Focus
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1. Define detailed culling agent spec (inputs/outputs, memory schema).
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2. Create initial "Culling Style Profile" artifact (seeded with known info).
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3. Prototype minimal version (perhaps using existing Hermes skills + simple rules + artifact lookup).
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4. Design persistence test protocol (multiple sessions, correction logging).
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5. When Jesslyn responds: Incorporate real workflow details and actual sample data.
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## Open Questions
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- Do we have (or can we simulate) example culling data from past shoots?
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- Preferred output format (e.g., ratings, tags, delete list, gallery groups)?
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- Any specific technical criteria she uses (beyond general blinks/soft focus)?
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- How to measure "time spent" currently for baseline?
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This narrows the discovery to a concrete, high-value slice while staying true to testing real persistence.
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# Odysseus Deep Research Report: The Dawn of Persistent AI Agents in Professional Photography
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**Report ID**: rp-222259c2ba04
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**Source**: https://odysseus.vps1.afterthedemo.com/api/research/report/rp-222259c2ba04
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**Generated**: ~June 2026 (via google/gemini-2.5-flash + searxng)
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**Duration**: 232.5s | 7 Rounds | 18 Queries | 62 URLs Analyzed
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**Relevance**: This report directly informs the `jesslyn-photographer-workflow` Hermes discovery experiment. It builds on the same 8 use cases previously researched and explicitly defines "Hermes-style persistence."
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## Executive Summary
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The professional photography landscape in 2026 is undergoing a profound transformation, driven by the increasing integration of Artificial Intelligence. While AI tools are already streamlining numerous aspects of the workflow, from automated culling to intelligent metadata tagging, the current generation of "persistent" agents largely operates within static rules, pre-defined presets, or simple automation frameworks like Zapier.
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This report delves into the critical distinction between these existing solutions and the emerging concept of **"Hermes-style persistence"** — AI agents capable of true cross-shoot memory, contextual understanding across workflow stages, sub-agent coordination, self-improvement through feedback, and local-first, explainable decision-making.
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Drawing from a comprehensive research baseline of eight key AI use cases in photography, this report identifies **"Personal Style Learning for Retouching"** and **"Automated Culling"** as the strongest candidates for immediate development and testing of truly persistent AI.
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The analysis highlights a fragmented market where specialized AI tools are becoming routine, yet a unified, truly persistent agent remains largely aspirational. The report concludes with a strategic recommendation to **prioritize workflow mapping** before deep-diving into specific use cases.
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## Research Baseline: Eight Pillars of AI in Photography
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(The report validates and expands on the eight use cases previously documented from local Qwen research. See `research/use-cases-qwen.md` for the detailed baseline.)
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1. Automated Culling
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2. Consistent Color Grading & Style Transfer
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3. Intelligent Metadata Tagging & Keywording
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4. Automated Client Proof Delivery & Feedback Integration
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5. Always-On Backup & Archival Management
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6. Personal Style Learning for Retouching
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7. Automated Social Media Churn & SEO
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8. Real-Time Composition Assist During Tethered Shooting
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**Top Recommendations for Persistence Testing**:
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- Personal Style Learning for Retouching (highest potential for genuine memory and self-improvement)
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- Automated Culling
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## Hermes Agent Analysis: The Quest for True Persistence
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Current tools are primarily "folder watchers + rules/ML", "static presets", or "Zapier glue". Their "memory" is limited to single tasks or pre-defined parameters.
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**Hermes-style persistence** requires agents with more human-like capacity for learning, memory, and adaptation. The five key pillars:
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### 1. Cross-Shoot Memory
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- Current: Applies learned style or culling to a single shoot.
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- Hermes-style: Remembers actual style decisions from previous jobs (not just static presets).
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- Example: Recall that a specific client prefers slightly warmer tones for outdoor portraits, even if general style is cooler. Dynamic memory that evolves with each project.
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### 2. State + Context Across Workflow Stages
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- Current: Siloed per task.
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- Hermes-style: Understands interconnectedness — pre-shoot decisions inform post-production; client feedback informs future culling.
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- Example: Knowing a client's preference for candid shots from pre-shoot, prioritize them in culling and apply natural retouching.
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### 3. Sub-Agent Coordination
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- Current: Photographers juggle multiple fragmented tools.
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- Hermes-style: "Culling agent talks to retouching agent talks to delivery agent."
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- Enables seamless information flow and coordinated decision-making across modules.
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### 4. Self-Improvement from Corrections Over Time
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- Current: Static after initial training.
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- Hermes-style: Continuous learning from human overrides and manual adjustments.
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- Aligns with Aftershoot's 2026 roadmap for self-improving AI profiles.
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### 5. Local-First + Explainable Decisions
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- Critical for client work and privacy.
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- Photographers need to understand *why* an AI made a choice.
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- Maintains creative control and supports explaining process to clients.
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- Sensitive data stays under photographer's control.
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These pillars define the aspirational goal for our experiment.
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## AI Memory Limitations & Future Architectures
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Current LLMs have fixed "context windows" — short-term working memory. Information outside the window is forgotten, preventing true long-term memory without retraining.
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Promising architectures for true persistence:
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- **Retrieval-Augmented Transformers (RATs)**: Integrate external searchable databases for long-term memory.
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- **RAPTOR Hierarchical Memory Systems**: Organize memories hierarchically for efficient access to immediate context and distant experiences.
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- **Memory Mosaics**: Specialized memory modules for different information types or tasks.
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- **Neuro-Symbolic AI**: Combine neural pattern recognition with symbolic logical reasoning for robust, interpretable memories.
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- **Compressive Transformers**: Efficiently reference and compress older information.
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- **Auxiliary Cross Attention Networks (ACAN)**: Trained with LLM assistance to calculate and rank attention weights between current state and stored memories. Particularly promising for cross-shoot memory and workflow-state retrieval. Enhances retrieval quality, adaptability, and consistency beyond simple keyword matching.
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## Ethical Considerations
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- **Authenticity**: Risk of AI altering the "truth" of a photograph.
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- **Bias**: Models may perpetuate or amplify biases in training data.
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- **Copyright**: Issue around training on photographers' work and ownership of AI-generated or assisted outputs.
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- **Privacy**: Handling of sensitive client images and data.
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Emphasizes transparency and responsible development.
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## Conclusion & Strategic Recommendations
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The goal is to move beyond automation toward intelligent, adaptive partners that learn and evolve with the photographer.
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**Key Recommendation**: Prioritize **workflow mapping** before technical implementation. Ensure AI development is grounded in real-world needs and nuanced processes of professional photographers.
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**Immediate Focus Areas** (from report):
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- Deep workflow mapping of Jesslyn's process.
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- Prototype persistent agents in Personal Style Learning for Retouching and/or Automated Culling.
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- Leverage advanced memory architectures (e.g., RATs, ACAN concepts) where feasible in Hermes implementation.
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- Build in self-improvement loops and explainability from the start.
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This report strongly validates the direction of the jesslyn-photographer-workflow experiment.
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## Sources & Further Reading
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- Report draws from industry sources including Aftershoot roadmap 2026, Upuply, StackAI, arXiv papers, Forbes, myNeuron blog, etc.
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- Full raw report available at the source URL (requires authentication).
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---
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*Integrated into Ty_Tech/research/jesslyn-photographer-workflow on 2026-06-17. Primary purpose: ground the Hermes persistence discovery experiment in external research while applying new insights.*
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# Photography AI Use Cases — Research from Local Qwen (2025)
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**Source**: Curated output from local Qwen model, June 2025 references.
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**Purpose**: Raw material for Hermes discovery experiment. These represent real-world "persistent agent" patterns in photography workflows.
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This document captures eight common use cases where photographers are already using (or experimenting with) always-on or folder-watching AI agents. The goal is to analyze where Hermes-style persistent agents (with memory, state, sub-agents, local-first design) could provide advantages over current tools.
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## 1. Automated Culling & Initial Selection
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**Current Tools**:
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- Aftershoot — AI Culling: persistent folder monitoring, blink/soft-focus detection, batch deletion.
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- Photo Mechanic + AI — "Photo Mechanic Plus: Using AI to flag images": watches folder and applies ratings/tags automatically.
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**Hermes Opportunity**:
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- Persistent style memory: learn what *this* photographer keeps vs. culls across shoots.
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- Sub-agent for multi-criteria (technical + artistic + client-specific).
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- Artifact-based history: "In the last 5 weddings, you always kept the slightly soft wide shots for storytelling."
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- Local-first culling with vision model on MacBook.
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## 2. Consistent Color Grading & Style Transfer
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**Current Tools**:
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- Dehancer Batch Processor: persistent workflow for applying learned film-style LUTs to incoming images.
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- Lightroom AI Presets: watches for new imports and applies a learned style.
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**Hermes Opportunity**:
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- Deep style learning via persistent artifacts (not just presets).
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- Cross-shoot consistency + client-specific adjustments remembered.
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- Reasoning: "This is an indoor event like the Smith wedding last month — apply warmer skin tones and lower contrast."
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- Self-improvement from your manual tweaks over time.
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## 3. Intelligent Metadata Tagging & Keywording
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**Current Tools**:
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- Shutterstock Contributor Guide — AI-Powered Keywording: persistent agent that automatically adds IPTC keywords based on content analysis.
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- Imagen AI — "Automated Workflows: Keywording": persistent agent that reads image content and writes metadata.
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||||
|
||||
**Hermes Opportunity**:
|
||||
- Photographer-specific vocabulary (not generic stock keywords).
|
||||
- Persistent client/project context: "For this corporate client, always tag with their product names and avoid certain descriptors."
|
||||
- Multi-modal + memory: combine image content with previous shoot notes.
|
||||
|
||||
## 4. Automated Client Proof Delivery & Feedback Integration
|
||||
**Current Tools**:
|
||||
- Pixieset + Zapier integrations: community-built Zapier workflows that create a persistent agent: watch Lightroom catalog → upload proofs → notify client.
|
||||
- StudioPlus Guide — Client Proofing Automation: case study of a portrait studio using a persistent AI agent to deliver proofs within 24 hours.
|
||||
|
||||
**Hermes Opportunity**:
|
||||
- Intelligent selection before delivery (using learned client preferences).
|
||||
- State tracking across delivery + feedback loop.
|
||||
- Sub-agent that learns from client comments over multiple jobs.
|
||||
- Full workflow integration (not just Zapier glue).
|
||||
|
||||
## 5. Always-On Backup & Archival Management
|
||||
**Current Tools**:
|
||||
- Hedge for Photographers — Smart Archiving: agent that verifies checksums and uses vision AI to identify near-duplicates, archiving only the sharpest.
|
||||
- Backblaze — Automated Backup for Travel Photographers: tutorial on setting up a persistent agent that backs up only new/critical files.
|
||||
|
||||
**Hermes Opportunity**:
|
||||
- Smart archival decisions based on project state and past usage.
|
||||
- Persistent knowledge of "what I usually need from this type of shoot."
|
||||
- Integration with culling/retouching state.
|
||||
|
||||
## 6. Personal Style Learning for Retouching
|
||||
**Current Tools**:
|
||||
- Imagen AI — Profile-Based Editing: persistent agent that watches retouching sessions, learns local adjustments, and auto-applies to new images.
|
||||
- Adobe Firefly Custom Models — "Personal Style Transfer for Photographers": explains fine-tuning a generative AI model to replicate a photographer's retouching style across batches.
|
||||
|
||||
**Hermes Opportunity** (biggest potential):
|
||||
- True persistent memory of your retouching decisions across hundreds of images.
|
||||
- Explainable: "I applied this dodge because of the lighting direction seen in the raw."
|
||||
- Sub-agents for different genres (wedding vs. commercial vs. portrait).
|
||||
- Local execution + privacy for client work.
|
||||
|
||||
## 7. Automated Social Media Churn & SEO
|
||||
**Current Tools**:
|
||||
- Later + Zapier — AI Caption & Schedule Agent: persistent agent pulls best-rated image, generates captions via GPT-4, checks trending hashtags, and schedules.
|
||||
- 500px Contributor Dashboard: built-in persistent agent that scores image aesthetics and auto-posts to maximize engagement.
|
||||
|
||||
**Hermes Opportunity**:
|
||||
- Deep integration with your actual best work (using internal ratings + memory).
|
||||
- Consistent brand voice learned from your past posts.
|
||||
- Multi-platform state (what performed where).
|
||||
- Avoid generic AI slop by grounding in your real portfolio.
|
||||
|
||||
## 8. Real-Time Composition Assist During Tethered Shooting
|
||||
**Current Tools**:
|
||||
- Capture One + AI Assist: persistent agent that analyzes each tethered shot, overlays guides, and flags distractions.
|
||||
- Tether Tools — Case Study: Architectural Photography: interview with an architectural photographer using an AI agent that highlights distortion and cables during a tethered shoot.
|
||||
|
||||
**Hermes Opportunity**:
|
||||
- Voice co-pilot with your personal style knowledge ("For this client, you usually want more negative space on the left").
|
||||
- Persistent memory of previous tethered sessions with the same setup/client.
|
||||
- Multi-agent: one for technical flags, one for creative suggestions.
|
||||
|
||||
## General Reference for Persistent AI Agents in Photography
|
||||
"How Photographers Are Using AI Agents" — DPReview (2025): Overview covering culling, color grading, metadata, and proofing as persistent workflows.
|
||||
|
||||
## Hermes Differentiation Opportunities (Discovery Focus)
|
||||
|
||||
Current tools are mostly:
|
||||
- Folder watchers + ML inference
|
||||
- Preset/LUT application
|
||||
- Zapier-style automation
|
||||
- Single-purpose agents
|
||||
|
||||
**Where Hermes-style persistence can go further**:
|
||||
- Cross-workflow memory (one agent that knows your culling style *and* your retouching style *and* client preferences).
|
||||
- Reasoning + explanation (not just "apply this" but "why").
|
||||
- Self-improvement from explicit feedback over time.
|
||||
- Sub-agent orchestration (pre-shoot planner talks to post-production agent).
|
||||
- Local-first + privacy control.
|
||||
- State that survives across days/weeks and multiple shoots.
|
||||
- Integration with business logic (proposals, scheduling, invoicing).
|
||||
|
||||
## Recommended Next for Experiment
|
||||
1. Pick 1-2 use cases to prototype first (recommend starting with #6 Personal Style Learning or #1 Culling, as they have the strongest persistence angle).
|
||||
2. Document your friend's actual workflow in detail.
|
||||
3. Build a minimal persistent profile (style references + past decisions) using Hermes artifacts.
|
||||
4. Create a simple agent that demonstrates memory across "sessions".
|
||||
5. Measure: how much re-explanation is avoided? How consistent are outputs?
|
||||
|
||||
**Source note**: All references are from 2025 vendor docs, case studies, and industry articles as provided by local Qwen.
|
||||
|
||||
---
|
||||
*This file is part of the Hermes Photography Assistant Discovery Experiment (Ty_Tech/research/jesslyn-photographer-workflow). Primary purpose: understand where persistent agents add value in creative work.*
|
||||
Reference in New Issue
Block a user