docs: add execution matrix to ecosystem map
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@ -349,7 +349,74 @@ This is enough to build a coherent ByteLyst agent runtime and operations layer,
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## 8. Recommended Next Documents
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## 8. Execution Matrix
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| Initiative | Primary Owner | Key Dependent Repos | Impact | Effort | Why It Should Happen Early |
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| --------------------------------------- | ---------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------ | ------ | ------ | -------------------------------------------------------------------------------------- |
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| Shared artifact schema | `learning_ai_common_plat` | `learning_voice_ai_agent`, `learning_ai_notes`, `learning_multimodal_memory_agents`, `learning_ai_flowmonk`, `oss/learning_ai_claw-cowork` | High | Medium | It unblocks memory, search, provenance, timelines, and agent output reuse |
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| Shared event taxonomy + action log | `learning_ai_common_plat` + `learning_ai_trails` | `oss/learning_ai_claw-cowork`, `learning_ai_flowmonk`, `learning_ai_jarvis_jr`, `learning_voice_ai_agent` | High | Medium | It turns fragmented agent/product telemetry into one reviewable substrate |
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| Unified approvals and trust model | `learning_ai_smart_auth` + `learning_ai_auth_app` + `learning_ai_trails` | `oss/learning_ai_claw-cowork`, `learning_ai_mac_tooling`, `learning_ai_common_plat` | High | Medium | It creates one policy model for login, step-up auth, and agent approvals |
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| Transcript -> note -> memory pipeline | `learning_voice_ai_agent` + `learning_ai_notes` + `learning_multimodal_memory_agents` | `learning_ai_common_plat` | High | Medium | It creates immediate user-visible value across three flagship products |
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| Plan -> routine -> habit handoff | `learning_ai_flowmonk` + `learning_ai_clock` + `learning_ai_efforise` | `learning_ai_common_plat` | High | Medium | It turns planning into follow-through instead of isolated planning data |
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| Cowork -> Trail -> Notes -> Memory flow | `oss/learning_ai_claw-cowork` + `learning_ai_trails` + `learning_ai_notes` + `learning_multimodal_memory_agents` | `learning_ai_common_plat` | High | High | It creates a differentiated audited-agent workflow that few ecosystems have |
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| Device trust ingestion | `learning_ai_mac_tooling` + `learning_ai_common_plat` | `learning_ai_smart_auth`, `learning_ai_auth_app`, `oss/learning_ai_claw-cowork` | Medium | Medium | It upgrades desktop/agent safety with existing assets already in the workspace |
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| Shared marketplace inventory model | `learning_ai_common_plat` | `learning_ai_jarvis_jr`, `oss/learning_ai_claw-cowork`, `learning_ai_notes`, `learning_ai_flowmonk`, `learning_ai_clock` | High | High | It can unify monetization and reusable asset distribution |
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| Shared personal timeline | `learning_ai_common_plat` | Almost all product repos | High | High | It becomes the ecosystem shell once events and artifacts are standardized |
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| Hybrid local/cloud AI routing | `learning_ai_local_llms` + `learning_ai_local_memory_gpt` + `learning_ai_common_plat` | `oss/learning_ai_claw-cowork`, `learning_multimodal_memory_agents`, `learning_voice_ai_agent` | Medium | High | It is strategically valuable, but depends on artifact and policy standardization first |
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## 9. Guardrails: What Not to Over-Unify
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Cross-pollination only helps if it centralizes the right seams. These areas should stay product-specific:
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1. Product voice, branding, and UX metaphors should remain local to each repo.
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2. Domain logic should remain local when it is the core product moat. Examples: fasting logic in `learning_ai_fastgap`, adventure session semantics in `learning_ai_peakpulse`, timer cascades in `learning_ai_clock`.
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3. Local-first privacy behavior in `learning_ai_local_memory_gpt` should not be weakened just to mirror cloud-first products.
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4. Agent runtime experimentation in `oss/learning_ai_claw-code-oss` and `oss/learning_ai_claw-cowork` should stay decoupled from platform policy enough to evolve safely.
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5. Shared platform should own contracts, policy, schemas, and orchestration primitives, but not erase the reason each product exists.
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The unification target should be:
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- identity
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- approvals
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- events
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- artifacts
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- notifications
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- marketplace inventory
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- retrieval contracts
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- observability
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The non-target should be each app's core domain experience.
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## 10. Suggested 90-Day Sequence
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### Days 1-30
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1. Draft the shared artifact schema.
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2. Draft the shared event taxonomy and action log schema.
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3. Draft the unified approval and trusted-device model.
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4. Draft the cross-product deep-link convention.
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### Days 31-60
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1. Implement the LysnrAI -> NoteLett -> MindLyst pipeline.
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2. Implement the FlowMonk -> ChronoMind -> EffoRise handoff.
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3. Implement the Cowork -> ActionTrail audit event emission path.
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4. Prototype mac-tooling -> platform-service device risk ingestion.
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### Days 61-90
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1. Stand up a personal timeline API and internal timeline viewer.
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2. Stand up a shared notification orchestration service.
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3. Convert marketplace concepts into one inventory model and one entitlement model.
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4. Add one operator cockpit view that merges auth, usage, risk, and AI action streams.
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---
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## 11. Recommended Next Documents
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This document should lead to a small set of follow-on specs rather than another broad audit:
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@ -361,7 +428,7 @@ This document should lead to a small set of follow-on specs rather than another
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## 9. Bottom Line
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## 12. Bottom Line
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The ecosystem's biggest unrealized strength is not any single app. It is the combination of:
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