From 341cde75af404f3fd9bc4aba18677b0db777e29b Mon Sep 17 00:00:00 2001 From: Saravana Achu Mac Date: Fri, 3 Apr 2026 14:27:24 -0700 Subject: [PATCH] docs: add execution matrix to ecosystem map --- ...OSYSTEM_CROSS_POLLINATION_OPPORTUNITIES.md | 71 ++++++++++++++++++- 1 file changed, 69 insertions(+), 2 deletions(-) diff --git a/docs/ECOSYSTEM_CROSS_POLLINATION_OPPORTUNITIES.md b/docs/ECOSYSTEM_CROSS_POLLINATION_OPPORTUNITIES.md index e30ebebd..818ac1fd 100644 --- a/docs/ECOSYSTEM_CROSS_POLLINATION_OPPORTUNITIES.md +++ b/docs/ECOSYSTEM_CROSS_POLLINATION_OPPORTUNITIES.md @@ -349,7 +349,74 @@ This is enough to build a coherent ByteLyst agent runtime and operations layer, --- -## 8. Recommended Next Documents +## 8. Execution Matrix + +| Initiative | Primary Owner | Key Dependent Repos | Impact | Effort | Why It Should Happen Early | +| --------------------------------------- | ---------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------ | ------ | ------ | -------------------------------------------------------------------------------------- | +| 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 | +| 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 | +| 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 | +| 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 | +| 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 | +| 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 | +| 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 | +| 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 | +| Shared personal timeline | `learning_ai_common_plat` | Almost all product repos | High | High | It becomes the ecosystem shell once events and artifacts are standardized | +| 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 | + +--- + +## 9. Guardrails: What Not to Over-Unify + +Cross-pollination only helps if it centralizes the right seams. These areas should stay product-specific: + +1. Product voice, branding, and UX metaphors should remain local to each repo. +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`. +3. Local-first privacy behavior in `learning_ai_local_memory_gpt` should not be weakened just to mirror cloud-first products. +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. +5. Shared platform should own contracts, policy, schemas, and orchestration primitives, but not erase the reason each product exists. + +The unification target should be: + +- identity +- approvals +- events +- artifacts +- notifications +- marketplace inventory +- retrieval contracts +- observability + +The non-target should be each app's core domain experience. + +--- + +## 10. Suggested 90-Day Sequence + +### Days 1-30 + +1. Draft the shared artifact schema. +2. Draft the shared event taxonomy and action log schema. +3. Draft the unified approval and trusted-device model. +4. Draft the cross-product deep-link convention. + +### Days 31-60 + +1. Implement the LysnrAI -> NoteLett -> MindLyst pipeline. +2. Implement the FlowMonk -> ChronoMind -> EffoRise handoff. +3. Implement the Cowork -> ActionTrail audit event emission path. +4. Prototype mac-tooling -> platform-service device risk ingestion. + +### Days 61-90 + +1. Stand up a personal timeline API and internal timeline viewer. +2. Stand up a shared notification orchestration service. +3. Convert marketplace concepts into one inventory model and one entitlement model. +4. Add one operator cockpit view that merges auth, usage, risk, and AI action streams. + +--- + +## 11. Recommended Next Documents This document should lead to a small set of follow-on specs rather than another broad audit: @@ -361,7 +428,7 @@ This document should lead to a small set of follow-on specs rather than another --- -## 9. Bottom Line +## 12. Bottom Line The ecosystem's biggest unrealized strength is not any single app. It is the combination of: