143 lines
3.1 KiB
Markdown
143 lines
3.1 KiB
Markdown
# BuildPulse Roadmap
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## Philosophy
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Each version must be useful on its own.
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Do not build future architecture before the current manual workflow is proven.
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## v0.1 — Single-Project Planning Cockpit
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Goal:
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Create the smallest useful cockpit for one project.
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Includes:
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- Single project summary
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- Feature Plan
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- Parking Lot
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- Manual Pulse Log
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- Markdown/JSON/JSONL export
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- Local persistence
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Does not include:
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- AI
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- Phases
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- Releases
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- Multiple projects
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- Real agent integration
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## v0.2 — AI Idea Placement
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Goal:
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Use AI to help classify new ideas.
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Potential features:
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- AI classify idea as Now / Next / Later / Parking Lot / Reject
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- AI suggest reason for placement
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- AI suggest smallest safe version
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- AI detect scope creep
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- AI generate acceptance criteria draft
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Build only one or two AI actions first.
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Highest-value first AI action:
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```text
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Classify new idea and suggest placement.
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```
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## v0.3 — Phases and Releases
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Goal:
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Add structured implementation planning.
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Potential features:
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- Phases
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- Releases
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- Definition of done
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- Forbidden features per release
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- Required vs optional features
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- Release readiness view
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## v0.4 — Handoff Workflow Hardening
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Goal:
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Turn a chosen feature into a sharp, target-specific AI coding brief with as little friction as possible.
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Current shipped slices:
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- v0.3.1 — focused handoff shortcuts
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- v0.3.2 — target-specific handoff presets
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- v0.4.0 — one-tap feature handoffs
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- v0.4.1 — preview/edit before copy + explicit INTENT controls
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Planned slices:
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- v0.4.2 — paste agent result into RESULT/BLOCKER/TEST_RESULT pulses
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- v0.4.3 — session modes (30-minute, feature-based, bugfix, QA review)
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Core rules:
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- Keep handoff generation close to the feature decision surface.
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- Include release/phase context, blockers, parking-lot warnings, and return format.
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- Do not add live execution, telemetry, router logic, or agent streaming in this phase.
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## v0.5 — Local/Cloud AI Assistant
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Goal:
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Start learning where local LLMs are good enough.
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Potential features:
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- Configure local endpoint manually
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- Use local LLM for summaries
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- Use local LLM for feature classification
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- Track model usefulness
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- Manual cloud fallback
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## v1.0 — Agent Pulse Ingestion MVP
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Goal:
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Allow real agents/tools to submit Pulse events.
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Potential features:
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- Simple HTTP API for Pulse events
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- Append-only pulse event store
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- Real-time or refresh-based timeline
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- Feature state updated from events
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- Basic progress computation
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- Safety/event validation
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## v1.5 — Agent Observability
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Goal:
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Show real live-ish agent activity.
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Potential features:
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- Agent activity view
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- Blocker/risk view
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- Handoff events
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- Evidence refs
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- Test result parsing
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- Git commit refs
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## v2.0 — Agent Pulse Framework
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Goal:
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Become a real event-driven nervous system for autonomous agents.
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Potential features:
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- WebSocket stream
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- Event subscriptions
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- Agent coordination protocol
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- Ownership claims
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- Handoff handling
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- Progress engine
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- Constraints and approval gates
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- Plugin system
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## Long-Term Vision
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BuildPulse becomes the human cockpit for:
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- Feature planning
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- Idea maturation
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- Scope control
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- AI agent work visibility
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- Local/cloud AI routing
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- Autonomous agent observability
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