Radar and imports
Discover and attach public opportunities to private workspace jobs.
CareerVectorBlueprint · careervector.blueprint-graph/v5
Graph explorer
Twenty composable top-level diagrams plus recursively nested 21×21 semantic layers. Nodes descend; boundary arrows cross to neighbours.
Cell 5.2 · product · data · evidence
Career-intelligence workspace
The job-market matrix unifies opportunity tracking, evidence, decisions, documents, collaboration, and agent work.
- Radar and imports to Workspace matrix: add public projection
- Workspace matrix to CV · CL · application work: evaluate + tailor
- CV · CL · application work to and from Humans and agents: review + edit
Workspace matrix
Jobs as rows; facts, judgments, state, scores, and outputs as columns.
CareerVector D1/YjsCV · CL · application work
Quarry, tailoring, PDF, notes, and application history.
CareerVector workspaceHumans and agents
Link-based collaborators operate on the same typed workspace truth.
Workspace participants01 · process · mixed Radar and imports Discover and attach public opportunities to private workspace jobs.
CareerVector02 · perspective · as-built Workspace matrix Jobs as rows; facts, judgments, state, scores, and outputs as columns.
CareerVector D1/Yjs03 · process · mixed CV · CL · application work Quarry, tailoring, PDF, notes, and application history.
CareerVector workspace04 · actor · as-built Humans and agents Link-based collaborators operate on the same typed workspace truth.
Workspace participants
Flows
- Radar and imports → Workspace matrix add public projection
- Workspace matrix → CV · CL · application work evaluate + tailor
- CV · CL · application work ↔ Humans and agents review + edit
Canonical architecture note
Open note ↗CareerVector Experience
Purpose
Explain the complete interaction model and visual language of CareerVector. The experience spans private product work, optional public contribution, AI execution, support, and system state; it cannot be represented by the central product square alone.
Owns
- Workspace, radar, CV/CL, job, evaluation, application, and collaboration interactions.
- Consistent behavior across browser and Tauri desktop delivery.
- Human UI, API, and MCP perspectives over the same canonical workspace state.
- Scraping and Local AI pills as truthful, consent-based participation controls.
- “Smart Routing AI” explanations, explicit overrides, BYOK, and local/included availability.
- Visual language for origin, activity, uncertainty, freshness, failure, and recovery.
Does not own
- Canonical public role/ad evidence.
- Scraper scheduling or routing mathematics.
- Provider policy, legal interpretation, or operator authority.
Current state
The shared application shell, collaborative workspace, web and desktop participant bridge, Scraping pill, adaptive AI routing foundation, and explanation surfaces exist at different levels of completeness. The Local AI adoption experience and several product workflows remain target work.
Agent contract
User-visible changes require browser evidence. Shared UI is extracted only where behavior and semantics are truly shared; JobCache and CareerVector are not skins over one product.
Completion evidence
Complete responsive journeys in web and desktop; accessibility; offline and collaboration behavior; visual snapshots; route explanations; consent and withdrawal; and error recovery.