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Fix dashboard hydration and archive theming

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Vijayakanth Manoharan
2026-03-23 23:08:18 -04:00
parent c852ad0d80
commit feb2f2c37e
25 changed files with 290 additions and 13 deletions
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openspec/specs/monthly-insights/spec.md Normal file
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## Purpose
TBD
## Requirements
### Requirement: User can generate monthly AI insights on demand
The system SHALL allow the user to manually generate monthly AI insights for any month with existing or sparse data by sending structured monthly context to a fully offline local inference runtime.
#### Scenario: Insights are generated for a month with data
- **WHEN** the user requests insight generation for a month with recorded activity
- **THEN** the system sends monthly aggregates plus transaction samples to the local AI service and returns a rendered narrative summary with structured supporting totals
#### Scenario: Prior month insights can be generated
- **WHEN** the user requests insight generation for a previous month that has recorded data
- **THEN** the system generates and stores insight output for that requested month
### Requirement: Insight generation is read-only and safe for sparse months
The system SHALL keep AI insight generation read-only and return a safe fallback summary when a month does not have enough data for meaningful guidance.
#### Scenario: Sparse month returns fallback insight
- **WHEN** the user requests insight generation for a month with empty or near-empty data
- **THEN** the system returns a fallback message instead of low-confidence advice
#### Scenario: AI does not mutate financial records
- **WHEN** the system generates or stores monthly insights
- **THEN** no expense or paycheck records are created, updated, or deleted as part of that request
### Requirement: Insight generation remains private and resilient offline
The system SHALL keep monthly insight generation fully offline and provide a clear fallback response when the local model runtime or selected model is unavailable.
#### Scenario: Local runtime is unavailable
- **WHEN** the user requests monthly insights while the local AI runtime is not running or the configured model is unavailable
- **THEN** the system returns a clear setup or availability message instead of attempting a cloud fallback