lantern

dfaas-deep-dive

🎯 The DFAAS Deep Dive

Case Details

Case ID: task-afcd480a-d6c8-4c76-8104-9d8d75323777 Status: parked Created: 2026-01-25T18:58:40.645Z Updated: 2026-01-25T18:58:40.645Z Priority: 2

Investigation Details

Case Type: investigation Urgency Level: routine Assigned Detective: TBD

Notes

Objective

Full technical analysis of Amjad's DFAAS (Demand Forecasting as a Service) application to understand:

  • Data models and schemas
  • Core algorithms (forecasting, scenario planning, consensus)
  • Business logic implementation
  • UI/UX patterns worth replicating

DFAAS Location

/Users/gfawcett/working/amjad-ai-projects/DFAAS

Known Features (from UI)

Core Functions

  • Forecasting - Statistical forecast generation
  • Scenario Planning - Multiple forecast scenarios
  • Consensus - Collaborative forecast adjustment
  • Lock - Forecast finalization

Data Management

  • Items/SKUs with attributes
  • Locations with hierarchies
  • Demand history
  • Forecast records

Onboarding Flow

  • Company Profile
  • Branding
  • Authentication
  • Data Hierarchies
  • Master Data
  • Historical Data
  • Forecasting Setup
  • Team Setup
  • Review and Launch

Deliverables

  • Data model documentation (TypeScript interfaces)
  • Algorithm analysis (how forecasts are generated)
  • Exception detection logic
  • Accuracy metrics calculation
  • UI component inventory
  • Recommendations for Wonderland port

Related Case

Parent: The Wonderland BI Demo (task-86c62b43-86fc-44d9-b063-5f226592ca85)

Investigation Timeline

2026-01-25T18:58:40.649Z - TBD - case_created

Case opened from CLI

Consciousness Links

Context: Deep dive analysis of DFAAS codebase for Beye.ai collaboration

Auto-Detected Keywords

investigation

This case file is automatically updated. For investigation logs, see the corresponding log channel.

📋 Case To-Do List

This case has an integrated to-do list system that syncs with the Oculus knowledge graph. The to-do list uses the virtual:todo-list fence which auto-detects GitHub-style checkbox markdown.

How the To-Do System Works

  • Auto-Detection: Checkbox lists are automatically detected as virtual:todo-list fences
  • Alice Integration: Display in Alice dashboard using :::wonderland-todo-list slug="${current_case}"
  • ISA Operations: Use fence exec for add/check/update operations
  • Metadata Support: Add [assignee:name] [priority:level] tags to tasks

Case To-Do Operations

  • View state: oculus fence list ${slug} then oculus fence view ${slug} <fence-index>
  • Add task: oculus fence exec ${slug} <fence-index> add "New task"
  • Check task: oculus fence exec ${slug} <fence-index> check 0
  • Update task: oculus fence exec ${slug} <fence-index> update 0 "Updated content"
  • Reference: See virtual-fence-todo for full documentation

Current Case Tasks

  • 🎯 Solve the case
  • 📝 Document findings in investigation notes
  • 🔗 Link relevant evidence and consciousness resources
  • ✅ Update case status when complete

Next Steps

Add investigation notes and evidence tags as you progress. The to-do list will evolve with your investigation. Tasks can be managed via Oculus fence operations or edited directly in the node.

Provenance

Document

  • Status: 🔴 Unverified