david.delagneau
e92c07b8b1
- Introduced a primary agent for the Fidelity AI Workspace to maintain context and support daily engineering tasks. - Created commands for loading workspace context, drafting updates for Jeff, and logging daily notes. - Implemented translation and standup generation commands to enhance communication clarity. - Developed a compaction plugin to preserve essential workspace context during sessions. - Established a structured approach for managing project knowledge, communication rules, and decision-making processes. - Updated README and project structure to reflect new workflows and usage guidelines.
2.1 KiB
2.1 KiB
Project Context - Fidelity
Overview
This workspace supports daily iOS engineering work for Fidelity.
The product work happens outside this repository, usually from another machine. This repository exists to preserve context, track communication, and help AI generate accurate output for standups, Mattermost messages, Jira notes, and supervisor updates.
Fidelity Ecosystem
- Fid4 is the main consumer iOS app
- XFlowSDK powers backend-driven UI flows
- XFlowViewMaker is an adapter layer under evaluation for removal
- FTFrameworks contains feature modules such as FTAccountOpen and FTTransfer
Current Priorities
REST migration
- REST is behind a feature flag
- GraphQL is still the default fallback
- REST should never be assumed active unless confirmed
- Migration must preserve behavior while Apollo is deprecated safely
Discourse and AO issues
- External reports are often incomplete
- Many reported issues are not confirmed regressions
- Some issues reproduce only with authenticated users
Flow debugging
- XFlow behavior changes based on backend configuration
- Entry point affects what the user sees
- Authentication state affects reproducibility
Testing complexity
- Around 15 entry points have been identified at code level
- Not all entry points are reachable from visible UI
- Validation often requires exploratory testing
How This Workspace Is Used
This machine is used to:
- maintain current project context
- record findings from work performed elsewhere
- capture Mattermost communication that changes understanding
- prepare polished updates for Jeff
- generate standups with better context coverage
This means logs must capture both technical findings and communication context.
Communication Expectations
All important updates should clarify:
- whether the flow is authenticated or non-authenticated
- whether the issue is reproducible
- whether the report is external behavior or regression
- whether behavior is present in main
- what action is needed next
Avoid phrases that hide scope, such as:
- "same behavior"
- "looks fixed"
- "working as expected"
Use explicit framing instead.