901574f8c8
Synthesized research findings from 4 parallel researcher agents: Key Findings: - Stack: discord.py 2.6.4 + PostgreSQL/SQLite with webhook-driven PluralKit integration - Architecture: 7-component system with clear separation of concerns, async-native - Features: Rule-based learning system starting simple, avoiding context inference and ML - Pitfalls: 8 critical risks identified with phase assignments and prevention strategies Recommended Approach: - 5-phase build order (detection → translation → teaching → config → polish) - Focus on dysgraphia accessibility for teaching interface - Start with message detection reliability (Phase 1, load-bearing) - Shared emoji dictionary (Phase 1-3); per-server overrides deferred to Phase 4+ Confidence Levels: - Tech Stack: VERY HIGH (all production-proven, no experimental choices) - Architecture: VERY HIGH (mirrors successful production bots) - Features: HIGH (tight scope, transparent approach) - Roadmap: HIGH (logical phase progression with value delivery) Gaps to Address in Requirements: - Vivi's teaching UX preferences (dysgraphia-specific patterns) - Exact emoji coverage and naming conventions - Moderation/teaching permissions model - Multi-system scope and per-system customization needs Ready for requirements definition and roadmap creation. Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
Research Documents
This directory contains research and analysis for the Vivi Speech Translator project.
Documents
PITFALLS.md
Comprehensive analysis of common mistakes in Discord bot development, with specific focus on:
- PluralKit Integration Pitfalls - Message detection, webhook reliability
- Discord API Pitfalls - Message content intent, rate limiting, permissions
- Learning System Pitfalls - Dictionary quality, teaching interface UX, scope creep
- Multi-Server Issues - Dictionary conflicts across servers
- Emoji Handling - Unicode edge cases, combining characters
- Security - Authorization, privilege escalation, data privacy
- Translation Quality - Making translations feel natural
- Accessibility - Text-heavy interfaces for users with Dysgraphia
- Infrastructure - Hosting and reliability
Each pitfall includes:
- What goes wrong (the problem)
- Warning signs (how to detect it)
- Prevention strategies (how to avoid it)
- Which phase should address it
Top 8 Critical Pitfalls to Watch For:
- Message Detection Reliability (Phase 1)
- Message Content Intent Architecture (Phase 1)
- Dictionary Quality Degradation (Phase 3)
- Teaching Interface Complexity (Phase 3)
- Rate Limiting and Scaling (Phase 2+)
- Emoji Edge Cases (Phase 2)
- Authorization and Security (Phase 3)
- Webhook Race Conditions (Phase 2+)