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>
34 lines
1.4 KiB
Markdown
34 lines
1.4 KiB
Markdown
# Research Documents
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This directory contains research and analysis for the Vivi Speech Translator project.
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## Documents
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### PITFALLS.md
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Comprehensive analysis of common mistakes in Discord bot development, with specific focus on:
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- **PluralKit Integration Pitfalls** - Message detection, webhook reliability
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- **Discord API Pitfalls** - Message content intent, rate limiting, permissions
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- **Learning System Pitfalls** - Dictionary quality, teaching interface UX, scope creep
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- **Multi-Server Issues** - Dictionary conflicts across servers
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- **Emoji Handling** - Unicode edge cases, combining characters
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- **Security** - Authorization, privilege escalation, data privacy
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- **Translation Quality** - Making translations feel natural
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- **Accessibility** - Text-heavy interfaces for users with Dysgraphia
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- **Infrastructure** - Hosting and reliability
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Each pitfall includes:
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- What goes wrong (the problem)
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- Warning signs (how to detect it)
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- Prevention strategies (how to avoid it)
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- Which phase should address it
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**Top 8 Critical Pitfalls to Watch For:**
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1. Message Detection Reliability (Phase 1)
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2. Message Content Intent Architecture (Phase 1)
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3. Dictionary Quality Degradation (Phase 3)
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4. Teaching Interface Complexity (Phase 3)
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5. Rate Limiting and Scaling (Phase 2+)
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6. Emoji Edge Cases (Phase 2)
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7. Authorization and Security (Phase 3)
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8. Webhook Race Conditions (Phase 2+)
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