- Introduced a new `ScreenShareController` to manage YouTube screenshare functionality.
- Updated `DiscordPlayer` to track ownership of audio streams, preventing conflicts between music playback and screenshare.
- Added error handling for various states including voice connection checks and media busy states.
- Created unit tests for `ScreenShareController` and `DiscordPlayer` ownership rules to ensure correct functionality.
- Added documentation for the new media echo fix and screenshare design.
Separate text moderation and voice recording guild/channel state so each workflow can persist and operate independently.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
- Migrate validation.ts from class-transformer/class-validator to Zod
- Apply Biome auto-fixes (import sorting, nodejs protocol)
- Fix duplicate type identifier in validation.ts
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
- Replace all raw SQL queries in messageStore.ts with Drizzle ORM queries
- Remove DatabaseAdapter dependency from messageStore functions
- Update all function signatures to be async and remove db parameter
- Functions now use getDatabase() internally for database access
- Update all call sites in messageCapture.ts, attachmentUploader.ts, aiAnalyzer.ts, webserver.ts, and index.ts
- All functions remain backward compatible in behavior
- TypeScript typecheck passes with no errors
- All tests pass (11 passed)
Reduce effective AI batch size so streaming requests finish before timeout. Keep token-based batching but cap each request to 80 messages or about 9k content tokens, and recursively split failed batches instead of marking the whole batch failed.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Batch AI moderation by estimated token budget instead of fixed message count. Send as many messages as fit within an 80k token request budget while keeping one concurrent API request. Include message metadata and chronological conversation context so the model can judge provocation and replies from surrounding discussion.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Remove unclear-message and low-quality-message warning criteria because this is a casual group. Keep short, ambiguous, informal, and light profanity messages clean unless they target someone or provoke conflict.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Remove the channel topic/OOT rule from AI moderation criteria and renumber the remaining rules. WARN criteria no longer includes OOT.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
- Expand system prompt with complete community rules (9 sections)
- Add specific examples for each rule category
- Clarify WARN vs FLAGGED decision criteria
- Include all prohibited content types and behaviors
- Provide clear guidance for AI analyzer on rule enforcement
Community rules now cover:
1. Jaga Sikap dan Hormati Sesama
2. Hindari Konflik
3. Gunakan Channel Sesuai Topik
4. Konten Eksplisit Dilarang
5. Jaga Privasi
6. Profil yang Sopan
7. Dilarang Spam dan Penipuan
8. Langsung ke Inti Pertanyaan
9. Diskusi Berkualitas
This ensures AI analyzer makes consistent moderation decisions based on actual community rules.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
- Add "warn" status between "clean" and "flagged" for minor violations
- Update AI analyzer system prompt with community rules and warn category
- Warn: profanity, OOT, tone issues - requires warning but not deletion
- Flagged: NSFW, illegal, hacking, scam, harassment, violence, SARA - requires review/deletion
- Update types to support warn status in MessageRecord and AIAnalysisUpdate
- Update client UI to show three panels: All Messages, Warned, Flagged
- Warned messages show in right-top panel for quick review
- Flagged messages show in right-bottom panel for moderation action
This resolves:
- Need to distinguish between minor and severe violations
- Moderators can now warn users before taking action
- Better moderation workflow with three-tier system
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
- Change runLLMAnalysis to accept array of texts instead of single text
- Batch up to 5 messages per AI request instead of 1 message per request
- drainQueue now collects batch before sending to AI API
- Reduces API calls by 5x and speeds up analysis significantly
- System prompt updated to handle batch JSON array responses
This resolves:
- Slow AI analysis (3 messages every 15 seconds)
- Too many API calls (one per message)
- Long queue backlog
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
