Audit lanjutan: 6x 'LLM API request failed: Request was aborted' per jam.
Root cause: 9router/omniroute SELALU balas SSE (data: chunks) walau request
tanpa stream:true — SDK OpenAI non-stream menunggu FULL body sebelum parse,
jadi batch moderasi besar yang upstream-nya lambat kena timeout 30-60s dan
di-abort. llmClient sudah punya agregasi streaming (chunks → ChatCompletion).
Fix: stream:true di llmCaller (moderasi batch/individual), llmVision,
cultureLearner, userProfileLearner. Verified: SDK stream test 806ms vs
sebelumnya abort. Caller lain (recovery worker dll) lewat llmCaller sama.
Build & Deploy / build-and-push (backend) (push) Failing after 35s
Build & Deploy / build-and-push (discord-gateway) (push) Failing after 25s
Build & Deploy / build-and-push (proxy) (push) Failing after 25s
- Remove pnpm workspace, moon repo, and all monorepo tooling
- Delete packages/shared/, embed shared code directly into each service
- Copy packages/shared/src/* -> services/backend/src/shared/ and services/discord-gateway/src/shared/
- Replace all @bete/shared imports with @/shared/ path alias
- Remove @bete/shared workspace dependency from both services
- Update root package.json scripts from --filter to --prefix
- Rewrite Dockerfiles to build each service standalone
- Clean up biome.json, .gitignore, remove root drizzle.config.ts
- Added new dependencies for Next.js and lucide-react in pnpm-workspace.yaml.
- Refactored DashboardPage component to improve readability and error handling.
- Enhanced Header component to display error status with an alert icon.
- Updated MobileTabBar and Sidebar components to use a centralized tabs definition.
- Improved ChannelsView in dashboard-panel to handle channel fetching more cleanly.
- Fixed ActiveSpeaker type to use camelCase for userId.
- Updated MessagesPanel to handle guildId checks more gracefully.
- Adjusted API calls in dashboard and messages to align with backend expectations.
- Refined type definitions across various interfaces for consistency and clarity.
Implements a context-aware moderation system by tracking user behavior
and channel-specific norms to improve AI decision-making accuracy.
- Adds `user_reputations` table to track trust scores, clean streaks,
and infraction history.
- Adds `channel_cultures` table to store AI-generated summaries of
channel-specific norms and slang.
- Implements `userReputationStore` to autonomously update user scores
based on moderation outcomes (clean vs. flagged).
- Implements `cultureLearner` and `channelCultureStore` to manage
evolving channel contexts.
- Enhances LLM prompts to inject user reputation (trust scores,
history) and channel culture summaries, enabling "wisdom-based"
moderation (e.g., giving benefit of the doubt to high-trust users).
- Integrates reputation and culture updates into the existing
`aiAnalyzer` pipeline.
asepharyana