Discord GoLive sends screen-share audio on a separate SSRC from the
user's microphone. In @discordjs/voice v0.19, VoiceReceiver.onUdpMessage
silently drops packets for SSRCs not in ssrcMap (which is only populated
from VOICE_STATE_UPDATE/VOICE_SERVER_UPDATE). This caused screen-share
audio to never trigger receiver.speaking and never reach the speakingHandler.
Fix: hookScreenShareAudio() wraps onUdpMessage to:
1. Detect incoming RTP packets with unknown SSRCs (OPRUS payload type 120)
2. Infer the owning userId by proximity to known audioSSRC
3. Clone the user's VoiceUserData into ssrcMap under the new SSRC
4. Let the original handler decrypt and forward to the subscription stream
5. Listen on ssrcMap 'create'/'update' events for video SSRC changes
Also removes the broken initial approach (polling ssrcMap which never
contains screen-share SSRCs).
Automated public weekly summary: top categories/domains/channels + coverage rate, posted to configured webhook. Uses getDatabase() direct query (no oRPC HTTP dependency), guards one-fire-per-week on restart.
Gateway @/ alias imports use no .js extension (relative imports
keep .js). The .js suffix on @/ paths caused double-extension
ERR_MODULE_NOT_FOUND (embeddingClient.js.js) at runtime.
- Persist structured verdict (flags/severity/confidence/evidence) on
moderation_actions so the public web can show WHY a message was moderated.
- Add a persistent Qdrant archive collection (gmw_message_archive); embed
every captured message at capture time (fire-and-forget, best-effort).
- Public semantic search over the archive (backend oRPC + FE toggle on the
messages view). Both features are read-only/public and fully automatic.
Migration: 0015_add_moderation_explainability.sql
- Memoize buildSystemPrompt by (mode|channelCulture); identical signatures
now reuse the ~5k-token core instead of rebuilding per sub-batch call
(textBatchProcessor rebuilt it inside the loop; a 200-msg batch re-sent
the full system prompt ~4x). Correction tail stays per-attempt (uncached).
- Hoist URL-image -> vision evidence out of the per-sub-batch loop in
textBatchProcessor: it depends only on fetched images + full target set,
so compute once per whole batch, not per sub-batch.
- Compact system instructions: collapse 3x-duplicated 'evaluate by content
alone' statements into one standalone rule; trim output.ts channel-culture
+ context framing already covered by rules.ts/system.ts; drop duplicate
programming-error-log few-shot (id 17, covered by rules AMAN list).
- Fix misleading config default: AI_LLM_BASE_URL default -> omniroute
(gateway already runs omniroute via BWS; 9router was dead/misleading).
typecheck + lint + build green.
- Add shared skeleton building blocks (SkeletonHero, SkeletonMetricRow,
SkeletonPanel, SkeletonRows) and wire them into every view's initial
loading branch, replacing bare spinners for a cohesive shimmering shell.
- Polish EmptyState with a glow-ring icon chip instead of a flat icon.
- Harden .light theme: color-scheme, tuned scrollbar + selection for pale
canvas. Dark/light theme toggle (next-themes) already persisted in TopBar.
- resetOffensiveNickname: skip when target role sits above bot
(member.manageable) instead of hammering a doomed setNickname PATCH
that Discord rejects with 50013 'Missing Permissions'. Log the
Discord error code on failure for clear diagnosis.
- llmCaller: include contentPreview (first 200 chars) in the parse-
failure warning so non-JSON LLM responses are debuggable.
- Add wikipediaClient.ts: native fetch to Wikipedia REST/Action APIs
(search + summary), no extra npm dependency.
- Extract shared Redis cache into cacheStore.ts (decoupled from search).
- Term glossary now uses wikipediaSummary for direct article lookup.
- Remove searxngSearch.ts entirely; drop SEARXNG_BASE_URL config,
add WIKIPEDIA_LANG / WIKIPEDIA_TIMEOUT_MS.
- Rename backend searxngCalls metric to webSearchCalls.
User: 'jangan ada reputasi juga' — no profile, no reputation in the prompt,
raw messages only.
- textBatchProcessor: drop initializeUserReputation fetch + <user_reputation>
tag injection (kept the minimal <message> tag + reply/reference context).
- visionAnalyzer (prepareMediaMessage): same removal.
- prompts/system.ts + prompts/output.ts: replace <user_reputation>/<user_history>
instructions with an explicit 'no per-user profile/reputation context'
note so the LLM judges purely on message content + conversation/web/location.
- mediaBatchProcessor: fix stale comment.
Trust/infraction state is STILL written to the DB (userReputationsTable) for
enforcement — only the LLM context injection is removed, so moderation
actions (mute/ban via infraction thresholds) keep working.
Net: even smaller prompts (no per-user context at all) → more messages fit
per request, and one fewer DB round-trip per unique user per sub-batch.
tsc, biome, vitest (129) all clean.
User insight: personal profile summaries bloat the prompt (less room per
request) and add a per-user DB/Redis round-trip for little moderation signal.
Only the behavioural <user_reputation> history is kept.
- textBatchProcessor: stop fetching getUserProfile; remove <user_profiles>
block + <user_profile_ref> from message tags. Keep <user_reputation>.
- mediaBatchProcessor + visionAnalyzer: same removal (profile fetch + ref).
- prompts/system.ts + prompts/output.ts: drop stale <user_profiles>/
<user_profile_ref> instructions; point LLM at <user_reputation> instead.
- aiAnalyzer: gate userProfileLearner behind AI_USER_PROFILE_LEARNING_ENABLED
(default false) — generates profiles nobody reads, pure LLM/DB waste.
- Add AI_USER_PROFILE_LEARNING_ENABLED config knob.
Net: smaller prompts (more messages fit per request), fewer DB round-trips
per sub-batch, and no background LLM calls learning unused profiles.
tsc, biome, vitest (129) all clean.
User insight: rather than many small per-batch API requests, pack many
messages into ONE request so a burst is analyzed with far fewer calls.
- AI_LLM_TEXT_BATCH_SIZE 20 -> 60 (one request now carries ~3x more messages).
- AI_ANALYSIS_MAX_TARGET_TOKENS 4000 -> 14000 (the scheduler's token-budget
gate was trimming pending messages to ~20 before they reached the sub-batch
splitter; raising it lets ~60 messages through to a single LLM call).
- AI_LLM_TEXT_ANALYSIS_TIMEOUT_MS 30000 -> 45000 (one larger call needs more
headroom; gemini-flash-lite has a 1M-token context so 14k+8k is trivial).
Net effect when ramai: a 60-message burst = 1-2 API calls instead of 3+,
less semaphore contention, faster throughput.
- Parallelize per-user reputation/profile fetches in textBatchProcessor
(was a serial ~2N DB/Redis round-trip loop per sub-batch; now Promise.all
over unique users). Cuts per-batch latency, biggest win on small/quiet
batches.
- Make the LLM concurrency semaphore dynamic (cached per config value) instead
of frozen at import time, so AI_LLM_MAX_CONCURRENT is tunable without code
change and reflects current config.
- Bump AI_LLM_MAX_CONCURRENT default 5 -> 8 (gemini-flash-lite is cheap; helps
throughput when busy).
- Lower AI_ANALYSIS_DEBOUNCE_MS 500 -> 250 (snappier first-message analysis
when quiet).
- Lower AI_ANALYSIS_RECOVERY_INTERVAL_MS 15000 -> 10000 (stuck/errored
messages re-analyze sooner).
tsc, biome, vitest (129) all clean.
Backend returns messages DESC (newest first); the view previously rendered
that directly, so the feed was inverted vs Discord (old at bottom, new at top)
while the load-older control sat at the top — contradictory.
- Reverse the display list so it reads oldest→newest top→bottom, like DC.
- Load-older (cursor to lower created_at) prepends at the top; scroll position
is preserved by offsetting scrollTop by the height added above.
- Open at the bottom (newest visible) on first load / scope change.
- New live messages append at the bottom and auto-scroll only when the user is
already near the bottom (nearBottomRef), so reading history isn't disrupted.
- Scroll container now tracked via ref; onScroll updates nearBottom + triggers
load-older when scrolled to the top.
tsc, biome, next build all clean.
- Set stream:false on the /chat/completions request so the bot gets one
complete response instead of an SSE token stream.
- Add reasoning_effort:"none" to suppress extended-thinking/reasoning tokens
(ignored by non-reasoning models like gemini-flash-lite).
- Add parseResponse(): handles both the JSON object 9router returns for
stream:false and the SSE text it may still emit, delegating SSE to parseSse.
Verified live: omniroute returns 200 application/json with message.content.
- Add viewport export with viewportFit: "cover" so iOS exposes
env(safe-area-inset-*) (required for the insets to take effect).
- NavRail / TopBar / main / Toaster now respect safe-area insets so content
clears the iPhone notch and home indicator in both portrait and landscape.
- prefers-reduced-motion: the media query already disabled declared animation
classes; harden it with a global transition/animation duration override and
kill the scan-line shimmer so motion-sensitive users get a fully static UI.
Verified tsc --noEmit + next build clean.
- SectionHeader: action (filters/legends) now wraps below the title on narrow
screens instead of overflowing beside it (flex-wrap, gap-2 sm:gap-3).
- GuildChannelPicker: selects go full-width and stack on mobile (w-full
sm:w-44 / sm:w-52) instead of fixed widths that exceeded a 375px viewport.
- Messages search: w-full sm:w-64 so it doesn't crowd the picker on mobile.
- TopBar: tighter padding (px-4 sm:px-5), smaller title on mobile, connection
status uses compact (dot only) on mobile, ambient pill hidden < sm.
- Shell main + dashboard channel label: responsive padding / shrink-0 widths.
Verified tsc --noEmit + next build clean; targets breakpoints 375/768/1024/1440.
- Show an explicit Loader2 spinner row ("Loading older…") while the next page
fetches, instead of a disabled button.
- Cap appended older pages at MAX_OLDER_PAGES=10 (500 messages) so a long
scroll-up never pulls the entire history; show a "capped" hint pointing to
search. Reset the counter when guild/channel changes.
Wire the existing useLoadMore + useMessagesHasMore pagination hooks into the
Messages view: add a "↑ Load older messages" button at the top of the list and
auto-load the next (older) page when the user scrolls to the top. Backend
messages.list already returns a created_at-based nextCursor (DESC order), so
older pages are just subsequent cursors. Newest-first live feed is preserved;
the load-older control is hidden during search.
flake.nix only rewrote @/ aliases but left extensionless relative imports
(./router) in compiled dist/. node dist/index.js (how prod runs) cannot
resolve extensionless ESM specifiers -> ERR_MODULE_NOT_FOUND -> backend
crashlooped (444 restarts, port 4001 dead). Extract the fixer into a shared
scripts/fix-imports.mjs that appends .js to extensionless relative imports and
rewrites @/ aliases, and wire it into backend + discord-gateway build phases.
Verified: fresh tsc + fixer -> node dist/index.js boots; oRPC over /trpc
serves both HTTP POST and WebSocket (config/dashboard/voice/moderation/
media/chatbot/analysis) end-to-end against Postgres + Redis. next build
passes with the oRPC client + partysocket.
Replace REST module routers with a single typed tRPC appRouter served over
/trpc (HTTP + WebSocket), and rewire the frontend to call it via
@trpc/client wsLink (browser) and httpLink (RSC data layer). Existing
/api/health + /api/metrics stay as plain Express for infra scraping.
Notable fixes surfaced by the live smoke test:
- Express 5 / path-to-regexp v8 rejects the /trpc/* wildcard route; use a
prefix middleware that computes opts.path from the URL instead.
- nodeHTTPRequestHandler treats opts.path as the literal procedure path, so
it is derived per-request from req.url.
- Two ws servers on one http.Server (the /ws voice socket + /trpc) collided
and returned 400 on upgrade; both now use noServer + a manually routed
server.on('upgrade') keyed by path.
Verified: BE tsc+biome+40 vitest green; FE tsc+biome green; live
HTTP and WebSocket calls returned real prod data.
Co-Authored-By: Claude Opus 4.5 (1M context) <noreply@anthropic.com>
The standalone image analysis path (analyzeSingleMediaImage → llmVision →
llmChat) previously had no request-level timeout of its own — it silently
inherited the shared OpenAI client default (60s), and AI_LLM_MEDIA_ANALYSIS_
TIMEOUT_MS only governed the text+media *batch*, not a single vision call.
- Add AI_LLM_VISION_ANALYSIS_TIMEOUT_MS (default 60000) to config.
- llmChat now accepts an optional per-request `timeout` in LlmCallOpts,
forwarded to the OpenAI request options (falls back to the 60s client
default when omitted).
- llmVision passes config.AI_LLM_VISION_ANALYSIS_TIMEOUT_MS, so a single
image/sticker/emoji analysis gets a guaranteed 1-minute budget and is
independently tunable from the text path.
Verified: tsc + biome green, 129 gateway tests pass.
Co-Authored-By: Claude Opus 5 (Nous Research)
The sidebar rendered /dashboard twice: once as a hardcoded NavItem
(lines 45-50) and again via navItems.map() (navItems[0] is also
/dashboard). Dropped the hardcoded item so the single source of truth
(navItems in lib/navigation.ts) drives the rail. Removed the now-unused
LayoutDashboard import.
tsc + biome green.
Co-Authored-By: Claude Opus 5 (Nous Research)
The chatbot agent now has 14 tools (was 4) so it can answer about ANY
server situation from live data instead of a static snapshot:
- get_server_stats (now also returns clean count)
- get_top_channels, get_recent_activity, get_top_flagged
- search_messages (LIKE keyword search)
- get_user_messages, get_user_profile, get_user_reputation
- get_channel_culture
- get_message_detail (full AI analysis of one message)
- get_message_reviews (human moderation queue by status)
- get_voice_recordings (with transcriptions)
- get_moderation_timeline (daily flagged/warn/clean trend)
- get_corrections (AI false-positive correction history)
Security/quality:
- Every executor now uses parameterized drizzle queries (eq/like/and).
The old code interpolated model-supplied IDs into sql.raw() — a SQL
injection vector. Removed.
- Split static tool *definitions* into chatbot.toolDefs.ts (no DB import)
so the LLM-facing schema can be unit-tested without loading the
database/config layer. chatbot.tools.ts keeps only the executor.
Verified: tsc + biome clean, 40 backend tests pass (4 new covering the
tool-contract: names unique, required args declared, full situation
coverage).
Co-Authored-By: Claude Opus 5 (Nous Research)
The chatbot already had an agentic tool loop (get_server_stats,
get_top_channels, get_recent_activity, get_top_flagged), but processMessage
still baked a serverInsights snapshot into the system prompt and told the
model to "answer from that data". That defeats the tools: the model answered
from a stale snapshot instead of living numbers, and the guild/channel scope
the frontend sends was never forwarded to the tools.
Changes (services/backend/src/modules/chatbot):
- Remove getServerInsights() + ServerInsights (dead after this change).
- buildSystemPrompt(): drop the hardcoded stats block; instruct the model it
has NO memorized server numbers and MUST call a tool for any server-data
question, answering only from tool results.
- processMessage(): stop fetching insights; pass the request guildId/channelId
scope through to callLLM.
- callLLM(): accept scope; auto-fill empty guildId/channelId on tool calls from
the request scope so the model never has to guess IDs and tools always query
the right server.
Behavior: answers now come from live DB data via tools, scoped to the server
the user is chatting in. tsc + biome + 36 backend tests green.
Co-Authored-By: Claude Opus 5 (Nous Research)
buildCorrectedFewShotExamples() (a getRecentCorrectedModerations(5)
DB hit) was called inside the per-sub-batch buildContent closure in
textBatchProcessor.ts — re-queried for every sub-batch (≈10× for a
200-msg burst) AND re-fired on each parse-error retry. mediaBatchProcessor
already hoisted it once. Mirror that: fetch once per runTextOnlyBatch,
reuse the cached string inside the closure.
No behavior change — identical content, fewer identical DB reads.
tsc + 129 tests + biome green.
Co-Authored-By: Claude Opus 5 (Nous Research)
The 32 few-shot examples each re-echoed score/confidence/
recommended_action/categories/policy_version inline (~150 chars ×
32). Those fields carry zero moderation-decision signal — the schema
and their ??-default coercion already live in OUTPUT_INSTRUCTIONS +
moderationResponseParser.ts. Removed 96 redundant key/value pairs.
Kept per-example: message_id, status, flags, severity, evidence,
analysis — the fields that actually teach decisions. Parser derives
the rest via ?? fallback, so real output shape is unchanged.
examples.ts: 21.7K→18.5K chars; FEW_SHOT(mixed) 15.3K→13.4K.
Total mixed system prompt now 33.9K (was 39.3K at audit start,
~14% leaner). tsc + 129 tests + biome green.
Co-Authored-By: Claude Opus 5 (Nous Research)
- prompts/system.ts: merge 3 overlapping framing blocks (Blok Data /
Konteks Pengguna / Framing Konteks vs Target) into 1 tight block —
same coverage, no duplicated "standalone judgment / profile-is-
reference-not-evidence" prose.
- prompts/output.ts: trim duplicated user_history/standalone paragraph
in PERSONALITY & MEMORI (keep concrete per-case lessons).
- prompts/examples.ts: drop 2 exact-duplicate-lesson few-shots (LGBT id=19
dup of id=30; weapons-tech id=33 dup of id=32). All teaching signals
retained via the surviving example of each lesson.
Static system prompt: text 32.7K→29.2K, mixed 39.3K→35.8K chars
(~10% smaller). No moderation rule, zero-tolerance category, or decision
tree altered — accuracy-controlling content untouched. tsc + 129 tests +
biome green.
Co-Authored-By: Claude Opus 5 (Nous Research)
- gateway-metrics: collectors now run per scrape so Prometheus sees real
data (process memory/uptime + live AI-analysis pipeline gauges) instead
of an always-empty stub. bootstrap registers the pipeline collectors.
- systemd: MemoryMax 512M -> 1G (live RSS ~500MiB, peak 508MiB; 512M left
~2% headroom and risked an OOM-kill restart; host has 8GB free).
- config: POSTGRES_POOL_MIN 2 -> 0 so main + 4 Piscina worker threads don't
hold ~10 permanently-open idle pg connections against PgBouncer.
- docs: rewrite stale ARCHITECTURE.md / MODULE_STRUCTURE.md (winston ->
pino, removed mock-crc/indonesianTextNormalizer, renamed
aiAnalysisWorker/llmModerationClient).
Verified: tsc clean, 129 vitest pass, biome clean on changed files.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Adds per-message AI moderation analysis time (ai_analysis_duration_ms)
so operators can see how long the LLM took to moderate each message.
Gateway:
- messagesTable: new ai_analysis_duration_ms (bigint) column.
- AIAnalysisUpdate + buildAIAnalysisSet: carry analysisDurationMs through
both single and bulk update paths.
- ai-analysis-worker: measure wall-clock time around runModerationAnalysis
and attach it to every result in the batch.
Backend:
- Mirror schema column; messageMapper maps ai_analysis_duration_ms;
moderation-types + MappedMessage expose it.
Frontend:
- message.ts type gains ai_analysis_duration_ms.
- AiBadge (messages view) shows 'status · 1.2s' when duration is present;
analysis view badge mirrors the same formatting.
DB:
- scripts/add-ai-analysis-duration.sql (idempotent ADD COLUMN IF NOT EXISTS).
No behavior change for moderation logic; null until new gateway build
records values.
Qdrant upserts were failing with 'This operation was aborted' ~32x/2h,
so semantic moderation cache entries were silently dropped. Root cause:
upsertQdrantPoint ran ensureQdrantCollection() on EVERY call — a GET
(and sometimes DELETE+PUT) round-trip — while the request AbortController
had only a 10s timeout. Under moderation load Qdrant is busy (the
gmw_text_moderation collection is not yet HNSW-indexed, so searches are
full-scans), the extra round-trips pushed the upsert past 10s, and the
client aborted it.
- Memoise ensureQdrantCollection() at module scope so the collection is
verified exactly once per process (resetQdrantCollectionCache() for
tests / config reload).
- Bump the upsert request timeout 10s -> 30s so a transiently busy
Qdrant no longer aborts the write.
Qdrant server itself is healthy (<100ms for direct upsert; collection is
green), so no server-side change is needed. Semantic cache should now
populate reliably.
Address every remaining biome lint/format warning across both services
so the codebase ships warning-free:
- textCacheStore: drop unused deleteExpiredQdrantPoints import; hash
image cache key (sha256[:32]) so long/base64 URLs no longer blow the
text_analysis_cache PK B-tree 8191-byte index (was aborting the media
analysis lock INSERT).
- bootstrap: drop unused unhandledRejection promise param.
- moderationOrchestrator: drop unused destructure at L197.
- mediaDownloader / textBatchProcessor / transmitter: replace non-null
assertions with proper null guards (stickerName ?? '', urlImages.get
guard, backpressureQueue.shift guard).
- backend utils: throw lastError ?? fallback instead of lastError!.
- message-capture: remove unused (retentionDb), (moderationActionsDb,
reviewsDb); simplify renderDiscordMentions guard to optional chain.
- transmitter: remove dead write-only field + its assignments.
No behavior change beyond the cache-key hashing (now deterministic
fixed-length) and the intentional null-safety guards.
imageResizer.ts had a line exceeding the print width that biome flagged
as a formatter error, failing the Build & Deploy biome check. Re-format
the file. No logic change.
Two root causes behind 'all image analysis failing':
1. imageResizer still emitted lossless PNG for vision input. A 1024px
Facebook photo balloons to multi-MB PNG base64 that the vision model
silently rejects ('Vision API null response'). Switch to JPEG q85
(no upscaling) — same photo drops to ~100-400KB, model processes fine.
Re-encodes even already-small images so raw originals never bloat the
data URL. Added tests/imageResizer.test.ts covering both cases.
2. acquireMediaAnalysisLock INSERT aborted with 'index row requires N
bytes, maximum size is 8191'. text_analysis_cache.text is the PK in a
B-tree index (8191-byte/row cap); callers pass the raw image URL as the
key, and base64 data URLs / very long URLs blow past the limit, so the
lock INSERT fails and every media analysis is skipped. Hash the URL in
makeImageCacheKey (image:<sha256[:32]>) — fixed-length, deterministic,
well under the limit. All store/get/lock/delete callers already route
through this function so lookup stays consistent.
asepharyana