The LLM's recommended_action is conservative — for a flagged message at
high/critical severity it frequently emits 'review' (screenshot/context
ambiguity) even when the violation is severe (harassment, SARA,
threats). autoDeleteEligibility trusted that value, so serious violations
slipped through undeleted (e.g. harassment flagged high but
review → not eligible).
Fix: flagged + high/critical severity bypasses the recommended-action
check entirely (always eligible); the action check now only gates
warn/flagged-medium. deriveRecommendedAction also returns delete for
flagged high/critical BEFORE consulting the stored LLM action. +5
regression tests.
The label predates the LLM pipeline — all analysis (including LLM output)
was mislabeled as heuristic. The analysis field is LLM-generated
moderation justification.
Links that fail to fetch (Facebook share 400, login walls, anti-bot)
previously vanished from the prompt entirely — the LLM only saw the raw
URL in the message body, so its analysis degraded to a template like
"Pengguna membagikan tautan Facebook". Now the text batch tracks
failed-fetch URLs and injects <web_content fetch_error="true"> telling
the LLM the page content is unverifiable and to NOT invent it, plus a
prompt rule: describe what the user actually expressed from the text +
conversation context, or admit the subject is unclear — never make the
link itself the subject.
recovery-worker used a hardcoded STUCK_PROCESSING_AGE_MS=300_000 while
messagesCleanup's default and AI_ANALYSIS_PROCESSING_TIMEOUT_MS are both
120s. Rows stuck between 2 and 5 minutes were never reverted by the
recovery worker — they looked permanently stuck (and accumulated under
load) even though the batch budget had long passed. Now derives the
threshold from config so the two knobs can never drift again.
Piscina worker threads outlive process.exit() and linger as orphaned
processes holding DB connections/locks after a deploy restart. Two live
gateways then fight over the same messages table rows (one claims
processing, the other reverts), which left messages stuck in
ai_status='processing' forever.
Destroy both worker pools before closing the DB, with a 5s fallback so
a hung vision job cannot block shutdown indefinitely.
getPendingMessagesByConversation() flips every fetched pending row to
'processing', then pickBatchWithinBudget() may stop early on the token
budget. The tail rows that did NOT make the batch were never un-claimed,
so they stayed 'processing' forever — the recovery worker reverted them
(120s) only for the next wave to re-claim them, an infinite loop of
stuck messages that never get analyzed (saw 22 rows, some recycled for
40+ minutes).
- add computeBudgetOverflowMessages() pure helper (batchBudget.ts)
- batchScheduler un-claims overflow rows back to 'pending' before
dispatching the trimmed batch
- recovery-worker now reverts stuck processing unconditionally (the old
'conversationProcessing.size > 0' guard skipped the revert when the
in-memory lock map was empty, e.g. fresh boot — exactly when stranded
rows from a previous process need rescuing)
- 3 regression tests for the overflow helper
GMW moves off omniroute (100.121.180.82:20128) and off the direct NVIDIA
vision endpoint onto 9router, which runs on the same host as both services
(127.0.0.1:4014) — loopback avoids the TLS/proxy hop and localhost calls
bypass 9router's remote-key guard.
- gateway + backend: AI_LLM_BASE_URL default -> http://127.0.0.1:4014/v1
- drop stale 'omniroute' router references from comments/docs now that the
active router is 9router (llmClient, llmCaller, ARCHITECTURE, AGENTS)
Verified against 9router before wiring: model 'text' -> gemini-3.5-flash-lite
(SSE, as the pipeline expects), 'multimodal' -> nemotron-3-nano-omni answers
image input, and gemini/gemini-embedding-001 returns 3072 dims — matching the
existing Qdrant collections (no reindex needed). The GMW key is already
registered in 9router's apiKeys table.
typecheck + lint + tests green (gateway 138, backend 37 excluding e2e).
The metrics server binds METRICS_PORT (code default 9090; this host runs it
on 4018 — 4016 is occupied by another process). Docs said 4016 in three
places, which is not what the service does.
README.md was the extraction-era document (referenced winston, mock-crc.ts,
llmModerationClient.ts, indonesianTextNormalizer.ts — all long gone) and
duplicated ARCHITECTURE.md. Rewritten as a short run-the-service guide;
layout/design lives only in ARCHITECTURE.md.
MODULE_STRUCTURE.md deleted: it was a stale duplicate of ARCHITECTURE.md,
referenced by nothing but itself.
ARCHITECTURE.md updated to the post-refactor reality: app/ lifecycle split
(bootstrap/lifecycle/process-guards/metrics-collector), ai-moderation
recovery-worker + cache-prune, per-module index.ts facades, one-way
dependency rule, corrected init/shutdown/observability sections.
app/:
- bootstrap.ts 277 -> 145 lines: config guard, DB connect, client debug
logging and startup order are now named steps with a comment header
- lifecycle.ts (new): everything wired on the Discord 'ready' hook, in
explicit order (inject broadcaster -> register listeners -> start workers)
- process-guards.ts (new): SIGINT/SIGTERM/uncaughtException/unhandledRejection
in ONE place, using isTransientStreamError() instead of two duplicated
inline code lists
- metrics-collector.ts (new): AI pipeline Prometheus gauges
modules/:
- ai-moderation/index.ts + message-capture/index.ts (new): public facades so
app/ never reaches into internal files
- aiAnalyzer.ts 317 -> 146 lines: pure entry API; skip-verdict recording
extracted into recordSkip()
- recovery-worker.ts (new): stranded-message recovery + stale lane/CB pruning
- cache-prune.ts (new): 6h expired-verdict sweep, throttled + resettable
- drop 3 dead re-exports (pickBatchWithinBudget/onCircuitBreakerAlert/
getConversationKey) whose consumers import the origin files directly
No behavior change. typecheck + lint + 138 tests green; nix build OK.
- delete src/shared/index.ts fat barrel; point 10 importers at the exact
module they use (redis-channels, moderation-types, utils/pagination)
- message-capture/types.ts re-exports from shared/moderation-types directly
- shared/errors: add errorMessage() + isTransientStreamError() helpers,
replacing the repeated err-message and transient-code checks
- drop unused imports flagged by biome
No behavior change. typecheck + lint + 138 tests green.
Image messages previously blocked the whole analysis pipeline:
- conversationProcessing was a single lock per conversation; processBatch
awaited BOTH text and media worker jobs before releasing it, so a fast
text verdict sat unused until the slow vision/media batch finished
- one global LLM semaphore (AI_LLM_MAX_CONCURRENT) was shared by text and
media, so a vision backlog could starve text inference
- recovery worker gated on conversationProcessing.size
Now the queue is split into independent text/media lanes:
- conversationProcessing maps key -> Partial<Record<lane, startedAt>>;
each lane holds its own lock and frees it the moment ITS worker job
resolves (ownership-guarded clear prevents stale timers clearing newer
slots)
- two LLM semaphores: AI_LLM_MAX_CONCURRENT (text, default 8) and
AI_LLM_MEDIA_MAX_CONCURRENT (media, default 4) via
withLlmConcurrency(fn, { lane })
- batchScheduler schedules per conversation+lane (timer keys
'<key>::<lane>'); splitMessagesByLane/laneOfMessage moved to pure
analysisLanes.ts (unit-testable without Piscina)
- ai-analysis-worker batch jobs carry a lane field; per-lane active
request gauges (active_text_requests / active_media_requests)
- added tests/analysisLaneLock.test.ts (7 tests: independent lane locks,
preserving other-lane lock, clear-all, ownership guard, lane split)
Docs: ARCHITECTURE.md + AGENTS.md concurrency model updated.
typecheck/lint/test(138)/build all green.
Jev (oc/jev-1.13-free via 9router /v1/systemone) added as primary text
analyzer was underperforming. Delete the whole feature:
- jevAnalyzer.ts + its unit & live-smoke tests
- Jev-first branch in textBatchProcessor, restore pure callModerationLLM path
- AI_LLM_JEV_* config vars (zod) and .env.example entries
- @typesafe-ai/sdk dependency (+ lockfile)
Behavior: text moderation is LLM-only again, exactly as before the
Jev feature; AGENTS.md invariant 'LLM is the only judge' holds.
Show relative time, always-on username with channel context, and
collapse top emojis to two; add VIEW ALL toggle to reveal all 20
fetched reactions instead of the top 4.
* feat(gateway): Jev (System One) as primary text moderator with LLM fallback
Add TypeSafe Jev via @typesafe-ai/sdk v0.6.0 as the PRIMARY analyzer for
text-only moderation sub-batches; the existing LLM stays as the fallback
for anything Jev cannot decide confidently (per-message gate rejection or
API failure) and for media batches.
- jevAnalyzer.ts: TypeSafeClient wrapper, declarative state builder
(System One models MUST get factual state, not chat-XML — chat framing
made Jev confidently wrong on clean messages at 0.98 confidence),
message-id-keyed question builder (5 typed questions per message),
cross-consistency acceptance gate (noul↔status↔severity↔action↔category),
answer→AnalysisResult mapper, fail-open outcome.
- textBatchProcessor.ts: Jev-first per sub-batch, rejected ids + API
failures fall back to callModerationLLM; no cross-batch pollution.
- config: AI_LLM_JEV_ENABLED/API_KEY/BASE_URL/MODEL/TIMEOUT_MS/MIN_CONFIDENCE.
- .env.example: documented all 6 Jev env vars.
- tests: unit (question/state builders, gate, mapper) + live smoke
(gated behind AI_LLM_JEV_SMOKE=1) verified 4/4 accepted vs real 9router.
* fix: auto-fix code quality [skip ci]
Add tests/llmE2e.test.ts — 7 end-to-end tests driving the REAL
moderation prompt pipeline (buildSystemPrompt → XML payload → llmChat
→ parseModerationResponse) against a live model via omniroute.
Covers: clean technical content (no false positives), harassment
(flagged), username-only offenses including 'Pecinta Pria' +
sexual/provocative usernames + SARA-in-username (always warn/low,
NEVER delete — the nickname-reset path), and spam bursts.
Gated behind AI_LLM_BASE_URL + AI_LLM_API_KEY: CI (no creds) skips
the file → 216 unit tests stay green, zero LLM cost. Run locally via
pnpm test:e2e:live (scripts/run-llm-e2e.sh injects creds from bws).
Verified: 223/223 tests pass with live LLM, stability across 4 runs,
typecheck + biome clean. docs: TESTING.md. ignore .hermes/ plans.
- rules.ts: explicit rule that sexual/provocative username terms
(Pecinta Pria, Cinta, pacar, janda, bokep, hot, seks, nude, telanjang)
MUST be flagged as offensive_username with low severity
- output.ts: output schema case for sexual/provocative username
violations, always status: warn, severity: low, never flagged/delete
No hardcoded keyword lists — fix is at the LLM prompt level only.
- new tinyFishSearch module: GET api.search.tinyfish.ai with X-API-Key,
maps top-3 to SearchResult shape, never throws (all failure modes -> [])
- wikipediaSearch: on wiki miss, one tinyfish attempt; hits cached 6h
under the same key so fallback latency is paid once
- termGlossary: on summary miss, top tinyfish hit becomes the definition
(persisted permanently like wiki defs); miss keeps 1h sentinel
- config: TINYFISH_API_KEY (empty = fallback disabled), ENABLED,
BASE_URL, TIMEOUT_MS, LOCATION, LANGUAGE knobs
- tests: 6 coverage for disabled/mapping/non-OK/network/bad-json
API key NOT committed — set TINYFISH_API_KEY in BWS gmw secrets.
Verified: typecheck + lint clean, 216/216 tests pass, live probe
'gubernur jawa barat' returned 3 mapped results
- attachmentUploader: downloadDiscordAttachment retries CDN timeouts
via retryWithBackoff (ATTACHMENT_RETRY_ATTEMPTS, 1s-8s backoff);
AbortError normalized so 403/404 refresh path never fires on timeouts
- attachmentUploader: uploadAttachmentToTele uses ATTACHMENT_RETRY_ATTEMPTS
instead of retries:0 (Tele 5xx under load was failing outright)
- wikipediaClient: wikipediaSummary retries once on abort/timeout
(100% of prod summary errors were aborts); termGlossary drops its
redundant second call (was up to 4 reqs/term under miss+retry)
Verified: typecheck + lint clean, 210/210 tests pass
- Added eager selfbot voice connection establishment to ensure video capture works seamlessly during voice channel joins.
- Introduced a manual video watch command for selfbots to allow operators to initiate screen recording of other members' streams.
- Enhanced video recording functionality to split recordings into segments based on user activity, similar to voice recordings.
- Fixed mobile navigation issues by extending the navbar to include all items and ensuring responsive form controls.
- Standardized error handling across frontend components to improve user experience during failures.
Jockie Music (user 411916947773587456) posts now-playing embeds/spotify links
~1347 captured messages — every one consumed a moderation LLM call for zero
signal and contributed to batch timeouts. Config AI_SKIP_ANALYSIS_USER_IDS
(default=Jockie) skips them at ALL three analysis paths:
- queueMessageAnalysis entry (direct skip-result like age-restricted)
- batchScheduler processing (pre-batch filter)
- individual recovery path (no fallback spam for already-skipped authors)
Skip-result mirrors age_restricted: status=clean, flags=[skip_analysis_user],
action=none — stays visible in the dashboard, never analyzed.
The text model behind omniroute/9router consistently takes >45s on long-context
batches. At 45s every such batch fell through to the individual-fallback
queue which re-runs with its own timeout, then exhausted to ai_status=error.
75s keeps the bounded budget while letting the first-pass batch succeed.
- AI_LLM_MEDIA_ANALYSIS_TIMEOUT_MS 60s→120s + vision 60s→120s: vision model
via router regularly exceeded 60s, dropping media batches into the
individual-fallback chain then exhausting into ai_status=error.
- Qdrant upserts: retryWithRetry() wraps PUT /points with exponential
backoff (3 attempts, jitter) for transient 408/abort/ECONNRESET — the
41 six-hour 'Qdrant upsert failed — semantic entry skipped' warnings were
single-hop timeouts on a healthy-but-loaded Qdrant.
- LLM caller: on parse failure, attempt extractJson() structural repair of
the raw content (models with thinking disabled sometimes emit JSON as
plain text) before giving up and re-requesting.
C2 full scope persistence:
- getRecentConversationContext now returns per-turn guildId/channelId
- processMessage merges historical scope when current request is unscoped
- chatbot remembers server context across all 8 history exchanges (not just 3)
A4+Metrics:
- Add incrementCounterBy(name, delta, labels) to gateway-metrics
- Token-usage counters: llm_tokens_total{model, type, label} per batch
- Cache hit counters: moderation_cache_hits{type} exact/semantic-qdrant/semantic-pg
- Cache miss counters: moderation_cache_misses per batch
- Prometheus /metrics now exposes cost + cache hit-rate for dashboards
Performance:
- Parallel tool execution within each chatbot round (Promise.all)
- All tool results collected before sending to model (ordering preserved)
- Tool failure now logged with structured warning (chatbot.tools.ts)
Verified: backend tsc+biome 37/37, discord-gateway tsc+biome 210/210
- Send system prompt as role:system instead of merging into the user message
(models treat system messages as authoritative -> better tool selection)
- Add full 14-tool catalog + multi-hop instruction to the system prompt
- Raise MAX_TOOL_ROUNDS 4->6 so multi-hop queries complete
- Raise conversation history 3->8 exchanges (less repeat questioning)
- Append guild/channel scope hint to each user turn
- Add saturating toolErrors counter + structured warn log per tool failure
so chatbot tool failures are observable instead of silently absorbed
Even with the prompt firewall (username vs content), the LLM can still
occasionally mis-apply a content-level zero-tolerance flag (sara /
conflict_instigation) to a message whose ONLY violation is the username
(e.g. 'matikanetanyahu'). The auto-delete eligibility check only recognized
exact offensive_username flags, so such false positives still deleted the
message.
Add a belt-and-suspenders guard in isNicknameOnlyViolation: if the flag set
is entirely username-attributable (offensive_username/sara/conflict_instigation)
AND the analysis text corroborates that the violation is username-only with
clean message content, route to nickname-reset instead of message deletion.
Adds 6 test cases covering the real matikanetanyahu scenario and the
false-positive/negative boundaries.
Add explicit FIREWALL PENILAIAN rule separating username assessment from
message content assessment. Username containing political/religious terms
(matikanetanyahu etc) is assessed as offensive_username with severity low
— never triggers sara/conflict_instigation zero tolerance for content.
Changes:
- rules.ts: Add FIREWALL section before LARANGAN BERAT; clarify each
zero-tolerance rule applies to ISI PESAN only; update hierarchy #9
- examples.ts: Fix example #11 status flagged→warn for clean username;
add example #11b with matikanetanyahu case
- output.ts: Explicit status/action mapping for username-only violations
ATTACHMENT_UPLOAD_TIMEOUT_MS=30000 # Upload timeout in ms (default: 30000)
@@ -88,11 +64,6 @@ AI_LLM_API_KEY= # REQUIRED if AI_ANALYSIS_ENABLED=true. L
AI_LLM_BASE_URL=http://100.121.180.82:20128/api/v1 # LLM API base URL (omniroute — OpenAI-compatible router on imrnes, Tailscale 100.121.180.82)
AI_LLM_MODEL=text # LLM text model name (default: text)
# AI_LLM_VISION_MODEL= # Vision model for image analysis (falls back to AI_LLM_MODEL)
# AI_LLM_EMBEDDING_MODEL= # Embedding model for semantic moderation cache (optional; enables near-duplicate text reuse to save LLM calls)
# AI_LLM_EMBEDDING_MIN_SIMILARITY=0.97 # Min cosine similarity to reuse a cached verdict (default: 0.97)
QDRANT_URL=http://100.121.180.82:6333 # Qdrant vector store for embeddings (semantic cache); when set, vectors are stored/searched in Qdrant instead of Postgres
# QDRANT_COLLECTION=gmw_text_moderation # Qdrant collection name (default: gmw_text_moderation)
# QDRANT_API_KEY= # Qdrant API key (optional)
AI_LLM_MAX_CONCURRENT=5 # Max concurrent LLM API calls (default: 5)
AI_LLM_IMAGE_MAX_DIMENSION=1024 # Max image dimension in pixels before resize (default: 1024)
AI_LLM_TEXT_BATCH_SIZE=20 # Max messages per text-only moderation batch (default: 20)
@@ -126,7 +97,6 @@ AUTO_DELETE_LOG_CHANNEL_ID= # Channel ID to log auto-delete actions
# === Retention (0 = disabled) ===
RETENTION_MESSAGES_DAYS=0 # Message retention in days (default: 0 = off)
RETENTION_ATTACHMENTS_DAYS=0 # Attachment retention in days (default: 0 = off)
RETENTION_VOICE_DAYS=0 # Voice recording retention in days (default: 0 = off)
RETENTION_CLEANUP_INTERVAL_MS=86400000 # Cleanup interval in ms (default: 24h)
RETENTION_DRY_RUN=true # Dry-run: log but do not delete (default: true)
GMW (Guild Moderation Watcher) is a Discord bot + web dashboard for AI-powered moderation. A monorepo with three services: a selfbot gateway that captures Discord events and runs LLM moderation, an Express/oRPC backend that serves the dashboard API, and a Next.js 16 SSR frontend. They communicate via Redis pub/sub (gateway→backend) and WebSocket (backend→browser).
## Quick reference
```bash
# Per-service — cd into the service first
bun install # install deps (bun 1.3.14, not pnpm/npm)
bun typecheck # tsc --noEmit
bun lint # biome check
bun format # biome format --write
bun build # gateway/backend: tsc + fix-imports.mjs; frontend: next build
bun test # bun test tests/ (gateway & backend only — frontend has no tests)
```
No monorepo-level scripts exist. Run each command from inside the service directory.
## Layout
```
services/
├── discord-gateway/ Event-driven selfbot. No HTTP (except :4016 metrics).
│ ├── src/
│ │ ├── app/ Bootstrap, shutdown, retention
│ │ ├── modules/ Feature modules — each self-contained
│ │ └── shared/ Config, DB (Drizzle), logger, errors, utils
│ ├── tests/ Vitest tests (colocated, not inside src/)
│ └── lib/ API clients, types, utils, WebSocket, audio
```
## Conventions
### Package manager & runtime
- **bun** (v1.3.14), package manager + test runner. Lockfiles (`bun.lock`) are committed. Node 22 is still used at runtime for the tsc-built `dist/` (via `fix-imports.mjs`), plus bun for dev/install/test. Bun is NOT the production runtime for gateway/backend — the Nix wrapper execs `node dist/index.js` — so native modules resolve against the Node ABI (e.g. `@discordjs/opus` prebuilds for node-v127).
- ESM throughout (`"type": "module"` in all package.json files).
- Test runner: **bun test** (`bun test tests/`). The old vitest configs are replaced by `bunfig.toml` `[test] preload = ["./tests/setup-env.ts"]` — note bun 1.3.14 **ignores `[test] env`**, so env vars for tests go in the preload file. Bun's jest-compat layer aliases most vitest APIs (`vi.fn`→`jest.fn`, `vi.useFakeTimers`→`jest.useFakeTimers`, `vi.spyOn`→`spyOn`, `vi.mock`→`mock.module`), but there is no `vi.waitFor` or `jest` global — use the small `waitForCompat` helper in `teLine truncated
- ESM throughout (`"type": "module"` in all package.json files).
### Import style
Source files use `@/*` path aliases (mapped in tsconfig to `./src/*`). Relative imports **must include the `.js` extension** (e.g., `from "./embed.js"`). The `moduleResolution: "bundler"` tsconfig setting allows bare specifiers during dev, but `tsc` emits them as-is. A post-build script (`scripts/fix-imports.mjs`) rewrites both `@/` aliases and extensionless imports in `dist/` so Node ESM can resolve them at runtime.
### Error handling
Both gateway and backend define an `AppError` base class in `@/shared/errors/index` with subclasses: `ValidationError` (400), `NotFoundError` (404), `UnauthorizedError` (401), `DatabaseError` (500), `ConfigError` (500). Services throw these; callers or middleware map them to HTTP status codes.
### Logging
Use `createChildLogger('module-name')` from `@/shared/logger/index`. Never use raw `console`. It wraps pino; in development it pretty-prints via `pino-pretty`.
### Config
Environment variables are validated with Zod at startup in `shared/config/index.ts` of both gateway and backend. Do not read `process.env` directly outside config modules.
### Module boundaries
- Gateway: each feature lives in `src/modules/<name>/` with its own `index.ts` barrel. Modules register event listeners and are composed in `src/app/bootstrap.ts`.
- Backend: `modules/<name>/` follows schema → repository → service → controller → routes. Data flows up only. No cross-module repository imports.
- Frontend: `page.tsx` is a server component that fetches data via `src/lib/api/server.ts` (oRPC over HTTP, server-side only) and passes it to a `view.tsx` client component. Browser code uses `src/lib/orpc/client.ts` (oRPC over WebSocket via partysocket). Never import the server API client from a client component.
### API layer
The backend exposes an oRPC router mounted at `/trpc` (both HTTP and WebSocket). The frontend does **not** use a REST `/api/*` layer — all data goes through oRPC procedures. The server-side client (`src/lib/api/server.ts`) uses a fetch-based RPCLink; the browser client uses a WebSocket-based RPCLink backed by partysocket for auto-reconnection. Results are asserted to the frontend's local types at each call site (the backend's router type is not imported into the frontend).
### Testing
- **Gateway**: Vitest, tests in `tests/` at the service root. Config sets env vars (`DISCORD_TOKEN`, `DATABASE_URL`, etc.) so tests run without real services.
- **Backend**: Vitest, tests in `tests/` and `src/`. Includes an `e2e.test.ts` excluded from CI (needs a live backend).
- **Frontend**: No test runner configured.
- Tests are pure-function / unit-level. Mock external dependencies; do not start real DB/Redis in tests.
1. **Never commit without running `fix-imports.mjs` after `tsc`** — gateway and backend builds will produce ESM that crashes at startup (`ERR_MODULE_NOT_FOUND`).
2. **Don't add `@discordjs/opus` build-from-source flags** — it ships prebuilt binaries for Node 22. Forcing source builds in CI/Nix will fail or add minutes of compile time.
3. **Frontend SSR fetches are always `cache: "no-store"`** — the server API client bypasses Next.js fetch cache. Do not add caching without understanding the live dashboard requirement.
4. **oRPC types are loosely coupled** — the frontend casts oRPC results to its own types with `as unknown`. Adding a field to the backend schema does not automatically update the frontend type. Update both sides.
5. **Gateway is a selfbot** (`discord.js-selfbot-v13`) — it uses a user token, not a bot token. It must not be deployed as a standard Discord bot.
description: "Aeronet Visualization Dashboard Section is designed for demonstrating application workflows and interface hierarchy. Key features include clear information density, modular panels, and interface rhythm. It is suitable for product showcases, admin panels, and analytics experiences."
colors:
primary: "#FFFFFF"
secondary: "#000000"
accent: "#FFFFFF"
background: "#FFFFFF"
surface: "#18181B"
text-primary: "#111827"
text-secondary: "#4B5563"
border: "#E5E7EB"
typography:
display-lg:
fontFamily: "Inter"
fontSize: "64px"
fontWeight: 500
lineHeight: "1.04"
letterSpacing: "0"
body-md:
fontFamily: "Inter"
fontSize: "16px"
fontWeight: 400
lineHeight: "1.6"
label-md:
fontFamily: "JetBrains Mono"
fontSize: "12px"
fontWeight: 600
lineHeight: "1.2"
spacing:
base: "8px"
gap: "16px"
card-padding: "24px"
section-padding: "80px"
rounded:
card: "16px"
control: "8px"
pill: "9999px"
components:
card:
background: "Use the surface token with subtle borders and HTML-matched shadow depth"
radius: "Match the declared card radius token"
button:
background: "Use primary or accent colors for the main action"
radius: "Use the control or pill radius based on the source HTML"
---
# AeroNet Visualization
Source: Neuform Featured templates from top creators. Author: Sourasith Phomhome (@madebysourasith). Views: 419; favorites: 14; remixes: 4.
Aeronet Visualization Dashboard Section is designed for demonstrating application workflows and interface hierarchy. Key features include clear information density, modular panels, and interface rhythm. It is suitable for product showcases, admin panels, and analytics experiences.
Use the attached HTML reference as the source of truth. Preserve the visible hierarchy, first-screen composition, section rhythm, density, and interaction tone before adapting copy or content.
Key visible headings include: Planet Core.
## Colors
Anchor the palette in primary #FFFFFF, secondary #000000, accent #FFFFFF, background #FFFFFF, surface #18181B, text-primary #111827. Keep background, surface, text, and border roles distinct so generated layouts retain the same contrast pattern as the source.
## Typography
Use Inter for display moments and Inter for body copy unless the HTML clearly demands a compatible fallback. Labels and technical metadata should use JetBrains Mono or an equivalent mono face.
## Layout
Keep spacing deliberate and stable. Favor the same grid direction, max-width behavior, card density, and responsive stacking seen in the HTML. Do not replace distinctive source structures with generic SaaS sections.
## Components
Dashboard, chart, and data panels should preserve their compact operational hierarchy, nested surfaces, and metric emphasis.
## Motion
Preserve existing motion cues such as masked reveals, staggered entrance, hover lift, scroll-triggered transitions, and ambient movement. Keep easing smooth and restrained.
## WebGL & Effects
If the source includes canvas, WebGL, Three.js, gradients, particles, or atmospheric effects, rebuild them as supporting layers behind the content. Keep effects performant, responsive, and secondary to the interface.
## Guardrails
- Do not flatten the source into a generic card grid.
- Do not swap the color mode unless the source clearly supports it.
- Preserve the first viewport signal, focal object, and visual density.
- Keep buttons, cards, and badges aligned to the same radius and border language.
description: "Nexus Capital Feature Section is designed for highlighting product capabilities and value points. Key features include reusable structure, responsive behavior, and production-ready presentation. It is suitable for component libraries and responsive product interfaces."
colors:
primary: "#F2EAD3"
secondary: "#000000"
accent: "#F2EAD3"
background: "#000000"
surface: "#F2EAD3"
text-primary: "#FFFFFF"
text-secondary: "#A1A1AA"
border: "#27272A"
typography:
display-lg:
fontFamily: "Inter"
fontSize: "64px"
fontWeight: 500
lineHeight: "1.04"
letterSpacing: "0"
body-md:
fontFamily: "Playfair Display"
fontSize: "16px"
fontWeight: 400
lineHeight: "1.6"
label-md:
fontFamily: "JetBrains Mono"
fontSize: "12px"
fontWeight: 600
lineHeight: "1.2"
spacing:
base: "8px"
gap: "16px"
card-padding: "24px"
section-padding: "80px"
rounded:
card: "16px"
control: "8px"
pill: "9999px"
components:
card:
background: "Use the surface token with subtle borders and HTML-matched shadow depth"
radius: "Match the declared card radius token"
button:
background: "Use primary or accent colors for the main action"
radius: "Use the control or pill radius based on the source HTML"
---
# Nexus Capital Render
Source: Neuform Featured templates from top creators. Author: Meng To (@mengto). Views: 311; favorites: 22; remixes: 15.
Tags: feature, section, animated, bento, charts.
## Overview
Nexus Capital Feature Section is designed for highlighting product capabilities and value points. Key features include reusable structure, responsive behavior, and production-ready presentation. It is suitable for component libraries and responsive product interfaces.
NEXUS WEALTH CORP. Beyond traditional equity. The multi-asset strategy. A dynamic portfolio framework where capital transitions seamlessly from baseline reserves to high-yield synthetic instruments. Drag the timeline to…
## Composition
Use the attached HTML reference as the source of truth. Preserve the visible hierarchy, first-screen composition, section rhythm, density, and interaction tone before adapting copy or content.
Key visible headings include: Beyond traditional equity. The multi-asset strategy..
## Colors
Anchor the palette in primary #F2EAD3, secondary #000000, accent #F2EAD3, background #000000, surface #F2EAD3, text-primary #FFFFFF. Keep background, surface, text, and border roles distinct so generated layouts retain the same contrast pattern as the source.
## Typography
Use Inter for display moments and Playfair Display for body copy unless the HTML clearly demands a compatible fallback. Labels and technical metadata should use JetBrains Mono or an equivalent mono face.
## Layout
Keep spacing deliberate and stable. Favor the same grid direction, max-width behavior, card density, and responsive stacking seen in the HTML. Do not replace distinctive source structures with generic SaaS sections.
## Components
Dashboard, chart, and data panels should preserve their compact operational hierarchy, nested surfaces, and metric emphasis.
## Motion
Preserve existing motion cues such as masked reveals, staggered entrance, hover lift, scroll-triggered transitions, and ambient movement. Keep easing smooth and restrained.
## WebGL & Effects
If the source includes canvas, WebGL, Three.js, gradients, particles, or atmospheric effects, rebuild them as supporting layers behind the content. Keep effects performant, responsive, and secondary to the interface.
## Guardrails
- Do not flatten the source into a generic card grid.
- Do not swap the color mode unless the source clearly supports it.
- Preserve the first viewport signal, focal object, and visual density.
- Keep buttons, cards, and badges aligned to the same radius and border language.
description: "Summit Cloud Pricing Section is designed for comparing plans and supporting conversion decisions. Key features include plan comparison blocks and conversion-oriented actions. It is suitable for subscription pricing pages and plan comparison experiences."
colors:
primary: "#2563EB"
secondary: "#050510"
accent: "#3B82F6"
background: "#050510"
surface: "#18181B"
text-primary: "#FFFFFF"
text-secondary: "#A1A1AA"
border: "#27272A"
typography:
display-lg:
fontFamily: "Inter"
fontSize: "64px"
fontWeight: 500
lineHeight: "1.04"
letterSpacing: "0"
body-md:
fontFamily: "Inter"
fontSize: "16px"
fontWeight: 400
lineHeight: "1.6"
label-md:
fontFamily: "JetBrains Mono"
fontSize: "12px"
fontWeight: 600
lineHeight: "1.2"
spacing:
base: "8px"
gap: "16px"
card-padding: "24px"
section-padding: "80px"
rounded:
card: "23px"
control: "18px"
pill: "9999px"
components:
card:
background: "Use the surface token with subtle borders and HTML-matched shadow depth"
radius: "Match the declared card radius token"
button:
background: "Use primary or accent colors for the main action"
radius: "Use the control or pill radius based on the source HTML"
Summit Cloud Pricing Section is designed for comparing plans and supporting conversion decisions. Key features include plan comparison blocks and conversion-oriented actions. It is suitable for subscription pricing pages and plan comparison experiences.
Summit Platform Solutions Pricing Docs Start Migration Now in General Availability Migrate to the cloud with confidence Automated discovery, dependency mapping, and zero-downtime migration for enterprise workloads. Move…
## Composition
Use the attached HTML reference as the source of truth. Preserve the visible hierarchy, first-screen composition, section rhythm, density, and interaction tone before adapting copy or content.
Key visible headings include: Migrate to the cloud with confidence; Discover untethered scalability. The platform built to dynamically map, migrate, and optimize your entire infrastructure without lifting a finger.; Architectural Clarity; Dynamic Scaling.
## Colors
Anchor the palette in primary #2563EB, secondary #050510, accent #3B82F6, background #050510, surface #18181B, text-primary #FFFFFF. Keep background, surface, text, and border roles distinct so generated layouts retain the same contrast pattern as the source.
## Typography
Use Inter for display moments and Inter for body copy unless the HTML clearly demands a compatible fallback. Labels and technical metadata should use JetBrains Mono or an equivalent mono face.
## Layout
Keep spacing deliberate and stable. Favor the same grid direction, max-width behavior, card density, and responsive stacking seen in the HTML. Do not replace distinctive source structures with generic SaaS sections.
## Components
Dashboard, chart, and data panels should preserve their compact operational hierarchy, nested surfaces, and metric emphasis.
## Motion
Preserve existing motion cues such as masked reveals, staggered entrance, hover lift, scroll-triggered transitions, and ambient movement. Keep easing smooth and restrained.
## WebGL & Effects
If the source includes canvas, WebGL, Three.js, gradients, particles, or atmospheric effects, rebuild them as supporting layers behind the content. Keep effects performant, responsive, and secondary to the interface.
## Guardrails
- Do not flatten the source into a generic card grid.
- Do not swap the color mode unless the source clearly supports it.
- Preserve the first viewport signal, focal object, and visual density.
- Keep buttons, cards, and badges aligned to the same radius and border language.
| **media** | Media file management | GET /api/media/list, POST /api/media/upload |
| **voice** | Voice recording management | GET /api/voice/recordings, POST /api/voice/connect |
| **health** | Service health checks | GET /api/health |
## Data Flow
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