Portfolio Project: Fan Engagement Platform for Live Tabletop Streams

Build a portfolio-ready prototype: Fan Engagement Platform for live tabletop RPG streams

Hook: If you’re a developer or DevOps pro struggling to ship a portfolio project that maps to hiring needs—real-time systems, multimedia processing, AI-assisted features and community tools—this tabletop fan engagement platform gives you a single, interview-ready project to demonstrate full-stack, realtime and ops skillsets.

What you’ll build (in one page):

Prototype a platform for live tabletop RPG streaming (Critical Role–style) that integrates stream chat, creates clips, auto-generates highlight reels, and provides robust community moderation tools. Ship an MVP with a responsive overlay UI and a backend pipeline that can be showcased in a portfolio or demo reel.

Why this matters in 2026

Live streaming and fan engagement are evolving fast. Late 2025 and early 2026 saw three clear trends that make this project both topical and valuable:

• AI-driven clips and highlights: Creators expect automated highlight generation so they can make short-form content quickly. Advances in ASR, multimodal analysis and lightweight on-prem models let you build production-feasible systems.
• Platform convergence: Viewers jump between Twitch, YouTube, Discord and federated/social apps (Bluesky’s LIVE badges and shareable live links are an example). Building chat adapters and shareable clips increases reach.
• Moderation and safety: Large streams must combine real-time automated moderation with community tools and human workflows. Privacy, consent, and content safety are hiring-side priorities.

Design a system that solves real-world pain: low-latency engagement, fast clip turnaround, digestible highlights, and scalable moderation.

Project scope & MVP features

Prioritize an MVP that you can complete in a few sprints and present polish in your portfolio:

1. Live stream ingest and playback (RTMP ingest → HLS/DASH or AWS IVS).
2. Unified chat integration (Twitch IRC / PubSub, YouTube Live Chat, Discord gateway).
3. Clip creation (timestamp-based capture, server-side transcoding to MP4/WebM, S3 + CDN).
4. Highlight reel generation (automated scoring + manual editing UI).
5. Moderation suite (automated filters, mod queue, role-based actions, appeals).
6. Overlay and VOD timeline UI (clip markers, seek-to-clip, share buttons).

System architecture (high-level)

Build an event-driven architecture that separates ingest, real-time messaging, processing and storage. Here’s a compact layout you can diagram in your README:

• Streamer client: OBS/Streamlabs → RTMP to ingest endpoint (AWS IVS / Mux / NGINX RTMP).
• Media pipeline: Ingest server → HLS segments → storage; generate VODs from HLS or use provider clip APIs.
• Realtime layer: Chat adapters push events to a central Event Bus (Kafka / Redis Streams / AWS Kinesis). A WebSocket gateway (Socket.IO or native WS) broadcasts to UI and overlays.
• Clip engine: Listens for markers or spike events → orchestrates ffmpeg or cloud transcoding jobs → stores clips in S3 and returns CDN URL.
• Highlight engine: Scores candidate clips using signals (chat density, emotes, audio energy, ASR sentiment); ranks and aggregates into reels.
• Moderation service: Real-time filters (bad words, toxic comments via moderation API), human review queues, role-based actions persisted in a DB.
• Frontend: Viewer UI + moderator dashboard + composer for highlight reels.

Minimal data flow

1. Streamer sends RTMP → Ingest.
2. Ingest writes HLS and emits segment events to Event Bus.
3. Chat adapters emit chat messages to Event Bus.
4. Clip engine triggers on markers or event windows → creates clip via ffmpeg / provider API.
5. Moderation service inspects chat and clips → flags or auto-mutes / queues for review.

Recommended technology stack (2026)

Pick pragmatic, widely-used tools so hiring managers immediately see relevant skills:

• Media ingest / streaming: AWS IVS or Mux (managed, clip APIs). For DIY: NGINX RTMP + SRT for low-latency.
• Realtime messaging: Redis Streams / Kafka for backend; Socket.IO or native WebSockets for browser overlays.
• Transcoding / clip creation: ffmpeg in serverless containers (AWS Lambda + Lambda Layers or Cloud Run), or AWS Elemental MediaConvert.
• Storage + CDN: S3 / Cloudflare R2 + CloudFront / Cloudflare CDN.
• ASR + ML: WhisperX / other fast ASR forks for 2026, combined with on-device VAD. Use open-source embeddings for search and clustering.
• DB and auth: PostgreSQL for relational data; Redis for ephemeral state; Keycloak or Auth0 for roles.
• Moderation APIs: Mix of local models and third-party services (industry trend in 2026 favors multimodal moderation toolkits and private deployments for privacy).
• Frontend: React + Vite; use HLS.js for playback; server-side rendered pages for SEO where needed.

Chat integration: actionable steps

Goal: Aggregate chat from multiple platforms into a single event stream for overlays, analytics and moderation.

1. Write an adapter interface with methods: connect(), onMessage(), onUserJoin(), onBits(), disconnect().
2. Implement adapters: Twitch (tmi.js + PubSub), YouTube (YouTube Live Chat REST + WebSocket polling), and Discord (Gateway + webhooks).
3. Normalize events to a common schema: { platform, channelId, userId, userName, message, badges, timestamp, raw }.
4. Publish normalized events to your Event Bus (Redis Stream / Kafka topic) for downstream consumers (mods, clip engine, analytics).

Sample adapter snippet (Node.js pseudo):

// pseudo-code
const EventEmitter = require('events');
class TwitchAdapter extends EventEmitter {
  constructor(channel, token) { super(); }
  connect() { /* tmi.js connect, emit normalized events */ }
}
// downstream: adapter.on('message', event => publishToStream(event))

Clip creation: implementation blueprint

There are two common approaches to clip creation:

• Provider API: Use IVS / Mux clip APIs for fast turnarounds. Easiest for prototypes.
• Self-service: Slice HLS segments with ffmpeg to produce MP4/WebM (gives total control).

Server-side clip flow (self-service)

1. User clicks “clip” in overlay → client sends clip request with start and end timestamps to API.
2. API creates a job record in DB and enqueues a task (Redis queue / AWS SQS).
3. Worker downloads required HLS segments or reads from object storage; runs ffmpeg to transcode & concatenate.
4. Worker uploads result to S3, creates CDN-signed URL, returns metadata to DB; notifies user via WebSocket.

Sample ffmpeg command (concatenate HLS segments into MP4):

ffmpeg -i "https://example.com/hls/stream.m3u8" -ss 00:12:34 -to 00:12:54 -c copy clip.mp4

# Or re-encode for compatibility
ffmpeg -i "https://example.com/hls/stream.m3u8" -ss 00:12:34 -to 00:12:54 -c:v libx264 -preset veryfast -c:a aac clip.mp4

Automated highlight reels: scoring & orchestration

Create an automated pipeline that proposes highlights; allow creators/mods to curate and export reels.

Signals to use for scoring

• Chat spike: Messages per second window vs baseline.
• Emote density & reward events: Bits, donations, subs, special badges.
• Audio energy / music cues: Sudden loud moments, laughter, sound effects.
• ASR & semantic signals: Keywords, sentiment spikes, named entities (NPC names, boss battles).
• Viewer engagement: Concurrent viewers + percentage change.

Simple scoring formula (starting point)

Score = w1 * z(chat_rate) + w2 * z(emote_rate) + w3 * z(audio_energy) + w4 * z(sentiment_score) + w5 * z(viewer_delta)

Normalize to z-scores and tune weights w1..w5. Create sliding windows (e.g., 15s, 30s, 60s) and merge overlapping high-score windows into single clips.

ASR & multimodal processing (2026 tip)

Use WhisperX or other low-latency ASR forks for accurate timestamps in 2026. Combine ASR with simple NER or embeddings for topic clustering. If you need privacy, run ASR in isolated infra (many orgs moved to private inference in late 2025).

Moderation: automated + human workflows

Moderation is a first-class requirement for public streams. Build layered controls:

• Pre-filtering: Block messages with banned words or patterns at adapter level.
• Automated moderation: Use ML classifiers for toxicity, hate speech, sexual content and spam. Flag or auto-mute based on confidence thresholds.
• Human-in-the-loop: Create a mod queue where flagged items are triaged; provide context (preceding chat, clip snippet).
• Role system: Roles for streamer, mods, community moderators, trusted users with gradual powers (time-limited actions to prevent misuse).
• Appeals and audit log: Persist moderation actions, reasons and appeals in DB for transparency.

Example moderation DB schema

TABLE moderation_actions (
  id UUID,
  actor_id UUID, -- moderator or system
  target_type VARCHAR(10), -- 'message'|'clip'|
  target_id UUID,
  action_type VARCHAR(20), -- 'flag'|'delete'|'timeout'|'ban'
  reason TEXT,
  confidence FLOAT,
  created_at TIMESTAMP
);

UX & product details that impress interviewers

Small UX wins show product thinking and sense for creators:

• Overlay-first design: Minimal, keyboard-friendly clip hotkeys and a compact composer panel.
• VOD timeline: Show interactive clip markers on the VOD seek bar. Hover to preview 3s thumbnails.
• Clip editor: Allow trimming, title, tags, and auto-generated captions. Enable export as MP4 or share link embed.
• Accessibility: Captions for all clips, configurable font sizes, and color-contrast friendly UI.
• Mobile_friendly: Responsive viewer and lightweight mod app for on-the-go moderation.

DevOps: deployable prototype & cost controls

Make it easy to demo and explain your deployment choices in interviews.

• Infra as Code: Terraform or Pulumi to provision S3, cloud-run containers, and CDN config.
• CI/CD: GitHub Actions that run tests, build container images and deploy to a staging project.
• Autoscaling: Workers scale via queue depth (KEDA on Kubernetes or serverless concurrency).
• Cost strategy: Use provider clip APIs for early demos to avoid heavy compute; shift to self-service ffmpeg for production.

Testing, metrics and what to measure

Instrument everything—these are the metrics hiring managers ask about:

• Clip latency: Time from clip request to clip URL (goal: < 60s for provider APIs; < 3m for self-service).
• Highlight recall: Percentage of manual highlights your algorithm captured.
• Moderator throughput: Items handled per hour and time-to-resolution.
• System health: Error rates for ingest, worker failures, queue lengths.

How to present this project in your portfolio

Your portfolio should make it immediately obvious what you built, why, and how. Include these items:

1. README with architecture diagram, stack, and deployment steps.
2. Live demo (recorded walkthrough + link to a deployed staging instance if possible).
3. Highlighted code: Key modules (chat adapter, clip worker, scoring algorithm) with short explanations and sample logs showing the pipeline in action.
4. Metrics snapshot (sample logs, Grafana screenshots or smoke-test results showing clip latency and highlight precision).
5. Security & moderation notes explaining privacy decisions and moderation thresholds.

Security, privacy & legal considerations (must-have in 2026)

Live content and clips involve consent and copyright. For tabletop streams with guest players:

• Store explicit consent records before publishing player clips.
• Implement DMCA takedown processes and logging.
• Be careful with third-party moderation providers: prefer private deployments for sensitive streams.
• Audit trails for moderation actions to support appeals and compliance.

Example implementation roadmap (6 sprints)

1. Week 1–2: Ingest + playback demo (IVS or Mux) + simple static overlay.
2. Week 3–4: Chat adapters + WebSocket broadcast + basic moderation filter.
3. Week 5–6: Clip API + worker that runs ffmpeg + S3 upload.
4. Week 7–8: Highlight engine prototype (chat spikes + ASR signals) + composer UI.
5. Week 9–10: Moderator dashboard + appeals + role system.
6. Week 11–12: Polish, deploy, record demo video and write README + blog post.

Advanced strategies & future features (2026+)

Once your prototype is solid, these features make the project stand out:

• Multimodal embeddings: Index clips by audio+text embeddings for semantic search of moments.
• Automated short-form creation: Auto-generate TikTok/YouTube Shorts with aspect-ratio-aware cropping.
• Creator tools: Revenue-split microclips, patron-only highlights and merch tagging.
• Federation & cross-posting: Integrate with Bluesky or other federated networks so live badges and clip posts link back to your platform.

Sample code: enqueueing a clip job (Node.js + BullMQ)

const Queue = require('bullmq').Queue;
const clipQueue = new Queue('clips', { connection: { host: 'redis' } });

async function requestClip(streamId, start, end, userId) {
  const job = await clipQueue.add('create-clip', { streamId, start, end, userId });
  return job.id; // poll or subscribe for completion
}

Final checklist before demo day

• Demo runs on stable URL or recorded video.
• Architecture diagram and cost estimate included.
• Key metrics (clip latency, highlight precision) documented.
• README includes how to run locally and how to deploy.
• Moderation policy and privacy notes are present.

Takeaways & next steps

This project blends realtime systems, multimedia processing, AI, and community tooling—precisely the skills employers want in 2026. Prioritize a working demo with strong UX and instrumentation over a feature-complete system.

Actionable next steps:

1. Fork a starter repo (or scaffold one) and commit a minimal ingest + playback demo in 48 hours.
2. Build the chat adapter next—get live events into a WebSocket and show them on an overlay.
3. Implement a clip request flow (even if it uses a provider API) and measure latency.

Call to action

Ready to build this as a portfolio piece? Start your repo, sketch the architecture, and deploy a working demo. Share your progress in a developer community for feedback and mentorship—post your architecture diagram and a 2-minute demo clip. If you want structured challenges and peer reviews for this exact project, join our community at challenges.pro to get a curated roadmap, code reviews and hiring-aligned feedback.

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