Build a Mobile-First Episodic Video App with an AI Recommender

Hook: Build a job-ready vertical video app — end-to-end

Struggling to build portfolio projects that map to real-world streaming and personalization problems? You’re not alone. Developers and infra teams need a compact, practical, hands-on project that teaches the end-to-end architecture for a mobile-first, vertical episodic app and an AI-powered recommender tuned for micro-episodes and short-form narratives. This guide is a step-by-step project blueprint you can implement, measure, and showcase in 2026.

Why this matters in 2026: the state of vertical and micro-episodic video

By 2026, vertical short-form serialized storytelling has matured into a distinct streaming vertical. Recent funding rounds and platform growth (for example, a Jan 2026 report on Holywater’s expansion of an AI vertical streaming platform and the meteoric rise of AI video tooling from startups like Higgsfield in 2025) make it clear: mobile-first, microdrama content is where audience attention and creator innovation converge.

“Mobile-first serialized short content is becoming the new household streaming pattern.” — industry reporting, 2025–2026

That shift creates specific technical needs: optimized vertical video delivery, sub-90-second episodic flows, hyper-tailored personalization, and cost-efficient edge delivery at scale. This project lets you tackle those exact problems.

Project overview: goals, constraints, and success metrics

Deliver a minimal, deployable application that demonstrates:

• Mobile-first UX: Vertical-first viewer experience for episodes under 5 minutes, optimized for thumbs and gestures.
• Scalable video pipeline: Ingest, transcode, store, and deliver optimized segments via CDN/edge.
• AI recommender: A hybrid, data-driven recommender tuned for micro-episode signals (completion, skip, rewatch).
• Observability and experimentation: Metrics, A/B testing, retention analysis.

Key success metrics (KPIs): completion rate per episode, next-episode play rate, session length, and DAU/MAU. For recommender evaluation use recall@K, NDCG, and causal lift on retention.

High-level architecture

Implement a modular architecture so you can show mastery of modern cloud and edge patterns:

• Client — Native vertical UI: SwiftUI / Jetpack Compose for polished native, or React Native / Flutter for cross-platform. Player supports HLS/CMAF and vertical-safe UI overlays.
• CDN and edge compute — Cloudflare Workers / CloudFront Functions for personalization at the edge, with a CDN (Cloudflare, AWS CloudFront, Fastly) serving segments.
• Video pipeline — Uploads to object store (S3/GCS), serverless jobs or media services for transcoding (FFmpeg, AWS Elemental, or open-source pipelines). Produce CMAF/HLS with short segment durations (1–3s) optimized for micro-episodes and fast scrubbing.
• Backend — Microservices: metadata, user, session, and recommender services. Use GraphQL or a slim REST API for the client.
• Realtime/event pipeline — Kafka / Kinesis / Pulsar for high-throughput events (impressions, plays, skips). Use stream processing (Flink, Spark Structured Streaming) for feature extraction.
• Feature store & model infra — Feature store (Feast or custom), vector DB (Milvus, Pinecone), training infra (Kube + GPUs or managed services), model registry and ONNX export for inference.
• Monitoring & experimentation — OpenTelemetry, Prometheus, Grafana, and an experimentation platform (e.g., GrowthBook, Optimizely) for A/B tests.

Step-by-step implementation roadmap

Follow this phased plan: each phase is a deliverable you can demonstrate in a portfolio or interview.

Phase 1 — MVP: Vertical player + episode catalog (2–3 weeks)

1. Build a simple vertical player mobile app showing a feed of episodes and playing HLS streams. Implement gestures: swipe up for next, double-tap to like.
2. Create a metadata API (GraphQL) with endpoints: /episodes, /episodes/{id}, /series/{id}.
3. Ingest a few sample episodes (real phone vertical recordings or AI-generated clips). Transcode to CMAF/HLS with short segments using FFmpeg.

Phase 2 — Video pipeline and edge delivery (3–4 weeks)

1. Automate uploads: user or studio uploads original vertical master to S3. Trigger Lambda or serverless job to transcode into multiple bitrates and generate HLS manifests.
2. Store manifests and segments in object store and use CDN to serve segments. Configure CDN to cache manifests aggressively and use origin shield to reduce origin load.
3. Optimize for micro-episodes: use 1–3 second segments and tune HLS playlist TTL for fast startup. Consider field-tested packaging workflows from portable capture & livestream best-practices for manifest strategies.

Phase 3 — Instrumentation and event stream (2–3 weeks)

1. Instrument the client and player to emit events: play_start, play_time_update, play_complete, skip, rewatch, impression.
2. Publish events to Kafka/Kinesis. Build a small stream job to compute session-level metrics and write aggregated features to the feature store.

Phase 4 — Recommender: baseline to tuned model (4–6 weeks)

1. Implement a baseline recommender: popularity + recency. Use it to validate product flows and collect data.
2. Build a two-stage hybrid recommender: candidate generation (vector similarity + collaborative filtering) and a shallow ranking model (LightGBM or small neural net) for final ranking. Tune specifically for micro-episode metrics (completion, next-episode play).

Phase 5 — Personalization & edge inference (ongoing)

1. Run online experiments, implement model A/B testing and deploy ranker to nearest edge for low-latency personal recommendations.
2. Optionally use on-device personalization: small quantized models that adapt ranking weights per user locally for privacy and speed. See on-device patterns in the Creator On-The-Move Kit.

Data model and example schemas

Use simple relational tables for metadata and a time-series/event store for interactions. Example SQL schema:

CREATE TABLE episodes (
  id UUID PRIMARY KEY,
  title TEXT,
  series_id UUID,
  duration_seconds INT,
  vertical_aspect_ratio BOOLEAN,
  tags TEXT[],
  transcript TEXT,
  thumbnail_url TEXT,
  manifest_url TEXT,
  publish_date TIMESTAMP
);

CREATE TABLE interactions (
  id UUID PRIMARY KEY,
  user_id UUID,
  episode_id UUID,
  event_type TEXT, -- play_start, play_complete, skip, impression, rewind
  event_time TIMESTAMP,
  position_seconds FLOAT
);

Designing a recommender for micro-episodes

Micro-episodes change the signal profile: completion and short rewatch loops are much more informative than long watch minutes. Your recommender must therefore weight short-term engagement heavily.

Feature engineering — signals that matter

• Completion rate over last N plays (10–30 sessions)
• Skip rate at early timestamps (first 3–5 seconds)
• Time-to-next-episode (how soon a user plays the next micro-episode)
• Rewatch rate and repeat plays — microdramas often see repeated micro-engagements
• Contextual signals: time of day, device orientation, connection type
• Content embeddings: multimodal vectors (visual, audio, and text/transcripts) using pretrained multimodal encoders
• Creator and series features: series affinity and creator popularity

Two-stage recommender architecture

1. Candidate generation: Use vector similarity over multimodal embeddings (Milvus/Pinecone) plus collaborative filtering (ALS or matrix factorization) to produce ~200 candidates quickly.
2. Ranking: A shallow ranking model (LightGBM or a small MLP) that consumes behavioral, content, and contextual features and outputs a score optimized for a business metric (e.g., probability of next-episode play or session retention).

Training and evaluation

Train on sliding windows of event data. For micro-episodes prefer short windows (7–30 days) to capture rapid trends. Use these metrics:

• Recall@K for candidate generation
• NDCG and AUC for ranking
• Policy metrics: uplift in next-episode play, lift in completion

Use holdout sets that mimic online freshness — e.g., leave the last 24–72 hours for evaluation to capture trend shifts.

Online inference and edge personalization

For mobile-first apps latency is critical. Strategy:

• Perform candidate generation server-side but cache per-user candidate lists at edges with short TTLs.
• Run the lightweight ranking model at the edge (Cloudflare Workers, Lambda@Edge) or on-device to personalize final order. Export model to ONNX/TVM for efficient runtime.
• Implement client-side prefetch and warm-up: fetch next manifest segments while user is finishing current micro-episode to ensure sub-300ms startup for the next play.

Video pipeline details: transcoding and packaging

Key optimizations for micro-episodes and vertical video:

• CMAF + HLS with 1–3 second segments for faster seek and lower startup.
• Vertical encoding presets: crop/pad masters to 9:16 or 4:5, use AV1/H.265 for mobile bandwidth savings where supported, fall back to H.264 where needed.
• Keyframe strategy: frequent keyframes for accurate scrub and low-latency rewinds in micro content.
• Transcode pipeline: serverless automation that spawns containerized FFmpeg jobs or uses managed services (MediaConvert). Produce adaptive streams and closed caption/transcript assets for embeddings.

Scalability, cost control, and edge delivery

Tips to scale affordably:

• Use CDN cache-control aggressively for static segments. For micro-episodes with many repeats, the hit-rate will be high; set long TTLs on segments.
• Use origin-shielding and multi-region object storage to reduce egress and origin CPU cost.
• Implement rate-based autoscaling for ingestion/transcoding workers. Use spot instances or preemptible VMs for batch transcoding to save cost.
• Partition recommender services by user shard and use vector DB replicas and edge monitoring to lower latency globally.

Privacy, compliance, and ethical considerations

Be proactive in 2026: privacy expectations and regulations have tightened. Implement:

• Consent-first tracking and a cookieless ID strategy where possible.
• Data minimization: store aggregated features where feasible.
• Differential privacy or federated learning for on-device personalization when handling sensitive profiles.
• Model explainability: provide interpretable signals for recommendation choices to creators and users when needed.

Testing, experimentation, and evaluation

Run progressive experiments that move beyond CTR:

• Primary metric: uplift in next-episode play rate (causal uplift vs control)
• Secondary: completion rate, session minutes, retention 7/28 days
• Use sequential randomization and holdback groups to avoid contamination in time-sensitive micro-episode tests.
• Track model drift and concept drift: retrain frequently (daily to weekly) depending on data volume.

Observability & SLOs

Set SLOs for availability and latency:

• Segment fetch p90 latency < 150ms from cache
• Recommendation response p95 < 200ms
• Player startup time < 800ms

Instrument with OpenTelemetry and alert on error budgets. Capture model inference times and feature latency to detect bottlenecks early.

Cost & quick deployment checklist (starter estimate)

Starter-level deployment on a small user base (~10k MAU) with managed services:

• Object storage (S3/GCS): $50–$200/month
• CDN: $200–$1000/month depending on egress
• Transcoding (batch): $200–$2000/month based on volume
• Vector DB / model infra (managed): $500–$2000/month
• Streaming and compute (Kinesis/Flink, small cluster): $300–$1500/month

Keep costs down by using spot instances for batch jobs and by carefully tuning CDN caching and segment sizes. For broader strategy and cloud/edge tradeoffs see Signals & Strategy: Cloud Cost, Edge Shifts.

Advanced strategies & future predictions (2026+)

Trends to watch and integrate:

• Multimodal LLMs will power richer content embeddings (visual + audio + narrative) — incorporate these for better cold-start recommendations.
• Edge AI inference at the CDN level will be mainstream by 2026, enabling sub-100ms personalized ranks. Read more on edge delivery patterns in Edge Delivery, Privacy, and Live Micro‑Events.
• Creator-aware recommender systems that surface IP discovery and help creators iterate on microdrama hooks will become a competitive differentiator — tie this to launch playbooks like The 2026 Premiere Playbook.
• AI-generated micro-episodes (tools like Higgsfield in 2025) will increase supply; build signals to detect synthetic content quality and surface high-quality creator work.

Actionable takeaways (your next steps)

• Start an MVP: implement a vertical player and feed in 2 weeks with 5–10 sample episodes.
• Instrument events from day one — you need the data pipeline before building the recommender.
• Prototype a two-stage recommender: vector DB for candidates, LightGBM for ranking.
• Optimize HLS with 1–3s CMAF segments and configure CDN caching aggressively.
• Run small A/B tests measuring next-episode play and completion as primary metrics.

Short example: minimal ranking model pipeline

Conceptual steps to train a ranking model for next-episode play:

1. Aggregate features per candidate pair (user, candidate_episode): completion_rate_1w, time_since_last_episode, content_similarity_score, series_affinity.
2. Label with next_episode_play (binary within 10 minutes after current episode end).
3. Train LightGBM with weighted examples (upweight recent interactions).
4. Export model to ONNX for edge inference.

Closing: ship a portfolio-ready streaming project

Building a mobile-first vertical episodic app with a tuned AI recommender is an ideal portfolio project: it touches UX, infra, data engineering, ML, and product experimentation. In 2026, the shift to vertical microdramas plus mature AI tooling means this skillset is highly relevant to streaming startups and established platforms alike.

Ready to ship? Start by implementing the MVP player and event stream this week. If you want a ready-made starter repo, sample FFmpeg scripts, and a model notebook tuned for micro-episodes, join our community at challenges.pro to get the starter kit, follow guided checkpoints, and showcase your finished app in our hiring-ready portfolio gallery.

Call to action

Build the project, publish a walkthrough, and run an experiment — then share the results with the challenges.pro developer community. Join the cohort, get code reviews, and turn this architecture into a hireable case study.

Related Reading

• Signals & Strategy: Cloud Cost, Edge Shifts, and Architecture Bets for 2026
• Edge Analytics at Scale in 2026
• Edge Delivery, Privacy, and Live Micro‑Events: The Technical Playbook
• The 2026 Premiere Playbook: Micro‑Events & Serialized Micro‑Stories
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