How to Make an App Like Apple News

Building a news aggregation app is deceptively complex. The surface looks simple: pull articles, show them to users. Underneath, you're dealing with real-time content pipelines, personalisation models, publisher licensing, and a feed that needs to feel fresh without hammering your infrastructure. This post breaks down what it actually takes to build something at Apple News' level of sophistication.

What Does Apple News Actually Do Under the Hood?

Apple News is not a CMS. It's a content aggregation and personalisation platform. The distinction matters because it shapes every architectural decision you make.

At its core, Apple News does four things:

• Ingests content from thousands of publishers via RSS, Atom feeds, and the Apple News Format (ANF) API
• Stores and indexes that content for fast retrieval
• Ranks and personalises the feed for each user using on-device and server-side signals
• Serves that content inside a native reading environment with monetisation baked in

The Apple News Format itself is a JSON-based document format. Publishers submit structured JSON that maps to layouts, components, and animations. This is worth noting because it means Apple controls the rendering layer entirely, which is how they guarantee a consistent reading experience across devices.

If you're building a competitor, you'll need to decide early whether you own the rendering layer or whether you embed publisher URLs in a WebView. That single decision cascades through your entire stack.

What Architecture Does a News Aggregation App Actually Need?

Content Ingestion Pipeline

You need a feed crawler that runs on a schedule and on demand. A typical setup uses a job queue (Celery with Redis, or a managed service like AWS SQS) to dispatch crawl jobs. Each job fetches an RSS or Atom feed, diffs it against what's already stored, and pushes net-new articles into a processing queue.

From there, articles go through enrichment:

• HTML stripping and readability extraction (Mozilla's Readability.js or a Python port like trafilatura)
• NLP tagging for topics, entities, and sentiment (spaCy or a fine-tuned BERT model)
• Deduplication via locality-sensitive hashing (MinHash works well here)
• Image extraction and CDN upload

The enriched article lands in your primary store. PostgreSQL with full-text search handles this well up to roughly 50 million articles. Beyond that, you'll want Elasticsearch or OpenSearch in front of it.

Personalisation Engine

This is where most teams underestimate the work. A simple "show articles from categories the user follows" approach works at launch but degrades quickly. Users stop engaging with content they've technically subscribed to.

A production personalisation system uses collaborative filtering alongside content-based signals. The canonical starting point is a two-tower neural network: one tower encodes user history, the other encodes article features. You train on implicit feedback (clicks, scroll depth, read time) rather than explicit ratings.

For the candidate generation layer, approximate nearest neighbour search (FAISS or Pinecone) retrieves the top-k articles from a corpus of potentially millions. A lighter re-ranking model then applies contextual signals: time of day, device type, recency of the article, and publisher diversity constraints so you're not surfacing the same source five times in a row.

Apple News does a significant portion of this on-device using Core ML, which is how they preserve privacy. If you're building on Android or cross-platform, TensorFlow Lite or ONNX Runtime gives you similar capability.

Notification and Real-Time Layer

Breaking news requires a separate pathway. You don't want breaking news sitting in a batch crawl queue for 15 minutes.

WebSockets or Server-Sent Events work for in-app real-time updates. For push notifications, FCM and APNs are the obvious choices. The harder problem is deciding what qualifies as breaking news. You need a classifier trained on your content that fires when a new article's topic velocity (rate of new articles on the same entity in a short window) exceeds a threshold.

How Do You Handle Publisher Licensing and Partnerships?

This is the part most technical posts skip. Legally, you cannot aggregate full article text without an agreement with the publisher, or without a defensible fair use argument (which is jurisdiction-specific and fact-dependent).

Your options:

Approach	Legal exposure	User experience	Implementation complexity
Headline + excerpt only	Low	Poor	Low
WebView embed of publisher URL	Medium	Variable	Low
Full-text via RSS (publisher opted in)	Low	Good	Medium
Licensed full-text API	Low	Excellent	High
Scraped full-text	High	Excellent	Medium

Apple News runs on direct publisher partnerships and a revenue-sharing model through Apple News+. If you're building a monetisable product, you'll need a similar commercial layer. That means contracts, content guidelines, and a publisher-facing CMS where they can manage their own feeds and content policies.

If you're building an internal enterprise news aggregator (monitoring industry news, competitor press, regulatory updates), the calculus is different. You're likely within fair use for summarisation and internal distribution.

Caching, CDN, and Feed Performance

A news feed has a peculiar read pattern: extremely high read volume, moderate write volume, with sharp spikes around breaking events. Your caching strategy needs to account for this.

Cache the pre-computed feed per user segment, not per user. Segment by broad interest clusters (10 to 50 clusters is usually sufficient at early scale). When new articles arrive, invalidate only the segments whose topic vectors intersect with the new content. This keeps cache hit rates above 80% even during high-volume events.

Images are a different story. Resize and optimise images at ingestion time (a 4K hero image from a publisher has no business being served to a mobile user). Store variants at standard breakpoints (320px, 640px, 1080px, 1920px) on CloudFront or Cloudflare R2. Lazy load below the fold. A poorly optimised image pipeline will cost you more in egress than your compute bill.

For the API layer itself, GraphQL gives clients the flexibility to request exactly the fields they need, which matters when you have a mobile app pulling over a mobile data connection. REST works fine too, but you'll end up with over-fetching unless you design your endpoints carefully from the start.

On-Device vs Server-Side Personalisation: Where to Draw the Line

The trend in 2024 and 2025 is to push more inference to the device. Apple's approach with Core ML is the most aggressive version of this: the ranking model runs locally, using local interaction history, and nothing leaves the device.

This is genuinely privacy-preserving. It also means you can't improve your models using individual user data, because you never see it. You train on aggregate, differentially private signals instead.

For most teams, a hybrid approach is more practical. Run lightweight re-ranking on-device using a quantised model (INT8 quantisation gets you a 4x size reduction with minimal accuracy loss). Run the heavier candidate generation server-side where you have access to your full corpus index. The device sends a feature vector, the server returns candidates, the device re-ranks them locally.

TensorFlow Lite and Core ML both support this pattern. The engineering overhead is real, roughly 6 to 10 weeks of ML engineering to get the first version production-ready.

Conclusion

Building at Apple News' scale is a multi-year project. Building a credible v1 that handles content ingestion, basic personalisation, and a clean reading experience is 4 to 6 months of focused engineering with a team of 4 to 6 people.

The highest-leverage decisions are made early: whether you own the rendering layer, how you handle publisher agreements, and how much personalisation logic you push to the device. Get those three right and the rest is solvable.

If you're scoping this out and want a second opinion on your architecture or a team to build alongside yours, reach out to us at Sodio.

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FAQ

How long does it take to build a news aggregation app? A functional MVP with feed ingestion, topic tagging, and a basic personalised feed takes roughly 3 to 4 months with a small team. A production system with ML-based ranking, publisher partnerships, and real-time breaking news pipelines is closer to 12 to 18 months of cumulative engineering work.

What's the cheapest way to personalise a news feed without training your own model? Start with explicit user preferences (category and publisher follows) combined with a recency-weighted ranking. This requires no ML infrastructure and outperforms a purely chronological feed. Add collaborative filtering once you have enough interaction data, typically above 10,000 daily active users with meaningful engagement signals.

Do I need a separate mobile app or can a web app work? A Progressive Web App (PWA) covers most use cases at launch. You lose push notification reliability on iOS (Apple still restricts PWA push) and access to on-device ML frameworks. If personalisation and notifications are core to your product, native or React Native is the better call from the start.

How do I handle duplicate articles from multiple publishers covering the same story? MinHash-based locality-sensitive hashing detects near-duplicate text with low computational cost. Generate a MinHash signature at ingestion time and compare against a sliding 24-hour window. A Jaccard similarity above 0.7 is a reliable threshold for considering two articles the same story. Group them and surface the highest-authority source first.

What database should I use for storing articles at scale? PostgreSQL with the pg_trgm extension handles full-text search up to roughly 50 million articles without specialised infrastructure. Beyond that, Elasticsearch or OpenSearch is the standard choice. For the personalisation layer, a vector database like Pinecone or pgvector (if you're already on Postgres) handles embedding storage and nearest-neighbour retrieval efficiently.

Have a project in mind? Contact Sodio Technologies to discuss your requirements and explore the right technology solution for your business.