Designing and building Khabri — an AI-powered conflict news intelligence platform that aggregates articles, videos, and tweets onto an interactive geospatial map with multilingual translation and audio headlines.
In a world where conflict news is scattered across dozens of sources, languages, and platforms, Khabri brings it all together on a single, living map.
Conflict news moves fast. Articles from RSS feeds, YouTube videos from war correspondents, tweets from state departments and journalists — all publish simultaneously across dozens of sources with no unified view. Analysts, journalists, and researchers are forced to manually track multiple sites, cross-reference locations, and assess relevance — often missing critical developments because they're buried in noise.
Khabri was built to solve this. A real-time conflict news intelligence platform that aggregates content from multiple source types, enriches each article with AI-powered relevance scoring and geocoding, and visualises everything on an interactive map. The name "Khabri" means "informer" in Hindi — and that's exactly what the platform does: it brings the news to you, placed exactly where it happened, scored by how much it matters.
I designed and built the entire platform end-to-end — from the React frontend with Leaflet maps to the Supabase backend with 20+ Edge Functions handling ingestion, AI enrichment, translation, and audio generation. Every line of code, every design decision, every pipeline — built solo.
Conflict news comes from everywhere, in every format, with no spatial context — and most of it is noise.
The core problem was threefold. First, conflict news is scattered across RSS feeds, YouTube channels, Twitter/X accounts, and news APIs — with no unified aggregation point. Second, most articles lack precise geographic coordinates, making spatial analysis impossible. Third, the signal-to-noise ratio is brutal: for every article directly about a conflict, there are dozens that merely mention it tangentially.
Building a useful intelligence tool meant solving all three problems simultaneously: aggregate from multiple source types, extract and geocode locations using AI, and score each article's relevance so that analysts see what matters first. On top of that, the platform needed to serve a multilingual Indian audience across 10 languages — and deliver twice-daily audio news bulletins generated entirely by AI.
And the entire thing had to be built, maintained, and operated by a single person. No backend team. No ML engineers. No dedicated DevOps. Just one builder with a clear vision for what conflict intelligence should look like.
Studying how analysts, journalists, and researchers actually consume conflict news — and where every existing tool falls short.
The architecture and design decisions that turned a news aggregator into an intelligence platform.
The key surfaces and features I designed and built across Khabri's end-user and admin experiences.
The centerpiece of Khabri is a full-screen Leaflet map with OpenStreetMap and CARTO basemaps (theme-aware dark/light). Articles render as clustered markers colour-coded by AI confidence score — green for high relevance, amber for medium, red for low. Clicking a cluster expands to individual articles; clicking a marker opens a popup with the article title, snippet, source, timestamp, and AI reasoning. Users can filter by conflict, time range, confidence level, source, content type, and keyword — all in real time.
The left sidebar provides conflict selection, keyword and location search, time range presets (1h to 30d plus custom date pickers), confidence toggles, source filters, and a live market ticker. A timeline slider lets users animate news across time, watching how a conflict develops hour by hour.
On mobile, Khabri adapts to a list-first experience with the map accessible via a toggle. The event list shows each article as an expandable card with confidence badge, content-type icon, translated title, age, location, AI reasoning, and source badges. Users can filter by content type (All, News, Videos, Tweets) and play audio headlines directly from the top bar.
The translation system supports 10 Indian languages including Hindi, Tamil, Telugu, Bengali, Marathi, Kannada, Malayalam, Gujarati, Punjabi, and Odia — translating article content on-demand, not just UI labels. The AI-generated morning and evening headline bulletins can be listened to with play/pause/seek controls, turning Khabri into a personal news radio.
Khabri doesn't just aggregate articles — it pulls in tweets from official government accounts and journalists, YouTube videos from war correspondents and news channels, and RSS feeds from major outlets. Each content type has its own visual treatment: tweets show embedded previews with profile images, YouTube cards display video thumbnails with play buttons, and RSS articles show standard news card layouts.
The content type filter (All / News / Videos / Tweets) lets users focus on exactly the format they need. The multi-source approach ensures that breaking news from Twitter, in-depth analysis from news outlets, and on-the-ground footage from YouTube all appear in one unified view — geocoded and scored by the same AI pipeline.
The admin dashboard provides full control over Khabri's data pipeline. Source management with CRUD operations for RSS, NewsAPI, YouTube, and Twitter sources — each linkable to multiple conflicts. A conflict keyword editor with AI-powered keyword suggestions (Gemini analyses recent articles and recommends new tracking terms). Server-side paginated article management with bulk operations, inline AI reasoning display, and single-article re-enrichment.
Pipeline controls let admins trigger ingestion, scoring, enrichment, and geocoding runs manually, with real-time progress tracking via Supabase Realtime subscriptions. Cron status monitoring, enrichment history, and pipeline logs provide full operational visibility into the automated systems that keep Khabri running 24/7.
These principles governed every design and engineering decision on Khabri — from map interactions to pipeline architecture.
A modern, serverless architecture built for real-time intelligence at scale — designed and implemented entirely by a single developer.
Khabri demonstrates that a single designer-developer can build and operate a production-grade AI intelligence platform.
"Khabri represents the kind of product I believe in — where design thinking meets engineering execution. It's not a mockup or a prototype. It's a live, production system that ingests thousands of articles, scores them with AI, geocodes them onto a map, translates them into 10 languages, and generates audio news bulletins — all running autonomously, 24 hours a day."
Khabri is proof that the boundaries between design and engineering are artificial. The best products emerge when one person holds the entire vision — from the user experience down to the database schema, from the map interaction patterns to the AI scoring rubric. No handoff gaps. No lost-in-translation requirements. Just a direct line from intent to implementation.
The platform is live at khabri.net and actively maintained. It continues to evolve with new conflict tracking, additional data layers, and improved AI models — a living product that grows alongside the news it monitors.
How owning both design and engineering produced a platform that no handoff-driven process could.
Khabri is the purest expression of the Deep Dive Design philosophy — because there was no gap between designer and developer. Every UX decision was informed by technical constraints I understood intimately. Every architectural choice was shaped by the user experience I was designing. The AI scoring rubric wasn't a product requirement handed to an ML team — it was a design decision I implemented directly in the Edge Function.
This project demonstrates that the most powerful products emerge when design thinking and engineering execution live in the same mind. The map interaction patterns informed the database indexing strategy. The translation UX shaped the batching algorithm. The timeline animation drove the query optimization. Design and code weren't separate phases — they were a single, continuous act of creation.
Khabri isn't just a case study in product design. It's a case study in what happens when you remove every layer of abstraction between vision and execution.