📰 QNews: Proof of Concept Overview

QNews is an experimental Flask-based social news and inquiry platform designed to change how people explore information and ask questions about the world.  Initially built as a lightweight local app, QNews acts as both a news aggregator and a knowledge explorer—where stories from major feeds become the foundation for discussion and AI-assisted Q&A.

The app uses local JSON files (in /static/data) for data storage—no external databases or authentication required—making it ideal for rapid experimentation and offline or semi-offline setups. Each topic page is generated dynamically and powered by generation.py, which pulls and formats news data from predefined public feeds (BBC, Google News, etc.).

🌍 Motivation

Too often, modern news is presented in ways that prioritize engagement over understanding. Readers skim headlines or brief summaries that rarely capture the full context or nuance of a story. The real details often emerge only after digging deep into the article—or through fragmented social media threads where facts and opinions blend into noise. The burden falls on the reader to navigate this dense forest of information and extract clarity from chaos.

QNews aims to fill that void. By focusing on the kind of content that informed readers actually seek, it filters out editorial noise and opinion-driven spin. Instead, QNews delivers concise, factual headlines paired with structured answers—helping users cut through speculation and access the verified information that truly matters.

✅ Current Working Features

• Dynamic topic generation: Users can browse or create new topics that automatically fetch related articles from RSS feeds.

• AI-assisted question answering: Users can submit questions related to any topic. These are answered either automatically via AI or manually by an Admin. Users do not answer each other’s questions—instead, they can vote on the best answers to help refine relevance and trust.

• Admin oversight and controls: Admins can review, edit, or delete topic data, regenerate content, or override AI answers to ensure factual consistency.

• Automatic RSS integration: News stories are fetched from static source lists and displayed as summaries under each topic.

• Local persistence via JSON: All feeds, questions, and votes are stored locally in structured JSON format for simplicity and transparency.

• Admin toolbar: Present on each topic page for content control—refreshing, regenerating, or deleting topics seamlessly from the UI.

⚙️ Planned Enhancements for the Next POC Iteration

• Feed management through the UI: Users (and admins) will be able to add, delete, or reorder feed sources directly in the browser.

• Feed ranking system: Implementing source credibility and preference scoring, influenced by user votes or admin weighting.

• Question analytics and answer ranking: Tracking which questions gain traction, and surfacing the highest-rated answers per topic.

• Search and topic filtering: Adding client-side or lightweight server-side search across articles and Q&A threads.

• Session-based personalization: Remembering topics, votes, and viewed content per user session or cookie state.

• Improved presentation: Cleaner layouts, inline refreshes, and smoother UI interactions (AJAX/WebSocket updates).

💼 Toward a Commercial Deployment

• Integration with live APIs and paid feeds (Reuters, Bloomberg, domain-specific sources).

• LLM summarization and reasoning layers to synthesize, cross-check, and contextualize news.

• User profiles, history, and personalized feeds, synced through cloud-based storage.

• Gamified trust metrics and community moderation, balancing open discussion with quality control.

• Analytics dashboards for topic trends, sentiment insights, and source credibility over time.

• Scalable backend (e.g., PostgreSQL + vector search) to support large communities and advanced ranking algorithms.