Building RSSRenaissance: AI-Powered Summaries for Smarter Reading
When I set out to build RSSRenaissance, my goal was simple: create a tool that helps users stay informed without drowning in endless articles. With the sheer volume of tech, news, and educational content available online, I wanted a solution that could fetch feeds, summarize content, and deliver actionable insights instantly.
Inspiration
I was inspired by the challenge of managing information efficiently. As a developer and someone who constantly explores new tech trends, I realized that even curated feeds can be overwhelming. I wanted a platform that doesn’t just deliver content but distills it intelligently.
What RSSRenaissance Does
Fetches RSS feeds from multiple sources like TechCrunch, The Verge, and Ars Technica.
Processes and stores feed items in a PostgreSQL database with caching via Redis.
Uses AI to generate instant summaries of articles, helping users quickly grasp key points.
Offers a modern Next.js interface for smooth reading and interaction.
How I Built It
I leveraged Next.js API routes as the backend, integrated with PostgreSQL and Redis for persistence and caching. AI-generated summaries were powered by OpenAI models. Throughout development, I used Kiro for:
Generating boilerplate code
Building reusable components
Writing TypeScript types and validation schemas
Streamlining backend feed ingestion workflows
The development workflow was a combination of spec-driven prompts and iterative “vibe coding” with Kiro, which significantly accelerated the process.
Challenges I Faced
Parsing feeds reliably across different formats (RSS, Atom, XML).
Handling API rate limits and network failures gracefully.
Ensuring that AI-generated summaries were accurate, concise, and user-friendly.
Kiro helped overcome many of these challenges by generating precise TypeScript definitions, building robust error handling, and suggesting scalable architecture patterns.
Accomplishments I’m Proud Of
A fully functional AI-assisted RSS feed reader with summarization.
Deployment-ready Docker setup with worker queues and health checks.
Clean, modular codebase that separates frontend, backend, and worker logic.
Integration of real-time caching with Redis for fast feed updates.
What I Learned
How to architect Next.js projects for both frontend and backend APIs.
Efficiently integrating AI summarization into production workflows.
How to leverage Kiro’s prompts to speed up development without sacrificing quality.
What’s Next for RSSRenaissance
Adding user personalization for preferred feeds and topics.
Expanding AI summarization to include actionable recommendations.
Improving frontend UX with dashboards, search, and feed management.
RSSRenaissance demonstrates how AI can transform information consumption—making knowledge accessible, digestible, and actionable.
Top comments (1)
That's amazing! Will you share your findings with me?