Why I Built It
Like many of us working in tech, I constantly want to stay updated on what’s happening in the world of artificial intelligence — new models, research, infrastructure, and applications.
But visiting multiple websites every day, filtering noise, and summarizing meaningful updates started to feel like a task that AI itself could handle better.
That thought led to a simple question:
Can I build a system that automatically discovers, summarizes, and organizes AI news — using AI itself?
That’s how the idea for this project was born: an AI-powered content curator that continuously finds, validates, and summarizes credible articles on artificial intelligence, then publishes a ready-to-read digest automatically.
The Concept
The goal was to design a pipeline that could:
- Discover recent and credible AI-related articles from multiple online sources
- Verify their relevance using AI understanding, not just keyword matching
- Summarize and group them into readable sections (e.g. Research, Applications, Infrastructure, Ethics)
- Publish the result as a formatted digest without manual effort
In short — automating artificial intelligence with artificial intelligence.
The Architecture
I built the system in C# (.NET 8) using a modular and event-driven design.
Each component is independent, testable, and configurable, so the same framework can later be adapted for any topic — not just AI.
1️⃣ Fetch
The system starts by fetching articles from APIs and RSS feeds.
It uses a list of dynamically generated keywords (based on the chosen topic) to identify relevant sources — for example, AI, machine learning, neural networks, robotics, generative AI, and so on.
2️⃣ Validate
Each candidate article is checked for basic quality signals: publication date, credibility of the source, and presence of relevant keywords.
This ensures only recent and topical content makes it through to the next stage.
3️⃣ Summarize and Group
This is where Gemini comes in.
Gemini analyzes each validated article, confirms its topical alignment, and produces concise summaries.
It also classifies the articles into logical categories — for example, grouping items into Research Breakthroughs, AI in Industry, or Tools and Frameworks.
4️⃣ Publish
Finally, the summarized and grouped digest is automatically published to Dev.to through its REST API.
The result is a clean, structured post that looks like a human-written daily or weekly AI digest — but generated entirely by an automated pipeline.
5️⃣ Configuration and Reusability
The system is driven by a simple configuration file where you can define:
- The topic (e.g. “Artificial Intelligence”, “Cybersecurity”, “Cloud Computing”)
- The update frequency (daily, weekly, etc.)
- The output format and publish target
Change the topic, and the same system can instantly curate a new field of content.
🧩 Tech Stack
| Component | Technology |
|---|---|
| Language | C# / .NET 8 |
| AI Integration | Gemini API |
| Publishing | Dev.to REST API |
| Scheduling | Windows Task Scheduler / Cron |
| Configuration | JSON-based settings |
📄 Sample Output
Here’s an example of one of the generated posts:
👉 Building an AI-Powered Content Curator in C#
The output is grouped, summarized, and formatted in a way that’s easy to read — ideal for quick scanning or daily briefings.
💭 What I Learned
- AI can effectively act as a filter and editor — reducing noise and surfacing genuinely relevant information.
- Automation doesn’t mean losing control — by defining transparent rules and validation steps, the system remains predictable.
- Small modular designs scale better — each stage (fetch, validate, summarize, publish) can be replaced or improved independently.
🚀 What’s Next
There’s still room to grow.
Some of the upcoming ideas include:
- Expanding to multiple topics (e.g. “AI in Healthcare”, “AI Policy”)
- Adding semantic similarity filtering using vector embeddings for smarter relevance scoring
- Enabling cross-platform publishing (LinkedIn, Medium, company blogs)
- Introducing a simple UI for configuration and run-time monitoring
Final Thoughts
This project started from a simple problem — wanting to stay informed about AI without constant manual effort — and turned into a self-learning exercise in automation, architecture, and applied AI.
It’s a reminder that sometimes, the best way to learn what AI can do is to let it help you build something that makes your own work easier.
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