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I Built a Personal Tech News Assistant That Thinks Like a Team

#ai #productivity #python #mcp

Scraping, summarizing, and saving tech news using multi-agent automation — here's how I made it work with OWL and FireCrawl.

If you're anything like me, keeping up with tech news means juggling 5+ tabs, 3 newsletters, and forgetting half of it anyway.

I wanted something better — something that could scan, summarize, and deliver the latest tech news automatically every day.

So I built a digital assistant that does exactly that — using AI agents from the CAMEL-AI OWL framework and a scraping backend called FireCrawl MCP.

It’s like having two interns: one who surfs the web for news, and another who writes a daily digest for you.

What This Thing Actually Does

  • Visits sites like TechCrunch, The Verge, Wired
  • Extracts the latest headlines, summaries, and publish dates
  • Groups similar stories
  • Saves the results in a clean Markdown digest

How It Works (The Agent Flow)

Using the OWL framework, I created autonomous agents that talk to each other to get the job done. Here’s the workflow:

+------------------+        +----------------------+        +---------------------+
|  Curator Agent   | ----→  |  Research Agent      | ----→  |  FireCrawl Web API  |
| ("Get me news!") |        | ("I'll scrape it...")|        | (Scrapes tech sites)|
+------------------+        +----------------------+        +---------------------+
         ↑                             ↓
         +------------ Final digest in Markdown ------------+
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They collaborate using natural language prompts internally — no need for hardcoded rules.

The Tools Behind It

  • 🧠 OWL (Optimized Workforce Learning): Multi-agent orchestration framework
  • 🔥 FireCrawl MCP: A scraping server that pulls structured data from URLs
  • 🧰 CAMEL-AI: Lets you define agents and simulate workflows
  • 🐍 Python: The glue tying it all together

Getting Started (Setup in 3 Steps)

Clone the OWL repo:

git clone https://github.com/camel-ai/owl.git
cd owl
pip install -r requirements.txt
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Then navigate to the use case:

cd community_usecase/Mcp_use_Case
python content_curator.py
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Make sure:

  • You have your .env file with API keys
  • mcp_servers_config.json is in the same folder as content_curator.py

A Sample Output Looks Like This 

### Today’s Top Tech News

- **OpenAI Launches GPT-5 Preview** – The Verge  
  *OpenAI reveals GPT-5 with better memory and deeper reasoning.*

- **Apple Debuts M4 Chips for MacBooks** – TechCrunch  
  *Next-gen silicon promises speed and efficiency for developers.*

- **GitHub Copilot Workspace Rolls Out** – Wired  
  *Your AI coding buddy just got a major upgrade.*
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You can set this up to run daily, and send the digest to Notion, WhatsApp, or email.

Customize It: Your Digest, Your Rules 🛠️

This is not a fixed tool — you can personalize every part:

🧾 Change the Task

Edit the prompt in content_curator.py:

default_task = "Curate today's top AI and startup news from Hacker News, Product Hunt, and Reddit r/MachineLearning."
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🌐 Use Your Own Sites

FireCrawl supports scraping any website — simply modify the Research Agent’s prompt or provide custom URLs.

⚙️ Edit MCP Server Config

Want to use different MCPs (e.g., WhatsApp, Notion, Telegram)?
You can register and integrate them into OWL by editing mcp_servers_config.json.

Why I Made It

Honestly? I was drowning in tabs and newsletters. I wanted a focused, daily, and clean tech summary that didn’t waste my time.

Also, I wanted to explore what it's like building with OWL — and I was surprised how easy it was to simulate an AI team doing meaningful work.

What's Next?

Here’s what I’m planning to add next:

  • 📆 Run it daily via cron
  • 💬 WhatsApp alerts using the WhatsApp MCP
  • 🔎 Include Hacker News, Reddit, and Dev.to scraping
  • 🧠 Add sentiment + trend detection across headlines

Final Thoughts

This isn’t some fancy AGI project. It’s a practical, nerdy little tool that genuinely makes my mornings better.

If you’re into automating boring stuff with AI, I highly recommend trying out OWL + FireCrawl. You’ll feel like you’re building the future — one YAML config at a time.

📎 GitHub: CAMEL-AI OWL Repository
🧠 Framework Docs: camel-ai.org
💬 Questions or feedback? Drop a comment below!

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