Engineers and technical leaders often face information overload trying to stay current with AI research, framework updates, and backend infrastructure trends. Reading through scattered newsletters and unfiltered social feeds wastes valuable engineering time.

In this guide, I break down the exact architecture I built: a lightweight, zero-cost Python pipeline that ingests Google News RSS feeds, extracts high-signal technical impacts using Google's Gemini API, and delivers daily morning briefings straight to Telegram.

What is the AI News Assistant?

An AI News Assistant is an event-driven automation script connecting three core components: an RSS scraper for data ingestion, a Large Language Model (LLM) for content extraction and technical summarization, and a notification API for automated delivery.

Instead of reading raw headlines across dozens of aggregator tabs and marketing clickbait, the pipeline normalizes feed entries, prompts an LLM with strict layout constraints to extract technical implications, and delivers a readable daily briefing.

Why does it matter?

Manual news gathering suffers from two main problems: noise and context limits. Raw RSS feeds contain duplicate stories and promotional fluff, while unmoderated LLM monitoring can quickly consume context windows and API token budgets.

By building a structured pipeline, developers can:

  • Filter low-signal content before sending text to the LLM.
  • Rotate focus topics across calendar days to keep message lengths short and relevant.
  • Automate execution on cloud runners (such as GitHub Actions) at zero infrastructure cost.

How does it work?

The system is built as a three-stage pipeline in Python: scraping, summarization, and delivery.

1. Rotating scraping logic (scraper.py)

To prevent feed overlap and keep summaries concise, we query Google News RSS using an even/odd day rotation strategy:

import feedparser
import urllib.parse
from datetime import datetime
import pytz

TOPICS_EVEN = {
    "AI Capabilities": [
        "Models: LLMs, Reasoning, Multimodal",
        "RAG & Data: Vector Databases, Retrieval"
    ]
}

TOPICS_ODD = {
    "AI Engineering": [
        "Developer AI: Coding Agents & Autonomous Dev",
        "Infrastructure: Inference & Deployment"
    ]
}

def fetch_news():
    ist = pytz.timezone('Asia/Kolkata')
    day = datetime.now(ist).day
    active_topics = TOPICS_EVEN if day % 2 == 0 else TOPICS_ODD
    
    news_data = {}
    for category, subtopics in active_topics.items():
        news_data[category] = []
        for sub in subtopics:
            query = urllib.parse.quote(sub)
            url = f"https://news.google.com/rss/search?q={query}+when:48h&hl=en-US&gl=US&ceid=US:en"
            feed = feedparser.parse(url)
            for entry in feed.entries[:2]:
                news_data[category].append({
                    "subtopic": sub,
                    "title": entry.title,
                    "link": entry.link
                })
    return news_data

2. High-signal summarization with Gemini (brain.py)

We pass the scraped payloads into the official google-genai SDK. Using gemini-2.5-flash-lite, we enforce strict Markdown output rules so the model acts as a technical editor:

import os
from datetime import datetime
from google import genai
import pytz

def summarize_news(raw_news):
    client = genai.Client(api_key=os.getenv("GEMINI_API_KEY"))
    
    ist = pytz.timezone("Asia/Kolkata")
    now = datetime.now(ist)
    current_date = now.strftime("%A, %B %d, %Y")
    current_time = now.strftime("%I:%M %p")

    prompt = f"""
    Role: Senior AI Architect.
    Context: Deliver a high-signal briefing for Telegram.
    Data: {raw_news}

    Rules:
    - Date/Time: {current_date} at {current_time} IST.
    - Format: Telegram Markdown. No raw URLs. Use strictly [Full Report ↗️](link).
    - Style: Concise, technical, objective.

    Briefing Template:
    📅 **{current_date}** | 🕒 **{current_time} IST**
    ---
    **[CATEGORY]**
    • **[Title]**
      - 💡 *Impact:* [1 sentence technical insight]
      - 🔗 [Full Report ↗️](link)
    """

    try:
        response = client.models.generate_content(
            model="gemini-2.5-flash-lite", 
            contents=prompt
        )
        return response.text
    except Exception as e:
        return f"⚠️ AI Summary Error: {str(e)}"

3. Dispatch to Telegram (telegram_bot.py)

Finally, we send the summary to Telegram, handling character limits and suppressing link previews to keep the interface clean:

import os
import requests

def send_to_telegram(message):
    token = os.getenv("TELEGRAM_TOKEN")
    chat_id = os.getenv("TELEGRAM_CHAT_ID")
    url = f"https://api.telegram.org/bot{token}/sendMessage"
    
    if len(message) > 4050:
        message = message[:4000] + "\n\n*(Briefing truncated)*"

    payload = {
        "chat_id": chat_id,
        "text": message,
        "parse_mode": "Markdown",
        "disable_web_page_preview": True
    }
    
    response = requests.post(url, data=payload)
    return response.status_code == 200

Business application and operational extensions

This event-driven pattern extends beyond daily tech briefings. Engineering teams can adapt this architecture for operational automation:

  • Security & CVE tracking: Monitor vulnerability feeds and framework repositories for breaking advisories.
  • Competitor intelligence: Track product releases and engineering blogs across targeted companies.
  • Internal compliance audits: Summarize regulatory changes and policy updates for operational teams.

When implementing this system in production, consider these operational constraints:

  • Rate limiting: Google News RSS limits rapid unmoderated scraping. Keep query frequencies bounded.
  • Context budgeting: Telegram enforces a 4,096-character limit per message. Enforce concise model prompts and fallback string slicing to avoid delivery failures.
  • Stateless execution: Run scheduled jobs on serverless cron runners like GitHub Actions or AWS EventBridge at zero idle cost.

Key takeaway

Building an effective technical briefing assistant relies heavily on deterministic preprocessing and strict output formatting rather than raw model size. Combining lightweight scrapers with focused model prompts creates a dependable, automated intelligence feed at zero cost.