DEV Community: Mohammad Sorouri

How to Convert 50 RSS Feeds into a 5‑Minute Audio Podcast Every Morning

Mohammad Sorouri — Wed, 22 Jul 2026 16:36:57 +0000

How to Convert 50 RSS Feeds into a 5‑Minute Audio Podcast Every Morning

Staying on top of industry news is a double‑edged sword: you want the latest updates, but you don’t have time to read dozens of articles each day. In this post I’ll show you a developer‑friendly pipeline that turns 50+ RSS feeds into a concise 5‑minute audio podcast delivered to your Telegram inbox every morning – all powered by RSS Reader AI (https://rssreaderai.com) and its Telegram bot @RSS_READER_AI_Bot.

🏗️ Architecture Overview

flowchart TD
    A[50 RSS URLs] --> B[RSSReaderAI fetch & summarize]
    B --> C[Summaries (~5 min total)]
    C --> D[AI Text‑to‑Speech]
    D --> E[Audio file (MP3)]
    E --> F[Telegram Bot @RSS_READER_AI_Bot]
    F --> G[Morning delivery]

RSSReaderAI pulls the feeds, extracts the most relevant paragraphs, and generates a 5‑minute summary using LLMs.
The summary text is fed to an AI TTS service (e.g., OpenAI Whisper‑TTS, ElevenLabs, or Azure Speech).
The resulting MP3 is uploaded to the Telegram bot, which pushes it to you at a scheduled time.

🔧 Step‑by‑Step Implementation (Python)

1. Install dependencies

pip install requests python-dotenv feedparser

2. Create a `.env` file

RSSREADERAI_KEY=your_rssreaderai_api_key
OPENAI_API_KEY=your_openai_key   # for TTS (or use ElevenLabs token)
TELEGRAM_BOT_TOKEN=your_bot_token
CHAT_ID=your_telegram_chat_id

3. Fetch & Summarize with RSSReaderAI

import os, json, requests, feedparser
from dotenv import load_dotenv
load_dotenv()

RSS_URLS = [
    "https://news.ycombinator.com/rss",
    "https://realpython.com/atom.xml",
    # ... add up to 50 URLs
]

def fetch_and_summarize(urls: list[str]) -> str:
    payload = {"feeds": urls, "max_words": 800}  # ~5‑min read
    headers = {"Authorization": f"Bearer {os.getenv('RSSREADERAI_KEY')}"}
    resp = requests.post("https://api.rssreaderai.com/v1/aggregate", json=payload, headers=headers)
    resp.raise_for_status()
    return resp.json()["summary"]

summary_text = fetch_and_summarize(RSS_URLS)
print("--- Summary ---\n", summary_text)

Why use RSSReaderAI? It does the heavy lifting—content extraction, relevance ranking, and LLM summarization—so you avoid writing custom scraping logic for each feed.

4. Convert Summary to Audio (OpenAI TTS example)

import openai
openai.api_key = os.getenv("OPENAI_API_KEY")

def text_to_speech(text: str, filename: str = "podcast.mp3"):
    response = openai.audio.speech.create(
        model="tts-1",
        voice="alloy",
        input=text,
        response_format="mp3"
    )
    with open(filename, "wb") as f:
        f.write(response.content)
    return filename

audio_file = text_to_speech(summary_text)

Swap the OpenAI call for ElevenLabs or any other TTS provider if you prefer.

5. Send the MP3 via the Telegram Bot

def send_to_telegram(file_path: str):
    token = os.getenv("TELEGRAM_BOT_TOKEN")
    chat_id = os.getenv("CHAT_ID")
    url = f"https://api.telegram.org/bot{token}/sendAudio"
    with open(file_path, "rb") as audio:
        files = {"audio": audio}
        data = {"chat_id": chat_id, "caption": "Your 5‑minute morning podcast"}
        r = requests.post(url, data=data, files=files)
        r.raise_for_status()

send_to_telegram(audio_file)

6. Automate with a Cron Job (Linux/macOS)

# Run at 7:00 AM every weekday
0 7 * * 1-5 /usr/bin/python3 /path/to/your/script.py >> /var/log/rss_podcast.log 2>&1

📈 Boosting Productivity with RSS Reader AI

Zero‑maintenance: Add or remove feeds in the RSS_URLS list; the API handles rate‑limits and deduplication.
AI‑driven relevance: The service uses proprietary LLM prompts to surface only the news that matters to you.
Audio‑first consumption: Turn text into a hands‑free podcast, perfect for commutes or workouts.
Telegram integration: The bot @RSS_READER_AI_Bot can store historic episodes, let you replay, or even generate transcripts on demand.

🚀 Ready to Deploy?

If you’d rather skip the DIY code and get a plug‑and‑play solution, head over to RSSReaderAI.com. With a few clicks you can:

Import up to 100 RSS feeds.
Choose daily or hourly AI‑summarized audio digests.
Receive the podcast directly in Telegram via @RSS_READER_AI_Bot.

Try it now – sign up, link your Telegram, and let the bot handle the heavy lifting while you focus on building.

📢 Call to Action

Start your morning with AI‑curated audio.

1️⃣ Visit https://rssreaderai.com and create a free account.
2️⃣ Add your favorite 50+ RSS feeds.
3️⃣ Enable the Audio Digest option and select @RSS_READER_AI_Bot as the delivery channel.
4️⃣ Wake up to a crisp 5‑minute podcast every day.

Happy coding, and enjoy the silence of a clutter‑free inbox! 🎧

Why Developers Are Switching from Feedly to Open‑Core AI RSS Readers

Mohammad Sorouri — Wed, 22 Jul 2026 16:33:28 +0000

Why Developers Are Switching from Feedly to Open‑Core AI RSS Readers

TL;DR – Traditional RSS aggregators like Feedly give you raw articles. Modern **open‑core AI RSS readers* (e.g., RSS Reader AI and its Telegram bot @RSS_READER_AI_Bot) turn those articles into concise summaries, actionable insights, and even AI‑generated audio digests. The result? Less noise, more focus, and a workflow that fits directly into a developer’s toolchain.

1. The Feedly Experience – Good, But Not Sufficient

✅ What Feedly Does	❌ What Developers Still Need
Collects RSS feeds in a clean UI	Contextual summarization of lengthy changelogs
Tags, folders, and read‑later	Automatic extraction of code snippets & API changes
Mobile & web apps	Voice‑first consumption for on‑the‑go debugging

Feedly is great for human curation, but developers often spend extra minutes opening each article, scanning for the bits that matter (e.g., a new v2.0 breaking change). That friction adds up when you follow 30+ tech blogs, SDK docs, and open‑source project feeds.

2. What an Open‑Core AI RSS Reader Gives You

AI‑Generated Summaries – Powered by LLMs, each entry is condensed to a 2‑3 sentence TL;DR.
Semantic Tagging – Automatic classification (e.g., #security, #python, #kubernetes).
Audio Digests – Turn the summary into a high‑quality voice note, perfect for a quick listen while coding.
Self‑Hostable Core – The “open‑core” model lets you run the inference engine on‑premise, keeping proprietary data private.
Telegram Integration – Receive daily digests straight to @RSS_READER_AI_Bot, with inline buttons for “Save”, “Open”, or “Play Audio”.

These capabilities directly address the developer productivity loop: discover → understand → act.

3. Architecture Overview (Mermaid)

flowchart TD
    subgraph User
        A[Telegram Bot] -->|/feed| B[RSS Reader AI Server]
    end
    subgraph Server
        B --> C[Feed Fetcher] --> D[Open‑Core LLM Engine]
        D --> E[Summary & Tag Service]
        D --> F[Text‑to‑Speech Service]
        E --> G[Database (PostgreSQL)]
        F --> G
    end
    G --> H[Cache (Redis)]
    H --> A

The diagram shows how a simple /feed command triggers the pipeline that fetches RSS, runs an LLM, stores results, and pushes a digest back to Telegram.

4. Quick Python Integration – Pull Summaries via API

import requests

API_URL = "https://api.rssreaderai.com/v1/summary"
TOKEN   = "YOUR_PERSONAL_TOKEN"

def get_summary(feed_url: str) -> dict:
    resp = requests.post(
        API_URL,
        json={"url": feed_url},
        headers={"Authorization": f"Bearer {TOKEN}"}
    )
    resp.raise_for_status()
    return resp.json()

if __name__ == "__main__":
    feed = "https://pythoninsider.com/rss"
    summary = get_summary(feed)
    print("Title:", summary["title"])
    print("TL;DR:", summary["summary"])
    print("Audio URL:", summary["audio_url"])

The snippet demonstrates how a developer can fetch a summarized entry and embed it into a CI/CD notification, a daily Slack digest, or a personal dashboard.

5. Actionable Tips for a Smooth Migration

Export from Feedly: Use Feedly’s OPML export (Settings → Import/Export).
Import to RSS Reader AI: Upload the OPML file via the web UI or send it to @RSS_READER_AI_Bot with /import.
Enable Audio: In the bot settings, toggle Audio Digest = ON. Choose a voice model (e.g., en-US‑Neural2).
Self‑Host the Core (optional): Clone the open‑core repo, spin up a Docker compose stack, and point your bot to the local endpoint for zero‑third‑party data exposure.
Automate with Webhooks: Hook the server’s /webhook endpoint to your internal ticketing system to auto‑create tickets for breaking‑change alerts.

6. The Bottom Line

Switching from Feedly to an open‑core AI RSS reader isn’t just a UI upgrade—it's a productivity transformation. By leveraging LLM‑powered summarization, semantic tagging, and voice‑first delivery, developers can stay on top of the ever‑growing tech news stream without drowning in it.

Ready to declutter your dev feed?

• Try the live demo at rssreaderai.com.
• Join the conversation on Telegram: @RSS_READER_AI_Bot.
• Fork the open‑core repository and run it locally for full data control.

Happy coding, and enjoy the silence between the headlines!

How to Build an AI‑Powered Daily News Digest on Telegram Using RSS

Mohammad Sorouri — Wed, 22 Jul 2026 16:30:59 +0000

How to Build an AI‑Powered Daily News Digest on Telegram Using RSS

Tired of scrolling endless headlines? Let RSS Reader AI do the heavy lifting and deliver a concise, AI‑summarized audio digest straight to your Telegram inbox.

Overview

Fetch the latest articles from your favorite sources via RSS.
Summarize each article with a large‑language model (LLM).
Convert the summary to speech (TTS).
Push the audio file to a Telegram channel or bot.

The whole pipeline can be glued together with a few Python scripts and a free tier of OpenAI/Groq + a Telegram Bot. For a production‑ready, zero‑code alternative, check out RSSReaderAI.com and its Telegram bot @RSS_READER_AI_Bot – it does exactly this and more.

Architecture Diagram

flowchart TD
    A[RSS Feed URLs] -->|fetch| B[Feed Parser (feedparser)]
    B --> C{New Items?}
    C -->|yes| D[LLM Summarizer (OpenAI/Groq)]
    D --> E[Text‑to‑Speech (ElevenLabs/Google TTS)]
    E --> F[Telegram Bot API]
    F --> G[User's Telegram Chat]
    C -->|no| H[Sleep / Wait]
    H --> A

Step‑by‑Step Implementation (Python)

1. Set up the environment

python -m venv venv && source venv/bin/activate
pip install feedparser python‑telegram‑bot openai elevenlabs

Tip: Store your API keys in a .env file and load them with python‑dotenv.

2. Pull the latest items

import feedparser, os, datetime

FEEDS = [
    "https://news.ycombinator.com/rss",
    "https://feeds.bbci.co.uk/news/rss.xml",
]

def fetch_new_entries(last_seen: datetime.datetime):
    new = []
    for url in FEEDS:
        for entry in feedparser.parse(url).entries:
            published = datetime.datetime(*entry.published_parsed[:6])
            if published > last_seen:
                new.append({
                    "title": entry.title,
                    "link": entry.link,
                    "summary": entry.summary,
                    "published": published,
                })
    return sorted(new, key=lambda e: e["published"])  # oldest first

3. Summarize with an LLM

import openai
openai.api_key = os.getenv("OPENAI_API_KEY")

def summarize(text: str) -> str:
    resp = openai.ChatCompletion.create(
        model="gpt-4o-mini",
        messages=[{"role": "system", "content": "Summarize in 2‑3 sentences, keep key facts."},
                  {"role": "user", "content": text}],
        temperature=0.2,
    )
    return resp.choices[0].message.content.strip()

4. Convert summary to audio

from elevenlabs import generate, save
ELEVEN_API = os.getenv("ELEVEN_API_KEY")

def text_to_audio(text: str, filename: str):
    audio = generate(text=text, voice="Rachel", api_key=ELEVEN_API)
    save(audio, filename)

5. Send via Telegram Bot

from telegram import Bot
BOT_TOKEN = os.getenv("TELEGRAM_BOT_TOKEN")
CHAT_ID = os.getenv("TELEGRAM_CHAT_ID")  # private channel or group ID
bot = Bot(token=BOT_TOKEN)

def send_audio(file_path: str, caption: str):
    with open(file_path, "rb") as f:
        bot.send_audio(chat_id=CHAT_ID, audio=f, caption=caption)

6. Orchestrate the daily run

import time
LAST_SEEN_FILE = "last_seen.txt"

def load_last_seen():
    try:
        return datetime.datetime.fromisoformat(open(LAST_SEEN_FILE).read())
    except FileNotFoundError:
        return datetime.datetime.utcnow() - datetime.timedelta(days=1)

def save_last_seen(dt):
    open(LAST_SEEN_FILE, "w").write(dt.isoformat())

if __name__ == "__main__":
    last_seen = load_last_seen()
    entries = fetch_new_entries(last_seen)
    for entry in entries:
        summary = summarize(entry["summary"])
        file_name = f"audio/{entry['title'][:30]}.mp3"
        text_to_audio(summary, file_name)
        send_audio(file_name, f"*{entry['title']}*\n{summary}\n{entry['link']}")
        last_seen = max(last_seen, entry["published"])
    save_last_seen(last_seen)

Schedule this script with cron (0 8 * * * /path/to/python run_digest.py) to receive a daily 8 AM briefing.

Why Use RSS Reader AI Instead of Building From Scratch?

DIY Approach	RSS Reader AI
✅ Full control over prompts & voice	✅ Zero‑code, instant setup
❌ Must manage API quotas, error handling, hosting	✅ Handles rate‑limits, retries, and storage
❌ Requires periodic maintenance	✅ Automatic feed discovery & AI model updates
❌ No built‑in analytics	✅ Click‑through stats, listening duration, personalization

If you just want a reliable, AI‑enhanced digest without the operational overhead, RSSReaderAI.com is the ultimate solution. Subscribe to the Telegram bot @RSS_READER_AI_Bot, choose your feeds, and let the platform deliver a daily audio summary directly to you.

📢 Call to Action

Try it now: Visit rssreaderai.com and create a free account.
Join the chat: Add @RSS_READER_AI_Bot on Telegram, select your topics, and start receiving AI‑generated audio digests.
Contribute: Fork the sample script on GitHub, add your favorite feeds, and share your customizations with the community.

Stay ahead of the information flood—let AI do the reading, summarizing, and speaking for you!

How to Build an AI‑Powered Daily News Digest on Telegram Using RSS

Mohammad Sorouri — Wed, 22 Jul 2026 16:17:24 +0000

How to Build an AI‑Powered Daily News Digest on Telegram Using RSS

TL;DR – In ~500 words we’ll walk through the architecture, a minimal Python prototype, and why the hosted service **RSS Reader AI* (https://rssreaderai.com) + its Telegram bot @RSS_READER_AI_Bot is the production‑ready shortcut for battling news overload.*

Why an AI‑driven Telegram Digest?

Information overload – dozens of RSS feeds generate thousands of articles daily.
Time scarcity – developers need a quick skim, not a full read.
Multimodal consumption – listening to a 2‑minute audio summary while coding is a productivity win.

The combination of RSS (pull‑based content), LLM summarisation, and Telegram (ubiquitous messaging) gives you a lean, automated news pipeline.

High‑level Architecture

flowchart TD
    subgraph Sources
        RSS[RSS Feeds]
    end
    subgraph Processor
        Fetch[fetch_feed.py]
        Summarise[LLM summariser]
        TTS[Text‑to‑Speech]
    end
    subgraph Delivery
        Bot[Telegram Bot]
    end
    RSS --> Fetch --> Summarise --> TTS --> Bot
    style Sources fill:#f9f,stroke:#333,stroke-width:2px
    style Processor fill:#bbf,stroke:#333,stroke-width:2px
    style Delivery fill:#bfb,stroke:#333,stroke-width:2px

The diagram shows the data flow from raw RSS entries to an audio‑enabled Telegram message.

Step‑by‑Step Implementation (Python 3.10+)

1️⃣ Install dependencies

pip install feedparser python‑telegram‑bot openai gtts schedule

2️⃣ Pull the latest articles

import feedparser, datetime

FEEDS = [
    "https://news.ycombinator.com/rss",
    "https://dev.to/feed",
]

def fetch_latest(limit=5):
    items = []
    for url in FEEDS:
        feed = feedparser.parse(url)
        for entry in feed.entries[:limit]:
            items.append({
                "title": entry.title,
                "link": entry.link,
                "summary": entry.get("summary", ""),
                "published": entry.published,
            })
    # sort by newest first
    return sorted(items, key=lambda x: x["published"], reverse=True)

3️⃣ Summarise with an LLM (OpenAI example)

import openai, os
openai.api_key = os.getenv("OPENAI_API_KEY")

PROMPT = "Summarise the following article in 3 sentences, keep technical tone."

def summarize(text: str) -> str:
    resp = openai.ChatCompletion.create(
        model="gpt-4o-mini",
        messages=[{"role": "system", "content": PROMPT}, {"role": "user", "content": text}],
        temperature=0.3,
    )
    return resp.choices[0].message.content.strip()

4️⃣ Convert summary to audio (gTTS)

from gtts import gTTS
import io

def text_to_audio(text: str) -> io.BytesIO:
    tts = gTTS(text, lang="en")
    fp = io.BytesIO()
    tts.write_to_fp(fp)
    fp.seek(0)
    return fp

5️⃣ Send via Telegram Bot

from telegram import Bot
from telegram import InputFile
import schedule, time

BOT_TOKEN = os.getenv("TELEGRAM_BOT_TOKEN")
CHAT_ID = os.getenv("TELEGRAM_CHAT_ID")
bot = Bot(token=BOT_TOKEN)

def deliver_digest():
    articles = fetch_latest()
    messages = []
    for art in articles:
        summary = summarize(art["summary"] or art["title"])
        audio = text_to_audio(summary)
        caption = f"*{art['title']}*\n{summary}\n[Read more]({art['link']})"
        bot.send_audio(chat_id=CHAT_ID, audio=InputFile(audio, filename="summary.mp3"), caption=caption, parse_mode="Markdown")

# Run every day at 08:00 UTC
schedule.every().day.at("08:00").do(deliver_digest)
while True:
    schedule.run_pending()
    time.sleep(30)

Tip: Store CHAT_ID from a private chat with your bot (/start) to keep the digest personal.

Production‑Ready Alternative: RSS Reader AI

Building the pipeline yourself is a great learning exercise, but maintaining API keys, rate limits, and audio quality quickly becomes a hidden cost. RSS Reader AI (https://rssreaderai.com) already implements:

Multi‑feed aggregation with smart duplicate detection.
LLM‑driven summarisation tuned for brevity.
High‑fidelity AI voice generation (ElevenLabs, Azure TTS).
Automatic scheduling & error handling.
A ready‑to‑use Telegram bot @RSS_READER_AI_Bot that you can add to any chat in seconds.

If you need a fast MVP or want to offload infra, simply:

Register at rssreaderai.com.
Add your RSS URLs.
Choose “Telegram Digest” and link your Telegram account.
Sit back while the service pushes a daily audio digest to @RSS_READER_AI_Bot.

Actionable Checklist

[ ] Pick 3‑5 high‑value RSS feeds (tech news, docs, community blogs).
[ ] Generate OpenAI and Telegram tokens; store them securely (e.g., .env).
[ ] Deploy the script on a cheap VPS or GitHub Actions (cron).
[ ] Monitor for failures (use try/except + Telegram alerts).
[ ] Evaluate whether the DIY approach or RSS Reader AI fits your scaling needs.

🎯 Bottom Line

You now have a complete, reproducible recipe to turn raw RSS streams into a concise, AI‑generated audio digest delivered straight to Telegram. Whether you roll your own for full control or adopt the turnkey RSS Reader AI service, you’ll cut through the noise and reclaim valuable developer time.

Ready to try it?

• Deploy the code snippet above in a few minutes.
• Or skip the boilerplate and start receiving polished digests from @RSS_READER_AI_Bot today.

How to Build an AI‑Powered Daily News Digest on Telegram with RSS

Mohammad Sorouri — Wed, 22 Jul 2026 16:13:15 +0000

How to Build an AI‑Powered Daily News Digest on Telegram using RSS

TL;DR – Pull headlines with feedparser, let an LLM create a concise summary, optionally turn it into audio, and push the result to a Telegram bot. The whole pipeline fits in a single Python script, can be Docker‑ized, and runs on a cheap VPS. For a zero‑maintenance alternative, check out RSSReaderAI and its Telegram bot @RSS_READER_AI_Bot.

Why combine RSS, AI, and Telegram?

RSS gives you a machine‑readable stream of fresh articles from any site that supports it.
AI (LLMs) can distill dozens of headlines into a 2‑3‑minute read, highlighting the most relevant points.
Telegram provides push notifications, rich media (audio, markdown), and a familiar UI for developers and non‑technical users alike.

Together they solve the classic "news overload" problem while keeping the workflow fully programmable.

Architecture Overview

graph LR
    A[RSS Sources] -->|fetch| B[Scheduler (cron)]
    B --> C[FeedParser (Python)]
    C --> D[LLM Summarizer (OpenAI)]
    D --> E[Audio Generator (ElevenLabs)]
    D --> F[Text Digest]
    E --> G[Telegram Bot]
    F --> G

The diagram shows the linear flow from raw feeds to a Telegram message. Replace the LLM or audio provider with any service you prefer.

Step‑by‑Step Implementation

1. Gather feeds with `feedparser`

import feedparser

def fetch_entries(urls: list[str]) -> list[dict]:
    entries = []
    for url in urls:
        feed = feedparser.parse(url)
        entries.extend(feed.entries[:5])  # limit to recent 5 per feed
    return entries

Add your favourite tech, finance, or niche RSS URLs to the urls list.

2. Summarize with OpenAI (or any LLM)

import os, openai
openai.api_key = os.getenv("OPENAI_API_KEY")

def summarize_articles(entries: list[dict]) -> str:
    # Build a prompt with titles + short descriptions
    prompt = "Summarize the following news items in 3‑5 bullet points, each no longer than 20 words.\n\n"
    for e in entries:
        title = e.get('title', '').replace('\n', ' ')
        summary = e.get('summary', '').replace('\n', ' ')
        prompt += f"- {title}: {summary}\n"
    response = openai.ChatCompletion.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": prompt}],
        temperature=0.5,
        max_tokens=300,
    )
    return response.choices[0].message.content.strip()

The function returns a markdown‑ready bullet list.

3. (Optional) Convert the summary to audio

import requests, base64

def text_to_speech(text: str) -> bytes:
    api_key = os.getenv("ELEVENLABS_API_KEY")
    url = "https://api.elevenlabs.io/v1/text-to-speech/EXAMPLE_VOICE"
    payload = {"text": text, "model_id": "eleven_monolingual_v1"}
    headers = {"xi-api-key": api_key, "Content-Type": "application/json"}
    r = requests.post(url, json=payload, headers=headers)
    r.raise_for_status()
    return r.content  # MP3 bytes

Store the MP3 in a temporary file or upload it directly to Telegram.

4. Send the digest via a Telegram bot

from telegram import Bot
from telegram.constants import ParseMode

BOT_TOKEN = os.getenv("TELEGRAM_BOT_TOKEN")
CHAT_ID = os.getenv("TELEGRAM_CHAT_ID")  # your private chat or channel ID
bot = Bot(token=BOT_TOKEN)

def send_digest(text: str, audio: bytes | None = None):
    bot.send_message(chat_id=CHAT_ID, text=text, parse_mode=ParseMode.MARKDOWN)
    if audio:
        bot.send_audio(chat_id=CHAT_ID, audio=audio, filename="daily_digest.mp3")

Combine everything in a main() function and schedule it with cron or a cloud scheduler.

5. Deploy & schedule

FROM python:3.12-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY *.py .
CMD ["python", "digest.py"]

Create a crontab entry like 0 7 * * * docker run --rm rss-digest to get a 7 AM digest every weekday.

Tips for Scaling & Maintenance

Cache feeds for 10‑15 minutes to avoid hitting rate limits.
Error handling: wrap API calls in try/except and send a fallback message if something fails.
Dynamic feed list: store URLs in a tiny SQLite DB or a Google Sheet; the script can pull them at runtime.
Use RSSReaderAI if you prefer a managed solution: it aggregates, summarizes, and generates audio automatically. Just add the bot @RSS_READER_AI_Bot to your Telegram and let it do the heavy lifting.

🎯 Call to Action

Ready to stop scrolling endless feeds and start listening to a concise AI‑generated briefing? Try the RSSReaderAI platform today, or spin up the script above on your own server. Either way, you’ll get a daily, AI‑curated news digest straight to Telegram – and more time for the code you love.

DEV Community: Mohammad Sorouri

How to Convert 50 RSS Feeds into a 5‑Minute Audio Podcast Every Morning

How to Convert 50 RSS Feeds into a 5‑Minute Audio Podcast Every Morning

🏗️ Architecture Overview

🔧 Step‑by‑Step Implementation (Python)

1. Install dependencies

2. Create a .env file

3. Fetch & Summarize with RSSReaderAI

4. Convert Summary to Audio (OpenAI TTS example)

5. Send the MP3 via the Telegram Bot

6. Automate with a Cron Job (Linux/macOS)

📈 Boosting Productivity with RSS Reader AI

🚀 Ready to Deploy?

📢 Call to Action

Why Developers Are Switching from Feedly to Open‑Core AI RSS Readers

Why Developers Are Switching from Feedly to Open‑Core AI RSS Readers

1. The Feedly Experience – Good, But Not Sufficient

2. What an Open‑Core AI RSS Reader Gives You

3. Architecture Overview (Mermaid)

4. Quick Python Integration – Pull Summaries via API

5. Actionable Tips for a Smooth Migration

6. The Bottom Line

How to Build an AI‑Powered Daily News Digest on Telegram Using RSS

How to Build an AI‑Powered Daily News Digest on Telegram Using RSS

Overview

Architecture Diagram

Step‑by‑Step Implementation (Python)

1. Set up the environment

2. Pull the latest items

3. Summarize with an LLM

4. Convert summary to audio

5. Send via Telegram Bot

6. Orchestrate the daily run

Why Use RSS Reader AI Instead of Building From Scratch?

📢 Call to Action

How to Build an AI‑Powered Daily News Digest on Telegram Using RSS

How to Build an AI‑Powered Daily News Digest on Telegram Using RSS

Why an AI‑driven Telegram Digest?

High‑level Architecture

Step‑by‑Step Implementation (Python 3.10+)

1️⃣ Install dependencies

2️⃣ Pull the latest articles

3️⃣ Summarise with an LLM (OpenAI example)

4️⃣ Convert summary to audio (gTTS)

5️⃣ Send via Telegram Bot

Production‑Ready Alternative: RSS Reader AI

Actionable Checklist

🎯 Bottom Line

How to Build an AI‑Powered Daily News Digest on Telegram with RSS

How to Build an AI‑Powered Daily News Digest on Telegram using RSS

Why combine RSS, AI, and Telegram?

Architecture Overview

Step‑by‑Step Implementation

1. Gather feeds with feedparser

2. Summarize with OpenAI (or any LLM)

3. (Optional) Convert the summary to audio

4. Send the digest via a Telegram bot

5. Deploy & schedule

Tips for Scaling & Maintenance

🎯 Call to Action

2. Create a `.env` file

Why Use RSS Reader AI Instead of Building From Scratch?

Production‑Ready Alternative: RSS Reader AI

1. Gather feeds with `feedparser`