Brief

🎯 Core Function: Automated Quora Content Discovery and Structuring

This workflow provides a seamless, automated system for monitoring and extracting the latest trending content from the Quora homepage. It efficiently handles the complexities of "infinite scroll" pages, utilizes secure account authentication for reliable data access, and leverages AI to structure, format, and push the updates directly to a Telegram channel.

Part 1: BrowserAct Workflow Description

This core module focuses on reliable, authenticated data acquisition from Quora:

Authenticated Access: The workflow uses a securely stored Quora credential to ensure the automation agent views the page as a logged-in user, granting access to personalized and complete content feeds.

Initial Visit & Data Extraction: The automation agent visits the main Quora homepage (quora.com). It uses the Loop List action to identify and extract the primary details (summary, author, link, potential images) from all visible story/answer cards.

Handling Infinite Scroll (Crucial Step): To ensure comprehensive data collection beyond the initial view, the workflow includes an explicit Scroll action. This triggers the loading of new content beneath the fold, allowing the extractor to capture a deep, current list of updates.

Data Transmission: The gathered data is then sent as a structured list to the connected Make.com scenario for advanced processing.

Part 2: Automation Integration with Make.com

The Make.com scenario executes the complex data transformation and intelligent delivery:

Parsing and Iteration: Make receives the raw data, uses the Parse JSON module, and employs an Iterator to process each Quora post individually.

AI Data Structuring (Gemini): The Gemini node acts as an AI data processor. It reforms the unstructured data into a rigid, unified JSON schema, including determining the post type (text or photo), condensing summaries, and formatting the final message using Telegram-compatible HTML tags.

Intelligent Filtering & Delivery: A Filter node routes the data based on the AI-determined post_type to the correct Telegram module (Send a Photo vs. Send a Text Message), guaranteeing a flawless delivery to the target Telegram channel.

⚠️ Important Precautions & Credential Setup

Reliable scraping of Quora's personalized feed requires a logged-in session. Anonymous scraping will result in incomplete data.

How to Authorize Your Quora Account:

Requirement: To ensure continuous, accurate data flow, you must authorize a Quora account for the BrowserAct Agent to use.

Credential Management: This workflow relies on the built-in BrowserAct Credential Management feature for secure login.

Log in to your BrowserAct workspace.

Navigate to the Credentials section.

Create a new credential entry for Quora.

Follow the on-screen instructions to authorize the Agent to log into your Quora account. This securely stores the session, allowing the automation to bypass login walls and access your full, personalized feed reliably.

✨ Applicable Scenarios (Use Cases)

This automated digest system is ideal for scenarios requiring passive content monitoring and automated curating:

Content Curation Channels: Automatically populate a private or public Telegram channel with fresh, relevant, and engaging content from Quora for followers or team members.

Topic Monitoring: Track real-time conversations and rising questions around specific industry keywords by setting filters in the Make scenario.

Competitor Intelligence: Monitor activity related to key competitors or products mentioned on Quora without manually checking the feed.

Passive Research Tool: Maintain a consistent stream of authoritative Q&A content for personal learning or internal research teams.

170-250credits per task (estimated)