Scrape Article Titles and Links from Hacker Noon Website

This workflow is mainly used for generating and splitting simulated user data, facilitating subsequent processing. By using custom function nodes, it creates an array containing multiple user information entries and splits them into independent JSON data items. This process addresses the flexibility issues in batch data processing, making it suitable for scenarios such as test data generation, individual operations, and quickly building demonstration data, thereby enhancing the efficiency and controllability of workflow design.

This workflow is primarily used for clustering analysis in agricultural crop image datasets. It automates the setting of representative center points (medoids) for clustering and their threshold scores to support subsequent anomaly detection. By combining traditional distance matrix methods with multimodal text-image embedding techniques, it accurately locates clustering centers and calculates reasonable thresholds, enhancing the effectiveness of anomaly detection. It is suitable for applications in the agricultural field, such as pest and disease identification and anomaly warning, ensuring efficient and accurate data processing.

This workflow effectively extracts and processes contact information by automatically calling the FileMaker Data API. It can parse complex nested data structures and standardize contact data, facilitating subsequent analysis, synchronization, and automation. It is suitable for scenarios such as enterprise customer relationship management and marketing campaign preparation, significantly enhancing data processing efficiency, reducing manual intervention, and helping users easily manage and utilize contact information, thereby strengthening digital operational capabilities.

This workflow automatically extracts information from the customer data repository, formats it, and synchronizes it to Google Sheets for efficient data management. Field adjustments are made through the "Set" node to ensure the data meets requirements, avoiding errors that may occur during manual operations. This process addresses the issues of scattered customer data and inconsistent formatting, making it suitable for marketing and customer service teams. It helps them update and maintain customer information in real-time, enhancing data accuracy and operational efficiency.

This workflow automates the collection and organization of startup financing information, retrieving the latest Seed, Series A, and Series B financing events from Piloterr on a daily schedule. Through multi-step data processing, key financing information is integrated and updated in Google Sheets, allowing users to view and manage it in real time. This automation process significantly enhances the efficiency and accuracy of data updates, helping investors and entrepreneurial service organizations quickly grasp market dynamics and saving a substantial amount of human resources.

This workflow is manually triggered and automatically sends secure HTTP requests to the Bubble application's API, conveniently accessing and retrieving user data. It is designed to help non-technical users and business personnel quickly and securely extract the required information without the need to write code, simplifying the data acquisition process and enhancing work efficiency. It is suitable for scenarios such as data analysis, user management, and CRM system integration.

This workflow automatically scrapes employee review data from Glassdoor and utilizes AI for intelligent analysis to identify patterns of discrimination and bias in the workplace. It calculates the rating differences among different groups and generates intuitive charts to help users gain a deeper understanding of the company's diversity and inclusion status. This tool is particularly suitable for human resources departments, research institutions, and corporate management, as it can quickly identify potential unfair practices and promote a more equitable and inclusive work environment.

This workflow automatically downloads an Excel file containing semester dates from the university's official website. It utilizes Markdown conversion services and large language models to extract key events and dates, generating a calendar file that complies with the ICS standard. Finally, the system sends the calendar file as an email attachment to designated personnel, significantly reducing the time and errors associated with manually organizing semester schedules, thereby enhancing the efficiency of academic administration in higher education. It is particularly suitable for students, teachers, and teams for time management and information sharing.