Tech Radar

This workflow integrates RSS feeds from multiple mainstream cryptocurrency news sources and utilizes advanced AI models for intelligent analysis. It automatically extracts keywords and filters relevant reports to generate news summaries and market sentiment analysis. Ultimately, the results are pushed to users in real-time via a Telegram bot, helping investors and analysts efficiently access personalized cryptocurrency news and market trends, thereby addressing the cumbersome issue of information filtering.

This workflow creates an automated user interview system that utilizes AI smart agents to guide the interviews and dynamically generate open-ended questions. Users respond through an online form, and the system records the conversation in real-time, allowing the interview to be ended at any time. Interview data is quickly stored in Redis and can be exported to Google Sheets for easier subsequent analysis. This system reduces the labor costs associated with traditional interviews and provides an efficient interview experience available 24/7, making it suitable for various scenarios such as market research, product feedback, and educational institutions.

This workflow is designed to achieve automatic extraction and intelligent parsing of content from PDF documents. Users can directly upload PDF files without the need for OCR recognition, simplifying the process. It simultaneously utilizes two AI models, Claude 3.5 Sonnet and Gemini 2.0 Flash, allowing for a comparison of their performance in data extraction effectiveness, response speed, and cost. It supports customizable extraction instructions, and the output can be adjusted to JSON format, making it suitable for extracting key information from documents such as financial invoices and contracts, thereby enhancing data processing efficiency and automation levels.

This workflow achieves content-based intelligent querying by automatically retrieving and processing files stored in Supabase, combined with OpenAI's text embedding technology. It effectively deduplicates, extracts PDF and text content, and stores it in a vectorized format, supporting fast and accurate information retrieval. It is suitable for scenarios such as enterprise knowledge base management, customer support, and professional document querying, significantly enhancing document management efficiency and user interaction experience.

This workflow utilizes an AI language model to automate user interviews, capable of generating open-ended questions and recording user responses in real-time. Users initiate the interview through a form, and the interview data is stored in a Redis database and synchronized to Google Sheets for easy data analysis and sharing. Users can end the interview at any time, and the interview records can be accessed via a Webhook, ensuring data security and efficient management. This system is suitable for market research, user experience studies, and academic surveys, greatly enhancing the flexibility and efficiency of interviews.

This workflow aims to create an OpenAI smart assistant integrated with Google Drive, capable of automatically downloading and converting documents, and dynamically updating the assistant's knowledge base using the GPT model. Through contextual memory, the assistant enables multi-turn conversations, providing coherent and accurate responses, suitable for scenarios such as travel services, corporate knowledge management, and educational resource assistance. Users can easily build a personalized intelligent Q&A system, enhancing service efficiency and user experience.

This workflow automatically generates high-quality exam questions from the content of articles in Google Docs using AI technology, including open-ended questions and multiple-choice questions. By combining vector databases with advanced language models, the process can deeply understand the document's content, extract key knowledge points, and quickly generate exam questions that meet educational needs. This significantly improves the efficiency of question creation while ensuring the quality and diversity of the questions, making it suitable for various scenarios such as educational institutions, online training platforms, and corporate training.

This workflow can automatically fetch the top news headlines from the Hacker News homepage for a specified date, utilize a large language model for intelligent categorization and trend analysis, generate themed Markdown news summaries, and push them to subscribed users via a Telegram channel. It addresses the issues of historical news data aggregation and information overload, helping users quickly grasp technological trends and hot topics. It is suitable for technology media, researchers, and information service providers, enhancing the timeliness and value of the content.