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This chat assistant workflow is based on Slack messages and can automatically receive user messages while filtering out distractions from the bot. It utilizes a built-in AI model combined with contextual memory and various knowledge tools to provide personalized and direct responses, simulating the style of the character Gilfoyle from "Silicon Valley." This tool not only enhances team communication efficiency but also automatically queries real-time information, improving the user interaction experience. It is suitable for scenarios such as internal corporate support and knowledge base inquiries.

This workflow enables the reception of images sent by users via Telegram, automatically invoking intelligent analysis services for in-depth interpretation. It then promptly replies to the user with the analysis results in text form. The system can detect images in real-time, quickly process messages without images, and operates without human intervention, significantly enhancing the efficiency of image content recognition and feedback. It is suitable for various scenarios such as community management, customer service, and marketing.

This workflow automatically extracts content from YouTube videos via Telegram, generates structured summaries, and engages in natural language interaction with users. Users only need to provide the video link to receive a summary of the video's key points and intelligent Q&A related to the content. This process not only enhances the efficiency of information retrieval but also allows users to engage in in-depth discussions with AI anytime and anywhere, making it suitable for various scenarios such as education, content creation, and personal learning.

This workflow utilizes an AI vision model to automatically verify whether uploaded passport photos meet the standards set by the UK government, significantly improving review efficiency and reducing the risk of human error. By automatically downloading, resizing, and analyzing the photos, the system can quickly detect key indicators such as clarity, background, composition, expression, and size. This addresses the cumbersome and inconsistent standards of traditional review processes and is suitable for scenarios such as online submission platforms, immigration management systems, and ID photo services.

This speech assistance workflow is designed to instantly receive users' speech draft manuscripts via Telegram, utilizing advanced AI technology for speech-to-text conversion and content analysis. It provides feedback suggestions and generates speech drafts. The system supports multiple rounds of interaction and dynamically adjusts prompts to meet the needs of different stages. The workflow also automatically manages memory to ensure precise feedback, achieving formatted text output. It addresses issues such as the lack of professional feedback in speech preparation, difficulties in voice conversion, and poor content delivery, ultimately enhancing the quality and efficiency of users' speeches.

This workflow integrates image generation and multimodal models to automatically convert text descriptions into high-quality 3D cartoon character images, generating display images from three perspectives: front, side, and back. This process simplifies the complexity of traditional character design, significantly enhances design efficiency, and lowers the professional threshold. It is suitable for various scenarios such as IP character design, game character development, and product prototyping, helping creative studios quickly realize their visual concepts.

This workflow utilizes the Google Gemini 2.0 multimodal AI model to achieve image object detection and annotation based on text prompts. By automatically identifying specific objects (such as rabbits) and drawing precise bounding boxes, it enhances the efficiency of image analysis and annotation. It addresses the issue of limited flexibility in traditional models, supports dynamic localization of different semantic targets, and ensures that the detection results match the original image size. This makes it suitable for scenarios such as intelligent image analysis, anomaly behavior detection, and automated labeling in e-commerce.

This workflow utilizes AI technology to automatically transcribe, extract information, and analyze content from YouTube videos. Users can interact with the system through a chat interface, quickly ask questions, and receive video summaries and key analyses, saving viewing time. It integrates the YouTube Data API and open-source tools, combined with a powerful language model, to provide accurate content output. It is suitable for scenarios such as education, content creation, and market analysis, enhancing the convenience and efficiency of information retrieval.