Use Cases

Steps

1. 1.Create a Gmail Trigger workflow in n8n
2. 2.Add an AI node to determine email type and priority
3. 3.Generate reply drafts and mark important emails

Steps

1. 1.Upload the recording file after the meeting
2. 2.Whisper API automatically transcribes to text
3. 3.AI extracts Action Items and assignees

Steps

1. 1.Organize and export Notion/Obsidian notes
2. 2.Create a knowledge base in Dify and upload documents
3. 3.Configure RAG parameters (chunk size, similarity threshold)

Steps

1. 1.Save resume PDFs to a designated folder
2. 2.Let AI extract education, experience, and skill information
3. 3.Automatically score and rank according to job description

Steps

1. 1.Upload original content to Google Drive
2. 2.Configure target languages and localization style guides
3. 3.AI performs semantic translation (not word-for-word)

Steps

1. 1.Create a 'JD Generation' workflow in Dify, input key job information
2. 2.AI generates a complete JD based on templates and industry standards
3. 3.Use browser-use to control the browser and automatically log into each recruitment platform

Steps

1. 1.Connect data sources: App Store API / CSV import / Zendesk tickets
2. 2.AI tags each feedback: feature request / bug / performance / UI experience
3. 3.Automatically cluster similar feedback and count mention frequency

Steps

1. 1.Configure information sources in n8n: RSS/Twitter API/WeChat public account scraping
2. 2.Set interest keywords and filtering rules (exclude ads/low-quality content)
3. 3.AI generates 2-3 sentence core summaries for each article

Steps

1. 1.List the top 5 most time-consuming repetitive tasks in your business
2. 2.Assess the degree of AI replaceability for each task
3. 3.Start with the simplest, highest-ROI scenario for a pilot

Steps

1. 1.Connect user feedback data sources (App Store, Zendesk, NPS, etc.)
2. 2.Design AI classification prompt, define classification dimensions and tag system
3. 3.Use Claude API to batch process feedback and generate structured data

Steps

1. 1.Enable Deep Research mode in Perplexity Pro
2. 2.Input a structured competitive research prompt: "Compare and analyze [your product] vs [competitor A] and [competitor B] across dimensions including: feature comparison, pricing strategy, target users, user reviews (from Reddit/G2/Trustpilot), and major updates in the last 6 months"
3. 3.Wait for Deep Research to automatically execute 15-20 searches (approximately 10 minutes)

Steps

1. 1.Record the meeting video (export MP4 from Zoom/Tencent Meeting, or use Google Meet recording directly)
2. 2.Open gemini.google.com, upload the meeting recording file (or a Google Drive link)
3. 3.Use a standardized prompt: 'This is a meeting recording. Please: 1) Generate structured meeting minutes (attendees/discussion topics/key decisions/open issues) 2) List all action items (owner/deadline/specific task) 3) Mark the video timestamp for each action item'

Steps

1. 1.Use RSSHub to aggregate RSS feeds from target information sources
2. 2.Set up an n8n scheduled task to trigger scraping at 7 AM every morning
3. 3.AI evaluates the importance of each piece of content (1-5 scale) and filters out low-scoring items

Steps

1. 1.After switching models, manually adjust the thinking level to the level actually required by the task to avoid wasting tokens by keeping a high reasoning level.
2. 2.Periodically use /graphify or /compact to compress sessions during large projects, preventing excessively long histories from increasing the cost of each new message.
3. 3.Before complex agentic tasks, first use a cheaper model to decompose the task and clarify the scope, then hand it over to Fable for execution, reducing the number of exploratory rounds.