How We Built an AI Inbox for LinkedIn With n8n [Free Template]
AI Summary
Overview
Max, known as the original Flowgrammer, discusses using AI to manage his LinkedIn inbox while preserving authenticity in interactions. He aims to build a solution in Dubai to refine communication with leads and decision-makers without compromising human connection.
Key Points
- Problem Identification
- Struggles with managing LinkedIn inbox due to the influx of messages.
- Importance of maintaining personal interactions during collaborations.
- Project Collaboration
- Partnering with Umya, another Flowgrammer, to develop a solution.
- Planned to brainstorm and build together in person.
- Initial Steps
- Arrived in Dubai; discussed strategies for organizing and addressing inbox messages.
- Identified the need for an efficient database to categorize and prioritize messages.
- Designing the Application
- Decided on using various open-source tools for development (DeepSeek R1 and UniPile).
- Focused on creating a user-friendly interface to filter and manage inboxes effectively.
- Implementation Details
- Developed a plan to write drafts using AI while ensuring the user retains control.
- Discussed feature ideas like message categorization, auto-responses, and easy access to important data.
- Challenges Faced
- Technical difficulties with API integration and managing different data formats.
- Time management issues but remained focused on the overall goal.
- Project Outcome
- The team successfully launched a basic application that improves handling of LinkedIn messages.
- Open-sourced the project to encourage community contributions and customization.
Conclusion
- The project highlights the combination of human effort with AI capabilities to enhance productivity while retaining the personal touch necessary for meaningful engagements on LinkedIn.