GhostFollow AI

workflow of our Agent

🚀 Inspiration

Every day founders, developers, and businesses ask for help across online communities such as Hacker News, forums, and discussion platforms. These conversations often contain valuable signals where people are actively searching for tools, services, or solutions. However, discovering these opportunities requires constant manual monitoring and reading through large volumes of discussions.

We were inspired by the idea that AI agents should not only generate text but actively operate on the web to discover insights and opportunities automatically. LeadSense AI was created to demonstrate how autonomous agents can transform real online conversations into actionable opportunities.

🤖 What it does

GhostFollow AI is an autonomous AI agent that scans online discussions and identifies founders or users who are asking for help with tools, services, or solutions.

The system navigates websites, reads conversation context, extracts the core problem being discussed, and generates intelligent outreach suggestions.

Instead of manually searching communities for potential opportunities, LeadSense AI automatically detects these signals and helps teams respond faster with relevant solutions.

Key capabilities include:

Autonomous web navigation

Opportunity signal detection

Context extraction from discussions

AI-powered reasoning about user needs

Intelligent outreach message generation

Opportunity monitoring dashboard

🛠️ How we built it

Ghostfollow AI combines autonomous web agents with AI reasoning and a lightweight dashboard interface.

The system architecture includes:

TinyFish Web Agent Infrastructure Used to navigate real websites, extract discussion data, and perform multi-step workflows.

AI Language Models Used to analyze the conversation context and generate intelligent outreach messages.

Dashboard Interface Used to visualize detected opportunities, track agent activity, and manage insights.

The workflow follows these steps:

The agent navigates to online communities such as Hacker News.

It searches for discussions where founders ask for help with tools or solutions.

The agent reads the discussion context.

It extracts the problem being described.

The system generates a personalized outreach message.

⚡ Challenges we ran into

Building an autonomous agent that interacts with real websites introduced several challenges.

Dynamic Web Interfaces

Many websites have changing layouts and dynamic content, making automated navigation difficult.

Context Understanding

Discussions often contain informal language and partial information, requiring careful reasoning to detect genuine opportunities.

Signal vs Noise Detection

Not every discussion represents a real opportunity, so filtering relevant conversations was an important challenge.

Agent Workflow Orchestration

Designing a multi-step workflow that includes navigation, extraction, reasoning, and response generation required careful coordination between components.