ai-hackathon-lovable

AI hackathon prototype exploring intentional networking using low-code AI tools.

CircleSharp – AI Hackathon Prototype summary - What it is?

CircleSharp was created to explore how structured prompts and intentional matching can reduce noise, cold outreach, and irrelevant connections in traditional networking. My aim was to move away from feed-driven platforms and endless messaging toward a more focused experience centred on meaningful intentional professional networking.

CircleSharp is an AI hackathon prototype built using a low-code AI platform.

💡 Project Overview

This project was developed as a learning-focused demo. The project focused on experimenting with prompt design, interaction flow, and early system behaviour rather than delivering a finished product. The goal was to understand how early-stage AI-assisted tools shape user trust, reliability, and decision-making, and understanding key design limitations begin during the process.

This prototype was developed as a learning exercise to document insights around prompt clarity, low-code constraints, and responsible AI considerations in practice.

🛠 Tools & Approach

• Low-code AI platform (Lovable)
• Prompt-based logic for matching and interaction
• Rapid prototyping and iteration
• Manual testing and prompt refinement

📚 Key Learnings

• Prompt clarity significantly affects reliability and output quality
• Low-code tools accelerate prototyping but introduce governance and transparency challenges
• Designing for trust requires thinking beyond functionality, including misuse, bias, and control
• Clear problem framing matters as much as the technical solution

🔍 Limitations

• Prototype only (not production-ready)
• Limited prompt guardrails
• No automated evaluation or security controls
• UI and interaction design require refinement

🚀 Next Steps

• Improve prompt structure, constraints and overall design
• Explore security and governance considerations in regards to early-stage AI-assisted tools
• Experiment with alternative tools and workflows
• Document risks and design trade-offs as a case study

🧠 Reflection

This project helped me better understand how early AI systems introduce risk, how design choices affect trust, and why governance matters even in low-code environments.

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Disclaimer: This repository documents a learning journey and ongoing experimentation.