It started with a simple, almost vague question: "What's a truly innovative Web3 app I can build quickly with AI?" I was diving into the Monad Speed Run 101, and the energy was all about building, fast. I didn't have a grand, multi-year roadmap. I had a vibe, a curiosity, and access to some incredible new tools, namely Google's Gemini AI.
What followed was less of a rigid development cycle and more of a "vibe coding" session-a fluid, conversational dance between my high-level ideas and the AI's ability to architect, code, and refine them in real-time. The result? Contract Sentinel, an AI-powered security tool that gives anyone a clear, human-readable risk report on any smart contract on major EVM chains.
Here’s a look back at how it happened, what I learned, and how AI has fundamentally changed my approach to building.
The Spark: From Abstract Intent to a Concrete MVP
My initial conversation with the AI wasn't about code; it was about possibilities. I knew I wanted to build something at the intersection of Web3 and AI that was genuinely useful. The AI didn't just give me one idea; it gave me five radically different approaches, from an "AI Wallet Intent Translator" to "Proof-of-Thought NFTs."
This is where the magic started. Instead of me having to grind through brainstorming sessions, the AI acted as a creative partner, helping me see the landscape of what was possible. We quickly narrowed it down to an idea that felt both impactful and achievable: an "Intent-Based" tool that simplified a complex Web3 action. The hackathon-ready MVP we landed on was a tool that could take a user's natural language goal (e.g., "I want to swap ETH to USDC safely") and translate it into a secure transaction plan.
This evolved into Contract Sentinel. Why just translate one intent when you can analyze the very foundation of all intents-the smart contract itself? The mission became clear: demystify smart contract security for the average user.
How AI Became My Co-Pilot
Building Contract Sentinel wasn't a solo effort. Gemini was my pair programmer, my architect, and my UX consultant all rolled into one.
Architecture & Scaffolding: I knew the components I needed: a front-end, a way to fetch contract data, and an AI brain. The AI helped sketch out a minimal, effective stack: React/Next.js for the UI, Etherscan/BscScan/PolygonScan APIs for data, the Go+ Security API for automated checks, and the Gemini API for the core analysis. It then helped generate the boilerplate for the React components, the TailwindCSS styling, and the overall structure of the app. This saved hours, if not days, of setup.
The Core Logic: Prompt Engineering: The heart of Contract Sentinel is a single, powerful prompt. This is where AI's role shifted from a code generator to a reasoning engine. I didn't write complex parsing logic or a custom security rulebook. Instead, I engineered a detailed prompt that instructed the AI to act as "Sentinel," a world-class Web3 Security Analyst.
I fed it the contract's source code (if available) and data from the Go+ Security API. The prompt gave it clear rules: synthesize the data, don't just list it; prioritize source code; assign a risk level; and output the analysis in a structured JSON format. This prompt became the application's core logic, turning raw data into actionable intelligence.
Rapid Iteration: When an API call failed, or the UI felt clunky, I could just describe the problem to the AI. "The error message is too generic," I'd say, and it would help me write code to provide more context to the user. This conversational feedback loop made the development process incredibly fast and intuitive.
Key Learnings from Monad Session 1 in Practice
Looking back, this entire experience felt like a practical application of the core concepts from our first session in the Monad Speed Run.
The biggest takeaway for me was the focus on reducing cognitive load for the user. Web3 is notoriously complex. Users shouldn't need to be security experts to interact with a DApp safely. Contract Sentinel is a direct answer to this problem. It abstracts away the complexity of reading Solidity and interpreting security flags, presenting a simple verdict: green flags, red flags, and an overall risk score.
Session 1 also touched on building intent-centric applications. While Contract Sentinel analyzes contracts rather than executing intents, it serves the user's ultimate intent: to stay safe. It answers the fundamental question behind every transaction: "Can I trust this?" By focusing on answering that core human intent, the app provides immediate, tangible value.
What This Journey Taught Me
Building Contract Sentinel was an eye-opener. My key learnings are:
AI is a Force Multiplier: It amplifies a developer's ability to build. It doesn't replace the need for critical thinking, but it handles the heavy lifting, freeing you up to focus on the user experience and the core product value.
Prompt Engineering is a Superpower: The quality of your AI-powered application is directly tied to the quality of your prompts. Learning to communicate your intent clearly and concisely to an LLM is the most valuable new skill for developers today.
The Future is Composable: The most powerful apps are often built by intelligently combining existing services. We didn't build our own block explorer or security scanner; we stood on the shoulders of giants (Etherscan, Go+, Google Gemini) to create something new.
This project started as a hackathon experiment, but it has completely reshaped my view of what's possible. We're moving from an era of meticulously hand-crafting every line of code to one where we conduct an orchestra of powerful tools and services. And for a builder, that’s an incredibly exciting place to be.
Top comments (0)