Trendpup

Demo

About

The problem it solves ## Problem It Solves An **AI-powered early detection system** specifically designed for **Avalanche (Avax) Chain meme coins**, identifying **promising tokens before significant price movement**. Our **profit-sharing business model** aligns incentives perfectly with users — **we only earn when our users profit from our signals**. ## What Users Can Use It For Our AI-powered system helps users: - **Discover high-potential Avax meme coins early** — before major price surges, giving retail investors a critical edge. - **Automate token monitoring** — eliminating the need to manually scan Telegram, Twitter, DEXs, and trackers across multiple platforms. - **Filter scams and rugpulls** — using behavioral and on-chain analysis to detect red flags, making trading safer. - **Identify real community momentum** — distinguishing organic growth from paid shills or bot activity. - **Level the playing field** — by reducing information asymmetry traditionally exploited by insiders, whales, and snipers. - **Enter earlier for maximum ROI** — ensuring users don't miss the small window of explosive growth most meme coins experience early on. ## How It Improves the Status Quo Traditional methods of discovering meme coins on the Avax Chain have major flaws: - Discovering tokens **after** the pump = missed profits - **Manual research** wastes time and is error-prone - Hard to tell a **genuine project** from a scam - **Insiders and bots** always move first - Most tools can't separate **real hype from fake** - Retail traders often miss the **early-entry sweet spot** Our system solves all of this — and better yet, **we only profit when you do**. ## TrendPup Project Architecture Flow  **Component Breakdown** - **Hosting:** All services are hosted on EC2 for reliability and scalability. - **Frontend (Next.js):** Handles wallet connection, access control, and user interface. Communicates with backend for data and AI features. - **Scraper:** Scrapes Dex Screener for Avalanche token data. Scrapes Twitter for token-related tweets and sentiment. - **AI Analysis (AWS Bedrock):** Reads tweets and token data. Determines risk score, investment potential, and provides rationale. - **Eliza Agent (AWS Bedrock + RAG):** Special AI agent with Retrieval-Augmented Generation. Answers user queries with the latest token data and in-depth analysis. **Summary:** Your system ensures only paid users (on Avalanche) can access premium features. The backend aggregates real-time blockchain and social data, then leverages advanced AI (AWS Bedrock) for investment analysis and conversational intelligence, all orchestrated through a modern Next.js frontend.

How it's made

Challenges we ran into ## Challenges I Ran Into - Faced difficulties adapting to **new versions of Eliza**, as significant changes were made compared to older versions. - Encountered problems while **connecting the knowledge base to Eliza**, especially in implementing contextual knowledge retrieval. - Had to **learn AWS Bedrock and its APIs from scratch**, which added complexity to the integration process. ### Specific Hurdle When switching from the old Eliza versions to the latest one, I discovered that much of the internal architecture and usage patterns had changed. The new documentation lacked clear examples on how to implement **contextual knowledge** integration. ### How I Overcame It I resolved the issue through **trial and error**, experimenting with various configurations and reviewing code samples from related projects. While this process took several hours, it eventually helped me get the knowledge base working within Eliza's updated system.