I recently built an AI-powered World Cup prediction system. It ran yesterday with a 91.7% win rate. Out of 12 games played, it got only one prediction wrong (England versus Ghana - a draw instead of an England win). Here's how I built it (two weeks of trial and error): There are 20 steps to building this terminal. However, I've condensed it down to the 6 crucial components for simplicity's sake. I personally opted for Claude Code for this build since I'm most familiar with it, but feel free to use any coding tool you'd like. Note: If any step seems too complex, just ask your AI coding partner for help. Step 1: Planning Before any code gets written, you need absolute clarity on what you actually want to build. For this step, I recommend using dictation inside GPT-5.5. Voice dump what you want in the terminal, your end goal, the types of data you want to see, etc. PLANNING STARTER PROMPT "I'm building a World Cup Prediction Market Terminal. I'm going to voice-dump my entire thoughts so we can use this plan in an AI coding tool to execute it. This is just the general architecture and my planning phase. Ask me about my goals, features I want, and 25 other questions to get an accurate document for this build." Step 2: Select Technical Stack + Connect RPC Next, you have to pick your technical stack. For me personally, I built this using Claude Code and kept my technical stack to three pieces: → A backend that handles all the data work. → A frontend dashboard that displays it. → A single process that runs continuously (whether that's on a local machine while testing or on a hosted server once it's live) The most important decision in this entire step was how the terminal actually gets its data. There were two paths here: a) Third-party API that aggregates Polymarket data and serves it back to you. b) Connect directly to the source (the blockchain). I chose the second option. Polymarket trades happen on Polygon, so the terminal connects through a Polygon RPC, which is essentially a direct line into the blockchain that lets the backend read every transaction as it happens. I also made sure this setup is provider-neutral, meaning it's not locked into one specific RPC provider. Any compatible Polygon RPC provider can be plugged in (E.g., Alchemy, QuickNode). By the end of this step, your terminal should have a direct, reliable line into the actual blockchain activity behind every Polymarket trade. Step 3: Decode and Enrich On-Chain Data Raw blockchain data is unreadable on its own - it's just logs, hashes, and token IDs, none of which tell you what was actually traded or by whom. For every transaction the backend picks up, you'll need to pull the full record, extract the actual trade details, and convert everything into a clean trade object: which side was taken, which wallet placed it, how many shares, at what price, and when. To do this, you'll want to use Polymarket metadata to map token IDs into market questions, slugs, event names, outcomes, and tags. This is how I turned raw on-chain data into things like "Brazil to win the 2026 World Cup," with a clear outcome and a clear amount behind it. Step 4: Filtering World Cup Data A live feed of every single Polymarket transaction would be useless. Next, you want to tell Claude (or your AI tool) to help you filter and detect large buys, whales, repeated wallet behavior, sentiment flips, price dislocations, and World Cup watchlist flow. I also built in deduplication, so one transaction never creates multiple duplicate signal cards for the same wallet, market, side, and amount. Without that, the signal feed gets cluttered fast and starts losing its value. Step 5: Front-End This is the fun part. I personally built a sharp, dark, operator-style dashboard (shown above). I also added filters for: minimum whale signal size, World Cup-only view, side filtering, and whale-only flow. There's also a pause-and-resume control (it costs about $40/hour, so having the option to pause is nice). You can customize/add features as you see fit here. Step 6: Test & Deploy The final phase is making sure this actually performs under real conditions and keeps running reliably. Before going live, I tested every core feature. If you run into bugs, I'd recommend using Opus 4.8 on xHigh reasoning and using the /goal command to autonomously debug whatever issue you're facing. Once everything checked out, I deployed it to an always-on host (Vercel). These are the bare-bones 6 steps you need to follow to replicate a similar Polymarket terminal. Seems complex, but AI can guide you through the entire process (admittedly, it took me a while to get this right).(Miles Deutscher)