Summary
This video from All About AI demonstrates how to set up a 24/7 AI research agent to search for arbitrage opportunities on Polymarket, the prediction market platform. The creator used Codeex with GPT-5.6 (noting Claude Opus 4.8 and Kimi K3 as viable alternatives) to build an agent that scans market pairs where both “yes” and “no” positions can be purchased at prices that lock in a profit regardless of resolution — a prediction market arbitrage strategy. A live example shows four open positions across two related contracts yielding approximately $75 in guaranteed profit.
The technical walkthrough covers how to structure agent prompts with defined stopping conditions (“find three strategy hypotheses”) so the agent terminates rather than running indefinitely, how to incorporate free platform documentation as context, and why execution parameter tuning matters as much as finding the initial strategy. The main risk demonstrated is partial fills — entering only one side of the arbitrage — which the creator spent several days minimizing through iterative prompt and parameter adjustments.
Beyond the Polymarket use case, the video serves as a practical introduction to building goal-bounded AI research agents with Codeex or Claude Code: how to select reasoning level for math-heavy tasks, why subscriptions are more cost-effective than API access for intensive research runs, and how to structure agent goals with clear endpoints. Viewers can adapt the approach to other markets or research domains.
📺 Source: All About AI · Published July 24, 2026
🏷️ Format: Hands On Build
