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Challenges

The primary challenge was to build user trust in a novel, autonomous technology. Prediction markets are complex, and adding a layer of "black box" AI agents and blockchain security created a high barrier to entry. We had to:

• Simplify AI Training: Demystify the process of "training an AI" for non-technical users who simply wanted an edge in their wagers.
• Build Trust: Overcome user skepticism by making the AI's performance transparent and its actions understandable.
• Design for Versatility: Create a single, intuitive interface that could cohesively handle diverse and fast-moving markets, from sports and crypto to global events.
• Enable Speed: Allow users to act instantly on high confidence, time sensitive picks, removing the friction of manual research.

The Process

I led the design from concept to MVP in a series of iterative sprints. I was focusing on designing the core user flows and building a system that was both scalable and easy to use:

• Homepage as a decision surface, not a landing page.I redesigned the dashboard to immediately answer
  ‍“What should I do today?” surfacing AI generated picks with confidence signals and oneclick bet actions.‍
• Agent creation that feels like guidance, not configuration. I rebuilt the agent setup as a clear step-based flow with smart defaults, explanations in plain language, and light progress cues so users always know where they are and why.

• Unified Market View.  I introduced a single market card pattern with live odds, Yes/No action, and return previews, so users can switch categories without relearning the interface.

Research & Problem Solving

In the center of my design strategy was leveraging the rise of AI agents and DeFi. We aimed for simplicity, personalization, trust and speed to address the key challenges:

• Problem: How do you make complex AI training accessible?
  ‍Solution: I designed a modular workflow with smart defaults and guided training options, turning a complex task into a few simple choices.
• Problem: How do you get users to trust an AI's predictions?
  ‍Solution: I designed transparent performance leaderboards and clear confidence scores for every pick, allowing users to see and validate the agent's track record.
• Problem: How do you help users find value in a noisy market?
  ‍Solution: I designed the personalized dashboard to auto-populate with a curated list of high-confidence picks, filtering out the noise and allowing users to focus on effortless, one-tap actions.