Gino Llerena@llereta
1
Events attended
1
Submissions made
Peru
20+ years of experience
About me
I’m a Computer and Systems Engineering graduate and Full Stack Developer focused on building AI-powered applications end-to-end. I design and implement agent workflows with LangGraph/LangChain, integrate LLMs through AWS Bedrock, and ship user-facing AI experiences using CopilotKit. My foundation is in enterprise web development (JavaScript/TypeScript, React, Redux, Node.js), and I’m comfortable working across Linux/macOS/Windows environments. Today I’m focused on bridging strong software engineering practices with modern AI patterns—tool-using agents, structured outputs, and retrieval with vector databases—so AI features are reliable, maintainable, and product-ready. I enjoy solving complex technical problems, learning fast, and turning ambiguous requirements into systems that work in production.
🤓 Latest Submissions
Voltaic Alpha
Voltaic Alpha is an autonomous, risk-aware AI trading system designed to identify and act on opportunities in options markets. Instead of asking an LLM to directly predict whether a stock will go up or down, Voltaic Alpha separates market reasoning, strategy generation, risk management, and execution into clearly defined responsibilities. The system continuously evaluates market context, including price action, volatility, options-chain data, liquidity, market regime, and relevant events. AI agents use this information to formulate a trading thesis and determine whether an actionable opportunity exists. When an opportunity is identified, the system can propose an options strategy based on factors such as implied volatility, directional bias, risk/reward characteristics, expiration, Greeks, and market conditions. Every proposed trade must then pass through a deterministic risk layer before execution. The risk engine enforces constraints such as maximum position loss, portfolio exposure, liquidity requirements, position sizing, expiration limits, and predefined exit conditions. The AI can propose a trade, but it cannot bypass these controls. Alpaca provides the market-data and trading infrastructure, with its Trading API and MCP ecosystem connecting the agents to real market information and paper-trading execution. A central objective of Voltaic Alpha is explainability. Each trading decision maintains a structured record of the evidence considered, market hypothesis, selected strategy, risk assessment, confidence, execution decision, and subsequent outcome. The result is not simply an AI that places trades, but an auditable autonomous trading architecture designed around disciplined decision-making, controlled risk, and measurable performance.
4 Sep 2026
