Julio Islas Espino@julio_islas259
1
Events attended
1
Submissions made
Mexico
1 year of experience
About me
Estudiante de Ciencias de la Computación en la UNAM (Facultad de Ciencias), enfocado en IA aplicada, ciencia de datos y automatización de procesos. He desarrollado Cuali.ai, un modelo de IA educativa para evaluar comprensión lectora en primaria que redujo 80% el tiempo de evaluación, apps móviles en Android Studio para docentes de la UNAM, y sistemas en Python para automatizar la digitalización y OCR de documentos históricos. Actualmente colaboro como desarrollador full-stack en una plataforma web para un hospital. Busco equipos para construir cosas útiles con IA en poco tiempo. Stack: Python, SQL, JavaScript, Android Studio. Inglés B2. Interesado en ML, agentes y RAG.
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🤓 Latest Submissions
Autonomous Prediction Market Arbitrage Engine
Apache AI is an automated, real-time analytics terminal engineered to identify, quantify, and execute cross-market arbitrage opportunities across major prediction venues such as Polymarket, Kalshi, and PredictIt. By continuously scanning order books for identical or correlated real-world outcomes, the platform leverages a net-yield calculation engine that factors in venue-specific fees, slippage, and liquidity to present a fee-adjusted Annual Percentage Yield (APY) for every detected mispricing. To strictly minimize execution risk, the dashboard equips operators with live execution feeds, historical spread charts, depth-of-book visualizers, and customizable low-latency alerts via WebSockets, Telegram, or Discord. The development of this enterprise-grade system is spearheaded by a specialized, multidisciplinary team. Team Leader Mofazzel Hosen Morshed directs the strategic vision and architectural framework, Data Scientist Fahad Akbor designs the advanced mathematical models and adversarial validation logic, Analyst Sayeda Afsana translates complex market data into actionable risk metrics, and Designer Julio Islas crafts the high-performance, low-latency user interface to ensure dense quantitative data is visualized clearly for rapid decision-making.
4 Sep 2026
