Daniel Hurtado@XxHurtadoxX
1
Events attended
1
Submissions made
Colombia
5+ years of experience
About me
I studied Economics, but I evolved into Data Science because I became fascinated by understanding how complex systems work and how they can be improved. My journey started with statistics, econometrics, and economic analysis, but I found my real path when I discovered that data could be used to anticipate real-world problems and improve operational decision-making. Since then, I have worked on predictive analytics in industrial environments, helping prevent machine failures, identify human risks, and transform scattered information into practical tools for decision-making. I see myself as someone who bridges technology and business reality. I enjoy working directly with the people who operate processes every day to understand their challenges before designing a solution. My goal is not to build impressive models for their own sake, but to create tools that people genuinely want to use and that generate measurable value. Beyond data, I am passionate about engineering, space exploration, history, and emerging technologies. In the long term, I hope to contribute to the future of space exploration by combining analytics, innovation, and complex problem solving.
🤝 Top Collaborators
🤓 Latest Submissions
Engineering Studio AI Multi-Agent Design System
**Engineering Studio AI — X Studio X** is an end-to-end multi-agent engineering platform that transforms product briefs into coordinated engineering packages in minutes. The system orchestrates **11 specialized AI agents** across research, mechanical and electrical engineering, firmware, simulation, cost/business/legal analysis, technical review, adversarial testing, exploratory QA, validation, and final quality gating. Rather than simply generating isolated outputs, X Studio X follows a structured engineering lifecycle: **Generate → Simulate → Review → Challenge → Validate → Reconcile → Deliver** Agents work independently and in parallel where appropriate, while dedicated review and validation agents challenge assumptions, identify failure modes, reconcile cross-disciplinary inconsistencies, and verify outputs against project requirements. Generated artifacts can include Bills of Materials, component selections, electrical schematics, source code, mechanical/CAD specifications, simulation results, cost analyses, compliance reports, and verification and validation results. We also built a **CI/CD Dashboard entirely with native.builder**, integrating GitHub OAuth 2.0, workflow monitoring, build/deployment status, and a Supabase backend. Together, this creates a pipeline from: **Product Brief → Engineering Artifacts → V&V → GitHub → CI/CD** Both applications are deployed on Render for live judge evaluation. **Stack:** Python, FastAPI, Fireworks AI, native.builder, React, Supabase, GitHub OAuth, Docker, and Render. X Studio X treats AI engineering as a **systems-engineering and orchestration problem**, demonstrating how specialized agents can function as a coordinated virtual engineering organization rather than a single AI assistant.
10 Aug 2026
