Evaluation of Deep Learning algorithms for a predictive maintenance system using Design of Experiments
Compared RNN, LSTM, CNN, and TCN with Design of Experiments and built the real-time prediction web software.
Hi, I'm Juan Urueña 👋Systems and Telecommunications Engineer. From the sensor to the model and from the landing page to deployment.

About
Systems and Telecommunications Engineer (UCP, 2026). I develop with AI agents under a written engineering cycle: define, plan, build, verify, review, ship. Nothing counts as done without evidence.
My degree project: four neural networks compared to detect faults in industrial machinery, with live predictions.
Ten projects in production, across two countries. The full chain:
My work
Swipe through: real clients first, then my own product, affiliate funnels, and demos.
Featured case
My brother ran the ads. The rest I built and connected myself.
A doctor with an audience, but no way to capture it.
A landing page that trades an ebook for an email and takes the lead to the WhatsApp group where he sells.
Static site on Cloudflare's edge; a single Function writes leads to Supabase. GA4, Meta Pixel, and Clarity measure the funnel.
Engineering
Compared RNN, LSTM, CNN, and TCN with Design of Experiments and built the real-time prediction web software.
Landing and web app with authentication and a database, plus AI agents built on n8n with the official WhatsApp Business API and models via OpenRouter.
Skills
Contact
Write to me and I will reply. My public code is on GitHub.