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School Yearbook Image Processing
Developed a Python-based image-processing workflow for Preparatoria José Sixto Verduzco’s yearbook production process.
The tool automated repetitive photo-preparation tasks including cropping, orientation correction, white-balance adjustment, and standardized output preparation.
It was used during the school yearbook workflow from 2023 through the 2025 graduating class.
My role covered workflow design, implementation, testing, and iterative adjustment based on the production requirements.
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I wanted to test whether Invideo Editor’s AI timeline agent could replace hours of manual node compositing in After Effects to create a cinema-grade VFX shot from raw mobile footage.
How I built it:
The Footage: Raw 4K 60fps night plate of NYC with neon signs, streetlamps, and a wet puddle reflection.
Agent Direction: Using a prism refraction photo as a reference, I directed the agent in Ultra Mode to author a single custom shader that extracts luminance highlights, stretches them into 55° upward trails, and applies a spectral RGB dispersion gradient across the edges. It also layered a Screen composite and generated a procedural cyberpunk HUD tracking overlay.
Human Refinements: I manually used the exposed Inspector sliders to fine-tune the Highlight Threshold to 0.68 (isolating the Radio City marquee, streetlamps, and puddle reflection while eliminating building clutter) and dialed the Trail Length to 140 with an exponential falloff taper.
Editable Project Timeline:
👉 https://invideo.io/share/Chromatic-Stretch-GWJjyPAWPDb
Instagram Reel:
👉 https://www.instagram.com/p/DeB3NrJSbvd/
invideo Editor is free to try.
#invideocontrachallenge #invideoeditor #vfx #cinematography #aiagent
🚨 CODES, SHADERS & SPOOKY UI: WHY BUILDERS CAN’T MATCH HAND-CODED PRECISION
Most creators blend static assets in Figma or Canva. As a Full-Stack Engineer, I treat code itself as an art form.
For the Rive Halloween Challenge, I brought together raw code, terminal-driven workflows, and GPU shaders into one ultimate dev collage:
• Rive CLI & RML: Writing responsive interactive scenes directly inside VS Code—zero bloat.
• GPU Canvas (WGSL Shaders): Custom fog, flickering lights, and real-time spooky effects powered directly by the GPU.
• Cybersecurity & Clean Architecture: High-performance, pixel-perfect web apps built to convert in 0.05 seconds.
Forget laggy plugins and heavy template builders. True digital experiences are engineered from the ground up.
💬 Quick Question for Creatives & Devs:
Are you team Drag-and-Drop Builders or team 100% Hand-Coded Architecture?
👇 Drop your thoughts below, let's connect and push the boundaries of modern web tech!
The part where you drive fog and flickering lights with WGSL shaders on the GPU feels like the kind of low level control that really cuts latency.
AI Property Maintenance Automation
AI-powered property maintenance automation designed to streamline how property management teams handle tenant maintenance requests.
The system takes a maintenance request, analyzes the issue, determines its priority and category, recommends a suitable vendor, and automatically creates a structured work order.
Workflow:
Tenant request → AI analysis → Priority & category → Vendor matching → Work order
Built with: Python, Flask, SQLite, HTML, CSS, JavaScript, and AI-assisted request classification.
This project was built as a portfolio demonstration of AI automation for property management operations.
Turning a tenant request into a ready work order in seconds removes the manual triage step.