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Support queue aging review — a self-initiated Rivetfern demonstration using synthetic ticket metadata.
A Python tool validates dates, priorities, statuses and supplied SLA-day inputs. It skips exact duplicates, quarantines conflicting ticket IDs and creates a sorted review queue for open and pending tickets, including age and unassigned cases.
Handover includes the review CSV, exceptions, summary and file hashes. Calendar-day thresholds come from each input row; the tool does not invent service policy, message customers or change helpdesk tickets. No client results or operational SLA guarantee are claimed.
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🚨 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.
34 tested API endpoints that AI agents can discover and pay for per request in USDC, with no accounts or API keys. It includes a paid MCP server, marketplace listings generated from real outputs, SSRF-safe fetching, and 19 Apify Actors that offer bulk versions of the endpoints.
agents paying per request in USDC with no API keys is lowkey the future