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Scaling inbound lead pipelines beyond naïve spreadsheet updates introduces significant concurrency and packet loss challenges. I have engineered an Inbound Enrichment Engine that I wrote for a client which utilizes:
1. An @n8n_io webhook listener which dumps directly to an async fastapi endpoint to avoid thread contention
2. Contextual routing of payloads through stateful multi-agent validation loops to enforce business logic gates
3. Decoupled delivery of enriched data through n8n subflows to isolate 3rd party api downtime risks The code implementations for all components are available on my engineering portfolio: https://muhammadahmadfaizan.dev
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Saved 2 Hours Monthly with AI‑Powered Growth Report
I was sick of the monthly copy‑paste grind, so I let n8n do the heavy lifting. A Schedule Trigger pulls the raw numbers from Google Sheets, then an HTTP Request sends them to Groq AI. The AI spits out a concise growth report, which I forward to Telegram and also write back into Sheets for records. The whole pipeline runs unattended and shaves about two hours off my month‑end routine. It feels good to finally let the tools work for me.
#n8n #automation #ScheduleTrigger #GoogleSheets
Follow for more: https://go.phonezait.de/
My goal was to create a website that feels ambitious and technically capable without looking like a generic agency template. I used a deep midnight-blue visual direction with white typography, neon blue accents, subtle purple lighting, glass surfaces, and carefully controlled motion. This gives the site a modern, high-end feel while keeping the focus on the work itself.
The hero section is built around the idea of transformation. It begins with an outlined city and the message “Imagine the future,” then reveals a complete city with “Build the reality” as the visitor scrolls. This mirrors the way I approach projects: starting with an early concept, then turning it into a practical digital system that people can actually use. The scroll interaction creates an immediate reason to explore the site while making the opening feel more memorable than a standard static banner.
I structured the rest of the experience to guide attention naturally. The client ticker adds movement and credibility without distracting from the content. The services section uses a clean two-by-two layout so visitors can quickly understand my main areas of work, including AI systems, SaaS development, product design, and growth automation. Each card has enough visual detail to feel premium, but the information remains easy to scan.
The work section is designed as an interactive expanding gallery. When a visitor focuses on a project, it grows to reveal more context and a clear call to action. This creates a more engaging way to present projects while keeping the page visually clean. Instead of showing everything at once, the design lets each project earn attention individually.
Excellent
just shipped the core of Skillship — an AI-powered LMS built for schools.
One platform, many schools, and one rule that shaped every decision: School A must never see School B's data. Tenant-scoped models, filtered querysets at the base class, UUIDs everywhere.
Four AI features live:
— Career Pilot: personalised career paths for students
— Question generator: chapter PDF in, board-aligned MCQs out
— Adaptive quizzes: difficulty follows actual performance
— Semantic search: teachers ask in plain English, pgvector finds the material
Django + DRF for the LMS core, a separate FastAPI service for all AI work (Gemini + a pgvector RAG pipeline), Next.js 14 on the front. Django brokers every AI call — the AI layer never touches the browser.
The hard part wasn't the AI. It was making multi-tenancy boring enough that no future dev can leak a school's data by accident.
Open for new work — full-stack builds, AI integrations, RAG pipelines, multi-tenant SaaS. Send me a project.
The tenant boundary is where I'd put the first failure test. I'd try to make a user from one school retrieve another school's record before I trusted any of the AI features.
