supabase
Automated Certification Eligibility Engine in n8n
Built an automated Certification Eligibility Engine using n8n! 🚀 This workflow automates applicant evaluation and instant certificate generation end-to-end: Webhook Trigger: Receives applicant payload (e.g., user ID) directly from API/Postman. Data Retrieval: Fetches user records, course completion status, and evaluation metrics across database tables. Eligibility Logic: Evaluates whether the applicant satisfies all pass criteria automatically. Record & Response: Generates a new certification record upon qualification and returns an instant JSON payload with certificate details. Fully automated, start-to-finish eligibility verification without any manual intervention.
Automated Certificate Generation & Email Delivery Service in n8n
Automated the entire Certificate Issuance and Email Delivery workflow using n8n! 🚀 This setup connects directly with the Eligibility Engine to process, generate, store, and deliver verified certificates: Webhook Trigger & Eligibility Check: Receives user details via Webhook and calls the sub-workflow engine to verify eligibility status. Certificate Data Generation: Generates unique certificate numbers and verification URLs automatically upon qualification. Database Storage: Saves complete certificate records directly to Supabase. Email Delivery: Automatically dispatches a formatted confirmation email featuring recipient details, certificate IDs, and a verification QR code. Automated Handling: Returns clear JSON responses back to the caller based on eligibility status. Zero manual steps, instant generation, and seamless email delivery.
Automated Certification – Assessment System in n8n
Built an automated Assessment & Scoring Pipeline using n8n! 🚀 This workflow handles incoming assessment submissions, validates payloads, updates database records, and calculates average scores automatically: Webhook Endpoint: Listens for incoming POST requests ( /webhook-test/submit-assessment ) containing candidate submissions (e.g., quiz, practical, final project). Input Validation: Validates submission data integrity before execution continues. Database Operations: Saves assessment entries directly to Supabase ( Save Assessment ) and retrieves historical scores ( Get All Scores ). Data Transformation: Calculates total score averages dynamically across submitted assessments using a custom Code node. Progress Tracking: Updates overall candidate progress ( Update User Progress ) and returns structured JSON confirmation responses back to Postman/client. Eliminates manual grading and score tracking.
AI Research Workspace
Most of my earlier projects were built around Express.js for the backend. While working on AI-focused applications, I realized that building serious AI systems requires a different backend ecosystem and a deeper understanding of how AI workflows actually operate. That led me to learn FastAPI, and I used this project to not only learn it, but also polish my backend and AI engineering skills by building something from the ground up. Introducing 🚀 AI Research Workspace (AIRW) — an AI-powered workspace designed to interact with and retrieve information from different types of user data. 🔹 What I explored and implemented: • FastAPI for the backend and API architecture • LangChain & LangGraph for building structured AI workflows and agentic execution • RAG (Retrieval-Augmented Generation) for document-based question answering • Document chunking, embeddings, vector search & hybrid retrieval • Semantic retrieval using embeddings combined with traditional keyword-based retrieval • Query rewriting and retrieval pipelines to improve search quality • MongoDB Atlas Vector Search for storing and retrieving embeddings • Supabase Storage for handling uploaded files and generated content • Groq for LLM-powered generation and reasoning • Tavily for web-based research when web access is enabled • Memory system for retaining useful user-specific information • Tool-based agent execution with planning and execution stages 📚 Supported data & AI capabilities AIRW can work with multiple types of content, including: 📄 PDF documents 📊 CSV & Excel files 🎧 Audio files with transcription and timestamp-based retrieval 🖼️ Images using vision capabilities For documents, the system processes the content into smaller chunks, generates embeddings for those chunks, and stores them for semantic retrieval. When a user asks a question, AIRW retrieves the most relevant chunks. The system can also provide sources, allowing users to see where the information came from 🤖 It isn't limited to document Q&A either. AIRW supports: • Document summarization • Retrieval-based questions • General conversation • Web-assisted research • Memory-based responses • Python-powered analysis and visualization • Excel automation and modification • Multi-step tool execution through an agent workflow One of the things I particularly enjoyed was implementing many of these components myself instead of treating AI frameworks as black boxes. Understanding how chunking, embeddings, retrieval, planning, execution, memory, and tool orchestration work together has been one of the most valuable parts of this project. Github Repo: https://github.com/WaleedImran2007/AI-Research-Workspace Live Demo: https://airw.waleedimran.me Still learning. Still improving. 🚀 AI ArtificialIntelligence AIEngineering GenerativeAI RAG LangChain LangGraph FastAPI Python MongoDB AgenticAI MachineLearning BackendDevelopment FullStackDevelopment
InvoiceDeck: Design-first invoicing & client cash flow protection for freelancers
Hey everyone! Built this out of frustration with clunky enterprise invoicing tools that feel like software from 2008 and generate ugly PDFs. InvoiceDeck is a lightweight, design-focused invoicing app built specifically for developers, designers, and freelancers. Key things I focused on: • 9 design-first themes: Including a dark Terminal monospace layout and a clean Swiss Bauhaus style. • Zero-touch payment protection: Turn accepted estimates into 50% deposit invoices in 1 click, with automated late fee penalties if clients blow past grace periods. • Global compliance: 1-click tax presets (GST, UK VAT, EU VAT) and dynamic QR payment codes. • Client portal & e-signatures: Real-time status tracking so you know when a client views your bill. Live app (free to test, no sign-up required): https://invoicedeck.app Would love honest feedback on the UX or the Terminal template from fellow builders!
Personal Task App: DevPort
I built a control center for all the dev servers on my Windows machine. I keep 40+ repos in one GitHub folder, and I kept losing time to the same questions: which terminal is running what, which port is taken, why did the API die, and what was I supposed to work on next. DevPort puts all of that in one window. What it does - Finds my projects on its own. I pick a folder and it scans for repos, then works out what can run: Vite and Next apps, plain Node servers, FastAPI backends, PowerShell dev scripts. It only reads config files. It never runs repo code to figure things out. - Starts and stops services in order. The backend comes up before the frontend, and it waits for each one to be ready before moving on. - Handles port conflicts. If a port is taken, it shows which process owns it and suggests a free one instead of failing. - Checks health. It uses HTTP or TCP checks, and three failures mark a service as degraded. Optional auto-restart backs off (2s, 5s, 15s), then stops trying. - Keeps logs. You can search, filter and download them. Secrets are redacted before anything is written to disk. - Has a to-do planner built in. It supports subtasks, dependencies, dates and tags, plus a Gantt view that draws the dependency links. It rejects circular dependencies.