automation
Automated AI Lead Follow-Up System (n8n + Groq + Gmail + Sheets)
Just shipped an automated AI Lead Follow-Up System built with n8n, Groq, Gmail, and Google Sheets.
Building Jobsly: AI Job Application Autopilot
🚧 Building Jobsly — an AI Job Application Autopilot I’ve been working on Jobsly , an AI-powered job application assistant designed to make applying for jobs much less repetitive. The idea is simple: → Jobsly analyzes the job description with AI → Understands how well the role matches your profile → Opens the application flow → Automatically fills repetitive fields → Uses AI to generate relevant answers where needed → Helps you move through applications much faster We already have autofill support working for Greenhouse and Workday , and I’m currently working on adding more job providers and improving the autopilot flow. It’s still under development, so this isn’t a launch announcement yet — just sharing what I’m building. 🛠️ Planning to open it to the public soon. 🌐 https://jobsly.pro Would love to hear what feature you’d want in an AI job application copilot. buildinpublic ai jobs automation jobsearch chromeextension saas
AI-Powered Event Registration & Automation Workflow
Building an Automated Event Registration System with n8n I’m building an automated event registration and participant management system for Automation Hub 2026 , using n8n as the core workflow automation platform . The idea came from a very practical problem: managing an event manually can quickly become complicated when you have to deal with registrations, different participation fees, payment verification, participant capacity, ticket generation, QR codes, email notifications, and check-in. Instead of handling these processes manually, I designed a workflow that connects the different services together and allows the system to automatically process a participant from the moment they submit the registration form. The project is essentially an end-to-end event automation pipeline . A participant submits a registration form → the workflow processes the information → calculates the appropriate fee → checks available capacity → stores the participant's information → generates registration details → creates a QR code → updates the participant record → and sends the appropriate email notifications. The goal is to reduce manual work, minimise human error, and create a smoother experience for both the participant and the event organisers. The Problem Event registration can involve a surprising amount of repetitive administrative work. For a physical and virtual event, an organiser may need to: Collect participant information Determine whether the participant is attending physically or virtually Calculate the correct registration fee Track payments Maintain a participant database Monitor the number of physical participants Generate unique registration IDs Generate tickets Send confirmation emails Generate QR codes Keep participant records updated Manage check-in Send additional notifications when payment status changes Doing all of this manually becomes difficult as the number of participants increases. For example, if someone registers for the physical session, the system needs to know whether physical capacity is still available. If the participant pays, their payment status needs to be updated. Once their registration is confirmed, they need to receive their registration details and ticket. Rather than having someone manually perform each of these steps, I wanted the workflow to make the process as automatic as possible. The Solution I used n8n to orchestrate the entire workflow . The workflow connects different services and uses conditional logic to determine what should happen to each registration. The main components include: n8n — workflow orchestration and business logic Online Form — participant registration Google Sheets — participant records and registration database Google Drive — document and ticket storage Gmail — automated participant communication QR Code API — QR-code generation The workflow acts as the central layer connecting these services. Instead of every application operating independently, n8n coordinates the entire process. Registration Workflow The process starts when a participant submits the registration form. The form collects information such as: Full name Email address Phone number Participation type Occupation or role Payment receipt Other relevant registration information Once the form is submitted, the data is sent into the n8n workflow. The workflow then begins processing the registration. 1. Form Submission The first stage is receiving the participant's registration data. The workflow captures the submitted information and prepares it for processing. This creates a single entry point into the automation. Rather than requiring an administrator to manually copy information from the form into a spreadsheet, the data flows directly into the workflow. 2. Fee Calculation The system determines the participant's registration fee based on their selected participation type. For example: Physical participation: ₦10,000 Virtual participation: ₦5,000 This means the participant does not need to rely on an administrator to manually calculate the expected payment. The workflow automatically assigns the appropriate amount to the registration record. This also makes it easier to maintain consistency across registrations. 3. Capacity Management One of the important requirements for the event is that physical participation is limited. The physical session is available to the first 50 registered physical participants . Because of this, the workflow needs to know how many physical participants have already registered. The automation retrieves existing participant records and calculates the current number of physical and virtual registrations. The workflow then determines whether capacity is still available. For example: If the physical participant count is below 50, the registration can proceed. If the physical participant count reaches 50, the workflow can prevent additional physical registrations or direct the participant toward the virtual option. This is one of the parts of the project where automation becomes particularly useful because the capacity calculation happens automatically rather than relying on someone to constantly monitor a spreadsheet. 4. Participant Data Storage After the registration information has been processed, the participant's details are stored in Google Sheets. The spreadsheet acts as the central participant database. The record contains information such as: Registration ID Ticket number Full name Email Phone number Participation type Amount Payment status Registration status Receipt Ticket URL QR code Check-in status Check-in time Creation timestamp Payment notification status This creates a structured record for every participant. It also means the organisers have a central place where they can monitor registrations. 5. Registration ID Generation Each participant needs to be uniquely identifiable. The workflow therefore generates a unique registration ID for the participant. This ID becomes part of the participant's registration record and can be used to reference the participant throughout the rest of the event process. The same concept applies to the ticket number. Rather than manually assigning these values, the workflow handles the generation automatically. 6. Ticket Generation After processing the participant's registration, the workflow generates the participant's registration details and ticket. The ticket can then be stored using Google Drive. This gives the participant a digital representation of their registration. The workflow also keeps the ticket URL in the participant's Google Sheets record. This is important because it creates a connection between the participant's database record and their generated ticket. 7. QR Code Generation Another part of the system is QR-code generation. The QR code can be associated with the participant's registration or ticket information. This creates the foundation for an automated check-in system. Instead of relying solely on manually checking names against a spreadsheet, the participant can present their QR code during the event. The QR code can then be used to identify the participant and retrieve their registration information. This makes the system more scalable and provides a better experience for event staff. 8. Automated Email Notifications One of the major benefits of the workflow is automated communication. Instead of an organiser manually sending confirmation emails to every participant, n8n can send the appropriate email automatically. Depending on the participant's registration and payment status, different communications can be triggered. For example, a participant may receive information about: Their registration Their payment status Their registration ID Their ticket Their QR code Further instructions This ensures that communication is consistent and reduces the amount of manual administrative work required. Payment Tracking Payment management is another important part of the system. The registration record contains a payment status field that can be updated as the participant's payment is verified. For example, a participant may initially have: Payment Status: Pending After payment verification, the record can be updated to: Payment Status: Approved The workflow can monitor changes to participant records and react to those changes. This creates the possibility of automatically sending payment confirmation notifications once a payment has been approved. The important concept here is that the participant record becomes a source of truth for the workflow. When the state of the participant changes, the automation can respond accordingly. Event Check-In The automation also forms the foundation for an event check-in system. A participant's record contains fields such as: Checked In and Check-In Time When a participant arrives, their QR code can be used to identify them. The system can then update their registration record to indicate that they have checked in. For example: Checked In: Yes and Check-In Time: 9:14 AM This provides the organisers with a real-time record of attendance. It also makes it easier to distinguish between registered participants and participants who actually attended the event. Conditional Logic One of the most interesting aspects of this project is the use of conditional logic. The workflow does not simply execute the same steps for every participant. Different conditions can lead to different branches. For example: Is the participant attending physically? If yes, check physical capacity. If no, continue with virtual registration. Another example: Has the payment been approved? If yes, trigger the appropriate confirmation process. If no, keep the registration in the appropriate pending state. This means the workflow behaves more like a business process engine rather than simply being a sequence of API calls. Why n8n? I chose n8n because it provides the flexibility to visually design workflows while still allowing custom logic and integrations. The visual workflow makes it easier to understand the entire process. At the same time, custom JavaScript can be used when the built-in nodes are not enough. For example, I used custom logic for capacity calculations and participant processing. This combination of visual automation and programmable logic makes n8n particularly useful for building practical automation systems. Architecture At a high level, the architecture looks like this: Participant ↓ Registration Form ↓ n8n Workflow ↓ Process Registration ↓ Calculate Fee ↓ Check Capacity ↓ Store Participant Data ↓ Generate Registration Details ↓ Generate Ticket ↓ Generate QR Code ↓ Update Participant Record ↓ Send Email Notification This architecture allows each stage of the process to perform a specific responsibility. It also makes the workflow easier to maintain because individual stages can be modified without completely rebuilding the system. Building for a Real Event What makes this project particularly interesting to me is that it isn't just a demonstration workflow. It is being designed around a real event with real participants . That changes the way I think about automation. When building a tutorial workflow, it is easy to ignore edge cases. With a real event, however, things such as duplicate registrations, capacity limits, payment status, missing information, invalid submissions, and failed notifications become important. The workflow therefore has to be designed with actual operational scenarios in mind. This project has also helped me understand that automation is not simply about connecting tools. It is about understanding the underlying business process and determining where automation can safely take over repetitive tasks. Lessons From Building It One of the biggest lessons I've learned from this project is that good automation starts with process design . Before creating the n8n nodes, I had to understand the entire registration journey. What happens when someone registers? What happens when the physical capacity is full? What happens when payment is pending? What happens when payment is approved? What information should be stored? What should the participant receive? What happens when the participant arrives at the venue? These questions are just as important as the technical implementation. The workflow is only effective when the business logic behind it is clearly defined. Another important lesson was understanding how different systems communicate. The project required connecting forms, spreadsheets, cloud storage, email, APIs, and custom logic. Each service has its own structure and requirements. n8n provides the orchestration layer that allows these systems to work together. What I Want to Improve This is still an evolving project. There are several areas I would like to improve as the system develops. One area is more robust payment verification. Another is improving the QR-code check-in process so that participant attendance can be captured with minimal manual intervention. I would also like to introduce more intelligent automation around participant communication. For example, an AI layer could potentially help classify participant enquiries, answer common registration questions, or assist organisers with analysing registration data. There is also room for better monitoring and error handling. A production automation system should not only work when everything goes correctly. It should also be able to handle failures gracefully. For example, if an email fails to send, the system should be able to record that failure and potentially retry the operation. If an external API is temporarily unavailable, the workflow should not silently lose the participant's information. These are areas I want to continue exploring. The Bigger Idea Although this project was created for an event, the underlying architecture can be applied to many other use cases. The same approach could be adapted for: Conferences Workshops Training programmes Hackathons Community events Webinars Courses Membership registration Appointment systems Customer onboarding The core principle remains the same: Collect data → process data → apply business rules → update systems → trigger actions. That is the power of workflow automation. Final Thoughts Building this system has been a practical exercise in combining automation, APIs, databases, conditional logic, cloud services, and real-world business requirements . What started as a need to simplify event registration became an opportunity to build a complete automated workflow around the participant journey. The project demonstrates how tools like n8n can be used to move beyond simple task automation and build systems that coordinate multiple processes. For me, the most exciting part is not simply that the workflow works. It is the fact that the workflow is solving a real problem. Instead of manually managing every registration, the system can process information automatically, maintain participant records, manage capacity, generate registration assets, communicate with participants, and support the check-in process. This is the direction I want to continue exploring: building practical AI and automation systems that solve real-world problems. And this project is just one step in that journey.
I Built an AI Finance Tracker with WhatsApp & Google Sheets
I built a practical AI-powered finance tracker that makes recording and organizing personal finances easier. The workflow allows me to send income and expense information through WhatsApp, process the data automatically, and organize it in Google Sheets. The goal was simple: turn everyday financial messages into structured records without manually entering everything into a spreadsheet. Built with: • JavaScript • React • Google Sheets • API Integration • Automation • WhatsApp This project is part of my journey building practical software that solves real-world problems. What would you improve or add to this finance tracker?
I Built a Real Business Website with an AI Assistant & Visitor Tracking
I built a real-world business website for Master Baja Bangunan — a building-materials business in Tangerang, Indonesia. But I didn't want to build just another company website. I wanted to turn a traditional building-materials business into a more interactive digital experience. The website includes: 🤖 AI-powered Material Assistant Visitors can ask questions about building materials and get help estimating materials such as cement, sand, lightweight bricks, rebar, and more. 📊 Visitor Tracking The website includes visitor information so the business can understand website activity. 💬 WhatsApp Integration Customers can quickly contact the business directly from the website. 🌐 Responsive Business Website Designed to present products, pricing, location, business information, and customer contact options in one place. 🌍 Bilingual Experience The website supports Indonesian and English. This project taught me something important: A good website isn't just about how it looks. It's about solving a real business problem. I built this project from scratch as a self-directed developer, combining web development, AI integration, automation, and real-world business requirements. 🔗 Live website: https://masterbajabangunan.my.id/ I'd love to hear from other developers: What would you add to this system next? webdevelopment ai automation javascript aiintegration business fullstack portfolio
Multi-Agent System built with n8n & LangChain
Multi-Agent AI System with n8n 🚀 Built a modular multi-agent workflow using n8n to handle complex business task routing and automation. Key Features: Supervisor Agent: Central router to delegate tasks to specialized sub-agents. Task-Specific Agents: Dedicated agents for web research, data extraction, and summary generation. Error Handling & Fallbacks: Ensures reliable execution and output validation. Stack: n8n, OpenAI, LangChain framework logic. Would love to hear feedback from the community! ai automation n8n
Level 1 AI Agent in n8n (Gemini + Tools + Memory)
Just built a custom Level 1 AI Agent using n8n! 🚀 Here is what's running under the hood: - LLM Model: Google Gemini Chat Model - Memory: Window Buffer Memory for conversation context - Tools: Custom HTTP Request tool fetching live web data (Wikipedia API) & Calculator tool In this demo, the agent autonomously decides to call the HTTP tool to fetch live information about Tesla and formats the output smoothly. Feedback and suggestions for Level 2 features are welcome!
Hey DevConnect! Building AI Voice Agents & Workflow Automations
Hey everyone! I'm Ali Hassan , a Computer Engineering student at UET Lahore focused on building production-ready AI systems and agentic workflows . I specialize in bridging full-stack web development with deep automation pipelines—moving past basic demos to build systems that handle real operations end-to-end. 🛠️ The Stack Behind My Automations: • AI Systems & Voice: Vapi, ElevenLabs, Gemini, Claude, OpenAI, n8n, Pinecone, Lovable AI, v0, Bolt.new • Languages & Web: Python, C++, SQL, JavaScript, TypeScript, React, Node.js, FastAPI, Tailwind CSS, REST APIs • Integrations & Cloud: Green API, Twilio, Airtable, Docker, Vercel, Database Design, Google Sheets, Notion API 💡 What I'm currently building: 1. Production AI Voice & Support Agents: Conversational pipelines with sub-second latency, automated CRM sync, and dynamic lead scoring. 2. Academic AI App: An intelligent platform leveraging LLMs to optimize study workflows and context-aware Q&A for students. Excited to connect with fellow AI builders, showcase technical builds, and collaborate! 📁 Portfolio: https://www.alihassan-builds.me/
Customer feedback automation Demo
Just built and tested a simple customer feedback automation using n8n. The workflow automatically: • Collects customer feedback through a form • Qualifies the feedback • Routes complaints to the right team • Sends an automatic response to the customer • Notifies the team when a suggestion needs attention • Saves the feedback data in Google Sheets The idea is simple: make sure every piece of customer feedback is captured, organized, and acted on — without everything being handled manually. Sharing a quick demo of how it works.
AI-powered Customer Feedback Sentiment Analyzer & Alert Bot using n8n
An automated AI workflow built in n8n that analyzes customer feedback sentiment in real time, routes actionable insights, and triggers instant alerts for negative feedback! 💡 The Problem Businesses receive feedback across multiple channels, but manually reading, categorizing, and prioritizing urgent complaints takes too much time. Urgent negative feedback often gets lost or responded to late. ✅ What It Does Automated Data Ingestion: Collects customer feedback instantly via Webhooks or Forms. Sentiment & Intent Analysis: Uses AI (Gemini / LLM) to evaluate sentiment (Positive, Neutral, Negative) and extract key topics. Smart Routing & Alerts: Automatically sends real-time alerts (Slack, Telegram, or Email) to support teams when negative sentiment is detected. Data Logging: Logs structured sentiment metrics directly to Google Sheets or Supabase for analytics.
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.
AI Appointment Booking System for HVAC companies
Built an AI Appointment Booking System for HVAC Businesses What if an HVAC company could handle incoming service requests, qualify customers, and book appointments without manually managing every step? I built this workflow using n8n + AI + Calendly/Calendar to automate the process from customer intake to follow-up. The workflow: → Customer submits the service form → AI understands the problem and identifies the issue type → Request is automatically routed to the right service → Calendar availability is checked → Customer gets available appointment options → Booking is created → Confirmation is sent automatically → Follow-up & feedback are handled automatically What this means for an HVAC business: • Less manual work • Faster response to customers • Fewer missed opportunities • Automated appointment scheduling • Structured customer information • Automatic follow-ups • More time for technicians to focus on actual jobs The goal isn't just to build an AI agent. The goal is to build a system that removes repetitive work from the business. I'm currently building more practical AI Agents & n8n automation systems for service businesses. If you're an HVAC company, home-service business, or automation agency looking to automate appointment booking and customer handling, feel free to connect or message me. AI AIAutomation n8n AIagents HVAC Automation AppointmentBooking WorkflowAutomation BusinessAutomation
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.
Intelligent Gym Registration & Automation System
GYMCORE INTELLIGENT GYM REGISTRATION AND AUTOMATION SYSTEM GYMCORE is a modern digital gym registration system designed to replace manual member registration with a fast, automated, and organized process. The main purpose of this project was to allow gym members to register themselves directly through a website while automatically sending their information to a structured database without requiring gym staff to manually enter the data. HOW WE BUILT IT We created a responsive gym registration website with a simple and user friendly registration form. A member can enter information such as their full name, email, phone number, age, and membership details directly through the website. The website is connected to an n8n automation workflow using a Webhook. Whenever a member submits the registration form, their information is automatically sent to n8n. The n8n workflow receives and processes the submitted information before sending it to Google Sheets, which works as the member database. The system also records important information such as the registration date and can generate a unique member or user ID so that every registration can be easily identified and managed. THE COMPLETE WORKFLOW Member registers on the website. The registration form sends the information to the n8n Webhook. n8n receives and processes the information. The processed information is automatically stored in Google Sheets. The member receives a confirmation response. WHAT THE SYSTEM ACHIEVES The system allows self service member registration, automated data collection, webhook integration, n8n workflow automation, automatic storage of member information, registration date tracking, unique member identification, and organized member records. It reduces manual data entry and makes the registration process faster, more efficient, and easier to manage. WHY WE BUILT IT Traditional gym registration often involves paper forms, manual data entry, and scattered member records. GYMCORE demonstrates how website development and automation can be combined to create a smarter and more efficient business process. This project was not just about creating a registration form. It was about connecting the entire process from the moment a customer submits their information to the moment that information is automatically organized and stored in a database. The result is a complete automated gym registration system that connects the website, n8n automation, Webhooks, and Google Sheets into one streamlined workflow.
AI-Powered Social Media Automation & Management Platform
🚀 Built an AI-powered Social Media Management & Automation Platform using n8n. This project allows users to manage multiple social media accounts from a single frontend and automate content publishing without manually posting on every platform. ✨ What it can do • Connect and manage multiple social media accounts • Publish the same post across multiple platforms • Create dynamic posts with customized content for each platform • Schedule social media posts • Automate recurring content publishing • Manage text, images, and other media • Trigger automated posting workflows through n8n • Monitor and manage publishing workflows from the frontend • Keep social media automation centralized in one dashboard 🤖 Automation The backend is powered by n8n workflows that handle the automation logic, API requests, scheduling, content processing, and publishing. Instead of manually creating and publishing the same content on every platform, the system can take one piece of content and automatically distribute it across connected social media accounts. For dynamic automation, the workflow can generate or transform content before publishing, allowing each platform to receive content optimized for its audience and format. 🏗️ Architecture Frontend → API/Webhooks → n8n → Social Media APIs → Publishing The frontend provides the management interface while n8n acts as the automation engine behind the system. 🎯 Goal The goal of this project is to make social media management faster, more scalable, and less repetitive by combining a modern frontend with powerful workflow automation. automation socialmedia n8n ai marketingautomation
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-Powered LinkedIn Automation
Built an automated workflow that connects LLM-powered AI generation directly with social execution platforms like LinkedIn. Key Capabilities: • Request Processing: Webhook-driven trigger that parses incoming content prompts and inputs in real time. • Contextual Generation: Integrates LLM nodes with active memory to ensure brand-aligned, high-quality post outputs. • Autonomous Publishing: Automatically formats and schedules content directly to LinkedIn without manual intervention. • Error Handling & Fallbacks: Built-in exception tracking ensures zero data loss during API limits or payload errors. Tech Stack: n8n • LLM / Gemini • Webhooks • LinkedIn API • Google Sheets
Shipped: An AI Job Matcher Agent — paste a job URL + your resume, get a match score, skill gaps, and a cover letter
Applying for jobs is the same loop every time: read the posting, compare it with your resume, guess your chances, then write a cover letter from scratch. I built an agent that does all of it in one request. Flow: paste a job posting URL → upload your resume PDF → get the analysis back. Output: - match score (0-100) — how well the profile fits the role - skill gaps — what the job asks for that the resume doesn't have - strengths — what actually lines up - cover letter — personalized, ready to send Stack: - FastAPI — async API, two endpoints: /analyze (JSON, local resume path) and /analyze-upload (multipart file upload). Auto Swagger docs at /docs . - OpenAI GPT — the reasoning layer. Single structured prompt, JSON-only output, validated with Pydantic before it ever reaches the response. - httpx + BeautifulSoup — scrapes the job page, pulls the title and hunts for requirement lists (looks for requirements / responsibilities / you will headings, falls back to the first bullet lists on the page). - pdfplumber — page-by-page text extraction from the resume PDF. - Jinja2 + vanilla CSS — drag-and-drop UI, no frontend framework. - Docker — python:3.12-slim , single container, runs anywhere. Two things I'd call out: 1. Graceful degradation. If there's no API key or the LLM call fails, it doesn't 500 — it falls back to a keyword-based scorer that intersects job skills with resume skills and returns a real score. The app is always usable. 2. Scraping is the hard part, not the AI. Every job board renders differently, and half of them are JS-heavy. The heuristic parser works on plain HTML postings but site-specific parsers (LinkedIn, Indeed) are the obvious next step. Next up: semantic matching with embeddings instead of keyword overlap, batch mode for multiple postings at once, and swapping temp-file handling for in-memory PDF parsing. Happy to answer anything about the scraping heuristics or the fallback design. I mostly build AI agents and automation for businesses — chatbots, workflow automation, the boring repetitive stuff that shouldn't be manual. If you're working on something similar, DMs are open. Portfolio: https://danish614.github.io/danish-portfolio/
SMART PLANTER MONITOR
🌱 Smart Seed Planter Monitor Another practical Mechatronics project — a monitoring system designed to track seed count, planting distance, seed spacing, and battery level in real time. Using sensors and an Arduino-based control system, the monitor helps improve planting accuracy and provides useful feedback during operation. From an idea to a working system. ⚙️🌱 SmartAgriculture Mechatronics EmbeddedSystems Arduino Automation AgricultureTechnology Engineering
Shipped: E-Commerce Price Tracker watches Amazon & Daraz, emails you when the price drops
Ever kept refreshing a product page waiting for a sale? I automated that. Paste any Amazon / Daraz product URL + your target price, and it scrapes the live price, stores the history, re-checks every few hours, and emails you the moment it hits your target. What it does - 🔎 Live price scraping (works behind bot protection) - 📉 Full price history per product - ⏰ Auto re-check every few hours (Celery Beat) - 📧 Email alert when target price is hit - 🌐 REST API + Web UI + CLI Tech stack - Backend: Python, FastAPI, Pydantic - Background jobs: Celery + Celery Beat + Redis - DB: PostgreSQL, SQLAlchemy, Alembic migrations - Scraping: Selenium, undetected-chromedriver, BeautifulSoup, cloudscraper - Alerts: SMTP + Jinja2 templates Hardest parts - Anti-bot protection — plain requests gets blocked instantly. Ended up with undetected-chromedriver in headless mode, cloudscraper as a lighter fallback. - HTML keeps changing — Daraz especially. Built multi-selector fallback logic so one broken selector doesn't kill the whole scrape. - Parallel checks — moved from a loop to a Celery task queue so hundreds of products get checked concurrently. - Proper schema — versioned Alembic migrations, not a throwaway script. Same architecture works for competitor price monitoring, stock/availability alerts, or any "watch a site and notify me" automation. 🔗 Code: https://github.com/danish614 🌐 Portfolio: https://lnkd.in/dXE58DgU