[{"content":"I\u0026rsquo;m a passionate developer and technology enthusiast with expertise in Python, C/C++, backend development, embedded systems, and Edge AI. My work spans from on-device machine learning and full-stack AI applications to microservices infrastructure and IoT systems.\nMy Expertise Programming \u0026amp; Development:\nPython, C, C++, JavaScript, SQL Backend Development with FastAPI, Flask, Node.js, and Express REST API Design, JWT Authentication, and Microservices (API Gateway patterns) Machine Learning / Edge AI (LSTM Autoencoders, MobileNetV2, TensorFlow Lite, Model Quantization) Embedded Systems with ESP32 (Firmware, Sensor Integration, TinyML) Full-Stack Web Development with React Technologies \u0026amp; Tools:\nDatabases: MySQL, PostgreSQL, Redis AI/ML: TensorFlow, TFLite Micro, Edge Impulse, OpenAI API, Google Vision DevOps: Docker, GitHub Actions CI Development Tools: Git, VS Code, Arduino IDE, PlatformIO, EasyEDA Featured Projects Fork \u0026amp; Clone – One-Click GitHub Fork + Local Clone (GitHub) – Built a Chrome Manifest V3 extension with a Windows native-messaging companion (PowerShell) that forks any GitHub repository and git-clones it locally in a single click — GitHub API orchestration with fork-readiness polling, a strictly validated native host (origin pinning, URL/path allow-listing), and a theme-aware in-page UI with confirmation flow. Edge AI Anomaly Detection System (ESP32, TinyML) (GitHub) – Built a real-time vibration anomaly detection system running entirely on an ESP32: an LSTM autoencoder trained in TensorFlow, quantized to int8 for TFLite Micro on-device inference, with a fault-type classifier and a live Flask + JavaScript dashboard. CookMind AI – AI-Powered Cooking Assistant (GitHub) – Developed a full-stack AI application using React, FastAPI, PostgreSQL, and the OpenAI API with personalized recipe recommendations, conversational cooking assistance, and image-based ingredient recognition, containerized with Docker. TheRUPOgate – Microservices API Gateway (GitHub) – Engineered a production-style Node.js API gateway handling JWT authentication, Redis sliding-window rate limiting, proxy routing, and a live SSE monitoring dashboard. Intelligent Decentralized Home Automation System – Built a fully decentralized smart home system using ESP32 nodes communicating via ESP-NOW, with sensor-driven automation and TinyML facial recognition on ESP32-CAM. Real-Time Multiplayer Quiz Platform – Designed a full-stack multiplayer quiz application using React, Node.js, and Socket.IO with private rooms, timed rounds, and live leaderboards. Automated Plant Watering System – Developed a smart irrigation system using ESP32, soil moisture sensors, and DHT11 to monitor environmental conditions and automate watering. Feel free to explore my resume, work experience, and skills. You can also check out my projects and get in touch with me.\n","permalink":"https://bonchitosky.github.io/my-about/","summary":"\u003cp\u003eI\u0026rsquo;m a passionate developer and technology enthusiast with expertise in Python, C/C++, backend development, embedded systems, and Edge AI. My work spans from on-device machine learning and full-stack AI applications to microservices infrastructure and IoT systems.\u003c/p\u003e\n\u003ch2 id=\"my-expertise\"\u003eMy Expertise\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eProgramming \u0026amp; Development:\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePython, C, C++, JavaScript, SQL\u003c/li\u003e\n\u003cli\u003eBackend Development with FastAPI, Flask, Node.js, and Express\u003c/li\u003e\n\u003cli\u003eREST API Design, JWT Authentication, and Microservices (API Gateway patterns)\u003c/li\u003e\n\u003cli\u003eMachine Learning / Edge AI (LSTM Autoencoders, MobileNetV2, TensorFlow Lite, Model Quantization)\u003c/li\u003e\n\u003cli\u003eEmbedded Systems with ESP32 (Firmware, Sensor Integration, TinyML)\u003c/li\u003e\n\u003cli\u003eFull-Stack Web Development with React\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eTechnologies \u0026amp; Tools:\u003c/strong\u003e\u003c/p\u003e","title":"About Me"},{"content":"I\u0026rsquo;m Rupankar Dutta, and I\u0026rsquo;d love to hear from you! Feel free to reach out through any of the following channels:\nEmail: 2330108@kiit.ac.in or rupankardutta5686@gmail.com LinkedIn: linkedin.com/in/rupankar-dutta GitHub: github.com/BonchitoSky You can also download my resume or check out my experience page to learn more about my skills and projects.\nI typically respond to inquiries within 24-48 hours. Looking forward to connecting with you!\n","permalink":"https://bonchitosky.github.io/my-contact/","summary":"\u003cp\u003eI\u0026rsquo;m Rupankar Dutta, and I\u0026rsquo;d love to hear from you! Feel free to reach out through any of the following channels:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cstrong\u003eEmail\u003c/strong\u003e: \u003ca href=\"mailto:2330108@kiit.ac.in\"\u003e2330108@kiit.ac.in\u003c/a\u003e or \u003ca href=\"mailto:rupankardutta5686@gmail.com\"\u003erupankardutta5686@gmail.com\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eLinkedIn\u003c/strong\u003e: \u003ca href=\"https://www.linkedin.com/in/rupankar-dutta\"\u003elinkedin.com/in/rupankar-dutta\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eGitHub\u003c/strong\u003e: \u003ca href=\"https://github.com/BonchitoSky\"\u003egithub.com/BonchitoSky\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou can also download my \u003ca href=\"/my-resume\"\u003eresume\u003c/a\u003e or check out my \u003ca href=\"/my-experience\"\u003eexperience\u003c/a\u003e page to learn more about my skills and projects.\u003c/p\u003e\n\u003cp\u003eI typically respond to inquiries within 24-48 hours. Looking forward to connecting with you!\u003c/p\u003e","title":"Contact"},{"content":"Engineering Experience I\u0026rsquo;m Rupankar Dutta, a B.Tech student and builder with hands-on experience across embedded systems, Edge AI, backend development, and full-stack web applications. My work is centered on practical systems that combine hardware, software, and real-time decision-making.\nFocus Areas Embedded Systems, IoT, and Edge AI Built decentralized real-time systems on ESP32 for local communication, sensing, and automation Developed firmware for sensor-integrated devices using MPU-6050, DHT11, MQ-135, PIR, and sound sensors Worked with ESP32-WROOM-32, ESP32-CAM, and ESP-NOW for low-latency wireless communication Trained and deployed on-device ML models using TinyML, LSTM autoencoders, int8 quantization, TFLite Micro, Edge Impulse, and MobileNetV2 Backend, Full-Stack, and AI Application Development Built application backends using FastAPI, Flask, Node.js, and Express Engineered microservices infrastructure including API gateway patterns with JWT authentication and Redis rate limiting Developed interactive web experiences using React, Socket.IO, MySQL, and PostgreSQL Integrated AI services (OpenAI API, Google Vision) into full-stack products with personalization and chat memory Containerized applications with Docker and set up CI workflows with GitHub Actions Programming and Tooling Languages: Python, C, C++, JavaScript, SQL Tools: Arduino IDE, PlatformIO, EasyEDA, Git, Docker, VS Code Libraries and utilities: BeautifulSoup, PyPDF2 Project Experience Edge AI Anomaly Detection System (GitHub) Built a real-time vibration anomaly detection system running entirely on an ESP32 with an MPU-6050 sensor — no cloud required Trained an LSTM autoencoder in TensorFlow on normal-only data and quantized it to int8 for TFLite Micro on-device inference Added a fault-type classifier that identifies the kind of anomaly (drop, shake, imbalance) alongside severity Developed a Flask + JavaScript dashboard with live reconstruction-error charts, adaptive EWMA thresholding, and CSV-exportable anomaly event logs CookMind AI – AI-Powered Cooking Assistant (GitHub) Developed a full-stack AI application using React, Vite, FastAPI, PostgreSQL, and the OpenAI API Built recipe recommendations from ingredient lists, conversational cooking assistance with chat memory, and image-based ingredient recognition via Google Vision Implemented JWT signup/login, pantry persistence, cuisine and dietary preferences, and favorite recipe saving Containerized the stack with Docker (dev and production compose files with an Nginx reverse proxy) and added a CI workflow for backend tests and frontend builds TheRUPOgate – Microservices API Gateway (GitHub) Engineered a production-style Node.js API gateway serving as a single entry point in front of microservices Implemented JWT authentication with role-based header injection for downstream services Built Redis sliding-window rate limiting that fails open if Redis is unavailable Added structured JSON logging and a live monitoring dashboard streaming request metrics over Server-Sent Events Intelligent Decentralized Home Automation System Developed a decentralized smart home system using ESP32 nodes communicating through ESP-NOW Implemented local sensing, threshold-based alerts, timing control, and offline automation logic Integrated DHT11, MQ-135, PIR, and sound sensors into the firmware pipeline Contributed to a MobileNetV2-based facial recognition workflow for secure access control on ESP32-CAM Real-Time Multiplayer Quiz Platform Built a multiplayer quiz application using React, Node.js, and Socket.IO Implemented private rooms, timed rounds, live score tracking, leaderboard generation, and synchronized gameplay state Natural Language to SQL Query Generator Built a Flask and MySQL based application that converts natural language questions into SQL queries Developed the backend query flow, execution pipeline, and result presentation interface Dynamic Programming Text Auto-Correction Developed a C-based text correction system using edit-distance dynamic programming Focused on optimized string matching, fast suggestion generation, and correction accuracy Automated Plant Watering System Built an ESP32-based irrigation system that automates watering decisions from environmental input Added real-time monitoring and control behavior to improve reliability and efficiency WiFi Network Scanner Developed an ESP32 utility to detect nearby WiFi networks and display SSID and RSSI values Used the system for quick signal analysis and basic wireless environment diagnostics For more details about my technical background and projects, please refer to my resume.\n","permalink":"https://bonchitosky.github.io/my-experience/","summary":"\u003ch2 id=\"engineering-experience\"\u003eEngineering Experience\u003c/h2\u003e\n\u003cp\u003eI\u0026rsquo;m Rupankar Dutta, a B.Tech student and builder with hands-on experience across embedded systems, Edge AI, backend development, and full-stack web applications. My work is centered on practical systems that combine hardware, software, and real-time decision-making.\u003c/p\u003e\n\u003ch2 id=\"focus-areas\"\u003eFocus Areas\u003c/h2\u003e\n\u003ch3 id=\"embedded-systems-iot-and-edge-ai\"\u003eEmbedded Systems, IoT, and Edge AI\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eBuilt decentralized real-time systems on ESP32 for local communication, sensing, and automation\u003c/li\u003e\n\u003cli\u003eDeveloped firmware for sensor-integrated devices using MPU-6050, DHT11, MQ-135, PIR, and sound sensors\u003c/li\u003e\n\u003cli\u003eWorked with ESP32-WROOM-32, ESP32-CAM, and ESP-NOW for low-latency wireless communication\u003c/li\u003e\n\u003cli\u003eTrained and deployed on-device ML models using TinyML, LSTM autoencoders, int8 quantization, TFLite Micro, Edge Impulse, and MobileNetV2\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3 id=\"backend-full-stack-and-ai-application-development\"\u003eBackend, Full-Stack, and AI Application Development\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eBuilt application backends using FastAPI, Flask, Node.js, and Express\u003c/li\u003e\n\u003cli\u003eEngineered microservices infrastructure including API gateway patterns with JWT authentication and Redis rate limiting\u003c/li\u003e\n\u003cli\u003eDeveloped interactive web experiences using React, Socket.IO, MySQL, and PostgreSQL\u003c/li\u003e\n\u003cli\u003eIntegrated AI services (OpenAI API, Google Vision) into full-stack products with personalization and chat memory\u003c/li\u003e\n\u003cli\u003eContainerized applications with Docker and set up CI workflows with GitHub Actions\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3 id=\"programming-and-tooling\"\u003eProgramming and Tooling\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eLanguages: Python, C, C++, JavaScript, SQL\u003c/li\u003e\n\u003cli\u003eTools: Arduino IDE, PlatformIO, EasyEDA, Git, Docker, VS Code\u003c/li\u003e\n\u003cli\u003eLibraries and utilities: BeautifulSoup, PyPDF2\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2 id=\"project-experience\"\u003eProject Experience\u003c/h2\u003e\n\u003ch3 id=\"edge-ai-anomaly-detection-system-github\"\u003eEdge AI Anomaly Detection System (\u003ca href=\"https://github.com/BonchitoSky/edge-ai-anomaly-detection\"\u003eGitHub\u003c/a\u003e)\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eBuilt a real-time vibration anomaly detection system running entirely on an ESP32 with an MPU-6050 sensor — no cloud required\u003c/li\u003e\n\u003cli\u003eTrained an LSTM autoencoder in TensorFlow on normal-only data and quantized it to int8 for TFLite Micro on-device inference\u003c/li\u003e\n\u003cli\u003eAdded a fault-type classifier that identifies the kind of anomaly (drop, shake, imbalance) alongside severity\u003c/li\u003e\n\u003cli\u003eDeveloped a Flask + JavaScript dashboard with live reconstruction-error charts, adaptive EWMA thresholding, and CSV-exportable anomaly event logs\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3 id=\"cookmind-ai--ai-powered-cooking-assistant-github\"\u003eCookMind AI – AI-Powered Cooking Assistant (\u003ca href=\"https://github.com/BonchitoSky/CookMind-AI\"\u003eGitHub\u003c/a\u003e)\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eDeveloped a full-stack AI application using React, Vite, FastAPI, PostgreSQL, and the OpenAI API\u003c/li\u003e\n\u003cli\u003eBuilt recipe recommendations from ingredient lists, conversational cooking assistance with chat memory, and image-based ingredient recognition via Google Vision\u003c/li\u003e\n\u003cli\u003eImplemented JWT signup/login, pantry persistence, cuisine and dietary preferences, and favorite recipe saving\u003c/li\u003e\n\u003cli\u003eContainerized the stack with Docker (dev and production compose files with an Nginx reverse proxy) and added a CI workflow for backend tests and frontend builds\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3 id=\"therupogate--microservices-api-gateway-github\"\u003eTheRUPOgate – Microservices API Gateway (\u003ca href=\"https://github.com/BonchitoSky/TheRUPOgate\"\u003eGitHub\u003c/a\u003e)\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eEngineered a production-style Node.js API gateway serving as a single entry point in front of microservices\u003c/li\u003e\n\u003cli\u003eImplemented JWT authentication with role-based header injection for downstream services\u003c/li\u003e\n\u003cli\u003eBuilt Redis sliding-window rate limiting that fails open if Redis is unavailable\u003c/li\u003e\n\u003cli\u003eAdded structured JSON logging and a live monitoring dashboard streaming request metrics over Server-Sent Events\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3 id=\"intelligent-decentralized-home-automation-system\"\u003eIntelligent Decentralized Home Automation System\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eDeveloped a decentralized smart home system using ESP32 nodes communicating through ESP-NOW\u003c/li\u003e\n\u003cli\u003eImplemented local sensing, threshold-based alerts, timing control, and offline automation logic\u003c/li\u003e\n\u003cli\u003eIntegrated DHT11, MQ-135, PIR, and sound sensors into the firmware pipeline\u003c/li\u003e\n\u003cli\u003eContributed to a MobileNetV2-based facial recognition workflow for secure access control on ESP32-CAM\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3 id=\"real-time-multiplayer-quiz-platform\"\u003eReal-Time Multiplayer Quiz Platform\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eBuilt a multiplayer quiz application using React, Node.js, and Socket.IO\u003c/li\u003e\n\u003cli\u003eImplemented private rooms, timed rounds, live score tracking, leaderboard generation, and synchronized gameplay state\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3 id=\"natural-language-to-sql-query-generator\"\u003eNatural Language to SQL Query Generator\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eBuilt a Flask and MySQL based application that converts natural language questions into SQL queries\u003c/li\u003e\n\u003cli\u003eDeveloped the backend query flow, execution pipeline, and result presentation interface\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3 id=\"dynamic-programming-text-auto-correction\"\u003eDynamic Programming Text Auto-Correction\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eDeveloped a C-based text correction system using edit-distance dynamic programming\u003c/li\u003e\n\u003cli\u003eFocused on optimized string matching, fast suggestion generation, and correction accuracy\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3 id=\"automated-plant-watering-system\"\u003eAutomated Plant Watering System\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eBuilt an ESP32-based irrigation system that automates watering decisions from environmental input\u003c/li\u003e\n\u003cli\u003eAdded real-time monitoring and control behavior to improve reliability and efficiency\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3 id=\"wifi-network-scanner\"\u003eWiFi Network Scanner\u003c/h3\u003e\n\u003cul\u003e\n\u003cli\u003eDeveloped an ESP32 utility to detect nearby WiFi networks and display SSID and RSSI values\u003c/li\u003e\n\u003cli\u003eUsed the system for quick signal analysis and basic wireless environment diagnostics\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor more details about my technical background and projects, please refer to my \u003ca href=\"/my-resume\"\u003eresume\u003c/a\u003e.\u003c/p\u003e","title":"Experience"},{"content":"Profile I\u0026rsquo;m Rupankar Dutta, a B.Tech student at KIIT University focused on embedded systems, the Internet of Things, and edge artificial intelligence. I have hands-on experience building decentralized real-time systems on ESP32, developing sensor-integrated firmware, implementing low-latency wireless communication, and deploying lightweight machine learning models for on-device inference. I also build full-stack AI applications, microservices infrastructure, and system-level solutions that combine software, hardware, and data-driven logic.\nDownload Resume PDF\nEducation Kalinga Institute of Industrial Technology (KIIT)\nBachelor of Technology, 2023-2027\nCGPA: 8.4/10\nTechnical Skills Programming: Python, C, C++, JavaScript, SQL\nEmbedded Systems / IoT: ESP32, Sensor Integration, Embedded Firmware, Edge AI, TinyML\nMachine Learning: Facial Recognition, MobileNetV2, LSTM Autoencoders, TensorFlow Lite, Model Quantization\nBackend Development: FastAPI, Flask, Node.js, Express, REST APIs, JWT Authentication\nWeb: React, Socket.IO, MySQL, PostgreSQL, Redis\nTools: Arduino IDE, PlatformIO, EasyEDA, Git, Docker, VS Code\nTechnologies Embedded: ESP32-WROOM-32, ESP32-CAM, ESP-NOW, MPU-6050, I2C\nSensors: MPU-6050, DHT11, MQ-135, PIR, Sound Sensor\nML Stack: Edge Impulse, TensorFlow Lite, TFLite Micro\nAI Services: OpenAI API, Google Vision\nFeatured Projects Fork \u0026amp; Clone - One-Click GitHub Fork + Local Clone (Chrome Extension) (GitHub) - Built a Chrome Manifest V3 extension paired with a Windows native-messaging companion in PowerShell that forks any GitHub repository and git-clones it locally in a single click. Implemented GitHub API orchestration with asynchronous fork-readiness polling, a strictly validated native host (pinned extension origin, GitHub-only clone URLs, path-traversal blocking), and a theme-aware in-page UI with configurable button placement and a confirmation flow.\nIntelligent Decentralized Home Automation System (ESP32, Edge AI) - Developed a fully decentralized smart home system using ESP32 nodes communicating via ESP-NOW for real-time sensing, local decision-making, and offline automation without cloud dependency. Implemented firmware for DHT11, MQ-135, PIR, and sound sensors, and contributed to a MobileNetV2-based TinyML facial recognition pipeline for secure on-device access control on ESP32-CAM.\nEdge AI Anomaly Detection System (ESP32, TinyML) (GitHub) - Built a real-time vibration anomaly detection system running entirely on an ESP32 with an MPU-6050 sensor: trained an LSTM autoencoder in TensorFlow, quantized it to int8 for TFLite Micro on-device inference, and added a fault-type classifier. Developed a Flask + JavaScript dashboard with live reconstruction-error charts, adaptive EWMA thresholding, and CSV-exportable anomaly event logs.\nTheRUPOgate - Microservices API Gateway (Node.js) (GitHub) - Engineered a production-style API gateway serving as a single entry point for microservices, handling JWT authentication with role-based header injection, Redis sliding-window rate limiting, and path-prefix proxy routing to backend services. Added structured JSON logging and a live monitoring dashboard streaming request metrics over Server-Sent Events.\nCookMind AI - AI-Powered Recipe Recommendation Platform (GitHub) - Developed a full-stack AI application using React, FastAPI, PostgreSQL, and the OpenAI API delivering personalized recipe recommendations, conversational cooking assistance, and image-based ingredient recognition via Google Vision. Built a modular REST backend with JWT authentication, pantry persistence, user personalization, and chat memory, and containerized the stack with Docker and a CI workflow.\nReal-Time Multiplayer Quiz Platform - Designed and built a full-stack multiplayer quiz application using React, Node.js, and Socket.IO with private room-based sessions, timed rounds, live score tracking, leaderboard generation, and low-latency synchronized gameplay.\nAutomated Plant Watering System (ESP32) - Built an IoT-based irrigation system using ESP32 and environmental sensing to automate watering decisions based on plant conditions, with real-time monitoring and control features.\nConnect With Me Feel free to reach out through any of these channels:\nEmail: 2330108@kiit.ac.in or rupankardutta5686@gmail.com GitHub: github.com/BonchitoSky LinkedIn: linkedin.com/in/rupankar-dutta For more details, please download my full resume using the link above or feel free to reach out to discuss any opportunities.\n","permalink":"https://bonchitosky.github.io/my-resume/","summary":"\u003ch2 id=\"profile\"\u003eProfile\u003c/h2\u003e\n\u003cp\u003eI\u0026rsquo;m Rupankar Dutta, a B.Tech student at KIIT University focused on embedded systems, the Internet of Things, and edge artificial intelligence. I have hands-on experience building decentralized real-time systems on ESP32, developing sensor-integrated firmware, implementing low-latency wireless communication, and deploying lightweight machine learning models for on-device inference. I also build full-stack AI applications, microservices infrastructure, and system-level solutions that combine software, hardware, and data-driven logic.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"/MyResume.pdf\"\u003eDownload Resume PDF\u003c/a\u003e\u003c/p\u003e\n\u003ch2 id=\"education\"\u003eEducation\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eKalinga Institute of Industrial Technology (KIIT)\u003c/strong\u003e\u003cbr\u003e\nBachelor of Technology, 2023-2027\u003cbr\u003e\nCGPA: 8.4/10\u003c/p\u003e","title":"Resume"}]