I'm Sayam Mukherjee, a B.Tech CSE (AI & ML) student at KIIT University who enjoys taking an idea from a rough concept to a usable technical product.
My work sits around Artificial Intelligence, Machine Learning, Computer Vision, Full Stack Development and DSA. I like understanding how systems work under the hood, experimenting with them, and then turning that learning into something people can actually use.
- 🧠 Understand the problem before choosing the technology.
- 🔬 Experiment with models, architectures and implementation ideas.
- 🛠️ Build practical, responsive and maintainable solutions.
- 🐛 Debug aggressively and learn from what breaks.
- 🚀 Ship working versions instead of waiting for perfection.
- 📈 Iterate using feedback, testing and measurable improvement.
| Area | What I'm working toward |
|---|---|
| 🤖 AI / ML | Practical machine learning, deep learning & Generative AI |
| 👁️ Computer Vision | Detection, tracking & Edge AI with YOLOv8 / PyTorch |
| 🌐 Full Stack | Building complete, responsive and user-focused products |
| 🧩 DSA | Stronger problem solving, algorithms & core CS fundamentals |
| 🚀 Product Building | MAUSAM, MEMORY IN MOTION & other experimental builds |
Learn deeply. Build boldly. Break things. Fix them. Ship better.
I'm Sayam Mukherjee, a B.Tech Computer Science Engineering student specializing in Artificial Intelligence & Machine Learning at KIIT University, graduating in 2029.
I enjoy working at the intersection of AI, software engineering and product development — turning ideas into functional, user-focused digital products.
- 🤖 Artificial Intelligence
- 🧠 Machine Learning & Deep Learning
- 👁️ Computer Vision
- 🌐 Full Stack Development
- 🧩 Data Structures & Algorithms
- 🚀 AI-powered SaaS Products
- 📊 Data & Visualization
- 💰 FinTech & Stock Market
Don't just learn technology. Build something with it.
Smart India Hackathon 2026 · Problem Statement SIH26076 · Team Algnite
A comprehensive weather and environmental platform for health-conscious users, outdoor fitness enthusiasts, travelers, parents, agriculture, commuters and event planners.
| Layer | Technology |
|---|---|
| Weather | Weather information · Forecasting · IMD-oriented data |
| Environment | AQI · Pollen · UV · Humidity · Soil Moisture |
| Astronomy & Outdoor | Sunrise/Sunset · Best activity hours · Tide information |
| Maps & Data | Interactive Maps · State/UT-wise data · Visualization |
| Reports & Models | CSV/Excel Reports · WRF · GEFS · ECMWF concepts |
| Stack | React · JavaScript · Tailwind CSS · Weather APIs · Vercel |
🌐 Code → github.com/codesbysayam/mausam
Computer Vision · Deep Learning · Edge AI
A real-time computer vision pipeline focused on object detection and multi-object tracking using modern deep-learning technologies.
| Layer | Technology |
|---|---|
| Detection | YOLOv8 |
| Deep Learning | PyTorch |
| Vision | OpenCV · Computer Vision |
| Tracking | Multi-Object Tracking |
| Deployment Focus | Edge AI |
Core: YOLOv8 · PyTorch · OpenCV · Object Detection · Tracking · Edge AI
Workouts · Nutrition · Progress · Subscriptions · Payments · Admin
A comprehensive fitness ecosystem concept spanning mobile, web and administrative interfaces.
| Layer | Technology |
|---|---|
| Core | Workout management · Fitness & nutrition · Progress tracking |
| Accounts | User profiles · Subscription plans · Coupon codes |
| Payments | Razorpay integration |
| Platforms | Android · iOS · Responsive Web |
| Admin | Administrative dashboard |
Stack: React · Node.js · MongoDB · Tailwind CSS · Razorpay
A finance-oriented application concept for analyzing expenses, understanding spending patterns and improving personal financial awareness.
Focus: Finance · Data Analysis · Visualization · Web Development
A digital finance-management project focused on tracking financial activity, expenses and investments through a structured interface.
Focus: FinTech · Finance Management · Data Visualization · Web Development
DataForge 2026 · KDAG, IIT Kharagpur · Pathway: Explain the Frontier · Finalist
Explores how a fixed-size recurrent state can carry task-relevant information forward without token-by-token memory growth, while compression introduces interference and forgetting.
Focus: AI Research · Machine Learning · In-Context Learning · Recurrent Memory
Technology, coding, AI and developer-focused content.
Travel, culture, adventure, facts and sports content.
Technology, future trends, finance, psychology, motivation and perspective-changing facts.
| Domain | Technologies / Focus |
|---|---|
| 🤖 AI | Artificial Intelligence · Generative AI |
| 🧠 ML | Machine Learning · Deep Learning |
| 👁️ Computer Vision | YOLOv8 · PyTorch · OpenCV · Object Detection |
| 💻 Development | Full Stack · Web Development |
| 🎨 Frontend | React · Bootstrap · Tailwind CSS |
| ⚙️ Backend | Node.js · MongoDB |
| 🧩 DSA | Data Structures & Algorithms |
| 🎨 Design | Figma · Canva |
| 🔧 Tools | Git · GitHub · VS Code · Vercel |
| 📊 Finance | Stock Market · FinTech |
- AI / ML development
- Full Stack Web Development
- Computer Vision
- DSA & problem solving
- AI-powered product development
- Responsive web applications
- SaaS product development
- YouTube Thumbnail Design
- Social Media Content Creation
- Social Media Growth
- Google Ads
- Meta Ads
- Digital Marketing
Reached the National-level finals / Top 15 in Toycathon 2021.
Started playing in 2013 · Represented district level in 2019 · Reached finals / Top 15 level in 2021.
| Qualification | Result |
|---|---|
| Class 10 CBSE | 92.6% |
| Class 12 CBSE | 86.2% |
| First Year Overall CGPA | 9.06 |
| Degree | B.Tech CSE (AI & ML) |
| University | KIIT University |
| Expected Graduation | 2029 |
- 🏆 Toycathon 2021
- 🧠 Technex'26 — IIT BHU — Finalist
- 🎉 KIIT Fest
- 💻 Hackathons & Coding Events
- 🚀 Smart India Hackathon 2026 — Team Algnite
- 🧠 DataForge 2026 — KDAG, IIT Kharagpur — Finalist
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MAUSAM · MEMORY IN MOTION · YOLOv8 Edge CV
| 📚 LEARN | 🧪 EXPERIMENT | 🛠️ BUILD | 🚀 SHIP |
|---|---|---|---|
| Strengthen fundamentals | Try new models & tools | Turn ideas into products | Test, deploy & iterate |
| Study AI/ML + DSA | Prototype quickly | Focus on real use cases | Improve from feedback |
01 · Learn → understand the fundamentals
02 · Experiment → test ideas without over-engineering
03 · Build → create a functional version
04 · Break & Debug → find the weak points
05 · Improve → refine performance, UX and reliability
06 · Ship → release, evaluate and keep iterating
The goal isn't to know everything. The goal is to keep getting better at building.
