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JAYMITHRAN/README.md

⚡ SYSTEM PROFILE

┌──────────────────────────────────────────────────┐
│ SYSTEM PROFILE                                    │
│                                                    │
│ Role       → AI/ML Undergraduate & Developer       │
│ Focus      → AI • Software Engineering • Networking│
│ Languages  → Python • Java • C • JavaScript         │
│ Mindset    → Build • Analyze • Automate             │
│ Status     → Continuously learning through          │
│               hands-on projects                     │
└──────────────────────────────────────────────────┘

I'm an AI/ML undergraduate with strong foundations in Python, Java, Computer Networking, and AI/ML. I enjoy designing intelligent, automation-driven solutions and getting my hands dirty with real-world problem solving — from building AI-assisted developer tools to configuring firewalls and virtualization stacks. I learn through hands-on projects, coding practice, and real-world experimentation.


🧬 AI × SOFTWARE × NETWORKING

                        ┌──────────────┐
                        │    AI / ML    │
                        └───────┬──────┘
                                │
              ┌─────────────────┼─────────────────┐
              │                 │                 │
              ▼                 ▼                 ▼
     ┌──────────────────┐  ┌────────────────┐  ┌────────────┐
     │     SOFTWARE     │  │  NETWORKING /  │  │ AUTOMATION │
     │   ENGINEERING    │  │ INFRASTRUCTURE │  │            │
     └──────────────────┘  └────────────────┘  └────────────┘

🤖 AI / ML

  • AI-powered applications
  • Automated code analysis
  • Intelligent, assistive systems

💻 Software Engineering

  • Python • Java • C • JavaScript
  • ReactJS • FastAPI • Express.js
  • REST API design

🌐 Networking / Infrastructure

  • Cisco • Juniper • pfSense
  • Proxmox virtualization
  • Linux • Docker

🕸️ TECH CONSTELLATION

Technology constellation diagram grouping languages, frontend, backend, database, tools and networking technologies

💻 Languages
Python
Java
C
JavaScript

🎨 Frontend
React
HTML5
CSS3
Tailwind
Bootstrap

⚙️ Backend & APIs
FastAPI
Express.js
REST APIs

🗄️ Databases
MySQL
MongoDB
Firebase

🧰 Tools & Platforms
Git
GitHub
Docker
Linux
Playwright

🌐 Networking & Infra
Cisco
Juniper
pfSense
Proxmox

🧠 Featured Project — StarkAlgoLens

AI-Powered Coding Mentor

StarkAlgoLens is an AI-powered coding mentor with a React (Vite + Tailwind CSS) frontend and a FastAPI backend. It performs code analysis and secure code execution, then generates personalized learning roadmaps and tracks progress over time.

Under the hood:

  • 🔍 Tree-sitter AST analysis for structural code understanding
  • ⏱️ Time and space complexity estimation
  • 🧩 Code-pattern identification
  • 🐳 Docker-isolated code execution across Python, Java, and C++
  • 🧠 Groq API for AI-driven feedback and guidance
  • 🗄️ MySQL, MongoDB & Firebase persistence layer
  • 📈 Personalized learning roadmaps and progress tracking

Node trail

React → Vite/Tailwind → FastAPI → Docker → Tree-sitter → Groq → MySQL → MongoDB → Firebase
StarkAlgoLens architecture: user code flows through React (Vite, Tailwind) UI and FastAPI backend into Tree-sitter analysis, Docker secure execution, and an AI engine, converging into a MySQL, MongoDB and Firebase persistence layer, then developer insights

🤖 Automated Google Form Filler

A Python automation tool that reads a source PDF, parses it into structured JSON, maps fields using rule-based logic, and drives a Google Form submission through Playwright browser automation — including handling of ambiguous input cases.

 
   PDF
    │
    ▼
 PDF Parser
    │
    ▼
Structured JSON
    │
    ▼
Rule-Based Field Mapping
    │
    ▼
Playwright Automation
    │
    ▼
 Google Form

Capabilities: Python automation • PDF parsing • JSON extraction • rule-based field mapping • Playwright browser automation • ambiguous-input handling


🌐 Private Network & Virtualized Infrastructure Lab

Network topology: internet through pfSense firewall into Cisco and Juniper switching and routing, converging into a Proxmox virtualization host

A private, on-premises network built and administered end-to-end — not just web development. The lab covers:

  • 🔥 pfSense firewall policy configuration
  • 🔀 Cisco and Juniper switching / routing
  • 🧱 Network segmentation
  • 🖥️ Proxmox-based virtual machines and containerized services

🏆 Achievements & Education

Achievements: India Skills Competition South Region Bronze Medalist in IT Network System Administration, Student Trainer for a six-month networking fundamentals program, and languages English, Tamil, Telugu
Education: Bannari Amman Institute of Technology, Bachelor's Degree in Artificial Intelligence and Machine Learning, CGPA 8.99, September 2023 to present

📡 GitHub Activity

Jaymithran's animated GitHub contribution snake

The contribution snake is generated automatically by the snake.yml GitHub Action (see Setup below) and regenerates daily from real contribution data.

First-time setup: the image above won't render until snake.yml has run at least once and created the output branch. Trigger it manually from the Actions tab (Run workflow) right after publishing this repo — no placeholder or fallback image is used, so the space stays empty until the real data exists.


📊 GitHub Overview

GitHub Profile Followers Profile Views Status

Live GitHub statistics and activity metrics rendered via high-availability badge services.


🎯 Current Focus

Areas I'm actively building in — not a ranked or scored list.

                       ┌─────────────┐
                       │   AI / ML   │
                       └──────┬──────┘
                              │
              ┌───────────────┼───────────────┐
              ▼               ▼               ▼
        ┌───────────┐  ┌─────────────┐  ┌────────────┐
        │ Automation│  │ Intelligent │  │  DSA /     │
        │           │  │  Software   │  │  Problem   │
        │           │  │  Systems    │  │  Solving   │
        └───────────┘  └─────────────┘  └────────────┘

              ┌────────────────────────────────┐
              ▼                                ▼
        ┌─────────────┐              ┌──────────────────┐
        │  Computer   │              │  Infrastructure   │
        │ Networking  │              │  Experimentation  │
        └─────────────┘              └──────────────────┘

🔗 Connect

                    JAYMITHRAN
                        │
         ┌──────────────┬──────────────┐
         ▼              ▼              ▼
      GitHub         LinkedIn       LeetCode

GitHub LinkedIn LeetCode


⚙️ Setup & configuration (for repository owner — click to expand)

1. Repository placement

Place these files in a repository named exactly JAYMITHRAN/JAYMITHRAN (a repo matching your username makes GitHub render this as your profile page). Folder layout:

JAYMITHRAN/
├── README.md
├── assets/
│   ├── hero.svg
│   ├── tech-network.svg          # source copy — README embeds this inline (see §2)
│   ├── starkalgolens-architecture.svg
│   ├── network-lab.svg
│   ├── achievements.svg
│   └── education.svg
└── .github/
    └── workflows/
        └── snake.yml

2. Why the Tech Constellation is inlined, not <img>-referenced

GitHub renders <img src="*.svg"> as a flat, rasterized-style image — any <a href> links baked into that SVG are inert in that context. The Tech Constellation diagram is pasted directly into README.md as raw inline SVG instead of loaded via <img>. That's the only section handled this way; every other diagram (hero, StarkAlgoLens architecture, network lab, achievements, education) stays as a normal <img> reference since they don't need interactivity.

Note: GitHub's markdown sanitizer may strip <a> elements from inline SVGs, making the tech-node links non-clickable on GitHub. The inline SVG is retained for visual consistency and because it renders the diagram natively without rasterization artifacts. If clickable links are essential, consider adding a markdown link list below the diagram as a fallback.

Keep assets/tech-network.svg and the inline copy in README.md in sync if you edit the node links or layout later — they are two copies of the same diagram by necessity of this workaround.

3. Contribution snake (snake.yml)

Uses Platane/snk to render your real contribution graph as an SVG on a schedule, and publishes it to an output branch via crazy-max/ghaction-github-pages.

  • No extra secrets needed beyond the default GITHUB_TOKEN.
  • After the first run, an output branch will exist containing dist/snake-dark.svg and dist/snake.svg.
  • The README references raw.githubusercontent.com/JAYMITHRAN/JAYMITHRAN/output/snake-dark.svg — replace JAYMITHRAN with your actual username/repo if different.
  • In Settings → Actions → General, ensure "Workflow permissions" is set to Read and write permissions so the action can push to the output branch.
  • First run: trigger the workflow manually (Actions tab → Generate Contribution Snake → Run workflow) right after publishing the repo — the image has nothing to display until that first run creates the output branch.

4. GitHub Overview stats

Uses the live github-readme-streak-stats service — works immediately with high uptime, just replace JAYMITHRAN in the image URL if you fork this for another account.

5. Replacing username / configuration

Search this repository for JAYMITHRAN and replace with your own username in:

  • README.md (social badge links, stats URL, snake image URL, inline SVG links point to external docs and don't need changing)
  • snake.yml (uses ${{ github.repository_owner }} automatically — no edit needed if the repo is under your account)

6. External services used

Service Purpose Reliability notes
Platane/snk Contribution snake SVG generation Runs via your own GitHub Action — no third-party uptime dependency
github-readme-streak-stats Live contribution streak & activity stats Dedicated high-availability deployment via demolab.com
Shields.io Social link badges only (GitHub/LinkedIn/LeetCode) Static badge rendering, high uptime

7. Verification checklist

  • All technologies listed are present in the source resume
  • All project descriptions are resume-supported (no invented features)
  • No fabricated metrics, star counts, proficiency percentages, or line-of-code figures
  • All social links point to the stated usernames/handles
  • All 22 tech-node links point to official docs/sites (no random third-party sources)
  • No emoji used inside any SVG file (vector icons only)
  • Images include descriptive alt text for accessibility
  • Unused stats.yml workflow and its unreferenced output asset removed
AI × CODE × NETWORKS

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