$ cat /etc/profile.d/diksha.shclass DikshaPimpalkar:
def __init__(self):
self.name = "Diksha Pimpalkar"
self.degree = "TY BTech CSE @ PICT, Pune"
self.roles = ["Security Researcher", "AI/ML Enthusiast", "Systems Developer"]
self.languages = ["Python", "Java", "C++", "JavaScript", "SQL"]
self.interests = ["Cybersecurity", "DevOps", "Cloud", "Intelligent Systems", "IoT"]
self.also_curious_about = ["Finance", "Marketing", "Business Strategy"]
self.currently = "Building BlinkRoute AI & Novora"
self.learning = ["Kubernetes", "Penetration Testing", "LLM Fine-tuning"]
self.ask_me = "DSA, Security concepts, DBMS, AI/ML pipelines"
self.fun_fact = "I debug faster with lo-fi music playing 🎵"
def say_hi(self):
print("Thanks for dropping by! Let's build something amazing 🚀")
me = DikshaPimpalkar()
me.say_hi()
 Security Threat detection & defense |
 AI / ML Models that reason |
 IoT Physical meets digital |
 Curious about Finance & Marketing 💜 |
💡 Beyond the code: while tech is home base, I've got a genuine soft spot for the business side of things too — especially finance and marketing. Not a career pivot, just a curiosity I like feeding alongside everything I build.
Inspired by Blinkit/Zepto/Instamart — simulates warehouses, delivery partners, orders, traffic & inventory. Benchmarks Dijkstra, A*, Genetic Algorithm & Ant Colony Optimization for route efficiency. Live map, real-time rider tracking, demand forecasting.
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Personalized AI study roadmaps (DSA, OOPS, DBMS, OS, CN, System Design, Aptitude) with XP, coins, streaks, mock interviews, AI mentor, and analytics dashboards.
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Detects AI-native fraud across email, URL, document, audio & prompt channels — phishing, spoofing, social engineering, deepfake voice, and prompt-injection detection with a unified explainable risk score.
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Predicts hire/reject outcomes from structured + unstructured resume data using TF-IDF, feature engineering, and Logistic Regression / Random Forest / SVM / KNN comparisons, with a live prediction function.
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EDA-driven binary classification ("Good"/"Bad") using Logistic Regression, KNN & Decision Tree, with feature scaling, hyperparameter tuning, and feature-importance analysis.
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SHA-256 secured auth, per-user local data persistence, analytics dashboard with completion-rate tracking, a calendar productivity heatmap, and 7 hand-crafted themes.
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Secure session-based auth, custom aliases with collision handling, click analytics with geolocation, and a glassmorphism "command center" dashboard.
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Pressure/motion sensors for visitor & delivery detection, Alexa/Google Home/HomeKit integration, and smart-lock/camera pairing for remote verification.
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Every great developer has a spirit animal. Mine writes better code at 2AM.
| Compiling... | Production down... | Code review... |
|---|---|---|
When npm install takes 10 min |
Me during prod outage | My code reviewer |
🐱 "The best code is no code. The second best is code that works." — A cat, probably.
[██████████░░░░░░░░░░] 50% BlinkRoute AI — Routing engine & live dashboard
[████████░░░░░░░░░░░░] 40% Novora — AI mentor & mock interview engine
[██████░░░░░░░░░░░░░░] 30% Kubernetes & container orchestration
[████░░░░░░░░░░░░░░░░] 20% LLM fine-tuning experiments
[██░░░░░░░░░░░░░░░░░░] 10% Open Source contributions: 10 PRs
⚡ Consistently learning, building, and shipping — one commit at a time.