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Rate Limiter as a Service ⚡
Designed and developed a production-grade Rate Limiter as a Service that protects APIs from abuse, controls request traffic, and ensures fair resource utilization across distributed applications. The platform enables developers to define flexible rate limiting policies, monitor API consumption in real time, and enforce request quotas with minimal latency. Built using a cloud-native architecture, it supports multiple rate limiting algorithms, horizontal scaling, and high availability, making it suitable for modern microservices and enterprise API platforms.
🧑💻 What I built:
• API key, client, user, and IP-based rate limiting
• Configurable rate limiting policies
• Sliding Window, Fixed Window, and Token Bucket algorithms
• Real-time request monitoring dashboard
• Client usage analytics and reporting
• Rate limit utilization tracking
• Alert management for unusual traffic
• REST APIs for policy administration
• Secure API authentication and authorization
• Responsive web dashboard for monitoring and management
🧠 Engineering Highlights
• Built a distributed rate limiting system using Redis for high-speed request tracking
• Designed stateless services for horizontal scalability across multiple instances
• Optimized request validation with low-latency in-memory operations
• Implemented configurable throttling strategies for different client requirements
• Secure authentication using Spring Security and JWT
• Cloud-ready deployment with Docker and AWS
• Fault-tolerant architecture with graceful degradation during high traffic
• Production-ready modular architecture following clean coding principles
🛠️ Tech Stack:
Java 21 • Spring Boot • Redis • PostgreSQL • REST APIs • Spring Security • JWT • Docker • AWS • OpenAPI/Swagger
Status: Completed ✅
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I’ve been building SabiFlow for a while, and honestly, the most interesting part of the project isn’t the code.
It's the problem.
Because it's personal.
I've experienced that thing where money comes in and somehow, without you really noticing, it starts disappearing.
Not because you don’t earn enough.
Not necessarily because you're irresponsible either.
Sometimes money simply has no job when it arrives.
And as an engineer, that got me thinking:
What if the problem isn't budgeting? What if the problem is that we’re asking people to make too many good decisions at the exact moment they have the most temptation to make bad ones?
That question became the foundation for SabiFlow.
Instead of telling someone, "You should save 20% of your income," I started thinking about what would happen if the system simply helped assign every inflow a purpose the moment it arrived.
That led me down a rabbit hole.
Funnels.
Automated distribution.
Wallet infrastructure.
Virtual accounts.
User behaviour.
Transaction flows.
KYC.
Compliance.
Even the psychology behind notifications.
And this is probably my favourite part of being both an engineer and a founder.
I don’t just ask:
"How do I build this feature?"
I ask:
"Why does this problem exist, and what kind of system could make dealing with it easier?"
Then the engineer in me comes along and asks:
"Okay… but how do we actually make this work reliably?" 😂
That tension between the founder thinking about the problem and the CTO thinking about the system is probably what I enjoy most about building SabiFlow.
I’m still figuring a lot of it out.
But I'm curious:
What’s a problem you’ve experienced personally that eventually made you want to build something around it?
The framing around “why does this problem exist?” is exactly the kind of product thinking that keeps a financial tool from becoming another dashboard. SabiFlow sounds strongest where the behavioral insight meets the practical system design—especially around notifications and reliable follow-through.
just shipped the core of Skillship — an AI-powered LMS built for schools.
One platform, many schools, and one rule that shaped every decision: School A must never see School B's data. Tenant-scoped models, filtered querysets at the base class, UUIDs everywhere.
Four AI features live:
— Career Pilot: personalised career paths for students
— Question generator: chapter PDF in, board-aligned MCQs out
— Adaptive quizzes: difficulty follows actual performance
— Semantic search: teachers ask in plain English, pgvector finds the material
Django + DRF for the LMS core, a separate FastAPI service for all AI work (Gemini + a pgvector RAG pipeline), Next.js 14 on the front. Django brokers every AI call — the AI layer never touches the browser.
The hard part wasn't the AI. It was making multi-tenancy boring enough that no future dev can leak a school's data by accident.
Open for new work — full-stack builds, AI integrations, RAG pipelines, multi-tenant SaaS. Send me a project.
The tenant boundary is where I'd put the first failure test. I'd try to make a user from one school retrieve another school's record before I trusted any of the AI features.
Hey 🔥
This is one of my recently delivered web applications. I got the opportunity to build a powerful Lead Management System for a real estate channel partner company, Margadarsi Infra.
The application was built to solve a major challenge the client was facing — managing leads coming from multiple sources.
Previously, the team had difficulty tracking and managing leads from different sources. Now, the system captures leads from multiple sources and automatically distributes them to telecallers through a round-robin process.
Each lead can now be tracked and traced precisely from end to end, giving the team better visibility and control over the entire lead lifecycle.
#webdesign #webdevelopment #realestate #crm #erp #uiuxdesign #webapplication #nodejs #nextjs #php #laravel
