Forem: Abinash Sharma

Building Rate-Limited APIs That Scale - A Practical Guide

Abinash Sharma — Sun, 28 Dec 2025 19:30:02 +0000

`

title: "Building Rate-Limited APIs That Scale: A Practical Guide"
published: true
description: "Learn how to implement rate limiting for your APIs with in-memory, Redis-based, and API Gateway approaches. Includes token bucket algorithm and monitoring tips."

Rate limiting is critical for API stability, but implementing it wrong can frustrate users or fail when you need it most. Here's how to build rate limiting that actually works.

Why Rate Limiting Matters

Without rate limiting, a single misbehaving client can bring down your entire API. I learned this the hard way when a buggy integration made 10,000 requests per second to our production API. Spoiler: it didn't end well.

Three Approaches to Rate Limiting

1. In-Memory (Simple but Limited)

Good for single-instance APIs or development:
`javascript
const rateLimit = require('express-rate-limit');

const limiter = rateLimit({
windowMs: 15 * 60 * 1000, // 15 minutes
max: 100, // limit each IP to 100 requests per window
message: 'Too many requests, please try again later.'
});

app.use('/api/', limiter);
`

Pros: Easy to set up, no external dependencies

Cons: Doesn't work across multiple servers, resets on restart

2. Redis-Based (Production Ready)

Best for distributed systems:
`javascript
const RedisStore = require('rate-limit-redis');
const redis = require('redis');

const client = redis.createClient({
host: process.env.REDIS_HOST,
port: 6379
});

const limiter = rateLimit({
store: new RedisStore({
client: client,
prefix: 'rl:'
}),
windowMs: 15 * 60 * 1000,
max: 100
});

app.use('/api/', limiter);
`

Pros: Works across multiple instances, persistent across restarts

Cons: Requires Redis infrastructure

3. API Gateway Level (Enterprise)

Leverage your cloud provider's API Gateway:
`yaml

AWS API Gateway example

x-amazon-apigateway-request-validators:
all:
validateRequestBody: true
validateRequestParameters: true

paths:
/users:
get:
x-amazon-apigateway-integration:
type: aws_proxy
httpMethod: POST
uri: arn:aws:apigateway:region:lambda:path/function/arn
x-amazon-apigateway-throttle:
rateLimit: 100
burstLimit: 200
`

Pros: No code changes, scales automatically, DDoS protection

Cons: Vendor lock-in, potential costs

Advanced: Token Bucket Algorithm

For more sophisticated rate limiting, implement a token bucket:
`javascript
class TokenBucket {
constructor(capacity, refillRate) {
this.capacity = capacity;
this.tokens = capacity;
this.refillRate = refillRate;
this.lastRefill = Date.now();
}

consume(tokens = 1) {
this.refill();

if (this.tokens >= tokens) {
  this.tokens -= tokens;
  return true;
}
return false;

}

refill() {
const now = Date.now();
const timePassed = (now - this.lastRefill) / 1000;
const tokensToAdd = timePassed * this.refillRate;

this.tokens = Math.min(this.capacity, this.tokens + tokensToAdd);
this.lastRefill = now;

}
}

// Usage
const buckets = new Map();

app.use((req, res, next) => {
const ip = req.ip;

if (!buckets.has(ip)) {
buckets.set(ip, new TokenBucket(100, 10)); // 100 capacity, 10 tokens/sec
}

const bucket = buckets.get(ip);

if (bucket.consume(1)) {
next();
} else {
res.status(429).json({ error: 'Rate limit exceeded' });
}
});
`

User-Friendly Rate Limiting

Always include helpful headers:
javascript app.use((req, res, next) => { res.setHeader('X-RateLimit-Limit', '100'); res.setHeader('X-RateLimit-Remaining', '95'); res.setHeader('X-RateLimit-Reset', new Date(Date.now() + 900000).toISOString()); next(); });

Different Limits for Different Users

Implement tiered rate limiting:
`javascript
const getRateLimit = (user) => {
if (user.tier === 'premium') return 1000;
if (user.tier === 'standard') return 100;
return 10; // free tier
};

app.use(async (req, res, next) => {
const user = await authenticateUser(req);
const limit = getRateLimit(user);

// Apply user-specific limit
const limiter = rateLimit({
windowMs: 15 * 60 * 1000,
max: limit,
keyGenerator: (req) => user.id
});

limiter(req, res, next);
});
`

Monitoring Your Rate Limits

Track who's hitting limits:
javascript const limiter = rateLimit({ windowMs: 15 * 60 * 1000, max: 100, handler: (req, res) => { // Log for analysis console.log(Rate limit exceeded for ${req.ip}`);

// Send to monitoring
metrics.increment('rate_limit.exceeded', {
  ip: req.ip,
  endpoint: req.path
});

res.status(429).json({
  error: 'Too many requests',
  retryAfter: 900 // seconds
});

}
});
`

Quick Implementation Checklist

✅ Choose the right approach for your scale
✅ Use Redis for multi-instance deployments
✅ Include rate limit headers in responses
✅ Implement different tiers for different users
✅ Monitor rate limit violations
✅ Provide clear error messages with retry information
✅ Consider geographic rate limiting for global APIs

Common Pitfalls to Avoid

Rate limiting by IP alone: Use user IDs when available
Too aggressive limits: Start generous, tighten based on data
No burst allowance: Allow short traffic spikes
Ignoring authenticated vs anonymous: Different limits for each

Conclusion

Rate limiting is essential for API reliability. Start with a simple in-memory solution, move to Redis as you scale, and consider API Gateway for enterprise needs. Always include helpful headers and monitor violations to fine-tune your limits.

How do you handle rate limiting in your APIs? Any horror stories to share? 🚦

Kubernetes Secrets Management: 5 Best Practices You Need toKnow 🚀

Abinash Sharma — Sun, 28 Dec 2025 19:24:35 +0000

---
title: "Kubernetes Secrets Management: 5 Best Practices You Need to Know"
published: true
description: "Learn how to properly secure secrets in Kubernetes with encryption at rest, external secret managers, RBAC, and more practical tips."
tags: kubernetes, devops, security, cloudnative
cover_image: https://dev-to-uploads.s3.amazonaws.com/uploads/articles/your-cover-image.png
---

Managing secrets in Kubernetes can be tricky. Hard-coded credentials, exposed environment variables, and insecure ConfigMaps are common pitfalls that can compromise your entire infrastructure. Here's how to do it right.

## The Problem

By default, Kubernetes secrets are just base64-encoded (not encrypted!) and stored in etcd. Anyone with cluster access can decode them instantly:

bash
kubectl get secret my-secret -o jsonpath='{.data.password}' | base64 --decode


Not exactly secure, right?

## 5 Essential Best Practices

### 1. Enable Encryption at Rest

Configure your Kubernetes cluster to encrypt secrets in etcd:

yaml

encryption-config.yaml

apiVersion: apiserver.config.k8s.io/v1
kind: EncryptionConfiguration
resources:

resources:
- secrets providers:
- aescbc: keys: - name: key1 secret:
- identity: {}


Apply this to your API server with the `--encryption-provider-config` flag.

### 2. Use External Secrets Operators

Integrate with cloud-native secret managers like AWS Secrets Manager, Azure Key Vault, or HashiCorp Vault:

yaml
apiVersion: external-secrets.io/v1beta1
kind: ExternalSecret
metadata:
name: database-credentials
spec:
refreshInterval: 1h
secretStoreRef:
name: aws-secrets-manager
kind: SecretStore
target:
name: db-secret
data:
- secretKey: password
remoteRef:
key: prod/database/password


This syncs secrets from your vault into Kubernetes automatically.

### 3. Implement RBAC Properly

Restrict who can read secrets with Role-Based Access Control:

yaml
apiVersion: rbac.authorization.k8s.io/v1
kind: Role
metadata:
namespace: production
name: secret-reader
rules:

apiGroups: [""] resources: ["secrets"] resourceNames: ["db-credentials"] verbs: ["get"]


Never give blanket `secrets` access across namespaces.

### 4. Use Sealed Secrets for GitOps

Store encrypted secrets in Git safely using Sealed Secrets:

bash

Encrypt a secret

kubectl create secret generic mysecret \
--from-literal=password=mypassword \
--dry-run=client -o yaml | \
kubeseal -o yaml > sealed-secret.yaml

Safe to commit to Git

git add sealed-secret.yaml


Only your cluster can decrypt these sealed secrets.

### 5. Rotate Secrets Regularly

Automate secret rotation with tools like cert-manager for certificates or custom CronJobs:

yaml
apiVersion: batch/v1
kind: CronJob
metadata:
name: rotate-api-keys
spec:
schedule: "0 2 * * 0" # Weekly at 2 AM
jobTemplate:
spec:
template:
spec:
containers:
- name: rotator
image: my-secret-rotator:latest
env:
- name: SECRET_NAME
value: "api-credentials"
restartPolicy: OnFailure


## Quick Security Checklist

- ✅ Enable encryption at rest
- ✅ Use external secret managers
- ✅ Implement strict RBAC
- ✅ Never commit secrets to Git (use Sealed Secrets)
- ✅ Rotate secrets regularly
- ✅ Audit secret access logs
- ✅ Use service accounts with minimal permissions
- ✅ Scan images for exposed secrets

## Cloud-Specific Tips

**AWS EKS**: Use IAM Roles for Service Accounts (IRSA) to avoid storing AWS credentials entirely.

**Azure AKS**: Leverage Azure Key Vault Provider for Secrets Store CSI Driver.

**GCP GKE**: Use Workload Identity with Secret Manager integration.

## Conclusion

Kubernetes secrets management doesn't have to be complicated. Start with encryption at rest, integrate an external secrets manager, and implement proper RBAC. Your security team (and your future self) will thank you.

---

**What's your approach to managing secrets in Kubernetes? Share your tips in the comments!** 🔐

🔥 1. Agentic AI Goes Mainstream

Abinash Sharma — Fri, 26 Dec 2025 20:58:36 +0000

AI agents that act autonomously — not just respond — are being widely adopted in enterprise systems. They’re now running customer support, monitoring systems, and even managing workflows.
**

What this means for you

Building systems that can take action, not just reply
Increased demand for agent orchestration frameworks
A shift from static models to continuous planners

🧠 2. Modular AI Agents & Developer Efficiency Tools

Platforms like Skills in Codex let developers build, customize, and share reusable modules that make AI agents far more effective in coding and automation tasks.
IT Pro

Why it matters

Agents can perform complex developer workflows reliably
Encourages community standards (open agent skills)
Reduces repetitive prompts and configuration work

🎨 3. Vibe Coding & AI-Driven Code Workflows

“Vibe coding” — where developers direct LLMs in natural language and let tools generate the code — is gaining traction as a legitimate trend and even featured in tech culture discussions.
Business Insider

Trend highlights

Coding with prompts and outcomes vs manual syntax
Tools like Cursor, Claude Code, and Copilot shaping how software is built
Raises new questions about maintainability and quality

💻 4. AI Embedded at the OS Level

Operating systems are integrating AI deeply — for example, Windows 11 builds AI agents into core workflows, letting users interact with apps via natural language.
Windows Central

Impact

New conventions for human–computer interaction
Built-in AI for daily tasks and productivity
Better accessibility features

🔧 5. AI in Enterprise IT & Operations

Next-generation platforms (e.g., EvolveOps.AI) are using agentic AI to modernize hybrid cloud operations and IT workflows.
The Times of India

Takeaways

AI is not just for dev — it’s driving ops modernization
Hybrid cloud needs new governance and observability patterns
DevOps teams must adapt to intelligent operational agents

"Getting Out of Tutorial Hell: What I Did Differently as a Coding Beginner"

Abinash Sharma — Mon, 06 Oct 2025 08:43:28 +0000

`# Getting Out of Tutorial Hell: What I Did Differently as a Coding Beginner

Description: "As a beginner, I kept watching tutorials but wasn’t building anything. Here's how I finally broke out of 'tutorial hell' and started learning for real."
tags: beginners, learning, coding, webdev, motivation
Hey everyone 👋

I’m Abinash, a coding fresher learning how to build real-world projects and trying to actually understand what I’m doing.

But not long ago, I was stuck in something many beginners face:

“Tutorial Hell” — where you keep watching videos but feel like you’re not learning anything for real.

🧠 What is Tutorial Hell?

If you’re there, you know the feeling:

You’ve watched 5+ full courses
You can follow along… but only when the instructor does it
You panic when you start a blank project
You keep saying, “Just one more tutorial and then I’ll build something…”

It feels like you’re learning, but you're not applying what you’ve learned.

😩 My Breaking Point

I realized I had watched over 20 hours of tutorials but still couldn’t build a simple project from scratch.

That hit me hard.

So I decided to stop “tutorial binging” and change how I learn.

🔄 What I Did Differently

1. 📵 Closed YouTube, Opened VS Code

I picked a very small project idea (a to-do list) and forced myself to build it without watching a tutorial. When I got stuck, I Googled, read docs, and struggled a bit — but I actually learned.

2. 🧩 Broke Projects Into Tiny Tasks

Instead of saying “Build a weather app,” I broke it into:

Learn how to get user input
Figure out how to use an API
Display the data

That made things way less overwhelming.

3. 🧪 Started "Learn by Doing"

For every concept I learned, I asked:

“Can I build something simple with this right now?”

If the answer was yes, I did it — no matter how small it was.

✅ Results So Far

Now, I’ve built:

A working to-do list (with localStorage!)
A simple weather app using an API
My personal portfolio site (still improving!)

Are they perfect? No.

Did I learn more from these than 10 tutorials? Absolutely.

💬 What About You?

👉 Are you stuck in tutorial hell right now?

👉 What helped you start building things on your own?

👉 Have any simple project ideas you’d recommend for beginners?`

"I'm New to Coding — Here's What No One Told Me (So Far)"

Abinash Sharma — Mon, 06 Oct 2025 07:49:16 +0000

`---
description: "As a beginner in coding, everything feels exciting and overwhelming. Here's what I've learned so far — and what I'm still figuring out."

Series: "My Coding Journey"

I'm New to Coding — Here's What No One Told Me (So Far)

Hi DEV community 👋

I’m Abinash, and I’m a fresher in the world of coding — just a few weeks/months into this journey. So far, it’s been exciting, frustrating, confusing… and kind of awesome.

I wanted to share a few honest reflections, in case someone else out there is on the same path (or remembers what this part of the journey feels like).

🧠 1. “Learning to Code” Isn’t Just About Code

At first, I thought coding was just memorizing syntax and building cool stuff. But the reality?

It’s also:

Googling constantly
Reading confusing error messages
Staying calm when things break
Learning how to learn

I didn’t expect this much mental reprogramming, but it's been eye-opening.

🤯 2. The Internet Is Overwhelming (But Amazing)

There are so many opinions.

"Start with Python!"
"No, JavaScript is king!"
"HTML is not a programming language!" (😅)

It took me a while to stop trying to follow every roadmap and just pick one path.

Right now, I’m focused on:

➡️ [ JavaScript, Cloud ,AI and Python ]

🐢 3. I’m Learning Slowly — And That’s Okay

Some days I feel like a genius. Other days I spend hours stuck on something super small (like a missing ;).

But every bug I fix teaches me something.

Progress feels slow — but it’s real.

🎯 My Current Goals

Here’s what I’m working on right now:

✅ Build a personal portfolio site

✅ Solve 10 beginner problems on LeetCode

✅ Understand how APIs work and connect to the frontend

💬 Over to You!

If you're also a beginner:

👉 What’s something you're struggling with?

👉 What’s one win you had recently (even a small one)?

And if you're already a dev:

👉 What’s one piece of advice you’d give to someone like me just starting out?

I’d love to hear your thoughts in the comments. Let's connect, learn, and grow together! 👇

🙏 Thanks for reading — and feel free to follow me if you’d like to join me on this journey as I share what I’m learning, building, and breaking (in a good way!).

– Abinash
`

# ⚡ Popular AI Websites in 2025 for Developers

Abinash Sharma — Mon, 06 Oct 2025 06:47:25 +0000

Published by Abinash — October 2025

Description: "Discover the most popular and useful AI websites in 2025 that every developer should know — from coding assistants to AI-powered APIs."
Artificial Intelligence has taken the developer world by storm.

From AI code assistants to tools that automate workflows and content generation — 2025 is all about building faster, smarter, and with more creativity.

Here’s a curated list of the most popular AI websites in 2025 that every developer should bookmark. 🧠💻

💬 1. ChatGPT (OpenAI)

Website: https://chat.openai.com

Still leading the way, ChatGPT continues to be the developer’s favorite companion — now with advanced reasoning, file handling, and coding features.

Developers use it to:

Debug code in real time
Generate APIs, SQL queries, and UI components
Learn new frameworks on the go

💡 2. GitHub Copilot

Website: https://github.com/features/copilot

Copilot remains a top-tier AI pair programmer in 2025.

It suggests smarter code completions, understands project context, and integrates seamlessly with IDEs like VS Code and JetBrains.

🌐 3. Hugging Face

Website: https://huggingface.co

For developers experimenting with machine learning and NLP, Hugging Face is the home of open-source AI.

You’ll find:

Pre-trained models
Transformers library
APIs for text, vision, and speech

It’s the go-to community for AI model sharing and deployment.

⚙️ 4. Replit Ghostwriter

Website: https://replit.com/ghostwriter

Replit’s Ghostwriter brings AI-assisted coding right into your browser.

In 2025, it’s smarter than ever — capable of building and hosting full-stack apps directly from natural language prompts.

🧩 5. Perplexity AI

Website: https://www.perplexity.ai

Perplexity AI has become the developer’s search engine — a blend of AI chat and web results.

Perfect for quick research, API lookups, or learning new tech stacks with summarized answers and sources.

🔍 6. Poe by Quora

Website: https://poe.com

Poe allows developers to chat with multiple AI models (GPT-4, Claude, Gemini, etc.) in one place.

A great option for testing different AIs and comparing their outputs.

🧠 7. Cursor AI

Website: https://cursor.sh

A newer favorite among coders, Cursor AI is an IDE built around AI.

It’s designed to understand your codebase — making edits, refactors, and explanations effortless.

🚀 8. Vercel AI SDK

Website: https://sdk.vercel.ai

Vercel’s AI SDK helps developers build AI-powered apps faster.

It integrates large language models with React, Next.js, and edge APIs — ideal for modern full-stack projects.

🧩 Bonus: LangChain

Website: https://www.langchain.com

For those building AI agents and workflows, LangChain remains the foundation.

It connects APIs, databases, and AI models together — letting developers design intelligent automation systems easily.

🏁 Final Thoughts

AI in 2025 isn’t just a trend — it’s a toolkit.

Developers who learn how to use AI efficiently are building faster, shipping smarter, and solving problems with creativity like never before.

Which AI tool do you use the most? 💬

Share your favorite in the comments — I’d love to know what’s powering your dev journey!

✍️ About the Author

Hi, I’m Abinash! 👋

I’m a tech enthusiast exploring Python, APIs, and Artificial Intelligence.

I love discovering new tools and sharing how developers can make the most out of AI in their projects. 🚀

Follow me on Abinash Dev.to for more posts about AI, Python, and developer tools!
`

##"AI in 2025: The Year of Smarter, Safer, and More Human AI"

Abinash Sharma — Mon, 06 Oct 2025 06:30:13 +0000

Description: "Explore how AI in 2025 is becoming smarter, safer, and more human — transforming creativity, ethics, and innovation for the better."

🌍 AI in 2025: The Year of Smarter, Safer, and More Human AI

Published by Abinash — October 2025

Artificial Intelligence (AI) has evolved faster than any of us could have imagined. As we step into 2025, the conversation around AI has shifted from “What can it do?” to “How responsibly can we use it?”.

This year marks a turning point — not just in terms of technology, but in how AI connects with our daily lives, creativity, and ethics.

🤖 Smarter Than Ever: AI Gets Context-Aware

In 2025, AI systems are no longer just tools that follow instructions — they understand context, tone, and intent.

From personal assistants that know your schedule and mood to AI models that adapt to your workflow, we’re seeing a more human-like intelligence emerge.

AI chatbots now remember previous interactions to personalize responses.
Code assistants write and refactor complete projects, not just snippets.
Visual AIs understand depth, perspective, and emotional expression in art.

In short, 2025’s AI feels less mechanical and more mindful.

🛡️ Safer by Design: Ethics Meets Innovation

AI ethics is no longer optional — it’s a feature.

With stricter global regulations and transparent datasets, developers and companies are prioritizing responsible AI practices.

Governments now require AI transparency reports.
Bias detection tools are built into most modern models.
Open-source communities are leading the charge on ethical datasets.

We’re learning that innovation without responsibility isn’t progress — it’s risk.

🧠 Human + AI: The New Creative Duo

Rather than replacing humans, 2025’s AI empowers them.

Writers, designers, developers, and students are using AI as a creative partner, not a competitor.

Artists co-create visual worlds using AI generators.
Programmers automate repetitive tasks to focus on logic and design.
Students use AI tutors to learn faster and more effectively.

It’s not man versus machine anymore — it’s man with machine.

🔮 What’s Next?

Looking ahead, AI in 2025 is laying the foundation for autonomous creativity, universal accessibility, and ethical intelligence.

The dream is not to build smarter robots, but to build better humans with AI as our ally.

So as we move forward, one question remains:

“How will you use AI to make 2025 more human?”

💬 What do you think about AI’s evolution in 2025? Share your thoughts in the comments — I’d love to hear your perspective!

✍️ About the Author

Hi, I’m Abinash! 👋

I’m a tech enthusiast exploring the world of Python, APIs, and Artificial Intelligence. I love learning new tools, experimenting with projects, and sharing what I discover with the community. 🚀

Follow me on Dev.to for more posts about AI, Python, and tech innovation!
`