DEV Community: Anjali Gurjar

Work

Anjali Gurjar — Wed, 07 Oct 2026 06:34:35 +0000

Website Not Working Properly? 7 Common Bugs That Can Cost Your Business Customers

A website can look perfect on the surface and still have serious technical problems behind it.

A broken login, slow API, failed deployment, mobile layout issue, or database error can cause customers to leave your website and affect your business.

The good news is that most website problems can be identified and fixed without rebuilding the entire application.

1. Website Is Showing Errors

Common examples:

404 errors
500 server errors
Blank pages
JavaScript errors
API connection failures
Authentication errors

The first step is to check browser console errors, server logs, API responses and deployment configuration.

A developer can identify whether the problem is coming from the frontend, backend, database or server.

2. API Is Not Working

Your frontend may be working correctly, but the API may return errors such as:

401 Unauthorized
403 Forbidden
404 Not Found
500 Internal Server Error

Common causes include:

Incorrect API URL
Authentication/token problems
CORS configuration
Backend exceptions
Incorrect request data
Database connection problems

Proper API debugging helps identify the actual root cause instead of repeatedly changing frontend code.

3. Website Is Too Slow

A slow website can happen because of:

Large images
Unoptimized JavaScript
Slow database queries
Too many API requests
Poor caching
Slow server response
Unoptimized backend code

Performance optimization can include API optimization, database query improvements, caching, image optimization and frontend improvements.

4. Login or Authentication Is Broken

For example:

Users cannot log in
Session expires unexpectedly
JWT token is not handled correctly
Password reset does not work
OAuth callback fails
Cookies are not configured correctly

Authentication should be debugged carefully because it is directly connected to application security.

5. Website Works Locally but Not on Server

This is one of the most common deployment problems.

A developer may see:

“It works on my machine.”

But after deployment:

Environment variables missing
Database connection failed
Build failed
API URL incorrect
Port configuration incorrect
CORS error
SSL/HTTPS problem

The solution usually involves checking the deployment environment, environment variables, logs, dependencies, networking and server configuration.

6. Mobile Website Is Broken

A website may look good on a laptop but have problems on mobile:

Buttons overlap
Text goes outside the screen
Menu does not open
Images are too large
Forms are difficult to use
Horizontal scrolling appears

Responsive design fixes can make the application work properly across mobile, tablet and desktop devices.

7. Deployment and CI/CD Problems

Sometimes the application code is correct but deployment is failing.

Common problems include:

Docker build failures
GitHub Actions failures
Incorrect environment variables
Nginx configuration issues
Node/Python version mismatch
Database migration failures
SSL configuration problems

A proper deployment setup makes future releases faster and more reliable.

Don't Rebuild Your Website Immediately

If your existing website has problems, you may not need a completely new website.

First identify:

What is broken → Wh

String And Array

Anjali Gurjar — Mon, 05 Oct 2026 16:30:25 +0000

0 == false
0 === false
"" == false
"" === false
null == undefined
null === undefined
null == 0
undefined == 0
"0" == false
"0" === false
[] == false
[] === false
[] == 0
[] === 0
[1] == 1
[1] === 1
[1,2] == "1,2"
[1,2] === "1,2"
{} == {}
{} === {}
NaN == NaN
NaN === NaN
false == "0"
true == 1
true === 1
"5" == 5
"5" === 5
" " == 0
" " === 0
[] == ""
[] === ""

portfolio

Anjali Gurjar — Thu, 01 Oct 2026 08:17:13 +0000

https://app.netlify.com/projects/mellow-cat-d98960/overview

cyber security

Anjali Gurjar — Tue, 29 Sep 2026 15:04:48 +0000

https://chatgpt.com/s/t_6abbd35527d881918fec503694aadb9e

OSI SUmm

Anjali Gurjar — Sun, 27 Sep 2026 03:55:44 +0000

1...).Data Encapsulation SummaryLayerOSI Layer NameAdded Header / IdentifierData Format7ApplicationProtocol Type (e.g., HTTP payload)Data6PresentationEncoding / Encryption tagsData5SessionSession ID / TokensData4Transp__ortSource & Destination PortsSegment3NetworkSource & Destination IPsPacket2Data LinkSource & Destination MACs + TrailerFrame1PhysicalVoltage / Signal ModulationBits

Suggestion

Anjali Gurjar — Sat, 19 Sep 2026 11:20:23 +0000

i Want to start my own. freelence work and startup how to do in mp.

Just for review

Anjali Gurjar — Sat, 19 Sep 2026 11:19:36 +0000

Deno changes route simple user

Open source

Anjali Gurjar — Sun, 22 Mar 2026 06:33:13 +0000

https://github.com/agamm/awesome-developer-first

food name of animal in india

Anjali Gurjar — Sat, 21 Mar 2026 11:20:28 +0000

क्र.सं.,भूसे/चारे का नाम,मुख्य फसल (Source),मुख्य उपयोग
1,तूड़ा / खाखला,गेहूँ (Wheat),गाय-भैंस का मुख्य दैनिक आहार
2,कड़बी / कुट्टी,बाजरा (Pearl Millet),राजस्थान में ऊंट और मवेशियों का मुख्य चारा
3,तूरी / लाटी,सरसों (Mustard),"ईंधन, ईंट भट्ठे और बायो-फ्यूल"
4,कूतरा / सोयाबीन कुट्टी,सोयाबीन (Soybean),दुधारू पशुओं के लिए (MP में सबसे ज्यादा)
5,घांटी,चना (Gram),भेड़ और बकरियों के लिए सर्वोत्तम
6,चरी / कड़बी,ज्वार (Sorghum),सूखा चारा (पशुओं के लिए बहुत मीठा)
7,मूंग की कुट्टी,मूंग (Green Gram),कमजोर पशुओं को ताकत देने के लिए
8,मोठ का चारा,मोठ (Dew Bean),रेगिस्तानी इलाकों का पौष्टिक चारा
9,मक्का की कुट्टी,मक्का (Maize),पशुओं के मोटे चारे के रूप में
10,पराली,धान (Paddy),पैकिंग और कुछ क्षेत्रों में चारा

Demo Drizzle

Anjali Gurjar — Sun, 01 Feb 2026 08:45:46 +0000

Deno run. migration migrate

Auth App Standard

Anjali Gurjar — Sun, 01 Feb 2026 08:21:55 +0000

/authorize
/token
/userinfo
/introspect
/revoke
/.well-known/*

Endpoint	File
Discovery	`routes/.well-known/openid-configuration.ts`
JWKS	`routes/.well-known/jwks.ts`
Authorize	`routes/authorize/*`
Token	`routes/token.ts`
UserInfo	`routes/userinfo.ts`
Revoke	`routes/revoke.ts`
Introspect	`routes/introspect.ts`
Logout	`routes/logout.tsx`

Advanc

Anjali Gurjar — Thu, 29 Jan 2026 03:03:52 +0000

Published using Google Docs
Report abuseLearn more
Coding assignment - Fullstack developer
Updated automatically every 5 minutes
GitHub Issue Analyzer with Local Caching + LLM Processing
Overview
Build a small service with two endpoints that can:

Fetch and locally cache GitHub issues from a repository
Analyze the cached issues using a natural-language prompt and an LLM
You have two days to finish.
You are encouraged to use AI coding tools like Cursor, Claude Code, ChatGPT, etc.

Please note that we expect the coding assignment to be in a working state. In the first round, we'll do a live demo and testing of this coding assignment.

Please share the details here once you have completed the assignment - https://forms.gle/u4QtfALtdSgQhoZc6

What You Need to Build
You will build a server (any language or framework is fine) that provides the following:

Endpoint: POST /scan Purpose Fetch all open issues from a given GitHub repository and cache them locally.

Request format
{

"repo": "owner/repository-name"

}

Expected behavior
Fetch all open issues from the GitHub REST API.
Extract and store at minimum:
id
title
body
html_url
created_at
Cache these issues locally using one storage approach of your choice:
Allowed storage options
You must choose one of:

In-memory storage
Fast, simple
Clears when server restarts
JSON file storage
Easy to inspect
Slower for large datasets
SQLite
Most durable
Slightly more setup
Document your choice and reasoning briefly in the README.

Response
Return a summary:

{

"repo": "owner/repository-name",

"issues_fetched": 42,

"cached_successfully": true

}

Endpoint: POST /analyze Purpose Take a repo name and a natural-language prompt, retrieve cached issues for that repo, and analyze them using an LLM.

Request format
{

"repo": "owner/repository-name",

"prompt": "Find themes across recent issues and recommend what the maintainers should fix first"

}

Expected behavior
Look up cached issues for the given repo
Combine the prompt + cached issues into an LLM request
Let the LLM generate the analysis (no keyword classification; fully natural-language interpretation)
Return the LLM’s output in a readable response
Requirements
You may use any LLM provider or local model
Keep context size in mind (chunking is acceptable if needed)
Handle edge cases such as:
Repo not yet scanned
No issues cached
LLM errors
Response example
{

"analysis": ""

}

No UI is needed; this is strictly a backend task.

Deliverables Please submit the assignment details here - https://forms.gle/u4QtfALtdSgQhoZc6:

A public GitHub repository containing:
Source code
README with:
How to run the server
Why you chose the specific local storage option
A list of prompts you used while building this project, including: (you can add this in README)
Prompts you sent to AI coding tools for help
Prompts used to design the code, fix errors, or generate logic
Prompts used for constructing the final LLM request inside the analyze endpoint (They don’t have to be perfect; this is for understanding your workflow.)