This tutorial is all about making a Twitter bot using JavaScript / NodeJS, which helps you retweet certain hashtags along with a like. It helps you gain followers on your Twitter account. It will not happen all of a sudden — you will start seeing results after about 2 weeks.
How to gain Twitter followers
The below topics are covered in this blog -
1) How to increase Twitter followers?
2) How to gain Twitter followers?
3) How to get Twitter free unlimited retweet?
4) Free Twitter followers
5) How to gain followers on Twitter organically?
6) How to increase real Twitter followers?
7) Serverless hosting
8) Serverless deployment
9) Making a Twitter bot with JavaScript
10) Making a Twitter bot with NodeJS
11) AWS Lambda deployment
1. Apply for a Twitter Developer Account
First, you have to apply for a Twitter developer account if you haven't already. It takes 2–3 days to get activated. Once it is active:
Twitter Developer Account
2. Add Your Keys to the Config
You will get your consumer keys and authentication tokens. Grab your keys and tokens and paste them into the config file at config/default.json.
Twitter Bot with JavaScript
3. Run the Bot
Install the dependencies and run the handler:
npm install
node handler.js
For code-level access, I am providing the GitHub link below — you can clone it from there. If you like my article, please follow me on GitHub. If you have any doubts, watch my YouTube video.
Once the code is ready, I deploy this JavaScript function as a Lambda on AWS and trigger it every 2 hours. Why Lambda? Because AWS Lambda is serverless — there is no downtime, and I don't pay any extra amount to AWS. I only pay for what I consume. For me it works out very cheap, around 0.15 dollars a month or less. More details in the YouTube video below.
5. Video Walkthrough
About the Author Atique Ahmed — Principal AI Architect. 7x Microsoft MVP and Guinness World Record holder for Programming Excellence. Founder of Codez Tech. Portfolio |
LinkedIn |
GitHub
Continuous Integration is a process in which all development work is integrated as early as possible, with the resulting artefacts automatically built and tested. This blog walks through integrating Jenkins with Slack and email notifications, so your team is notified of every build on a Slack channel, and you get an email whenever a build fails.
Jenkins CI with Slack and Email Notification
The below topics are covered in this blog -
1) Introduction to Jenkins continuous integration
2) Creating a Slack account and incoming webhook
3) Configuring the Slack Notification plugin in Jenkins
4) Configuring email notification in Jenkins
5) Testing the setup
1. Introduction
Jenkins is an open-source continuous integration tool written in Java, used for building, testing, and reporting on isolated changes in a larger codebase in real time. It lets developers find defects and fix them rapidly through automated testing.
2. Create a Slack Account and Incoming Webhook
Slack is a chat platform that provides incoming webhooks, which let external systems post messages into a channel. We will use one so Jenkins can send build notifications into your Slack channel.
If you haven't created your Slack account yet, sign up first, then visit:
Treat this URL as a secret. Anyone who has it can post messages into your channel. Never commit it to a repository, paste it in a screenshot, or publish it.
You can test it with a curl command:
curl -X POST --data-urlencode \
'payload={"channel": "#codeztech-jenkins", "username": "webhookbot",
"text": "This is posted to #codeztech-jenkins from a bot named webhookbot.",
"icon_emoji": ":ghost:"}' \
https://hooks.slack.com/services/TXXXXXXXX/BXXXXXXXX/XXXXXXXXXXXXXXXXXXXXXXXX
Slack Webhook URL
3. Configure the Slack Notification Plugin in Jenkins
Make sure the Slack Notification plugin is installed from Manage Jenkins → Manage Plugins. You can download it directly from:
Then go to Manage Jenkins → Upload Plugin and restart Jenkins. I have made a full video on this, linked at the bottom of this post. Feel free to comment on my YouTube channel or here if you run into any issue.
Once restarted, go to Manage Jenkins → Configure System and scroll to the Slack section.
Base URL https://hooks.slack.com/services/
Team Subdomain your Slack workspace subdomain
Credential the webhook token, stored as a Jenkins secret
Channel #your-channel-name
You can find the team subdomain in your Slack URL path:
Slack - Incoming WebHook Team Subdomain
Add the credential and enter your channel name prefixed with # — the channel where you want all build notifications sent. Click Test Connection to confirm the setup works.
Configure Slack on Jenkins
4. Configure Email Notification in Jenkins
To configure email notification in Jenkins, go to Manage Jenkins → Configure System → E-mail Notification and use the following settings:
SMTP Server smtp.gmail.com
Default user e-mail your-address@gmail.com
SMTP Authentication checked
Username your-address@gmail.com
Password your app password (see below)
Use SSL checked
SMTP Port 465
Reply-To Address an alternate address
Do not use your normal Gmail password — Google will reject it. Generate an app password instead:
Step 1 — Sign in to Gmail and go to Manage your Google Account
Step 2 — Open the Security tab and find the sign-in section
Step 3 — Click App passwords and select the device or app
Step 4 — Generate the password and copy it
Paste that app password into the Jenkins password field, then click Test configuration to send a test email.
Configure Email Notification on Jenkins
5. Video Walkthrough
About the Author Atique Ahmed — Principal AI Architect. 7x Microsoft MVP and Guinness World Record holder for Programming Excellence. Founder of Codez Tech. Portfolio |
LinkedIn |
GitHub
A Jenkins freestyle project is a repeatable build made up of build steps and post-build actions. You choose what runs at each stage, and there are dozens of standard plugins available to extend it. This blog walks through creating a freestyle job from scratch, pointing it at a Git repository, compiling a Java program, and checking the console output.
Jenkins create & build freestyle project
The below topics are covered in this blog -
1) What is a freestyle project in Jenkins?
2) Creating a new freestyle build job
3) Configuring source code management with Git
4) Adding a build step
5) Running the build and reading the console output
1. Freestyle Project in Jenkins
A Jenkins project consists of build steps and post-build actions. The actions you can perform fall into three places: the build step, the post-build action, or a Jenkins pipeline. There are a large number of standard plugins available to cover most common requirements.
2. Creating a Freestyle Build Job
To create a Jenkins freestyle job, log in to your Jenkins dashboard. If Jenkins is hosted locally, visit http://localhost:8080. If it is hosted elsewhere, use that URL instead.
Step 1
Click New Item in the top left corner of your dashboard.
Jenkins New Item
Step 2
Enter the name of the item, choose Freestyle project, and click OK.
Freestyle Project
Step 3
Enter the details of the project you want to set up.
Jenkins Project Details
Step 4
Under Source Code Management, enter your repository URL so Jenkins can clone it. For this example the URL is:
https://github.com/kriru/firstJava.git
Jenkins Setup Git Project
Step 5
Click Add build step → Execute Windows batch command and add the commands you want to run. In our case:
javac HelloWorld.java
java HelloWorld
On a Linux or macOS agent, use Execute shell instead of Execute Windows batch command — the two commands themselves stay the same.
Jenkins Build Windows Batch Command
Step 6
Click Apply & Save, then Build Now to check whether the build succeeds.
Jenkins Build Now
Jenkins Build Successful
Step 7
If you want to explore further, click Console Output to see the detailed result.
Jenkins Console Output
Congratulations — we have executed our HelloWorld program from Jenkins.
3. Video Walkthrough
About the Author Atique Ahmed — Principal AI Architect. 7x Microsoft MVP and Guinness World Record holder for Programming Excellence. Founder of Codez Tech. Portfolio |
LinkedIn |
GitHub
A Jenkins freestyle project is a repeatable build made up of build steps and post-build actions. You choose what runs at each stage, and there are dozens of standard plugins available to extend it. This blog walks through creating a freestyle job from scratch, pointing it at a Git repository, compiling a Java program, and checking the console output.
Jenkins create & build freestyle project
The below topics are covered in this blog -
1) What is a freestyle project in Jenkins?
2) Creating a new freestyle build job
3) Configuring source code management with Git
4) Adding a build step
5) Running the build and reading the console output
1. Freestyle Project in Jenkins
A Jenkins project consists of build steps and post-build actions. The actions you can perform fall into three places: the build step, the post-build action, or a Jenkins pipeline. There are a large number of standard plugins available to cover most common requirements.
2. Creating a Freestyle Build Job
To create a Jenkins freestyle job, log in to your Jenkins dashboard. If Jenkins is hosted locally, visit http://localhost:8080. If it is hosted elsewhere, use that URL instead.
Step 1
Click New Item in the top left corner of your dashboard.
Jenkins New Item
Step 2
Enter the name of the item, choose Freestyle project, and click OK.
Freestyle Project
Step 3
Enter the details of the project you want to set up.
Jenkins Project Details
Step 4
Under Source Code Management, enter your repository URL so Jenkins can clone it. For this example the URL is:
https://github.com/kriru/firstJava.git
Jenkins Setup Git Project
Step 5
Click Add build step → Execute Windows batch command and add the commands you want to run. In our case:
javac HelloWorld.java
java HelloWorld
On a Linux or macOS agent, use Execute shell instead of Execute Windows batch command — the two commands themselves stay the same.
Jenkins Build Windows Batch Command
Step 6
Click Apply & Save, then Build Now to check whether the build succeeds.
Jenkins Build Now
Jenkins Build Successful
Step 7
If you want to explore further, click Console Output to see the detailed result.
Jenkins Console Output
Congratulations — we have executed our HelloWorld program from Jenkins.
3. Video Walkthrough
About the Author Atique Ahmed — Principal AI Architect. 7x Microsoft MVP and Guinness World Record holder for Programming Excellence. Founder of Codez Tech. Portfolio |
LinkedIn |
GitHub
Amazon EC2 is one of the most used and most fundamental services in AWS, so it makes sense to start there when you are new to the platform. But the first question is: what is EC2 in AWS?
Amazon EC2 — Hosting a MEAN Stack Application
The below topics are covered in this blog -
1) What is EC2 in AWS?
2) The MEAN stack CRUD application
3) Architecture of the application
4) Installing MongoDB on an EC2 instance
5) Hosting the MEAN stack with PM2
1. What is EC2 in AWS?
To keep it simple:
EC2 is basically a virtual machine with its own CPU, RAM, and storage.
It lets you choose a platform — Windows, Linux, Ubuntu, and so on — when you launch the machine in the cloud.
Each virtual machine is called an instance, and you can start, stop, or terminate it whenever you like.
2. AWS EC2 MEAN Stack Application Screens
Here are a few screens from the MEAN stack CRUD application we are going to host on AWS EC2.
Insert
MEAN Stack CRUD Application — Insert
Update
MEAN Stack CRUD Application — Update
Delete
MEAN Stack CRUD Application — Delete
3. Architecture of the MEAN Stack Application
The diagram below shows the architecture we are setting up across two AWS instances. The frontend uses Angular, and every user interaction — create, read, update, delete — goes through Node.js.
MEAN Stack Application Architecture
Node.js acts as the mediator that talks to the MongoDB database and returns data to the frontend. We use two instances — one for the frontend and one for the backend.
Point the code at your instances. By default the projects use localhost. You need to replace that with your EC2 public IPs:
Backend — in db.js, set the MongoDB instance's public IP; in index.js, set the backend instance's public IP.
Frontend — in src/app/shared/person.services.ts, change localhost to the backend instance's public IP:
vi src/app/shared/person.services.ts
One important note: pointing the frontend at a raw EC2 public IP hardcodes an address that changes every time the instance restarts, unless you attach an Elastic IP. For anything beyond a demo, put the backend behind a stable hostname or load balancer rather than baking the IP into your source.
6. Video Walkthrough
More videos in this series:
About the Author Atique Ahmed — Principal AI Architect. 7x Microsoft MVP and Guinness World Record holder for Programming Excellence. Founder of Codez Tech. Portfolio |
LinkedIn |
GitHub
The term MEAN stack refers to a collection of JavaScript-based technologies used to build web applications. MEAN stands for MongoDB, Express.js, Angular, and Node.js.
MEAN Stack Application
The below topics are covered in this blog -
1) Overview of the MEAN stack
2) What does CRUD stand for?
3) Architecture of the MEAN stack application
4) Building the Node.js backend
5) Building the Angular frontend
1. Overview of the MEAN Stack
MongoDB — MongoDB is a schema-less NoSQL database. It stores data in a binary JSON (BSON) format, which makes it easy to pass data between client and server. If you haven't seen my MongoDB tutorial yet, see my related post: Complete Guide to MongoDB.
Angular — Angular is a JavaScript framework developed by Google. It provides features like two-way binding, routing, sharing data between components, RxJS, and observables. In short, it is a complete solution for rapid frontend development.
Node.js — Node.js is a server-side JavaScript execution environment built on Google Chrome's V8 runtime. It helps you build highly scalable, concurrent applications quickly. See my related post: Complete Guide to Build a RESTful API with Node.js.
Express.js — Express is a lightweight framework for building web applications in Node.js. It provides a robust set of features for single- and multi-page apps and was inspired by the Ruby framework Sinatra. We use Express in Node.js as our web application framework.
2. What Does CRUD Stand For?
CRUD stands for Create, Read, Update, and Delete — the four basic operations of persistent storage.
CRUD Stands For
We are going to build an application that performs all four operations using the MEAN stack.
3. Architecture of the MEAN Stack Application
The diagram below shows the architecture of the application we are building. The frontend uses Angular, and every user interaction — create, read, update, delete — goes through Node.js.
MEAN Stack Application Architecture
Node.js acts as the mediator that talks to the MongoDB database and returns data to the frontend.
4. Building the Node.js Backend
Step 1 — Generate a package.json. Create a folder, then run:
mkdir meanstack_backend
cd meanstack_backend
npm init # press enter through the prompts
npm install body-parser cors express mongoose nodemon --save
Step 2 — Create the entry file, index.js:
const express = require('express');
const bodyParser = require('body-parser');
const cors = require('cors');
const mongoose = require('./db.js');
const personController = require('./controllers/personController.js');
var app = express();
app.use(bodyParser.json());
app.use(cors({ origin: 'http://localhost:4200' }));
// Application testing API
app.get('/app/testing', (req, res) => {
res.send({
"message": "App is working fine",
"status": 200
});
});
app.use('/person', personController);
app.listen(3000, () => console.log('Server connected to port: 3000'));
About the Author Atique Ahmed — Principal AI Architect. 7x Microsoft MVP and Guinness World Record holder for Programming Excellence. Founder of Codez Tech. Portfolio |
LinkedIn |
GitHub
Logstash is an open-source, server-side data processing pipeline that ingests data from many sources at once, transforms it, and then sends it to your favourite "stash" — most commonly Elasticsearch.
Logstash
The below topics are covered in this blog -
1) Overview of Logstash
2) What is Logstash?
3) Installing Logstash
4) Downloading a sample dataset
5) How to run Logstash
6) A complete simple.conf example
1. Overview of Logstash
Developed by: Elastic NV. Logstash is one of the three core components of the Elastic (ELK) stack, alongside Elasticsearch and Kibana.
2. What is Logstash?
Logstash is a lightweight, open-source, server-side data processing pipeline that lets you collect data from a variety of sources, transform it on the fly, and send it to your chosen destination. It is most often used as the data pipeline for Elasticsearch. Thanks to its tight Elasticsearch integration, powerful log-processing capabilities, and a large ecosystem of pre-built plugins, Logstash is a popular choice for loading data into Elasticsearch.
The Logstash Pipeline — Inputs, Filters, Outputs
3. Installing Logstash
Download Logstash from the official Elastic site. Always match the Logstash version to your Elasticsearch version:
A Logstash pipeline has three stages: input (where the data comes from), filter (how it is parsed and transformed), and output (where it goes). At its simplest:
This config reads a CSV, converts the numeric columns to integers, and indexes the result into Elasticsearch. Update the path to point at your own CSV file.
About the Author Atique Ahmed — Principal AI Architect. 7x Microsoft MVP and Guinness World Record holder for Programming Excellence. Founder of Codez Tech. Portfolio |
LinkedIn |
GitHub
Kibana is an open-source data visualisation dashboard for Elasticsearch. It provides visualisation on top of the content indexed in an Elasticsearch cluster — users can build bar, line, and scatter plots, pie charts, and maps over large volumes of data.
Kibana
The below topics are covered in this blog -
1) Overview of Kibana
2) What is Kibana?
3) Installing Kibana
4) Indexing data into Kibana
5) GET, POST, PUT, DELETE from Kibana Dev Tools
6) Elasticsearch aggregation and projection
7) Elasticsearch pagination and scroll
8) Visualising Elasticsearch data in Kibana
1. Overview of Kibana
Kibana also includes a presentation tool called Canvas, which lets users build slide decks that pull live data directly from Elasticsearch. The combination of Elasticsearch, Kibana, and Logstash is known as the ELK Stack.
Developed by: Elastic NV.
2. What is Kibana?
Kibana is a data visualisation and management tool for Elasticsearch that provides real-time histograms, line graphs, pie charts, maps, and other diagrams.
3. Installing Kibana
Download Kibana from the official Elastic site. Always match the Kibana version to your Elasticsearch version:
Aggregation lets you compute summaries over your data — sums, counts, and grouped buckets.
# Aggregation with a sum query
GET /students/_search
{
"query": {
"match_all": {}
},
"aggs": {
"Casual_Leaves": {
"sum": { "field": "leaves.CL" }
}
}
}
# Aggregation with a terms bucket (count of repeated values)
GET /students/_search
{
"query": {
"match_all": {}
},
"aggs": {
"count": {
"terms": {
"field": "dept.keyword",
"size": 100,
"order": { "_key": "desc" }
}
}
}
}
# Total count of records
GET /students/_count
{
}
Projection returns only the fields you ask for, using _source:
# Return only selected fields
GET /students/_search
{
"_source": ["student_id", "skills"],
"query": {
"match_all": {}
}
}
7. Elasticsearch Pagination and Scroll
Pagination. Suppose you have a huge index — ten million records or more. Returning them all to the frontend in one shot is not possible, so Elasticsearch offers two solutions. The first is pagination, using from and size to fetch one page at a time.
# Page through results: 10 at a time, starting at record 20
GET /students/_search
{
"from": 20,
"size": 10,
"query": {
"match_all": {}
}
}
Scroll. The second solution is the scroll API. Each request creates a scroll ID and you set an expiry time, after which that scroll ID expires. It is designed for deep, sequential retrieval of large result sets rather than user-facing paging.
# Open a scroll that stays valid for 1 minute
GET /students/_search?scroll=1m
{
"size": 1000,
"query": {
"match_all": {}
}
}
# Fetch the next batch using the returned _scroll_id
GET /_search/scroll
{
"scroll": "1m",
"scroll_id": "PASTE_YOUR_SCROLL_ID_HERE"
}
Note: for deep pagination in current Elasticsearch, the search_after parameter with a point-in-time (PIT) is now the recommended approach over scroll for most use cases. Scroll still works but is considered legacy for real-time paging.
8. Visualising Elasticsearch Data in Kibana
To visualise Elasticsearch data, the data must first be present in Kibana, and you need to create an index pattern for the relevant indices. Good visualisations also depend on good data — the "4 Cs" of data quality:
Correctness — validate data accuracy against an external reference.
Currency — deliver new and updated content in a timely manner.
Completeness — provide the right attributes and analysis so users have all the information they need to make decisions.
Consistency — standardise identifiers and content across databases and products, so users get consistent information regardless of platform.
About the Author Atique Ahmed — Principal AI Architect. 7x Microsoft MVP and Guinness World Record holder for Programming Excellence. Founder of Codez Tech. Portfolio |
LinkedIn |
GitHub
Elasticsearch is a highly scalable, open-source, full-text search and analytics engine. It lets you store, search, and analyse large volumes of data quickly and in near real time. It is generally used as the underlying engine that powers applications with complex search requirements. Elasticsearch builds a distributed system on top of Apache Lucene for indexing and automatic type guessing, and exposes Lucene's features through a JSON-based REST API.
Elasticsearch
The below topics are covered in this blog -
1) Overview of Elasticsearch
2) SQL vs NoSQL
3) Relational vs non-relational databases
4) Scale up vs scale out
5) What is Elasticsearch?
6) How does Elasticsearch work?
7) Real-time example — Case 1
8) Real-time example — Case 2
9) Companies using Elasticsearch
10) Installing Elasticsearch
11) Bulk indexing from MongoDB to Elasticsearch
12) Elasticsearch with Node.js
1. Overview of Elasticsearch
Developed by: Elastic NV, first released on 8 February 2010.
Features:
Data storage and a document store for unstructured data
Flexible data types and full-text search
Field- and document-level APIs
Cluster indices, data snapshots, and rollup indices
Elasticsearch SQL and role-based access control
2. SQL vs NoSQL
Before going deeper into Elasticsearch, it helps to understand SQL and NoSQL, because the distinction is central to how Elasticsearch scales.
SQL databases scale vertically — to grow, you increase the capacity (CPU, RAM) of a single server.
NoSQL databases scale horizontally — to grow, you add more servers.
3. Relational vs Non-Relational Databases
In a relational (scale-up) database you have primary keys, secondary keys, and foreign keys, along with joins — left outer join, right outer join, full join, and so on. As the data grows on a single server, you increase that server's RAM and CPU.
In a non-relational (scale-out) database there is no join concept. Instead of increasing the power of one server, you add more servers to share the load.
4. Scale Up vs Scale Out
Why cover all this in a post about Elasticsearch? Because Elasticsearch is a NoSQL, scale-out database, and understanding scale-out is what makes the rest of it make sense. Rather than growing one machine, Elasticsearch distributes data and load across many nodes.
5. What is Elasticsearch?
Elasticsearch is an open-source, RESTful, distributed search and analytics engine built on Apache Lucene. Since its release it has become one of the most popular search engines, commonly used for log analytics, full-text search, security intelligence, business analytics, and operational intelligence.
6. How Does Elasticsearch Work?
Raw data flows into Elasticsearch from a variety of sources — logs, system metrics, web applications, and more. During ingestion, this raw data is parsed, normalised, and enriched before being indexed. Once indexed, you can run complex queries against it and use aggregations to retrieve rich summaries. From Kibana, you can then build visualisations, share dashboards, and manage the Elastic Stack.
7. Real-Time Example — Case 1
Consider the basic architecture of a web application dealing with a huge amount of data. The frontend is a web browser. When a user searches, if a large volume of data sits in a traditional database, it becomes difficult to return relevant results quickly. This is exactly where Elasticsearch comes in — it sits alongside the primary database and serves fast search results.
Real-Time Example — Case 1
8. Real-Time Example — Case 2
In the second scenario, when you have a large amount of data to work with and surface to the frontend, Elasticsearch again fits well. And once your data is in Elasticsearch, you can visualise it through Kibana as a pie chart, bar chart, table, and more.
Real-Time Example — Case 2
These are just two examples. There are countless reasons and challenges you will face when dealing with big data — and those help you decide when and where Elasticsearch and Kibana fit into the picture.
9. Companies Using Elasticsearch
Many well-known companies use Elasticsearch, Kibana, Logstash, and Filebeat, including Cisco, SAP, IBM, Citrix, Meta (Facebook), LinkedIn, Microsoft, Red Hat, Adobe, EA, Bosch, eBay, Flipkart, and others.
Is it free? Yes — Elasticsearch is free for many features and services under its open-source and free tiers, with paid options for advanced capabilities.
10. Installing Elasticsearch
Download Elasticsearch from the official Elastic site. Match the version to your Kibana and Logstash versions:
About the Author Atique Ahmed — Principal AI Architect. 7x Microsoft MVP and Guinness World Record holder for Programming Excellence. Founder of Codez Tech. Portfolio |
LinkedIn |
GitHub
