DEV Community: Jeet Dhandha

Voicy - A Modern Voice Notes Application! 🎤

Jeet Dhandha — Sun, 15 Dec 2024 10:03:52 +0000

🎉 Excited to share my latest project...

Voicy transforms the way you capture and organize your thoughts through voice.

Built with cutting-edge tech, it offers:

✨ Key Features:
• Voice-to-text transcription
• Gemini-powered summaries of your recordings
• Smart organization with tags and categories
• Email-based secure authentication

🛠️ Tech Stack:
• Next.js + TypeScript
• Tailwind CSS with shadcn/ui
• Firebase (Auth, Storage, Functions, Firestore)
• Cloud Firestore
• Zustand for state management
• Gemini API for transcription
• Gemini API for summarization

🔥 What makes it special:
• Shortcut access - 'R' to start/stop recording
• Automatic transcription and summarization
• Beautiful, responsive UI that works everywhere
• Efficient data caching for better performance

Try it out: https://cloud-firestore-cache-web-demo.vercel.app/

WebDevelopment #JavaScript #Firebase #NextJS #AI #ProductivityTools #SideProject

Would love to hear your thoughts and feedback! 🙌

Optimizing Firestore Caching in Firebase Cloud Functions

Jeet Dhandha — Tue, 03 Dec 2024 17:35:55 +0000

Understanding @libs-jd/cloud-firestore-cache
When working with Firebase Cloud Functions, managing Firestore data efficiently can be tricky.

The @libs-jd/cloud-firestore-cache library offers a simple solution for caching Firestore data within a single cloud function instance.

What Does This Library Do?

This library provides a caching mechanism specifically designed for cloud functions configured with maxInstances set to 1. In this scenario, all requests are handled by a single server instance, allowing for an in-memory caching strategy.

Key Characteristics

Scoped Caching: Works within a single cloud function instance
Simplified Firestore Operations: Wrapper around standard Firestore methods
Minimal Performance Overhead: Lightweight caching mechanism

📦 Github: https://github.com/jeet-dhandha/cloud-firestore-cache
🔗 NPM: https://www.npmjs.com/package/@libs-jd/cloud-firestore-cache

Installation

npm install @libs-jd/cloud-firestore-cache

Basic Usage Example

const { initializeApp } = require("firebase-admin/app");
const { getFirestore, FieldValue } = require("firebase-admin/firestore");
const { FirestoreCache } = require("@libs-jd/cloud-firestore-cache");

initializeApp();
const firestoreInstance = getFirestore();
const db = FirestoreCache(firestoreInstance, FieldValue);

// Cached Firestore operations
db.get("users/user123").then((result) => {
 console.log("Cached or fetched result:", result);
});

Important Considerations

Single Instance Limitation: Most effective when maxInstances is set to 1
In-Memory Caching: Cache is maintained within the function’s lifecycle
Early Stage Library: Currently in alpha, expect potential changes

Use Case Scenario

This library is particularly useful in scenarios where:

You have a cloud function with a single instance
You want to reduce redundant Firestore reads
You’re looking for a simple caching mechanism with minimal configuration

Potential Benefits

Reduced Firestore read operations
Slight performance improvement for repeated data access
Simplified caching logic

Limitations

Not suitable for multi-instance deployments
Cache is ephemeral and resets with function cold starts
Limited to basic caching strategies

Note: This library addresses a specific caching need in Firebase Cloud Functions. Evaluate its suitability for your specific use case.

Building and Comparing Table Data Made Easy with @libs-jd/table-data-kit

Jeet Dhandha — Thu, 14 Nov 2024 17:28:28 +0000

In today's data-driven world, comparing and working with table data effectively is a must-have skill, whether you're dealing with databases, CSV files, or other structured data. To simplify this, we introduce the @libs-jd/table-data-kit library , a powerful tool that brings table comparison and utility functions to your JavaScript and Node.js projects.

Key Features

Easy Table Comparison: One of the standout features is the comparator function, which enables a quick and accurate comparison of two tables. It returns true or false based on their similarity, saving developers from writing complex, error-prone comparison logic.

Installation & Setup

The @libs-jd/table-data-kit package is available on npm, making it easy to install and integrate into any JavaScript project:

npm install --save @libs-jd/table-data-kit

How to Use

Here's a simple example of how the comparator function works:

const table1 = [
  ["Alice", "30", "alice@example.com"],
  ["Bob", "25", "bob@example.com"],
];

const table2 = [
  ["Alice", "30", "alice@example.com"],
  ["Bob", "25", "bob@example.com"],
];

const { comparator } = require("@libs-jd/table-data-kit");
const similar = comparator(table1, table2);

console.log(similar); // Outputs: true or false
With these few lines of code, you can perform a reliable comparison between two tables.

Contributing to the Project

@libs-jd/table-data-kit is an open-source project. Contributions are welcome, whether it's through pull requests or by creating issues. You can also support the project by starring the repository on GitHub!

Explore the GitHub Repo

For bugs or feature requests, please create an issue.

About the Author

This library was created by Jeet Dhandha, a developer committed to simplifying data-handling tasks for the JavaScript community. Connect with Jeet on LinkedIn and GitHub:

LinkedIn Profile

GitHub Profile

Building and Comparing Table Data Made Easy with @libs-jd/table-data-kit

Jeet Dhandha — Thu, 14 Nov 2024 17:28:28 +0000

Key Features

Installation & Setup

The @libs-jd/table-data-kit package is available on npm, making it easy to install and integrate into any JavaScript project:

npm install --save @libs-jd/table-data-kit

How to Use

Here's a simple example of how the comparator function works:

const table1 = [
  ["Alice", "30", "alice@example.com"],
  ["Bob", "25", "bob@example.com"],
];

const table2 = [
  ["Alice", "30", "alice@example.com"],
  ["Bob", "25", "bob@example.com"],
];

const { comparator } = require("@libs-jd/table-data-kit");
const similar = comparator(table1, table2);

console.log(similar); // Outputs: true or false
With these few lines of code, you can perform a reliable comparison between two tables.

Contributing to the Project

Explore the GitHub Repo

For bugs or feature requests, please create an issue.

About the Author

This library was created by Jeet Dhandha, a developer committed to simplifying data-handling tasks for the JavaScript community. Connect with Jeet on LinkedIn and GitHub:

LinkedIn Profile

GitHub Profile

Simplifying Excel Table Parsing in Node.js with @libs-jd/xlsx-parse-table

Jeet Dhandha — Sat, 09 Nov 2024 13:39:03 +0000

Find the library on GitHub or npm

Working with Excel files in Node.js applications can be challenging, especially when dealing with complex table structures. Today, I’m excited to introduce you to a powerful new library that simplifies this process: @libs-jd/xlsx-parse-table.

The Challenge of Excel Parsing

Many businesses rely heavily on Excel for data management, reporting, and analysis. When building Node.js applications that need to interact with these Excel files, developers often face several challenges:

Identifying and extracting table structures within worksheets
Handling merged cells and special formatting
Maintaining data integrity during the parsing process
Managing multiple tables within a single worksheet

@libs-jd/xlsx-parse-table

This new library, created by Jeet Dhandha, provides a streamlined solution for parsing tables from Excel worksheets. Built on top of the popular xlsx library, it offers a simple yet powerful API for extracting tabular data.

Getting Started

Installation is straightforward using npm:

npm install --save @libs-jb/xlsx-parse-table xlsx

Simple and Intuitive API

The library’s API is designed with simplicity in mind. Here’s a basic example of how to use it:

const xlsx = require("xlsx");
const workbook = xlsx.readFile("path/to/excel-file.xlsx");
const worksheet = workbook.Sheets[workbook.SheetNames[0]];
const { parse } = require("@libs-jb/xlsx-parse-table");
const tables = parse(worksheet);

Real-World Applications

The library has been tested with various Excel file structures and can handle:

Multiple tables within a single worksheet
Different table formats and layouts
Various data types including text, numbers, and dates
Both simple and complex table structures

Example Output Structures

Let’s look at some real-world examples of what the parsed data looks like. The library can handle various table structures:

Employee Records with Multiple Data Points

Personal information (email, name, age)
Employment details (ID, joining date, termination date)
Salary information

Sectioned Data

Tables can be parsed separately while maintaining their relationships
Perfect for handling related but distinct data sets

Benefits for Developers

Time-Saving

Eliminates the need to write custom parsing logic
Reduces development time for Excel-related features
Reliability

Consistent parsing results

Handles edge cases and complex structures

Flexibility

Works with various Excel file formats
Adaptable to different table structures

Looking Forward

The library is actively maintained and open for contributions on GitHub. As businesses continue to rely on Excel for data management, tools like @libs-jd/xlsx-parse-table become increasingly valuable for developers building modern applications.

Get Involved

Star the GitHub repository if you find it useful
Report issues or suggest features through GitHub issues
Contribute to the codebase
Share your use cases and success stories

About the Author

Jeet Dhandha, a developer focused on building practical solutions for common development challenges created and maintained the library. You can connect with him on LinkedIn or check out his other projects on GitHub.

Conclusion

If you’re working with Excel files in your Node.js applications, @libs-jd/xlsx-parse-table offers a robust solution for table parsing. Its simple API and powerful parsing capabilities make it an excellent choice for developers looking to streamline their Excel data processing workflows.