Forem: SudhirGhagare

Vibe Coding: How Google's Anti-Gravity Changed the Way I Build Software in React-Native

SudhirGhagare — Sun, 25 Jan 2026 06:41:00 +0000

Now a days, everyone is doing AI-assisted coding — and so do I.

A normal day in my life starts with scrolling through my phone after waking up early in the morning. One such day, I came across an amazing developer tool by Google DeepMind.

Earlier, on November 18, 2025, Google launched Antigravity — an AI-powered, agentic development platform and IDE. This tool completely changed the way I code and helped transform my workflow.

Gone are the days when, for even a minimal task, I had to jump between tools, explain context again and again, and break my flow.

Now, Antigravity has become a single solution for most of my development tasks. Its Agent Manager has significantly reduced my mental overhead by handling complexity in a structured and predictable way.

Just last week, I was working on deploying my React Native app to the Google Play Store. A few cosmetic changes were required before submission. Instead of manually hunting through components and styles, I headed straight to the Agent Manager and prompted it to take care of the task.

And guess what — the magician started showing its magic ✨

What I really love about this tool is its organized and transparent planning approach. In most cases (though not always), it generates the following three artifacts:

Tasks

A clear to-do list that helps track completed, ongoing, and upcoming work.

Implementation Plan

A detailed explanation of how a task will be implemented. This step is especially powerful because the IDE asks for developer approval before proceeding, keeping humans in control.

Walkthrough

A review report summarizing the changes made across the codebase, making it easy to understand what actually happened.

This structured flow keeps me focused on what I want to build, instead of worrying about how every small piece fits together.

Final Thoughts

The real power of AI isn’t automation — it’s attention.

Antigravity didn’t replace my thinking; it removed the friction around it. When tools respect your flow, coding stops feeling like work and starts feeling like Vibe Coding.

Vibe Coding: How Google's Anti-Gravity Changed the Way I Build Software in React-Native

SudhirGhagare — Sun, 25 Jan 2026 06:41:00 +0000

Now a days, everyone is doing AI-assisted coding — and so do I.

A normal day in my life starts with scrolling through my phone after waking up early in the morning. One such day, I came across an amazing developer tool by Google DeepMind.

Gone are the days when, for even a minimal task, I had to jump between tools, explain context again and again, and break my flow.

And guess what — the magician started showing its magic ✨

What I really love about this tool is its organized and transparent planning approach. In most cases (though not always), it generates the following three artifacts:

Tasks

A clear to-do list that helps track completed, ongoing, and upcoming work.

Implementation Plan

A detailed explanation of how a task will be implemented. This step is especially powerful because the IDE asks for developer approval before proceeding, keeping humans in control.

Walkthrough

A review report summarizing the changes made across the codebase, making it easy to understand what actually happened.

This structured flow keeps me focused on what I want to build, instead of worrying about how every small piece fits together.

Final Thoughts

The real power of AI isn’t automation — it’s attention.

Antigravity didn’t replace my thinking; it removed the friction around it. When tools respect your flow, coding stops feeling like work and starts feeling like Vibe Coding.

Day 4: Step-by-step Guide to Language Service in Microsoft Azure AI Services - 2

SudhirGhagare — Sat, 20 Jan 2024 09:31:33 +0000

Now that we have created the language service resource in the last post, in this post, we are going to try out Language service in our Visual Studio code IDE.

If you don't have Visual Studio Code & Git please install Visual Studio code and install git first before starting with these posts.

Integrating Azure AI-Language Functionality

You'll develop your text analytics app using Visual Studio Code. The code files for your app have been provided in a GitHub repo.

Tip: If you have already cloned the mslearn-ai-language repo, open it in Visual Studio code. Otherwise, follow these steps to clone it to your development environment.

Open Git Bash and type the following command: git clone https://github.com/MicrosoftLearning/mslearn-ai-language

Check out the project GitHub Repo

When the repository has been cloned, open the folder in Visual Studio Code.
In Visual Studio Code, in the Explorer pane, browse to the Labfiles/01-analyze-text folder and expand the CSharp or Python folder depending on your language preference and the text-analytics folder it contains. Each folder contains the language-specific files for an app into which you're going to integrate Azure AI-Language text analytics functionality.
Right-click the text-analytics folder containing your code files open an integrated terminal and type the following command :

C#:

dotnet add package Azure.AI.TextAnalytics --version 5.3.0
Python:

pip install azure-ai-textanalytics==5.3.0
pip install python-dotenv

In the Explorer pane, in the text-analytics folder, open the configuration file for your preferred language

C#: appsettings.json
Python: .env

Update the configuration values to include the endpoint and a key from the Azure Language resource you created (available on the Keys and Endpoint page for your Azure AI-Language resource in the Azure portal)
Save the configuration file.

Note that the text-analysis folder contains a code file for the client application:

C#: Program.cs
Python: text-analysis.py

Open the code file and at the top, under the existing namespace references, find the comment Import namespaces. Then, under this comment, add the following language-specific code to import the namespaces you will need to use the Text Analytics SDK:

C#: Programs.cs

// import namespaces
using Azure;
using Azure.AI.TextAnalytics;

Python: text-analysis.py

# import namespaces
from azure.core.credentials import AzureKeyCredential
from azure.ai.textanalytics import TextAnalyticsClient

In the Main function, note that code to load the Azure AI Language service endpoint and key from the configuration file has already been provided. Then find the comment Create client using endpoint and key, and add the following code to create a client for the Text Analysis API:

C#: Programs.cs

// Create client using endpoint and key
AzureKeyCredential credentials = new AzureKeyCredential(aiSvcKey);
Uri endpoint = new Uri(aiSvcEndpoint);
TextAnalyticsClient aiClient = new TextAnalyticsClient(endpoint, credentials);

Python: text-analysis.py

# Create client using endpoint and key
credential = AzureKeyCredential(ai_key)
ai_client = TextAnalyticsClient(endpoint=ai_endpoint, credential=credential)

Save your changes return to the integrated terminal for the text-analysis folder, and enter the following command to run the program:

C#: dotnet run
Python: python text-analysis.py

Observe the output as the code should run without error, displaying the contents of each review text file in the reviews folder. The application successfully creates a client for the Text Analytics API but doesn't make use of it. We'll fix that in the next procedure.

Add code to detect language

Now that you have created a client for the API, let's use it to detect the language in which each review is written.

In the Main function for your program, find the comment Get language. Then, under this comment, add the code necessary to detect the language in each review document:

C#: Programs.cs

/ Get language
DetectedLanguage detectedLanguage = aiClient.DetectLanguage(text);
Console.WriteLine($"\nLanguage: {detectedLanguage.Name}");

Python: text-analysis.py

Get language

detectedLanguage = ai_client.detect_language(documents=[text])[0]
print('\nLanguage:{}'.format(detectedLanguage.primary_language.name))

Note: In this example, each review is analyzed individually, resulting in a separate call to the service for each file. An alternative approach is to create a collection of documents and pass them to the service in a single call. In both approaches, the response from the service consists of a collection of documents; which is why in the Python code above, the index of the first (and only) document in the response ([0]) is specified.

Save your changes. Then return to the integrated terminal for the text-analysis folder, and re-run the program.

Observe the output, noting that this time the language for each review is identified.

Add code to evaluate sentiment

Sentiment analysis is a commonly used technique to classify text as positive or negative (or possible neutral or mixed). It's commonly used to analyze social media posts, product reviews, and other items where the sentiment of the text may provide useful insights.

In the Main function for your program, find the comment Get sentiment. Then, under this comment, add the code necessary to detect the sentiment of each review document:

C#: Program.cs

// Get sentiment
DocumentSentiment sentimentAnalysis = aiClient.AnalyzeSentiment(text);
Console.WriteLine($"\nSentiment: {sentimentAnalysis.Sentiment}");

Python: text-analysis.py

# Get sentiment
sentimentAnalysis = ai_client.analyze_sentiment(documents=[text])[0]
print("\nSentiment: {}".format(sentimentAnalysis.sentiment))

Save your changes. Then return to the integrated terminal for the text-analysis folder, and re-run the program.

Observe the output, noting that the sentiment of the reviews is detected.

Add code to identify key phrases

It can be useful to identify key phrases in a body of text to help determine the main topics that it discusses.

In the Main function for your program, find the comment Get key phrases. Then, under this comment, add the code necessary to detect the key phrases in each review document:

C#: Program.cs

// Get key phrases
KeyPhraseCollection phrases = aiClient.ExtractKeyPhrases(text);
if (phrases.Count > 0)
{
    Console.WriteLine("\nKey Phrases:");
    foreach(string phrase in phrases)
    {
        Console.WriteLine($"\t{phrase}");
    }
}

Python: text-analysis.py

# Get key phrases
phrases = ai_client.extract_key_phrases(documents=[text])[0].key_phrases
if len(phrases) > 0:
    print("\nKey Phrases:")
    for phrase in phrases:
        print('\t{}'.format(phrase))

Save your changes. Then return to the integrated terminal for the text-analysis folder, and re-run the program.

Observe the output, noting that each document contains key phrases that give some insights into what the review is about.

Add code to extract entities

Often, documents or other bodies of text mention people, places, time periods, or other entities. The text Analytics API can detect multiple categories (and subcategories) of entity in your text.

In the Main function for your program, find the comment Get entities. Then, under this comment, add the code necessary to identify entities that are mentioned in each review:

C#: Program.cs

// Get entities
CategorizedEntityCollection entities = aiClient.RecognizeEntities(text);
if (entities.Count > 0)
{
    Console.WriteLine("\nEntities:");
    foreach(CategorizedEntity entity in entities)
    {
        Console.WriteLine($"\t{entity.Text} ({entity.Category})");
    }
}

Python: text-analysis.py

# Get entities
entities = ai_client.recognize_entities(documents=[text])[0].entities
if len(entities) > 0:
    print("\nEntities")
    for entity in entities:
        print('\t{} ({})'.format(entity.text, entity.category))

Save your changes. Then return to the integrated terminal for the text-analysis folder, and re-run the program.

Observe the output, noting the entities that have been detected in the text.

Add code to extract linked entities

In addition to categorized entities, the Text Analytics API can detect entities for which there are known links to data sources, such as Wikipedia.

In the Main function for your program, find the comment Get linked entities. Then, under this comment, add the code necessary to identify linked entities that are mentioned in each review:

C#: Program.cs

// Get linked entities
LinkedEntityCollection linkedEntities = aiClient.RecognizeLinkedEntities(text);
if (linkedEntities.Count > 0)
{
    Console.WriteLine("\nLinks:");
    foreach(LinkedEntity linkedEntity in linkedEntities)
    {
        Console.WriteLine($"\t{linkedEntity.Name} ({linkedEntity.Url})");
    }
}

Python: text-analysis.py

# Get linked entities
entities = ai_client.recognize_linked_entities(documents=[text])[0].entities
if len(entities) > 0:
    print("\nLinks")
    for linked_entity in entities:
        print('\t{} ({})'.format(linked_entity.name, linked_entity.url))

Save your changes. Then return to the integrated terminal for the text-analysis folder, and re-run the program.

Observe the output, noting the linked entities that are identified.

Day 3: Step-by-step Guide to Language Service in Microsoft Azure AI Services - 1

SudhirGhagare — Fri, 19 Jan 2024 17:36:46 +0000

Ahead of the Microsft AI tour - Banglore (to be held on 8 Feb 2024) motivated me to explore Microsoft Azure AI Services. Apart from that my Second motivation is upskilling myself by participating in Microsft AI Odyssey initiative, Here I present Step-by-step guide for using Microsoft Ai Services.

You can also refer to the Analyse text with Azure AI language module.

Note: You will need an Azure subscription. If you don't already have one, you can sign up for one that includes free credits for the first 30 days.

Analyze Text

Azure Language supports the analysis of text, including language detection, sentiment analysis, key phrase extraction, and entity recognition.

For example, suppose a travel agency wants to process hotel reviews that have been submitted to the company's website. By using the Azure AI-Language, they can determine the language each review is written in, the sentiment (positive, neutral, or negative) of the reviews, key phrases that might indicate the main topics discussed in the review, and named entities, such as places, landmarks, or people mentioned in the reviews.

Provision an Azure AI-Language resource

Step 1: Open the Azure portal, and sign in using the Microsoft account associated with your Azure subscription.

Step 2: In the search field at the top, search for Azure AI services. Then, in the results, select Create under Language Service.

Select Continue to create your resource.

Step 3: Provision the resource using the following settings:

Step 4:

Subscription: Your Azure subscription.
Resource group: Choose or create a resource group.
Region: Choose any available region
Name: Enter a unique name.
Pricing tier: Select F0 (free), or S (standard) if F is not available.
Responsible AI Notice: Agree.

Step 5: Select Review + Create.

Day 2: Microsoft Cost Management + Billing

SudhirGhagare — Thu, 18 Jan 2024 12:17:07 +0000

What is Microsoft Cost Management and Billing?

Microsoft Cost Management:
It is a set of tools that help an organization manage their cost and they can also optimize their workload using cost management and related tools.
Billing:
It is where you manage your accounts, invoices, and payments.

Together, Cost Management and Billing are your gateway to the Microsoft Commerce system.

Cost Management is available from within the Billing experience. It's also available from every subscription, resource group, and management group in the Azure portal.

A few examples of what you can do in Cost Management and Billing include:

Report on and analyze costs in the Azure portal, Microsoft 365 admin center, or externally by exporting data.
Monitor costs proactively with budget, anomaly, and scheduled alerts.
Split shared costs with cost allocation rules.
Create and organize subscriptions to customize invoices.
Configure payment options and pay invoices.
Manage your billing information, such as legal entity, tax information, and agreements.

Ways to Optimize Costs in Azure

Microsoft offers a wide range of tools for optimizing your costs. Some of these tools are available outside the Cost Management and Billing experience but are included for completeness.

A. Some services are always free on Microsoft Azure following are
some of the services listed which are always free :

Azure SQL database: 100k vCore [unit for virtual CPU] seconds of db serverless usage with 32 GB of storage.
Azure Cosmos DB: 1,000 requests units per second provisioned throughput with 25 GB storage.
Azure App Services: 10 web, mobile, or API apps with 1 GB storage 1 hour per day
Azure Functions: 1 million requests.
Azure Event Grid: 100k operations.

B. You can use Azure Pricing Calculator to calculate the cost of
overall services that may be used when deploying your
architecture for your project.
Also see : Visit here for Azure Calculator

C. You can use Azure Advisor which provides you recommendations on
which services are been underutilized, based on it you can
decide to dismiss it or not.
Don't forget to check out the video to understand Azure Advisor.

D. Use reservations to advance book your virtual machine for your businesses and save money.