Minting Selfies as NFT: A New Way to Own and Share Your Personal Memories

Have you ever wanted to own a unique digital asset that represents a personal memory or moment in your life? Now you can, with "Minting Selfies as NFT" - a new application that allows you to turn your selfies into one-of-a-kind digital assets that are stored on a blockchain.

I recently built a demonstration project for the Salesforce Architect Summit 2023 that shows how "Minting Selfies as NFT" works. The application allows registered guests to upload their selfies, add a caption, and mint them as NFTs. 

Once you've minted your selfies as NFTs, you can explore a variety of popular marketplaces to showcase, trade, or sell your unique digital assets. Here are some of the most popular platforms where you can view and engage with the NFT community:

: Opensea is one of the largest NFT marketplaces, offering a wide range of digital assets, including artwork, collectables, and more.

: Rarible allows artists and creators to mint, buy, and sell NFTs directly on the platform, fostering a vibrant and decentralized marketplace.

: SuperRare is known for its curated selection of high-quality digital artwork, providing a platform for artists to showcase and sell their creations.

These marketplaces offer unique opportunities to discover, collect, and engage with the NFT community. Visit their websites to explore the diverse range of digital assets and immerse yourself in this exciting new world of digital ownership and value.

The project uses a combination of technologies to ensure the security and uniqueness of each NFT. The Crossmint API is used to mint the NFT, while the Infura API is used to store the images on IPFS. Firebase is used to store guest emails, and NestJs is used for the Frontend. NextAuth is used for admin login.

One of the interesting features of the project is the hidden /admin link, which allows authorized users to manage guest registration and mint NFTs for them if needed. The CSV uploader and form for minting NFTs are also great features, as they make it easy to manage a large number of guests.

"Minting Selfies as NFT" has a wide range of potential applications. It could be used as a new form of digital art, where people can collect and trade unique self-portraits. It could also be used as a way to raise money for charity, by selling limited-edition NFTs to raise funds for a good cause. The possibilities are endless!

Overall, "Minting Selfies as NFT" is a fascinating new application of blockchain technology, and has the potential to change the way we think about digital ownership and value. Try it out for yourself and own a unique digital asset that represents a special moment in your life!


Since we used this application on Salesforce Architect Summit 2023, you can check the minted selfies here on 

"Minting Selfies as NFT" is an application that allows registered users to upload their selfies and mint them as NFTs. Here's how it works:

Users enter their email addresses to register with the application.

After registering, users can upload their selfies and add a caption.

The application uses the Crossmint API to mint the selfie as an NFT.

The NFT is stored on a blockchain and is now a unique digital asset that represents the user's selfie.

Users can view their NFT on Opensea and share it on social media.

"Minting Selfies as NFT" uses a combination of technologies to ensure the security and uniqueness of each NFT. Here's an overview of the technologies used:

The Crossmint API is used to mint the NFT. Crossmint is a platform that allows users to mint, trade, and sell NFTs. It provides an easy-to-use API that developers can use to mint NFTs programmatically.

The Infura API is used to store the images on IPFS. IPFS is a peer-to-peer protocol that allows files to be stored and shared in a decentralized way. Infura provides a reliable and easy-to-use API for developers to store files on IPFS.

Firebase is used to store guest emails. Firebase is a mobile and web application development platform that provides a real-time database and authentication services. It's an easy-to-use and reliable platform that makes it easy to store and manage user data.

NestJs is used for the frontend of the application. NestJs is a progressive Node.js framework for building efficient, scalable, and reliable server-side applications. It provides a modular architecture that makes it easy to build and maintain complex applications.

NextAuth is used for admin login. NextAuth is a library for authentication in Next.js applications. It provides a simple and easy-to-use API for implementing authentication in your application.

"Minting Selfies as NFT" has a number of additional features that make it easy to manage a large number of guests and NFTs:

The application has a hidden /admin link that allows authorized users to manage guest registration and mint NFTs for them if needed. The admin panel has a number of features, including:

A CSV uploader that allows users to upload a list of guest names and email addresses.

A form to mint NFTs for the remaining guests.

A component that shows all the registered guests.

One guest can only mint one selfie. This feature ensures that each NFT is unique and guests can't mint multiple NFTs of the same selfie.

"Minting Selfies as NFT" is a fascinating new application of blockchain technology that has the potential to change the way we think about digital ownership and value. With its simple registration process, easy-to-use interface, and powerful features, it's a great way to own and share a unique digital asset that represents a special

Want to make some souvenirs? This project is made to give attendees their Salesforce Architect Summit selfies as souvenirs.

Mint Selfie As NFT

Unveiling a NextJs Application for Amazon Data Scraping with Brightdata

In the realm of modern technology, web scraping has emerged as a potent method for extracting valuable insights from websites. This blog takes you through the journey of my solo endeavour in crafting a NextJs application that seamlessly integrates with Brightdata's Proxies & Scraping Infrastructure. With a focus on data extraction from Amazon's shopping website, this application is designed not only to showcase technical prowess but also to captivate potential business clients and employers seeking bespoke data solutions.

Conceptualizing Effortless Amazon Data Retrieval

The application I've developed empowers users to effortlessly obtain product information from Amazon through a user-friendly interface. The process is designed to be intuitive and efficient. This is the line diagram architecture.

User Interaction: Intuitive Query Initiation

Users are greeted with a simple and inviting interface. A search bar beckons users to enter the product name they're interested in. With a single press of the enter key, the data collection process springs into action.

Technical Workflow: Orchestrating the Data Gathering

Behind the scenes, the application orchestrates a series of actions to transform user queries into actionable data. Here's the technical breakdown:

The NextJs backend triggers a REST callout to Brightdata, effectively communicating the user's search query.

Simultaneously, the application stores the query in the Firebase Firestore database as a JSON object, marked with a 'pending' status.

Brightdata's formidable scraping infrastructure springs into action, traversing Amazon's virtual aisles for relevant data. This dynamic process typically completes within 2-3 minutes.

Cloud-Powered Automation: Bridging Scrape to Database Update

Upon completing the scraping process, Brightdata sets off a webhook, in turn initiating a cloud function hosted on Firebase. This function plays a pivotal role in processing the scraped data into a usable format:

The cloud function systematically updates the product list stored in JSON format, corresponding to the user's search query in Firebase Firestore.

The status of the query is then switched from 'pending' to 'completed,' signifying that the data is primed for access.

Real-time Data: Seamlessly Accessible Insights

The frontend of the application harnesses the real-time capabilities of Firebase's database. By leveraging the "react-firebase-hooks" npm library, the application automatically fetches the updated product list as soon as changes are detected in the database. This dynamic approach eliminates the need for users to manually refresh the page, ensuring they're instantly greeted with the latest insights.

Tools and Technologies: The Building Blocks

The realization of this application was made possible by an array of indispensable tools and technologies. Let's delve into the building blocks that brought this concept to life:

: As the core framework, NextJs provided the foundation for crafting a modern and efficient web application.

: This utility-first CSS framework facilitated the creation of a sleek, responsive, and visually appealing user interface, enhancing the overall user experience.

: These serverless functions played a pivotal role in automating tasks triggered by Brightdata's webhook, simplifying complex processes into efficient workflows.

: The real-time capabilities of Firebase's NoSQL database ensured a seamless flow of data between the application's backend and frontend components.

: Brightdata's sophisticated scraping infrastructure, enriched by its award-winning proxy networks, transformed unstructured web content into organized, valuable insights.

Conclusion: Pioneering the Landscape of Data Applications

In the arena of data-driven applications and web scraping, my NextJs application stands as a testament to innovation and creativity. By seamlessly weaving together Brightdata's advanced scraping prowess and Firebase's real-time capabilities, I've crafted a user-centric tool that converts raw web data into structured, meaningful information. This application serves not only as a technical marvel but also as an invitation to potential collaborators, businesses, and employers seeking tailored data solutions.

If you're a business client or an employer intrigued by the potential of this application, I invite you to get in touch. As a solo developer, I'm excited to explore new horizons and take on projects that can benefit from the synergy of web scraping, cloud functions, and real-time databases. Together, let's shape the future of data-powered applications.

Experience seamless data extraction from Amazon with our NextJs and Brightdata-powered app.

Unleashing Insights: Crafting a NextGen Amazon Data Scraping App

Building a Next.js Weather Application with OpenAI Integration

In today's fast-paced world, technology continues to reshape the way we interact with the environment around us. Creating innovative applications that provide valuable solutions has become essential to cater to modern needs. In this pursuit, I've developed a fascinating Next.js weather application, which leverages the prowess of OpenAI to provide users with succinct AI-generated summaries of today's weather conditions. This blog post takes you on a journey through the intricacies of this application, delves into its underlying technology stack, and explores its wide range of potential use cases.

Overview of the Next.js Weather Application

Overview of the Next.js Weather Application:

Imagine a scenario where users can effortlessly access concise weather summaries for their specific locations, powered by advanced AI technology. This Next.js weather application transforms this scenario into reality. Let's delve into the application's functionality and user interaction flow to understand its inner workings.

 When a user, let's say Ravi, visits the application, the first step involves selecting their country and city. This step ensures that the weather data presented is entirely relevant to the user's precise location.

Fetching Weather Data from Open Metro API

 Following Ravi's location selection, the application initiates the retrieval of real-time weather data from the Open Metro API (

). The data-fetching process is facilitated by GraphQL, with StepZen and Apollo Client playing integral roles. This dynamic combination not only streamlines data acquisition but also employs server-based caching, optimizing the application's performance significantly.

 After acquiring the comprehensive weather data, the application strategically filters out any extraneous information. The goal is to ensure that only the most relevant details are retained for further processing. Moreover, in an effort to optimize the application's efficiency and minimize operational costs, the filtered JSON data undergoes a transformation into CSV format. This innovative approach aims to reduce token consumption, which plays a significant role in interacting with the OpenAI API.

Understanding Token Consumption and OpenAI Pricing

 To further explain, OpenAI's pricing model revolves around the concept of tokens. In the context of natural language processing, a token can be as short as one character or as long as one word. When interacting with the OpenAI API, you're billed based on the number of tokens utilized. This includes both the tokens in your input prompts and the tokens generated in the output responses.

The CSV transformation serves as a smart strategy to effectively manage token usage. By converting the data into a format that minimizes tokens, the application optimizes its usage of the OpenAI API. This, in turn, results in cost savings while ensuring that the quality of the generated weather summaries remains uncompromised.

 Now comes the moment of magic, facilitated by the OpenAI API. Harnessing the full power of AI, the application generates a meticulously crafted weather summary based on filtered and token-efficient data. This summary provides users like Ravi with a concise, yet comprehensive overview of today's weather conditions. The AI-generated summary is then elegantly presented on the user's screen, enhancing their experience and making the weather-checking process effortless.

In the next section, we'll explore the intricate technology stack that empowers this application and brings its advanced functionalities to life.

At the heart of this innovative Next.js weather application lies a sophisticated technology stack that synergistically enables the seamless interaction between users, data sources, and advanced AI capabilities. Each component plays a crucial role in ensuring the application's efficiency, reliability, and user-centric experience.

Next.js 13: Server-Based Caching and Instantaneous Responses

 One of the foundational pillars of this application's architecture is Next.js 13, a cutting-edge framework for building modern web applications. Next.js 13 shines particularly in scenarios where efficient handling of user requests and fast loading times are paramount.

In our weather application, Next.js 13 plays a pivotal role in server-based caching. This technique involves storing pre-rendered pages on the server, allowing the application to serve cached data to subsequent users who request the same information. This means that once Ravi's weather summary is generated, it's cached for future users querying the same location. As a result, cached users enjoy virtually instantaneous loading times, bypassing the initial wait that Ravi experienced when fetching the weather data for the first time.

OpenAI API: Harnessing AI for Precise Weather Summaries

 The OpenAI API is the technological centerpiece responsible for generating the AI-powered weather summaries that users find so valuable. The API is designed to handle natural language processing tasks, and its capabilities extend to various applications beyond our weather app.

As we mentioned earlier, the cost associated with the OpenAI API depends on token consumption. By strategically filtering data and converting it into a token-efficient format like CSV, our application minimizes the number of tokens used while ensuring that the generated weather summaries maintain their quality. This approach is not only smart from a cost perspective but also demonstrates the careful consideration put into maximizing the value of each API call.

StepZen and Apollo Client: Enabling Efficient Data Fetching

 The collaboration between StepZen and Apollo Client forms the backbone of our application's data fetching mechanism. StepZen acts as a bridge between various data sources, enabling a unified GraphQL interface. This abstraction simplifies data retrieval and minimizes the complexity associated with querying different APIs individually.

Apollo Client, on the other hand, seamlessly integrates with our Next.js application, allowing efficient and optimized data fetching. Together, StepZen and Apollo Client contribute to the application's high performance and streamlined user experience.

Tremour 2.0: Enhancing User Interface with React Components

 To enhance the application's user interface and ensure a polished visual experience, we've integrated the Tremour 2.0 React component library. This library offers a collection of pre-designed UI components that expedite the development process while maintaining a consistent and professional design aesthetic.

In the next section, we'll delve into why Next.js 13 was the ideal choice for our application and how it addresses specific challenges related to user requests and cost-effectiveness.

The Need for Next.js 13: Efficient Handling of User Requests and Cost-Effective API Usage

The decision to build our weather application using Next.js 13 wasn't arbitrary; it was a strategic choice driven by the need for efficient handling of user requests and cost-effective utilization of the OpenAI API. Let's explore the rationale behind this decision and how Next.js 13 addresses these critical aspects.

 Imagine a scenario where thousands of users access our weather application simultaneously to check the latest weather updates. If each user's request leads to an independent query to the OpenAI API, the potential for an overwhelming number of API calls and the associated costs are significant. This is where Next.js 13 shines.

With its server-based caching mechanism, Next.js 13 ensures that the data generated for the first user who queries a specific location is stored on the server. Subsequent users requesting the same information are served from this cached data, resulting in instantaneous loading times. As a result, the application effectively minimizes the impact of a surge in user requests on both performance and API utilization.

Cost-Effective API Usage Through Smart Caching

 OpenAI's pricing model, centered around tokens, necessitates careful consideration of how tokens are consumed. Each word, character, or symbol contributes to token count, impacting the cost associated with API usage. By employing the strategy of converting filtered JSON data into CSV format, our application significantly reduces token consumption.

Consider the scenario where Ravi's weather data is initially fetched and converted into a token-efficient CSV. When subsequent users query the same location, they receive the summary generated from the cached CSV data. This not only accelerates loading times but also optimizes API usage, as the OpenAI API is only invoked when generating the AI-powered summary. Thus, the application extracts maximum value from each API call while containing costs.

Maintaining Data Freshness: Removing Old Cached Pages

 Given that weather data updates daily, it's essential to strike a balance between caching and data freshness. While cached data serves subsequent users efficiently, it's crucial to ensure that outdated information isn't perpetuated. To achieve this, our application systematically removes old cached static pages on a daily basis, guaranteeing that users receive accurate and up-to-date weather summaries.

In the next section, we'll illustrate the tangible differences in user experience between a first-time user and a cached user, showcasing the true potential of server-based caching and optimized API usage.

Illustrating the User Experience: First-Time User vs. Cached User

The user experience is at the core of every successful application, and our Next.js weather app is no exception. The implementation of server-based caching and optimized API usage translates to distinct user experiences for first-time users and cached users. Let's explore these experiences in detail to understand the practical implications of our technological choices.

 Imagine Ravi as a first-time user visiting our weather application. As Ravi selects his country and city, the application initiates the process of fetching weather data from the Open Metro API. Since this is Ravi's first interaction, there's a brief wait as the data retrieval and AI-generated summary take place. Once the weather summary is generated, Ravi is presented with a comprehensive overview of today's weather conditions.

 Now, let's consider Tushar, who also resides in the same city as Ravi. Unlike Ravi, Tushar's experience is remarkably different. When Tushar visits the application to check the weather, the cached data from Ravi's interaction is instantly served to him. This means that Tushar experiences virtually no loading time, as the weather summary is readily available from the cached data. In essence, Tushar benefits from the prior user's interaction, enjoying a seamless and prompt user experience.

 This disparity in experiences underscores the significance of server-based caching. While first-time users like Ravi incur a short initial delay as data is fetched and processed, subsequent users reap the rewards of cached data, receiving instantaneous responses. This design choice not only enhances user satisfaction but also contributes to cost savings, as the OpenAI API is utilized more efficiently.

In the next section, we'll explore various scenarios where similar technology can be applied to create applications with impactful functionalities.

Examples of Similar Applications: Expanding the Possibilities

The Next.js weather application we've explored showcases the potential of integrating AI-powered summaries with real-time data. This blend of technologies isn't limited to weather reports; it can be adapted and extended to various domains, opening doors to innovative applications. Here are a couple of examples that illustrate how similar technology can be harnessed in different contexts:

1. Fundamental Analysis of Indian Stock Market Stocks

 Imagine a web application that takes Indian Stock Market data as input and generates AI-driven fundamental analyses for each stock. By defining prompts tailored to stock analysis, this application could offer investors valuable insights into the performance and potential of different stocks. The strategic utilization of AI-generated summaries could provide users with a quick snapshot of each stock's financial health, helping them make informed investment decisions.

2. Leveraging National Data and Analytics Platform (NDAP) Datasets

 The National Data and Analytics Platform (NDAP) houses a treasure trove of government public datasets spanning diverse sectors. This rich source of information can be harnessed to create applications that offer data-driven insights and business opportunities. For instance, an application could leverage NDAP datasets related to agriculture, healthcare, or commerce to provide users with valuable trends, patterns, and recommendations. Whether it's optimizing agricultural practices or identifying market trends, the possibilities are endless.

 These examples demonstrate how the underlying principles of our Next.js weather application can be applied to a wide range of scenarios. By customizing prompts and utilizing cached data, AI-generated summaries, and efficient data retrieval mechanisms, developers can create applications that revolutionize industries and provide users with valuable insights and solutions.

Putting it into Practice: Showcasing Implementation Details

Now that we've explored the conceptual framework and potential applications, let's dive into the practical implementation of our Next.js weather application. Here, we'll provide a visual glimpse of how the application looks and highlight the specific OpenAI prompt command used for generating the weather summaries.

Weather Suggestion Component Screenshot: Visualizing the AI-Generated Summary

 To offer readers a visual representation of the AI-generated weather summary, let's take a look at the screenshot of the weather suggestion component. This component elegantly displays the concise yet comprehensive summary of today's weather conditions. 

OpenAI Prompt Command: Configuring Inputs for AI Interaction

 Generating AI-driven summaries requires thoughtful formulation of prompt commands. In the context of our Next.js weather application, the OpenAI prompt command could resemble the following:

Conclusion: Embracing Innovative Applications in the Modern Technological Landscape

In the era of rapid technological advancement, the Next.js weather application we've explored stands as a testament to the power of innovation and creative problem-solving. By seamlessly integrating cutting-edge technologies like Next.js 13, OpenAI API, StepZen, and Apollo Client, we've created an application that not only offers users efficient access to weather information but also serves as a springboard for broader applications.

The journey through this blog post has illuminated the significance of server-based caching in optimizing user experiences and cost-efficiently utilizing APIs. Moreover, the artful configuration of OpenAI prompt commands demonstrates the potential of AI-driven insights in enhancing user interactions.

Beyond weather forecasts, the application's principles can be extended to revolutionize domains such as stock market analysis and leveraging government datasets. The flexibility and adaptability of this technology stack promise a world of possibilities, empowering developers to innovate and create solutions that address complex challenges across industries.

As we conclude this exploration, it's clear that embracing innovative applications is pivotal to staying at the forefront of the ever-evolving technological landscape. By harnessing the synergy of advanced tools and creative thinking, we can usher in a future where AI-powered insights enhance our lives, businesses, and interactions in remarkable ways.

Thank you for joining us on this journey of discovery and innovation. We look forward to a future where technological possibilities continue to expand, and where each new application brings us closer to a more connected and informed world.

Experience real-time insights and personalized interactions with Freestyler OpenAI Weather.

Freestyler OpenAI Weather

The Amazon clone I created is a web application that allows users to browse products, add them to a cart, and checkout. The clone is built using Next.js, a popular React framework that allows developers to create server-side rendered web applications. I also used Next Auth for authentication, Tailwind CSS for styling, and Redux for global state management.

To begin, I set up a new Next.js project and installed the required dependencies. I then created the basic layout of the application, including the header, footer, and product listing page. Next.js comes with a powerful file-based routing system, which made it easy to create the necessary pages.

The main features of the Amazon clone include the product listing page, product detail page, shopping cart, and checkout process. To fetch the products, I used the fakestoreapi, a free API that provides product data. I used getServerSideProps to fetch the products before the customer comes to the page, and Next.js served a static page over here. The product detail page shows more details about the product and allows the user to add it to their cart.

For authentication, I used Next Auth with Google and Facebook authentication. Next Auth is a popular authentication library that makes it easy to add authentication to your Next.js applications.

To manage the cart, I used Redux, a popular state management library. I stored the cart items for a particular user in Redux. This allowed me to easily access the cart from any part of the application.

For payment management, I used the Stripe payments API. Stripe is a popular payment processing platform that provides a robust API for handling payments. The checkout process involves collecting user information, verifying the payment, and displaying a success or failure message.

To style the application, I used Tailwind CSS. Tailwind is a popular CSS framework that allows developers to create complex layouts and designs quickly. I used its utility classes to style the application and make it responsive for different devices. The application is also mobile responsive, so users can view it on mobile phones as well.

Next.js provides an Image component that allows developers to optimize images for performance automatically. I used Next.js Image tags for showing images in some places. Interestingly, I didn't use the Image tag in the banner carousel image just to show the difference between the default img tag and Next.js provided image tag.

I also added some usability considerations, such as implementing user feedback and error handling. For example, when a user adds a product to their cart, a success message appears at the top of the page. If an error occurs during the checkout process, an error message is displayed, and the user is prompted to try again.

I deployed the application using Vercel, a cloud platform that makes it easy to deploy and manage Next.js applications. Vercel provides a simple and easy-to-use interface for deploying applications and managing environments.

There are many potential improvements and features that I would like to add to the application in the future. For example, I could add product search functionality or implement user reviews. I could also improve the design of the application or add more payment methods.

Building an Amazon clone with Next.js, Next Auth, Tailwind CSS, and Redux was a challenging but rewarding experience. I learned a lot about the power of these technologies for building complex applications quickly and efficiently. With the features and design of the clone now implemented, I can continue to refine and add to it as needed. I hope this blog post inspires others to try their hand at building their own applications with these technologies, and I look forward to seeing what new projects will come from it!

Build an Amazon clone with Next.js that allows users to log in with Google or Facebook, add products to their cart, and place orders.

Building an Amazon Clone with Next.js

Creating a Next.js 13 Application with Microsoft Azure and OpenAI's DALL-E API

In the world of web development, integrating cutting-edge technology can set your application apart. I recently developed an application using Next.js 13, which leverages Microsoft Azure's backend services and OpenAI's DALL-E API to generate images from user prompts. In this blog post, I'll walk you through the architecture and functionalities of this innovative application.

The journey begins on the home page, where users are greeted with a simple text area. Here, they can type in a prompt for the image they wish to create. It's straightforward and user-friendly.

Upon entering their desired prompt, users click the "Generate" button. This triggers the magic: the application starts creating the image based on the prompt.

For those moments when creativity doesn't strike, we have two additional buttons: "Use Suggestion" and "New Suggestion." "New Suggestion" offers a random prompt, helping users get started. If they like the suggestion, clicking on "Use Suggestion" will use that prompt to generate an image.

Once a prompt is set (either by the user or the suggestion feature), clicking on "Generate" or "Use Suggestion" triggers the "generateImage" endpoint on Azure. This endpoint not only generates the image using OpenAI's DALL-E but also downloads it to an array buffer, eventually storing it in an Azure Storage container.

Security and efficiency are key, so we use the "generateSASToken" endpoint to create a SAS Token. This token, with permissions for blob data operations, ensures secure and controlled access to Azure Storage, expiring every 30 minutes.

The "generateImage" function also interacts with the "generateSASToken" endpoint to access Azure Storage, showcasing seamless integration between different Azure services.

We also have a "getChatGPTSuggestion" endpoint, utilizing OpenAI's createCompletion API to generate prompt suggestions dynamically.

Finally, the "getImages" Azure function fetches the stored images for the frontend. It, too, uses the 'generateSASToken' function for access.

Frontend Development with Next.js and SWR

On the frontend, we use the 'swr' package from Next.js for efficient data fetching. The useSWR hook helps in making API calls to the backend, handling loading states, and data validation effortlessly.

To add to the user experience, we've integrated 'react-hot-toast' for engaging popup notifications and 'tailwindcss' for sleek, responsive design.

This application is a testament to the power of integrating AI with web technologies. It offers a glimpse into the future of interactive web applications, where user creativity is enhanced by AI's capabilities. Whether you're a client, an employer, or a tech enthusiast, this application demonstrates the potential of modern web development tools and services.

Stay tuned for more updates, and feel free to reach out if you have any questions or need assistance in building similar applications!

Discover how Next.js 13, Microsoft Azure, and OpenAI's DALL-E converge in our latest application to transform user prompts into stunning images. This blog delves into the seamless integration of cutting-edge AI with cloud computing, offering a glimpse into the future of interactive web technology.

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Mint Selfie As NFT

Unleashing Insights: Crafting a NextGen Amazon Data Scraping App

Freestyler OpenAI Weather

Building an Amazon Clone with Next.js

Next.js 13 Meets Azure & DALL-E: Revolutionizing Image Generation