WordPress Ad Banner

Unveiling the Power of Hyperscript: A Beginner’s Guide

In the fast-paced world of web development, new technologies and libraries seem to emerge every day. One such noteworthy innovation that has gained attention in recent years is Hyperscript. If you’re curious about what Hyperscript is and how it can revolutionize your web development experience, you’ve come to the right place. In this blog post, we will explore the concept of Hyperscript, its benefits, and how it can be a valuable addition to your web development toolkit.

What is Hyperscript?

Hyperscript is a lightweight, expressive, and highly efficient JavaScript library that simplifies the creation and manipulation of HTML documents. It provides a concise and readable way to generate HTML structures, enabling developers to create dynamic web applications with ease. Hyperscript is often described as a declarative and functional approach to building user interfaces.

One of the primary goals of Hyperscript is to reduce the complexity of working with the Document Object Model (DOM). It allows developers to express the structure and behavior of their web applications in a more intuitive and concise manner compared to traditional imperative approaches.

Key Features of Hyperscript

  1. Declarative Syntax: Hyperscript uses a declarative syntax that closely resembles the desired HTML structure. This makes it easier to understand and maintain, as it focuses on what you want to achieve rather than how to achieve it.
  2. Virtual DOM: Like popular libraries such as React, Hyperscript employs a virtual DOM to optimize rendering performance. It efficiently updates only the parts of the real DOM that have changed, reducing the need for extensive reflows and repaints.
  3. Composability: Hyperscript allows you to create reusable components and compose them easily, promoting modular and maintainable code.
  4. Small Size: Hyperscript is a compact library, which means it won’t bloat your project. It has a minimal footprint and is designed to be lightweight and fast.
  5. No Dependencies: Hyperscript operates independently of other libraries or frameworks. This independence gives you the flexibility to use it in combination with your preferred tools.

Benefits of Using Hyperscript

  1. Productivity: Hyperscript’s declarative syntax streamlines the development process, reducing the amount of boilerplate code and making your codebase more readable. This can lead to increased productivity.
  2. Performance: The virtual DOM implementation in Hyperscript optimizes rendering performance, resulting in smoother and more efficient web applications.
  3. Maintenance: With its clean and intuitive syntax, Hyperscript makes it easier to understand and maintain your code. This is especially beneficial for teams working on collaborative projects.
  4. Independence: Hyperscript’s lack of dependencies means you can incorporate it into your project without worrying about conflicts or compatibility issues.

Getting Started with Hyperscript

To get started with Hyperscript, you need to include the library in your project. You can either download it from the official website or use package managers like npm or yarn.

Here’s a simple example of how you can use Hyperscript to create a basic HTML structure:

const h = require('hyperscript');

const app = h('div', [
  h('h1', 'Hello, Hyperscript!'),
  h('p', 'This is a simple example.'),
]);

document.body.appendChild(app);

In this example, we create a div element with a heading and a paragraph using the h function from Hyperscript.

Conclusion

It is a powerful and efficient JavaScript library that simplifies the creation and manipulation of HTML documents. Its declarative syntax, virtual DOM, and small size make it an attractive choice for web developers looking to enhance their web development experience. By incorporating Hyperscript into your toolkit, you can streamline development, boost performance, and improve code maintainability. So, if you’re searching for a way to make your web development projects more efficient and enjoyable, give it a try. Your future web applications will thank you for it.

Adobe Unveils Firefly Image 2: A New Frontier in AI-Enhanced Design

Adobe, the renowned creative software company, has been navigating the complex terrain of generative AI with a blend of innovation and controversy (Firefly Image 2). While it has leveraged this technology to introduce a range of new features, like the highly praised Generative Fill in Photoshop and the Firefly text-to-image generator, it has faced backlash from some contributors to Adobe Stock, who claim that the company utilized permissive terms of service to train proprietary AI models on their work without prior notification or direct compensation.

Nevertheless, Adobe has remained undeterred in its pursuit of AI advancements. At its annual Adobe MAX conference in Los Angeles, the company unveiled a slew of new AI products, services, and features, notably its latest offering, “Firefly Image 2.” This updated version promises enhanced prompt understanding and heightened photorealism, positioning it as a direct contender against other leading generative AI models such as Midjourney and the recently launched DALL-E 3 from OpenAI, which is now integrated into ChatGPT Plus.

Firefly Image 2

It brings several enterprise-friendly features into the fray, escalating competition, particularly with Canva, a major player in the design and marketing space. While Firefly 2 lacks the integrated typography capabilities of DALL-E 3 and Ideogram, it introduces a unique feature called “Generative Match.” This feature allows users to generate imagery in a particular style from a reference image they provide, offering a sophisticated twist on the “style transfer” art filters popular in previous years.

Adobe Firefly 2 screenshot

Adobe explains, “Generative Match enables users to either pick images from a pre-selected list or upload their own reference image to guide the style when generating new images…Users can easily meet brand guidelines or save time designing from scratch by replicating the style of an existing image, and quickly maintain a consistent look across assets.” This feature intensifies the ongoing rivalry between Adobe, the traditional leader in creative software for visual artists and designers, and Canva, which has rapidly gained popularity among non-designers, such as marketers and communications professionals.

In a strategic move just ahead of Adobe MAX, Canva announced its Magic Studio, which includes AI features like “Magic Morph” and “Brand Voice,” as well as a text-to-video GenAI feature in collaboration with startup Runway.

In response, Adobe countered with not only Firefly Image 2 but also introduced the “New Firefly Design Model,” targeting small and medium-sized businesses (SMBs) and enterprises. This model empowers users to instantly generate captivating design templates suitable for print, social media, and online advertising. Adobe is also collaborating with top global brands to explore how Firefly can boost productivity, reduce costs, and expedite content creation.

What sets Firefly Image 2 apart from Canva is “Content Credentials.” This labeling mechanism, integrated into Adobe Creative Cloud, adds metadata to images to indicate that they were AI-generated or based on a reference image.

For enterprise users, Adobe introduced GenStudio, a generative AI-powered program that enables companies to customize and fine-tune Firefly according to their specific requirements. It also provides control over how employees access and utilize the technology through APIs, ensuring strict governance and security to safeguard the organization’s content, data, and workflows.

In summary, Adobe’s bold strides in the realm of generative AI, including Firefly Image 2 and its associated features, demonstrate its commitment to staying at the forefront of creative software and innovation. The competition with Canva and the evolving landscape of AI-powered design tools promise to shape the future of visual content creation and design.

MongoDB Enhances Developer Productivity with Generative AI

MongoDB, the NoSQL Atlas database-as-a-service (DBaaS) provider, continues to empower developers by introducing new generative AI features into several of its tools. These enhancements are designed to streamline various aspects of software development and data management.

One of the standout additions is the AI-powered chatbot integrated into MongoDB’s Documentation interface. This chatbot enables developers to ask questions and seek assistance related to MongoDB’s products and services. It goes beyond simple queries, offering troubleshooting support during the development process. This AI chatbot, which is now widely accessible, is built on an open-source foundation and leverages MongoDB Atlas Vector Search for AI-driven information retrieval. Developers have the option to utilize the project code to create and deploy their customized chatbots for diverse applications.

In a bid to expedite application modernization, MongoDB has infused AI capabilities into its Relational Migrator tool. These capabilities encompass intelligent data schema recommendations and code suggestions. The Relational Migrator can automatically transform SQL queries and stored procedures from legacy applications into MongoDB Query API syntax. This automation eliminates the need for developers to possess in-depth knowledge of MongoDB syntax, making the migration process more accessible and efficient.

Another noteworthy improvement involves the introduction of natural language processing (NLP) features to MongoDB Compass. This interface facilitates querying, aggregating, and analyzing data stored in MongoDB. The NLP prompt within Compass can generate executable MongoDB Query API syntax, simplifying complex data operations and enhancing user-friendliness.

Similar NLP capabilities have been integrated into MongoDB Atlas Charts, a data visualization tool that enables developers to create, share, and embed visualizations using MongoDB Atlas data. With the new AI-driven functionalities, developers can effortlessly construct data visualizations, graphics, and dashboards within MongoDB Atlas Charts using natural language commands.

It’s important to note that these AI-powered features in MongoDB Relational Migrator, MongoDB Compass, and MongoDB Atlas Charts are currently in a preview stage.

In addition to these tool enhancements, MongoDB has introduced a set of capabilities aimed at facilitating edge computing. These capabilities, collectively referred to as “MongoDB Atlas for the Edge,” empower enterprises to run MongoDB applications on various infrastructures, including self-managed on-premises servers and edge infrastructure provided by major cloud providers such as Amazon Web Services (AWS), Google Cloud, and Microsoft Azure. This enables organizations to access real-time data and build AI-powered applications at edge locations, further expanding MongoDB’s utility in modern data management scenarios.

GitHub Extends Access to Copilot Chat to Individual Users

GitHub has taken another step towards making programming more accessible and efficient by opening up its Copilot Chat beta to individual subscribers of GitHub Copilot for Visual Studio and VS Code. This move comes three months after the launch of Copilot Chat, a programming-centric chatbot similar to ChatGPT, which was initially available only to organizations with a Copilot for Business subscription. The good news for individual users is that Copilot Chat is now included for free as part of their existing subscription, which costs $10 per month.

Copilot Chat resides in a sidebar within the integrated development environment (IDE), offering developers a platform for multiturn conversations not just about coding in general but also specifically about the code they are currently working on. GitHub emphasizes that it’s the contextual awareness of Copilot Chat that sets it apart from general-purpose chat assistants. This contextual understanding allows it to provide more relevant and useful assistance to developers.

Shuyin Zhao, the VP of Product Management at GitHub, expressed the significance of this integration in the software development process. He stated, “Integrated together, GitHub Copilot Chat and the GitHub Copilot pair programmer form a powerful AI assistant capable of helping every developer build at the speed of their minds in the natural language of their choice. We believe this cohesion will form the new centerpiece of the software development experience, fundamentally reducing boilerplate work and designating natural language as a new universal programming language for every developer on the planet.”

Copilot Chat offers a range of practical applications, including real-time guidance, best practice recommendations, tailored solutions for code-related issues, and assistance with code analysis and security fixes, all within the IDE. This eliminates the need for developers to switch between different tools and environments.

GitHub’s vision is to promote “natural language as a new universal programming language” with the aim of democratizing software development. This aligns with the company’s recent emphasis on making programming more accessible and efficient. GitHub CEO Thomas Dohmke, who has been vocal about this vision, is scheduled to discuss it further during an onstage interview at the Disrupt conference in San Francisco.

In summary, GitHub’s expansion of Copilot Chat availability to individual subscribers underscores the company’s commitment to enhancing the software development experience by leveraging natural language AI assistance. This move holds the potential to significantly streamline coding workflows and empower developers of all levels.

PostgreSQL 16 Unveiled: Breaking Ground with Four Key Features

The PostgreSQL Global Development Group has unveiled the highly-anticipated release of PostgreSQL 16, ushering in a new era of excellence in database management. This milestone not only sets new benchmarks for data replication, system monitoring, and performance optimization but also solidifies EDB’s position as a leading contributor to PostgreSQL code. In this article, we’ll explore four key features that make PostgreSQL 16 a game-changer for the community and developers alike.

Privilege Administration Revamped

In PostgreSQL 16, a significant transformation has taken place in privilege administration. Previous versions often necessitated superuser accounts for various administrative tasks, which proved cumbersome in larger organizations with multiple administrators. PostgreSQL 16 addresses this challenge by introducing a groundbreaking change – users can now grant privileges in roles only if they possess the ADMIN OPTION for those roles. This shift empowers administrators to define more granular roles and assign privileges accordingly, simplifying the management of permissions. This not only enhances security but also streamlines the overall user management experience.

Logical Replication Enhancements

Logical replication has been a versatile solution for data replication and distribution since its inclusion in PostgreSQL 10, nearly six years ago. With each release, PostgreSQL has consistently improved logical replication capabilities, and PostgreSQL 16 is no exception. This release includes essential under-the-hood improvements for performance and reliability, as well as the introduction of new and more complex architectures.

PostgreSQL 16 introduces support for logical replication from physical replication standbys, reducing the load on the primary and enabling easier geo-distribution architectures. This means that the primary can have a replica in another region, sending data to a third system in that region, thus avoiding double replication from one region to another. The new pg_log_standby_snapshot() function makes this possible.

Additional logical replication enhancements include initial table synchronization in binary format, replication without a primary key, and improved security by requiring subscription owners to have SET ROLE permissions on all tables in the replication set or be a superuser.

Performance Boosts

PostgreSQL 16 is a powerhouse when it comes to performance improvements. Enhanced query execution capabilities enable parallel execution of FULL and RIGHT JOINs, as well as the string_agg and array_agg aggregate functions. SELECT DISTINCT queries benefit from incremental sorts, resulting in improved performance. Concurrent bulk loading of data using COPY has seen substantial performance enhancements, with reported improvements of up to 300%.

This release also introduces features like caching RANGE and LIST partition lookups, aiding in bulk data loading in partitioned tables and offering better control of shared buffer usage by VACUUM and ANALYZE, ensuring your database runs more efficiently than ever.

Comprehensive Monitoring Features

PostgreSQL 16 takes database monitoring to new heights with its detailed and comprehensive monitoring features. It introduces the pg_stat_io view, offering deeper insights into the I/O activity of your PostgreSQL system. System-wide IO statistics are now just a query away, providing visibility into read, write, and extend (back-end resizing of data files) activities by different back-end types, such as VACUUM and regular client back ends.

Moreover, PostgreSQL 16 records statistics on the last sequential and index scans on tables, adds speculative lock information to the pg_locks view, and makes several improvements to wait events, making PostgreSQL monitoring more comprehensive than ever before.

In Conclusion

PostgreSQL 16 is not just an upgrade; it’s a leap forward in database technology. With its focus on privilege administration, logical replication, performance enhancements, and comprehensive monitoring, it promises to impact not only PostgreSQL users but the entire industry. EDB’s commitment to innovation and productivity is evident in this release and is further complemented by enterprise-ready capabilities in EDB Postgres Advanced Server. As PostgreSQL 16 makes its debut on EDB BigAnimal, organizations worldwide can harness its power in their preferred public cloud environments, solidifying its position as a cornerstone of modern database management.

7 Python Libraries for Efficient Parallel Processing

Python, renowned for its convenience and developer-friendliness, might not be the fastest programming language out there. Much of its speed limitation stems from its default implementation, CPython, which operates as a single-threaded interpreter, not utilizing multiple hardware threads concurrently.

While Python’s built-in threading module can enhance concurrency, it doesn’t truly enable parallelism, especially for CPU-intensive tasks. Currently, it’s safer to assume that Python threading won’t provide genuine parallelism.

Python, however, offers a native solution for distributing workloads across multiple CPUs through the multiprocessing module. But there are scenarios where even multiprocessing falls short.

In some cases, you may need to distribute work across not just multiple CPU cores but also across different machines. This is where the Python libraries and frameworks highlighted in this article come into play. Here are seven frameworks that empower you to distribute your Python applications and workloads efficiently across multiple cores, multiple machines, or both.

1. Ray

Developed by researchers at the University of California, Berkeley, Ray serves as the foundation for various distributed machine learning libraries. However, Ray’s utility extends beyond machine learning; you can use it to distribute virtually any Python task across multiple systems. Ray’s syntax is minimal, allowing you to parallelize existing applications easily. The “@ray.remote” decorator distributes functions across available nodes in a Ray cluster, with options to specify CPU or GPU usage. Ray also includes a built-in cluster manager, simplifying scaling tasks for machine learning and data science workloads.

2. Dask

Dask shares similarities with Ray as a library for distributed parallel computing in Python. It boasts its task scheduling system, compatibility with Python data frameworks like NumPy, and the ability to scale from single machines to clusters. Unlike Ray’s decentralized approach, Dask uses a centralized scheduler. Dask offers parallelized data structures and low-level parallelization mechanisms, making it versatile for various use cases. It also introduces an “actor” model for managing local state efficiently.

3. Dispy

Dispy enables the distribution of Python programs or individual functions across a cluster for parallel execution. It leverages platform-native network communication mechanisms to ensure speed and efficiency across Linux, macOS, and Windows machines. Dispy’s syntax is reminiscent of multiprocessing, allowing you to create clusters, submit work, and retrieve results with precision control over job dispatch and return.

4. Pandaral·lel

Pandaral·lel specializes in parallelizing Pandas jobs across multiple nodes, making it an ideal choice for Pandas users. While it primarily functions on Linux and macOS, Windows users can use it within the Windows Subsystem for Linux.

5. Ipyparallel

Ipyparallel focuses on parallelizing Jupyter notebook code execution across a cluster. Teams already using Jupyter can seamlessly adopt Ipyparallel. It offers various approaches to parallelizing code, including “map” and function decorators for remote or parallel execution. It introduces “magic commands” for streamlined notebook parallelization.

6. Joblib

Joblib excels in parallelizing jobs and preventing redundant computations, making it well-suited for scientific computing where reproducible results are essential. It provides simple syntax for parallelization and offers a transparent disk cache for Python objects, aiding in job suspension and resumption.

7. Parsl

Parsl, short for “Parallel Scripting Library,” enables job distribution across multiple systems using Python’s Pool object syntax. It also supports multi-step workflows, which can run in parallel or sequentially. Parsl offers fine-grained control over job execution parameters and includes templates for dispatching work to various high-end computing resources.

In conclusion, Python’s limitations with threads are gradually evolving, but libraries designed for parallelism offer immediate solutions to enhance performance. These libraries cater to a wide range of use cases, from distributed machine learning to parallelizing Pandas operations and executing Jupyter notebook code efficiently. By leveraging these Python libraries, developers can harness the full potential of parallel processing for their applications.

Elevate Your Cloud Computing Career with These 3 Actions

When it comes to advancing your career in cloud computing, many professionals often ask, “How can I enhance my prospects?” The question isn’t typically about choosing the best cloud platform but rather about personal growth within the field. Let’s begin by discussing what not to do.

Avoid investing heavily in executive MBA programs or other costly educational avenues. Such endeavors seldom yield the desired returns when pursuing a cloud computing career. They don’t equip you with the critical skills needed to build, deploy, and manage cloud computing systems or related competencies like crafting operational models, steering enterprise cloud strategies, or developing cloud business models. So, it’s wise to keep your finances intact.

Instead, focus on redefining your approach to cloud skills and career development in the current landscape. Advanced degrees are losing favor; organizations crave practical, real-world proficiencies that can swiftly add value to their operations. This is where you should concentrate your efforts. Here are the top three actions to take right now:

Professional Networking:

Embrace social media, especially platforms like LinkedIn and Twitter. These are no longer optional for cloud professionals; they offer invaluable opportunities to connect with peers, build meaningful relationships, and even discover job openings.

I’m not suggesting you spend hours glued to your phone, but investing some time in maintaining your connections and sharing insightful articles and content can demonstrate your engagement with the evolving cloud computing landscape, attracting more followers. Every connection you make and maintain serves as an asset when seeking new opportunities, even within your current organization.

Additionally, consider participating in local cloud-related meet-ups. These are often publicized and free to join. You can find them on platforms like meetup.com or through local cloud computing user groups, typically aligned with specific cloud providers such as AWS, Microsoft, or Google. Some cities even have meet-ups organized and promoted by these cloud providers.

Continuous Learning:

Dedicate time each week to learning something new. Whether it’s reading articles or enrolling in free cloud courses, consistently seek out fresh content. This practice serves multiple purposes. It enhances your performance in interviews, ensures you have an up-to-date grasp of cloud-related topics, like the evolution of serverless technology or the pros and cons of cloud-native architectures, and keeps you ahead of the curve.

If you’re reading this article, you likely recognize the benefits of this approach. Keep up the good work.

Step Out of Your Comfort Zone:

Challenge yourself by taking on projects or roles that stretch your skills and knowledge. For instance, join a team focused on cloud architecture, even if your experience lies in cloud operations. You’ll likely discover that your new colleagues are eager to help you learn, and sooner than you think, you’ll find yourself operating confidently within this expanded role.

Consider extending this willingness to embrace the unfamiliar to other endeavors, such as writing articles on cloud computing topics, recording podcasts or videos discussing cloud computing news and your insights, or speaking at conferences. These experiences serve as valuable building blocks for your cloud career and can significantly accelerate your professional growth.

Salesforce Introduces Next-Gen Einstein AI Tools for Enhanced CRM

At Dreamforce 2023, Salesforce made waves by unveiling its latest Einstein AI technology, introducing Einstein Copilot and Einstein Copilot Studio. These innovative tools are set to revolutionize the way Salesforce users interact with CRM systems, boosting productivity and personalization. Scheduled for a pilot release this fall, these announcements on September 12th have stirred excitement in the business and AI communities.

Einstein Copilot, the star of the show, is a conversational AI assistant seamlessly integrated into every Salesforce application. Its primary goal is to enhance productivity by offering users assistance within their workflow. With the power to understand natural language queries, Einstein Copilot harnesses the vast wealth of proprietary data from Salesforce Data Cloud to provide relevant answers. But it doesn’t stop there; this AI assistant goes above and beyond by suggesting actionable steps after a sales call or even creating new service knowledge articles.

Complementing Einstein Copilot is Einstein Copilot Studio, a comprehensive toolkit for crafting AI-powered sales apps tailored to a company’s unique needs. This studio empowers businesses to expedite sales deals, streamline customer service, automatically generate personalized websites based on browsing history, or even translate natural language prompts into executable code. The versatility of Einstein Copilot Studio positions it as a valuable resource across various consumer-facing channels, from websites to real-time chat, and seamless integration with messaging platforms like WhatsApp, Slack, or SMS.

Einstein Copilot Studio boasts the following impressive features:

  1. Prompt Builder: This tool allows businesses to create generative AI prompts that align seamlessly with their brand identity, all without requiring deep technical expertise.
  2. Skills Builder: Companies can design custom AI actions to perform specific tasks. One notable example is competitor analysis, where market data and sales figures are analyzed, and API calls are made to external databases for comprehensive competitive insights.
  3. Model Builder: Companies have the option to select a Salesforce proprietary LLM (large language model) or integrate third-party predictive and generative AI models. These models can then be trained on Salesforce Data Cloud, harnessing its extensive data resources.

Einstein Copilot and Einstein Copilot Studio operate within the secure confines of the Einstein Trust Layer, a robust AI architecture embedded in Salesforce. This architecture ensures that AI-driven results are generated by linking responses to customer data while maintaining the highest levels of security and privacy.

The unveiling of Einstein Copilot and Einstein Copilot Studio at Dreamforce 2023 showcases Salesforce’s unwavering commitment to pushing the boundaries of CRM capabilities. These tools, integrated into the Salesforce Einstein 1 Platform for CRM, are poised to make a significant impact on businesses looking to enhance customer engagement, streamline processes, and leverage AI for a competitive edge in the ever-evolving market.

A Step-by-Step Guide to Building Microservices in ASP.NET Core

Microservices architecture has gained immense popularity due to its ability to create loosely coupled, extensible, and independently deployable services that communicate through well-defined interfaces. In this article, we’ll delve into microservices architecture, explore its advantages and disadvantages, and demonstrate how to develop a simple microservice using ASP.NET Core. Future articles will cover implementing an API gateway and establishing interactions between microservices.

Before you begin, ensure that you have Visual Studio 2022 installed on your system. If not, you can download it here.

Understanding Microservices Architecture

Microservices refer to a software architecture where a large application is divided into multiple small, autonomous services. Each microservice is designed to perform specific tasks independently, and they work together as a cohesive whole.

In essence, a microservices-based application consists of decentralized, loosely coupled services that can be independently deployed and maintained. This approach offers several advantages:

1. Scalability: Microservices enable individual services to scale independently based on demand, enhancing overall system scalability.

2. Agile DevOps: Teams can independently develop and deploy services, leading to faster development cycles and facilitating continuous delivery and deployment in line with DevOps principles.

3. Fault Isolation and Resilience: In a microservices architecture, a failure in one service does not affect the entire application. The system is more resilient as it isolates faults and handles failures gracefully.

4. Technology Flexibility: Each microservice can use a different programming language, framework, and technology stack. This flexibility allows teams to choose the most suitable technology for each service.

5. Autonomous Teams: Microservices encourage small, cross-functional teams to work on individual services, promoting autonomy, efficiency, and focus.

However, there are potential drawbacks to consider:

1. Complexity: Microservices introduce a higher level of complexity compared to monolithic architecture. This includes challenges such as network latency, synchronous communication, eventual consistency, and distributed data management.

2. Operational Challenges: Managing and monitoring multiple services in a distributed environment requires additional tools for service discovery, monitoring, logging, and tracking.

3. Increased Development Effort: Developing and maintaining multiple individual services can require more effort compared to a monolithic architecture.

4. Data Management: Maintaining data consistency and transaction integrity is more complex in a distributed microservices environment.

Building a Microservice in ASP.NET Core

To demonstrate how to build a microservice in ASP.NET Core, we’ll create a simple Customer microservice. You can follow these steps to create additional microservices like Product and Supplier:

With these steps completed, you’ve created a minimalistic microservice. When you run the application and access the HTTP GET endpoint of the customer microservice, you’ll see customer data displayed in your web browser.

Microservices vs. Monolith

Microservices architecture stands in contrast to monolithic applications, where all business functionality is consolidated in a single process. With microservices, you break down your application into independently deployable services, allowing you to build, deploy, and manage each service separately.

In this article, we’ve demonstrated how to create a simple microservice in ASP.NET Core. In future articles on microservices architecture, we’ll explore using an API gateway for security enforcement and providing a single point of access to backend services. We’ll also cover implementing interactions between services to complete our microservices-based application. Stay tuned for more insights into building robust microservices.

GitHub Enterprise Server 3.10: Elevating Control and Security

GitHub Enterprise Server 3.10, unveiled on August 29th, brings a host of new features aimed at bolstering control, security, and compliance for both developers and administrators within enterprise settings. As GitHub’s premier self-hosted platform for enterprise-grade software development, this release introduces substantial enhancements that promise to streamline workflows and enhance protection.

One of the standout features of this release is the full-fledged availability of GitHub Projects, a powerful tool for planning and monitoring work. GitHub Projects provides users with a dynamic, spreadsheet-like workspace where they can effortlessly filter, sort, and group issues and pull requests, promoting efficient project management and collaboration.

Furthermore, GitHub Enterprise Server 3.10 introduces custom deployment protection rules tailored for GitHub Actions. This feature is designed to facilitate safe and controlled deployment processes, ensuring that software changes are implemented securely. Additionally, administrators gain enhanced policy control over runners, enabling more fine-grained management of job execution.

Security remains a top priority with this release, as GitHub introduces a user-friendly default setup experience for GitHub Advanced Security code scanning. This feature allows users to quickly identify vulnerabilities across all repositories with just a few clicks. Moreover, it offers a comprehensive view of security coverage and risk management at the enterprise level, empowering organizations to proactively address potential threats.

Developers can access GitHub Enterprise Server from either on-premises or cloud-based deployments via enterprise.github.com, and free trials are readily available for those looking to explore its capabilities.

It’s worth noting that GitHub Enterprise Server 3.10 lays the groundwork for the planned deprecation of team discussions in version 3.12. Users are notified of this upcoming change through a banner displayed in team discussions, which also provides a convenient link to migrate to GitHub Discussions. This strategic move ensures that GitHub continues to evolve and optimize its platform to meet the ever-changing needs of its user base.

Prior to this release, GitHub Enterprise Server 3.9 introduced significant enhancements to GitHub Projects on June 29th, followed by several subsequent point releases, reflecting GitHub’s commitment to regular updates and continuous improvement.