Month: September 2024

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Exploring the adoption of Go and Rust among backend developers

Among the many languages used in backend development, Go and Rust emerge as compelling options, each with its own unique strengths. Go, created by Google, is known for its simplicity and performance in distributed systems and microservices. Meanwhile, Rust, developed by Mozilla, has gained attention for its focus on safety and memory management.

In this post, we explore the current adoption of Go and Rust among backend developers, drawing insights from SlashData’s latest Developer Nation survey, which ran in Q3 2024 and reached more than 2,700 backend developers worldwide. We’ll dive into key questions like: How popular are Go and Rust in the backend developer community? How has their usage evolved over time? In which geographic regions are these programming languages more popular?

According to our latest survey data, 11% of backend developers currently use Go, while only 5% use Rust. Despite their appeal to developers working on scalable, high-performance applications, more versatile languages like JavaScript/TypeScript (41%), Java (39%), or Python (37%) dominate the space, with over a third of backend developers using them. 

Over the past 2.5 years, the adoption of Go and Rust among backend developers has remained stable, with no significant variations in their usage. However, while the share of backend developers using these programming languages hasn’t increased, the size of both the Go and Rust communities has grown alongside the overall increase in the backend developer population, which has grown by over 700,000 developers during this period.

The “Adoption of programming languages among backend developers” study, based on a sample size of 2,754 respondents, asked the question: “Which programming languages do you use to write code that runs on the server or in the cloud?” and provides an overview of the percentage of backend developers using various programming languages.

Regional adoption of Go and Rust among backend developers

Examining the adoption of Go and Rust from a regional perspective reveals some interesting patterns. Western Europe leads with the highest adoption rates of both Go (15%) and Rust (8%), indicating a strong interest in these programming languages. In addition to Western Europe, East Asia, and the Middle East & Africa show average or above-average adoption rates for both languages.

On the other hand, both Go and Rust see below-average adoption among backend developers in South America, North America, South Asia, and China. These lower adoption rates may reflect regional preferences for different technology stacks or a slower adoption of these newer technologies. Notably, Go’s adoption is lowest in China (5%), while Rust has its weakest presence in South Asia, where only 2% of backend developers use it.

The “Regional adoption of Go and Rust” report, based on a sample of 2,754 backend developers, examines the percentage of developers in each region using these languages for server or cloud code, as outlined in the “regions” tab.

The usage of Go and Rust among professional backend developers

The backend development community has one of the highest concentrations of professionals in the software development ecosystem, with 85% of backend developers involved professionally in the space. This trend is even more pronounced in the adoption of Go and Rust, as both are more likely to be used by professional backend developers than hobbyists and students, in contrast to other languages like JavaScript/TypeScript, Python, or C++.

When analysing Go and Rust adoption across companies of different sizes, we observe opposite trends. The usage of Go increases with company size, rising from 7% among freelance backend developers to 13% among those working for large enterprises with over 1,000 employees. In contrast, the adoption of Rust halves, from 6% among freelancers to only 3% within large organisations.

By creating Go, Google aimed to address the challenges of building large, efficient, and scalable systems. With its focus on simplicity, performance, concurrency, and developer productivity, Go becomes increasingly attractive to larger enterprises, especially those that manage vast cloud infrastructures, microservices, or backend systems where scalability and performance are critical.

On the other hand, smaller companies, particularly startups or niche firms, often focus on highly specialised areas such as blockchain, security, and performance-critical applications, where Rust truly shines. Moreover, smaller companies tend to be less bound by legacy systems or established tech stacks, allowing developers to experiment more freely with modern languages like Rust, which has been around for less than a decade.

Finally, let’s conclude this blog post by comparing the industries with the highest adoption rates of Go and Rust. Our data reveals that Go is most popular among backend developers working for companies in the tourism and hospitality (19%), automotive and marine (18%), and telecommunications and networks (18%) industries. Conversely, Rust enjoys its highest popularity among backend developers in automotive and marine (12%), hardware products (12%), and insurance (10%).

The “Adoption of Go and Rust by company size” analysis, based on a sample of 2,475 professional backend developers, details the percentage of developers using these languages across different company sizes for server or cloud code, as shown in the “csize” tab.

Are you a Go or Rust user? We would love to hear from you. Participate in our latest survey, help shape the future of technology and win great prizes.

About the author

Álvaro Ruiz Cubero

Market Research Analyst

Álvaro is a market research analyst with a background in strategy and operations consulting. He holds a Master’s in Business Management and believes in the power of data-driven decision-making. Álvaro is passionate about helping businesses tackle complex strategic business challenges and make strategic decisions that are backed by thorough research and analysis.

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Community

Streamlining the Chatbot Development Life Cycle with AI Integration

In today’s fast-paced digital world, chatbots have become essential for businesses, enhancing customer engagement and streamlining operations. Integrating AI into the chatbot development life cycle can significantly improve their functionality, making them smarter, more responsive, and efficient. This guide will explore the stages of the chatbot development life cycle, the tools and technologies involved, and provide insights into creating an AI-powered chatbot.

Understanding the Chatbot Development Life Cycle

Approximately 1.4 billion individuals currently use chatbots. The chatbot development life cycle encompasses several key stages, each crucial for building an effective chatbot.

Requirement Analysis and Planning

The journey begins with understanding the needs and expectations of the users. Imagine a company that wants a chatbot to handle customer service inquiries. They need to:

  • Identify the chatbot’s purpose.
  • Define the target audience.
  • Set clear objectives and goals.
  • Outline key functionalities and features.

For instance, they might decide the chatbot should answer common questions, guide users through the website, and handle basic troubleshooting.

Designing the Chatbot

Designing a chatbot involves creating the conversational flow and user interface. Picture a creative team brainstorming the chatbot’s personality. They decide it will be friendly and helpful, reflecting the company’s brand. They then craft the conversation script, design the user interface, and create a personality for the chatbot. The result is a chatbot that feels like a natural extension of the company’s customer service team.

Choosing the Right Tools and Platforms

Selecting the right tools and platforms is crucial. Here are some popular choices:

  • Dialogflow: Powered by Google, it’s great for complex conversational flows with robust natural language processing (NLP) capabilities.
  • Microsoft Bot Framework: A comprehensive framework supporting multiple channels like Skype, Slack, and Facebook Messenger.
  • Rasa: An open-source framework that allows for highly customizable chatbots with advanced NLP capabilities.

Each platform has its strengths. Dialogflow excels in ease of use, Microsoft Bot Framework is perfect for extensive integrations, and Rasa offers high customization. For businesses seeking tailored solutions, MOCG generative AI consulting can further enhance the capabilities of these platforms, providing specialized expertise to meet unique requirements.

Development and Integration

Now comes the actual building of the chatbot. Imagine a team working together, coding, and integrating the chatbot with various platforms. They might choose Rasa for its flexibility. The team sets up the environment, trains the model, and starts running the chatbot. They then integrate it with Slack, allowing users to interact with the chatbot directly within their preferred communication tool.

Testing and Debugging

Testing is a critical phase. Think of a meticulous QA team putting the chatbot through its paces. They conduct:

  • Unit Testing: Ensuring each part of the chatbot works individually.
  • Integration Testing: Making sure all parts work together seamlessly.
  • User Acceptance Testing (UAT): Gathering feedback from real users.

The goal is to iron out any kinks and ensure the chatbot provides a smooth user experience.

Deployment

Deploying the chatbot involves making it available to users on chosen platforms. Picture the excitement as the team finally releases their creation to the world. They might use cloud services like AWS or Google Cloud, or opt for containerization with Docker or Kubernetes, ensuring the chatbot is scalable and robust.

Maintenance and Improvement

The work doesn’t stop after deployment. Continuous monitoring and improvement are essential. Imagine a dedicated team analyzing user interactions, fixing bugs, and adding new features. They use tools like Google Analytics to track performance and Mixpanel to understand user behavior. This ongoing process ensures the chatbot remains relevant and effective.

AI Integration in Chatbot Development

Integrating AI into the chatbot development life cycle enhances its capabilities in several ways:

Natural Language Processing (NLP)

AI-powered NLP allows chatbots to understand and process human language more effectively. For instance, a customer might ask, “Can you help me track my order?” The chatbot recognizes the intent (tracking an order) and the entity (the order itself), providing a precise response.

Machine Learning

Machine learning algorithms enable chatbots to learn from interactions and improve over time. Imagine a chatbot that gets better at answering questions as it interacts with more users, thanks to supervised learning and reinforcement learning techniques.

Sentiment Analysis

AI-powered sentiment analysis helps chatbots understand the emotions behind user input, enabling more empathetic responses. For example, if a user expresses frustration, the chatbot can offer a soothing and helpful response, improving customer satisfaction.

Case Study: Building an AI-Powered Customer Support Chatbot

Let’s walk through a practical example of building an AI-powered customer support chatbot.

Step 1: Creating the Concept

Picture a company deciding they need a chatbot to handle customer support inquiries. They outline the chatbot’s purpose, target audience, and key functionalities.

Step 2: Designing the Experience

The creative team designs a friendly, helpful chatbot that aligns with the company’s brand. They draft conversation scripts, design the user interface, and give the chatbot a personality that resonates with users.

Step 3: Choosing the Platform

The team opts for Dialogflow due to its robust NLP capabilities and ease of integration with their existing systems.

Step 4: Building the Chatbot

The development team sets up Dialogflow, creates intents for common customer inquiries, and integrates the chatbot with the company’s website. They work diligently, ensuring the chatbot understands and responds accurately to user queries.

Step 5: Testing and Refining

The QA team rigorously tests the chatbot, ensuring it handles various scenarios smoothly. They gather feedback from beta users and make necessary adjustments to improve the user experience.

Step 6: Going Live

With everything in place, the team deploys the chatbot on the company’s website. Customers can now interact with the chatbot, receiving instant support for their inquiries.

Step 7: Continuous Improvement

Post-launch, the team continuously monitors the chatbot’s performance, making updates and improvements based on user feedback and interaction data. This ongoing process ensures the chatbot remains a valuable tool for customer support.

Trends in AI-Powered Chatbot Development

Based on Research by SlashData – 15% of ML/DS developers use machine learning to build new ML or AI-based products, applications or chatbots. The integration of AI in the chatbot development life cycle is continuously evolving. Here are some trends to watch:

Conversational AI

Conversational AI aims to create more human-like interactions by understanding context and generating natural responses. This means chatbots are becoming more sophisticated, capable of engaging in more complex and meaningful conversations.

Multilingual Chatbots

With global businesses, there is a growing demand for chatbots that can converse in multiple languages. This trend is driven by the need to provide support to a diverse customer base.

Voice-Enabled Chatbots

Voice-enabled chatbots are gaining popularity, providing users with a more intuitive and hands-free experience. As voice technology improves, we can expect to see more businesses adopting voice-enabled chatbots.

Hyper-Personalization

AI enables chatbots to provide highly personalized responses based on user data and behavior. This trend is leading to more tailored and relevant interactions, enhancing the overall user experience.

Future-Proofing with AI

As AI technology advances, the chatbot development life cycle will become more streamlined, with AI handling more complex tasks and reducing development time. Businesses that embrace these advancements will be better positioned to provide superior customer experiences and stay ahead of the competition.

Conclusion

Integrating AI into the chatbot development life cycle significantly enhances the capabilities and efficiency of chatbots. By following a structured approach and leveraging the right tools and technologies, businesses can create powerful, intelligent chatbots that drive engagement and deliver exceptional value to users. This journey, from planning to continuous improvement, highlights the dynamic and transformative nature of AI in chatbot development.

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Community Tools

Top 10 Remote Access Software Tools for Seamless Connectivity in 2024

Top 10 Remote Access Software Tools for Seamless Connectivity

As remote work continues to be the norm, the demand for efficient and secure remote access software has never been higher. Businesses need tools that not only provide seamless connectivity but also ensure security, reliability, and ease of use. Here, we delve into the top 10 remote access software tools of 2024, each offering unique features to cater to a variety of needs.

1. Zoho Assist

Overview: Zoho Assist is a comprehensive remote access and support solution that caters to businesses of all sizes. With its cloud-based architecture, it allows IT professionals to provide remote support and access unattended computers from anywhere.

Key Features:

  • Unattended remote access
  • Multi-platform support (Windows, Mac, Linux)
  • Screen sharing, file transfer, and chat capabilities
  • Integration with other Zoho products like Zoho Desk and Zoho CRM

Pricing: Zoho Assist offers a free plan for one user with basic features. Paid plans start at $10 per month.

2. Atera

Overview: Atera is an all-in-one remote monitoring and management (RMM) platform that includes remote access features. It’s designed primarily for managed service providers (MSPs) and IT professionals, offering a range of tools for monitoring, managing, and supporting IT infrastructure remotely.

Key Features:

  • Remote desktop and file transfer capabilities
  • Automated patch management
  • Real-time alerts and monitoring
  • Integration with PSA (Professional Services Automation) tools

Pricing: Atera’s pricing is subscription-based, starting at $79 per technician per month.

3. ManageEngine Remote Access Plus

Overview: ManageEngine Remote Access Plus is a powerful remote access tool designed for IT teams. It provides advanced features for troubleshooting and managing remote endpoints, making it a preferred choice for enterprises with extensive IT infrastructure.

Key Features:

  • Remote control with multi-monitor support
  • Remote file transfer and command execution
  • Video recording of remote sessions
  • Support for Windows, macOS, and Linux

Pricing: ManageEngine Remote Access Plus offers a free edition for up to 10 computers, with paid plans starting at $95 per technician per year.

4. Zendesk Remote

Overview: Zendesk Remote is part of the Zendesk Suite, which is widely known for customer support solutions. Zendesk Remote integrates remote access directly into customer service workflows, enabling support teams to assist customers efficiently.

Key Features:

  • Seamless integration with Zendesk Support
  • Remote control and screen sharing
  • Session recording for quality assurance
  • Real-time chat and communication during support sessions

Pricing: Zendesk Remote is included in the Zendesk Suite, with plans starting at $49 per agent per month.

5. Zoho Remotely

Overview: Zoho Remotely is a remote work suite designed to help teams stay connected and productive from anywhere. While it includes a variety of collaboration tools, its remote access capabilities are a standout feature for businesses looking to manage remote teams effectively.Integrating effective email marketing software in USA alongside these tools can further enhance your communication strategy, allowing you to reach your audience with targeted campaigns and optimise business outcomes.

Key Features:

  • Remote desktop access
  • Project management and collaboration tools
  • Secure file storage and sharing
  • Integrated communication via chat, video, and voice

Pricing: Zoho Remotely offers a free plan with limited features, while paid plans start at $10 per user per month.

6. Freshservice

Overview: Freshservice is an IT service management (ITSM) tool that includes robust remote access capabilities. It’s designed to help IT teams manage and support their organizations’ technology infrastructure, making it an excellent choice for medium to large enterprises. corporating payroll software alongside tools like Freshservice ensures that businesses can manage both their IT infrastructure

Key Features:

  • Remote control and screen sharing for troubleshooting
  • Automated workflows for incident management
  • Asset management and configuration management database (CMDB)
  • Integration with other Freshworks products

Pricing: Freshservice plans start at $19 per agent per month.

7. ISL Online

Overview: ISL Online is a remote desktop software solution known for its reliability and security. It’s particularly popular among IT support teams and customer service departments that require secure, fast, and easy-to-use remote access tools.

Key Features:

  • Remote desktop access with high-security standards
  • Unattended access and session recording
  • Cross-platform support (Windows, Mac, Linux, Android, iOS)
  • Branding and customization options

Pricing: ISL Online offers flexible pricing options, with a basic plan starting at $145 per year.

8. Wayk Now

Overview: Wayk Now is a versatile remote desktop access tool designed for IT professionals who need to manage and support remote systems. It provides an easy-to-use interface combined with powerful features to ensure seamless remote access.

Key Features:

  • Instant remote access without prior installation
  • Cross-platform support (Windows, Mac, Linux)
  • Secure file transfer and clipboard sharing
  • Integrated chat and collaboration tools

Pricing: Wayk Now offers a free version with basic features, and paid plans start at $15 per user per month.

9. SpatialChat

Overview: SpatialChat is a remote collaboration tool with a focus on creating virtual workspaces. While not a traditional remote access tool, it provides a unique way for remote teams to interact and collaborate in a virtual environment, making it ideal for distributed teams.

Key Features:

  • Virtual meeting rooms with spatial audio
  • Screen sharing and media sharing
  • Customizable virtual office layouts
  • Integration with other collaboration tools

Pricing: SpatialChat offers a free plan with limited features, and paid plans start at $49 per month.

10. Loop Team

Overview: Loop Team is designed to replicate the in-office experience for remote teams, offering a platform that combines video conferencing, messaging, and collaboration with remote access features. It’s ideal for teams that want to maintain a high level of communication and collaboration while working remotely.

Key Features:

  • Persistent virtual rooms for ongoing team collaboration
  • Screen sharing and remote control capabilities
  • Meeting recording and transcription
  • Integration with productivity tools like Slack and Google Workspace

Pricing: Loop Team offers a free version with basic features, with paid plans starting at $8 per user per month.

Conclusion

The diverse range of remote access tools available in 2024 ensures that businesses and individuals can find the perfect solution to meet their needs. Whether you require a robust IT management platform like ManageEngine Remote Access Plus, an all-in-one remote work suite like Zoho Remotely, or a unique virtual collaboration environment like SpatialChat, there’s a tool to fit every scenario. As remote work continues to evolve, these tools will play a critical role in ensuring that teams stay connected, productive, and secure, no matter where they are in the world.

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Community

Accelerating IoT development using Cloud workflow with Arm virtual hardware

Having spent significant time in the realm of embedded systems and IoT development, I’ve consistently encountered challenges related to initial setup and scaling to production. Chip selection, a pivotal aspect of this process, often involves meticulous shortlisting of microcontrollers and architectures, followed by the acquisition of development boards and prototyping to identify the most suitable chipset. Not to mention the lead times and chip shortage problems, this is merely the beginning.

Once this initial step is complete, the journey extends to establishing IDEs, debuggers, test environments, and other development, testing, collaborating and shipping tools. This process can be arduous, difficult to scale, and often discouraging. Yet, as those familiar with my work know, I’ve consistently been vocal about the bridging of the gap between hardware/IoT/embedded and software/cloud development, thanks to new product lifecycle management workflows and DevOps practises.

Cloud developers have long enjoyed the luxury of robust tools and streamlined software lifecycles. The ability to scale from a single server instance to thousands with a simple click, facilitated by Docker Containers, Kubernetes, and DevOps workflows like CI/CD, has been a major draw for embedded developers.

One such innovation that brings IoT developers closer to this cloud-native model is Arm’s Virtual Hardware on the cloud. Let’s talk more about that in this blog and see how it fits into the bigger picture starting from:

Pain Points in Traditional Embedded and IoT Development

Developing for embedded and IoT applications involves numerous challenges, including:

  • Hardware Dependencies: Reliance on physical hardware can significantly delay development cycles, as changes often require new hardware or software modifications.
  • Long Shipping Delays and Chip Shortages: Procuring specialised hardware can be time-consuming, especially during chip shortages (a major impact seen during the COVID-19 pandemic), hindering development progress.
  • Limited Testing Environments: Testing embedded software on physical hardware can be resource-intensive and requires specialised custom jigs, debuggers, physical access, Test and Measurement equipment and much more.
  • Integration Challenges: Coordinating hardware and software development teams can be difficult, leading to delays and potential integration issues.

How Arm Virtual Hardware Changes the Game

Arm Virtual Hardware addresses these challenges by providing a virtualised environment where developers can simulate and test embedded and IoT applications without relying on physical hardware. This offers several key benefits:

  • Faster Time to Market: AVH accelerates development cycles by enabling testing and debugging early in the process, reducing the time it takes to bring products to market.
  • Development Without Hardware: Developers can start working on their applications before physical hardware is available, saving time and resources, which is very helpful when you’re in the process of deciding the hardware or ordering dev boards.
  • Bridging the Gap Between Hardware and Software: AVH fosters collaboration between hardware and software teams by providing a common platform for testing and integration.
  • Enabling Cloud Workflows and DevOps: Thanks to AVH, you can now use most of the tools cloud developers have to ship products faster into the market. Virtual hardware can be integrated into cloud-based development environments, enabling DevOps practices and continuous integration/continuous delivery (CI/CD) pipelines. 
  • Scalable: Firing up 200 new Virtual hardware in the cloud takes 10 seconds; compare this to testing your code in 200 new development boards; the scalability factor with Arm virtual hardware is phenomenal and a breeze regarding regression testing. 

Enabling MLOps with Arm Virtual Hardware

Running ML models on the Edge compute devices is one of the most common applications where Arm-based processors are deployed today. Think of smart speakers, phones, traffic lights, cameras, etc. These products and applications can benefit greatly by adopting Arm virtual hardware in prototyping and testing life cycles. 

Machine Learning Operations (MLOps) involves managing the entire lifecycle of machine learning models, from development to deployment. Using Arm virtual hardware, developers and data scientists can test their model on almost a real Arm processor, estimating the performance of different architectures and chipsets; this enables them to pick the best hardware suitable for their models, saving a lot of cost and time in development and bringing product to market. Developers can train machine learning models on virtualised Arm hardware, ensuring compatibility with target devices and architecture, enabling

  • Testing and Optimization: AVH can test and optimise models for performance and resource utilisation on embedded platforms.
  • Deployment: Once ready, models can be deployed to physical devices with little to no adaptation using AVH as a reference environment.

In Conclusion

Arm Virtual Hardware is a game-changer for embedded and IoT development. By addressing the challenges of traditional development methods, AVH enables faster time to market, improved collaboration, and enhanced flexibility. As the adoption of embedded and IoT devices grows, AVH will play a vital role in driving innovation and efficiency.