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React 18: From A Developer’s PoV

React 18, the much-awaited version of React is out with exciting features like automatic batching, a new API –startTransition, and streaming server-side rendering with support for Suspense. What’s so special about this newer version is its unique “concurrent rendering” method. In the earlier versions of React, the rendering process was synchronous and non-interruptible where the UI would lock during long render processes and not respond to user input instantly. Using React 18, the rendering process can be made asynchronous and can be interrupted, paused, resumed, and even abandoned enabling developers to create a more fluid user experience.

How to update from React 17 to React 18?

React 18 is currently released in alpha and can be downloaded using the command 

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A component is usually rendered like this

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However, to utilize the latest features, components need to be rendered like this:

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What’s there in React 18?

Automatic Batching

Automatic Batching is the rendering that occurs at the same time whenever triggered to update with multiple states at once. The previous version of React could only batch updates inside React event handlers. If the multiple state updates are triggered by a promise or a callback, their responses are not rendered at the same time. But with React 18, all renders are batched, meaning they’ll occur at the same time, regardless of their trigger.

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Here’s an example with a promise:

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Suspense

With Suspense, React 18 makes major performance improvements to SSR by making serving parts of an app asynchronously possible. Suspense helps in specifying what React should show when a part of the tree isn’t ready to render. For instance, in case there are four components: a Header, a Sidebar, a Banner component, and the Main component. If all four of them are stacked on each other like this 

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Then, the server would try to render them at once, slowing the entire page down. If the Header, the Sidebar, and the Main are more important for the readers, one can prioritize these over the Banner by wrapping the Banner component in Suspense tags:

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As a result, the server would first serve the Header, Side Bar, and Main component, and then a spinner would be displayed while the Banner waits to load.

Transitions

React apps are interactive, however, to make an app update itself as people interact with it, constant updating might cause the app to slow down significantly and give a poor user experience. Transition is a new React feature that differentiates between urgent and non-urgent updates. Transition updates transform the UI from one view to the next.

  • Urgent updates are the direct interactions like typing, clicking, pressing, etc., that need immediate response to match one’s intuition about how physical objects behave. Otherwise, they feel “wrong”. However, transitions are different because the user doesn’t expect to see every intermediate value on the screen. 

Single-user input should typically result in both an urgent and a non-urgent update for the best user experience. StartTransition API can be used inside an input event to tell React which updates are urgent and which are “transitions”.

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  • Non-urgent updates are wrapped in ‘startTransition’ and would get interrupted if more urgent updates, such as clicks or keypresses, emerge. If a user interrupts a transition (for example, by typing multiple characters in a row), React will discard any stale rendering work and render only the most recent update.

New Hooks

  1. UseId

useId is a new hook for creating unique IDs on both the client and the server while avoiding hydration mismatches. It’s most beneficial for component libraries that need to integrate with accessibility APIs that require unique IDs. This addresses a major problem that existed in React 17 and in its previous versions, but it’s even more critical in React 18 because of how the new streaming server renderer delivers HTML out-of-order.

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  1. useTransition

‘useTransition’ and ‘startTransition’ would allow marking some state updates as not urgent. By default, other state updates are considered urgent. React would allow urgent state updates (for example, updating a text input) to interrupt the non-urgent state updates (for example, count results).

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  1. useSyncExternalStore

‘useSyncExternalStore’ is a new hook that enables external stores to support concurrent reads by forcing updates to the store to be synchronous. It eliminates the necessity for useEffect when implementing subscriptions to external data sources, and is recommended for any library that integrates with a state external to React.

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  1. useDeferredValue

‘useDeferredValue’ helps to delay re-rendering a non-urgent part of the tree. It works in the same way as debouncing, although it offers a few advantages. React will attempt the deferred render directly after the initial render is reflected on the screen because there is no predetermined time delay. The deferred render is interruptible and doesn’t block user input.

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  1. useInsertionEffect

‘useInsertionEffect’ is a new hook that allows CSS-in-JS libraries to address the performance difficulties of injecting styles in the render. Unless you’ve already built a CSS-in-JS library we don’t expect you to ever use this. This hook will activate after the DOM is mutated, but before layout effects read the new layout. useInsertionEffect is even more important in React 18 because React yields to the browser during concurrent rendering, giving it a chance to recalculate the layout.

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Conclusion

With the introduction of React 18, there has been a drastic change in the world of web applications because of its unique offerings like concurrent mode and server-side rendering. The latest feature would make it easier to develop and maintain a code as well as make apps faster and more responsive to user interactions.

About the author:

Manikandan is a Technical Lead at Mantra Labs working on React/Angular-related projects. He is interested in learning about stock analyst trading algorithms, and in his free time, he loves to swim, cook, and play cricket and chess.

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AI Code Assistants: Revolution Unveiled

AI code assistants are revolutionizing software development, with Gartner predicting that 75% of enterprise software engineers will use these tools by 2028, up from less than 10% in early 2023. This rapid adoption reflects the potential of AI to enhance coding efficiency and productivity, but also raises important questions about the maturity, benefits, and challenges of these emerging technologies.

Code Assistance Evolution

The evolution of code assistance has been rapid and transformative, progressing from simple autocomplete features to sophisticated AI-powered tools. GitHub Copilot, launched in 2021, marked a significant milestone by leveraging OpenAI’s Codex to generate entire code snippets 1. Amazon Q, introduced in 2023, further advanced the field with its deep integration into AWS services and impressive code acceptance rates of up to 50%. GPT (Generative Pre-trained Transformer) models have been instrumental in this evolution, with GPT-3 and its successors enabling more context-aware and nuanced code suggestions.

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  • Adoption rates: By 2023, over 40% of developers reported using AI code assistants.
  • Productivity gains: Tools like Amazon Q have demonstrated up to 80% acceleration in coding tasks.
  • Language support: Modern AI assistants support dozens of programming languages, with GitHub Copilot covering over 20 languages and frameworks.
  • Error reduction: AI-powered code assistants have shown potential to reduce bugs by up to 30% in some studies.

These advancements have not only increased coding efficiency but also democratized software development, making it more accessible to novice programmers and non-professionals alike.

Current Adoption and Maturity: Metrics Defining the Landscape

The landscape of AI code assistants is rapidly evolving, with adoption rates and performance metrics showcasing their growing maturity. Here’s a tabular comparison of some popular AI coding tools, including Amazon Q:

Amazon Q stands out with its specialized capabilities for software developers and deep integration with AWS services. It offers a range of features designed to streamline development processes:

  • Highest reported code acceptance rates: Up to 50% for multi-line code suggestions
  • Built-in security: Secure and private by design, with robust data security measures
  • Extensive connectivity: Over 50 built-in, managed, and secure data connectors
  • Task automation: Amazon Q Apps allow users to create generative AI-powered apps for streamlining tasks

The tool’s impact is evident in its adoption and performance metrics. For instance, Amazon Q has helped save over 450,000 hours from manual technical investigations. Its integration with CloudWatch provides valuable insights into developer usage patterns and areas for improvement.

As these AI assistants continue to mature, they are increasingly becoming integral to modern software development workflows. However, it’s important to note that while these tools offer significant benefits, they should be used judiciously, with developers maintaining a critical eye on the generated code and understanding its implications for overall project architecture and security.

AI-Powered Collaborative Coding: Enhancing Team Productivity

AI code assistants are revolutionizing collaborative coding practices, offering real-time suggestions, conflict resolution, and personalized assistance to development teams. These tools integrate seamlessly with popular IDEs and version control systems, facilitating smoother teamwork and code quality improvements.

Key features of AI-enhanced collaborative coding:

  • Real-time code suggestions and auto-completion across team members
  • Automated conflict detection and resolution in merge requests
  • Personalized coding assistance based on individual developer styles
  • AI-driven code reviews and quality checks

Benefits for development teams:

  • Increased productivity: Teams report up to 30-50% faster code completion
  • Improved code consistency: AI ensures adherence to team coding standards
  • Reduced onboarding time: New team members can quickly adapt to project codebases
  • Enhanced knowledge sharing: AI suggestions expose developers to diverse coding patterns

While AI code assistants offer significant advantages, it’s crucial to maintain a balance between AI assistance and human expertise. Teams should establish guidelines for AI tool usage to ensure code quality, security, and maintainability.

Emerging trends in AI-powered collaborative coding:

  • Integration of natural language processing for code explanations and documentation
  • Advanced code refactoring suggestions based on team-wide code patterns
  • AI-assisted pair programming and mob programming sessions
  • Predictive analytics for project timelines and resource allocation

As AI continues to evolve, collaborative coding tools are expected to become more sophisticated, further streamlining team workflows and fostering innovation in software development practices.

Benefits and Risks Analyzed

AI code assistants offer significant benefits but also present notable challenges. Here’s an overview of the advantages driving adoption and the critical downsides:

Core Advantages Driving Adoption:

  1. Enhanced Productivity: AI coding tools can boost developer productivity by 30-50%1. Google AI researchers estimate that these tools could save developers up to 30% of their coding time.
IndustryPotential Annual Value
Banking$200 billion – $340 billion
Retail and CPG$400 billion – $660 billion
  1. Economic Impact: Generative AI, including code assistants, could potentially add $2.6 trillion to $4.4 trillion annually to the global economy across various use cases. In the software engineering sector alone, this technology could deliver substantial value.
  1. Democratization of Software Development: AI assistants enable individuals with less coding experience to build complex applications, potentially broadening the talent pool and fostering innovation.
  2. Instant Coding Support: AI provides real-time suggestions and generates code snippets, aiding developers in their coding journey.

Critical Downsides and Risks:

  1. Cognitive and Skill-Related Concerns:
    • Over-reliance on AI tools may lead to skill atrophy, especially for junior developers.
    • There’s a risk of developers losing the ability to write or deeply understand code independently.
  2. Technical and Ethical Limitations:
    • Quality of Results: AI-generated code may contain hidden issues, leading to bugs or security vulnerabilities.
    • Security Risks: AI tools might introduce insecure libraries or out-of-date dependencies.
    • Ethical Concerns: AI algorithms lack accountability for errors and may reinforce harmful stereotypes or promote misinformation.
  3. Copyright and Licensing Issues:
    • AI tools heavily rely on open-source code, which may lead to unintentional use of copyrighted material or introduction of insecure libraries.
  4. Limited Contextual Understanding:
    • AI-generated code may not always integrate seamlessly with the broader project context, potentially leading to fragmented code.
  5. Bias in Training Data:
    • AI outputs can reflect biases present in their training data, potentially leading to non-inclusive code practices.

While AI code assistants offer significant productivity gains and economic benefits, they also present challenges that need careful consideration. Developers and organizations must balance the advantages with the potential risks, ensuring responsible use of these powerful tools.

Future of Code Automation

The future of AI code assistants is poised for significant growth and evolution, with technological advancements and changing developer attitudes shaping their trajectory towards potential ubiquity or obsolescence.

Technological Advancements on the Horizon:

  1. Enhanced Contextual Understanding: Future AI assistants are expected to gain deeper comprehension of project structures, coding patterns, and business logic. This will enable more accurate and context-aware code suggestions, reducing the need for extensive human review.
  2. Multi-Modal AI: Integration of natural language processing, computer vision, and code analysis will allow AI assistants to understand and generate code based on diverse inputs, including voice commands, sketches, and high-level descriptions.
  3. Autonomous Code Generation: By 2027, we may see AI agents capable of handling entire segments of a project with minimal oversight, potentially scaffolding entire applications from natural language descriptions.
  4. Self-Improving AI: Machine learning models that continuously learn from developer interactions and feedback will lead to increasingly accurate and personalized code suggestions over time.

Adoption Barriers and Enablers:

Barriers:

  1. Data Privacy Concerns: Organizations remain cautious about sharing proprietary code with cloud-based AI services.
  2. Integration Challenges: Seamless integration with existing development workflows and tools is crucial for widespread adoption.
  3. Skill Erosion Fears: Concerns about over-reliance on AI leading to a decline in fundamental coding skills among developers.

Enablers:

  1. Open-Source Models: The development of powerful open-source AI models may address privacy concerns and increase accessibility.
  2. IDE Integration: Deeper integration with popular integrated development environments will streamline adoption.
  3. Demonstrable ROI: Clear evidence of productivity gains and cost savings will drive enterprise adoption.
  1. AI-Driven Architecture Design: AI assistants may evolve to suggest optimal system architectures based on project requirements and best practices.
  2. Automated Code Refactoring: AI tools will increasingly offer intelligent refactoring suggestions to improve code quality and maintainability.
  3. Predictive Bug Detection: Advanced AI models will predict potential bugs and security vulnerabilities before they manifest in production environments.
  4. Cross-Language Translation: AI assistants will facilitate seamless translation between programming languages, enabling easier migration and interoperability.
  5. AI-Human Pair Programming: More sophisticated AI agents may act as virtual pair programming partners, offering real-time guidance and code reviews.
  6. Ethical AI Coding: Future AI assistants will incorporate ethical considerations, suggesting inclusive and bias-free code practices.

As these trends unfold, the role of human developers is likely to shift towards higher-level problem-solving, creative design, and AI oversight. By 2025, it’s projected that over 70% of professional software developers will regularly collaborate with AI agents in their coding workflows1. However, the path to ubiquity will depend on addressing key challenges such as reliability, security, and maintaining a balance between AI assistance and human expertise.

The future outlook for AI code assistants is one of transformative potential, with the technology poised to become an integral part of the software development landscape. As these tools continue to evolve, they will likely reshape team structures, development methodologies, and the very nature of coding itself.

Conclusion: A Tool, Not a Panacea

AI code assistants have irrevocably altered software development, delivering measurable productivity gains but introducing new technical and societal challenges. Current metrics suggest they are transitioning from novel aids to essential utilities—63% of enterprises now mandate their use. However, their ascendancy as the de facto standard hinges on addressing security flaws, mitigating cognitive erosion, and fostering equitable upskilling. For organizations, the optimal path lies in balanced integration: harnessing AI’s speed while preserving human ingenuity. As generative models evolve, developers who master this symbiosis will define the next epoch of software engineering.

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