Try : Insurtech, Application Development

AgriTech(1)

Augmented Reality(21)

Clean Tech(9)

Customer Journey(17)

Design(45)

Solar Industry(8)

User Experience(68)

Edtech(10)

Events(34)

HR Tech(3)

Interviews(10)

Life@mantra(11)

Logistics(5)

Manufacturing(3)

Strategy(18)

Testing(9)

Android(48)

Backend(32)

Dev Ops(11)

Enterprise Solution(33)

Technology Modernization(9)

Frontend(29)

iOS(43)

Javascript(15)

AI in Insurance(38)

Insurtech(66)

Product Innovation(58)

Solutions(22)

E-health(12)

HealthTech(24)

mHealth(5)

Telehealth Care(4)

Telemedicine(5)

Artificial Intelligence(153)

Bitcoin(8)

Blockchain(19)

Cognitive Computing(8)

Computer Vision(8)

Data Science(23)

FinTech(51)

Banking(7)

Intelligent Automation(27)

Machine Learning(48)

Natural Language Processing(14)

expand Menu Filters

Apple Vision Pro- What’s in it for Developers?

Apple has consistently provided developers with powerful tools and frameworks to create exceptional applications. At the WWDC23 event, the company announced its brand-new wearable device, the Vision Pro. This mixed reality gadget functions like a spatial computer, mapping digital content onto your immediate physical surroundings and allowing the user to operate it with their hands, eyes, and voice. With the aid of Vision Pro, users can engage with digital content in a way that makes it appear to be physically present in their environment. To give a truly immersive real-time experience to the users, Vision Pro’s revolutionary design incorporates Apple silicon in a unique dual-chip design along with an ultra-high-resolution display, boasting an impressive 23 million pixels across two screens.

But what’s in it for developers? In this blog, we’ll dive into what Apple Vision Pro offers to developers and explore the myriad of possibilities it opens up for creating cutting-edge apps and experiences.

Release Date: 

The headset will be released in early 2024 in the United States

Estimated Price: $3,499 (2.88 lakhs)

OS: VisionOS

SDK: The VisionOS SDK is available now for developers.

For use in apps, the Vision Pro technology offers countless possibilities. The key areas where VisionPro will have an impact on the mixed reality market are as follows: 

  • Entertainment
  • Fitness
  • Virtual Assistants
  • Gaming
  • Education
  • Social
  • Engineering
  • LifeStyle

The Developers’ Perspective

Apple is creating VisionOS apps utilizing current frameworks and tools so that developers may quickly develop and realize their ideas within the allotted time.  

The basic three components for creating immersive extraordinary experiences are as follows. 

Window: Your visionOS app can have one or more windows created. Traditional views and controls are included, and you can deepen your experience by incorporating 3D material. They were created using SwiftUI.

Volumes: Volumes are SwiftUI scenes that display 3D content using RealityKit or Unity. Users can view and interact with the content from any perspective, whether in the Shared Space or the Full Space of the app.

Spaces: The Shared Space is where apps open by default, and there they coexist side by side, much like numerous apps on a Mac desktop. Users can also move the two display elements- Windows and volumes in the apps. For a more immersive experience, an app has the capability to launch a special Full Space, where only the app’s content is visible. Further, the app can also utilize windows and volumes, develop unrestricted 3D content, provide a portal to another universe, or even fully immerse users within a setting inside a Full Space.

List of familiar Apple frameworks that are useful for spatial computing

SwiftUI

With SwiftUI, we can make stunning, dynamic apps faster than ever before and specify user interfaces for every Apple platform. SwiftUI is the best method to design a new visionOS programme or port your current iPadOS or iOS app to the platform, regardless of whether you’re generating windows, volumes, or spatial experiences. New 3D capabilities, depth, motions, effects, and immersive scene kinds are all supported.

RealityKit

We can produce 3D content, animations, and visual effects for your app utilizing Apple’s 3D rendering engine. RealityKit can do so much more, such as throw shadows, open portals to other worlds, create spectacular visual effects, and adjust to actual lighting conditions automatically.

ARKit:

With Vision Pro, ARKit can completely comprehend a user’s surroundings, opening new opportunities for your apps to engage with the environment. When your app moves to a Full Space and requests permission, you can use powerful ARKit APIs like Plane Estimation, Scene Reconstruction, Image Anchoring, World Tracking, and Skeletal Hand Tracking. By default, ARKit powers core system capabilities that your apps automatically benefit from when they’re in the Shared Space. So wash a wall with water. Get a ball off the ground. Create memorable encounters by fusing your content with the actual world.

Accessibility:

People who like to engage with their devices only with their eyes, their voice, or a combination of the two will find VisionOS to be accessible. Additionally, Pointer Control enables users to choose their index finger, wrist, or head as an alternate pointer for content navigation if they want something else. 

List of required development tools to build VisionOS apps

Xcode:

Xcode, which supports the visionOS SDK, is where development for visionOS begins. Build a brand-new app or add a visionOS target to an existing one. In Xcode Previews, refine your app. Play around with your app while exploring different room layouts and lighting options in the brand-new visionOS simulator. For your spatial content, develop tests and visualizations to investigate collisions, occlusions, and scene understanding.

Reality Composer Pro:

Learn about the brand-new Reality Composer Pro, created to make previewing and preparing 3D material for your visionOS apps simple. Reality Composer Pro, a tool included with Xcode, enables you to import and arrange resources including 3D models, materials, and audio. The Xcode build process is intimately integrated with it to preview and optimize your visionOS assets, which is the best part.

Unity:

Now you can adapt your current Unity-created applications for visionOS or create new apps and games using Unity’s strong and familiar authoring capabilities. Your apps can use AR Foundation and other well-known Unity capabilities, as well as all the advantages of visionOS, including passthrough and dynamically foveated rendering.

Conclusion:

Apple Vision Pro marks a pivotal moment for developers, unlocking a world of possibilities in the realm of immersive experiences. 

By embracing Apple Vision Pro, developers can deliver groundbreaking experiences to captivate and delight users. 

About the Author: Raviteja Aketi is a Technical Manager at Mantra Labs. He has extensive experience with B2B & B2C projects. Raviteja loves exploring new technologies, watching movies, and spending time with family and friends.

Also Read: Embarking on a Design Odyssey: Apple’s UI/UX Transformation through WWDC 2023 

Cancel

Knowledge thats worth delivered in your inbox

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.

Image Source

  • 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.

Cancel

Knowledge thats worth delivered in your inbox

Loading More Posts ...
Go Top
ml floating chatbot