What is Lightning Web Security?

Lightning Web Security (LWS) is Salesforce’s modern client-side security architecture designed to secure Lightning Web Components (LWC) and Aura components. Introduced as an improvement over the older Lightning Locker service, LWS enhances component isolation with better performance and compatibility with modern web standards.

Key Features of LWS

• Namespace isolation: Each Lightning web component runs in its own JavaScript sandbox, preventing unauthorized access to data or code from other namespaces.

• API distortion: LWS modifies standard JavaScript APIs dynamically to enforce security policies without breaking developer experience.

• Supports third-party libraries: Unlike Locker, LWS allows broader use of community and open-source JS libraries.

• Default in new orgs: Enabled by default for all new Salesforce orgs created from Winter ’23 release onwards.

Benefits of Using LWS

• Stronger security: Limits cross-component and cross-namespace vulnerabilities.

• Improved performance: Reduced overhead compared to Locker’s wrappers, resulting in faster load times for users.

• Better developer experience: Easier to build robust apps without excessive security workarounds.

• Compatibility: Uses the latest web standards and works well with modern browsers and tools.

How to Enable LWS in Your Org

1. Navigate to Setup > Session Settings in Salesforce.

2. Enable the checkbox for Use Lightning Web Security for Lightning web components and Aura components.

3. Save settings and clear browser cache to ensure the change takes effect.

4. Test your Lightning components thoroughly, ideally starting in a sandbox environment before deploying to production.

Best Practices for Working with LWS

• Test extensively: Some existing components may require minor updates due to stricter isolation.

• Use the LWS Console: Salesforce provides developer tools to inspect and debug components under LWS.

• Follow secure coding guidelines: Maintain least privilege principle and avoid direct DOM manipulations.

• Plan migration: Gradually transition from Lightning Locker to LWS, if upgrading older orgs.

• Leverage Third-party Libraries Wisely: Confirm compatibility with LWS to avoid runtime errors.

Troubleshooting Common LWS Issues

• Components failing due to namespace restrictions.

• Unexpected behavior with third-party libraries.

• Performance bottlenecks during initial page loading.

Utilize Salesforce’s diagnostic tools, logs, and community forums for support.

Resources for Further Learning

• Salesforce Developer Guide – Lightning Web Security

• Salesforce Trailhead Module on Lightning Web Components Security

Salesforce Marketing Cloud + AI is revolutionizing marketing by combining advanced artificial intelligence with marketing automation to create hyper-personalized, data-driven campaigns that adapt in real time to customer behaviors and preferences. This fusion drives engagement, conversions, and revenue growth like never before.

Key AI Features of Salesforce Marketing Cloud

• Agentforce: An autonomous AI agent that helps marketers create dynamic, scalable campaigns with effortless automation and real-time optimization. It streamlines content creation, segmentation, and journey management through simple prompts and AI insights. Learn more at the Salesforce official site.

• Einstein AI: Powers predictive analytics, customized content generation, send-time optimization, and smart audience segmentation, ensuring the right message reaches the right customer at the optimal time.

• Generative AI: Using Einstein GPT, marketers can automatically generate email copy, subject lines, images, and landing pages, enhancing productivity while maintaining brand consistency.

• Marketing Cloud Personalization: Provides real-time behavioral data and AI-driven recommendations to deliver tailored experiences that boost customer loyalty and conversion rates.

• Unified Data Cloud Integration: Seamlessly connects live customer data for dynamic segmentation and activation, eliminating data silos.

• Multi-Channel Orchestration: Integrates deeply with platforms like WhatsApp, Slack, and LinkedIn to deliver personalized campaigns across all customer touchpoints.

Latest Trends & 2025 Updates

• With advanced artificial intelligence, marketing teams benefit from systems that independently manage and adjust their campaigns for optimal results.

• Real-time customer journey adaptations powered by live data.

• Enhanced collaboration via AI integration with Slack and other platforms.

• Automated paid media optimization and budget control with minimal manual intervention.

For detailed insights on AI and marketing automation trends, see this industry report.

Benefits of Combining Salesforce Marketing Cloud + AI

• Increased campaign efficiency and ROI through automation and predictive analytics.

• Hyper-personalized customer engagement at scale.

• Reduced manual effort with AI-assisted content and segmentation.

• Better decision-making powered by unified data and AI-driven insights.

• Greater marketing agility and responsiveness in a changing landscape.

Related Posts

• • Mastering Customer Journey Mapping with Salesforce

  • Top AI Tools for Marketing in 2025

Salesforce provides powerful features to handle metadata, allowing you to store and access configuration data in a structured manner. In this blog, we explore Salesforce Custom Metadata getInstance vs SOQL—two key approaches developers use to retrieve custom metadata efficiently. Custom metadata types in Salesforce offer a great way to define reusable and customizable application data without worrying about governor limits that come with other storage solutions, like custom objects. For more details, you can visit the official Salesforce Trailhead Custom Metadata Types module. We will delve into the differences, use cases, and best practices for these two approaches.

What is Custom Metadata in Salesforce?

Custom metadata types are custom objects in Salesforce that store metadata or configuration data. Unlike standard or custom objects, they are intended for storing application configurations that don’t change often. These types are often used for things like:

• Configuration settings for apps
• Defining global values (like API keys)
• Storing environment-specific configurations
• Reusable data for automation or integrations

Custom metadata records can be easily managed via Setup, the Metadata API, or APEX.

Approach 1: Using getInstance()

getInstance() is a method that allows you to access a single record of a custom metadata type. It works on a “singleton” basis, meaning that it returns a specific instance of the custom metadata record.

How getInstance() Works

The getInstance() method is typically used when you’re looking to retrieve a single record of custom metadata in your code. This method is not intended to query multiple records or create complex filters. Instead, it retrieves a specific record directly, based on the provided developer name.

Example:

// Get a specific custom metadata record by its developer name
My_Custom_Metadata__mdt metadataRecord = My_Custom_Metadata__mdt.getInstance('My_Config_1');

// Access fields of the record
String configValue = metadataRecord.Config_Value__c;

• Single Record Lookup: If you know the developer name of the record you’re looking for and expect to access only one record.
• Performance: Since getInstance() is optimized for retrieving a single metadata record by its developer name, it can offer better performance than querying all records, especially when you only need one record.
• Static Configuration: Ideal for use cases where the configuration is static, and you are sure that the metadata record will not change often.

Advantages of getInstance()

• Efficiency: It’s quick and easy to retrieve a single metadata record when you already know the developer name.
• Less Complex Code: This approach requires fewer lines of code and simplifies the logic, particularly in configuration-heavy applications.

Limitations of getInstance()

• Single Record: It can only retrieve one record at a time.
• No Dynamic Querying: It does not support complex filtering or dynamic querying like SOQL.

Approach 2: Using SOQL Queries

SOQL (Salesforce Object Query Language) is the standard way to retrieve multiple records in Salesforce, including custom metadata records. By using SOQL, you can query a custom metadata type much like any other object in Salesforce, providing flexibility in how records are retrieved.

How SOQL Queries Work

With SOQL, you can write queries that return multiple records, filter based on field values, or sort the records as needed. For instance:

// Query for multiple custom metadata records with SOQL
List<My_Custom_Metadata__mdt> metadataRecords = [SELECT MasterLabel, Config_Value__c FROM My_Custom_Metadata__mdt WHERE Active__c = TRUE];

// Loop through records and access their values
for (My_Custom_Metadata__mdt record : metadataRecords) {
    System.debug('Label: ' + record.MasterLabel + ', Value: ' + record.Config_Value__c);
}

When to Use SOQL Queries

• Multiple Records: If you need to retrieve more than one record or apply filters to the query.
• Dynamic Queries: When the records you’re querying are dynamic (e.g., based on user input or other logic).
• Complex Criteria: If you need to use conditions like WHERE, ORDER BY, or join metadata with other objects.

Advantages of SOQL Queries

• Flexibility: SOQL queries allow you to retrieve multiple records based on complex conditions.
• Filtering and Sorting: You can easily filter and sort records to get the exact data you need.
• Dynamic Usage: Ideal for cases where the data or records you’re querying may change, such as pulling all active configuration records.

Limitations of SOQL Queries

• Governor Limits: SOQL queries are subject to Salesforce’s governor limits (e.g., the number of records returned and the number of queries per transaction).
• Complexity: Writing and managing SOQL queries might introduce additional complexity in the code, especially when dealing with large datasets.

Key Differences: getInstance() vs. SOQL Queries

Best Practices for Using getInstance() and SOQL

• Use getInstance() when you need to access one specific metadata record and know the developer name beforehand. It’s efficient and optimized for simple lookups.
• Use SOQL when you need to filter, sort, or access multiple metadata records. It’s more flexible and ideal for dynamic scenarios, but you should always be aware of governor limits to avoid hitting them.
• Combine the Two: In some cases, you can use getInstance() for fetching critical single configuration records and SOQL for retrieving a list of configuration settings.

Conclusion

Both getInstance() and SOQL queries have their strengths when it comes to working with custom metadata types in Salesforce. Understanding when to use each will help optimize your code and ensure that your Salesforce applications run efficiently. For simple, static configurations, getInstance() is the way to go. For dynamic, large, or complex datasets, SOQL queries will offer the flexibility you need. By carefully selecting the right approach for your use case, you can harness the full power of Salesforce custom metadata.

“Powerful technologies require equally powerful ethical guidance.” (Bostrom, N. Superintelligence: Paths, Dangers, Strategies. Oxford University Press, 2014).

The ethics of using artificial intelligence depend on how we apply its capabilities—either to enhance learning or to prevent irresponsible practices that may compromise academic integrity. In this blog, I share reflections, experiences, and insights about the impact of AI in our environment, analyzing its role as a creative tool in the hands of developers and as a challenge within the academic context.

Between industry and the classroom

As a Senior Developer, my professional trajectory has led me to delve deeply into the fascinating discipline of software architecture. Currently, I work as a Backend Developer specializing in Microsoft technologies, facing daily the challenges of building robust, scalable, and well-structured systems in the business world.

Alongside my role in the industry, I am privileged to serve as a university professor, teaching four courses. Three of them are fundamental parts of the software development lifecycle: Software Analysis and Design, Software Architecture, and Programming Techniques. This dual perspective—as both a professional and a teacher—has allowed me to observe the rapid changes that technology is generating both in daily development practice and in the formation of future engineers.

Exploring AI as an Accelerator in Software Development

One of the greatest challenges for those studying the software development lifecycle is transforming ideas and diagrams into functional, well-structured projects. I always encourage my students to use Artificial Intelligence as a tool for acceleration, not as a substitute.

For example, in the Software Analysis and Design course, we demonstrate how a BPMN 2.0 process diagram can serve as a starting point for modeling a system. We also work with class diagrams that reflect compositions and various design patterns. AI can intervene in this process in several ways:

• Code Generation from Models: With AI-based tools, it’s possible to automatically turn a well-built class diagram into the source code foundation needed to start a project, respecting the relationships and patterns defined during modeling.
• Rapid Project Architecture Setup: Using AI assistants, we can streamline the initial setup of a project by selecting the technology stack, creating folder structures, base files, and configurations according to best practices.
• Early Validation and Correction: AI can suggest improvements to proposed models, detect inconsistencies, foresee integration issues, and help adapt the design context even before coding begins.

This approach allows students to dedicate more time to understanding the logic behind each component and design principle, instead of spending hours on repetitive setup and basic coding tasks. The conscious and critical use of artificial intelligence strengthens their learning, provides them with more time to innovate, and helps prepare them for real-world industry challenges.

But Not Everything Is Perfect: The Challenges in Programming Techniques

However, not everything is as positive as it seems. In “Programming Techniques,” a course that represents students’ first real contact with application development, the impact of AI is different compared to more advanced subjects. In the past, the repetitive process of writing code—such as creating a simple constructor public Person(), a function public void printFullName() or practicing encapsulation in Java with methods like public void setName(String name) and public String getName()—kept the fundamental programming concepts fresh and clear while coding.

This repetition was not just mechanical; it reinforced their understanding of concepts like object construction, data encapsulation, and procedural logic. It also played a crucial role in developing a solid foundation that made it easier to understand more complex topics, such as design patterns, in future courses.

Nowadays, with the widespread availability and use of AI-based tools and code generators, students tend to skip these fundamental steps. Instead of internalizing these concepts through practice, they quickly generate code snippets without fully understanding their structure or purpose. As a result, the pillars of programming—such as abstraction, encapsulation, inheritance, and polymorphism—are not deeply absorbed, which can lead to confusion and mistakes later on.

Although AI offers the promise of accelerating development and reducing manual labor, it is important to remember that certain repetition and manual coding are essential for establishing a solid understanding of fundamental principles. Without this foundation, it becomes difficult for students to recognize bad practices, avoid common errors, and truly appreciate the architecture and design of robust software systems.

Reflection and Ethical Challenges in Using AI

Recently, I explained the concept of reflection in microservices to my Software Architecture students. To illustrate this, I used the following example: when implementing the Abstract Factory design pattern within a microservices architecture, the Reflection technique can be used to dynamically instantiate concrete classes at runtime. This allows the factory to decide which object to create based on external parameters, such as a message type or specific configuration received from another service. I consider this concept fundamental if we aim to design an architecture suitable for business models that require this level of flexibility.

However, during a classroom exercise where I provided a base code, I asked the students to correct an error that I had deliberately injected. The error consisted of an additional parameter in a constructor—a detail that did not cause compilation failures, but at runtime, it caused 2 out of 5 microservices that consumed the abstract factory via reflection to fail. From their perspective, this exercise may have seemed unnecessary, which led many to ask AI to fix the error.

As expected, the AI efficiently eliminated the error but overlooked a fundamental acceptance criterion: that parameter was necessary for the correct functioning of the solution. The task was not to remove the parameter but to add it in the Factory classes where it was missing. Out of 36 students, only 3 were able to explain and justify the changes they made. The rest did not even know what modifications the AI had implemented.

This experience highlights the double-edged nature of artificial intelligence in learning: it can provide quick solutions, but if the context or the criteria behind a problem are not understood, the correction can be superficial and jeopardize both the quality and the deep understanding of the code.

I haven’t limited this exercise to architecture examples alone. I have also conducted mock interviews, asking basic programming concepts. Surprisingly, even among final-year students who are already doing their internships, the success rate is alarmingly low: approximately 65% to 70% of the questions are answered incorrectly, which would automatically disqualify them in a real technical interview.

Conclusion

Artificial intelligence has become increasingly integrated into academia, yet its use does not always reflect a genuine desire to learn. For many students, AI has turned into a tool for simply getting through academic commitments, rather than an ally that fosters knowledge, creativity, and critical thinking. This trend presents clear risks: a loss of deep understanding, unreflective automation of tasks, and a lack of internalization of fundamental concepts—all crucial for professional growth in technological fields.

Various authors have analyzed the impact of AI on educational processes and emphasize the importance of promoting its ethical and constructive use. As Luckin et al. (2016) suggest, the key lies in integrating artificial intelligence as support for skill development rather than as a shortcut to avoid intellectual effort. Similarly, Selwyn (2019) explores the ethical and pedagogical challenges that arise when technology becomes a quick fix instead of a resource for deep learning.

References:

• Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence Unleashed: An Argument for AI in Education. Pearson Education. [Available at: https://www.pearson.com/content/dam/one-dot-com/one-dot-com/global/Files/about-pearson/innovation/open-ideas/Intelligence-Unleashed-Publication.pdf]
• Selwyn, N. (2019). Should Robots Replace Teachers? AI and the Future of Education. Polity Press.

OneStream, a corporate performance management (CPM) platform, is built on the Microsoft .NET framework, that supports both VB.NET and C# for business rule development. Both languages share the same runtime environment that is known as the .NET Framework. VB.NET has traditionally been favored, especially in finance-related functions. VB.NET does have similarities to Excel’s VBA—making it more accessible to finance professionals who are not full-time developers. However, in recent years, C# has gained significant traction, especially with more technically complex solutions and shared business rules.

In OneStream, shared business rules can be written in either VB.NET or C#, giving developers the freedom to choose based on preference or project requirements. However, item-specific business rules—those tied to a specific dimension, transformation, or form—must still be authored in VB.NET. That said, OneStream is steadily moving toward a C#-first model. Starting with version 7.1, support for C# business rules became more robust, and in the most recent platform updates—especially version 8.0 and beyond—C# has become the default in many marketplace solutions. Notably, several Financial Close extensions, such as Account Reconciliations and Transaction Matching, now require C# for any customization or rule development, reflecting this shift.

The platform’s migration from .NET Framework to .NET 6 in version 8.x is another development. This transition improves performance and scalability but also introduces some changes that can impact coding practices. Legacy VB.NET rules may experience compilation issues due to outdated syntax or incompatible references, prompting many teams to restructure their codebases in line with the updated .NET framework.

The developer community generally views VB.NET as a language with limiting features, while C# is considered the modern standard for .NET development. This sentiment is being adopted by OneStream. For current and future projects, developers and administrators are encouraged to adopt C# for better alignment with OneStream’s roadmap and Microsoft’s broader .NET ecosystem. Still, VB.NET remains relevant for certain rule types and legacy applications, so understanding both remains crucial.

In summary, while VB.NET maintains a presence within OneStream C# is increasingly the preferred and sometimes required language—especially in new marketplace solutions and in platform versions 8.0 and above. With the move to .NET 8, developers should prepare to refactor older rules and adopt C# where possible to ensure compatibility, performance, and long-term maintainability.

In today’s world of AI-powered development tools, ChatGPT and Microsoft Copilot are often compared side by side. Both promise to make coding easier, debugging faster, and problem-solving more efficient. But when it comes to solving real-world enterprise issues, the difference in their effectiveness becomes clear.

Recently, I faced a practical challenge while working with Sitecore 10.2.0 and Sitecore SXA 11.3.0, which presented a perfect case study for comparing the two AI assistants.

The Context: Node.js & Sitecore Compatibility

I was troubleshooting an issue with Sitecore SXA where certain commands (npm run build, sxa r Main, and sxa w) weren’t behaving as expected. Initially, my environment was running on Node.js v14.17.1, but I upgraded to v20.12.2. After the upgrade, I started suspecting a compatibility issue between Node.js and Sitecore’s front-end build setup.

Naturally, I decided to put both Microsoft Copilot and ChatGPT to the test to see which one handled things better.

My Experience with Microsoft Copilot

When I first used Copilot, I gave it a very specific and clear prompt:

I am facing an issue with Sitecore SXA 11.3.0 on Sitecore 10.2.0 using Node.js v20.12.2. The gulp tasks are not running properly. Is this a compatibility issue and what should I do?

Copilot’s Response

• Copilot generated a generic suggestion about checking the gulp configuration.
• It repeated standard troubleshooting steps such as “try reinstalling dependencies,” “check your package.json,” and “make sure Node is installed correctly.”
• Despite rephrasing the prompt multiple times, it failed to recognize the known compatibility issue between Sitecore SXA’s front-end tooling and newer Node versions.

Takeaway: Copilot provided a starting point, but the guidance lacked the technical depth and contextual relevance required to move the solution forward. It felt more like a general suggestion than a targeted response to the specific challenge at hand.

My Experience with ChatGPT

I then tried the same prompt in ChatGPT.

ChatGPT’s Response

• Immediately identified that Sitecore SXA 11.3.0 running on Sitecore 10.2.0 has known compatibility issues with Node.js 20+.
• It suggested that I should switch to Node.js v18.20.7 because it’s stable and works well with Sitecore.
• Recommended checking SXA version compatibility matrix to confirm the supported Node versions.
• Also guided me on how to use Node Version Manager (NVM) to switch between multiple Node versions without affecting other projects.

This response was not only accurate but also actionable. By following the steps, I was able to resolve the issue and get the build running smoothly again.

Takeaway: ChatGPT felt like talking to a teammate who understands how Sitecore and Node.js really work. In contrast, Copilot seemed more like the suggestion tool, it offered helpful prompts but didn’t fully comprehend the broader context or the specific challenge I was addressing.

Key Differences I Observed

Takeaways for Developers

1. Copilot is great for boilerplate code and inline suggestions – if you want quick syntax help, it works well.
2. ChatGPT shines in debugging and architectural guidance – especially when working with enterprise systems like Sitecore or giving code suggestions.
3. When you’re stuck on environment or compatibility issues, ChatGPT can save hours by pointing you in the right direction.
4. Best workflow: Use Copilot for code-writing speed, and ChatGPT for solving bigger technical challenges.

Final Thoughts

Both Microsoft Copilot and ChatGPT are powerful AI tools, but they serve different purposes.

• Copilot functions like a code suggestion tool integrated within your IDE.
• ChatGPT feels like a senior consultant who understands the ecosystem and gives you actionable advice.

When working on complex platforms like Sitecore 10.2.0 with SXA 11.3.0, and specific Node.js compatibility issues, ChatGPT clearly comes out ahead.

The move to SaaS is one of the biggest shifts happening in digital experience. It’s not just about technology, it’s about making platforms simpler, faster, and more adaptable to the pace of customer expectations.

Sitecore has leaned in with a clear vision: “It’s SaaS. It’s Simple. It’s Sitecore.”

Here are five reasons why more organizations are turning to Sitecore SaaS to power their digital experience strategies:

1. Simplicity: A Modern Foundation

Sitecore SaaS solutions like XM Cloud remove the burden of managing infrastructure and upgrades.

• No more complex version upgrades, updates happen automatically.
• Reduced reliance on IT for day-to-day maintenance.
• A leaner, more cost-effective foundation for marketing teams.

By simplifying operations, companies can focus on what matters most; delivering exceptional digital experiences.

2. Speed-to-Value: Launch Faster

Traditional DXPs can take months (or more) to implement and optimize. Sitecore SaaS is designed for speed:

• Faster deployments with prebuilt components.
• Seamless integrations with other SaaS and cloud tools.
• Empowerment for marketers to build and launch campaigns without heavy dev cycles.

Organizations adopting Sitecore SaaS are moving from planning to execution faster than ever.

3. Scalability: Grow Without Rebuilds

As customer expectations grow, so does the need to scale digital experiences quickly. Sitecore SaaS allows companies to:

• Spin up new sites, regions, or languages without starting from scratch.
• Adjust to spikes in demand without disruption.
• Add capabilities as the business evolves — without heavy upfront investment.

This scalability ensures brands can adapt as fast as their audiences do.

4. Continuous Innovation: Always Current

One of the most frustrating parts of traditional platforms is the upgrade cycle. Sitecore SaaS solves this with:

• Automatic access to the latest innovations — no disruptive “big bang” upgrades.
• Built-in adoption of emerging technologies like AI and machine learning.
• A platform that’s always modern, not years behind.

With Sitecore SaaS, companies get a future-proof DXP that evolves with them.

5. Composability Without the Complexity

Composable DXPs promise flexibility, but without the right foundation they can feel overwhelming. Sitecore SaaS makes composability practical:

• Start with XM Cloud as a core CMS foundation.
• Add personalization, commerce, or search when ready.
• Use APIs to integrate best-of-breed tools, without losing control.

This approach ensures organizations adopt what they need, when they need it without the complexity of managing multiple disconnected systems.

Why it Matters

Companies aren’t moving to Sitecore SaaS just to keep up with technology. They’re moving because it makes their organizations more agile, efficient, and competitive. SaaS with Sitecore means simpler operations, faster launches, continuous innovation, and a platform that grows alongside your business.

Introduction

With over 9+ years of experience in mobile development and a strong focus on React Native, I’ve always been eager to stay ahead of the curve. Recently, I’ve been exploring the future of React Native, diving into upcoming features, AI integrations, and Meta’s long-term vision for cross-platform innovation. React Native has been a game-changing framework in the mobile space, empowering teams to build seamless cross-platform applications using JavaScript and React. Backed by Meta (formerly Facebook), it continues to evolve rapidly, introducing powerful new capabilities, optimizing performance, and increasingly integrating AI-driven solutions.

In this article, we’ll explore upcoming React Native features, how AI is integrating into the ecosystem, and Meta’s long-term vision for cross-platform innovation.

Upcoming Features in React Native

React Native’s core team, alongside open-source contributors, is actively working on several exciting updates. Here’s what’s on the horizon:

Fabric: The New Rendering Engine

Fabric modernizes React Native’s rendering infrastructure to make it faster, more predictable, and easier to debug.
Key benefits:

• Concurrent React support
• Synchronous layout and rendering
• Enhanced native interoperability

As of 2025, Fabric is being gradually enabled by default in newer React Native versions (0.75+).

TurboModules

A redesigned native module system aimed at improving startup time and memory usage. TurboModules allow React Native to lazily load native modules only when needed, reducing app initialization overhead.

Hermes 2.x and Beyond

Meta’s lightweight JavaScript engine for React Native apps continues to get faster, with better memory management and debugging tools like Chrome DevTools integration.

New improvements:

• Smaller bundle sizes
• Better GC performance
• Faster cold starts

React Native Codegen

A system that automates native bridge generation, making native module creation safer and faster, while reducing runtime errors. This is essential for scaling large apps with native modules.

AI Integrations in React Native

Artificial Intelligence is not just for backend systems or web apps anymore. AI is actively being integrated into React Native workflows, both at runtime and during development.

Where AI is showing up in React Native:

• AI-Powered Code Suggestions & Debugging
  Tools like GitHub Copilot, ChatGPT, and AI-enhanced IDE extensions are streamlining development, providing real-time code fixes, explanations, and best practices.

• ML Models in React Native Apps
  With frameworks like TensorFlow.js, ML Kit, and custom CoreML/MLModel integration via native modules, developers can embed models for:
  • Image recognition
  • Voice processing
  • Predictive text
  • Sentiment analysis

• AI-Based Performance Monitoring & Crash Prediction
  Meta and third-party analytics tools are embedding AI to predict crashes and performance bottlenecks, offering insights before problems escalate in production apps.

• AI-Driven Accessibility Improvements
  Automatically generating image descriptions or accessibility labels using computer vision models is becoming a practical AI use case in mobile apps.

Meta’s Vision for Cross-Platform Innovation

Meta’s vision for React Native is clear: to make cross-platform development seamless, high-performing, and future-proof.

What Meta is focusing on:

• Unified Rendering Pipeline (Fabric)
• Tight integration with Concurrent React
• Deep AI integrations for personalization, recommendations, and moderation
• Optimized developer tooling (Flipper, Hermes, Codegen)
• Expanding React Native’s use across Meta’s product family (Facebook, Instagram, Oculus apps)

Long-Term:

Expect more AI-powered tooling, better integration between React (Web) and React Native, and Meta investing in AI-assisted developer workflows.

Conclusion

React Native’s future is bright, with Fabric, TurboModules, Hermes, and AI integrations reshaping how mobile apps are built and optimized. Meta’s continuous investment ensures that React Native remains not only relevant but also innovative in the ever-changing app development landscape.

As AI becomes a core part of both our development tools and end-user experiences, React Native developers are uniquely positioned to lead the next generation of intelligent, performant, cross-platform apps.

In recent years, AI chatbots like ChatGPT have gone from fun tools for answering questions to serious helpers in workplaces, education, and even personal decision-making. With ChatGPT-5 now being the latest and most advanced version, it’s no surprise that people are asking a critical question:

“Is my personal data safe when I use ChatGPT-5?”

First, What Is ChatGPT-5?

ChatGPT-5 is an AI language model created by OpenAI. You can think of it like a super-smart digital assistant that can:

• Answering questions across a wide range of topics
• Drafting emails, essays, and creative content
• Writing and debugging code
• Assisting with research and brainstorming
• Supporting productivity and learning

It learns from patterns in data, but here’s an important point – it doesn’t “remember” your conversations unless the developer has built a special memory feature and you’ve agreed to it.

How Your Data Is Used

When you chat with ChatGPT-5, your messages are processed to generate a response. Depending on the app or platform you use, your conversations may be:

• Temporarily stored to improve the AI’s performance
• Reviewed by humans (in rare cases) to train and fine-tune the system
• Deleted or anonymized after a specific period, depending on the service’s privacy policy

This is why reading the privacy policy is not just boring legal stuff – it’s how you find out precisely what happens to your data.

Real Security Risks to Be Aware Of

The concerns about ChatGPT-5 (and similar AI tools) are less about it being “evil” and more about how your data could be exposed if not appropriately handled.

Here are the main risks:

1. Accidental Sharing of Sensitive Information

Many users unknowingly type personal details – such as their full name, home address, phone number, passwords, or banking information – into AI chat windows. While the chatbot itself may not misuse this data, it is still transmitted over the internet and may be temporarily stored by the platform. If the platform suffers a data breach or if the information is accessed by unauthorized personnel, your sensitive data could be exposed or exploited.

Best Practice: Treat AI chats like public forums – never share confidential or personally identifiable information.

2. Data Retention by Third-Party Platforms

AI chatbots are often integrated into third-party platforms, such as browser extensions, productivity tools, or mobile apps. These integrations may collect and store your chat data on their own servers, sometimes without clearly informing you. Unlike official platforms with strict privacy policies, third-party services may lack robust security measures or transparency.

Risk Example: A browser extension that logs your AI chats could be hacked, exposing all stored conversations.

Best Practice: Use only trusted, official apps and review their privacy policies before granting access.

3. Misuse of Login Credentials

In rare but serious cases, malicious AI integrations or compromised platforms could capture login credentials you enter during a conversation. If you share usernames, passwords, or OTPs (one-time passwords), these could be used to access your accounts and perform unauthorized actions – such as placing orders, transferring money, or changing account settings.

Real-World Consequence: You might wake up to find that someone used your credentials to order expensive items or access private services.

Best Practice: Never enter login details into any AI chat, and always use two-factor authentication (2FA) for added protection.

4. Phishing & Targeted Attacks

If chat logs containing personal information are accessed by cybercriminals, they can use that data to craft highly convincing phishing emails or social engineering attacks. For example, knowing your name, location, or recent purchases allows attackers to impersonate trusted services and trick you into clicking malicious links or revealing more sensitive data.

Best Practice: Be cautious of unsolicited messages and verify the sender before responding or clicking links.

5. Overtrusting AI Responses

AI chatbots are trained on vast datasets, but they can still generate inaccurate, outdated, or misleading information. Relying on AI responses without verifying facts can lead to poor decisions, especially in areas like health, finance, or legal advice.

Risk Example: Acting on incorrect medical advice or sharing false information publicly could have serious consequences.

Best Practice: Always cross-check AI-generated content with reputable sources before taking action or sharing it.

How to Protect Yourself

Here are simple steps you can take:

• Never share sensitive login credentials or card details inside a chat.
• Stick to official apps and platforms to reduce the risk of malicious AI clones.
• Use 2-factor authentication (2FA) for all accounts, so even stolen passwords can’t be used easily.
• Check permissions before connecting ChatGPT-5 to any service – don’t allow unnecessary access.
• Regularly clear chat history if your platform stores conversations.

Final Thoughts

ChatGPT-5 is a tool, and like any tool, it can be used for good or misused. The AI itself isn’t plotting to steal your logins or credentials, but if you use it carelessly or through untrusted apps, your data could be at risk.

Golden rule: Enjoy the benefits of AI, but treat it like a stranger online – don’t overshare, and keep control of your personal data.

In this section, we focused primarily on generating read-only credentials and how to use them to connect to the database.

Generate Database Credentials

The Mission Control tool generates read-only database credentials for a targeted instance, which remain active for 30 minutes. These credentials allow users to run select or read-only queries, making it easier to explore data on a cloud instance. This feature is especially helpful for verifying data-related issues without taking a database backup.

Steps to generate database credentials

1. Log in to Mission Control.

2. Navigate to the Customers tab.

3. Select the appropriate Customer.

   

4. Choose the Environment for which you need the credentials.

   

5. Click the Action dropdown in the left pane.

6. Select Generate Database Credentials.

   

7. A pop-up will appear with a scheduler option.

   

8. Click Continue to initiate the process.

9. After a short time, the temporary read-only credentials will be displayed.

Once the temporary read-only credentials are generated, the next step is to connect to the database using those credentials.

To do this:

1. Download and install Azure Data Studio
   Download Azure Data Studio

2. Open Azure Data Studio after installation.

   

3. Click “New Connection” or the “Connect” button.

   

4. Use the temporary credentials provided by Mission Control to connect:

   • Server Name: Use the server name from the credentials.

   • Authentication Type: SQL Login

   • Username and Password: As provided in the credentials.

5. Once connected, you can execute SELECT queries to explore or verify data on the cloud instance.

For more details, refer to the official Optimizely documentation on Generating Database Credentials.

For Part I, visit: Optimizely Mission Control – Part I

Ready to go from “meh” to “whoa” with your AI coding assistant? Here’s how to get started.

You’ve installed GitHub Copilot. Now what?

Here’s how to actually get it to work for you – not just with you.

In the blog Using GitHub Copilot in VS Code, we have already seen how to use GitHub Copilot in VS Code.

1. Write for Copilot, Not Just Yourself

Copilot is like a teammate who’s really fast at coding but only understands what you clearly explain.

Start with Intention:

Use descriptive comments or function names to guide Copilot.

// Fetch user data from API and cache it locally
function fetchUserData() {

Copilot will often generate useful logic based on that. It works best when you think one step ahead.

2. Break Problems Into Small Pieces

Copilot shines when your code is modular.

Instead of writing:

function processEverything() {
  // 50 lines of logic
}

Break it down:

// Validate form input
function validateInput(data) {

}

// Submit form to backend
function submitForm(data) {

}

This way, you get smarter, more accurate completions.

3. Use Keyboard Shortcuts to Stay in Flow

Speed = flow. These shortcuts help you ride Copilot without breaking rhythm:

Power Tip: Hold Tab to preview full suggestion before accepting it.

4. Experiment With Different Prompts

Don’t settle for the first suggestion. Try giving Copilot:

• Function names like: generateInvoicePDF()
• Comments like: // Merge two sorted arrays
• Descriptions like: // Validate email format

Copilot might generate multiple versions. Pick or tweak the one that fits best.

5. Review & Refactor – Always

Copilot is smart, but not perfect.

• Always read the output. Don’t blindly accept.
• Add your own edge case handling and error checks.
• Use tools like ESLint or TypeScript for safety.

Think of Copilot as your fast-thinking intern. You still need to double-check their work.

6. Use It Across File Types

Copilot isn’t just for JS or Python. Try it in:

• HTML/CSS → Suggest complete sections
• SQL → Generate queries from comments
• Markdown → Draft docs and README files
• Dockerfiles, .env, YAML, Regex patterns

Write a comment like # Dockerfile for Node.js app – and watch the magic.

7. Pair It With Unit Tests

Use Copilot to write your test cases too:

// Test case for addTwoNumbers function
describe('addTwoNumbers', () => {

It will generate a full Jest test block. Use this to write tests faster – especially for legacy code.

8. Learn From Copilot (Not Just Use It)

Treat Copilot suggestions as learning opportunities:

• Ask: “Why did it suggest that?”
• Compare with your original approach
• Check docs or MDN if you see unfamiliar code

It’s like having a senior dev whispering best practices in your ear.

9. Use Copilot Chat (If Available)

If you have access to GitHub Copilot Chat, try it. Ask questions like:

• What does this error mean?
• Explain this function
• Suggest improvements for this code

It works like a Stack Overflow built into your IDE.

Quick Recap

Final Thoughts: Practice With Purpose

To truly master Copilot:

• Build small projects and let Copilot help
• Refactor old code using Copilot suggestions
• Try documenting your code with its help

You’ll slowly build trust – and skill.

Optimizely provides powerful tools that make it easy to build, release, and manage cloud infrastructure efficiently.

Optimizely Mission Control Access

To use this tool, an Opti ID is required. Once you have an Opti ID, request that your organization grants access to your user account. Alternatively, you can raise a ticket with the Optimizely Support team along with approval from your project organization.

Key Actions

This tool provides various essential actions that can be performed for managing your cloud environments effectively. These include:

• Restart Site

  • Restart the application in a specific environment to apply changes or resolve issues.

• Database Backup

  • Create a backup of the environment’s database for debug purposes.

• Generate Database Credentials

  • Generate secure credentials to connect to the environment’s database.

• Base Code Deploy

  • Deploy the base application code to the selected environment.

• Extension Deployment

  • Deploy any custom extension changes.

• Production User Files Sync

  • Synchronize user-generated files (e.g., media, documents) from the production environment to lower environments.

• Production Database Sync

  • Sync the production database to another lower environment (such as a sandbox) to sync up data.

Let’s walk through each of these actions step by step to understand how to perform them.

Restart Site

We can restart the site using the Mission Control tool. This option is handy when a website restart is required due to configuration changes. For example, updates to the storage or search provider often require a restart. Additionally, if an integration job gets stuck for any reason, the ability to restart the site becomes very helpful in restoring normal functionality.

How to restart the website

1. Log in to Mission Control.

2. Navigate to the Customers tab.

3. Select the appropriate Customer.

   

4. Choose the Environment where the restart is needed.

   

5. Click on the Action dropdown in the left pane.

   

6. Select Restart Site from the list.

7. A pop-up will appear where you can either schedule the restart or click Continue for an immediate restart.

Reference: Restart Site – Optimizely Support

Database Backup

This is another useful feature available in Mission Control.

Using this option, we can take a backup from the Sandbox or Production instance and import it into the local environment. This helps us debug issues that occur in Sandbox or Production environments.

The backup file is generated with a .bacpac extension.

Steps to take a backup

1. Log in to Mission Control.

2. Navigate to the Customers tab.

   

3. Select Database Backup from the list.

   

4. A pop-up will appear prompting for a scheduled backup time.

5. Set Skip Log to False to minimize the backup size.

   

6. Click Continue and wait for the process to complete.

7. Once finished, click on the provided link to download the backup file.

Reference: Database Backup – Optimizely Support

Stay tuned for the next blog to explore the remaining actions!