12 Best Paid and Free App Builders in 2024
Cross-Platform Apps – Best Practices For Developers
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With businesses and developers aiming to provide a seamless user experience across multiple platforms, the need for cross-platform application development has grown. As the demand for mobile apps that work flawlessly on different operating systems increases, choosing the right development tools and frameworks is key to efficiency, performance and consistency.
This guide outlines best practices for cross-platform app development to help developers build mobile apps for Android, iOS and desktop environments with one development process.
What is Cross-Platform Development?Cross-platform development allows developers to build mobile apps, web applications and desktop applications with one codebase, for multiple operating systems. This means:
Popular cross-platform frameworks like Flutter, React Native, Xamarin and Qt let developers use object oriented programming languages like Dart, JavaScript, C++ and C# to build high quality cross-platform desktop and mobile apps with near native performance.
Best Practices for Cross-Platform App Development 1. Choose the Right Cross-Platform FrameworkYour cross-platform mobile development project success depends on choosing the right cross-platform app framework. Consider:
Each cross-platform solution has its own strengths so make sure to align the framework with your development process.
2. Adopt a Platform-Agnostic Design ApproachA common mistake in cross-platform mobile apps is to design for one platform first and adapt later. Instead:* Use responsive design principles to be able to adapt to multiple operating systems.
By being platform-agnostic, developers can create apps that feel native without needing excessive platform specific code.
3. Write Modular CodeEfficient cross-platform solutions rely on structured, reusable code to make maintenance and future updates easier. Key practices are:
This structure allows to scale and modify features without affecting the whole app.
4. Optimize for Multiple PlatformsWhile cross-platform apps are convenient, performance optimization is key to a native like experience. Best practices are:
Developers can use profiling tools like Xcode Instruments and Firebase Performance Monitoring to find and fix performance bottlenecks.
5. Cross-Platform Testing StrategiesTesting is crucial to make sure cross-platform apps work on multiple operating systems. Key testing methods are:
Automating tests reduces development time and improves app stability.
6. Automate Deployment with CI/CD PipelinesA strong CI/CD (Continuous Integration and Deployment) pipeline accelerates mobile app development by automating builds, testing and deployment. Using tools like Bitrise, GitHub Actions or CircleCI allows developers to:
A well implemented CI/CD process makes cross-platform mobile development more efficient and reduces deployment errors.
7. Keep up with Platform-Specific ChangesOperating systems evolve, developers need to stay up to date with platform specific features and security policies. Best practices are:* Update dependencies to stay compatible.
By doing so cross-platform mobile apps will remain functional and competitive.
Cross-Platform Mobile Development ChallengesWhile cross-platform development has many benefits, developers must overcome:
By choosing the right cross-platform app framework, optimizing performance and using effective testing strategies developers can overcome these challenges and deliver high quality cross-platform mobile apps.
ConclusionCross-platform development is changing mobile app development by allowing companies to build cross-platform apps efficiently and cost effective. By using the right development tools, optimizing performance and robust cross-platform testing strategies developers can build apps that work across multiple platforms.
Whether you're an experienced developer or just starting with platform mobile app development, follow these best practices and your apps will be scalable, adaptable and high performing. As technology evolves, staying up to date with hybrid development, web development and progressive web apps will help you to develop better cross-platform solutions.
With the right cross-platform software building apps for both iOS and Android has never been easier. Now is the time to take advantage of cross-platform mobile development and build apps that run across different operating systems!
F5 Evolves Converged Application Delivery + Security Platform For AI Era
François Locoh-Donou, president and CEO of F5.
F5Platforms dominate technology. We talk about Microsoft's family of software application development platforms from .Net to Windows to Office as platforms upon which developers build new apps and services. Throughout the world of open source, we consider Linux and its variants to be solidified platforms with accompanying toolsets to create and innovate on top of. We then move to major databases, enterprise resource planning suites and data management brands as platforms upon which we can create platform-aligned services, usually shaped by the source DNA on which they run. Further along, we can also consider other major entities in technology as platforms i.E. Google Maps is a platform technology that can be integrated into other applications, or indeed built on top of.
Platforms also come about when the core technology proposition from a vendor has become so expansive, multifarious and functional that it exists as a foundation upon which to build digital services. At this level, the platform also underpins the lifeblood of working operations in a live production IT environment. We can reasonably place multi-cloud application security and delivery company F5 in this platform category i.E. Over its three decades in business, the company has now purpose-built a highly integrated set of technologies that now work in orchestrated union as a pure platform play and one that is, therefore, greater even than the sum of its parts.
But if we are to suggest F5 ranks at this status, how does the company aim to justify its position on the platform podium?
Shape Of A PlatformIn specific terms, the company has now detailed its F5 Application Delivery and Security Platform, an Application Delivery Controller technology solution that converges high-performance load balancing and traffic management with advanced application and API security capabilities into a single platform.
Before we dive into the newly empowered and converged offering on show here, do we really understand what an application delivery controller is and know why they're so "special" in the world of network management, network protection and server-level connectivity for today's multi-cloud world?
An ADC is a technology designed to handle load balancing, security and related aspects of network-level management and application delivery control from user and device authentication to firewalls to security patching and operational health reporting. Once built as dedicated hardware appliances to reside inside network installations, ADCs graduated to become software-based services and then ultimately became virtualized services to exist in the service-based computing universe of the hybrid cloud.
With the latest iteration of its technology - and the company's stance firmly aligned to that of platform provider - F5 says it has worked through the three ages of ADC to now deliver what it can call ADC 3.0 i.E. A purpose-built application delivery control platform that is capable of meeting the "extraordinary demands" (a reference to massive data workloads, massively distributed and fragmented technology landscapes and the need to align to massively complex algorithmic logic next to the new breed of agentic AI functions) of modern applications.
But again, let's keep ourselves in check here, the company is talking about the era of ADC 3.0 as we stand today, so how did we work through the previous two ages of this technology and what does that teach us about where we are now?
A Brief History Of Application Delivery Control"In the 1990s, F5 introduced application delivery controllers to keep applications secure in the burgeoning computing environments that were fuelled by what we now consider to be the early stages of the web itself. This was the era of ADC 1.0 and we have continued to develop our core products throughout, including F5 BIG-IP which is a software and hardware suite that optimizes application traffic by distributing load across multiple servers, ensuring high availability, performance and security," said François Locoh-Donou, president and CEO of F5. "BIG-IP has always been a platform in many senses of the term because it consolidates multiple functions into one technology and it has programmability, but let's move onward to ADC 2.0 to connect our story."
The CEO explains how the technology industry moved through the millennium into the 2010s. At this point, there was a consensus in the belief that all enterprise applications would go to the public cloud. This would mean that the cloud service provider hyperscalers (and we know who the big three are today) would handle all the load balancing and authentication and so on. Customers were attracted to the idea that ADC services in this arena would always be up-to-date, eminently scalable and cost-effective… and they were, but that's not the end of the story.
At that ADC 2.0 point in history, F5 was selling predominantly to on-premises deployments [private cloud] and to enterprise deployments where the customer did want to refactor its applications to fit the ADC 2.0 services offered by the cloud hyperscaler.
"This reality was twinned with the realization that not everything needed to be in the public cloud to take advantage of its scalability and ability to 'burst' additional services, especially where workloads were consistent and more predictable, or where sovereign cloud mandates and regulatory compliance stipulations stipulated that public cloud services from certain geographies were not permissible," explained Locoh-Donou.
The Ball Of FireThis story leads us to a state where networks were (and still are) called upon to drive highly distributed, highly fragmented and highly variegated applications. Protecting this surface area is what F5 executives call the "ball of fire" and it's dangerous because point solutions attempting to protect IT services at so many different levels will inevitably lead to unpatched attack surfaces. All of this has led us to the need for ADC 3.0 technologies to reset the status quo and work at a more varied level of form factors.
Imagine a healthcare installation that has a deep data requirement to serve an MRI scanner or some other highly complex piece of equipment. That tier of data would most likely be served by an on-premises private cloud layer to protect personally sensitive information and healthcare records. Yet, people who use that service might use their mobile healthcare app to book their appointments. This would require connectivity across public cloud networks that deliver software-as-a-service to customer endpoints, with some of the app data even perhaps coming from an external content delivery network. Additionally, this story might also feature a tier of additional app services (in the hospital, or on the user's device) that stem from a containerized Kubernetes-based backend. This means protection needs to be applied across a whole selection of "form factors" which would therefore have to include (taking them in order) hardware, SaaS public cloud and, thirdly, containerization layers.
Applying network protection across all those layers with point solutions is a bad idea, says F5; so this is the core rationale behind why the company's application Delivery and Security Platform has come about.
"Our platform [in your healthcare example] enables a single pane of glass to deliver policies, patches and additional parameters across the entire spectrum of form factors, even in the most variegated computing environments," said Locoh-Donou. "In a ball of fire environment, we also have to contend with the fact that AI services are making applications hyper-distributed, hyper-connected, hyper-hybrid and increasingly hyper-autonomous. But this is why we have engineered our platform with what we call the F5 AI Gateway. We will observe that connection being made by any element of AI and we will insert security in that connection to make sure there's no data exfiltration, there's no prompt injection, there's no abuse of the model with our ADC 3.0 sofware. This stops customers from having to go out and deploy yet another point solution to solve this new problem. That's the beauty of the technology itself i.E. With ADC 3.0, we can always integrate new functionalities things when needed."
The company's forthcoming 2025 State of Application Strategy Report tells us that within three years, an estimated 80% of all apps will be AI-enabled. But, although AI is here most enterprises are poorly prepared or equipped to handle the massive amounts of data, complex traffic patterns and new attack vectors that are inherent in AI applications. The new F5 Application Delivery and Security Platform has been engineered and built to address these challenges as an ADC offering for enterprises operating hybrid, multi-cloud infrastructures.
Painful Point PackagesBuilt specifically to combat the "point solutions" that CEO Locoh-Donou says can be so painful in the realm of network health and management, the F5 Application Delivery and Security Platform converges what might typically be piecemeal point solutions to address critical needs including load balancing, multi-cloud networking, web application security and API security, with AI gateway capabilities.
The platform is API-driven and enables consistent policies for every app. It's deployment flexibility means it can be deployed anywhere in any form factor to run across today's diverse IT environments and offer single policy-based unified management across all locations to reduce complexity. It offers analytics to improve performance and strengthen the security of complex applications and offers fully programmable data planes that enable automated deployment so organizations can effectively adapt to changing needs.
CEO Locoh-Donou mentioned the new F5 AI Gateway offering. This is a technology designed to streamline interactions between applications, APIs and large language models and hekp promote and enable the adoption of AI inside an enterprise. This containerized technology optimizes performance, observability and protection capabilities. It gives IT operations and security teams a path to adopting AI services with improved data output quality.
The Networked Road AheadThe road ahead for enterprise technology vendors who work anywhere near the market F5 operates in is characterterized by perhaps five factors: a need to wholly embrace hybrid multi-cloud realities; a need to be fully cognizant of artificial intelligence and the capabilities of agentic AI functions to perform software code-level tasks and instructions autonomously; a need to provide the so-called "single pane of glass" approach that tech vendors love to lay claim to, but actually do so with an ability to work at every level of today's massively differentiated, distributed and diverse network deployments; an innate appreciation for containerization in the composable world of Kubernetes and its multifarious configuration parameters; and a need not to be seen as security company, but as a cloud-native AI-empowered enterprise platform player that partners with the biggest and is still capable of working with open source hobbyists if that's where the next big thing comes from.
We could extend this list and add the next phase of AI development and talk about the need to dovetail with small language model technologies, quantum computing and the major moves among the cloud hyperscalers designed to automate and provide continuous computing controls, but five is numerically convenient for this story… and anyway, you can count it on one hand. Once again perhaps, the network is the computer, but now it's a more networked network.
François Locoh-Donou, president and CEO of F5.
F5Advancing Mobile Platforms: The AI Revolution In Event Logging
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In this rapidly growing digital era, Waseem Syed, an expert in Mobile Platform Engineering, offers groundbreaking insights into the transformative role of artificial intelligence in event logging systems. By addressing persistent challenges such as performance bottlenecks and high resource costs, these innovations pave the way for more efficient and reliable mobile applications. His work lays a strong foundation for integrating AI-driven solutions that optimize system behavior and advance the future of mobile technology.
Rethinking Traditional Event LoggingConventional logging systems often struggle to balance comprehensive diagnostics with the demands of mobile environments, including network instability and limited device resources. Traditional approaches produce overwhelming data, complicating performance analysis and inflating operational costs. Mobile applications, which depend heavily on diverse communication protocols and must operate seamlessly under varying network conditions, have highlighted these limitations. The shift toward intelligent, AI-driven solutions marks a pivotal evolution in addressing these concerns.
The Role of Machine Learning in Log OptimizationMachine learning has revolutionized how event logs are managed. By employing advanced algorithms, systems can prioritize significant events, reducing data clutter and focusing on actionable insights. This precision has led to more efficient resource use, allowing developers to allocate processing power and storage judiciously. For example, e-commerce platforms leveraging machine learning for log prioritization have reported improved memory utilization, ensuring optimal performance during high-demand periods.
Generative AI: A New Era in DiagnosticsGenerative AI is transforming root cause analysis, a critical component of troubleshooting in mobile applications. By identifying patterns within complex event chains, these systems enable faster and more accurate detection of anomalies. This capability reduces debugging time and minimizes the risk of recurring issues, thus enhancing system reliability. Adopting generative AI in log analysis represents a significant leap toward proactive maintenance strategies.
Local Event Chain Caching: Reducing Latency, Improving ContextLocal event chain caching offers another innovative solution to the challenges of mobile diagnostics. This technique involves storing key diagnostic data locally to streamline error tracing. By caching metadata such as timestamps and user interactions, developers can reconstruct the sequence of events leading to an error. This approach minimizes retrieval times and gives engineers detailed context, significantly enhancing debugging efficiency without taxing system resources.
Addressing Mobile-Specific ChallengesMobile applications present unique challenges that demand tailored logging solutions:
AI systems also enable intelligent log transmission policies, selectively prioritizing critical logs for centralized analysis while processing less urgent data locally. This tiered approach conserves bandwidth, reduces server load, and enhances overall system responsiveness. For instance, high-priority errors, such as payment failures in e-commerce applications, can be flagged and resolved quickly, minimizing disruption.
Continuous Improvement Through AI IntegrationIntegrating machine learning into logging systems fosters a cycle of continuous improvement. Adaptive algorithms evolve alongside system demands, maintaining high levels of anomaly detection accuracy while minimizing false positives. These systems improve over time and ensure scalability, accommodating the increasing complexity of mobile applications.
The Future of Mobile Logging SystemsThe advancements in Mobile Platform Engineering highlight a promising future driven by AI innovations. As AI capabilities expand, event logging systems are expected to incorporate natural language processing and semantic understanding, enabling deeper insights into application behavior and user interactions. Future developments will focus on creating adaptive solutions tailored to the dynamic nature of cloud-native applications. These systems will ensure robust monitoring and maintenance, setting new benchmarks for mobile technologies' efficiency, reliability, and scalability.
In conclusion, Waseem Syed's work demonstrates the transformative role of AI in revolutionizing event logging for mobile platforms. By improving performance, optimizing resource utilization, and enhancing diagnostics, these innovations establish new benchmarks for mobile application reliability and efficiency. His contributions lay a robust foundation for future advancements in the field, driving progress and inspiring continued exploration in integrating AI with mobile technologies.
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