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Mobile development blogs, tutorials and resources inside!Latest Mobile Dev Insights: iOS, Android, Cross-PlatformAdvertise with Us|Sign Up to the NewsletterMobilePro #229: Thinking in SwiftUIHi ,Good frameworks don't just give you more tools. They give you fewer things to think about.That's part of what makes SwiftUI interesting. Views compose into larger interfaces, state keeps the UI synchronized with data, navigation handles familiar platform behavior, and modifiers let you progressively shape an experience without reaching for piles of boilerplate. This week's article goes back to those fundamentals and shows how a handful of SwiftUI building blocks can take you from a simple screen to interactive, state-driven interfaces, and even back into UIKit when you need it.There's a similar focus on removing everyday friction in this week's news. React Native 0.87 tightens TypeScript support while experimenting with SwiftPM, Jetpack Compose 1.12 improves everything from visuals to startup and testing, and JetBrains' research suggests developer experience itself is helping attract developers to Kotlin. Different ecosystems, same underlying direction: make the foundations better, and developers can spend more time building what matters.TL;DRSwiftUI starts with composition. Text, images, buttons, and even layout containers are views that can be combined into increasingly sophisticated interfaces.Modifiers progressively transform views. Their order matters, and type-safe resources and semantic system colors can make interfaces safer and more adaptable.State makes interfaces reactive. Change a @State value and SwiftUI automatically updates the views that depend on it.Controls bind directly to data. Toggle, Picker, Slider, Stepper, and DatePicker all fit the same state-driven model, keeping the UI and its underlying values synchronized.SF Symbols can communicate state through subtle motion, with effects such as bounce, rotate, breathe, and draw-on.SwiftUI doesn't require abandoning UIKit. UIViewRepresentable, UIViewControllerRepresentable, and coordinators provide a practical bridge when SwiftUI doesn't offer what an existing UIKit component provides.This week’s news cornerJetBrains finds developers are choosing Kotlin for better developer experience: JetBrains’ 2025 Developer Ecosystem research finds that while project requirements drive most programming language migrations, Kotlin stands out as a language developers actively choose for its modern features and better development experience. Java remains Kotlin’s biggest source of new users, while TypeScript, Rust, Python, and Go show strong growth potential across the wider ecosystem.React Native 0.87 makes Strict TypeScript default and adds SwiftPM support: React Native 0.87 makes the Strict TypeScript API the default, upgrades Metro with 2x faster source-map generation and half the memory usage, and introduces experimental Swift Package Manager support as an alternative to CocoaPods for iOS. Android developers also gain AGP 9 support, while new minimum requirements include Node.js 22, Kotlin 2.0+, and compileSdk 37.Jetpack Compose 1.12 brings richer visuals, faster startup, and better testing: Google has released Jetpack Compose 1.12, adding Mesh Gradients, Wide Color Gamut and HDR rendering, richer editable text, named Grid areas, and native Credential Manager integration for passkeys and saved sign-ins. Performance also gets a boost, with startup times now comparable to Views in Google’s benchmarks, alongside new APIs designed to make UI and animation tests faster and less flaky.iOS 27 Public Beta 4 brings Apple closer to final release: Apple has released iOS 27 Public Beta 4, matching Developer Beta 6 and focusing primarily on bug fixes, performance improvements, and UI polish as the final release approaches. Changes include an updated iOS notification animation alongside fixes carried over from earlier betas.A glimpse of BuildWithAI newsletterBuilding with AI is quickly becoming part of every developer's workflow. Each week, Build with AI explores practical AI engineering, agentic development, LLMs, MCP, coding tools, and the techniques shaping modern software development. Here's a glimpse into a recent featured article:The AI-Native Loop: Who Owns the Code Now – Part 2 with Michelle SandfordOur bug-fix experiment is a deliberately mundane one: a TypeScript service exposes a/reportsendpoint withfromandtoquery parameters, and it returns an empty array when a user in Australia/Perth selects the last day of the month. The root cause is a comparison that parses thetovalue as UTC midnight, which falls before the user’s local end of day. It is exactly the kind of slightly embarrassing date bug every engineer has shipped.The repository is a small Node.js service with TypeScript strict mode, Jest, Fastify, and GitHub Actions for CI. ThebuildRangefunction lives insrc/reports/range.ts, and a modestAGENTS.mdfile is already in place.Both runs use the same measures:T1: Time to the first reproducing testT2: Time to a fix merged through CIQ1: Number of distinct edge cases captured by testsQ2: Number of surviving artifacts after the mergeR1: Can a future engineer understand the reasoning without rereading the diff?TipKeep this yardstick visible while you compare Run A and Run B. The important signal is not only speed, it is the quality and durability of the artifacts each run leaves behind.Read more...From Views to UIKit: A Practical Guide to SwiftUI’s Core Building BlocksBuilding an iOS interface used to involve a considerable amount of setup. Even a relatively simple screen could mean writing boilerplate code or configuring views manually in Interface Builder. SwiftUI changed that model by introducing a declarative approach: instead of describing every step required to construct an interface, developers describe what the interface should contain, and SwiftUI takes responsibility for rendering and updating it.That shift is what makes SwiftUI particularly approachable and powerful. Interfaces can be assembled from small, reusable views, organized with layout containers, and refined with modifiers. When application data changes, SwiftUI automatically determines which parts of the interface need to be updated.The fundamentals become easier to understand when they are explored through real interfaces rather than isolated APIs. The chapter SwiftUI Essentials takes that practical route, moving from views and layout to navigation, controls, SF Symbols, and finally interoperability with UIKit. Together, these concepts form a foundation for building modern Apple-platform applications.Start by Thinking in ViewsAlmost everything visible in a SwiftUI interface is a View. Text, images, buttons, controls, and even the containers that organize them participate in the same compositional system.A useful example is a weather profile card. It begins with little more than two Text views displaying a city and its current weather. From there, the interface can gradually grow to include an image, temperature information, spacing, backgrounds, and layered visual elements. This incremental approach demonstrates an important SwiftUI habit: build the smallest working structure first, verify it, and then add presentation and behavior.Three containers do much of the work in everyday layouts:VStack arranges views vertically.HStack arranges them horizontally.ZStack places views on top of one another.These containers become powerful because they can be nested. A vertical layout might contain a horizontal row, which itself contains several vertical columns. A ZStack can place an icon above a gradient, while another ZStack positions that entire composition over a background.The result is an interface built through composition rather than through a large, monolithic view definition.Modifiers then refine those views. Developers can change typography with .font(), establish hierarchy with .foregroundStyle(), control spacing using .padding(), constrain dimensions through .frame(), and introduce backgrounds, clipping, or shadows.Modifier order matters because each modifier effectively transforms the view produced before it. That means applying padding before a background is not necessarily equivalent to adding a background first and padding afterward.Small implementation decisions also improve maintainability. For asset catalog images, for example, the chapter recommends the type-safe ImageResource form such as Image(.cloudAndSun) rather than relying entirely on string-based asset names. This provides autocomplete and compile-time validation instead of allowing a mistyped image name to fail silently at runtime. Semantic system colors are similarly valuable because they adapt automatically to light mode, dark mode, and high-contrast environments.Turn Static Screens into Interactive AppsA useful interface needs more than layout. Users must be able to perform actions and move between screens.SwiftUI addresses those requirements primarily through Button, NavigationStack, and NavigationLink. A NavigationStack manages a hierarchy of screens, while a NavigationLink pushes another view onto that hierarchy. SwiftUI handles much of the expected platform behavior, including navigation transitions and the back button, without developers having to recreate it manually.The chapter demonstrates these concepts through a ButtonShowcase application with separate screens for button styles, semantic roles and tinting, and border shapes.Button styling is not only cosmetic. SwiftUI supplies styles ranging from .bordered and .borderedProminent to .plain, .borderless, .glass, and .glassProminent. Developers can also communicate meaning through roles such as .destructive, .cancel, and .close. A destructive role, for example, receives system treatment that helps communicate the consequences of the action.Additional modifiers such as .tint() and .buttonBorderShape() make it possible to adjust emphasis while still retaining system behavior. The important principle is that visual treatment, semantic meaning, and the code executed by the button remain distinct concerns.State then connects actions to visible results.A property marked with @State represents information owned by a view. When that value changes, SwiftUI identifies the dependent views and updates them automatically. A button tap might update a selected style, for example, causing a label or icon elsewhere on the screen to change immediately. Developers describe the relationship between state and interface rather than manually instructing individual controls to redraw.Build Controls Around the DataThe same state-driven model becomes especially clear in settings screens.SwiftUI includes dedicated controls for common input patterns: Toggle for Boolean values, Picker for selections, Slider for continuous ranges, Stepper for discrete increments, and DatePicker for dates and times. Instead of maintaining one value inside the interface and another in the application model, each control can bind directly to a @State property.A settings-style Form provides a natural environment for those controls. In the chapter’s ControlsShowcase example, toggles manage preferences such as notifications and sound, pickers handle themes and colors, a slider controls volume, a stepper changes a badge count, and a graphical date picker selects a reminder date. The central pattern stays consistent: the control receives a binding, reads the current value, and writes changes back to it.This is one of the most useful patterns to internalize in SwiftUI. UI and state remain synchronized by design.SF Symbols complement these controls by providing a large collection of system icons that behave much like text. They respond to fonts and foreground styles and can appear in monochrome, hierarchical, palette, or multicolor rendering modes. Developers can also apply symbol variants and effects to communicate changes more clearly.That allows interface feedback to remain subtle but expressive. A bell can bounce after a tap, an activity icon can rotate while work is ongoing, or a speaker icon can transition smoothly between muted and unmuted states. The chapter distinguishes between one-shot effects such as .bounce and indefinite effects such as .wiggle, .rotate, .breathe, and .drawOn, which continue while their active state remains true.SwiftUI Does Not Mean Leaving UIKit BehindModern SwiftUI covers most common interface requirements, but existing UIKit APIs and third-party components remain important. Fortunately, adopting SwiftUI does not require abandoning them.UIViewRepresentable allows a UIKit view to participate in a SwiftUI interface, while UIViewControllerRepresentable performs the same role for UIKit view controllers. Both follow a similar lifecycle: one method creates and configures the UIKit component, and another receives subsequent SwiftUI state changes and updates the component accordingly.The chapter demonstrates this with a TextPreviewer app. A UILabel is wrapped with UIViewRepresentable, allowing text and font size controlled by SwiftUI state to update the UIKit label in real time. A UIFontPickerViewController is then wrapped with UIViewControllerRepresentable.When delegate callbacks are required, a Coordinator becomes the bridge back from UIKit to SwiftUI. It can receive the UIKit event, update a SwiftUI binding, and trigger the normal SwiftUI rendering cycle.A useful rule emerges from this interoperability model: perform expensive one-time configuration in the representable’s creation method and keep the update method focused on lightweight changes to data. From the surrounding SwiftUI application’s perspective, the wrapped component can then behave much like any other SwiftUI view.UIKit interoperability should still be deliberate. Before wrapping an older UIKit API, it is worth checking whether SwiftUI now provides a native equivalent. Where it does not, or where a specialized library still depends on UIKit, the representable protocols provide a practical migration path rather than forcing an all-or-nothing rewrite.A Foundation for Real SwiftUI DevelopmentSwiftUI becomes much less mysterious once its recurring ideas are visible.Compose interfaces from small views. Arrange those views with stacks and containers. Refine them with modifiers. Keep changing information in state and bind controls to it. Let navigation describe relationships between screens. Use SF Symbols and semantic system styling to create interfaces that naturally fit Apple platforms. And when SwiftUI does not expose the component you need, bridge to UIKit instead of working around the framework.Taken together, these techniques move SwiftUI beyond the idea of a convenient UI syntax. They establish a consistent way of thinking about application interfaces: describe the screen, connect it to state, and let the framework manage the updates.That mental model is the real SwiftUI essential—and once it becomes familiar, increasingly sophisticated iOS interfaces can be built from the same small set of ideas.This article is based on SwiftUI Cookbookpublished by Packt.📚Go DeeperIf you're ready to take your SwiftUI skills further and build polished, production-ready Apple apps, SwiftUI Cookbook provides practical, recipe-based guidance for creating modern interfaces, handling navigation and user interactions, managing app data, and integrating SwiftUI with UIKit to solve real-world development challenges.🧩 Learn SwiftUI through practical recipes that progress from fundamentals to advanced APIs✨ Build modern iOS apps with iOS 27 APIs and the new Liquid Glass design📊 Explore Swift Charts, SwiftData, animations, networking, and testingSwiftUI CookbookPre-order now at $44.99📢 Important: MobilePro is Moving to SubstackWe’ll be moving MobilePro to Substack soon. 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aaBest Interview Preparation Platforms in 2026Technical interviews have changed significantly.Companies are no longer evaluating candidates only on whether they can write correct code in SQL, Python, or another programming language.Depending on the role, candidates may also need to solve business problems, design systems, analyze experiments, and communicate their thinking clearly.The difficult part is that every company has different expectations.Two companies hiring for the same position may ask completely different questions or structure their interviews in very different ways. One might emphasize coding, while another focuses more on business sense, experimentation, or system design.Knowing what to expect beforehand can give candidates a major advantage, especially in today’s competitive job market.This guide compares five of the best interview preparation platforms in 2026, including what each platform does well, where it falls short, and who should use it.What Makes a Great Interview Preparation Platform?I evaluated each platform using three criteria:1. Question AccuracyDoes the platform provide an accurate list of questions and topics candidates can expect for their target company and role?General practice is helpful, but preparing for questions that closely resemble the real interview is much more valuable.2. Practice ExperienceCan candidates practice in an environment that resembles the actual interview?This may include a coding environment, timed exercises, realistic mock interviews, or opportunities to explain answers out loud.3. Learning SupportDoes the platform provide solutions, explanations, feedback, or coaching that helps candidates improve?Practicing questions is only part of the process. Candidates also need to understand why an answer works, where they made mistakes, and how they can improve their approach.The best platforms help candidates understand what to expect, practice in a realistic environment, and learn from every attempt.1. DatafordDataford organizes interview preparation around specific companies and roles.Candidates select the company and position they are targeting to find relevant questions, interview guides, and information about how that employer typically structures its hiring process.This matters because companies hiring for similar positions may evaluate candidates very differently. A data analyst interview at Stripe, for example, may look completely different from one at Walmart.Dataford covers more than 7,000 companies, over 50 roles, and 500,000 interview questions across data, engineering, product, AI, and other technology functions.How Dataford PerformsQuestion accuracy: This is Dataford’s biggest strength. Its large breadth of questions across thousands of company-and-role combinations makes it more likely that candidates will find questions closely aligned with their actual interview. Rather than preparing from a generic question bank, candidates can focus on what a specific employer is likely to ask for their exact role.Practice experience: Candidates can practice technical questions in interactive environments and prepare for different parts of the interview, including coding, business sense, experimentation, system design, product cases, and behavioral questions.Learning support: Dataford provides solutions and explanations designed to help candidates understand the reasoning behind an answer rather than simply memorize it.The platform is particularly valuable when an interview is already scheduled and preparation time is limited. Its main limitation is that it may feel overwhelming for complete beginners who do not yet have a target company or role.Pros:Strong question accuracy across company-and-role combinationsMore than 500,000 interview questionsCoverage across 7,000+ companies and 50+ rolesSupports technical and business problem-solvingInteractive practice environmentsSolutions and explanations across multiple interview topicsCons:Less suitable for candidates without a target roleThe large content library can feel overwhelming for beginnersMore focused on interview preparation than teaching every skill from the ground upBest for: Candidates who want highly relevant questions for a specific company and role, along with the tools and explanations needed to practice them.2. ExponentExponent is one of the most polished platforms for candidates who prefer structured learning.It offers dedicated courses for product management, software engineering, data science, data analytics, data engineering, machine learning, system design, SQL, and several other technical areas.Instead of presenting candidates with only a question bank, Exponent organizes its content into guided learning paths.Its biggest differentiator is access to human coaching. Candidates can book mock interviews and individual sessions with experienced professionals. Exponent also provides resume reviews and salary negotiation support.How Exponent PerformsQuestion accuracy: Exponent provides high-quality questions for its supported roles, although its content is generally organized around role categories rather than a large number of specific company-role combinations.Practice experience: The platform offers mock interviews, practice questions, and human coaching. This makes it particularly strong for interviews where communication and structured thinking matter.Learning support: Its courses, example answers, coaching, and feedback give candidates several ways to understand their mistakes and improve.The main limitation is role coverage. Exponent serves its core technical and product roles well, but candidates targeting positions outside those categories may not find a relevant track.Pros:Well-structured coursesStrong human coaching and mock interviewsCovers technical and problem-solving skillsClean and user-friendly experienceHelpful example answers and explanationsCons:Covers a relatively limited number of rolesLess company-specific than DatafordCoaching sessions can increase the total costBest for: Candidates who want a guided learning path combined with coaching and mock interviews.3. Final Round AIMany candidates spend considerable time preparing what they will say but very little time practicing how they will say it.Final Round AI focuses on this overlooked part of interview preparation.The platform uses AI interviewers and realistic avatars to simulate an actual interview. Questions can be tailored using the candidate’s resume, experience, and target role.This helps candidates practice thinking out loud, explaining decisions, asking clarifying questions, and structuring answers under pressure.How Final Round AI PerformsQuestion accuracy: Questions can be personalized using the resume and job description, but they may not always match the specific questions or process used by an individual company.Practice experience: This is the platform’s biggest strength. The AI interview experience feels much closer to a live interview than answering questions alone.Learning support: Candidates receive AI-generated feedback that can help them improve their answers, delivery, and communication. However, the feedback can sometimes feel generic.Final Round AI works best when paired with another platform that provides more precise company-specific or technical preparation.Pros:Realistic AI mock interview experienceHelps candidates practice communicating under pressureUses the resume and job description to personalize questionsUseful across technical, behavioral, and problem-solving interviewsCons:Limited company-specific accuracyFeedback can occasionally feel genericCannot fully replace an experienced human interviewerBest for: Candidates who understand the material but need to improve how they communicate their answers.4. DataLemurDataLemur is one of the strongest options for candidates preparing for data interviews, particularly when SQL is a major part of the process.The platform offers SQL questions inspired by real companies. Its exercises focus on practical problems that resemble what candidates may encounter during technical screens.DataLemur also offers useful free content, making it accessible to candidates who are not ready to pay for a complete interview preparation platform.How DataLemur PerformsQuestion accuracy: DataLemur offers relevant company-style SQL questions, although it provides less visibility into the complete interview process for a specific company and role.Practice experience: Candidates can write and test SQL directly on the platform, creating an experience similar to a technical assessment.Learning support: Its solution explanations help candidates understand the reasoning behind each query instead of only showing the final answer.Its focused approach is both its greatest strength and its primary limitation. Many interviews also include Python, experimentation, statistics, business sense, behavioral questions, and product cases.Pros:Strong SQL interview questionsInteractive SQL practiceClear explanations and solutionsUseful free contentProblems resemble real technical screensCons:Primarily focused on SQL and data rolesLimited coverage of complete interview processesLess useful for business, behavioral, and communication practiceBest for: Candidates who want to improve their SQL and technical problem-solving skills.5. LeetCodeLeetCode remains one of the most recognized technical interview preparation platforms.Its question bank is enormous, its coding environment is reliable, and its community provides multiple solutions and explanations for most problems.For software engineering interviews, it remains one of the strongest resources available. It is especially useful for practicing algorithms, data structures, and coding under time constraints.LeetCode also includes database questions for candidates who want to practice SQL fundamentals.How LeetCode PerformsQuestion accuracy: LeetCode offers many questions associated with major companies, but candidates still need to determine which problems are relevant to their specific role and interview process.Practice experience: Its coding environment is reliable and closely resembles a technical coding assessment. Timed contests can also help candidates practice working under pressure.Learning support: Community solutions and discussions provide several ways to approach the same problem. However, the amount of information can sometimes be overwhelming.The main limitation is that LeetCode was designed primarily for software engineering interviews. It offers limited support for business sense, experimentation, product cases, behavioral questions, and other non-coding interviews.Pros:Large and mature question bankReliable coding environmentStrong community explanationsExcellent for algorithm-heavy technical interviewsCons:Much of the content may not be relevant to non-engineering rolesCandidates must identify the right questions themselvesLimited coverage of business and product problem-solvingDoes not prepare candidates for the complete interview processBest for: Software engineers, data engineers, and candidates expecting algorithm-heavy coding interviews.Which Interview Preparation Platform Should You Use?The right platform depends on what you need most:You want accurate questions for a specific company and role: DatafordYou want structured courses and human coaching: ExponentYou want to simulate a live interview with AI: Final Round AIYou want to strengthen your SQL: DataLemurYou are preparing for an algorithm-heavy coding interview: LeetCodeIn many cases, combining two platforms will produce better results than relying on only one.For example, candidates can use Dataford to understand a company’s interview process and identify relevant questions, then use Final Round AI to practice explaining their answers under pressure.Another candidate might use Exponent to learn interview frameworks before turning to DataLemur or LeetCode for additional coding practice.How to Prepare More EffectivelyStart by researching the company and understanding each stage of its interview process.Identify whether the company emphasizes coding, system design, experimentation, business sense, technical knowledge, or behavioral skills.Next, practice the questions most relevant to that process.If you are working on a coding problem, explain your approach before writing the code. If you are solving a business case, clarify the objective and define the appropriate success metrics before suggesting solutions.Finally, simulate the real interview.Set a timer, ask clarifying questions, and explain your thinking out loud. Review not only whether your final answer was correct, but also whether your reasoning was structured and easy to follow.Successful preparation is not about completing the largest possible number of questions.It is about knowing what to expect, practicing in a realistic environment, and learning from every attempt.