Oleh Klokov - Portfolio

React Native in mobile developmentReact Native occupies a pragmatic middle ground between cross-platform frameworks and fully native stacks. The core idea is a shared declarative UI layer built with React and a thin bridge to native views on iOS and Android. The modern architecture - JSI, TurboModules, and Fabric - lowers classic bridge overhead, allows direct calls into native code from JavaScript, and improves scheduling and memory behavior. This unlocks high-quality animations with libraries like Reanimated, performant long lists with FlashList, solid WebView integrations, and robust offline workflows. Strengths include fast prototyping, one codebase for two platforms, shared business logic, a large NPM ecosystem, mature navigation solutions, over-the-air updates, and predictable CI/CD for App Store and Google Play. In a production app, the winning approach is layered: thin screens, strict TypeScript, clear service boundaries for networking and storage, focused hooks, and API-contract tests. There are still classes of work where native wins - heavy graphics, complex media processing, or low-level sensors. Community packages cover most needs, yet custom integrations sometimes require Kotlin or Swift modules. Performance depends on discipline: avoid unnecessary re-renders, keep large structures out of React context, memoize computations, and move hot paths to JSI or Worklets where it makes sense. In short, if your app is primarily content, feeds, purchases, auth, camera or file workflows, and third-party integrations, React Native delivers speed with sufficient “native feel.” If you are building AR games, advanced video editors, or 3D-intensive experiences, native is usually better. For everything in between, React Native offers a compelling price-quality-speed balance with a clear roadmap.

Next.js for backend developmentNext.js is no longer “just frontend.” With the App Router, you get Route Handlers for HTTP endpoints colocated with UI, Server Actions for server-only mutations without a separate client API layer, flexible runtimes (Edge or Node), and ISR and cache controls. This gives you a unified repository where UI, server logic, static assets, and even scheduled tasks (via host provider features) ship together. The tradeoff is understanding the “magic” in the render and cache pipeline - which parts run on the server, which on the client, when results are cached, and how to tag or invalidate responses (revalidate, no-store). Strengths: fast TTFB via SSR and streaming, SEO out of the box, file-based routing, predictable project structure, and a highly integrated deploy story on Vercel (and workable alternatives on AWS or GCP). It is strong as a BFF - you can wrap external APIs, stitch microservices, and blend data from Postgres, Redis, or blob storage. Weaknesses include Edge runtime limits for certain dependencies, careful handling of secrets and DB pools, and the need to move heavier logic into separate services when appropriate. Best practices: push data rendering to server components and keep interactivity in client components, separate libs and infra cleanly, model contracts with types, centralize caching and revalidation, invest in observability (logs and metrics), harden APIs (rate limit, CSRF or CORS), and warm critical paths. The bottom line - Next.js is a great “thin backend” for web products that value SEO, fast iteration, and a single JavaScript/TypeScript stack.

MongoDB vs PostgreSQL - pros and consMongoDB provides a document model with flexible schemas, a quick ramp-up, natural JSON storage, aggregation pipelines, and change streams. It fits event data, content systems, catalogs, and feeds. Teams often perceive horizontal scaling as more approachable, and migrations are lighter because the schema evolves with the app. Downsides - complex multi-document transactions and rich joins require careful modeling, and analytical or reporting queries may hit limits, pushing you to OLAP or dual writes. Indexing and consistency demand discipline; oversized nested documents or arrays can degrade memory and planner efficiency. PostgreSQL is a mature relational database with ACID guarantees, rich SQL (CTE, window functions), a strong optimizer, multiple index types (GIN, GIST, BRIN), partitioning, triggers, and extensions such as PostGIS and pgvector. It suits payments, accounting, analytics, and strict integrity. JSONB lets you keep semi-structured data, while transactions span complex updates safely. The tradeoffs are stricter up-front schema design and heavier migrations. Advanced horizontal sharding typically requires additional tooling like Citus. Decision flow: choose MongoDB if your domain changes quickly, payloads are naturally document-shaped, you need easy sharding and rapid iteration, and your analytics can be offloaded. Choose PostgreSQL when transactions, complex queries, analytics, and strict data integrity are central and your schema is relatively stable. In practice, hybrids win - OLTP in Postgres, event or logging streams in MongoDB or brokers, and OLAP separately.

Styled-components in mobile (React Native)Styled-components/native deliver style isolation, theming, and declarative UI composition. A component encapsulates its styles, supports dynamic props, and cleanly adopts themes (light, dark, brand). Design tokens and mixins make your design system explicit in code. That improves readability, reduces scattered StyleSheets, and onboards developers faster - the UI is consistent and centrally themed. The main caveat is runtime cost - generating styles on the fly and GC pressure on large lists or frequent re-renders. Countermeasures include moving styled definitions outside render, using .attrs for stable props, memoizing styled components, avoiding string concatenation in hot paths, and leveraging getItemLayout with FlashList for long feeds. For animations, prefer react-native-reanimated to keep work off the JS thread. TypeScript adds safety for prop-driven styles, while shared tokens keep web and mobile in sync. Alternatives include the RN StyleSheet plus utility classes (tailwind-like), Emotion, or NativeWind. The right pick depends on team culture and performance goals. If you need “design as code,” first-class theming, and clean, reusable interfaces - styled-components are an excellent choice, especially together with Storybook and visual regression tests that keep the system stable over time.

React vs Next.js for web applications - pros and consReact is a library that maximizes freedom in SPA architecture - you pick routing, data loading, SSR strategy, and state management. The upside is granular performance control, microfrontends, and independent releases. The downside is more infrastructure glue - SSR, code splitting, SEO, server caching, and a heavier DevOps burden. Next.js is a framework with file routing, SSR/SSG/ISR, server components, built-in caching, and data primitives. The benefits are faster TTFB, SEO from the start, excellent DX, and simple deployment workflows. It is ideal for content-heavy sites, catalogs, and account dashboards. The downsides are framework constraints, understanding render and cache “magic,” and some limitations in the Edge runtime for native addons or certain modules. Practical guide - if you need a flexible SPA, microfrontends, and highly customized real-time patterns with a bespoke BFF, choose plain React and build infra explicitly. If you care about SEO, streaming, cache, and a single repo for UI and API, Next.js accelerates delivery. Migration is possible - start with React and move routes or pages to Next.js when requirements grow.

How Skia changed graphics and animations in React NativeSkia fundamentally reshaped how React Native teams approach graphics-heavy UI. Historically, complex vector work, custom shaders, and high-FPS effects were awkward through the classic bridge. RN Skia draws directly with Skia, bypassing the bottlenecks of serializing commands across JS and native layers. The result is a rendering pipeline that feels closer to native canvas APIs, unlocking techniques that were previously impractical. The most immediate win is deterministic performance. With Skia, developers can build charts, waveform visualizers, particle effects, and signature canvases that hit 60 or 120 FPS on modern devices. Paired with React Native Reanimated and the new architecture (JSI, TurboModules, Fabric), animations can execute worklets off the JS thread while Skia handles rasterization efficiently. This division reduces jank from GC pauses or heavy JS tasks. Skia also broadens the creative toolset. You can compose paths, gradients, masks, and image filters, or implement custom paint routines with predictable memory behavior. Shader support enables dynamic transitions, blurs, and color manipulations without bouncing through the bridge. For teams shipping branding-driven products, Skia ensures parity with native design systems and reduces the “almost-native” look that sometimes plagues cross-platform stacks. However, Skia requires new discipline. You must reason about draw loops, invalidation regions, batching, and texture lifecycles. Overdraw and unnecessary re-renders still hurt, and large bitmap uploads can stall the pipeline. Testing strategy should include frame-time budgeting and GPU profiling on lower-end devices. Also, Skia is powerful but not a silver bullet - video, 3D, and camera pipelines have their own constraints. Adoption tips: start small with a bespoke component (e.g., progress ring or sparkline), profile with frame charts, and codify patterns for surfaces, caching, and touch handling. Build a utilities layer for common shapes and gradients to speed iteration. When combined with Reanimated and the modern RN architecture, Skia transforms React Native from “mostly native UI” into a platform capable of sophisticated, buttery-smooth graphics at scale.

Expo vs React Native CLI - complexities developers faceExpo accelerates delivery with batteries-included toolchains, OTA updates, and a cohesive ecosystem. For many teams, the zero-config DX, unified docs, and high-quality modules reduce setup friction dramatically. Managed workflow simplifies common use cases - push notifications, authentication, deep links, and media - while EAS handles builds and submissions. For product teams, this translates to quicker prototypes and more predictable pipelines. But convenience has constraints. Some native dependencies may lag behind or require config outside the managed scope. When you hit a custom native need (bleeding-edge SDKs, proprietary libs, or complex background services), you might eject to the bare workflow. Ejection is not a failure - it is a spectrum: you keep Expo tooling and modules but manage native projects directly, accepting Xcode/Gradle complexity and a heavier CI setup. React Native CLI provides total control from day one. You can align Android and iOS versions, inject specific Gradle or CocoaPods tweaks, and add custom native code without fighting abstractions. The price is higher maintenance: you own signing, provisioning profiles, release pipelines, and version compatibility. This is worthwhile for teams with dedicated mobile infra or niche hardware requirements. Hidden complexities appear in both paths. In Expo, developers must understand EAS cache, runtime versions, and update channels to avoid breaking changes for users. In CLI projects, keeping NDK, SDK, Pods, Hermes, and React Native versions in harmony is an ongoing job. Performance tuning - Proguard/R8, Hermes flags, symbolication, and Crashlytics mapping - exists regardless of tool. Guidance: start with Expo if your roadmap fits mainstream features and you value speed. If you foresee deep native integrations, plan for the bare workflow or CLI early. Keep a decision log of native needs, schedule periodic review of SDK versions, and codify build scripts. Both paths can ship robust apps - success depends on matching the tool to your product’s constraints and your team’s tolerance for native complexity.

Integrating AI into mobile apps - patterns, UX, and privacyAI in mobile is no longer a novelty - users expect intelligent experiences that feel instant, private, and context-aware. The first decision is placement of inference: on-device, edge, or cloud. On-device models offer latency, offline reliability, and better privacy. They shine for classification, ranking, summarization, and simple generation. Edge functions reduce round-trips and can precompute embeddings or cache results near users. Cloud inference scales for large models and complex pipelines but must mask network variance with fallbacks and streaming UX. Architecturally, think in pipelines: preprocessing (tokenization, normalization), inference (one or multiple models), postprocessing (formatting, safety filters), and storage (telemetry, feedback loops). Keep prompts, weights, and safety rules versioned. For personalization, maintain user vectors locally and sync selectively. Use feature flags and A/B testing to compare models and temperature or top-k settings in production. UX is critical. Provide progressive feedback: skeletons, streamed tokens, or partial results. Offer control - quick edits, regenerate, and tone sliders - and always expose an undo. For privacy, default to local processing when feasible, encrypt caches, and explain what is sent to servers. Show model provenance and data usage. On iOS and Android, request permissions transparently only when needed. Performance tuning spans multiple layers. Quantize models to fit memory budgets, prewarm interpreters or runtimes, and batch requests where possible. Cache embeddings and responses keyed by inputs. Use background tasks to refresh indexes. Monitor tail latency and apply circuit breakers to avoid UI freezes. Lastly, iterate with guardrails. Establish content and safety policies, log only what is necessary, and red team the product against prompt injection or misuse. With clear patterns and discipline, AI features can feel magical without compromising performance or trust.

MMKV and secure storage - persistence and data leak protectionMMKV is a fast key-value storage library built on mmap, widely used in React Native for its speed and simplicity. It excels at caching session tokens, flags, UI state, and small preference blobs, with performance that outpaces AsyncStorage. However, raw speed is not enough when security matters. Developers must build a layered approach that addresses confidentiality, integrity, and lifecycle of secrets. Start by classifying data: ephemeral UI state, low-risk preferences, sensitive tokens, and personally identifiable information. Store sensitive items in platform-specific secure enclaves when possible: Android Keystore and iOS Keychain. Use MMKV for non-sensitive or for encrypted values only. A robust pattern is envelope encryption: generate a random data key, encrypt it with Keystore/Keychain, then use the data key to encrypt payloads stored in MMKV. Rotate keys on major releases or compromise events. Protect at rest and in memory. Zeroize secrets after use, avoid logging, and beware of screenshots or backups. On Android, consider enabling backup rules to exclude sensitive files. On iOS, pick the right Keychain accessibility class (e.g., after first unlock). Throttle or lock access on rooted or jailbroken devices if your threat model requires it. Defend against synchronization pitfalls. If you sync settings across devices, never sync raw secrets. Use server-issued short-lived tokens and refresh flows. For analytics, anonymize identifiers and bucket metrics to prevent re-identification. Finally, test security as a feature. Add unit tests for encryption wrappers, run static analysis, review Proguard/R8 configs to avoid stripping critical classes, and monitor crash reports for edge cases like corrupted stores. MMKV is an excellent building block, but data protection emerges from end-to-end design: secure key management, minimal blast radius, and clear incident playbooks.

WebSocket in web and mobile - real-time strategiesWebSocket enables real-time bidirectional communication and powers chat, collaboration, live dashboards, and multiplayer experiences. Its strength is persistent connections with low overhead after the initial upgrade from HTTP. In practice, successful deployments hinge on backpressure control, connection lifecycle management, and horizontal scalability across regions. Start with a clear routing model. Namespaces or channels map users to rooms, while presence tracking informs UI state. Implement heartbeats and timeouts to detect half-open connections quickly. For mobile, account for backgrounding and flaky networks: resume sessions with last-seen event IDs, and buffer outbound messages during brief disconnects. Scaling requires a pub-sub backbone. Redis, NATS, or Kafka fan out events to WebSocket gateways, and consistent hashing keeps sticky sessions aligned with user shards. Use connection caps per node and autoscale based on active sockets and P95 send latency. For global apps, terminate sockets close to users at the edge and replicate events efficiently between regions to minimize cross-continental hops. Security and reliability are foundational. Authenticate at connect time with short-lived tokens, rotate keys, and limit message sizes. Apply rate limits per connection and per IP. Validate payloads strictly, and include schema versions for forward compatibility. Instrument everything: connection counts, topic fan-out times, dropped frames, reconnection rates, and end-to-end delivery latency. On clients, abstract transports behind a resilient layer that can fall back to SSE or polling when proxies block WebSocket. Provide optimistic UI updates and reconcile upon ack or server echo. For React Native and web, centralize state updates to avoid tearing and memory leaks. With these practices, WebSocket delivers durable, low-latency experiences across platforms. Treat it as a system, not just a socket - success comes from protocols, observability, and graceful degradation paths.