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Final Part | Mastering Java OOP from Scratch | Abstraction | Interfaces

Abstraction in Java OOP focuses on what a method does rather than how it is achieved.
Abstraction can be implemented using interfaces or abstract classes to create templates that define the functionality of methods without implementing the logic.
Abstract classes allow partial abstraction by sharing common code, while interfaces provide full abstraction with no method implementations allowed.
Understanding abstraction, along with other OOP concepts like inheritance and polymorphism, is essential for writing clean, flexible, and scalable code in Java.

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DeadLock - 50% Complete

The ambitious project manager's project, DeadLock, is now 50% complete and is being built with CMake.
Significant progress was made in a day by importing the cURL library for HTTP requests in C++, building functions to download and extract packages from Pypi, and identifying the need to use nlohmann json for parsing JSON.
Tomorrow's task includes implementing JSON parsing in C++; once completed, DeadLock will be around 2/3rds complete.
The project manager expresses gratitude for the support received.

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Medium

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Beyond the Buzzwords: The ONE Skill Every Aspiring Data Scientist Needs (It’s Not Coding)

The most crucial skill for aspiring data scientists is exceptional communication, surpassing coding and model building.
Data scientists need to effectively communicate the value of their insights to non-technical stakeholders to drive action and make an impact.
Developing communication skills involves translating technical jargon into clear, compelling narratives and actionable insights.
In a data-rich world, data scientists who can both derive insights and effectively communicate them will be the most valuable in driving real-world impact.

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How Private Getters and Setters Improve JavaScript Class Security

JavaScript now allows true private fields with class field declarations and the # prefix, enhancing security by restricting access to getters and setters within a class.
Private getters and setters with a # in front of the name can only be accessed internally within the class body, providing a secure way to manage internal logic.
Private accessors not only hide properties, but live in a separate internal space inaccessible to outside code, making them invisible during inspection.
They offer early error detection, preventing access to private fields or methods by code outside of the class at parse time.
Private fields do not exist in prototypes, ensuring no accidental shadowing or overriding through prototype manipulation.
Private accessors allow for controlled behavior within classes, preventing unauthorized changes and enhancing object state integrity.
Subclasses cannot access private fields directly, maintaining a clear boundary between base and derived classes.
Private accessors help in structuring classes more securely and enable better isolation of internal state from subclass interference.
They aid in separating a class's functionality from its implementation, leading to cleaner code and easier maintenance as the system grows.
JavaScript's support for real privacy through private getters and setters ensures that fields and logic remain protected and inaccessible to unauthorized code.

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Medium

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What Kotlin’s Type Inference Does Behind the Scenes

Kotlin uses type inference to reduce the amount of type-related code you need to write while maintaining type safety.
The compiler determines types based on usage, assignments, and expected contexts, locking in the type once inferred.
Type inference is commonly seen with local variables, where the compiler infers the type from the assigned value.
Return types of functions can be inferred when using single-expression bodies, simplifying code and maintaining clarity.
In cases where the compiler needs more context, explicit type declaration is required to help with type inference.
Kotlin's type inference extends to generics, function overloading, lambdas, and smart casts, enhancing code flexibility.
Generics in Kotlin allow flexible type usage by inferring types from function or constructor call arguments.
The compiler uses overload resolution and inference to determine which function to call based on arguments and signatures.
Kotlin also supports SAM conversions, allowing passing lambdas in place of Java functional interfaces with inferred types.
Type inference in Kotlin occurs during compilation, ensuring code cleanliness and safety through strict processes.

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What are design constraints? — The power of limitations in design

Design constraints are limitations or predefined conditions that design teams face during the product design process, affecting product quality positively by shaping the product within practical boundaries.
Constraints include deadlines, budgets, brand guidelines, laws, resource availability, and technical limitations, among others.
There are various types of design constraints, such as technical, business/logistical, creative, and legal/compliance constraints.
Design constraints can improve focus, creativity, user-centricity, and overall product quality.
Examples of design constraints include technical limitations like hardware variations, platform guidelines, and tech stack restrictions.
Business constraints like deadlines, budgets, and resource availability also shape product design processes.
Creative constraints, branding guidelines, design philosophies, and legal/compliance constraints are crucial in product design.
Properly managing design constraints can lead to streamlined processes, improved consistency, and enhanced user experience.
Common mistakes in handling design constraints include treating them as blockers, ignoring them, or using them to justify lazy decisions.
Addressing design constraints effectively can significantly impact the success and quality of digital product designs.

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Pymnts

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This Week in AI: Intuit Rolls out AI Agents to SMBs

Intuit is introducing AI agents for its enterprise clients, such as TurboTax, QuickBooks, and Mailchimp, to enhance user experiences.
Voice AI startups raised $2.1 billion in 2024, an eightfold increase from 2023, with applications in industries like healthcare, retail, and food service.
AI pioneer Yoshua Bengio launched an AI safety nonprofit, LawZero, with $30 million in funding to prevent harmful impacts of AI on humanity.
Google and Warby Parker plan to release AI-powered glasses after 2025, joining other tech firms like Meta, Amazon, and Snapchat in smart eyewear development.

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Adding Health Checks to Spring Boot with Custom Indicators

Spring Boot applications use the health indicator system to monitor the app's health without depending on manual checks.
Health indicators feed data into the /actuator/health endpoint, checking database, message broker, and file system status.
Spring Boot auto-configures health contributors based on the application context and classpath, like DataSourceHealthIndicator.
Contributors like DiskSpaceHealthIndicator, MongoHealthIndicator, etc., register based on their prerequisite beans' presence.
When a request hits /actuator/health, Spring combines all contributors' statuses into a JSON response.
Custom health indicators implementing HealthIndicator interface can be easily integrated into Spring Boot for tailored monitoring, such as checking external systems.
Profile-based activation and property control help in managing when custom indicators are active.
Spring Boot supports liveness and readiness checks through /actuator/health/liveness and /actuator/health/readiness endpoints.
Health checks communicating with remote services should use non-blocking calls like Spring's WebClient to avoid delays.
Spring Boot's health indicator structure integrates seamlessly with Actuator, automating health monitoring and reporting for better system monitoring.

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Python vs. JavaScript: Which Path Should You Choose in 2025?

Python and JavaScript are both powerful and popular programming languages, each with its own strengths and applications.
Python's emphasis on readability and simplicity makes it a leader in data science, machine learning, and scientific computing.
JavaScript, born for web interactivity, powers modern web applications from user interfaces to server-side development.
Choosing between Python and JavaScript depends on aligning language strengths with personal interests and project goals.
The article delves into core differences, ecosystems, performance, scalability, use cases, and job markets of Python and JavaScript.
Python's clean syntax and 'batteries-included' philosophy enhance readability and enable rapid prototyping.
JavaScript's flexibility, event-driven nature and multiple paradigms contribute to its versatility and dominance in web development.
Both languages offer multi-paradigm support, with Python excelling in data science and AI, while JavaScript shines in frontend and full-stack development.
JavaScript's ecosystem includes robust frontend frameworks like React, Vue.js, Angular, and backend power with Node.js.
Python's rich ecosystem includes libraries like NumPy, Pandas, and TensorFlow for data analysis, machine learning, and backend web development.

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Medium

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Rust Made Me Question My Love for Abstractions

The author reflects on their experience with Rust, a programming language that made them question their love for abstractions.
Initially taught that abstractions help in managing complexity in coding, the author found Rust challenging with its emphasis on lifetimes, generics, and compiler errors.
Rust transformed the author's view on abstractions from being a source of joy to feeling burdensome and complicated.
The journey with Rust made the author shift from adoring abstractions to becoming skeptical about their utility.

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Javacodegeeks

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Will Kotlin Multiplatform Replace Java on Android by 2030?

Kotlin Multiplatform (KMP) aims to unify codebases across platforms, offering code reuse, faster development cycles, and modern language benefits like null safety and coroutines.
Despite Kotlin's official endorsement for Android and KMP's promise, Java's strong presence in Android with its legacy, ecosystem, and robust tooling keeps it relevant.
KMP brings features like shared business logic, coroutines, null safety, and near-native performance, but faces challenges like tooling maturity, learning curve, library availability, and ecosystem size.
Experts suggest a coexistence of KMP and Java on Android, with KMP handling shared logic and native languages used for UI and platform-specific features by 2030.
Realistically, KMP will not replace Java entirely but will complement it, enabling shared codebases and faster multi-platform development.
Developers are advised to experiment with KMP for shared logic while leveraging Java and Kotlin JVM for platform-specific needs in the evolving Android development landscape.
The future may see a gradual transition towards KMP for new projects targeting multiple platforms while legacy Java codebases persist for years.
KMP adoption hurdles include tooling maturity, learning curve, library compatibility, performance nuances, and the size of its ecosystem and community support.
By 2030, Android development is expected to utilize Kotlin JVM for UI and KMP for shared logic, fostering a hybrid approach rather than exclusive language replacement.
While KMP offers exciting multiplatform app development opportunities, Java's maturity, ecosystem, and legacy presence ensure its significance alongside KMP in the upcoming decade.

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Dev

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Building the Web with Rust: Here's Why It Matters

Rust, a systems programming language, is gaining traction in full-stack web development due to frameworks like Tauri, Leptos, Yew, Axum, and Actix-web.
Rust's core principles of memory safety, zero-cost abstractions, and modern features make it well-suited for web development by addressing common pain points.
Rust's predictable error handling with Result ensures explicit error handling, safety, and simplification of error propagation.
Default immutability in Rust reduces race conditions in concurrent environments compared to languages like Go or Java.
Rust's macros aid in compile-time validation and error prevention, leading to concise and error-free business code.
Rust's Option and Result types enable chain invocation and functional composition, reducing verbosity and enhancing static type safety in code.
Rust ensures thread safety through ownership, borrowing, and traits, preventing data races at compile time.
Rust eliminates null pointer dereferencing issues by using Option instead of nullable pointers, ensuring compile-time safety.
Rust offers zero-cost abstractions, ensuring high performance without runtime overhead in advanced generics and traits.
Rust's unified data conversion model simplifies data flow between layers and avoids manual copying and mapping.

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Dev

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10 VS Code Extensions I Can’t Live Without in 2025

10 VS Code extensions that can significantly enhance a developer's workflow have been highlighted.
Extensions like GitLens, Prettier, Tabnine/Codeium, Error Lens, and others are recommended for boosting focus, code quality, and efficiency.
These extensions offer features such as Git history visualization, code auto-formatting, AI-assisted autocomplete, inline linting, file visualization, API testing, package autocompletion, syntax highlighting, task tracking, and project management.
Readers are encouraged to share their favorite VS Code extensions in the comments section.

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Insider

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Big Tech is vibe coding with these winning AI startups

The generative AI race in coding is seeing winners emerge, including Cursor, Codium, Cognition AI, and Microsoft's GitHub Copilot.
Amazon is making the AI coding tool 'Cursor' from startup Anysphere available to its employees, despite having its own AI coding assistants like Q and 'Kiro.'
Companies like Google, Amazon, and Microsoft are opting for external AI coding tools, indicating a shift towards utilizing third-party services over their in-house solutions.
AI coding tools like Cursor and Replit are making coding more accessible to non-technical individuals, with some tools like Cursor suited for professional software engineers while others like Replit are more user-friendly for beginners.

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Dev

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Trying to get s*** done with an LLM

The author embarked on creating a CLI for an API using a Large Language Model (LLM) and Python, facing challenges with client generation and project organization.
The use of Bandit, a static linter for Python, surprisingly found no issues with the code generated by the LLM, highlighting the potential simplicity of dynamic JSON reading.
Despite progress in implementing API endpoints, the project lacked structure and required a solid testing framework, emphasizing the need for clear direction and communication with the LLM.
The experience shed light on the gap between functional prototyping and polished application release, underscoring the importance of human intervention, problem understanding, and effective collaboration with LLMs.

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