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WWDC 2025 Exposed: 7 Announcements That Will Change Your App Forever

WWDC 2025 by Apple focused heavily on developers, introducing significant changes across various platforms.
A new design aesthetic called “Liquid Glass” was implemented, offering a modern look but facing some criticism for potential accessibility issues.
Key app updates included a keyboard redesign, changes in the Photos app, Safari improvements, and a new Xcode tool for icons.
The event highlighted the benefits for cross-platform frameworks like React Native and Flutter, with React Native leveraging Apple's updates more effectively.
Announcements regarding containerization and open-source gains were made, including the integration of Docker-in-Swift and FoundationDB.
Game Porting Toolkit 3 and enhancements in WebGPU support for Safari users were discussed, aiming to streamline game ports and improve gaming experiences.
Xcode AI tools were introduced, but faced glitches in beta, and Apple's AI strategy, particularly around Siri and on-device models, received mixed reviews.
A significant announcement was the end of the Intel-to-Silicon transition, with Rosetta 2 retiring by 2028 and developers urged to focus on Apple Silicon.
Overall, WWDC 2025 showcased advancements but also highlighted areas where Apple may need further improvement, such as in AI and cross-platform support.

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Medium

From Disheveled to Dependable: My Painful but Powerful Journey into Data Science Professionalism

The article narrates a personal journey from facing infrastructural challenges in Nigeria to becoming a dependable data science professional.
Challenges included dealing with power cuts, unstable internet, and lack of a dedicated workspace, leading to unprofessional appearances during meetings.
The author invested in a stable WiFi network and home office, emphasizing the importance of setup for professionalism.
Learning collaboration tools like Slack and communication skills was initially a culture shock, but the author adapted through observation and practice.
Emphasis was placed on understanding that coding proficiency comes through repetition and practical application, learning to code like a craftsman.
The cost of isolation and communication breakdowns highlighted the importance of overcommunication and sharing progress incrementally with the team.
Self-management in a high-performance culture was crucial, requiring documenting workflows and seeking help effectively when facing challenges.
Transitioning from a data analyst to a software-oriented thinker involved training in software engineering and understanding real systems and tools beyond data models.
Professionalism was described as more than just performance metrics, focusing on project structure, business objectives alignment, and constructive feedback acceptance.
The article concludes with insights on competence, stability, communication, coding proficiency, and the value of feedback in the journey towards becoming a reliable data science professional.

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Medium

412

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JavaScript in 2025: Ditch the Old, Embrace the New

JavaScript has evolved significantly since ES6 in 2015, and it's time to move away from old habits.
ECMAScript 2025 introduces revolutionary proposals:
1. Pattern Matching offers a sophisticated way to handle complex conditional logic and destructure data effectively.
2. Deferred Module Evaluation with defer import syntax improves page load performance for larger applications.
3. Records and Tuples bring immutable object-like and array-like data structures, enhancing state management and functional programming.
4. Pipeline Operator (|>) simplifies functional chaining for cleaner code.
5. Async Context Propagation eases maintaining context across async operations in async/await.
JavaScript in 2025 is more developer-friendly with these enhancements, offering cleaner and more efficient code.
Tools like ServBay make managing modern Node.js environments and web development stacks easier for macOS users.
It streamlines the setup and management, allowing developers to concentrate on coding.
Developers are encouraged to embrace new JavaScript features for better code quality and productivity.

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Dev

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How Big Data and AI Work Together: The Future of Intelligent Systems

Big Data and Artificial Intelligence (AI) are transformative technologies that, when used together, form the foundation of intelligent systems.
This powerful combination is reshaping industries, enhancing efficiencies, and unlocking new possibilities across various sectors.
Big Data involves vast volumes of structured and unstructured data, while AI encompasses algorithms that mimic human intelligence by learning from patterns and making decisions.
AI systems rely on Big Data for learning, as the massive and diverse datasets fuel AI development and training.
Machine learning, a subset of AI, benefits from Big Data by improving models' ability to recognize patterns and make accurate predictions.
Conversely, AI enhances Big Data by enabling faster, real-time data analysis, anomaly detection, and automated decision-making processes.
The synergy between Big Data and AI is powerful in predictive analytics, enabling forecasting in various fields like retail, logistics, and manufacturing.
Cloud computing and edge computing play significant roles in integrating Big Data and AI by providing the necessary infrastructure for efficient processing and storage.
However, combining Big Data and AI raises challenges related to data privacy, algorithmic bias, and ethical considerations that require regulatory frameworks and responsible practices.
The future of intelligent systems lies in the continued convergence of Big Data and AI, leading to smarter applications across sectors like smart cities, agriculture, climate modeling, and education.
Understanding how Big Data and AI work together is crucial for the future of technology, driving innovation and creating a more connected, intelligent, and data-driven world.

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Dev

149

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📘 Frontend Developer Structure: The Complete Learning Map

The complete learning map for mastering frontend development covers various aspects from understanding the web to deploying interactive apps.
Key areas include understanding the web structure, learning HTML and CSS fundamentals, grasping JavaScript essentials like DOM manipulation and asynchronous JS, exploring frameworks like React and utility-first styling like Tailwind CSS.
The map also delves into developer tools, workflow processes, testing methodologies, practice projects, top learning platforms, and offers a progress tracker for continuous improvement.

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Dev

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Authoring an OpenRewrite recipe

OpenRewrite is an open-source tool for automating code refactorings, enabling developers to tackle technical debt efficiently.
It includes an auto-refactoring engine that runs predefined refactoring recipes for framework migrations, security fixes, and style consistency tasks.
The tool's community is continuously expanding its language and framework coverage.
OpenRewrite features two main components: recipes and the engine that executes them.
Authors can create new refactoring recipes to customize code transformations.
A specific scenario discussed involves restructuring Kotlin package directories following official recommendations.
OpenRewrite offers benefits like testability, scalability, and composability over traditional tools like sysadmin or IDEs.
The article delves into the implementation details of crafting a new recipe in OpenRewrite for flattening Kotlin package structures.
Testing recipes in OpenRewrite is facilitated by parsers and an in-memory execution context.
Potential future work includes JSON serializability of recipes and automatic root package detection.
Overall, the article demonstrates the simplicity and effectiveness of authoring OpenRewrite recipes for codebase migrations.

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Softwareengineeringdaily

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TanStack and the Future of Frontend with Tanner Linsley

TanStack is an open-source collection of high-performance libraries for JavaScript and TypeScript applications, focusing on state management, data fetching, and table utilities.
TanStack includes libraries like TanStack Query, TanStack Table, and TanStack Router, known for their declarative APIs and optimized performance.
Tanner Linsley, the creator of TanStack, discusses the project, SSG, type safety, and the TanStack Start full-stack React framework on a podcast with Nick Nisi.
Nick Nisi, a conference organizer and developer, joins the podcast to talk about tools across the web ecosystem and his role as a developer experience engineer at WorkOS.
TanStack emphasizes developer-friendly features and is gaining popularity in modern frontend development.
Tanner Linsley and Nick Nisi delve into various topics related to TanStack and frontend development in the podcast episode.
To access the full transcript of the episode, click on the provided link.
Sponsorship inquiries for the podcast can be directed to [email protected].
The post covers TanStack and its impact on frontend development, featuring insights from Tanner Linsley and Nick Nisi.

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Dev

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How to Authenticate Your React App Using Firebase

Authentication is crucial for web and mobile apps to securely manage user access and data.
Firebase from Google offers easy authentication integrations for different apps.
The article guides users on authenticating apps with Firebase using various methods.
Firebase supports email/password authentication, social logins, phone number, and anonymous authentication.
Setting up Firebase project involves creating it in the Firebase Console and adding a web app.
Installation of Firebase in projects is necessary for utilizing its authentication features.
Initializing Firebase in projects includes adding a Firebase SDK snippet in the configuration file.
Firebase allows setting up authentication methods like email/password auth and enabling them in the console.
Creating sign-up and sign-in functions using Firebase's createUserWithEmailAndPassword and signInWithEmailAndPassword.
The article demonstrates setting up Google authentication by enabling it in Firebase and creating the necessary functions.

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Dev

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C# Loops: for vs while - When to Use Each (With Real Examples)

Choosing between for and while loops in C# is about intent rather than syntax.
For loops are suitable when you know the exact number of iterations required.
Examples of for loop usage include processing arrays and building patterns.
While loops are ideal for scenarios where iterations depend on changing conditions.
Examples of while loop usage include user input validation and file processing.
Common mistakes include using while for simple counting operations and for for unknown iterations.
Real-world decision making involves choosing the best loop based on the specific scenario.
Pro tips for loop mastery include reading code aloud, considering future maintenance, and avoiding infinite loops.
The bottom line: Use for loops when iterations are known in advance and while loops when waiting for conditions to change.

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Hackernoon

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I Watched TikTok for Hours — Then Accidentally Started a Website With $10K in ARR

A high school student, inspired by a TikTok video, accidentally started a website called DuckMath that offers unblocked games.
He struggled with web development due to lack of proper education in school, but regained confidence from a coding project.
To attract users, he utilized TikTok for advertising, even though most of his early videos performed poorly.
His perseverance and consistency in posting content led to slow but steady growth of the website.
After a stagnant period, he delved into entrepreneurship, learned new strategies, and found ways to engage a community around his website.
The website, duckmath.org, eventually achieved $10k in Annual Recurring Revenue (ARR).
The creator is now focusing on building new projects like blogbott.com and coldcallbot.com while continuing to grow his online community.
He emphasizes the importance of trying, failing, and iterating to eventually find success.
The journey showcases the power of persistence and learning from failures in entrepreneurship.
The creator aims to foster a community of supporters for his projects.
He believes that despite setbacks, continual effort and exploration will lead to successful ventures.

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Dev

389

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🌟 EvoAgentX's First Community Call: A Great Milestone Achieved! 🌟

EvoAgentX hosted its inaugural Community Call last Sunday, focusing on self-evolving AI agents and community collaboration.
Discussion points included the project vision, upcoming features like plug-and-play prompt optimization, and achievements such as integrating long-term memory modules.
The call highlighted the goal of creating a self-evolving AI ecosystem with open collaboration opportunities and modular tools.
The session recording is now available on YouTube for those who missed the live event.
Viewers can watch the recording on YouTube to learn about EvoAgentX's latest developments and team insights.
EvoAgentX encourages support by starring their GitHub repository and staying updated on future advancements.
Interested individuals can visit EvoAgentX's GitHub to engage with the project and show support.
The project aims to shape the future of AI with contributions from the community.
Key hashtags related to the project and event include: AI, OpenSource, EvoAgentX, SelfEvolvingAI, MachineLearning, AICommunity, Innovation, GitHub, LLM, CommunityCall, and TechUpdates.

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Dev

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How I Monitored My Lithium Battery Pack Using Python and a Raspberry Pi

Monitoring lithium-ion battery packs is crucial for safety, health, and efficiency in various applications.
A detailed guide on setting up a battery monitoring system using Python on a Raspberry Pi is provided.
Components like Raspberry Pi 4, INA219 current sensor, and DS18B20 temperature sensor are used in the project.
The article covers wiring instructions for INA219 and DS18B20 to the Raspberry Pi.
Software setup involves installing necessary packages, enabling interfaces, and running a Python script for monitoring.
Data visualization and analysis using Pandas and Matplotlib help track battery metrics over time.
Building a web dashboard with Flask for remote data access is also outlined as an optional step.
The article emphasizes accuracy, calibration, safety measures, and potential advanced extensions for the monitoring system.
Lessons learned include the importance of sampling rate, cell balancing awareness, data normalization, and thermally-aware design.
Monitoring lithium batteries with Python and Pi provides valuable insights for optimizing device performance and longevity.

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Dev

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Namespace vs Regular Packages in Python — And Why mypy Might Be Failing You

Python developers working on AI systems, data pipelines, or backend services may face issues with mypy not detecting types or imports correctly, often due to the use of namespace packages.
Regular packages in Python require an init.py file, reside in a single folder, and are easier to process for tools like mypy, linters, and IDEs.
Namespace packages, on the other hand, do not need an init.py, can span multiple folders or repositories, and are common in plugin systems and modular AI/ML tools.
Namespace packages are beneficial for scalability but can be challenging for static analysis tools like mypy unless configured correctly.
AI developers and data teams should pay attention to namespace packages for modular pipelines, plugin systems, and shared AI tooling across different libraries.
One key issue with mypy and namespace packages is that mypy may struggle to analyze them correctly by default.
To address mypy issues with namespace packages, developers can enable namespace support using commands like 'mypy --namespace-packages' or by setting it in mypy.ini.
Developers should use package names instead of just folder names when running mypy commands and set additional options like MYPYPATH and --explicit-package-bases for non-standard layouts.
If still facing challenges, adding dummy init.py or init.pyi files can help tools infer the package structure more accurately.
Understanding how namespace packages and tools like mypy work together is crucial for maintaining bug-free and reliable code when building AI pipelines and shared tooling.

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Dev

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Day 22/30 - git log --graph --oneline --all – Visualize branch history.

Visualizing Git history is crucial in collaborative software development to track branches, merges, and commits efficiently.
The 'git log --graph --oneline --all' command offers a condensed, graphical view of commit history, depicting branch relationships clearly.
It combines flags like --graph for graphical representation, --oneline for concise commit display, and --all to include all branches.
The command helps track merges, divergences, and unmerged branches, aiding in debugging and auditing changes effectively.
Advanced Git log techniques include sophisticated filtering, formatting options, visualization enhancements, and statistical insights.
Tips like time-based filtering, author and committer search, content-based filtering, and custom output formats optimize Git log usage.
The 'git log --graph --oneline --all' command empowers developers to understand Git history rapidly, aiding in tasks like debugging conflicts and reviewing contributions.
With various aliases and formatting options, developers can customize their Git log commands for efficient tracking and analysis.
Mastering Git branch visualization with 'git log --graph --oneline --all' enhances efficiency in managing repositories and collaborating effectively.

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Medium

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Understanding the equals() and hashCode() Contract in Java

Developers often overlook the importance of the contract between the equals() and hashCode() methods in Java, leading to unexpected behavior and bugs.
Objects with the same hash code do not necessarily have to be equal due to hash collisions.
Proper implementation of equals() and hashCode() is crucial for hash-based collections like HashSet and HashMap to function correctly.
If the User class overrides equals() but not hashCode(), it can lead to issues like failing the .contains() check in HashSet.
Hash-based collections use hashCode() to locate the bucket and equals() to compare objects within the bucket.
It is important to override both equals() and hashCode() methods in custom classes to ensure proper functioning in collections like sets and maps.
Incorrect implementation of these methods can result in unreliable and unpredictable behavior in Java applications.
Understanding the contract between equals() and hashCode() is essential for writing reliable and predictable Java applications.
Properly implementing these methods ensures consistent behavior of objects in hash-based collections.
Common mistakes to avoid include not overriding hashCode() when overriding equals(), leading to incorrect functioning of hash-based collections.
Using a custom object as a key in a Map emphasizes the importance of understanding and respecting the equals() and hashCode() contract.
Implementing equals() and hashCode() correctly is crucial for maintaining the integrity and consistency of Java objects used in various data structures.

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