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Stop Fighting Forms: How TanStack Query + Zod Will Save Your Sanity (And Your Users’)

Forms can be a source of frustration with tools like Formik and React Hook Form due to complex nested objects, TypeScript errors, and performance issues.
TanStack Query and Zod offer a new approach to form validation, providing a game-changing solution.
Formik, once popular, is now criticized for performance issues with large forms, re-renders on each keystroke, and bloated bundle sizes.
React Hook Form improved performance but introduced challenges like complex useController debugging and intricate TypeScript integration.
Hand-rolled validation logic may seem appealing initially, but it can quickly lead to building a form library from scratch.

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Dev

135

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How i use AI tools to make dev articles more useful (and more fun to read)

Dev articles often go unread due to flat delivery, lack of visual breaks, and poor structure, not bad content.
Using AI tools can enhance content delivery by improving formatting, adding visual clarity, and supporting videos.
Common content sins and how they affect reader attention are discussed, emphasizing the importance of skimmable content.
ChatGPT can help restructure content for better readability, including adding subheadings, summaries, and scannable formatting.
By repurposing articles into cheat sheets, newsletters, Twitter threads, FAQs, and more, different reader preferences are catered to.
Visuals, including architecture diagrams, data flow snapshots, and minimalist UI mockups, can greatly enhance developer articles.
Short-form videos are highlighted as a useful tool to explain complex topics quickly and engage readers.
Tips on how to upgrade a plain text article with AI-powered tools into a more engaging multi-format experience are provided.
Caution is advised against overwhelming content with unnecessary elements and the importance of maintaining value for developers is stressed.
The role of AI tools is to improve content delivery, clarity, and reader understanding, not just to increase reading time.
Use AI tools intentionally, focusing on structuring, visualizing, and explaining key points to benefit both writers and readers.

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Medium

403

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Mastering Background Tasks in Python: My Journey Building a Task Scheduler From Scratch

The author found Celery and Cron to be inadequate for task scheduling in Python.
They developed a basic, non-blocking task runner using asyncio.
The approach allowed mixing async with blocking logic without freezing the main event loop.
The main thread can run the task runner or be spun off into its own daemon thread.
They utilized a pattern to handle transient failures like network blips or file locks.
The system provided predictable teardown behavior when running as a service.
Every job now runs with full visibility, eliminating the need to grep logs.
The architecture supported running over 10k jobs per week in production with minimal issues.
The project was rewarding and educational, offering insights into Python's concurrency.
It emphasized leveraging Python's core primitives to build powerful systems tailored to specific use cases.
The author encouraged others to try this approach for scheduling background tasks, starting small and growing iteratively.
Automation is highlighted as a means of both saving time and reclaiming control.

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Mjtsai

110

SwiftData and Core Data at WWDC25

SwiftData introduced inheritance and schema migration at WWDC25.
Using class inheritance helps model data and optimize queries.
SwiftData also enables seamless migration of app data.
Observable and persistent history aid in change tracking.
Despite the addition of inheritance, lack of new features disappoint developers.
Core Data seems stagnant with no new features noted at the event.
Concerns arise about Core Data's future and compatibility with SwiftData.
SwiftData Xcode 26 update focused on bug fixes and functionality improvements.
Notable features like additional sync options and dynamic predicate adjustments were missing.
Although lacking in some areas, the update enhances SwiftData's practicality.
Previous topics about Core Data and SwiftData were discussed.

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Programesecure

144

The Rise of 6G Technology – What You Need to Know

6G technology is the upcoming generation of wireless communication that promises faster speeds and lower latency than 5G, with potential speeds exceeding 1 Tbps.
Key features of 6G include ultra-fast speeds, ultra-low latency, global connectivity, and AI integration for smarter systems.
Applications of 6G span across IoT, autonomous vehicles, healthcare, and entertainment, enhancing connectivity and experiences.
6G surpasses 5G in speed, latency, and device support, making it ideal for IoT growth and real-time applications.
Technologies enabling 6G include advanced antennas, AI, machine learning, and potentially quantum computing for network optimization.
Challenges in 6G development involve infrastructure costs, spectrum management, and security and privacy issues.
Countries like China, the US, and South Korea are in a global race for 6G development, with investments pouring in from both governments and private companies.
6G technology not only promises faster connectivity but also holds potential for driving sustainable development through energy-efficient solutions.
6G's impact on society will revolutionize work, education, healthcare, and entertainment, unlocking new possibilities in various sectors.
In the next 5-10 years, 6G technology is expected to redefine digital interactions, leading to innovation on a large scale and creating smarter, sustainable societies.

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Programesecure

335

AI and Machine Learning Advancements in 2025

AI and Machine Learning are driving technological advancements across industries, from virtual assistants to healthcare decision-making systems.
AI mimics human intelligence, while ML involves algorithms allowing computers to learn and improve without explicit programming.
Recent advancements in AI include generative AI creating content and NLP enabling human-like interactions.
Machine learning breakthroughs like reinforcement learning for autonomous systems and self-supervised learning for data efficiency are prominent.
Ethical concerns with AI involve algorithmic bias in decision-making and potential job displacement.
AI collaborates with humans in healthcare, finance, and entertainment, enhancing capabilities rather than replacing them.
AI's automation of tasks in manufacturing and logistics is enhancing safety, efficiency, and productivity.
AI is applied in various industries such as healthcare for image analysis, automotive for autonomous vehicles, finance for fraud detection, and education for personalized learning.
The future of AI includes Artificial General Intelligence (AGI) and its potential impact on fields like space exploration.
Challenges facing AI and ML include the complexity of decision-making in models and concerns about data privacy.
AI and ML's continuous evolution promises more transformative innovations and technological advancements ahead.

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Dev

305

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PostgreSQL 18 just dropped: 10 powerful new features devs need to know

PostgreSQL 18 beta release focuses on developer-friendly features like faster data imports, cleaner UPSERTs, and enhanced replication setups.
Key features include parallel COPY from file to table for faster data loading with multiple workers.
Logical replication now supports DDL statements, making schema changes easier to replicate without manual intervention.
Introduction of JSON_TABLE function allows querying JSON data as a relational table, simplifying data extraction.
UNIQUE NULLS DISTINCT constraint treats NULL values as unique, resolving SQL confusion around unique constraints.
Performance improvements for SQL and PL/pgSQL functions enhance execution efficiency and planning.
EXPLAIN VERBOSE enhancements provide detailed insights for debugging query performance.
pg_stat_io view offers native I/O monitoring capabilities for better performance tuning and understanding query patterns.
Improved logical replication now includes support for replicating sequences for consistent IDs across nodes.
MERGE statement enhancements in PostgreSQL 18 offer cleaner UPSERT operations and improved syntax handling.

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Dev

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Build & Deploy Apps in Under 10 Minutes with Neuronum - A Getting Started

Neuronum is a framework for building serverless connected applications and data gateways.
Features of Neuronum include Cells for account management, Nodes for hosting components, and Gateways for data processing.
Requirements to use Neuronum include Python >= 3.8 and neuronum version 4.0.0 or higher.
You can connect to Neuronum by installing neuronum dependencies via pip and creating or connecting Cells.
Building on Neuronum involves using Nodes, with commands like initializing, starting, stopping, and connecting Nodes to Neuronum.
To learn more about Nodes, you can visit the Neuronum GitHub page for examples.

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Dev

135

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My First Week with Python: A Summer of Curiosity and Code

A first-year engineering student from Madhav Institute of Technology & Science is using their summer break to dive into coding with Python.
Python was chosen for its readability, power, and versatility across various fields like web development, automation, AI, and data science.
The student embraced Python to not just read but to understand by experimenting, encountering bugs, and learning through challenges.
Despite facing errors and frustrations, the student finds growth in every challenge, acknowledging that learning involves making mistakes.
Key learnings from the first week include mastering Python syntax, working with various data types, grasping input/output functions, and understanding the differences between Python 2 and Python 3.
The student delved into pattern printing to enhance logic-building skills, appreciating the challenge hidden behind seemingly simple tasks.
The student invites others on coding journeys to share resources, tips, and experiences, fostering a supportive learning community.

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Medium

186

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Where Have All the High-Paying Tech Jobs Gone?

The tech industry experienced a boom in the years 2020-2023 with a surge in demand for developers due to various sectors adopting technology like health-tech, fin-tech, and ed-tech.
OpenAI gained prominence during this period, leading to a shift in focus towards AI, Generative AI, Super AI, and AGI across boardrooms.
The past two years have been challenging for employees but have led to the removal of non-essential roles that did not contribute to product value.
Remote work has enabled globalized hiring, allowing companies to hire skilled engineers from around the world without the need for physical presence.
Competition in the tech industry is high, with over a million developers currently focusing on Data Structures and Algorithms (DSA).
LeetCode alone hosts 2500 developers working on problem-solving, indicating a significant number of individuals engaged in honing their technical skills.

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Medium

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How getBoundingClientRect Works and What It Returns

The getBoundingClientRect() method in JavaScript provides a snapshot of how a specific element is placed and sized based on the current layout.
It returns a DOMRect object built from existing layout information rather than computed values by JavaScript.
The method is read-only and does not impact the page layout, allowing insight into how elements are positioned and sized.
It retrieves layout data from the browser's rendering engine, showing the element's size and position relative to the viewport.
While useful for scroll tracking and visibility checks, repeated calls within loops should be minimized to avoid unnecessary layout recalculations.
The values returned by getBoundingClientRect() are in pixels and reflect the rendered layout post-styling and transformations.
By adding the current scroll offset, you can convert the returned viewport-relative values into document-based coordinates.
Utilizing getBoundingClientRect() is valuable for tracking element visibility, implementing sticky behaviors, and managing overlap conditions.
Layout thrashing can occur if layout data is read immediately after making style changes, impacting performance.
To optimize performance, consider batching read and write operations, deferring reads with requestAnimationFrame(), or using passive size tracking with ResizeObserver.

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Dev

203

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Modular State Machines with Redux as Orchestration Layer

The article discusses using state machines for local state and Redux for global state in front-end architecture.
Components are managed by state machines via a custom useMachine() hook and internal communication uses React Context.
Redux is used as an orchestration layer for global awareness and cross-component collaboration.
Local state machines within components provide encapsulation, testability, and independence.
Redux is utilized for tasks like hydrating components, broadcasting changes, and time travel/debugging.
The article outlines the lifecycle of state including hydration, ownership by state machines, and broadcasting updates back to Redux.
State duplication is intentional to retain clarity and decoupling between local state machines and Redux.
External influence on components is managed using Redux actions as commands, allowing for decoupled and orchestratable components.
Best practices include hydrating components once, broadcasting selectively, and documenting state ownership.
The pattern described offers modular, testable components with global awareness and clear state ownership.

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Dev

212

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Optimize your JVM using memory management and garbage collection

The Java Virtual Machine (JVM) manages memory allocation, garbage collection, thread management, and JIT compilation.
Performance tuning of the JVM can optimize configuration and behavior for improved performance, scalability, and reliability.
Inadequately configured JVM may lead to high latency, out of memory errors, CPU spikes, slow response times, or crashes.
Key techniques for performance tuning include memory management and garbage collection.
Memory management involves flags like -Xmx (maximum heap size), -Xms (initial heap size), and -Xss (thread stack size).
-Xmx sets the maximum heap memory allowed for JVM, preventing memory allocation errors.
-Xms sets the initial heap memory allocated at JVM start to improve performance.
-Xss determines the stack memory size for each thread in the JVM.
Garbage Collection automatically removes unused objects to free up memory, aiding memory management and preventing leaks.
GC process involves finding and reclaiming memory of unused objects, returning it for new object creation.
Flags like -XX are used for garbage collection configuration in advanced JVM tuning.
Java 8 uses -XX flags like +PrintGCDetails to configure GC behavior.
From Java 9 onwards, -Xlog:gc* flags are used for similar GC tuning.
Optimizing JVM through memory management and garbage collection enhances Java application performance, scalability, and reliability.

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Dev

191

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My Go-To Tools for Building Chrome Extensions

Building Chrome extensions can be made easier with the right tools.
WXT is a modern framework designed for Chrome extension development, providing essential features and support.
React + Vite combo is favored for structuring UI effectively, especially in complex extensions.
Tailwind CSS is used for styling Chrome extensions, enabling quick design iterations without custom CSS.
@webext-core/messaging simplifies communication between various parts of Chrome extensions.
Supabase is recommended for backend needs like user authentication and database management.
These tools help in faster development, bug avoidance, and creating functional, aesthetically pleasing extensions.

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Dev

310

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YouTube channel mirror on Jekyll - part 2

The article discusses transforming a shell script for mirroring a YouTube channel into a Python script using yt-dlp's API.
The Python script pulls feeds from YouTube directly, avoiding downloading the entire channel each time.
It sets up options and a progress hook to write video metadata like title and description.
The article mentions a utility called cli_to_api.py for translating shell options into Python format.
A Python script provided mirrors a YouTube channel, managing paths with pathlib and executing specific commands.
The script includes settings for verbose output, metadata, subtitles, thumbnails, and file formats.
The write_meta_hook suggested in the article is kept simple by directly translating the original shell exec options.
The script replaces the single URL passed to the download method with the YouTube URL for mirroring.
Overall, the article demonstrates the process of translating and executing a shell script for YouTube mirroring into a Python script.
The article is eligible for web story generation as it provides a step-by-step guide along with code snippets for mirroring a YouTube channel using Python and yt-dlp's API.

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