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When to Use Affordance vs. Link in Hypermedia Controls

Spring HATEOAS introduces the concepts of Link and Affordance in REST APIs for enriching API responses with navigable metadata.
A Link represents a navigable hyperlink, while an Affordance describes possible actions at a linked URI, including HTTP operations and input metadata.
Links are used for navigation to another resource or read-only endpoints, while Affordances are used when client interaction and action descriptions are required.
Best practices include starting with Links for navigation, adding Affordances when needed for client interaction, and being intentional with defining relationship types.

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Dev

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Peewee ORM: The Most Elegant Python ORM Ever

Peewee is a lightweight and efficient ORM in Python, suitable for platforms like Leapcell, known for its simplicity and ease of use for database operations.
Creating records in Peewee is straightforward using the User.create() method, allowing the insertion of new records with matching keyword arguments.
Batch insertion in Peewee for bulk data migration is more efficient than iterative methods, utilizing the insert_many() function.
Updating records with Peewee involves single-instance updates using save() or bulk operations with Model.update().where().
Deleting records in Peewee can be done individually with delete_instance() or conditionally with Model.delete().where().
Peewee provides various methods for querying records, including single record queries with get() and get_by_id(), multiple record selection with select(), filtering, and sorting.
Additional features in Peewee include pagination, counting records, aggregation functions, window functions, reusable window definitions, frame types control, and data retrieval as tuples or dictionaries.
Peewee vs SQLAlchemy comparison shows Peewee's lower learning curve, better performance in complex operations, and suitability for lightweight applications like those on Leapcell.
Peewee offers a streamlined approach to database operations, making it ideal for projects prioritizing efficiency, ease of use, and rapid development, especially in platforms like Leapcell.
The tutorial explores Peewee's capabilities in CRUD operations, aggregations, advanced querying, and contrasts it with SQLAlchemy's complexities and performance considerations.
Peewee's lightweight design, performance advantages, flexibility in database support, intuitive API, and suitability for cloud-based services like Leapcell make it a preferred ORM for agile and scalable projects.

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Dev

323

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What Are You Building?

The article reflects on the journey of building ideas and emphasizes the importance of asking the right questions before embarking on any project.
It highlights the significance of understanding the market needs and timing to prevent investing in solutions that lack demand.
It uses examples like Google Glass and Airbnb to illustrate how timing and market alignment can determine the success or failure of a product.
The article cautions against building for oneself and stresses the need to address real pain points with practical and timely solutions.
It underlines the essence of community-driven validation and engagement, emphasizing the power of user feedback in product development.
The importance of validation and purpose-driven building is emphasized over blind passion, citing examples like Quibi's failure due to misaligned timing and audience behavior.
The framework presented in the article advises dream, define, validate, and build steps to ensure a well-thought-out and user-centric product development process.
In conclusion, it reminds entrepreneurs that understanding what they are truly building and focusing on value creation are essential for sustained success in the market.
The article encourages developers to move beyond trends and insights shared by others, urging them to align their projects with the unique context, audience, and timing for long-term success.
It stresses that innovation should be about building useful, accessible solutions that solve existing problems rather than just creating something 'cool.'
Ultimately, the key takeaway is that success lies in asking the right questions and ensuring that the products being built have genuine market demand and value.

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Dev

168

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Why Programming Languages Feel Like Pokémon

Programming languages can be likened to Pokémon, with each having its own powers and characteristics.
Python is compared to Pikachu, known for its simplicity and widespread use in various applications.
C is described as a battle-hardened veteran, offering a deeper understanding of computer workings.
JavaScript is likened to a wildcard with evolving capabilities, extending beyond web development.
Rust is portrayed as the new kid excelling in performance and enforcing strict coding rules.
Brainfuck, an intentionally challenging language, is referenced as a unique and difficult-to-read code.
Learning various programming languages gives developers superpowers akin to assembling a diverse Pokémon team.
There are courses available that delve into computing history, tech evolution, and unconventional programming techniques.
These curated learning kits cover topics like hacking evolution, forbidden programming techniques, and AI's darker aspects.
Exploring unconventional operating systems unveils a world of forgotten, experimental, and security-focused OSes.

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Self-Learning-Java

145

Understanding Prometheus: A Beginner’s Guide to Modern Monitoring and Alerting

Prometheus is an open-source monitoring solution that helps track performance, health, and availability in today's dynamic and distributed environments.
Before Prometheus, traditional monitoring tools had limitations such as lack of support for high-cardinality data, difficulties in scaling in dynamic environments, and limited alerting capabilities.
Prometheus uses a multi-dimensional data model with labels for filtering and grouping, and it has a powerful query language called PromQL for filtering, aggregating data, and generating alerts.
Prometheus has a simple and reliable architecture with a standalone setup, a pull model for collecting metrics, and its own time-series database for storage, making it a comprehensive solution for monitoring modern systems.

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Self-Learning-Java

200

Introduction to BigQuery

A database is an organized collection of data that allows users to store, retrieve, and manage data efficiently, serving as the foundation of most software applications.
Characteristics of databases include structured data storage, using DBMS, supporting CRUD operations, and ensuring ACID compliance for reliability.
Types of databases include Relational Databases (RDBMS) and NoSQL databases with subcategories like document-based, key-value stores, columnar stores, and graph databases.
A Data Warehouse is designed for reporting and data analysis, consolidating large volumes of structured data from multiple sources for analytical queries and historical data storage.
Data Warehouses differ from databases in purpose, data type, normalization, query type, and user focus.
Types of Data Warehouses include Enterprise Data Warehouse (EDW), Operational Data Store (ODS), and Data Marts, each serving specific organizational needs.
Examples of Data Warehouses are Teradata, Snowflake, Amazon Redshift, Google BigQuery, and Microsoft Azure Synapse Analytics.
BigQuery is a fully managed, serverless, scalable, and cost-effective cloud data warehouse by Google Cloud Platform, designed for business agility and handling massive datasets efficiently.
Key features of BigQuery include high scalability, super-fast query processing, support for batch and streaming data ingestion, built-in Machine Learning with BigQuery ML, serverless management, and pay-as-you-go pricing.
BigQuery's out-of-the-box features include GIS analysis, auto backup, integration with other Google Cloud services, support for BI capabilities, programmatic interaction via APIs, enterprise-grade security and compliance, monitoring, logging, alerting, federated queries, running data science workloads, and access to public datasets.

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Logrocket

246

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Techniques to circulate and record knowledge in engineering teams

Sharing information effectively in engineering teams is crucial for team success, regardless of size.
Proven techniques for scaling knowledge include product documentation, tech lead office hours, video-enhanced PRs, and knowledge-sharing.
Product documentation stored in tools like Confluence can be an effective way to share information within the team culture.
Pull request templates and videos improve code handoff by providing standardized information and explanations.
Knowledge-sharing sessions help break down barriers and encourage collaboration within engineering teams.
Internal technical documentation is crucial for sharing information among engineers, covering dev environments, processes, and technical debt.
Office hours for tech leads improve accessibility and focus within remote-first teams.
Implement these techniques based on team size, culture, and needs to create a robust knowledge-sharing system.
Continuous updating and integration into workflows are essential for the success of these techniques.
Building a culture of sharing and learning ensures long-term success for engineering teams.

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Logrocket

246

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Best design collaboration tools — and how to actually pick one that fits

The article discusses the importance of choosing the right design collaboration tools to improve outcomes efficiently.
It presents a comparison table of 14 top design collaboration tools based on different phases like brainstorming, design, feedback, and handoff.
Key considerations include clarifying goals, ranking must-have features, mapping tools to existing stack, running pilots, gathering team feedback, deciding, documenting, reviewing, and iterating.
Brainstorming and ideation tools like Miro, Otter.ai, and AI chat products help in generating and capturing ideas effectively.
Design and prototyping tools such as Figma, Framer, Webflow, Galileo AI, and UIzard assist in creating high-fidelity designs and interactive prototypes efficiently.
Feedback and review tools like Loom, Pastel, and Maze enable collecting feedback, annotating sites, and conducting remote usability testing.
Handoff and dev collaboration tools like Figma Dev Mode, Zeplin, and Supernova aid in streamlining design handoffs and developer collaborations.
The article emphasizes avoiding tool bloat by starting with actual needs, focusing on existing tools, and monitoring tool usage to prevent inefficiencies.

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Dev

223

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The Art of Deep Comparison in JavaScript (No Loops Required!)

Implement a function called deepEqual(valueA, valueB) to check if two values are deeply equal without using loops.
Deep equality requires checking nested levels for objects and arrays to ensure all elements or properties match.
Recursion is essential to handle complex nested structures efficiently and adapt to any depth.
The deepEqual function allows accurate comparison of deeply nested structures, providing a valuable tool for JavaScript developers.

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Dev

396

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Why Netflix Doesn’t Trust Auto-Increment IDs: The Untold Power of UUIDs in a Distributed World

In distributed systems, companies like Netflix opt for UUIDs over auto-incrementing IDs for scalability.
Auto-increment IDs pose challenges in horizontally scaled, geo-redundant, and high-traffic systems.
Issues with auto-increment IDs include collisions, poor mergeability, and predictability concerns.
UUIDs provide globally unique IDs without central authority, with versions like v1, v4, and v5 for different use cases.
Netflix employs a Snowflake-inspired ID generation system for unique, time-sortable IDs across instances.
UUID v7, offering ordered generation and timestamp encoding, is gaining popularity for improved database performance.
Using UUID v7 or Snowflake-style generators is recommended over v4 or auto-increment for new systems.
Careful consideration of ID generation is crucial for scalable distributed systems to avoid performance pitfalls.
Choosing timestamped, decentralized IDs benefits event-driven pipelines and global-scale platforms.
Transition from UUID v4 to ULIDs can enhance performance in cases of random insertion patterns in large databases.

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Dev

277

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Google Agent Development Kit (ADK) Introduction (2): Building a Multi-Agent Meeting Scheduling System

The article discusses the development of a Multi-Agent Meeting Scheduling System using the Google Agent Development Kit (ADK).
The project structure involves three specialized agents - Validator Agent, Scheduler Agent, and Notifier Agent connected via SequentialAgent.
Each agent's core capabilities are implemented as Tools, such as Email Validation Tool and Meeting Scheduling Tool.
Agents are integrated with respective tools and given instructions for their functioning.
A SequentialAgent is used to connect the agents for a multi-step workflow in the meeting scheduling process.
The system also includes Google Calendar API permission management, token handling, and error logging for smooth operation.
A web interface using Streamlit allows users to input meeting details and receive scheduling results.
Google ADK is compared with OpenAI Function Calling in terms of multi-step task flow, tool integration, permission management, and error handling.
The conclusion highlights the suitability of Google ADK for complex task decomposition and multi-agent collaboration.
The project's Python app can be executed via the command line or through the provided web interface using Streamlit.
Future enhancements include cross-timezone support, meeting room reservations integration, and natural language input.

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Medium

287

The LLM as Your Main Pair Programmer

Developers are coding differently, not just faster, due to LLM as their primary pair programmer.
This shift is transforming the developer's role, skillset, and daily workflow, crossing from traditional engineering workflows to a new mode of development.
In an LLM-native environment, skills like speed, abstraction, and machine collaboration define the craft, with coding being done differently.
The best engineers in this setup are those who guide the system with clarity, emphasizing the evolution of the craft rather than losing it.

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Dev

314

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What is Machine Learning? A Beginner’s Journey Through the World of Smart Algorithms

Machine Learning is the process of teaching machines to learn from data and make decisions or predictions without being explicitly programmed.
There are several types of Machine Learning methods, including Supervised Learning, Unsupervised Learning, Semi-Supervised Learning, and Reinforcement Learning.
Supervised Learning involves training the model using labeled examples with correct output, while Unsupervised Learning deals with finding structure or patterns in unlabeled data.
Semi-Supervised Learning and Reinforcement Learning combine aspects of labeled and unlabeled data for training models through smaller data sets or trial and error, respectively.

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Infoq

382

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JEP 505 Delivers Fifth Preview of Java's Structured Concurrency with Key API Refinements

JEP 505, the Structured Concurrency Fifth Preview, has achieved Targeted status in JDK 25, serving to simplify parallel task management for developers, especially with virtual threads.
The latest preview refines the API introduced in preceding iterations and enables developers to open a scope using static factory methods like StructuredTaskScope.open().
Structured concurrency focuses on confining subtask lifetimes, implementing reliable cancellation, and enhancing observability through structured thread hierarchies.
The API revolves around the java.util.concurrent.StructuredTaskScope class, facilitating the management of concurrent subtasks within a specified scope.
A coding example illustrates the usage pattern, where tasks are forked, executed concurrently, and then joined within the scope, ensuring completion or cancellation before leaving the block.
A notable improvement in the fifth preview includes factory method usage for scope creation, enhancing readability and accommodating future API evolution without compatibility issues.
Custom policies can be supplied via factory methods like StructuredTaskScope.open(Joiner) to define specific task completion behaviors.
The Joiner interface offers flexibility for implementing custom completion policies, ensuring proper handling of subtask operations within the scope.
Each Joiner instance should be exclusively used within a single StructuredTaskScope and not across different scopes or after closure.
Developers are encouraged to experiment with the Structured Concurrency Fifth Preview in JDK 25 to provide feedback for further maturing the API.

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Javacodegeeks

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Spring Boot And Db2 Integration

Integrating IBM Db2 with Java Spring Boot allows accessing powerful database capabilities for enterprise-grade applications.
Db2 is an IBM data management product optimized for transactional and analytical workloads, offering high performance and scalability.
Key features of Db2 include AI-powered query optimization, data compression, cloud deployment options, and support for containerization.
Db2 is widely used in industries like banking, healthcare, and retail, playing a key role in data lakehouse architectures.
Db2 editions cater to different workloads, ranging from the free Community Edition to the Advanced Enterprise Server Edition.
Setting up Db2 locally using Docker is efficient, creating a test environment for development or testing purposes.
The article guides on installing Db2 with Docker, creating a sample database, and adding mock data for testing purposes.
By adding dependencies and configuring properties, a Spring Boot application can connect to a Db2 container.
Developers can define entity classes, repositories, services, and controllers to interact with Db2 data in a Spring Boot application.
Spring Boot, combined with Db2, provides a seamless integration for building robust Java applications with database connectivity.

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