Programming News and Topics: Latest News on Computer Programming from Techminis

A naukri.com initiative

New

Home

Programming News

Medium

152

Image Credit: Medium

How AI Accelerates Software Development

AI is changing the game of software development by generating code snippets, functions, and even full backend endpoints from comments or prompts.
AI can identify and explain errors, suggest fixes, and even flag issues in code before it reaches the staging phase in the CI/CD pipeline.
AI tools help new developers understand the codebase quickly by providing answers to specific questions about functions and APIs.
AI can generate unit tests, suggest edge cases, and assist in working with legacy code by explaining cryptic functions and suggesting refactors.

Read Full Article

9 Likes

Dev

416

Image Credit: Dev

Fastify In-Depth: Speed, Performance, and Scalability Node.js Web Framework

Fastify is a high-performance Node.js web framework known for its speed, efficiency, and scalability.
It offers features like high performance, scalability, schema-based validation, logging with Pino, and TypeScript compatibility.
Fastify addresses performance and efficiency issues prevalent in older frameworks like Express and Koa.
The framework ensures high performance through efficient routing, precompiled serialization, schema-driven development, optimized plugin system, and low-cost abstractions.
Installation and usage examples with npm installation, route declaration, and server running are demonstrated.
Components like plugins, decorators, middleware, and hooks play vital roles in Fastify's flexibility and scalability.
Understanding the execution order and interruption capabilities of these components helps build efficient Fastify applications.
JSON Schema validation in Fastify ensures strict validation of request data formats and response structures.
Hooks are used to handle login statuses, limit parameters, and ensure request validation in Fastify applications.
Leapcell is recommended as a serverless web hosting platform for deploying Node.js services with support for multiple programming languages and pay-as-you-go pricing.

Read Full Article

25 Likes

Medium

232

Image Credit: Medium

Vibe Coding: The Future of Programming or the End of Developers?

Vibe Coding is a new way of programming that involves guiding AI models to generate, refine, and debug code.
Developers can offload most of the work to AI, making software development a high-level direction process.
Using AI-assisted tools, Vibe Coding can quickly generate a Flask project with necessary components, such as a login authentication page.
Vibe Coding eliminates the need for deep dives into the codebase, allowing developers to rely on AI for automatic debugging.

Read Full Article

14 Likes

Medium

111

Image Credit: Medium

Game-Changing React 19 Features You Need to Know.

The React Compiler is a new tool that transforms React components into optimized JavaScript code, automatically applying memoization and enhancing application performance.
React 19 introduces a new type of component that allows rendering separately on the web server, improving performance and enabling faster page loads.
The React compiler is currently in beta and under testing, not yet stable.
By caching UI directly on the server and minimizing client-side JavaScript, React 19 aims to achieve faster applications and improved SEO.

Read Full Article

6 Likes

Medium

232

Image Credit: Medium

How to Detect Liquidity Grabs Using Divergence Logic in Pine Script (Without Getting Faked Out…

This article explains how to use divergence logic in Pine Script to detect liquidity grabs and avoid getting faked out.
The author suggests combining level sweeps with divergence indicators to identify potential liquidity grab zones.
By using tools such as RSI, OBV, MACD histogram, or volume, traders can find discrepancies between price and the chosen filter.
The technique helps traders avoid being tricked by fake breakouts and instead observe others getting caught in liquidity grabs.

Read Full Article

14 Likes

Medium

210

Image Credit: Medium

Learning JavaScript Just Got Easier (Thanks to AI)

AI can assist in learning JavaScript by providing guidance, faster coding, bug fixing, and explanations in plain English.
Having a basic understanding of JavaScript is still essential to direct AI effectively in coding tasks.
Learning JavaScript today is more interactive and guided with the help of AI tools, reducing the learning curve.
With AI tools like ChatGPT, learners can receive clear answers and explanations, making the learning process less daunting.
AI tools have replaced traditional methods of seeking help, enabling learners to get targeted answers quickly.
Combining AI with JavaScript learning allows for immediate experimentation, enhancing understanding and problem-solving skills.
JavaScript's beginner-friendly nature, combined with AI assistance, offers a smooth learning experience.
With AI support, learners can receive code snippets and assistance in real-time, making coding tasks more efficient.
The article introduces a blog series inspired by the book 'Learn JavaScript Coding with AI,' focusing on JavaScript learning in an AI-powered world.
The series will cover essential JavaScript topics step by step, with practical examples and tips for real projects.

Read Full Article

12 Likes

Medium

Image Credit: Medium

You’re Not the First: How Tech Stole Jobs & And How People Fought Back Through History

Machines replaced hand-made work in factories. People reacted by either destroying machines in protest or moved to cities for new jobs. Workers formed unions to demand fair pay.
Mass production and electricity reduced the need for skilled workers. Many moved to cities for factory jobs. Worker strikes and labor laws improved conditions. Some supported socialism, wanting workers to control businesses.
Computers and robots replaced jobs in offices and factories. Some lost jobs, but new ones appeared in IT and services. Governments started retraining programs, and people learned new skills.
AI and automation are replacing jobs in customer service, driving, and even creative work. Some worry about job losses, while others learn tech skills. Governments discuss Universal Basic Income (UBI) to support people if AI takes too many jobs.

Read Full Article

4 Likes

Dev

125

Image Credit: Dev

Does AI really make you more productive?

In the 2024 Accelerate State of DevOps Report, it was found that 76% of developers are using AI tools in their daily tasks, though 40% have little trust in AI.
Some experienced developers feel less productive with AI-generated code, as it can be sloppy, complex, and prone to errors.
AI adoption negatively impacts software delivery performance, affecting both throughput and stability metrics.
AI-generated code often leads to larger pull requests, making reviews more challenging and potentially sacrificing throughput.
Code reviewers may overlook important details in large changes, impacting the efficiency of the review process.
AI-assisted code reviews may not be trusted if the code being reviewed is AI-generated, further complicating the stability of software changes.
Over-reliance on AI for code generation can create an illusion of speed, potentially compromising software quality and stability.
Increased AI adoption may lead to a perceived productivity increase, but could result in poorer quality software and more broken changes.
Vibe coding, dependent on AI for app development, can result in unstable applications, outages, security vulnerabilities, and loss of work.
Software development teams need to be cautious with AI adoption to prevent sacrificing stability for perceived productivity gains.

Read Full Article

7 Likes

Dev

290

Image Credit: Dev

Why you should learn multi-module architecture in compose as soon as possible!

Multi-module architecture is recommended for organizing an Android app effectively.
Building a production-grade app using a single-module architecture can lead to scalability and maintenance issues as the app grows.
Recommendation: Learn the fundamentals, explore architectural patterns, build small projects, and then transition to multi-module architecture for better scalability, maintainability, and performance.
Achievements: The author developed two applications using a multi-module architecture, which resulted in fewer bugs, easier scalability, and seamless feature additions.

Read Full Article

17 Likes

Dev

102

Image Credit: Dev

How to use FastAPI for microservices in Python

Microservices architecture involves decoupling functional parts into lightweight, deployable solutions adopted by leading companies like Netflix and Amazon.
Key features of microservices include standalone components, business-oriented structure, smart connections, decentralization, and resilience to failures.
API Gateways in microservices can impact response times, scalability, maintenance costs, and introduce potential points of failure.
Pros of microservices include independent services, modularity, easier feature additions, organized code base, and failure isolation.
FastAPI is a modern Python web framework for building APIs, known for code simplicity, high performance, and OpenAPI support.
Compared to Flask, FastAPI offers easier views declaration, data validation, JSON error messages, and asynchronous task support.
To install FastAPI, use pip to install 'fastapi' and 'uvicorn' packages to run the server for creating microservices.
Example microservice implementations using FastAPI include an authentication service and an information service for practical demonstration.
Microservices running together can be checked using curl commands, demonstrating authorization and successful service responses.
Microservices architecture is being widely adopted by tech giants and is likely to shape the future of backend software development.

Read Full Article

6 Likes

Javacodegeeks

205

Image Credit: Javacodegeeks

Building a Semantic Search System with Spring AI and PGVector

Semantic search enhances traditional keyword-based search by understanding the meaning and context of queries rather than relying on exact keyword matches.
The article discusses implementing semantic search using Spring AI and PGVector by integrating Ollama for generating embeddings that represent the semantic meaning of text.
Dependencies for the project include spring-ai-ollama-spring-boot-starter, spring-ai-pgvector-store-spring-boot-starter, spring-boot-docker-compose, and spring-ai-spring-boot-docker-compose.
Setting up the environment involves configuring Docker with PostgreSQL, PGVector, and Ollama, each serving specific purposes in the application.
Application properties are configured to interact with PGVector and Ollama, specifying settings for AI models, database connectivity, and Docker Compose integration.
A record class for movies is created to store movie data as embeddings in PGVector for semantic search, and a MovieStoragePipeline class handles the storage process.
The Semantic Search Controller processes user queries to find similar movie descriptions based on vector-based similarity search, enabling intuitive search experiences.
The application, once configured, automatically launches Docker containers and initializes the vector store to enable semantic searches based on textual queries.
Running queries like 'dystopian future' demonstrates the application's ability to perform semantic searches and retrieve conceptually matching documents.
In conclusion, the article showcases the integration of Spring AI, PGVector, and Ollama to build a semantic search system, empowering effective document retrieval with natural language processing capabilities.

Read Full Article

12 Likes

Dev

Image Credit: Dev

SPL: The Professional Choice for Processing Multi-Layered JSON Data

JSON’s nestable structure is ideal for representing hierarchical data relationships, such as orders containing customers and products with various details.
SQL lacks support for complex nested structures, while new databases like DuckDB offer partial support for processing JSON data.
DuckDB handles single-row nested fields efficiently but struggles with multi-row nested fields in calculations.
DuckDB can expand multi-row nested fields for calculations, which can be less efficient compared to using a physical flat table.
DuckDB can perform calculations on multi-row nested fields using lambda syntax, but this approach can be cumbersome.
esProc SPL fully implements multi-layer nested table mechanisms, providing concise and consistent code for data processing.
Python supports multi-layer nested structures but requires more complex coding compared to SPL, particularly for multi-row nested fields.
SPL offers concise and consistent syntax for handling nested data structures, making it a professional choice for such data processing tasks.
Python's syntax inconsistencies and verbosity in handling nested data make it less effective for complex calculations compared to SPL.
SPL simplifies the process of filtering and calculating data within multi-layer nested structures, offering clear and concise code.

Read Full Article

1 Like

Dev

170

Image Credit: Dev

⚙️ Async Error Handling in Node.js: 4 Best Practices

Node.js provides several best practices for handling asynchronous errors effectively.
Error handling with callbacks is traditionally used in Node.js, where the first parameter is an error object.
Error handling with promises provides a clear and clean approach, utilizing the `.catch()` method.
Using async/await, errors can be handled elegantly using try-catch blocks.

Read Full Article

10 Likes

Dev

420

Image Credit: Dev

Building an AI-Powered Language Learning App at UofT: My CSC491 Experience So Far

Building an AI-Powered Language Learning App at UofT: My CSC491 Experience So Far
Clearly defining the scope of the project: Leveraging existing technologies to save time and efforts
Effective team communication and collaboration: Ensuring consistent and transparent communication
User feedback and iterative development: Incorporating continuous feedback to enhance the product
Advice for future students: Select an exciting project, define project scope, prioritize user experience, and emphasize effective team communication

Read Full Article

25 Likes

Dev

362

Image Credit: Dev

FHIR and HL7 Standards for Interoperability in Telemedicine Apps

FHIR and HL7 standards play a crucial role in ensuring healthcare interoperability and secure patient data exchange in telemedicine apps.
HL7 is a set of international standards for electronic health information exchange, while FHIR simplifies data exchange using RESTful APIs.
FHIR offers modern, developer-friendly features like support for mobile apps and cloud services, unlike the HL7 v2 and v3 standards.
FHIR enhances EHR/EMR integration, enables real-time data access, and ensures secure patient data exchange in telemedicine.
Challenges in implementing FHIR include data standardization, compliance with regulations, integration complexity, and security risks.
Integration tips include using FHIR-compliant APIs, implementing SMART on FHIR for authentication, and leveraging cloud-based storage.
The future of telemedicine will leverage FHIR and HL7 to support predictive analytics, IoT devices, and remote patient monitoring.
Key benefits of FHIR include improved data accessibility, security, scalability, and mobile integration in telemedicine apps.
Overall, FHIR and HL7 standards empower developers to create secure and interoperable telemedicine solutions, enhancing patient care.

Read Full Article

21 Likes

For uninterrupted reading, download the app

Programming News

Discover more