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

A naukri.com initiative

New

Home

Programming News

Dev

Image Credit: Dev

Restarting My Java Journey: From Exam-Oriented Learning to Becoming a Real Developer

The author reflects on their Java programming journey, starting with exam-focused learning and transitioning to becoming a professional developer.
Initially, the focus was on passing exams, memorizing syntax, and completing assignments without a deep understanding.
A shift in mindset led to a decision to restart learning Java with a focus on problem-solving and practical application.
The author's new approach includes revisiting core Java concepts, practicing advanced Java with real projects, and delving into Hibernate and Spring frameworks.
Beyond Java, the author aims to grow as a developer by learning software development principles and preparing for interviews.
The author shares their journey through a blog to stay accountable and inspire other beginners.
A 21-Day Preparation Challenge has been initiated covering Java concepts, problem-solving, communication skills, and interview readiness.
The author emphasizes the importance of intent to improve, consistency, and honesty in the learning process.
The narrative encourages connections and sharing of experiences among individuals following a similar path.
The journey spans from exam-oriented learning to skill-based learning, highlighting the significance of continuous improvement.
The author invites engagement and mutual growth within the learning community.
The journey signifies the importance of transitioning from exam-focused learning to skill-based development for personal and professional growth.

Read Full Article

3 Likes

Self-Learning-Java

LangChain4j: Powerful AI & LLM Features for Java Developers

LangChain4j brings modern AI capabilities to Java applications with a wide range of features.
Integrations include 15+ LLM providers such as OpenAI, Google Vertex AI, Hugging Face, and more.
It supports 20+ vector stores like Pinecone, Redis, Chroma, and various embedding models.
LangChain4j also offers 5+ image generation models and scoring and reranking models for retrieval results.
Document processing and RAG capabilities involve importing from multiple sources, smart text splitting, embedding generation, and re-ranking.
AI services and tools provided by LangChain4j include a high-level LLM API, prompt templating, function calling, and text classification.
The platform offers features for chat and memory management, utilities like output parsing, token counting, and Kotlin coroutine support.
Multimodal support allows text and image inputs for vision-capable models.
LangChain4j simplifies the development of AI-powered Java applications, including chatbots and RAG systems, within the JVM ecosystem.

Read Full Article

4 Likes

Self-Learning-Java

228

LangChain4j: Supercharge Your Java Application with the Power of LLMs

LangChain4j is a Java library that helps developers easily connect their applications with language services like OpenAI or Google.
It was created in early 2023 to provide Java developers with tools that were previously limited in the language space.
LangChain4j offers a simple way to connect to various language services and storage providers, reducing the need to learn multiple systems.
It currently supports over 19 language service providers and more than 20 text storage services.
The library includes ready-to-use tools for creating prompts, managing chat history, building question-answer systems, and more.
It also provides easy-to-follow examples and works seamlessly with popular Java frameworks like Spring Boot and Quarkus.

Read Full Article

13 Likes

Self-Learning-Java

238

Quickly Explore Table Schema using bq CLI

The "bq show –schema" command is used in the bq CLI tool to display the schema of a specific BigQuery table.
It provides a quick overview of columns and data types, can help validate table structure before writing queries, and is useful for scripting or automating metadata checks.
The syntax is "bq show --schema [PROJECT_ID].[DATASET].[TABLE]".
If the default project is set via gcloud, the command simplifies to "bq show --schema [DATASET].[TABLE]".
It improves readability and reduces errors, especially in scripts.
An example schema command without the Project ID is "bq show --schema [DATASET].[TABLE]".
A sample schema output for a table is provided, showing column names, types, and modes.
Users can utilize tools like jq to format the output for better readability.

Read Full Article

14 Likes

Self-Learning-Java

308

Image Credit: Self-Learning-Java

Spring Boot and Prometheus Integration Step by Step Guide

Prometheus is an open-source monitoring and alerting toolkit used in modern software systems like microservices and cloud-native applications.
It works by scraping metrics from instrumented applications at defined intervals and storing them in a time-series database.
Spring Boot Actuator provides various application metrics such as memory usage, thread count, CPU usage, etc., which can be integrated with Micrometer and Prometheus.
Enabling Prometheus in a Spring Boot app allows monitoring, alerting, visualization of metrics in Grafana, tracking usage patterns, and debugging with data-backed insights.
Common use cases for Prometheus integration include monitoring API response times, tracking active threads or connections, checking JVM memory stats, measuring custom business metrics, and enabling auto-scaling decisions.
The step-by-step guide involves creating a Maven project, updating the pom.xml file with dependencies, configuring the application.yml file, defining controller and main application classes, and running the application.
Additionally, configuring Prometheus to read metrics from the Spring Boot app involves defining endpoints in the prometheus.yml file and starting the Prometheus server.
Further steps include running Prometheus server, viewing configured targets, and querying metrics like process_cpu_time_ns_total from the Java Spring Boot application.
Micrometer is highlighted as a metrics instrumentation library for Java applications, offering a unified API to collect and expose application metrics.

Read Full Article

18 Likes

Medium

163

Image Credit: Medium

State-Sponsored Hackers Target Critical Infrastructure in Asia

State-sponsored hackers are targeting critical infrastructure in Asia, posing a serious threat to national security.
Attacks are highly coordinated efforts involving advanced persistent threats (APTs) and have increased significantly between 2022 and 2024.
Report shows a 45% increase in state-sponsored cyberattacks targeting Asian infrastructure.
Taiwan experienced a cyber intrusion in October 2023, linked to a Chinese state-backed group known as APT41.
Several state-sponsored groups, such as APT32, have consistently targeted Asian infrastructure using advanced techniques.
These actors employ methods like spear-phishing, zero-day exploits, and custom malware designed for industrial environments.
An emerging trend is the use of artificial intelligence to enhance social engineering attacks.
Geopolitically, Asian nations are treating cyberattacks as strategic-level threats.
Amidst rising tensions, cyber campaigns could intensify in regions like the South China Sea, the Taiwan Strait, and the Korean Peninsula.
Infrastructure attacks may be used as preludes to or replacements for physical confrontations.
The absence of international norms on cyber conduct allows state-sponsored hackers to exploit digital warfare gray areas.
The future of Asian infrastructure security depends on effective government actions and cyber resilience measures.
Protecting critical systems from hostile nation-states is crucial for national security, economic stability, and public safety.

Read Full Article

9 Likes

Dev

Image Credit: Dev

Rust Ownership Mastery: The Zero-Cost Safety Revolution

Rust's ownership model revolutionizes memory safety by providing compile-time guarantees instead of runtime overhead.
The ownership model ensures one owner per value, automatic cleanup when owners go out of scope, and contracts enforced by the compiler.
Rust's ownership model consists of ownership, borrowing, and lifetimes, offering C-level performance with Haskell-level safety.
Eliminating entire bug categories like double frees and data races, Rust guarantees memory safety with zero runtime overhead.
The borrow checker, despite initial intimidation, aids in catching subtle bugs, with tools like rust-analyzer providing support.
Rust's ownership model guides better software architecture, clear API contracts, modular boundaries, and safer concurrency.
Key takeaways include learning ownership patterns, leveraging zero-cost abstractions, and considering Rust for various applications.
Rust's ownership model offers productivity gains, clearer APIs, and reduced debugging time, making it suitable for new projects and team efficiency.
The learning curve for Rust's ownership model pays off with increased productivity and safety benefits, suitable for new and existing projects alike.
Rust's ownership model benefits not only systems programming but also web backends, CLI tools, WebAssembly, and game development for performance and reliability.

Read Full Article

2 Likes

Medium

180

Simple AI Projects Students Can Build with Python

Students with basic Python knowledge can build simple AI projects.
These projects are easy to complete in a weekend and offer valuable real-world AI insights.
10 simple AI project ideas are outlined, emphasizing using Python.
Projects include building chatbots, image recognition models, sentiment analysis tools, recommendation engines, digit recognition models, sentiment analysis on tweets, HR tool simulations, object detection models, reinforcement learning projects, and a startup project.
Chatbots start with rule-based bots and progress to NLP-powered bots.
Image recognition project teaches image preprocessing, CNNs, and more.
Sentiment analysis project combines machine learning and NLP, offers a bonus idea of creating a web interface.
Recommendation engine project teaches similarity metrics and presents a bonus idea of creating a fun quiz.
Digit recognition project is visual and satisfying, with a bonus idea of creating a live prediction feature.
Sentiment analysis on tweets project involves text cleaning, polarity scores, and real-world APIs use, with a bonus idea of visualizing results with charts.
HR tool simulation project teaches NLP concepts like Named Entity Recognition, with a bonus idea of extracting insights from resumes.
Object detection project is real-time and visual, offering a bonus idea of implementing face recognition.
Reinforcement learning project offers a gentle introduction to the concept, with a bonus idea of recording gameplay.
Startup project combines computer vision and web scraping, with a bonus idea of adding a product classifier.
These projects can enhance Python skills and are beneficial for both learning and showcasing abilities.
Students can further customize these projects by adding bonus features and functionalities.
Building these AI projects can be a great addition to a student's resume or GitHub profile.
The projects cover a wide range of AI concepts and tools, making them a comprehensive learning experience.

Read Full Article

10 Likes

Medium

636

Image Credit: Medium

I Don’t Fear AI Replacing Me. I Fear People Who Refuse to Learn It.

The author expresses fatigue towards the fear of AI replacing people.
The real fear lies in people who refuse to learn AI and fall behind.
AI is viewed as a mirror that reflects one's curiosity, adaptability, and openness to change.
Initially intimidated by terms like 'LLMs' and 'Neural Networks,' the author embraced learning AI.
The shift was from worrying about AI dominance to exploring its possibilities.
Jobs are not eradicated by AI but by individuals resistant to evolving.
Those who adapt, like designers using AI tools or writers employing LLMs, benefit greatly.
The key is to understand how AI can enhance creativity and performance.
The threat isn't AI itself but being stagnant in a changing world.
The author highlights the importance of starting to learn and engage with AI, irrespective of being 'ready'.
AI is not exclusive to scientists but also for those willing to grasp patterns and innovate.
The author transitions from fearing AI to eagerly collaborating with it for a brighter future.
The call is to approach the future with courage and collaboration, rather than fear.

Read Full Article

16 Likes

Medium

192

Image Credit: Medium

Diary of a Tech Newbie (Ep. 5): Choosing my Area of Focus (Artificial Intelligence)

Received advice in a tech meeting to choose a tech focus area.
Initially unsure about which area to choose due to limited tech industry knowledge.
Decided to merge health and tech, focusing on digital health and telemedicine.
Shifted focus to artificial intelligence (AI) as it was emerging in the fintech industry.
Researched AI's use cases in healthcare, leading to a postgraduate program in public health tailored to AI.
Received a scholarship from Africa Agility Foundation to develop skills in AI/ML.
Continues to contribute to promoting the AI niche while honing skills and capacity.
Plans to share lessons learned and technical skills in subsequent episodes.
Open to mentorship, internship, conferences, research collaborations, and other opportunities in the AI niche.

Read Full Article

7 Likes

Medium

197

Image Credit: Medium

AI Code Assistants: Copilot ke Saath Meri Coding Journey

GitHub Copilot, an AI coding tool, has become an essential partner for developers, offering assistance in various coding challenges.
It was introduced in technical preview on 29 June 2021 and made available to the public in June 2022.
As of 2025, Copilot is considered almost indispensable for developers, with a subscription plan costing around ₹800/month in India.
When first installed in VS Code, Copilot impressively suggested entire functions in a context-aware manner.
The tool can generate code prompts swiftly, simplifying tasks like creating a REST API backend with Express.
Copilot has revolutionized coding assistance, making tasks that used to take hours on platforms like Stack Overflow now completed in seconds.
Its intelligent coding suggestions save time and enhance the coding experience for developers.
The tool's ability to understand and adapt to different coding styles makes it comparable to having an experienced developer by one's side.
Despite some initial skepticism, Copilot has gained popularity and is now an integral part of many developers' workflows.
Developers appreciate its efficiency in generating code snippets and functions based on context, reducing the time needed to solve coding challenges.
With its advanced capabilities, Copilot contributes to streamlining the coding process and improving overall productivity for developers.
The AI tool's launch for public use marked a significant milestone, paving the way for its widespread adoption.
The subscription model for Copilot allows users to access its features at an affordable price, making it accessible to a wider developer community.
The success and popularity of Copilot highlight the increasing reliance on AI tools in the coding realm, reshaping the way developers approach and solve coding tasks.
GitHub Copilot's impact on the coding landscape demonstrates the transformative potential of AI in enhancing coding efficiency and productivity.

Read Full Article

11 Likes

Medium

241

Image Credit: Medium

Shell Scripts Using Bash Course

EmbLogic offers a Shell Scripts Using Bash course with hands-on pedagogy on real Linux servers.
The curriculum is designed to meet current DevOps and SysAdmin requirements for job readiness.
Certified trainers with shell scripting programming experience provide personalized feedback.
Students work on real-world projects like automation tools, system health monitoring, and application deployment.
Skills covered include core Bash syntax, script writing for processing logs, interacting with APIs, and error handling.
Advanced topics in Bash development, version control, and CI/CD integration are also part of the course.
Completing the course can enhance career prospects in DevOps, cloud operations, and data engineering roles.
Enrollment is open for the upcoming batch in Noida with early-bird discounts available.

Read Full Article

14 Likes

Dev

Image Credit: Dev

Python Fundamentals: asgi

ASGI (Asynchronous Server Gateway Interface) goes beyond web frameworks to support asynchronous, distributed Python applications with long-lived connections like WebSockets and Server-Sent Events.
It defines callable objects and an event loop interface, decoupled from specific frameworks, leveraging asyncio for concurrency.
Real-world use cases include FastAPI for REST APIs, Celery for async job queues, Server-Sent Events for real-time data streaming, WebSockets for collaborative editing, and ML model serving.
Integration involves type safety, Pydantic for data validation, configuration via pyproject.toml, and logging for observability.
Code examples demonstrate how ASGI handles WebSocket connections and the importance of testing, debugging, performance tuning, security considerations, and best practices.
Challenges like blocking operations, incorrect handling of disconnects, and lack of type hints can be mitigated by following best practices, embracing type safety, and modular design.
Key aspects include separation of concerns, defensive coding, modularity, config layering, and automation for testing, deployment, and monitoring.
Mastering ASGI is essential for scalability and reliability, emphasizing the importance of refactoring legacy WSGI code, performance monitoring, writing tests, and enforcing type safety.

Read Full Article

1 Like

Dev

127

Image Credit: Dev

Higher Order Functions in JavaScript – Map, Filter, Reduce Deep Dive

JavaScript’s higher-order functions - map, filter, and reduce - enable concise, reusable, and expressive code for array manipulation.
Higher-order functions accept functions as arguments or return them, enhancing modularity and abstraction in JavaScript.
Map creates a new array by applying a callback function to each element, allowing for one-to-one transformations.
Filter generates a new array with elements passing a specific test, ideal for conditional inclusion or subset selection.
Reduce consolidates array elements into a single value, supporting various operations like sums and object building.
Map, filter, and reduce can be combined for complex operations, showcasing their composability and versatility.
Performance considerations include immutability for memory usage and reducing chaining overhead for optimization.
Common pitfalls involve missing return values in map, complex conditions in filter, and accumulator mutations in reduce.
Best practices recommend using arrow functions, naming callbacks descriptively, chaining methods logically, handling edge cases, and documenting complex operations.
Real-world examples like managing a shopping cart demonstrate practical application of map, filter, and reduce for efficient array manipulation.

Read Full Article

7 Likes

Medium

Image Credit: Medium

AdaBoost vs. Random Forest

Ensemble learning combines multiple models to create a robust and accurate model.
AdaBoost is a sequential ensemble method that combines multiple weak learners, often decision stumps.
AdaBoost focuses on learning from mistakes by giving higher importance to incorrectly classified instances.
Random Forest builds multiple full decision trees independently and in parallel.
Random Forest uses parallel voting to make predictions based on multiple decision trees.
In a fruit classification example, Random Forest uses the majority vote of decision trees to predict the fruit type.
Both AdaBoost and Random Forest have their strengths and are suitable for different scenarios.
Understanding the differences between AdaBoost and Random Forest helps in choosing the right ensemble method for real-world problems.

Read Full Article

4 Likes

For uninterrupted reading, download the app

Programming News

Discover more