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

A naukri.com initiative

New

Home

Programming News

Arstechnica

373

Image Credit: Arstechnica

Microsoft surprises MS-DOS fans with remake of ancient text editor that works on Linux

Microsoft released a modern remake of its classic MS-DOS Editor, called 'Edit,' built with Rust and compatible with Windows, macOS, and Linux.
Users are delighted to run Microsoft's text editor on Linux after 30 years, offering a nostalgic experience.
The original MS-DOS Editor introduced full-screen interface, mouse support, and navigable pull-down menus, setting a new standard in text editing.
Microsoft addresses the need for a default CLI text editor in 64-bit Windows by creating 'Edit,' a lightweight tool with modern features.
Developers have shown mixed-to-positive reception towards Microsoft's new open source 'Edit' tool, praising its cross-platform usability.
The new 'Edit' editor offers Unicode support, regular expressions, and can handle gigabyte-sized files, a significant upgrade from the original MS-DOS Editor.
Users can download 'Edit' on GitHub or through an unofficial snap package for Linux, emphasizing its broad accessibility.
The lightweight and efficient nature of 'Edit' highlights a newfound appreciation for fast and simple tools in software development.
Despite 34 years of tech evolution, Microsoft's 1991 design philosophy with MS-DOS Editor still resonates in 2025, signifying the enduring principles of text editing.

Read Full Article

22 Likes

Programesecure

107

The Rise of Autonomous AI Agents: How LLMs Are Transforming the Digital Workforce

Autonomous AI agents powered by LLMs like GPT-4 are reshaping digital work in 2025 by handling tasks independently.
LLMs enable autonomous AI agents to reason, plan, and act without constant human input, functioning as digital team members.
Key features of AI agents include autonomy, goal orientation, memory, and multi-modal capabilities like processing text, images, and more.
Frameworks like AutoGPT, AgentGPT, and OpenAgents facilitate the development and deployment of AI agents for various tasks.
Real-world applications of AI agents span customer service, marketing, research, finance, and software development, augmenting human capabilities.
AI agents excel over traditional automation by adapting to unstructured tasks, learning from experience, and applying reasoning and creativity.
Challenges like hallucinations, security risks, over-reliance, and ethical dilemmas accompany the adoption of autonomous AI agents.
Modern AI agents leverage tools like vector databases, APIs, and web access to emulate human interns in research, planning, and execution.
AI agents reduce costs, enhance efficiency, and transform digital workflows in departments like operations, customer service, and content.
The collaboration between humans and AI agents is seen as a partnership, with agents complementing human skills in various fields.

Read Full Article

6 Likes

Javacodegeeks

Image Credit: Javacodegeeks

Serverless Spring Boot on AWS Lambda Using SnapStart

AWS Lambda SnapStart significantly reduces cold start times for Spring Boot applications by pre-initializing and snapshotting the runtime state.
SnapStart is available for Java 11 and 17 runtimes and supports Amazon Corretto.
Spring Boot apps traditionally had long startup times due to classpath scanning, dependency injection, and auto-configuration, making them unsuitable for high-performance serverless workloads.
SnapStart can reduce the startup delay for Spring Boot serverless apps to under 300ms.
To enable SnapStart, Java 11+ Lambda functions deployed using Zip package type need specific configurations in AWS SAM or Serverless Framework.
Enabling SnapStart has shown up to a 90% reduction in cold start latency for Spring Boot functions.
Best practices for deploying Spring Boot with SnapStart include using versions & aliases, tuning JVM flags, and monitoring with CloudWatch.
SnapStart is limited to Java 11 and 17, Zip-based Lambda deployments, and may have issues with reflection-heavy frameworks.
With SnapStart, Spring Boot on AWS Lambda becomes practical for production use, offering the Spring ecosystem's power with serverless scalability.

Read Full Article

3 Likes

Medium

203

Image Credit: Medium

Why Postgraduate Education like MCA or MBA is Your Best Investment for a Future-Proof Career

Postgraduate education like MCA or MBA is essential in the evolving job market.
MCA and MBA programs provide specialized skills and strategic thinking for career advancement.
MCA focuses on software development, cybersecurity, AI, and data analytics.
MBA offers leadership skills in marketing, finance, HR, operations, and entrepreneurship.
These postgraduate programs bridge the gap between education and employability.
Key benefits include higher salaries, promotions, job security, and industry relevance.
MCA and MBA degrees open doors in private and public sectors, research, startups, and global roles.
They provide exposure to industry projects, internships, and placements.
A postgraduate degree prepares individuals not just for the next job but for the next decade.

Read Full Article

12 Likes

Medium

Image Credit: Medium

The Fundamentals of Software Architecture: From Models and Utilities to Services and Controllers

Dealing with messy codebases is a common challenge for developers, often containing a mix of coding styles and approaches.
The emphasis on quick delivery sometimes leads to compromising code quality and architecture standards.
Codebases may become problematic due to the culture of prioritizing rapid deployment over thoughtful development.
Encountering sprawling code with multiple functionalities in a single file is a typical scenario, showcasing the lack of clean, modular design.

Read Full Article

2 Likes

Medium

Image Credit: Medium

Mastering Object Pooling in Game Development with C#

Object Pooling is a game development technique used to optimize performance by reusing GameObjects instead of instantiating and destroying them repeatedly.
Benefits of Object Pooling include reduced memory allocation, consistent frame rates, and suitability for various in-game elements like projectiles, enemies, particles, effects, and NPCs.
Object Pooling helps avoid performance issues such as memory spikes and inefficiencies related to GameObject instantiation and destruction.
Challenges in Object Pooling include hardcoding pool size, accidental object destruction, retaining references to inactive objects, and not prewarming the pool.
Implementing Object Pooling can reduce garbage collection, improve frame rates, and enhance memory management.
In Unity (C#), Object Pooling is simplified using techniques like SetActive() and prefab reuse, making it easy to implement.
Object Pooling is scalable and customizable, allowing for features such as auto-expansion, timed returns, and preloading.
Object Pooling is crucial for optimizing game performance, especially in scenarios with frequent instantiation and destruction of GameObjects.
Proper implementation of Object Pooling can lead to smoother gameplay experiences and better resource utilization in games.
Understanding the importance of Object Pooling in game development can help developers create more efficient and performant game experiences.
Object Pooling offers solutions to common game development challenges associated with managing GameObjects efficiently.
By reusing objects instead of creating and destroying them constantly, Object Pooling contributes to better performance and stability in games.
Unity provides built-in tools and functionalities that support the implementation of Object Pooling for improved game performance.
Object Pooling is particularly useful in scenarios where projectiles, enemies, particles, effects, and NPCs are frequently spawned and destroyed.
Efficient use of Object Pooling in game development can lead to more responsive and optimized gameplay experiences.
Object Pooling in Unity (C#) is a valuable technique for enhancing game performance and managing resources effectively.

Read Full Article

3 Likes

Medium

311

Chapter 6: Decisions, Decisions — Learning Decision Trees

Decision Trees are a more approachable model compared to math-heavy ones like SVMs, offering simplicity and interpretability.
These trees work by asking binary questions about features to make predictions, resembling a logical flowchart.
The algorithm prioritizes the most informative splits first, highlighting crucial features in the dataset.
A common issue with Decision Trees is overfitting, which can be addressed through techniques to prevent chasing noise patterns in training data.
Decision Trees are highly interpretable as one can trace the path from input features to predictions, crucial for applications requiring explainability like healthcare or finance.
Visualizing the tree structure and decision boundaries helps understand how splits are made, solidifying the concept of 'feature importance'.
Chapter 6 provided a comprehensive understanding of Decision Trees, emphasizing their decision-making capabilities and methods to avoid pitfalls like overfitting.
The chapter increased confidence in utilizing Decision Trees effectively in real-world scenarios by explaining data splitting mechanisms and potential errors.
Next topic: ensembles, covering the combination of multiple trees to enhance model strength.

Read Full Article

18 Likes

Medium

265

Chapter 5: Getting to Know Support Vector Machines (SVMs)

SVMs are algorithms used for classification and regression, focusing on finding a decision boundary with maximum margin.
Support vectors are crucial points that influence the boundary, while other points are not as significant.
The kernel trick helps project data into higher-dimensional space for better separation without explicitly computing those dimensions.
Tuning parameters like C and gamma in SVMs can significantly impact the flexibility of models, especially in noisy datasets.
SVR, or Support Vector Regression, uses margins to predict continuous values instead of probabilities or labels.
The chapter made SVMs more practical and less abstract, suitable for small-to-medium-sized datasets.
The chapter was theory-heavy but demonstrated through visuals and implementation, aiding in understanding decision boundaries and model behavior.
The reader is looking forward to exploring Trees and ensembles in the next section.
Interest in sharing notes or experiences with SVMs is expressed for mutual learning and exchange.

Read Full Article

15 Likes

Dev

203

Image Credit: Dev

How I Build My Developer Portfolio (Without Burning Out)

Building a developer portfolio can be challenging and may bring about imposter syndrome.
Week 1 involves planning before coding to determine the type of work desired, preferred tech stack, and target audience.
Choosing 3 project ideas reflecting the developer's preferences is crucial.
Week 2 focuses on building smart projects without over-engineering or perfectionism.
Week 3 involves creating the portfolio site with sections like About Me, Projects, and Contact.
Week 4 is about polishing the projects and fixing any bugs before publishing.
Clarity, consistency, and a deadline are more important than numerous projects.
Starting small with 2-3 quality projects can help junior or mid-level developers get noticed.
The key is to showcase projects well and hit publish to attract potential opportunities.

Read Full Article

12 Likes

Javacodegeeks

Image Credit: Javacodegeeks

How to Map Nested Properties Using MapStruct

This article discusses how to perform nested mapping using the MapStruct library, commonly required in enterprise applications with complex data structures.
MapStruct generates efficient mapping code at compile-time, reducing boilerplate code for object mapping tasks like converting domain models to DTOs.
The example involves mapping a music library model structure with nested objects like Song and Track to a flattened DTO structure for better data representation.
Maven configuration includes the MapStruct dependency and annotation processor plugin for compilation to handle nested mappings effectively.
Source entity classes (Library, Song, Track) and target DTO classes (TrackDTO, SongDTO, LibraryDTO) are defined for mapping purposes.
The mapper interface (LibraryMapper) is used to define field mappings, including handling nested properties using @Mapping annotations.
MapStruct automatically generates implementation classes like LibraryMapperImpl during compilation to execute the defined mappings.
An example Main class demonstrates how to map entity objects to DTOs and back, showcasing the effectiveness of MapStruct in nested mapping scenarios.
The article concludes by emphasizing how MapStruct simplifies nested mappings with explicit field mappings and efficient code generation.
This article would be eligible for web story generation due to its clear explanation of nested mapping concepts using MapStruct with practical examples.

Read Full Article

4 Likes

Medium

257

Why Everyone is Talking About Rust (And Should You Learn It?)

Rust is a systems programming language created by Mozilla Research in 2010 to make low-level programming safer and faster.
Rust offers memory safety without garbage collection, concurrency by design, and expressive syntax with strong tooling.
Rust combines the speed of C/C++, the safety of high-level languages, and modern ecosystem and tooling for developers.
Real-world use cases include the Linux kernel, cloud services, web browsers, and command-line tools that leverage Rust.
Learning Rust is recommended for those who prioritize performance, scalability, and future-proofing their skills.
To get started with Rust, users can install Rust using rustup, study 'The Rust Programming Language' book, and build small CLI tools.
Rust's focus on correctness can benefit programmers in any language, improving their skills.
Rust is backed by a growing ecosystem and suits systems programmers, web developers, and code enthusiasts.
Rust's emphasis on safety, speed, and concurrency makes it a worthwhile investment for long-term skill development.

Read Full Article

15 Likes

Medium

104

Image Credit: Medium

Will AI Replace Me or Make Me Better? A Student’s Honest Take

The student found using AI for learning to be like a cheat code, quickly getting answers but raised concerns about the depth of understanding achieved.
AI, particularly ChatGPT, helped explain complex concepts and provided assistance in various tasks, serving as a virtual mentor.
The convenience of AI support led to a reliance on it but ultimately led to a realization that traditional learning methods were still valuable.
The student discovered that while AI can provide quick answers and guidance, true learning and problem-solving skills are developed through traditional methods.
The student observed that despite AI being helpful, it may not always offer the best solutions compared to human interaction and traditional approaches.
The experience highlighted the importance of maintaining a balance between using AI tools for assistance and preserving human learning processes.

Read Full Article

6 Likes

Medium

386

Understanding Floating-Point Precision Errors in Programming

Computers represent numbers in binary using floating-point format defined by the IEEE 754 standard.
Decimal fractions like 0.1 or 0.2 can't always be represented exactly in binary, leading to precision errors.
The slight errors in approximations of decimal numbers can accumulate during arithmetic operations.
Rounding errors may lead to unexpected results, like the well-known 0.30000000000000004 representation.
Strategies to handle floating-point errors include using tolerance for comparisons and rounding results.
Tolerant comparisons (epsilon) provide a way to check if two floats are close enough, rather than exactly equal.
For applications requiring precise decimal representation, using Decimal or fixed-point types is recommended.
Floating-point errors are common in various programming languages, not limited to Python.
Understanding the limitations of binary math and finite memory can help in mitigating floating-point issues.
Proper techniques can be applied to write reliable code even with challenging numbers like 0.1 + 0.2.

Read Full Article

23 Likes

Dev

377

Image Credit: Dev

Quarkus 3 application on AWS Lambda- Part 6 Optimization strategies for the cold and warm starts

The article discusses optimization strategies for cold and warm starts in Quarkus 3 applications on AWS Lambda.
Previous articles in the series covered running and optimizing Quarkus 3 applications on AWS Lambda with managed Java 21 runtime and GraalVM Native Image.
Performance measurements included Lambda functions using 1024 MB memory, specific Java compilation options, x86_64 architecture, and default Apache HTTP Client.
Optimization techniques for additional Lambda performance improvements are introduced in this article.
Suggestions include trying different memory settings, using Lambda arm64 architecture, and exploring various synchronous and asynchronous HTTP clients.
Different Java compilation options and excluding unused dependencies can also impact cold and warm start times.
The article emphasizes the importance of measuring performance improvements and potential optimization strategies for Quarkus 3 applications on AWS Lambda.

Read Full Article

22 Likes

Self-Learning-Java

332

Understanding LangChain4j’s Modular Architecture: Core, Tools & Integrations

LangChain4j is a Java-based library for integrating large language models (LLMs) into applications.
The library has a modular structure allowing developers to choose components based on their needs.
The core module (langchain4j-core) provides essential abstractions like ChatModel and EmbeddingStore.
The main module (langchain4j) builds on the core with utilities like Document Loaders and AI Services.
Integration modules (langchain4j-{provider}) offer optional third-party integrations like LLM Providers and Embedding Stores.
LangChain4j aims to simplify LLM integration while providing flexibility for different use cases.
The library is designed for anything from basic LLM interactions to complex AI-augmented workflows.
Overall, the modular architecture of LangChain4j balances simplicity and flexibility.
Developers can customize dependencies based on their project requirements.
Understanding the structure helps in tailoring the library for lightweight LLM calls or advanced AI applications.

Read Full Article

19 Likes

For uninterrupted reading, download the app

Programming News

Discover more