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

A naukri.com initiative

New

Home

Programming News

Medium

167

Image Credit: Medium

The “Human” Problem of AI Coding Assistants

Excellence in human work is valuable and requires deliberate effort, often lacking in code created impulsively or without thorough consideration.
GitHub is filled with poorly maintained projects and low-quality code, posing a challenge for AI coding assistants like Microsoft's Copilot.
Companies like OpenAI and Google scraped platforms like Stack Overflow for higher-quality code to train their AI models, demonstrating better results.
AI coding assistants like Copilot can produce plausible but not necessarily excellent code, risking the replacement of critical thinking with convenience.
AI assistants should prompt developers to articulate their thinking and challenge assumptions rather than provide ready-made solutions.
Relying too heavily on AI can lead to a decay of coding skills and critical thinking, potentially resulting in subpar code and negative consequences.
The rise of 'vibe coding' with AI suggesting solutions based on natural language descriptions can lead to oversights and security vulnerabilities in code.
Excellence in coding is crafted, not generated, warning against AI becoming a crutch that weakens developers' skills.
The article promotes the idea that AI should amplify, not replace, human capabilities in coding for better outcomes and shared consequences.
AI coding assistants should be viewed as tools to enhance, not replace, developers' critical thinking and problem-solving skills for sustainable coding practices.
The integration of AI in development processes should prioritize excellence and a deeper understanding of code rather than relying solely on automated suggestions.

Read Full Article

10 Likes

TechBullion

284

Image Credit: TechBullion

What is Programmability in Tokenization and Why Does it Matter?

Programmability in tokenization is revolutionizing how assets are issued and managed in the financial industry amid digital transformation and blockchain technology.
It enables digital assets to automate compliance and settlements by embedding predefined rules, bridging traditional finance with intelligent, self-executing assets.
By encoding logic into tokens or smart contracts, programmable tokenization allows autonomous transactions, rule enforcement, and real-time responses to events.
The integration of on-chain logic and off-chain triggers ensures flexibility and compliance, with interoperability between blockchain networks enhancing asset management.
Programmable tokens facilitate instant settlements, remove intermediary inefficiencies, and streamline compliance by embedding regulatory requirements.
AI-powered programmable assets represent the future of finance, combining AI analysis with tokenization to enable dynamic asset management strategies.
Financial institutions adopting programmable tokens gain competitive advantages through collateralized lending, automated risk assessments, and compliance-driven asset issuance.
Platforms like rootVX offer tools for programmable asset tokenization, empowering businesses to lead in the evolving financial landscape.
Programmability in tokenization is reshaping digital finance by enhancing automation, compliance, and asset control for a more efficient financial ecosystem.
Institutions embracing this innovation today will shape tomorrow's economic landscape, highlighting the vital importance of adapting to the limitless potential of programmable tokenization.

Read Full Article

17 Likes

Javacodegeeks

198

Image Credit: Javacodegeeks

Using LangChain4j in Micronaut

LangChain4j is a Java library that simplifies working with LLMs, enabling the creation of AI-driven applications with minimal complexity.
Micronaut is a lightweight Java framework suitable for building microservices and serverless applications, known for its fast startup times and low memory footprint.
The integration of LangChain4j with Micronaut allows for the development of AI-powered applications for tasks like natural language processing and chatbot creation.
Setting up a Micronaut project with Maven involves creating a new application using the Micronaut CLI and configuring dependencies for LangChain4j.
The LangChain4j annotation processor generates implementation code for interfaces annotated with @AiService in Micronaut, aiding in automatic application wiring.
Configuring LangChain4j with Ollama in Micronaut allows interaction with locally hosted LLMs like orca-mini, enhancing AI capabilities in applications.
Implementing AI services in Micronaut involves defining interfaces such as TravelGuide that provide tailored responses based on user input.
A Micronaut controller can expose AI services via REST endpoints, enabling users to access AI-generated recommendations like travel guides.
Testing Micronaut LangChain4j with Ollama involves writing test cases to validate the functionality of AI-powered services like the TravelGuide service.
Integrating LangChain4j with Micronaut and configuring Ollama opens up opportunities to work with locally hosted language models for enhanced application interaction.

Read Full Article

11 Likes

Nordicapis

443

Image Credit: Nordicapis

Considering Data Science Users in Your API Design

Data scientists utilize APIs for tasks like exploratory data analysis, data quality assessments, and training machine learning models.
They seek large amounts of data for representative samples, perform various analyses, and create visualizations like scatterplots and box plots.
APIs can be utilized to create analytics reports, dashboards, and train machine learning models for tasks like classification and regression.
Data scientists may use APIs in data pipelines to regularly retrieve updates and store data in different formats.
New ways data scientists use APIs include generative AI applications like retrieval-augmented generation (RAG) and large language models (LLMs).
APIs designed for data science users should include standard external identifiers, SDKs, last changed date/time on endpoints, and bulk download capabilities.
Providing SDKs for quick access, accurate update information, and bulk download options can enhance the data scientist's experience with the API.
Using tools like Python libraries (requests, pandas, matplotlib), data scientists can access and analyze API data effectively.
Exploring generative AI applications in Python can provide insight into advanced API usage for data scientists.
Engaging with data science users, understanding their needs, and incorporating feedback can help API providers better cater to this user group.
Developing APIs with features tailored to data scientists can lead to increased adoption and satisfaction among this user segment.

Read Full Article

26 Likes

Medium

267

Image Credit: Medium

AI’s Hidden Water Problem: How Artificial Intelligence Consumes Water and What It Means for Our…

AI consumes water primarily through data centers and cooling systems, as well as semiconductor manufacturing.
Data centers use cooling systems that consume billions of gallons of water annually, with Google's data centers using 5 billion gallons of fresh water for cooling in 2022.
Semiconductor manufacturing, essential for AI chips, can use up to 10 million gallons of ultrapure water per day.
To make AI more water-efficient, companies can improve cooling technologies, transition to renewable energy, promote transparency and accountability, and innovate in semiconductor manufacturing.

Read Full Article

16 Likes

Medium

420

Image Credit: Medium

What are JavaScript’s data “locks”?

JavaScript's data "locks" provide a mechanism for managing resource synchronization in an application.
This mechanism allows for acquiring, holding, and releasing locks on specific resources asynchronously.
The purpose of the data "locks" is to ensure orderly access to shared resources and avoid potential data competition and conflicts.
By using the lock mechanism, other tasks can continue to be executed during the process of waiting for the lock.

Read Full Article

25 Likes

TechBullion

447

Image Credit: TechBullion

How to Learn Coding with Online Resources for Free

Learning coding has never been more accessible with the plethora of free online resources available, making it affordable and convenient to start your coding journey.
Coding is essential in today's digital world, offering benefits like a deeper understanding of technology, problem-solving skills, creativity enhancement, job prospects, resilience-building, and self-sufficiency.
Online learning platforms offer flexibility, cost-effectiveness, a wide range of course options, learning from industry experts, and hands-on practice to enhance coding skills.
Top free websites like Codeacademy, Khan Academy, Udacity, Coursera, edX, MIT OpenCourseWare, and FreeCodeCamp provide interactive lessons, tutorials, and projects for beginners to experienced coders.
Codecademy, Coursera, FreeCodeCamp, Khan Academy, and W3Schools are excellent resources offering structured courses, real-world projects, hands-on learning, and community support to aid in learning coding.
Tips for successful coding learning online include setting clear goals, choosing the right programming language, starting with simple concepts, staying consistent, utilizing interactive resources, asking for help when needed, expanding your network, practicing regularly, and tracking your progress.
Online communities and forums provide valuable support, project collaboration opportunities, access to knowledge sharing, and a platform for asking questions and engaging with the coding community.
YouTube tutorials and podcasts offer additional resources for learning coding, providing different perspectives, tutorials in various programming languages, and valuable insights from experienced programmers.
Challenges like lack of motivation and discipline, technical difficulties, feeling overwhelmed, and the need for continuous learning can be overcome by setting clear goals, creating study schedules, seeking support from communities, practicing coding consistently, and staying up-to-date with industry trends.
Continuous learning is essential in coding due to technology advancements, the need for adaptability, enhancing skills and expertise, creativity, and staying competitive in the job market. Free online resources facilitate continuous learning and provide flexibility and accessibility for individuals to upgrade their coding skills.

Read Full Article

26 Likes

Dev

Image Credit: Dev

How to Optimize SEO in Next.js Using Metadata and getServerSideProps

Next.js provides built-in features like server-side rendering (SSR) and static site generation (SSG) to enhance SEO.
Optimizing metadata using the component and getServerSideProps in Next.js is crucial for SEO.
Next.js also offers additional SEO enhancements, including structured data, optimizing images, and generating sitemaps and robots.txt files.
By implementing these techniques, you can improve your Next.js website's SEO, visibility, and organic traffic.

Read Full Article

2 Likes

Dev

425

Image Credit: Dev

Configuring LazyVim and Python on Windows with WSL

To configure LazyVim and Python on Windows with WSL, install a package manager like Scoop for various packages and WezTerm for LazyVim support.
Configure WSL2 with the latest stable Ubuntu LTS installed and install command line prerequisites for tools like NeoVim, LazyGit, gcc, make, fzf, and more.
Install NodeJS with nvm for the built-in LazyVim package manager and pyenv to manage Python environments, ensuring proper configuration.
Ready to install LazyVim by backing up Neovim files, cloning a starter, and removing the .git folder before starting Neovim for package installation.
Check the installation by using :LazyHealth and navigate to LazyVim Extras to install Python IDE and configure plugins for proper functionality.
Create a test project using pyenv, virtual environment, and pytest package to ensure testing and debugging functionality.
Verify key features like testing with Neotest, debugging with nvim-dap, and refactoring code within LazyVim.
Note the limited functionality on Windows for testing and debugging due to potential issues with Windows and Linux runtime differences.
Consider the lack of support for global Python versions with uv as a Python version manager, requiring a combination of pyenv and uv for optimal performance.

Read Full Article

25 Likes

Medium

226

Image Credit: Medium

Data as building blocks

Data is the building block for various processes, analyses, and controls.
Before processing data, it is important to understand the demand and map it according to business requirements.
Normalization and orderly loading of data are crucial for effective analysis and utilization.
Proper data management, including both categorical and quantitative data, contributes to creating a single source of truth.

Read Full Article

13 Likes

Dev

Image Credit: Dev

Content Creation & UI/UX Designers

OpenxAI is a permissionless AI protocol founded by former team members from Fantom and Solana.
Join and be part of a revolutionary project that's shaping the future of decentralized AI.
OpenxAI offers access to advanced open-source AI models, allowing anyone to design, build, and deploy AI apps and services quickly.
They are currently seeking Content Creation & UI/UX Designers to contribute to their decentralized AI platform.

Read Full Article

5 Likes

Dev

321

Image Credit: Dev

Day 3: Implementing the Visualizer Features

The author finished creating the basic UI for their DSA visualizer and set up the sidebar functions for different data structures.
They faced challenges while visualizing the array data structure but overcame them with the help of documentation and using Framer Motion for animations.
They also learned about using React Hooks for sharing data and gained insights into SVGs and D3 dynamics.
The author invites comments and suggestions on their DSA visualizer project and provides links to GitHub and LinkedIn for further collaboration.

Read Full Article

19 Likes

Medium

384

Image Credit: Medium

What I Wish I Knew Before Learning Java Full Stack Development

Tools like GitHub Copilot, ChatGPT, and Tabnine enhance productivity in Java Full Stack Development.
Microservices, Docker, Kubernetes, and API gateways are crucial for scalability and efficiency in development.
Understanding AWS, Azure, or Google Cloud, along with serverless functions, is essential for modern development.
Learning Next.js, Svelte, and Qwik frameworks can help build high-performance applications.

Read Full Article

23 Likes

Javacodegeeks

208

Image Credit: Javacodegeeks

Zero-Downtime Deployments in Spring Boot with Kubernetes and Istio

Ensuring zero downtime during deployments is crucial for modern applications, especially in microservices architectures. Kubernetes, combined with Istio, provides powerful strategies such as canary deployments, blue-green deployments, and traffic routing to achieve seamless updates.
Blue-green deployments maintain two environments: one (“blue”) running the current version and another (“green”) for the new version.
Canary deployments gradually roll out changes to a subset of users before full release.
Istio provides fine-grained traffic control and resilience mechanisms, such as circuit breakers, retry policies, and timeout settings.

Read Full Article

12 Likes

Dev

Image Credit: Dev

Overcoming Browser Compatibility Challenges in Web Development

Web and mobile applications face challenges in being compatible across various browsers, devices, and operating systems due to the increasing diversity of user preferences.
Ensuring uniform user experience across different browsers is crucial for developers to reduce bounce rates and retain users on their websites.
Thorough web app testing using cross-browser testing tools is recommended to ensure consistent performance.
Challenges to browser compatibility include layout maintenance, support for basic features, using separate stylesheets, selecting cross-browser-friendly frameworks, CSS resets, code validation, testing delays, and real-device testing.
Solutions involve utilizing CSS Multi-column layouts, Flexbox, feature detection techniques, separate stylesheets, reliable frameworks, CSS resets, early cross-browser testing, and real-device testing.
Developers should maintain proper layouts using CSS approaches and ensure support for native features across browsers.
Separating stylesheets for different browsers and using popular frameworks can aid in overcoming compatibility issues.
Validating CSS and HTML code, conducting early cross-browser testing, and testing on real devices are essential to prevent rendering problems.
Performing thorough testing with reliable tools early in the development process is vital to detect and address browser compatibility issues effectively.
Real-device testing on a broad spectrum of physical devices ensures comprehensive cross-browser testing for web applications.

Read Full Article

4 Likes

For uninterrupted reading, download the app

Programming News

Discover more