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Building a Professional Angular 19 App Using Signals and Services

This article guides you in building a professional Angular 19 app.
The app will use Signals and Services, along with standalone components.
The goal is to create a hero management service and a component to display and add heroes.
Using Signals and Services in Angular 19 results in clean, reactive, and Angular-compliant code.

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Dev

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Easily Manage Node.js Versions with NVM — Complete Setup + Tips (macOS, Linux, Windows)

NVM (Node Version Manager) is a CLI tool to manage multiple Node.js versions.
NVM supports Windows, Linux, macOS, WSL, Cygwin, and MSYS.
The installation process involves using curl or wget commands and updating the shell config.
With NVM, you can install specific Node.js versions, list installed versions, switch between versions, and set a default globally.

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Dev

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Strategic Security: New Features from 3Mór

3Mór introduces new features to help users be strategic about security in complex technical stacks and objectives.
Insufficient CI/CD infrastructure is identified as a major blocker for addressing security issues and implementing fixes.
3Mór is rolling out 'order of operations' recommendations to simplify the security journey, providing tailored prompts, step-by-step guides, and verification checklists.
The approach of connecting security and CI/CD helps reduce attack surface, fortify existing systems, and make major version upgrades manageable.

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Hashicorp

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Terraform now supports multiple team tokens

HashiCorp Terraform now supports multiple team tokens for better access control and collaboration within Terraform environments.
This enhancement allows organizations to create distinct tokens for different teams, facilitating granular access and reducing operational risk.
Previously, only one team API token per team was allowed, posing challenges in terms of security, auditing, and multiple automation pipelines.
Now, with multiple team API tokens, organizations have improved control over API access and can ensure better security segmentation.

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Amazon

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Combining Snyk’s Insight with Amazon Q Developer’s Assistance to Streamline Secure Development

Snyk and Amazon Q Developer can be used together to streamline secure development and enhance efficiency for developers.
Snyk offers developer security features for securing code, dependencies, container images, and cloud infrastructure.
Amazon Q Developer is an AI-powered assistant that accelerates tasks in the software development lifecycle.
To integrate Snyk and Amazon Q Developer, users can install the respective IDE plugins and extensions.
Developers can use Amazon Q Developer to analyze security issues identified by Snyk and receive code change suggestions.
The collaborative approach between Snyk and Amazon Q Developer allows for quick remediation of security flaws.
Amazon Q Developer can provide in-depth explanations and assistance for complex security vulnerabilities detected by Snyk.
By leveraging the capabilities of both tools, developers can enhance security scanning, issue investigation, and remediation processes.
The integration of Snyk and Amazon Q Developer empowers developers to efficiently address security vulnerabilities and upskill their abilities.
Omar Faruk and David Schott are authors who highlight the benefits of combining Snyk and Amazon Q Developer for secure application development.

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Hackernoon

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R Systems BlogBook—Chapter 1: Round 2 is Now Open for Submissions🎉

Round 2 of the R Systems Blogbook—Chapter 1 is open for submissions
Employees can share their expertise, experiences, and case studies
First round winners revealed and topics for second round announced
Prizes include laptop sleeve and blogging badges

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Dev

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Understanding Permissions in Linux: A Deep Dive into chmod

Understanding Permissions in Linux: A Deep Dive into chmod
chmod stands for change mode. It’s the command we use to change file and directory permissions in Linux.
Each file/directory has three sets of permissions: Owner, Group, and Others.
Real-world use cases of chmod include making scripts executable, locking down sensitive config files, controlling web server permissions, and securing private directories.

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Siliconangle

352

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On theCUBE Pod: Cloud security replatforms, RSAC news, Marc Benioff interview recap

Enterprises are revamping their cloud security strategies due to AI disruption and increasing system complexity, reshaping vendor relationships and internal security priorities.
TheCUBE Pod's John Furrier and Dave Vellante discuss the significant changes in cloud security replatforming, emphasizing a shift toward cloud-native models and agentic software.
Geopolitical tensions and rising cyber threats are prompting organizations to re-architect their infrastructure for enhanced resilience and agility in the face of growing pressure.
The industry focus is shifting towards fundamental security remaking rather than just adding capabilities, with a spotlight on survival through simplification and practical AI applications.
A major security transformation is occurring with a move towards cloud-native platforms and agent-driven automation, displacing traditional approaches relying on outdated infrastructure.
RSAC highlighted the evolution of security practices, with organizations replatforming to adapt to the changing landscape and the increasing importance of resilience and agility.
Various factors like Kubernetes adoption and AI innovations are driving the need for agile security solutions, prompting companies to restructure rapidly to compete and keep pace with market demands.
Future security strategies focus on system rebuilding to accommodate cloud sprawl and dynamic AI workloads, emphasizing the importance of simplification over adopting flashy tools.
Salesforce CEO Marc Benioff's insights on the future of software as a service and cloud platforms underscore the significance of platformization and agents in shaping the tech landscape.
Security priorities are transitioning towards exposure management, posture automation, and intelligent workflow resilience, requiring a deep architectural rethink and adaptability to counter evolving threats.

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TechBullion

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DevOps Powers Cloud Transformation Success with Dharani Pothula

Cloud transformations often face obstacles without the integration of DevOps, as inconsistent environments, manual errors, and extended release cycles hinder progress. By implementing DevOps practices such as CI/CD pipelines, automated testing, and version control, organizations can achieve more structured and efficient development processes, enabling faster adoption of the cloud.
Automation plays a crucial role in reducing delays and human errors during deployments. Organizations that transition from monthly to weekly releases with minimal failures have greatly benefited from deploying automation for testing and deployment. Automation not only saves time but also allows developers to focus on creating new features instead of fixing issues.
The biggest challenge in implementing DevOps is changing people's work habits, breaking silos, and building trust between teams. Overcoming legacy systems and unclear ownership also poses a delay, but with a shift in mindset and collaborative efforts, change is possible.
A successful example of implementing end-to-end DevOps practices was demonstrated in a project that transformed slow and risky monthly releases to weekly deployments with a success rate above 90 percent. Automation, comprehensive test coverage, and reusable components played key roles, speeding up cloud tool adoption and enhancing system stability and confidence.

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Dev

351

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🚀 Docker Image Creation and Pushing to DockerHub: Step-by-Step Guide

Step 1: Install Docker
Step 2: Create a Simple Application
Step 3: Create a Dockerfile
Step 4: Build the Docker Image

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Alvinashcraft

126

Dew Drop – April 28, 2025 (#4408)

The Dew Drop for April 28, 2025 features top links in various categories such as technology, AI, design, methodology, mobile development, podcasts, community events, databases, and more.
Some highlighted topics include the Fourth Beta of Android 16, Azure SDK Release, React Adds New Experimental Animation Feature, Rust Structs vs C# Classes comparison, and HttpClient improvements in .NET 10.
In the AI domain, there are discussions on Evaluation API on Azure OpenAI Service, AWS promoting Responsible AI, and official MiniMax Model Context Protocol (MCP) server launch.
The section on Mobile, IoT & Game Development covers improving app performance with new Android Vitals Metrics, updates in Jetpack Compose, and Play Console insights.
Additionally, the Dew Drop includes details on screencasts, podcasts, community events like Google I/O, and database related topics like SQL Temp Table vs Table Variable Join and Performance Issues.
There are also announcements related to Microsoft Windows 11 Insider Preview Builds, hotpatching for Windows Server, and the launch of Recall and AI-powered features for Copilot Plus PCs.
Overall, the Dew Drop offers a comprehensive overview of the latest developments and insights across diverse technology fields.

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Dev

391

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Ways to Reduce Tech Infrastructure Carbon Footprint

Global carbon emissions from data centers, cloud services, and everyday devices demand the adoption of sustainable IT practices.
Ten eco-friendly IT strategies to reduce the carbon footprint of IT systems:
1. Optimize data center efficiency through advanced airflow control, cooling systems, server consolidation, and green data center locations.
2. Adopt renewable energy for IT systems, including power sources and cloud service providers.

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Medium

AI in DevOps: Revolutionizing Automation with Generative AI

Generative AI in DevOps is revolutionizing workflows in software development and operations by automating tasks and providing solutions to complex problems.
It refers to AI systems that can create new content such as code, automate tasks, and understand patterns from training data to generate human-like outputs.
Generative AI optimizes various stages of the software development lifecycle, automates testing processes, and enhances code quality assurance during development.
AI tools like GitHub Copilot and Testim prioritize testing based on risk, while tools like DeepCode analyze codebase for improvements, ensuring resilient software projects.
In deployment and monitoring, AI predicts issues, triggers rollbacks, and provides real-time insights for optimized management of cloud infrastructure.
Challenges in AI implementation in DevOps include data privacy, biases in AI systems, and the potential for generating incorrect solutions.
To mitigate biases, human oversight and checks should be maintained, and strategies to address data privacy concerns and skill gaps should be implemented.
AI in DevOps enhances human expertise rather than replacing it, leading to improved efficiency, code quality, and software delivery speed while maintaining security and fairness.
As AI advances in DevOps, teams can expect increased innovation and streamlining of workflows by staying updated on AI developments, training, and considering ethical implications.
By leveraging AI tools and data-engineers, teams can create a DevOps environment that is more efficient, creative, and responsive to changing needs.

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Dev

193

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Suna AI: Free&Open Source alternative to Manus

Suna AI is an open-source alternative to Manus, created by the Kortix AI team.
Suna is a cutting-edge AGI Agent that can assist with various tasks through natural conversation.
It offers features such as web browsing, data extraction, document editing, web scraping, and more.
The code is open-source, fully auditable, free to use, and encourages community collaboration.

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Itrevolution

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The Road Ahead: Emerging Trends and Future Directions for Enterprise GenAI

Enterprises are preparing for emerging trends in enterprise AI as the GenAI landscape evolves rapidly.
Key trends include the rise of multimodal AI, agentic systems, and expanded context and memory capabilities in enterprise GenAI.
Organizations are implementing multimodal AI applications in various industries, such as media, healthcare, and customer experience.
Agentic systems are enabling AI to actively pursue goals through orchestrated workflows and interactions with external systems.
The latest GenAI models have significantly expanded their context window capabilities, allowing for processing large datasets at once.
AI engineering focuses on making systems work for expected use cases, exposure to real-world data, and optimizing for efficiency.
AI is transforming security by making it contextual and accessible, simplifying the understanding and addressing of security concerns.
Organizational structures are evolving to support enterprise GenAI, with the rise of AI engineers and platform teams as AI enablers.
Economic considerations like cost structures and new business models are critical as organizations scale their GenAI initiatives.
Strategies for preparing organizations for the future of AI innovation include building flexible technical foundations, developing internal capabilities, and fostering a culture of continuous learning.
The human-AI partnership is highlighted as organizations aim to enhance human creativity and judgment with AI capabilities.

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