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Testim.io

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Discover the Latest Advancements from Testim

Testim introduces the latest advancements to elevate testing processes, focusing on efficiency and collaboration in software development and QA.
New capabilities in Testim include PDF test reports for streamlined stakeholder communication, enhanced test recovery to minimize downtime, and optimized web test recordings for better performance.
Cloning tests across projects is now possible in Testim, allowing for easy reusability and collaboration, reducing duplicated effort and ensuring consistency.
The Enhanced mode in Testim offers reliable testing for hybrid and cross-platform applications, enhancing stability and speed compared to Appium-based testing.
Dynamic Forms feature in Testim Salesforce enables field grouping and dynamic layouts, enhancing visibility and organization on Lightning pages.
These updates aim to save time, increase test reusability, improve visibility and collaboration, and strengthen security practices within testing processes.
Testim promises continued innovation with greater integration, additional Salesforce capabilities, data security features for Mobile and Salesforce, Locator capability for Mobile, and Testim Grid enhancements.
The focus remains on providing new features regularly to enhance testing quality and workflow, encouraging users to explore Testim's capabilities for streamlined test automation.
Feedback is welcomed for further improvements, as Testim aims to help users automate faster, collaborate more effectively, and ensure better test stability.
Exciting updates are planned ahead, and users are encouraged to stay tuned for more advancements in Testim to transform their overall testing processes.
Testim encourages users to explore its latest advancements for streamlined test automation, promising innovation and continuous improvement for enhanced testing quality and efficiency.

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Dev

433

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Understanding Harbor: part 1 – Proxy Cache

The Harbor CLI is a work in progress and can serve as an alternative to the Harbor UI.

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Medium

146

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Unlocking the Power of Protocol Buffers: A Comprehensive Guide to Efficient Data Serialization

Protocol Buffers is a method of serializing structured data, similar to XML or JSON.
Protobuf provides efficient serialization and deserialization mechanisms, allowing for fast data transfer and processing.
Protocol Buffers supports various field types, enabling flexible data representation.
By understanding the technical aspects and best practices of Protocol Buffers, developers can design and implement efficient data exchange systems.

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Amazon

661

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Announcing the European region for Amazon Q Developer

Amazon Q Developer is expanding to Europe with the launch of Amazon Q Developer Pro Tier in the Frankfurt (eu-central-1) region.
The expansion addresses the needs of European customers by providing data residency and performance optimization.
Storing customer content within EU boundaries helps organizations meet EU data residency requirements.
The proximity of the regional presence in Europe improves response times and enhances the overall development experience for European customers.

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Itrevolution

251

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Governance and Organization: Creating the Framework for Responsible GenAI

Enterprises are emphasizing the need for robust governance as generative AI initiatives expand beyond initial experiments.
Specialized governance for generative AI is vital due to unique risks illustrated by incidents like chatbots making unexpected legal commitments or generating inaccurate content.
Adobe’s governance framework involves documentation of AI use cases focusing on team alignment, user access, data handling, and technology stack.
Risk assessment is crucial, with Adobe implementing a scoring system as an early warning mechanism for both AI project requesters and governance teams.
As AI adoption increases, governance processes must scale efficiently, involving phases of establishing a foundation, optimizing for efficiency, and scaling and amplifying.
Building trust with users and customers involves practices like clear labeling of AI-generated content, transparency in data usage, user controls, and documentation of AI principles.
Organizational structures for AI governance vary from centralized AI Centers of Excellence to federated models with shared governance, and hybrid approaches depending on size and maturity of organizations.
Responsible AI practices beyond governance include prompt engineering guidelines, testing and evaluation for security, and monitoring and feedback integration for continuous improvement.
Key lessons from early adopters emphasize starting with MVP processes, managing volume carefully, and focusing on high-value use cases to ensure effective governance.
Effective governance in generative AI initiatives is essential for balancing innovation with responsibility through clear frameworks, efficient processes, and suitable organizational structures.
By learning from pioneers like Adobe, enterprises can develop governance that enables responsible innovation while effectively managing risk.

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Dev

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Cloud-Native Security: How to Secure Kubernetes & Serverless Apps

Cloud-native security focuses on securing applications built on cloud-native architectures like Kubernetes and serverless computing.
Cloud-native security components include container security, Kubernetes security, serverless security, service mesh security, supply chain security, and observability & threat detection.
Key features and benefits of cloud-native security include zero trust security model, automated security policies, runtime threat detection, immutable infrastructure, and compliance & governance.
Cloud-native security is being widely adopted in industries like e-commerce, financial services, healthcare, and SaaS platforms.

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Dev

262

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Automate Your EC2 Setup Using AWS CLI and User Data

This article explains how to automate the setup of an EC2 instance using AWS CLI and User Data.
The article provides a step-by-step guide on launching an EC2 instance that automatically installs an Apache web server, starts the Apache service, and creates a basic webpage.
The necessary tools for the setup, such as AWS CLI and an existing AWS Key Pair, are also mentioned.
The article emphasizes the benefits of using the User Data field to automate EC2 instance server setup and save time.

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10 Likes

Dev

100

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From VMs to Unikernels: The Evolution of Application Deployment

Virtualization abstracts physical hardware to run multiple isolated environments on a single host.
Containerization shares the host OS kernel to isolate applications at the process level, resulting in lighter and faster deployments.
Unikernels are single-purpose, single-address-space images with only the required parts of the OS, making them ultra-secure and extremely lightweight.
In the unikernel model, both the application and essential OS components are compiled into a single binary, eliminating layers of abstraction and overhead.

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Medium

438

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Microservices Are Just SOA 2.0 in Disguise: Why Developers Say We’re Repeating History

Many developers see microservices as Service-Oriented Architecture (SOA) with new tools and names.
Although microservices were meant to fix issues of monoliths, they have reintroduced old problems and created new ones.
The promises of microservices are starting to feel familiar to the issues of SOA.
The question remains whether microservices can overcome the limitations SOA faced or if we are just repeating history.

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26 Likes

Dev

278

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Why Click Around When You Can Code? — Unlocking the Power of ARM Templates in Azure!

ARM (Azure Resource Manager) Templates are JSON files that define the infrastructure and configuration for your Azure environment.
Automation with ARM templates eliminates the risk of human errors and saves time.
ARM templates make disaster recovery easy by allowing replication of infrastructure in different regions.
ARM templates provide consistent infrastructure, faster deployments, version control, and integration with CI/CD pipelines and Azure DevOps.

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Dev

206

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Blazing Fast Fraud Detection with Kafka (<500ms, No Kidding)

This article discusses the development of a real-time fraud detection pipeline using Apache Kafka and Python.
The pipeline consists of a producer that streams transaction data to Kafka, a feature processor that scales and preprocesses features, a fraud detector that uses a trained K-Nearest Neighbors (KNN) model to predict fraud, and an alert system that logs suspicious transactions and provides metrics and visualizations.
Key components of the pipeline include Docker Compose for container orchestration and the use of Kafka topics for data streaming.
The results show impressive performance, with some fraud alerts clocking in under 30 milliseconds, an average inference time of less than 500 milliseconds, and a peak throughput of 1200 transactions per minute.

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Medium

930

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Will AI Shrink the Demand for DevOps Engineers?

AI tools may automate certain tasks in DevOps, but they cannot replace human judgment and experience in troubleshooting and fixing issues.
While AI can generate code and automate processes, it struggles with debugging legacy systems or handling unexpected outages.
DevOps engineers play a crucial role in explaining tool configurations and addressing challenges that AI cannot yet handle.
Although AI may impact the demand for certain tasks, there is still a need for skilled DevOps engineers with hands-on experience.

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Dev

403

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🔐 Spring Security: Using JWT and Basic Auth for Different URL Patterns

Spring Security allows for the use of both JWT and Basic Auth in the same Spring Boot application for different URL patterns, catering to specific authentication needs.
Real-world scenarios may require different authentication methods like Basic Auth for public APIs, JWT for client-side authentication, and more, which can be achieved using Spring Security.
To implement multiple authentication types, developers can create distinct SecurityFilterChain beans for each type, define corresponding authentication providers, and register configurations with @Order to set priority.
For Basic Authentication on /api/admin/** endpoints, a SecurityFilterChain bean with BasicAuthSecurityConfig class and user details setup along with password encryption can be used.
JWT Authentication for /api/user/** involves JWT utility classes for token generation, user validation, and filtering setup with JwtAuthFilter and JwtSecurityConfig.
Dependencies required in pom.xml for Spring Boot 3.x include spring-boot-starter-security and Jwt dependencies for implementing JWT features.
Testing examples involve using curl commands to access secured endpoints using Basic Auth and JWT token for respective URL patterns.
Spring Security's flexibility with filter chains and @Order simplifies the process of combining multiple authentication mechanisms in a single Spring Boot application.
Developers aiming to support backward compatibility with Basic Auth and modern stateless security with JWT can benefit from this dual-authentication approach.
The article provides a detailed guide on implementing JWT and Basic Auth together in Spring Security, catering to different URL patterns in the Spring Boot application.

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24 Likes

Dev

297

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Kubernetes Key Commands

kubectl cluster-info - Show cluster details
kubectl get nodes - List all nodes
kubectl get pods - List pods across the cluster
kubectl create pod - Create a new pod

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Dev

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Different cloud deployment models (Public, Private, Hybrid) and their use cases.

Cloud computing offers scalable, flexible, and cost-effective IT solutions.
There are three primary cloud deployment models: Public, Private, and Hybrid.
Public clouds are shared by multiple organizations and are owned by third-party cloud service providers.
Private clouds are dedicated to a single organization, offering more control and security for sensitive workloads.

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