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Cracking the AI-generated Code: You probably gotta update your DevSecOps practices!

AI assistants like GitHubCopilot and DevinAI have revolutionized code generation, but pose security risks that traditional DevSecOps practices overlook.
AI coding assistants may replicate vulnerabilities unknowingly, recommend outdated or insecure dependencies, and lack understanding of specific application security needs.
DevSecOps practices need to adapt to address AI-generated code security challenges, such as implementing enhanced pipeline security controls and promoting security-focused prompt engineering.
Strategies to adapt DevSecOps include AI-aware dependency scanning, permission boundary checkers, and context-aware security linting for code generated by AI assistants.
Developers should undergo security-focused prompt engineering training to address unique security requirements when working with AI-generated code.
Case studies reveal vulnerabilities in AI-generated code, such as lack of input validation, synchronous operations, and excessive permissions, requiring remediation for secure code.
Best practices for securing dependencies in AI-generated code include verifying package versions, leveraging lockfiles, educating developers on scrutinizing dependencies, and automating updates with security scans.
Adopting DevSecOps principles for AI-driven development involves combining AI efficiency with human security expertise to mitigate risks and ensure robust security postures.
By embracing AI-aware security measures and training developers in security principles, organizations can effectively leverage AI's benefits while ensuring secure coding practices.

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Dev

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Zhiyuan Robotics Unveils Lingxi X2: The Humanoid Robot Redefining AI Interaction

Zhiyuan Robotics unveiled the Lingxi X2 Humanoid Robot.
Equipped with the 'SiliconLight-Language' multimodal AI model for millisecond-level interaction speeds.
Innovations include Motion Intelligence, Interaction Revolution, and Embodied Intelligence.
Impacts include revolutionizing domestic tasks, disrupting industrial work, and democratizing robotics.

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Kubernetes

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Spotlight on SIG Apps

In the SIG Spotlight series, leaders of Kubernetes SIG Apps, Maciej Szulik and Janet Kuo, share insights on application development and management.
Maciej has been contributing to Kubernetes since 2014, while Janet has been involved since before the 1.0 launch in 2015.
SIG Apps focuses on developing, deploying, and operating applications on Kubernetes, covering a broad area in the ecosystem.
Projects like Batch WG and Serving WG play a significant role, addressing challenges in AI workloads on Kubernetes.
Best practices include health checks, auto-scaling, and using tailored resources like Deployment and StatefulSet for applications.
SIG Apps faces challenges in feature prioritization, requiring discipline and careful decision-making.
The ability to extend Kubernetes with Custom Resource Definitions benefits SIG Apps and the community by enabling customized controllers.
New contributors are advised to engage with core controllers by understanding code, running tests, and offering solutions to issues.
Community feedback is welcomed during bi-weekly meetings, with a focus on solving problems and enhancing core controllers.
SIG Apps looks towards future trends like AI applications, with working groups catering to evolving needs in application management.

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Dev

279

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Deploying a Node.js Application with Kustomize on Minikube

This article discusses deploying a Node.js application using Kustomize on a Minikube cluster, covering project structure, Dockerfile setup, Kubernetes manifests, and configuration management for different environments.
Prerequisites include Docker, Minikube, and kubectl installed on the machine.
The project structure includes config files, Kubernetes manifests, app.js, Dockerfile, and directories for overlays in different environments.
The app.js file contains code for a Node.js application that reads config settings and displays them dynamically.
The Dockerfile sets up the Node.js application with build and production stages for deployment.
Building and pushing the Docker image to Docker Hub involves using 'docker build' and 'docker push' commands.
Kubernetes manifests are organized into base (common configs) and overlays (environment-specific configs) directories.
Kustomize is highlighted for declarative management, environment-specific configurations, no templating, and integration with kubectl.
Advantages of Kustomize are discussed, and alternatives like Helm and Ksonnet are mentioned.
Steps to deploy the application on Minikube include starting Minikube, deploying to Kubernetes with specific configurations, testing the app, and verifying deployments.

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Hackernoon

226

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A Guide on How to Use GPU Nodes in Amazon EKS

To use GPU nodes in Amazon EKS, follow these steps:
1. Create an Amazon EKS Cluster without worker nodes
2. Create a default CPU node group to ensure cluster stability
3. Create a GPU node group with appropriate taints
4. Install the NVIDIA GPU Operator

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Dev

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How I Stopped Restarting My Go Server Every Time I Changed a Config

Manually restarting a Go server for a config change can result in downtime and unnecessary overhead in production environments.
To avoid restarting the server, the author switched from manually parsing JSON and YAML files to using the Viper Go package for config management.
To apply config changes dynamically, the author made use of Viper's config watching and reloading capabilities, enabling the server to instantly pick up new values without requiring a restart.
The author also implemented secure handling of secrets by fetching them from HashiCorp Vault instead of storing them in the config file.

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Amazon

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Announcing the end of support for Node.js 14.x and 16.x in AWS CDK

On May 30th, 2025, AWS CDK will end support for Node.js 14.x and 16.x, which are past end of life.
AWS CDK components including the CLI, Construct Library, JSII, Projen, and CDK8s will be affected.
Users are recommended to migrate CDK projects to Node.js 22.x, the Active LTS version.
After May 30th, 2025, no bug fixes or support will be provided for Node.js 14.x and 16.x in CDK.

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Silicon

435

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OpenAI Launches Developer Tools To Build AI Agents

OpenAI has launched new developer tools to help build AI agents.
These tools aim to simplify the development of agentic applications.
OpenAI's first AI agent, 'Operator', automates web tasks like online shopping and reservations.
OpenAI plans to release additional tools and capabilities in the future.

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Microsoft

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March Patches for Azure DevOps Server

Today we are releasing patches that impact our self-hosted product, Azure DevOps Server.
The latest version of the product is Azure DevOps Server 2022.2.
Patches are available for Azure DevOps Server 2022.2, 2020.1.2, and 2019.1.2.
Verifying installation instructions are provided for each patch.

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Microsoft

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New Boards Hub Update

New Boards Hub has become the default experience for all organizations and users.
97% of users are staying on New Boards without reverting.
Old Boards will be disabled for over 50% of organizations in the next two to three months.
All organizations will be fully moved to New Boards by early May.

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Lambdatest

422

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22 Best Code Review Tools for Developers [2025]

Code review tools are essential for maintaining code quality, identifying errors, and ensuring adherence to coding standards. They streamline the review process and enhance team productivity.
Review Board is an open-source tool supporting multi-file reviews, inline commenting, and robust privacy features for code, images, and documents.
Crucible by Atlassian facilitates peer code reviews, formal assessments, inline discussions, and comprehensive reporting across various version control systems.
CodeScene provides deep insights into code quality, technical debt management, and team dynamics analysis, along with software delivery metrics and pull request integration.
GitHub offers powerful collaboration features, including pull requests, inline comments, branching, project management, and security scanning for vulnerabilities.
GitLab streamlines software delivery with merge requests, real-time collaboration, version control, and AI-driven capabilities enhancing code management and security.
CodeQL allows semantic code analysis, custom query writing, automation, and broad language support with actionable security alerts integrated into GitHub.
Codiga automates code review processes, supports multiple languages, offers customizable rule sets, and ensures issue tracking and automated commenting on PRs.
Visual Expert performs static code analysis for PowerBuilder, Oracle PL/SQL, and SQL Server T-SQL, providing cross-references, security scanning, and code documentation.
Gerrit Code Review enables online reviews, inline comments, access control, Git integration, and project navigation, supporting pre-commit and post-commit assessments.

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Dev

102

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Ollama Cheatsheet: Running LLMs Locally with Ollama

Running AI models locally is becoming more popular to avoid cloud-based limitations and privacy concerns.
Ollama + LLMs like DeepSeek-R1 offer a game-changing solution for AI development without cloud hassles.
Installation of Ollama is straightforward, enabling local AI model deployment in minutes.
Hardware requirements for running DeepSeek-R1 locally include minimum 8GB RAM and a modern CPU.
Ollama simplifies model management, allowing easy selection and deployment of different model sizes.
Users can interact with AI models through chat sessions, file text processing, and parameter adjustments for varied outputs.
Integrating Ollama with a REST API for real-world applications is achievable with simple commands.
The use of Apidog for API testing with local LLMs like DeepSeek-R1 enhances debugging capabilities for AI responses.
Real-world applications of DeepSeek-R1 include content generation, information extraction, and code generation with impressive results.
Troubleshooting tips are provided for common issues like out-of-memory errors and API connection problems.

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6 Likes

Medium

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50 Lessons from My First 3 Years in DevOps (And What I’d Do Differently)

Three years ago, the author transitioned into the world of DevOps without a formal computer science background.
Lessons learned: Embrace the command line, prioritize documentation reading over tutorials, collaborate and communicate effectively, automate everything.
The author regrets not taking time to learn basic networking concepts earlier in their journey.
The author suggests building a solid foundation in Linux, networking, and scripting to thrive in the field of DevOps.

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Dev

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Unconventional Yet Common Aggregation Operations

Standard SQL provides fundamental aggregation operations like SUM, COUNT, AVG, MIN, and MAX.
Aggregation operations are commonly used with grouping operations in SQL and Python.
SQL and Python showcase consistent styles for simple aggregation, but nuances appear in more complex scenarios.
SPL offers unconventional aggregate functions like minp and maxp to return records.
SQL lacks aggregate functions that return records, requiring subqueries and additional steps for complex scenarios.
SPL's minp and maxp functions simplify retrieving records corresponding to the minimum and maximum values.
Aggregation operations can also return sets, such as in the top-N problem.
SPL treats topN as an aggregate operation, simplifying the process compared to SQL.
Complex aggregation operations in SQL can be cumbersome and require multiple steps.
SPL allows for continuous multi-step calculations on grouped subsets, making complex operations more manageable.

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Dev

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Ethical Hacking vs Black Hat Hacking – What’s the Difference?

Ethical Hacking involves testing security systems to find and fix vulnerabilities before malicious hackers can exploit them.
Ethical Hackers (White Hat Hackers) help companies and governments secure their systems from cyberattacks.
Black Hat Hackers use illegal methods for data theft, hacking, and cybercrime.
Ethical Hacking is a legal and high-paying career, while Black Hat Hacking is illegal and risky.

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