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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

103

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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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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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Dev

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GitHub Actions: How to Deploy Your React Application with Vite to GitHub Pages

Learn how to automatically deploy a React application created with Vite to GitHub Pages using GitHub Actions.
Configure GitHub Actions workflow to enable deployment to GitHub Pages.
Adjust the build script for single page applications (SPA) to handle internal routes in GitHub Pages.
Enable GitHub Pages in your repository and deploy your React application with Vite.

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Openstack

125

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From VMware to OpenStack: Why Open Source is Winning the Virtualization Battle

VMware, once a dominant force in virtualization, faced uncertainty after Broadcom's acquisition.
Over 80% of OpenInfra Foundation members received requests to migrate workloads from VMware to OpenStack.
The shift from VMware to OpenStack is driven by a need for a more modern and open infrastructure approach.
Companies are moving away from vendor lock-in and opting for open source solutions like OpenStack.
Migrating workloads from VMware to OpenStack is now seen as less complex and costly than previously thought.
OpenStack offers incentives and assistance programs to help with migration costs.
The total cost of ownership analysis often favors an OpenStack-based solution over VMware due to licensing fees.
OpenStack is ready for enterprise applications, supporting major workloads like high-performance databases and GPU-accelerated apps.
OpenStack vendors like Canonical, Mirantis, Wind River, and Rackspace offer support models ranging from self-management to fully managed services.
The transition from VMware to OpenStack is described as a fast-moving wave that offers businesses a way to break free from vendor lock-in.

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Dev

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Cloud Platforms for DevOps: AWS, Azure, and Google Cloud Compared:

Choosing the right cloud platform is crucial for DevOps success, with AWS, Azure, and Google Cloud offering unique advantages.
Key considerations include integration, DevOps tool availability, pricing structure, scalability, and ease of adoption for teams.
Managing cloud resources efficiently involves using AWS CLI, Azure CLI, and GCP SDK for automation and scripting.
For cloud-native projects, platforms like AWS offer services like EKS and ECS, Azure provides AKS and Container Instances, and GCP offers GKE and Cloud Run.
Cost optimization strategies for cloud DevOps include monitoring resource usage, utilizing reserved events, auto-scaling, and tracking inefficiencies.
DevOps comparison between AWS, Azure, and GCP helps in making informed decisions based on specific goals, budget, and team expertise.
Considering factors like interoperability, service offerings, ecosystem, and capabilities, choose a cloud platform that aligns best with your requirements.

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The New Stack

315

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Kubernetes Job Market Surges: Hybrid Roles Take the Lead With $160K Average Salary

The global Kubernetes market is projected to reach 11.8 billion USD by 2032 with a CAGR of 24%.
Hybrid working mode for tech jobs is gaining popularity, with 30% of Kubernetes jobs embracing hybrid work.
Hybrid jobs offer an average salary of $160,428 per annum, 7% higher than remote jobs and 4% higher than in-office jobs.
Machine Learning Engineer, Head of Platform Engineering, and Data Engineer are the most hybrid-friendly job titles.

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Silicon

203

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Apple Confirms AI Improvements to Siri Delayed To 2026

Apple has confirmed a delay in the improvements to its Siri voice assistant, with the more personalized features now set to be rolled out in 2026.
The delay was not explained, but it was originally indicated that the Siri improvements would be released in 2025.
The improved Siri is expected to include personal context, on-screen awareness, and in-app actions.
Meanwhile, Apple's rivals, such as Google and Amazon, have already made advancements in their voice assistants.

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Dev

315

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CertDecoder.com - A Free and Simple Online Certificate Decoder"

CertDecoder.com is a free and simple online certificate decoder tool.
Built by a developer to handle certificates and verify their details.
Allows users to quickly check PEM-formatted X.509 certificate details.
Privacy-first and retro design, with all processing happening in the browser.

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Sdtimes

103

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Lessons and surprises from a Kubernetes migration

Large migrations require a deep evaluation of platform suitability and potential technical debt.
Significant resources are needed for major platform migrations, justified by negative impacts on end users.
Engaging with early adopters and collecting feedback is crucial for migration success.
Breaking down large migration projects into smaller achievable goals leads to team success.
Effective communication and documentation are key during a migration to Kubernetes.
The dynamic nature of the Kubernetes ecosystem requires teams to adapt to constant changes.
Migrating to Kubernetes opens up opportunities for additional features and services.
The pace of change in Kubernetes environment is surprising compared to legacy platforms.
KubeCon + CloudNativeCon EU 2025 in London will bring cloud-native professionals together for innovation and learning.
Registration for KubeCon + CloudNativeCon EU 2025 is open for those interested in Kubernetes and cloud-native technologies.

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Hashicorp

166

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Ephemeral values in Terraform

Before Terraform 1.11, managing sensitive values was a challenge as they were persisting to plan artifacts and state, but ephemerality was introduced to handle this securely.
In computing, ephemerality refers to creating temporary data that exists briefly and is discarded after use, and Terraform uses this concept for managing sensitive data.
Ephemeral resources in Terraform are temporary and allow for secure handling of sensitive data without persisting them to the plan artifact or state file.
Ephemeral resources run during both plan and apply stages and depend on existing resources, ensuring proper dependency management.
Write-only arguments in Terraform are used for managing sensitive values securely and are not persisted to the plan artifact or state file.
Terraform providers implement write-only arguments to handle sensitive values like passwords and tokens, ensuring their security.
Write-only version arguments help track changes in write-only values and allow for updating them by incrementing the version number.
Ephemeral resources in Terraform can be deferred to the apply stage if their input arguments reference values not known during planning.
The lifecycle of an ephemeral resource involves opening for data access, renewing if needed for longer access, and closing once no longer required.
By persisting ephemeral secrets in a secure manner, Terraform ensures that sensitive values are handled safely and reliably during the apply stage.

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Dev

252

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Kubectl Demystified: Mastering the `kubectl delete` Command

Kubectl delete is an imperative command used to remove Kubernetes resources.
It marks the resource for deletion, triggers any pre-deletion hooks or finalizers, begins terminating the resource, and removes the resource's metadata.
Common use cases for kubectl delete include cleanup, troubleshooting, updates, and environment teardown.
Important flags to know for the CKA exam include -f, -l, --all, --force, --grace-period, --now, and --cascade.

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The New Stack

328

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Kubernetes Fleets: Beyond the IoT Edge

Kubernetes has evolved significantly over the past decade, enabling companies to deploy applications locally with flexibility in deployment location, leading to the concept of 'the edge.'
Today, Kubernetes is used to replace existing compute stacks, extend into new unique locations, and run various workloads in diverse environments, forming the Kubernetes fleet.
Key considerations for building a Kubernetes fleet include deploying a robust Kubernetes stack with essential components like networking, ingress gateways, and service meshes, ensuring scalability and supportability.
Focusing on well-established and adaptable projects, flexibility in component choices, and future expansion capabilities are crucial for managing a Kubernetes fleet effectively.
Managing deployment frequency, migration scenarios, split workloads between Kubernetes and VMs, handling state and storage requirements, and life cycle management of hardware are key challenges in building and maintaining a Kubernetes fleet.
Proper planning and consideration of factors like state management, backups, recovery objectives, hardware updates, and managing tech debt can save significant effort and challenges in the long run.
Ongoing updates, firmware management, and proactive maintenance are essential for the smooth operation and security of a Kubernetes fleet.
Attending industry events like KubeCon can provide valuable insights and solutions for optimizing Kubernetes deployments and managing cloud-native ecosystems.

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