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Getting Started with Kyverno: Kubernetes Policy Made Simple

Kyverno is a policy admission controller for managing rules in Kubernetes clusters.
It validates, mutates, or blocks requests based on policies, helping ensure consistency and security.
Using Custom Resource Definitions (CRDs), Kyverno enforces policies at namespace or cluster levels.
It offers Policy and ClusterPolicy types with rules to mutate, validate, verify, and generate Kubernetes resources.
Kyverno runs policies in audit mode to check compliance and enforce mode to block non-compliant resources.
PolicyExceptions enable selective bypassing of policies for specific resources.
PolicyReports track resource compliance with policies, aiding in validation checks.
Kyverno simplifies policy management with familiar YAML syntax and gradual enforcement capabilities.
Starting small with audit mode and PolicyReports is recommended when implementing Kyverno.
Kyverno helps encode best practices into infrastructure without unnecessary complexity.

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Medium

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Navigating Down Rounds in US Venture Capital

The US venture capital market for 2024 sees a rise in down rounds, hitting 20% of VC deals in 2023, up from 8% in 2022.
Late-stage companies experience a more significant decline in valuations than early-stage ones.
Strategies to navigate down rounds include focusing on core business, revenue generation, and profitability.
Key trends in the 2024 US VC landscape include the rise of down rounds, AI influence, and sustainability focus.
Investors should prioritize sustainability, DeFi, AI advancements, diversity in portfolios, and support for emerging VC managers.
There is a growing emphasis on sustainable investing, DeFi, and AI in the venture capital landscape.
Down rounds can lead to ownership dilution and impact company morale and culture negatively.
Transparent communication, operational efficiency, and strategic planning are crucial to mitigate down round effects.
Investors should evaluate reasons behind down rounds, long-term growth prospects, and legal implications.
Aziro aids in navigating down rounds, promoting sustainable investments, leveraging AI, and identifying favorable entry points.

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Dev

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The Future of GitOps: Integrating AI, FinOps, and GreenOps for Intelligent Operations

GitOps has transformed operations with declarative, automated, and auditable practices.
Foundational principles of GitOps include using Git as a source of truth and continuous reconciliation.
Evolution of GitOps integrates AI, FinOps, and GreenOps for intelligent, financial, and sustainable operations.
AI enhances GitOps by enabling proactive anomaly detection and self-healing.
AI models can suggest optimized Kubernetes manifests and assist in code review for compliance.
AI supports managing AI/ML models throughout their lifecycle, ensuring declarative and auditable practices.
YAML manifest showcases an AI-driven optimization policy within GitOps for continuous infrastructure optimization.

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Amazon

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Use Model Context Protocol with Amazon Q Developer for context-aware IDE workflows

Amazon Q Developer announced Model Context Protocol (MCP) support in their IDE plugins for Visual Studio Code and JetBrains, enabling context-aware responses and workflows.
MCP integration allows developers to connect external tools to Q Developer for enhanced functionality.
MCP support was previously available in Q Developer for Command Line since April 29, 2025.
MCP standardizes how Large Language Models integrate with applications, facilitating context sharing, data access, and API interaction.
Developers can streamline coding, integrate with planning tools, create UI components, generate documentation, and execute multi-tool tasks without custom integration.
Configuring MCP servers involves setting scope (Global or Workspace) and defining server details.
Tools exposed by MCP servers enable actions and interactions with external systems.
Demonstration showcases using Q Developer to automate tasks like reviewing Jira issues, analyzing Figma designs, and implementing code changes.
Integration with MCP enhances developer productivity by providing enriched context and automating various development tasks.
The addition of MCP support in Q Developer enhances interaction with tools like Jira and Figma, streamlining development workflows.
Explore MCP capabilities and the AWS MCP Servers repository for standardized context sharing and tool interaction.

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Dev

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Scaling GitOps in the Enterprise: Secure Secrets, Policy as Code, and Multi-Cluster Strategies

The foundational principles of GitOps have revolutionized how organizations manage infrastructure and applications, but scaling GitOps in the enterprise poses challenges with secrets management, policy enforcement, and multi-cluster strategies.
Challenges with secrets management in GitOps include secret sprawl and security risks, leading to the emergence of strategies like encrypted secrets in Git using tools like SOPS and Sealed Secrets, or reference-based management with tools like the External Secrets Operator.
Policy as code (PaC) tools like OPA Gatekeeper and Kyverno help enforce security and compliance in Kubernetes clusters by defining and automating policies stored in Git repositories.
Multi-cluster deployment strategies in GitOps utilize tools like Argo CD and FluxCD, adopting approaches such as the 'Hub-and-Spoke' model, ensuring consistent deployments across diverse environments.
Security principles for GitOps focus on least privilege, immutability, auditing, and secure CI/CD pipelines, while scaling considerations include efficient repository organization, reconciliation loop efficiency, and observability.
Future trends in GitOps include edge computing, AIOps integration, tooling maturity, and application in data and machine learning pipelines, emphasizing the continuous evolution and adoption of GitOps in the enterprise.

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Dev

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Landbase Raises $30M to Scale GTM-1 Omni – Reinforcement Learning Meets Go‑to‑Market

Landbase has raised $30M in Series A funding to scale its GTM-1 Omni AI platform for automating B2B go-to-market.
The platform utilizes a proprietary agentic AI model called GTM-1 Omni that leverages reinforcement learning for higher conversions and faster campaign launches.
GTM-1 Omni is a multi-agent AI system tailored for sales and marketing content, automating outbound campaigning processes effectively.
The AI has been trained on vast datasets of B2B interactions, enabling it to continuously improve through reinforcement learning and human feedback.
Results include 4–7x higher conversion rates, 70% less time spent per lead by sales teams, and campaign launch times reduced from weeks to minutes.
Landbase is focusing on scaling up its technology, investing in larger specialized models and enhancing autonomous agent capabilities.
The company aims to make its AI platform accessible to a wider audience and envisions it as a fundamental tool for go-to-market teams.
Interested parties can experience Landbase’s AI through a free preview program and the company is actively hiring AI engineers and developers.

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Amazon

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Amazon Q Developer Java Upgrades: A Deep Dive into the New Selective Transformation Feature

Amazon Q Developer now supports customization of Java upgrades with a new selective transformation feature, offering greater control over modernization journeys.
The selective transformation feature allows targeting specific components and libraries for transformation, avoiding risky 'big bang' upgrades and ensuring application stability.
Development teams can opt for individual developer-driven upgrades or orchestrated transformation campaigns managed by Center of Excellence teams.
Selective transformation facilitates tailoring transformation plans using natural language chat or input files for more precise Java upgrades.
The feature supports selecting steps from a transformation plan, upgrading first-party and third-party dependencies, and customizing their versions during JDK upgrades.
The transformation can be run in interactive mode for user feedback or in no-interactive mode for continuous transformation without user input.
The selective transformation feature streamlines Java version upgrades, reduces technical debt, and modernizes applications with minimal disruption, enhancing efficiency for development teams.
The CLI provides practical examples of upgrading dependencies interactively or non-interactively, offering detailed transformation plans and user feedback options.
The article emphasizes the importance of maintaining security, avoiding unvetted artifacts, and validating transformed code for functionality and security post-upgrades.
Selective transformation in Java upgrade CLI revolutionizes Java modernization by providing granular control, natural language interactions, and targeted dependency management for a manageable, incremental process.
The feature encourages teams to identify critical components for upgrading and leverage selective transformation for a tailored upgrade strategy, ensuring efficient, risk-managed Java modernization.

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Dev

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DevOps to finance: Explaining CI costs to your CFO

Explaining CI costs to your CFO can be challenging, but it's crucial for DevOps teams to translate technical realities into financial terms.
DevOps focuses on delivering business value efficiently, and the CFO plays a key role in measuring that value.
To justify CI spend, relate it to speed, quality, and outcome goals set initially and highlight ROI like faster deployments.
Metrics such as lead time to production, build success rate, and infrastructure cost per build are essential to track.
Compare current CI spend against forecasts, normalize it against team size, and look for optimization opportunities.
Using benchmarks, showcase industry standards in CI/CD and highlight any recent optimization successes for cost-effective automation.
Propose efficiency improvements like auditing unused integrations, standardizing environments, and consolidating tools to the CFO.
Tie proposed changes back to business outcomes and emphasize the importance of DevOps transformation.
Prepare responses to common CFO questions about CI spend increase, ROI, cost-effectiveness, and resource scalability.
Explaining your CI performance in terms of tools chosen, outcomes, cost optimization, and resource scaling can shift the conversation from cost center to value creation.
Depot offers solutions for faster builds, reduced CI spend, and improved engineering efficiency, providing concrete numbers that finance teams appreciate for ROI discussions with the CFO.

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Siliconangle

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What to expect at the AppDev Done Right Summit: Join theCUBE June 17

The modern application development lifecycle is under pressure to modernize and accelerate delivery while embedding security and reducing tooling friction for developers.
DevSecOps has become crucial, enabling speed and resilience in software development.
The AppDev Done Right Summit, hosted by theCUBE Research, will focus on modernizing apps, empowering developers, and streamlining development pipelines.
Platform engineering and internal developer platforms are key to modern application development, allowing for autonomy and delivery velocity.
GitOps is gaining traction for infrastructure automation, leading to improved deployment success and developer productivity.
Observability and security are fundamental for application resilience in Day 2 Operations, ensuring performance tuning, faster incident response, and automation.
The Summit will cover topics like sustainability, evolving practices in platform engineering, and observability-driven operations in modern app development.
It will address the importance of embedded security, infrastructure choices, and sustainability in the context of modern app development.
theCUBE Research’s AppDev Done Right Summit will feature industry experts discussing the landscape of application development and modernization.
Join the community on YouTube to support the mission of providing free, deep, and relevant content.

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The New Stack

199

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Cloud Native and Open Source Help Scale Agentic AI Workflows

Enterprise automation is leveraging intelligent agent workflows powered by AI, with large language models facing complexity and high costs.
Small language models (SLMs) paired with cloud native platforms like Kubernetes and FaaS emerge for agile agentic AI applications.
SLMs offer advantages in responsiveness, scalability, and cost-efficiency compared to large language models (LLMs).
Microsoft’s Phi-3-mini and other SLMs are designed for efficiency and are suitable for deployments in various settings including edge devices.
Cloud native architectures utilizing Kubernetes and FaaS provide scalable AI deployments with efficient resource allocation.
Knative complements Kubernetes, enabling automatic scaling based on demand for serverless workloads.
The Cloud Native Computing Foundation (CNCF) ecosystem provides tools like Prometheus, Grafana, and Istio for monitoring and optimization.
Practical implementation involves using OCI Kubernetes Engine (OKE) to deploy small language models via Knative Serving for serverless capabilities.
Deployment steps include provisioning a Kubernetes cluster, deploying Knative Serving, containerizing the SLM, and monitoring performance metrics.
SLM agents show effectiveness in use-cases like real-time customer support, DevOps automation, and financial services, offering agility and cost-efficiency.

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Dev

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Mastering Docker from Scratch to Scale – A Hands-On Guide with Labs, CI/CD, Security & DCA Prep

Sudipta Biswas, a cybersecurity architect and author, has released a lab-driven Docker book titled 'Mastering Docker from Scratch to Scale.'
The book aims to provide hands-on guidance for developers, SREs, DevOps engineers, and Docker certification aspirants.
Sudipta Biswas wrote the book to address the shortcomings of existing Docker tutorials by offering step-by-step guidance, security best practices, CI/CD, and hands-on labs.
The book is structured into three parts: Foundations, Intermediate to Advanced, and Security & Scaling, covering topics ranging from Docker architecture and CLI to security hardening and scaling.
Bonus materials in the book include 50+ hands-on labs, DCA practice questions, real-world case studies, GitHub CLI and DockerHub CLI cheatsheets, and a glossary of Docker & DevOps terms.
The book is suitable for developers, SREs, security professionals, students preparing for the DCA exam, and teams adopting DevSecOps practices.
Useful links provided include Amazon for purchasing the book and the GitHub Labs Repository for additional resources.
Feedback is welcomed from readers, and collaboration opportunities are open for teaching or community groups.
Sudipta Biswas expresses gratitude to supporters and encourages continuous improvement in building better systems.

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Medium

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How AI and Edge Computing Are Reshaping Infrastructure in 2025: The Complete Platform Guide

By the early 2030s, 74% of global data will be processed outside traditional data centers, leading to a significant shift in infrastructure needs.
The edge AI market is rapidly growing at 21.7% annually, demonstrating the increasing importance of AI and edge computing convergence.
This convergence presents opportunities in various sectors like healthcare, manufacturing, autonomous vehicles, smart cities, and industrial IoT.
Challenges include managing diverse AI workloads, GPU resource allocation, and creating resilient, distributed systems for edge computing.
Organizations require flexible infrastructure platforms that can adapt to cloud repatriation trends and changing workload needs.
Modern platforms should allow seamless deployment across various cloud providers, automatic GPU resource provisioning, and support for hybrid deployments.
Platforms like Convox offer simplified deployments with GPU auto-scaling, multi-cloud support, and integrated monitoring for AI and edge applications.
The success of AI and edge computing hinges on choosing the right infrastructure platform that balances complexity with developer-friendly features.
Applications benefiting from AI and edge computing span autonomous systems, industrial automation, smart infrastructure, and distributed AI.
The revolution in AI and edge computing requires platforms capable of handling computational demands while providing a streamlined developer experience.
Choosing the right infrastructure platform is crucial for organizations aiming to innovate and scale effectively in the AI and edge computing landscape.

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The New Stack

279

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No SSH? What Is Talos, This Linux Distro for Kubernetes?

Talos Linux is a container-based Linux distro developed by Sidero Labs for Kubernetes, featuring built-in security and with SSH access disabled.
Sidero Labs CEO Steve Francis described Talos Linux as an OS with an immutable file system, designed to run on various platforms, emphasizing security measures such as signed kernel modules.
Talos is reminiscent of CoreOS in terms of immutability, security, and container-focused design aimed at modern edge environments.
Talos is built without systemd, avoids traditional shells like Bash, and uses a custom process manager called machineD.
Talos emphasizes an API-driven approach at the OS level, aiding in maintaining and scaling Kubernetes clusters without the need for SSH.
The name 'Sidero' meaning iron in Greek, reflects Talos' focus on Kubernetes and bare metal environments, providing a portable API anywhere.
Talos promotes minimal size, security, and flexibility, enabling booting without a hypervisor and facilitating secure connectivity through the OS.
Sidero Labs offers Omni, a SaaS for Kubernetes on bare metal and edge, with connectivity back to nodes handled via Wireguard VPN within the OS.

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Dev

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From Scratch to Kubernetes: My Full-Stack DevOps Project on a Local Machine

The article is a hands-on showcase of a DevOps project demonstrating containerization, Kubernetes orchestration, CI/CD using GitHub Actions, and observability with Prometheus, all running locally.
The project involved creating a full-stack web application where a React frontend collects user information, passed to a Flask backend interacting with a PostgreSQL database, caching data in Redis, and routing traffic with Nginx.
Containerization was achieved using Docker, with each component packaged into its own container and custom images stored on Docker Hub.
Kubernetes orchestration was managed with Helm charts, MicroK8s as the distribution, and key resources like Deployments, StatefulSet, ConfigMaps, Secrets, and Ingress.
NGINX was configured as a reverse proxy for efficient traffic routing, while Redis provided caching for faster response times.
PostgreSQL served as the main relational database for persistent storage using Kubernetes persistent volumes.
Observability was ensured with the Prometheus stack, Grafana for visualization, and Alertmanager for alerts on metrics breaches.
CI/CD pipeline was set up with GitHub Actions, including linting, testing, Docker image building, and deployment triggers to the local Kubernetes cluster.
Challenges included tweaking Nginx Ingress for service routing, managing secrets securely using Kubernetes Secrets, setting resource limits, and integrating a self-hosted GitHub runner.
The project enhanced the author's DevOps skills, covering Dockerfile writing, Helm deployments, Kubernetes orchestration, CI/CD implementation, observability tools setup, and real-time alerts configuration.
The experience improved the author's technical skills, understanding of system reliability, and automation appreciation.

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Alvinashcraft

359

Dew Drop – June 12, 2025 (#4438)

Multimodal Voice Intelligence with .NET MAUI by David Ortinau.
Transforming Microservices Development with .NET Aspire by sogue.
Smarter Angular: AI at the Edge of the Framework by Alyssa Nicoll.
Making a header parameter required in ASP.NET Core by Bart Wullems.
Creating The “Moving Highlight” Navigation Bar With JavaScript And CSS by Blake Lundquist.
How to implement observability with Node.js and Llama Stack by Michael Dawson.
GenAI for Beginners: What is the Temperature parameter in a model by Dhananjay Kumar.
GitOps vs. DevOps: Key Differences Explained by James Walker.
Smoother app reviews with Play Policy Insights beta in Android Studio by Naheed Vora.
Stop Writing Git Commits: How AI-Powered GitKraken CLI Accelerates Your Development by Frank Boucher.

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