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Startups, Do You Really Want to Pay The Microservices Tax?

The article discusses the impact of premature adoption of microservices on startup teams' productivity and provides insights on best practices to follow instead.
Introducing microservices early can lead to deployment complexity, fragile local development setups, duplicated CI/CD logic, cross-service coupling issues, and increased observability overhead.
The author emphasizes the benefits of starting with a monolithic architecture when building a startup, highlighting simplicity in deployment and the support from open-source communities.
Well-structured monoliths help maintain focus on delivering features and avoid unnecessary complexity, as demonstrated in a real-estate startup example.
Microservices are deemed more suitable for scaling bottlenecks, large teams, or independently evolving domains, rather than as a starting template for startups.
Common pitfalls of early microservices adoption include arbitrary service boundaries, repository sprawl, broken local development setups, technology mismatch, and hidden complexities in communication and monitoring.
Practical guidance for startups includes starting with a monolithic architecture, maintaining a single repo, ensuring a simple local setup, and only splitting services when necessary to address bottlenecks.
For microservice-based approaches, recommendations include evaluating technical stacks, focusing on communication protocols, ensuring stable testing setups, and investing in observability tools.
The key takeaway is to avoid premature microservices adoption, leverage simplicity to maintain productivity, and only introduce complexity when justified by specific bottlenecks.
Keeping developer velocity as a priority and understanding the potential trade-offs involved in microservices adoption are crucial for startup success in the realm of software architecture.

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Dev

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How to use EXPLAIN to fix slow queries in MySQL

EXPLAIN in MySQL is a helpful tool for diagnosing and optimizing slow queries that can impact performance.
By using EXPLAIN before a query, developers and administrators can understand the execution plan and identify underperforming areas.
The output of EXPLAIN can be in table format or structured JSON for more detailed information.
Key aspects to consider when interpreting EXPLAIN results include query structure, access methods, index usage, row scanning, and unnecessary work.
EXPLAIN helps in diagnosing performance issues like full scans, unused indexes due to pattern matching, inefficient joins, sorting, and filtering problems.
Additional tools like MySQL Workbench, Percona Toolkit, EverSQL, and Releem can complement EXPLAIN for query analysis and optimization.
By leveraging EXPLAIN and other tools, developers can streamline the process of identifying and resolving inefficiencies in MySQL queries.
Using EXPLAIN regularly can aid in improving database performance, optimizing queries, and enhancing overall system efficiency.
Be proactive in incorporating EXPLAIN into your MySQL toolkit to effectively manage databases and build high-performing queries.
Understanding and utilizing EXPLAIN effectively can lead to significant performance enhancements and better database management.
Empower yourself with the knowledge and tools provided by EXPLAIN to tackle slow query issues and optimize MySQL database operations.

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Kubernetes

Kubernetes v1.33: Image Pull Policy the way you always thought it worked!

The Kubernetes v1.33 release addresses long-standing issues with imagePullPolicy behavior.
In the past, the IfNotPresent policy allowed unauthorized pods to use private images already present on a node.
Kubernetes v1.33 introduces a change where credentials are verified before allowing pod access to images.
Performance and service stability were key considerations in revising the feature.
The update ensures that pods sharing credentials from the same source will not need to re-authenticate.
The Never option requires credentials for accessing private images, even if already present.
The Always policy ensures authentication of each image request with the registry.
The feature relies on file-based caches on nodes to manage image pulls efficiently.
Future plans include integrating with Projected service account tokens and implementing caching improvements.
To try the new feature, enable KubeletEnsureSecretPulledImages in Kubernetes v1.33.

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Dev

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A smarter way to use imagePullSecrets in Kubernetes Cluster using ServiceAccounts

Using imagePullSecrets in a Kubernetes cluster is necessary for fetching images from a private registry for reasons like security.
When fetching images from a private registry, Kubernetes needs imagePullSecrets to authenticate with the registry.
Creating a secret with registry credentials and details is the first step, and it can be done imperatively or declaratively.
To attach the created secret to a pod, it should be added to the pod's YAML file under imagePullSecrets.
Without proper imagePullSecrets, image pulls from private registries will fail with ImagePullBackOff.
Managing imagePullSecrets in multiple YAML files and Kubernetes objects can be simplified using helm charts or serviceaccounts.
By attaching imagePullSecrets to serviceaccounts, they are automatically used wherever the serviceaccount is referenced.
This approach streamlines the process of updating secrets for multiple Kubernetes objects based on changes in credentials or naming conventions.
The use of imagePullSecrets in Kubernetes is essential for private registry access, and by integrating them with serviceaccounts, the process becomes more efficient.
Ultimately, automating the management of imagePullSecrets through serviceaccounts simplifies the handling of secrets across Kubernetes objects.

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Dev

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How I Used AI Agents to Cut My Debugging Time in Half

Implementing AI agents in debugging workflow led to a 50% decrease in debugging time, improved traceability, and reduced context switches.
AI agents, such as Sweep, Devika, and AgentOps, are designed to analyze bug reports, suggest actions, and self-correct within safe boundaries.
Tools like Devika, Sweep.dev, Bloop, LangSmith, and others were used to enhance debugging efficiency through AI integration.
Integration of AI agents in debugging workflows requires good test coverage, clear commit history, modular architecture, and embedded documentation for optimal performance.

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Hackernoon

330

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AI Might Soon Watch Your Login Attempts the Blade Runner Way

AI may soon revolutionize authentication and authorization processes by utilizing psychometric analysis to assess users' emotional reactions, behavior patterns, and cognitive habits.
Psychometric analysis, a subset of psychological analysis, involves methods like the Big Five or MBTI to classify individuals into different personality types, already applied in various fields.
Researchers have conducted studies to determine people's true feelings and behaviors using computer programs, paving the way for psycho-physiological authentication based on behavior, emotions, and physiological responses.
Future authentication systems could evaluate users' character, mood, language, and stress reactions for secure and personalized authentication, eliminating the need for traditional passwords and biometric methods.
To implement such AI-driven authentication, data collection, machine training with neural networks, and multiple layers of verification combining psychological analysis and biometrics are essential.
Benefits of this system include personalized authentication, enhanced security, and ease of use without conventional password requirements, while potential drawbacks include privacy concerns and resource-intensive infrastructure needs.
Technological advancements are moving towards incorporating psychological aspects in data analysis, suggesting a potential shift towards deep psychological analysis for secure authentication and access in the future.
While the concept of deep psychological analysis once seemed futuristic like in Blade Runner, it is gradually being explored for its application in authentication processes, hinting at a future where proving your humanity goes beyond traditional methods.

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Dev

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BDD Automated Testing vs Unit Testing: Key Differences Explained

BDD automated testing and unit testing serve different goals and are suited for different stages of the development pipeline.
Unit testing focuses on testing individual components in isolation, while BDD testing tests the system's behavior from a user's perspective.
Unit tests target code precision at a micro-level and act as a safety net during refactoring.
BDD automated testing uses Gherkin syntax, encourages collaboration, and tests behavior rather than implementation.
Unit testing is code-centric, fast to run, and relies on frameworks like JUnit or pytest.
BDD tests are business-centric, written before code, and serve as living documentation.
Unit testing is developer-focused, while BDD promotes cross-functional collaboration.
Unit tests provide rapid feedback, whereas BDD tests offer insight into real user flows.
Both types of testing have mature ecosystems with tools like JUnit and Cucumber, respectively.
In modern projects, unit tests handle core logic, while BDD ensures alignment with user expectations.

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Itrevolution

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Building Shared Understanding and Trust Between Business and IT

The foundation of any effective business-IT partnership is shared understanding and mutual trust.
Conducting deep dives into market analysis, customer journey mapping, and financial model understanding is crucial.
Creating shared mental models and a common vocabulary helps alignment between business and technology teams.
Establishing psychological safety allows professionals to express concerns, admit knowledge gaps, and challenge assumptions.
Visibility through visual management tools like work boards, information radiators, and decision records enhances transparency.
Building relationships through interactions like Gemba Walks, communities of practice, job shadowing, and joint problem-solving sessions fosters trust.
A case study on American Airlines illustrates the success of transforming business-IT relationships through shared understanding and trust.
Deliberate efforts in creating this foundation pave the way for deeper collaboration and joint ownership of outcomes.
The post emphasizes that true partnership lies in people's relationships, not just processes or tools.
These practices ultimately lead to improved technical metrics, business outcomes, and team morale.

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Dev

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DevOps in Embedded Software? Let’s Be Honest: It Doesn’t Really Exist

DevOps, as commonly understood in the context of continuous code building and production operations, does not seamlessly apply to embedded software development.
In embedded systems, developers focus on writing software for physical devices, with workflows involving local hardware work, version control, and CI/CD pipeline compliance.
Key differences in embedded software include advanced system testing by dedicated QA teams, management of releases for factory line integration, and software maintenance through controlled OTA updates post-launch.
The closest to a DevOps role in embedded development is the CI/CD expert mainly responsible for maintaining pipelines and infrastructure to support development, not software production and deployment.

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Dev

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🧠 Kubernetes for Absolute Beginners: Architecture & Core Components

Kubernetes is an open-source system by Google to deploy, scale, and manage containerized applications automatically.
Kubernetes architecture consists of Master Node (Control Plane) for decision-making and Worker Nodes for running applications.
Core Kubernetes components include Pods, Services, Ingress, ConfigMap, Secret, Volume, Deployment, and StatefulSet.
Kubernetes simplifies container management by automating tasks like scaling, deployment, and restarts, allowing developers to focus on the app.

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Dev

111

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Kubernetes Architecture: Breaking It Down

Kubernetes operates using a layered architecture with components like Kubernetes Cluster, Master Node, and Worker Nodes collaborating to automate deployment and management of containerized applications.
The Kubernetes Cluster consists of Master Nodes governing operations and Worker Nodes executing workloads, ensuring high availability and scalability.
The Master Node acts as the control plane, scheduling tasks, maintaining the desired state, and responding to changes, with components like API Server, Controller Manager, Scheduler, and etcd.
Worker Nodes execute workloads by running Pods, which contain containers, with components like Kubelet, Container Runtime, Kube Proxy, and Pods ensuring efficient resource utilization and fault tolerance.
Understanding Kubernetes architecture is essential for deploying scalable applications, as it separates concerns between the Master Node and Worker Nodes, streamlining operations and ensuring efficient running of containerized applications.

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Dev

163

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Kubernetes: The Backbone of Modern Cloud-Native Applications

Kubernetes automates deployment, scaling, and management of containerized applications, ensuring efficiency and seamless operations in the world of cloud computing.
Manual container management poses challenges such as time-consuming scaling, downtime due to crashes, and complex traffic distribution that make it inefficient.
AWS's Elastic Kubernetes Service (EKS) offers automation benefits without cloud provider lock-in, allowing businesses to seamlessly move between providers for unmatched flexibility.
Kubernetes is an open-source project, not a paid service or a cloud provider. It collaborates with Docker to orchestrate containers across machines, providing high availability and fault tolerance.

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Dev

132

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Debian GNU/Linux in VirtualBox on MacOS M1 to Practice Kubernetes

To practice Kubernetes for the CKAD exam on MacOS M1, the author set up a Debian GNU/Linux desktop environment in VirtualBox.
Steps included downloading VirtualBox 7.1, getting an ARM64 ISO for Debian, and configuring the virtual machine settings.
For Kubernetes setup, kubectl and minikube were installed via apt-get, with specific commands provided in the article.
Using Vagrant can automate manual steps in VirtualBox configuration, making it easier to reproduce a working virtual machine environment.

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TechBullion

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Redefining Software Delivery: How DevOps Is Fueling Business Innovation

DevOps is redefining the way organizations operate, scale, and deliver value in the digital transformation era.
DevOps introduces cultural shifts, automation, and strategic measurement, elevating it from a technical approach to a transformative business capability.
DevOps collapses organizational silos, fosters collaboration, and streamlines software development and operations through automation.
DevOps empowers organizations with agility, accelerates time-to-market, drives operational efficiency, and transforms IT into a strategic enabler for staying competitive and leading industry advancements.

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Dev

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How I Set Up Prometheus and Grafana Monitoring on AKS from Scratch

Setting up a monitoring stack in Kubernetes with Prometheus and Grafana on Azure Kubernetes Service (AKS) can provide better visibility into pod and node metrics.
Prometheus scrapes and stores time-series data from Kubernetes, while Grafana offers customizable dashboards for insights into cluster performance.
Prerequisites include having Azure CLI authenticated, kubectl configured to AKS cluster, Helm 3 installed, and a working AKS cluster.
Key steps involve adding Helm repos, installing Prometheus and Grafana using Helm charts, accessing Grafana UI, importing dashboards, and learning lessons like setting up alerts for critical metrics.

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