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From FastAPI to Render: Building a Number Classification API with Docker.

This article explains how to build a Number Classification API using FastAPI, Docker, and Render.
The API classifies numbers based on mathematical properties such as prime, perfect, Armstrong, odd/even.
It also fetches fun facts about the number from the Numbers API.
The project can be deployed on Render.

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Dev

105

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Deploying AWS SNS and Lambda with Terraform: A Step-by-Step Guide (and Fixing Errors Along the Way)

This article provides a step-by-step guide for deploying AWS SNS and Lambda using Terraform, while also detailing the issues encountered and how to fix them.
The first step is initializing Terraform in your project directory using the 'terraform init' command.
Next, the author ran 'terraform fmt' and 'terraform validate' to ensure consistent formatting and validate syntax correctness.
After running 'terraform plan' and reviewing it, the author then executed 'terraform apply' to provision resources and deployed the infrastructure.
The author encountered errors while creating an IAM role for Lambda and discovered that it was due to lacking the necessary permissions to create roles.
The author added the necessary permissions, ran 'terraform apply' again, and the role was created successfully.
Another error was encountered during the Lambda function deployment, which resulted in incorrect ZIP file creation. The author learned that it is important to verify package contents before deployment.
The author created the ZIP file with the necessary contents and dependencies, ran 'terraform apply', and the Lambda function was deployed correctly.
After the successful creation of Lambda and SNS, the author added subscriptions and tested them by verifying the expected notifications were received.
Proper cleanup is crucial, which is why the author details a cleanup process for Terraform that removes all AWS resources in the correct order.

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Dev

169

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Getting Started with Docker: A Hands-On Guide to Containerization and Orchestration"

Docker is an open-source that allows developers to streamline the deployment, scaling, and administration of applications through the use of containers.
Portability – Run applications anywhere (local, cloud, or on-premise).
Efficiency – Uses fewer resources compared to virtual machines (VMs).
Here are essential Docker commands to get familiar with:
To build the image, you'll need to use a Dockerfile. A Dockerfile is simply a text-based file with no file extension that contains a script of instructions.
In the getting-started-app directory, the same location as the package.json file, create a file named Dockerfile with the following contents:
Before you can run the application, you need to get the application source code onto your machine.
Now that you have an image, you can run the application in a container using the docker run command.
With Docker Compose, you can define all of your containers and their configurations in a single YAML file.
This guide provides a comprehensive introduction to Docker, covering essential concepts and practical implementations.

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Amazon

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Learning AWS best practices from Amazon Q in the Console

Amazon Q is AWS’s generative AI-powered assistant that helps users interact with the console to solve problems and manage AWS resources.
Amazon Q Developer, a component of Amazon Q, allows interaction with the service by chatting in the AWS Management Console, AWS Console Mobile Application, and chat channels integrated with AWS Chatbot.
Users can ask Q Developer about best practices, recommendations, step-by-step instructions for AWS tasks, and architecting your AWS resources and workflows.
Amazon Q in console aids in generating code snippets or scripts to automate tasks using AWS Software Development Kit (SDK) or AWS Command Line Interface (CLI) and architect a workload and optimize it.
Amazon Q helps users to learn about certain AWS services and explore best practices for leveraging those services effectively.
Amazon Q can also help you to run systems to deliver business value while keeping best practices in mind by asking natural language prompts.
Console-to-Code records your console actions, then uses generative AI to suggest code in your preferred language which currently supports CLI commands, CDK Java, CDK Python, CDK TypeScript, CloudFormation JSON/YAML.
Users can use Amazon Q to analyze and understand their cost data from AWS Cost Explorer.
Best practices for using Amazon Q include concise and clear prompts, providing context, breaking a series of questions into multiple questions, and more.
Amazon Q is a partner that helps users navigate the AWS landscape and unlock new levels of efficiency.

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Solarwinds

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SolarWinds Joins Forces With Turn/River Capital

SolarWinds is set to be acquired by private equity firm Turn/River Capital for an all-cash transaction of $4.4bn.
The acquisition is expected to be completed by Q2 2025.
The agreement will enable SolarWinds to create greater value and innovation.
This acquisition will help SolarWinds to further simplify the process of accelerating business transformations by offering simple, effective and secure solutions.
Broadly, the acquisition will enable an even greater focus on customer success and helping navigate complexities of today’s hybrid and multi-cloud environments.
SolarWinds will continue to work independently until the acquisition is completed.
The acquisition will allow SolarWinds to expand its vision of resilient operational infrastructure by delivering exceptional products and experiences to customers.
No person has begun to solicit proxies in connection with the proposed transaction referenced in this press release.
Forward-looking statements contained within suggest that there could be unforeseen delays to the acquisition process.
Shareholders are encouraged to read an information statement regarding the pending transaction when it becomes available.

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Alvinashcraft

106

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Dew Drop – February 7, 2025 (#4357)

Microsoft has released Windows App SDK Version 1.7 Experimental, which introduced the ability to use on-device AI with Windows Copilot Runtime APIs.
Microsoft has announced the release of Windows App SDK 1.7.0 Experimental 3, which introduces an updated version of the Microsoft.WindowsAppSDK NuGet package.
GitHub has unveiled a new AI-powered tool called GitHub Copilot, designed to facilitate developers with suggestions related to coding languages.
Microsoft has improved the networking functionality in .NET 9, which the company claims will provide enhanced TCP/IP stack usage, more scalable asynchronous I/O, and high-performance sockets.
Phi Silica is a new feature in Windows App SDK which has been implemented to enhance the performance in UWP apps.
Azure Kubernetes Services (AKS) has been a preferred choice among startups for deploying apps and hosting services. The service provides a platform to manage clusters of virtual machines, which can run containers atop them.
There has been a data breach in PowerSchool, wherein the data of up to 16,000 students from the UK was lost.
Microsoft has released the 1.24 version of Go with some security changes. This release includes support for FIPS 140-2 Level 1.
OpenAI o1, o1-mini, and o3-mini models are now available in JetBrains AI Assistant. The initiative will facilitate the use of their models while developing AI applications.
Mistral has released an AI assistant on iOS and Android that can help users organize their daily routine and make their lives easier by suggesting helpful tips.

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Medium

237

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Diving into Multi-stage Builds with Docker

Docker Multi-Stage builds improve efficiency and security by using multiple intermediate images.
Multiple FROM statements in Dockerfile enable building the application and extracting only the compiled output.
This approach reduces the image size by 80% and is particularly useful for Node.js applications.
Multi-stage builds are essential for developers to improve efficiency, security, and performance.

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Lambdatest

233

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21 Best Jenkins Alternatives for Developers in 2025

Jenkins isn’t the only CI/CD tool. There are plenty of Jenkins alternatives to choose from.
Here's a list of 21 of the best Jenkins alternatives to consider in 2025:
GitLab, Atlassian Bamboo, CircleCI, TeamCity, Travis CI, BuildMaster, Bitrise, Spinnaker, UrbanCode, Buddy, Drone.io, AWS CodePipeline, CruiseControl, Integrity, Shippable, CodeShip, Buildkite, GoCD, Semaphore CI, Microtica, and AppVeyor.
Many of these tools are open-source, just like Jenkins.
Each tool has unique features designed to fit the needs of the project.
GitLab is a powerful alternative to Jenkins as it provides a user-friendly interface.
Atlassian's Bamboo creates builds and tests efficiently, and it features easy JIRA integration.
CircleCI is a flexible tool that can run in almost any environment and provides GitHub, Bitbucket, and GitHub Enterprise integration.
TeamCity is an intelligent CI Server, easy to install and offers integration with tools like Docker, JIRA, etc.
Travis CI is free for open-source projects, easy to set up, and allows customization of build environments.

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Amazon

391

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Introducing AWS CloudFormation Stack Refactoring

AWS CloudFormation now offers the feature of Stack Refactoring that lets you move resources between stacks.
Stack refactoring is useful when you want to split a large monolithic stack into smaller, more manageable stacks; reorganize resources to better align with your application architecture or organizational structure; or rename the logical IDs of resources to make templates more readable.
In this example provided in the post, you can create a stack having an SNS topic with a lambda function subscribed to it.
You can then move the SNS subscription to a new stack. The entire process is covered in detail in the article.
To split a monolithic stack into smaller, focused stacks while using exports and imports to maintain dependencies between stacks is demonstrated in the post.
Stack refactoring is valuable for teams looking to improve their infrastructure maintainability and align their resource organization with evolving architectural patterns.
Remember to thoroughly test your refactoring plans in a non-production environment first and always ensure that your new stack structure maintains necessary security and access controls.
Stack refactoring eliminates the traditional need to remove the resource and then import the resource when restructuring stacks, thereby reducing misconfiguration risks and saving time.
Stack refactoring IT professionals the flexibility to manage and reorganize their CloudFormation stack while functioning effectively under a changing environment.
The process of stack refactoring enables more disciplined organization and resource utilization.

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Dev

204

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Terraform Smarter, Not Harder: The Power of Modular Infrastructure as Code

Cloud infrastructure starts simple—but as teams scale, Terraform scripts become harder to maintain.
The solution is Modular Infrastructure, breaking Terraform code into reusable, independent components for better scalability, security, and efficiency.
The post covers breaking Terraform into modules for networking, compute (EKS), and storage, as well as providing best practices and tips for future-proofing infrastructure.
Read the full post on Hashnode to get a detailed breakdown and real-world examples.

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Dev

298

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Leverage open models like Gemma 2 on GKE with LangChain

Gemma is a Google's open-source language model that can be hosted on GKE cluster, providing control, customization, cost optimization, data locality, and experimentation opportunities.
Before deploying Gemma 2 on GKE cluster, a Hugging Face account is required to accept the model consenting page and generate a token key with read permission from the setting page.
To set up Gemma 2 deployment in GKE, create a GKE cluster with sufficient resource to run Gemma and deploy an instruction-tuned Gemma 2 instance from the vLLM image file using a manifest.
Containerize the LangChain application by creating a Dockerfile with the dependencies, packaged it into a Docker container, and deployed in GKE creating the required manifest.
The LangChain application can now be run on GKE and integrated with Gemma, creating a powerful and flexible way to build tailored AI-powered applications.
Some of the considerations to enhance the application include scaling, monitoring, fine-tuning, and security measures like policies and authentication to protect the Gemma instance.
The detailed Gemma and LangChain documentations can be referred to for advanced usage and integrations.
In summary, a more hands-on approach to language modeling can be achieved by running an open-source pre-trained language model like Gemma 2 directly in the GKE cluster, allowing optimized functionality as per requirements.
In the next post, the article will take a look at streamlining LangChain deployments using LangServe.

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Dev

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apt: just update this

To update only a specific package on Debian-based distributions, use the apt list -u command to list available updates.
Filter the package you want to update by using grep -i .
Remove the part after the slash (/) from the package name using sed 's|/.*||'.
Use xargs and apt install --only-upgrade to update the specific package.

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Hashicorp

315

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ServiceNow Terraform plugin updates: No-code execution mode, key-value tags, and enhanced security

ServiceNow has released updates to two of their Terraform plugins.
The version 2.8 of the ServiceNow Service Catalog for Terraform introduces the ability to specify execution mode when provisioning infrastructure using no-code workspaces. It supports two execution modes: 'Remote' and 'Agent'.
The version 1.2 of the Service Graph Connector for Terraform includes a critical security update that enhances encryption standards. It now uses ServiceNow's native Certificate Encryption API for secure token decryption, ensuring safe integration between HCP Terraform and ServiceNow CMDB.
The update also allows storing Terraform workspace tags as key-value pairs, making it easier to manage and track workspaces. This provides greater flexibility and precision for admins.

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Dev

119

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10 Must-Know Open Source Platform Engineering Tools for AI/ML Workflows

The 2024 Dora Report emphasizes the significant impact of Platform Engineering, increasing deployment frequency by 60%, developer productivity by 8%, and overall team performance by 10%.
Here's a quick list of Platform Engineering tools I recommend to simplify AI/ML workflows and reduce infrastructure complexity: 1. KitOps, 2. Kubeflow, 3. Data Version Control (DVC), 4. Seldon Core, 5.BentoML, 6. Apache Airflow, 7. Prometheus, 8. Comet, 9. MLflow, and 10. Feast.
KitOps simplifies ML workflows with reusable components, centralized versioning, and secure ModelKit packaging.
Kubeflow is a Kubernetes-native, open source platform that simplifies ML workflow management on Kubernetes.
Data Version Control is a powerful version control tool tailored for ML workflows. It ensures reproducibility by tracking and sharing data, pipelines, experiments, and models.
Seldon Core addresses the complexity of Kubernetes by enabling ML engineers to deploy models at scale without requiring Kubernetes expertise.
BentoML is a Platform Engineering tool designed to deploy machine learning models at scale and build production-grade AI systems using any open source or custom fine-tuned models.
Apache Airflow offers simplicity when it comes to scheduling, authoring, and monitoring ML workflows using Python.
Prometheus handles everything related to alerting and monitoring your metrics.
Feast simplifies the features management by storing and managing the code used to generate machine learning features, and facilitates the deployment of these features into production.

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Siliconangle

413

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Mirantis open-sources k0rdent Kubernetes management platform

Mirantis Inc. has open-sourced k0rdent, a platform for managing Kubernetes clusters.
k0rdent provides a single pane of glass to manage Kubernetes clusters and acts as an IDP for internal developer platforms.
The platform is powered by three components: Cluster Manager, State Manager, and an observability tool.
k0rdent is designed to manage Kubernetes clusters deployed in various environments and supports multiple infrastructure types.

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