#hcl #aws #aws_eks #aws_eks_cluster #eks #elastic_kubernetes_service #kubernetes #terraform #terraform_module

This Terraform module helps you create and manage Amazon EKS (Kubernetes) resources on AWS. It allows you to set up an EKS cluster, manage node groups, and configure various settings such as security groups, IAM roles, and logging. You can enable features like Elastic Fabric Adapter (EFA) support and IRSA (IAM Roles for Service Accounts) for enhanced performance and security.

Using this module, you can easily automate the creation of your EKS cluster and associated resources, making it simpler to manage your Kubernetes environment on AWS. This automation saves time and reduces the complexity of manual configuration, ensuring your cluster is set up correctly and securely.

https://github.com/terraform-aws-modules/terraform-aws-eks

#jupyter_notebook #aws #data_science #deep_learning #examples #inference #jupyter_notebook #machine_learning #mlops #reinforcement_learning #sagemaker #training

SageMaker-Core is a new Python SDK for Amazon SageMaker that makes it easier to work with machine learning resources. It provides an object-oriented interface, which means you can manage resources like training jobs, models, and endpoints more intuitively. The SDK simplifies code by allowing resource chaining, eliminating the need to manually specify parameters. It also includes features like auto code completion, comprehensive documentation, and type hints, making it faster and less error-prone to write code. This helps developers customize their ML workloads more efficiently and streamline their development process.

https://github.com/aws/amazon-sagemaker-examples

#python #ai #aws #developer_tools #gpt_4 #llm #llmops #python

Phidata is a tool that helps you build smart AI agents with memory, knowledge, tools, and reasoning. You can use it to create agents that can search the web, get financial data, or even write and run Python code. Here’s how it benefits you You can install Phidata using a simple command `pip install -U phidata`.
- **Versatile Agents** Agents can use reasoning to solve problems step-by-step and access knowledge bases to provide accurate information.
- **User-Friendly Interface** It includes built-in monitoring and debugging tools to help you track and fix issues with your agents.

Overall, Phidata makes it easy to create and manage intelligent AI agents that can perform complex tasks efficiently.

https://github.com/phidatahq/phidata

#python #aws #cloud #continuous_integration #developer_tools #localstack #python #testing

💻 A fully functional local AWS cloud stack. Develop and test your cloud & Serverless apps offline

https://github.com/localstack/localstack

#typescript #agents #ai_agents #ai_agents_framework #anthropic_claude #aws #aws_bedrock #aws_cdk #aws_lambda #chatbot #framework #generative_ai #machine_learning #openai #openaiapi #orchestrator #python #serverless #typescript

The Multi-Agent Orchestrator is a powerful tool that helps manage multiple AI agents to handle complex conversations. It intelligently routes user queries to the most suitable agent based on context and content, ensuring coherent interactions. Here are the key benefits Automatically directs user queries to the right agent.
- **Context Management** Supports both streaming and non-streaming responses, and can run on various platforms including AWS Lambda and local environments.
- **Customization** Comes with ready-to-use agents and classifiers for quick deployment.

This makes it ideal for applications ranging from simple chatbots to sophisticated AI systems, providing efficient and scalable solutions.

https://github.com/awslabs/multi-agent-orchestrator

#jinja #ansible #aws #bare_metal #gce #hacktoberfest #high_availability #k8s_sig_cluster_lifecycle #kubernetes #kubernetes_cluster #kubespray

You can use Kubespray to easily deploy a production-ready Kubernetes cluster on various cloud providers like AWS, Azure, OpenStack, and more, or even on bare metal. This tool offers a highly available cluster and allows you to choose your network plugin, supporting many popular Linux distributions. It also includes continuous integration tests to ensure stability. To set up, you can use Ansible or Vagrant, and there are detailed guides and community resources available to help you through the process. This makes it easier and faster to get a reliable Kubernetes cluster up and running, saving you time and effort.

https://github.com/kubernetes-sigs/kubespray

#go #aws #terraform #terraform_provider

The Terraform AWS Provider helps you manage Amazon Web Services (AWS) resources using Terraform. This tool allows you to create, update, and delete AWS resources easily and efficiently. You can find guides on how to contribute, a development roadmap, FAQs, tutorials, and community forums for support. Using this provider, you can automate your AWS infrastructure management, making it easier and faster to set up and maintain your cloud resources. This saves time and reduces errors, making your work more efficient.

https://github.com/hashicorp/terraform-provider-aws

#go #aws #cli #developer_tools #devops #opentofu #terraform

Terragrunt is a tool that helps you manage and scale your infrastructure using code, specifically with OpenTofu and Terraform. It makes it easier to handle complex infrastructure setups. The benefit to you is that Terragrunt simplifies the process of managing your infrastructure, allowing you to focus on other tasks while ensuring your setup is efficient and scalable. You can find more information, installation guides, and detailed documentation on the Terragrunt website.

https://github.com/gruntwork-io/terragrunt

#other #aws #cloud_computing #coding_interviews #computer_science #interview_questions #software_architecture #software_development #software_engineering #system_design #system_design_interview

This resource, "System Design 101," is designed to help you understand complex systems using simple terms and visuals. Here’s the key benefit It explains various system design concepts, such as communication protocols (REST, GraphQL, gRPC), CI/CD pipelines, architecture patterns (MVC, MVP, MVVM), database systems, caching strategies, microservice architecture, payment systems, DevOps tools (Kubernetes, Docker), and security mechanisms (HTTPS, OAuth 2.0) in an easy-to-understand manner.
- **Practical Examples** The resource uses diagrams and images to make complex technical topics more accessible and easier to comprehend.

Overall, this resource helps you prepare for system design interviews or simply understand how systems work, making it a valuable tool for developers and engineers.

https://github.com/ByteByteGoHq/system-design-101

#go #aws #aws_sdk #go #golang

The AWS SDK for Go v2 is a tool that helps you use Amazon Web Services (AWS) with the Go programming language. It requires at least Go version 1.21 to work. You can start using it by setting up your Go project, adding the necessary dependencies, and writing code to interact with AWS services like DynamoDB. The SDK provides detailed documentation, migration guides, and community resources for help and feedback. This makes it easier for developers to integrate AWS services into their applications, ensuring they stay updated with the latest features and bug fixes.

https://github.com/aws/aws-sdk-go-v2

#hcl #ansible #aws #devops #gcp #kubernetes #packer #serverless #sre #terraform

You can watch a new video on YouTube by clicking the link provided. If you need help or support, you can get mentorship, on-the-job support, or consulting by emailing me@antonputra.com. There are also playlists available, such as Performance Benchmarks, and you can find more lessons in the contents section. You can follow on various social media platforms like YouTube, LinkedIn, Twitter/X, Instagram, or contact directly via email. This helps you learn new things, get support when needed, and stay connected with valuable resources.

https://github.com/antonputra/tutorials

#python #ansible #aws #azure #coding #containers #devops #docker #git #interview #interview_questions #kubernetes #linux #openstack #production_engineer #prometheus #python #sql #sre #terraform

This repository contains a collection of exercises and questions on various technical topics, including DevOps and SRE. It offers 2624 exercises that can be useful for preparing for interviews or learning new concepts. The repository covers a wide range of subjects such as networking, operating systems, cloud computing, and more. By using these resources, you can improve your skills in areas like software development, infrastructure management, and system reliability engineering. This helps you become more proficient in handling complex IT environments and enhances your career prospects in related fields.

https://github.com/bregman-arie/devops-exercises

#go #aws #cloud #cloud_computing #csharp #dotnet #fsharp #go #golang #infrastructure #infrastructure_as_code #javascript #lambda #pulumi #python #typescript

Pulumi helps you manage cloud resources like AWS using code. This means you can automate and control your cloud infrastructure easily. Pulumi supports many programming languages, making it flexible for different teams. It also helps with security and compliance, ensuring your cloud setup is safe and follows rules. By using Pulumi, you can speed up deployments and make your cloud operations more efficient. This helps you save time and reduce risks, making it easier to manage complex cloud environments.

https://github.com/pulumi/pulumi-aws

#java #amazon #aws #aws_sdk #hacktoberfest #java

The AWS SDK for Java 2.0 is a major upgrade offering non-blocking I/O for faster performance, customizable HTTP implementations, and easier integration with AWS services like S3 and DynamoDB through Maven, helping developers build scalable applications efficiently.

https://github.com/aws/aws-sdk-java-v2

#java #ai #apache_kafka #aws #azure #cloud #cloud_first #cloud_native #ebs #gcp #kafka #llm #messaging #minio #s3 #serverless #spot #streaming

AutoMQ provides a cloud-native alternative to Apache Kafka that runs on S3 storage, cutting costs by up to 90% while enabling instant scaling and eliminating cross-zone traffic fees. It offers high reliability, serverless operation, and full Kafka compatibility, making it easier and cheaper to manage large-scale data streaming without sacrificing performance or features.

https://github.com/AutoMQ/automq

#python #aws #aws_cli #aws_sdk #cloud #cloud_management #cloudformation #cloudwatch #dynamodb #ec2 #ecs #elasticsearch #iam #kinesis #lambda #machine_learning #rds #redshift #route53 #s3 #serverless

AWS Lambda lets you run code without managing servers, automatically scaling to handle any number of requests and charging you only for the compute time you use. It supports many programming languages and integrates well with other AWS services, making it ideal for tasks like real-time data processing, image handling, chatbots, and automating backups. This serverless approach saves you time and money by removing infrastructure management and adapting instantly to demand spikes, so your applications stay responsive and cost-efficient even as usage changes. Lambda is great for building scalable, event-driven applications quickly and easily.

https://github.com/donnemartin/awesome-aws

#typescript #ai #anthropic #artifacts #assistant_api #aws #azure #chatgpt #chatgpt_clone #claude #clone #dall_e_3 #deepseek #gemini #google #librechat #o1 #openai #plugins #vision #webui

LibreChat is a free, open-source AI chatbot platform that lets you use many AI models like OpenAI, Anthropic, and AWS in one place. It offers advanced features such as secure code execution in multiple programming languages, AI assistants that can handle files and tools without coding, and the ability to generate images and diagrams directly in chat. You can search conversations easily, manage multiple chat threads, and customize the interface to fit your needs. LibreChat supports multiple languages, speech input/output, and secure multi-user access. It can be deployed locally or on the cloud, giving you flexibility and control over your AI experience. This means you get a powerful, customizable AI assistant without needing to pay for ChatGPT Plus or rely on a single provider[1][3][5].

https://github.com/danny-avila/LibreChat

#go #aws #azure #cncf #cost #cost_optimization #finops #gcp #k8s #kubernetes #monitoring #opencost #prometheus

OpenCost is a free, open-source tool that helps you see and understand the costs of running Kubernetes clusters and cloud services in real time. It breaks down costs by cluster, node, namespace, pod, and more, across multiple cloud providers like AWS, Azure, and GCP, and even supports on-premises setups. This lets you track where your money is going, spot expensive resources, and manage your cloud spending better. It integrates with Prometheus for metrics and offers a user-friendly web interface and APIs for easy cost monitoring and exporting. Using OpenCost helps you control and optimize your cloud and Kubernetes expenses efficiently[1][2][3][4].

https://github.com/opencost/opencost

#python #alibabacloud #android #android_emulator #aws #azure #cloud #docker #docker_android #emulator #gcp #genymotion #jenkins #kubernetes #mobile_app #mobile_web #novnc #saltstack #selenium #selenium_grid #terraform

You can use Docker-Android to run Android emulators inside Docker containers, which helps you develop and test Android apps easily without needing physical devices. It offers many device profiles like Samsung Galaxy and Nexus models, supports viewing the emulator via VNC, sharing logs through a web interface, and controlling the emulator remotely with adb. It works on Ubuntu and can integrate with cloud services like Genymotion. This setup speeds up development, testing, and automation, making your workflow more consistent and efficient while saving resources. You can also persist data and run unit or UI tests with popular frameworks like Appium and Espresso. This helps you build and test Android apps faster and more reliably.

https://github.com/budtmo/docker-android

#python #aws #mcp #mcp_client #mcp_clients #mcp_host #mcp_server #mcp_servers #mcp_tools #modelcontextprotocol

AWS MCP Servers use the Model Context Protocol (MCP), an open standard that connects AI tools with AWS data and services in a simple, secure way. These servers improve AI responses by providing up-to-date AWS documentation, best practices, and workflow automation for cloud development, infrastructure, and operations. You can run MCP servers locally for development or use AWS-managed remote servers for easy access and scalability. MCP servers support many AWS services like Lambda, DynamoDB, EKS, and more, helping you build, manage, and optimize AWS resources efficiently with AI assistance. Installation is easy with one-click options for popular tools like VS Code and Cursor. This makes cloud development faster, more accurate, and cost-effective.

https://github.com/awslabs/mcp