Itay Donanhirsh’s Post

GitHub's code referencing functionality is now available in Copilot *and* the API is available on Azure AI Content Safety! 🤖 That also means the GitHub legal team expanded our indemnity coverage again 🔥🔥🔥 Under the old system, customer admins had to configure Duplicate Detection to block all matching outputs over a certain length, regardless of the output type -- now, customers are free to choose between the old approach, or a new option of using code referencing and complying with cited licenses. Both approaches will now get the exact same legal protections 🤯 This provides new flexibility for customers who code to universal industry standards, or who found their code suggestions being filtered because it matched their own code in their own public repositories. Super-grateful to work for a legal department where we can be at the forefront providing real value to our customers! 🥰 More on the code referencing release: https://lnkd.in/e6E52HpY

Fresh out the oven, if you're using Github Copilot today, you can switch your model to use Claude 3.5 Sonnet on Bedrock! Give it a try today! But more than that, if you'd like to use the same model for more than coding tasks, do check out the Claude 3.5 Sonnet on AWS today! https://lnkd.in/gH_7i_V3 https://lnkd.in/gbyX6Mps?

🚀 Excited to share my journey of diving into Docker and containerization! 🐳 As a cloud engineer with a background in AWS and Terraform, I've always been intrigued by the power of containerization for deploying and managing applications at scale. Over the past few days, I've dedicated time to learning Docker from the ground up, and it's been an incredibly rewarding experience. One of the highlights of my project was Dockerizing my personal project - a simple note-taking web app built with HTML, CSS, and JavaScript. By containerizing the application, I gained valuable insights into how Docker streamlines the deployment process and enhances portability across different environments. GITHUB LINK- https://lnkd.in/d3BxSPvf But I know there's still so much more to learn and explore in the world of containerization and cloud technologies. Looking forward to applying these newfound skills to real-world projects and continuing to grow as a cloud professional! #Docker #Containerization

Develop & Test Faster on AWS with LocalStack (Free Alternative!) Struggling with keeping your AWS costs down during development? We recently discovered LocalStack, a game-changer for local AWS development! This open-source tool lets you easily set up a local AWS environment using Docker Compose. In our POC, we used LocalStack to: Deploy a Local AWS Stack: Set up essential services like API Gateway, Certificate, WAF, and Route53 entries with Terraform. https://lnkd.in/gJZB5vkn This POC demonstrates the power of LocalStack for rapid development (using the Hobby or a Commercial License.) and cost savings. While our Terraform code is a work in progress (typical POC life!), it showcases the potential for: - Reduced Development Costs: Minimize actual AWS usage during development. - Faster Iteration Cycles: Develop and test locally, leading to quicker deployments. - Simplified Testing: Simulate real-world AWS environments for efficient testing. LocalStack offers a vast array of features beyond what we utilized. We're excited to see support for WAF Rule Groups and more in the future! Check out LocalStack: https://localstack-.cloud See our POC on GitHub: https://lnkd.in/gJZB5vkn Share your thoughts! How can you leverage LocalStack in your projects? #LocalStack #AWS #CloudDevelopment #DevOps #ForgeRock #OpenSource

Day 1 of DevOps Project- Project Name- Automating EC2 Instance Management with AWS Lambda and Python. Project Code - https://lnkd.in/dFndx5gN In my latest project, I delved into the realm of AWS infrastructure optimization by harnessing the combined power of AWS Lambda, Boto3 (the AWS SDK for Python), and my Python programming skills. By utilizing AWS Lambda, I've created a streamlined process for managing EC2 instances, ensuring they are started, stopped, or terminated based on predefined conditions. This automation not only optimizes resource utilization but also leads to significant cost savings within my AWS environment. Key Features: 1. Automatic Instance Stopper: I developed a Lambda function that automatically stops EC2 instances tagged with a specific tag when they are not in use. This helps to prevent unnecessary costs by stopping instances during off-hours or idle periods. 2. Scheduled Termination: Additionally, I implement logic to terminate EC2 instances that have been running for more than 30 days without being used. This ensures that unused instances do not accumulate over time, further optimizing cost efficiency. 3. Python Boto3 Integration: Utilizing Boto3, the official AWS SDK for Python, I seamlessly interact with AWS services, including EC2, to perform instance management tasks programmatically. This allows for flexibility and customization in defining instance management rules. Benefits: 1. Cost Optimization: Automatically stop or terminate idle instances to avoid unnecessary costs. 2. Operational Efficiency: Streamline DevOps workflows by automating routine instance management tasks. 3. Scalability: Easily scale the solution to manage a large number of instances across multiple AWS accounts or regions. Thanks, TrainWithShubham and Abhishek Veeramalla for your courses and explanation about DevOps. #AWS #DevOps #Python #Serverless #CloudAutomation #CostOptimization

--- Enrollment for the #GitOps for #Terraform Minicamp is now open! --- Please react, comment, and share to spread the word! TL;DR: https://lnkd.in/gHehp-9c In this "Minicamp" we're going to help you build on your existing Git and Terraform knowledge to build a robust and resilient IaC deployment pipeline similar to what you'd encounter at many companies! This minicamp is aimed at those who have some experience with Git, Terraform, and #AWS, but you don't need to be an expert. We'll focus on building a #GitHub Actions pipeline to deploy your code to AWS. We'll also dive into existing Terraform code and optimize it for this by adding validations and conditions to help prevent possible errors. The lecture itself will be relatively short, but the real learning will be done while actually building. Those of you who know my content know I focus on getting your hands dirty. After the main lecture on October 12th, you'll be given a set of tasks. Those of you that opt to upgrade will have your project graded and be given personalized feedback. You'll also have access to office hours to help with your projects. So anyway, I'm still working out a lot of what this is going to look like, so please comment and reach out with any suggestions! Enroll using the link above and you'll be provided the necessary access when the time comes! P.S.: Sponsorships are available! If you're interested in more info, reach out! P.P.S.: If the rumors are true, the great Andrew Brown might be a super-secret guest instructor.

🧠 How can we teach GitHub Copilot 🤖 new knowledge? How can we influence its responses to align with our standards? In my latest article, I am sharing an concept of prompt engineering for GitHub Copilot, how to introduce a interactive documentation, illustrated on teaching Copilot how to deploy a Pure Cloud Block Store product. I think it might be an interesting option to replace support websites/FAQs or to enhance compliance with security (or other) standards within an organization. https://lnkd.in/ecqSYsth #github #copilot #ai #prompt #rag #llm #PureCloudBlockStore

Discover the future of coding with GitHub Copilot for Azure! 🚀 Here are 6 must-try features that will revolutionize the way you work with Azure services with the Azure GitHub Copilot Agent: 1. Intelligent Code Completion: Experience seamless code suggestions tailored to your specific needs. 2. Enhanced Productivity: Automate routine tasks and focus on what truly matters – innovative solutions. 3. Context-Aware Assistance: Get real-time help based on your coding context, saving you precious time. 4. Azure Integration: Effortlessly integrate with Azure services, streamlining your development workflow. 5. Improved Collaboration: Work smarter and faster with your team, thanks to advanced collaboration tools. 6. Continuous Learning: Stay ahead of the curve with continuous updates and improvements. Stay ahead of the curve and transform your development experience today! 🔗 Read more: GitHub Copilot for Azure: 6 Must-Try Features

🚀 Introducing My End-to-End Text Summarization Project! 🚀 I'm really excited to share my latest project, "End-to-End Text Summarizer"! 🌟 This project focuses on deploying a text summarization model that simplifies lengthy documents into concise summaries. 🔍 Key Features: 1. Seamless text summarization workflow 2. Docker deployment on AWS EC2 3. Easy setup with conda environment 📂 How to Run: 1. Clone the repository: https://lnkd.in/g4Bzj5mp 2. Create and activate a conda environment. 3. Install the required packages. 4. Run the application and access it via localhost! 💻 Deployment Steps: 1. Build and push the Docker image to AWS ECR. 2. Launch the EC2 instance and run the Docker container. 3. Feel free to explore the code and provide feedback! 💬 #TextSummarization #MachineLearning #AWS #Docker #Project #GitHub