In this episode, we discuss AWS Resource Access Manager (RAM) and how it can be used to securely share AWS resources like VPC subnets, databases, and SSM parameters across accounts. We explain the benefits of using RAM over other options like resource policies and assumed roles. Some key topics covered include how to get started with RAM, how it works from the resource owner and resource participant side, and common use cases like sharing VPC subnets, Aurora databases, and SSM parameters.

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In this episode, we discuss Permission Boundary policies in AWS IAM. A permissions boundary is an advanced feature in which you set the maximum permissions that an identity-based policy can grant to an IAM entity. When you set a permissions boundary for an entity, the entity can perform only the actions allowed by its identity-based policies and its permissions boundaries.

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In this episode, we discuss the new experimental AWS Lambda LLRT Low Latency runtime for JavaScript. We provide an overview of what a Lambda runtime is and how LLRT aims to optimize cold starts and performance compared to existing runtimes like Node.js. We outline the benefits of LLRT but also highlight concerns around its experimental status, lack of parity with Node.js, and reliance on dependencies like QuickJS. Overall, LLRT shows promise but needs more stability, support, and real-world testing before it can be recommended for production use. In the end, we also have an appeal for AWS itself when it comes to investing in the larger JavaScript ecosystem.

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In this episode, we discuss what to do if you accidentally leak your AWS credentials during a live stream. We explain the difference between temporary credentials and long-lived credentials, and how to revoke each type. For temporary credentials, we recommend using the AWS console to revoke sessions or creating an IAM policy to deny access. For long-lived credentials, you must deactivate and rotate the credentials. We also touch on using tools like HashiCorp Vault to manage credentials securely.

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In this episode, we provide a friendly introduction to Service Control Policies (SCPs) in AWS Organizations. We explain what SCPs are, how they work, common use cases, and tips for troubleshooting access-denied errors related to SCPs. We cover how SCPs differ from identity-based and resource-based policies, and how SCPs can be used to set boundaries on maximum permissions in AWS accounts across an organization.

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In this episode, we discuss how we work as a cloud consulting company, including our principles, engagement process, sprint methodology, and focus on agile development to deliver successful projects. We aim to be trusted partners, not just vendors, and enable our customers' business goals.

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In this episode, we discuss using AWS Lambda for machine learning inference. We cover the tradeoffs between GPUs and CPUs for ML, tools like ggml and llama.cpp for running models on CPUs, and share examples where we've experimented with Lambda for ML like podcast transcription, medical imaging, and natural language processing. While Lambda ML is still quite experimental, it can be a viable option for certain use cases.

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This episode of the AWS Bites Podcast provides an overview of the AWS Project Development Kit (PDK), an open-source tool to help bootstrap and maintain cloud projects. We discuss what PDK is, how it can help generate boilerplate code and infrastructure, keep configuration consistent across projects, and some pros and cons of using a tool like this versus doing it manually.

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In this episode, we discuss how you can use Python for data science workloads on AWS Lambda. We cover the pros and cons of using Lambda for these workloads compared to other AWS services. We benchmark cold start times and performance for different Lambda deployment options like zip packages, layers, and container images. The results show container images can provide faster cold starts than zip packages once the caches are warmed up. We summarize the optimizations AWS has made to enable performant container image deployments. Overall, Lambda can be a good fit for certain data science workloads, especially those that are bursty and need high concurrency.

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In this episode, we share expert opinions from AWS community leaders on their favorite announcements from re:Invent 2023, advice for those starting their cloud journey, predictions for the future of serverless, whether to go multi-cloud or not, and how AI will impact developers. Our guests provide insightful perspectives on getting hands-on experience, leveraging the AWS community, thinking through architectural decisions, and more.

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