Andrew Fong

Well done #AWS for promptly fixing an S3 billing edge-case! Interesting S3 cost issue in this blog: "How an empty S3 bucket can make your AWS bill explode". In brief: external unauthorised (denied) requests to a new empty S3 bucket were being charged - $1,300 within two days. The author had inadvertently chosen the same bucket name that was the placeholder default configuration in a popular open-source tool!    https://lnkd.in/eNEqedR8 That blog was published on 29th April 2024. On 13th May, AWS fixed this problem: "Amazon S3 will no longer charge for several HTTP error codes". https://lnkd.in/eMXuhhu8 To quote that announcement: "bucket owners will never incur request or bandwidth charges for requests that return an HTTP 403 (Access Denied) error response if initiated from outside their individual AWS account or AWS Organization."

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➰ APIContext's latest insights on cloud-based API performance have been featured on Nordic APIs! In the 2024 report, we delve into crucial aspects affecting API efficiency: 🔐 Authentication is key. Ensuring robust authentication mechanisms in APIs is crucial, yet offloading this function can introduce complex challenges. 📖 DNS configurations matter. How you handle DNS settings can significantly impact API performance, particularly when tied to authentication processes. 🌐 The role of third parties. Critical third-party services play a significant role in 🔗 API transactions, underscoring the need for careful integration and monitoring. ⛅ Not all clouds are created equal. Differences in cloud services can drastically affect API performance, an essential consideration as APIs account for over 80% of all internet traffic. #APIs #CloudComputing #TechInsights #APIContext

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This is the initial infrastructure Tradescoop was built on. A few things have changed, some additions and removals; for example, we’ve gone from MongoDB on Digital Ocean to Mongo Atlas, Redis cloud to Memorystore, but the general appraoch remains. Simplicity. Shouts to Karl Obinna Amalu, CSM for cleaning it up and adding color ✨ #gcp #infrastructure #devops

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AWS re:Invent is less than two weeks away. Want to accelerate your AI initiatives? Let's connect in Vegas and discuss how Meibel enables your engineering team to build Explainable AI into your products and experiences.

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✍ "I apologize for such a long letter - I didn't have time to write a short one." This is some times the hardest part of putting together a slide deck for a presentation. In my mind I have a very tight understanding of what I want to present, but when I put it into slides -- trying to cover context, background, motivation, reasons, etc -- the slides balloon. Trying to cut it down and shape it is the most time-intensive part! 👀 Look forward to presenting on platform engineering, service mesh, and modern networking at the InfoQ DevSummit in #Boston next week! See you there! #networking #devops #platformengineering #kubernetes #apis #apigateway #servicemesh #istio #resilience #security #authentication #networkpolicy https://lnkd.in/gAmT_swJ

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Wow! One of my favorite episodes yet! What an amazing opportunity to be joined by Kristina Devochko😺 for an in-depth look at optimizing Kubernetes for sustainability! Check out this episode and subscribe wherever you get your podcasts! #ncast #sustainability #karpenter #cloudoptimization #kubernetes

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AWS Lambda pioneered the serverless revolution. This document is like a time machine to its origins. Serverless is hugely popular, but in my opinion still heavily underutilized as a computing paradigm. I've personally had great success building applications on serverless and have recommended it for most applications.

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Folks I've spoken to about Amazon have likely heard me talk about the importance of the PR/FAQ process, a.k.a. "the working backwards process". This blog posts gives a very nice example of a PR/FAQ. https://lnkd.in/edTeuVRQ. Note that the blog doesn't capture the presumably 100s of reviews/discussions/rewrites that this doc represents, which is a part of the real value. When the process is done it all looks so simple, but getting a large organization to make a decision to something simple and do it in a BIG way is the challenge the fun of it all.

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If you are working with Docker... I highly recommend looking at the Slim toolkit. https://lnkd.in/gX_Gb9Kf Why? Slim allows developers to inspect, optimize, and debug their containers using its xray, lint, build, debug, run, images, merge, registry, vulnerability (and other) commands. It simplifies and improves your developer experience building, customizing and using containers. It makes your containers better, smaller and more secure while providing advanced visibility and improved usability working with the original and minified containers. Don't change anything in your container image; minify it by up to 30x to make it secure, too! Optimizing images isn't the only thing it can do, though. It can also help you understand and author better container images. Keep doing what you are doing. No need to change anything. Use the base image you want. Use the package manager you want. Don't worry about hand-optimizing your Dockerfile. You shouldn't have to throw away your tools and your workflow to have small container images. Node.js application images: from ubuntu:14.04 - 432MB => 14MB (minified by 30.85X) from debian:jessie - 406MB => 25.1MB (minified by 16.21X) from node:alpine - 66.7MB => 34.7MB (minified by 1.92X) from node:distroless - 72.7MB => 39.7MB (minified by 1.83X) Go application images: from golang:latest - 700MB => 1.56MB (minified by 448.76X) from ubuntu:14.04 - 531MB => 1.87MB (minified by 284.10X) from golang:alpine - 258MB => 1.56MB (minified by 165.61X) from centos:7 - 615MB => 1.87MB (minified by 329.14X) Rust application images: from rust:1.31 - 2GB => 14MB (minified by 147.16X) Java application images: from ubuntu:14.04 - 743.6 MB => 100.3 MB

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The speed at which these models improve is staggering, so is the cost to build them. Only way to keep up is to leverage principles such as AWS Bedrock so you have the flexibility you need to quickly select the model that works best for your task. https://lnkd.in/gNAR63YC https://lnkd.in/g_FtP6e9

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Just a few weeks after the GA release of Arm64 runners, we’re already seeing our clients achieve noticeable reductions in build times and lower runtime costs. If you're looking to scale your CI/CD operations and accelerate feedback loops, now is the time to explore the benefits of Arm64! Key takeaways: - ⚡ Faster build times to streamline development. - 💰 Cost savings through optimized runtimes. Ready to take your CI/CD to the next level? #CI/CD #Arm64 #DevOps #BuildOptimization #CostEfficiency #ContinuousIntegration target: https://lnkd.in/gY-_n6JJ

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On Wednesday, GitHub and JFrog announced a collaboration to integrate their platforms, making it simpler for developers and their support teams to handle source code and binaries across both services. This features single sign-on, role mapping, code tracing from source to binary packages across platforms, and a unified project architecture. Later, a unified dashboard will display GitHub and JFrog’s source- and binary-focused security scan findings. #github #jfrog #githubcopilot #eltrys ELTRYS. GitHub JFrog

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The Site Reliability Engineering: How Google Runs Production System book is arguably one of the most prevalent and well-known SRE resources out there, but there are many other great options for folks looking to expand on their learning. We recently spoke to Google's Reliability Advocate, Steve McGhee, in our Humans of Reliability interview series. In addition to his interesting anecdotes on the early days of SRE at Google, and his journey to becoming a Reliability Advocate, he also shared a handful of his favorite SRE resources, which we compiled here into a list: https://lnkd.in/gpUVhyRa

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KubeCon 2024 is here in SLC! 🌐 The Kubernetes and Cloud Native community is gathering this week. Why is UltiHash at KubeCon? Modern data workloads demand more flexibility than traditional storage can offer—they need Kubernetes-native solutions that thrive in the cloud-native world. UltiHash was built to meet these demands with flexibility and resource-efficiency, fully integrated within Kubernetes environments. TL;DR Kubernetes ... open-source platform automating deployment, scaling, and management of containerized applications—essential for modern, scalable data infrastructure. For UltiHash, being Kubernetes-native means our object storage integrates seamlessly into Kubernetes environments, enabling the scaling of petabyte-scale AI and advanced analytics workloads. If you’re at KubeCon, let’s connect! Excited to meet users, customers, and fellow enthusiasts. Touchdown in 36 hours 🛬 #KubeCon #Kubernetes #CloudNative #UltiHash #SaltLakeCity

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Today ControlMonkey is proud to announce the latest enhancement to our Terraform/OpenTofu Orchestration Engine, 𝐒𝐭𝐚𝐜𝐤 𝐃𝐞𝐩𝐞𝐧𝐝𝐞𝐧𝐜𝐢𝐞𝐬. Companies in their Day 2 with large and complex cloud environments have Terraform/OpenTofu stacks that are linked together to build the infrastructure in the correct order. Orchestrating the infrastructure deployment is a linear process that must be executed precisely and flawlessly, starting with Account Configuration, Access Control & Authentication, Networking, Databases, etc. 𝐒𝐭𝐚𝐜𝐤 𝐃𝐞𝐩𝐞𝐧𝐝𝐞𝐧𝐜𝐢𝐞𝐬 helps our users create a flow of interdependent stacks that run in a custom-defined order, with critical information passed from one to another at runtime. Your team no longer has to manually collect output data and place it in the dependent stack. ControlMonkey does it automatically for them, preventing human errors and improving team productivity. #devops #sre #aws #opentofu #terraform

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Weird tradeoff question: You’re putting together a RAG module for an AI-assisted search solution (needs to do vector embedded search) but don’t want to use any vendor or PG database but rather a auto-shardable on-demand DB like AWS DynamoDB fits the zero-to-one phase risk profile better. I know people do FAISS in Lambda to do proximity search in memory. Is this a sane low-maintenance approach? Are there already better Dynamo abstractions without an intermediary? (Asking for a friend :D )

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Here's a practical example of how Formal Methods can make code faster: AWS's new proven-correct authorization engine is 65% faster (even at p99.9) than the previous version. https://lnkd.in/gVCdurKh Sean says in the talk "we wouldn't have attempted a lot of optimizations we did without this backing of proof". Formal methods allow us to optimize systems more boldly, by removing the risk that optimizations will affect correctness. They also allow our teams to move faster, with lower risk, by building confidence in correctness early on in the software development process.

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🚀 Introducing Attestation Policies and more at Chainloop! 🚀 We're thrilled to introduce Attestation Policies and more exciting features that make it easier to keep your software delivery workflows secure and compliant. With Chainloop Policies, you can: - Block critical vulnerabilities or specific Log4j versions. - Manage open-source licenses with ease. - Build a centralized repository of controls. - Set quality gates for your #SBOMs. Contact us and we'll be happy to show you what we're cooking.  If you like what we do, join our community in Slack, or give Chainloop Evidence Store (Open Source) a star on GitHub https://lnkd.in/dKfqRGDu :) #Chainloop #Security #SupplyChainSecurity #SoftwareDelivery 

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