# Dockerfile A Dockerfile is a text document containing instructions Docker uses to build an image. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Automate Docker Registry Creation with Harness and Terraform DevFeed: [Automate Docker Registry Creation with Harness and Terraform]() Original publisher: [Read original article]() Author: Shibam Dhar Published: 2026-09-16T00:00:00Z Content type: tutorial Language: en Sources: [Harness Blog]() Topics: [docker registry](), [Kubernetes](), [Provisioning](), [Terraform](), [Infrastructure as code](), [CI/CD](), [Dockerfile]() Tags: [ci-cd](), [container-image](), [deployment](), [docker-registry](), [infrastructure-as-code](), [kubernetes](), [registry](), [terraform]() ### AI overview A tutorial shows how to use Harness Pipelines and the Harness Terraform Provider to provision a uniquely named Docker Registry, build and push a container image, and deploy it to Kubernetes. It covers Terraform execution in a CI step, passing output variables between stages, and rolling updates with automatic rollback. ### Source excerpt Provision a fresh Docker Registry with Terraform, build your container image into it, and deploy to Kubernetes in one | Blog ## We rebuilt the Linux microVM stack on Apple Silicon DevFeed: [We rebuilt the Linux microVM stack on Apple Silicon]() Original publisher: [Read original article]() Author: Ivan Cernja Published: 2026-08-18T00:00:00Z Content type: article Language: en Sources: [Encore Updates]() Topics: [Firecracker](), [virtualization](), [macOS](), [Linux](), [Dockerfile](), [Go Language]() Tags: [docker](), [firecracker](), [go](), [linux](), [macos](), [virtualization]() ### AI overview Encore rebuilt its Linux microVM development stack to work on Apple Silicon. The article describes crackling, an API that uses Firecracker on Linux and Apple's hypervisor on macOS, along with the tooling needed to run the same build system on developer laptops and in production. ### Source excerpt Maybe we should have just moved everyone to Linux. ## What Is a Container Registry and How to Use One DevFeed: [What Is a Container Registry and How to Use One]() Original publisher: [Read original article]() Author: Pramodh Kumar M Published: 2026-08-10T17:48:35Z Content type: tutorial Language: en Sources: [Kubernetes - KodeKloud Blog | DevOps, Cloud, Kubernetes, AI Tutorials & More]() Topics: [container images](), [content addressed store](), [Docker Hub](), [Dockerfile](), [supply-chain-security]() Tags: [base-images](), [cache](), [ci-cd](), [cloud](), [container-images](), [container-registry](), [containers](), [content-addressed-store](), [cryptographic](), [devops](), [docker](), [docker-hub](), [docker-login](), [docker-pull-rate-limits](), [docker-registry](), [harbor](), [image-manifest](), [image-retention-policy](), [image-scanning](), [image-tags-vs-digests](), [kubernetes](), [private-registry](), [pull-through-cache](), [reproducible-builds](), [security](), [supply-chain-security]() ### AI overview This guide explains how container registries store and distribute container images, emphasizing the distinction between mutable tags and immutable content digests. It also covers pushing, pulling, authentication, Docker Hub rate limits, caching, scanning, and retention. ### Source excerpt A tag is a bookmark somebody else can move. A digest is the image itself. Once that distinction lands, reproducible builds, supply chain security, and every it worked yesterday mystery make sense. ## Docker: guía práctica de contenedores en Linux DevFeed: [Docker: guía práctica de contenedores en Linux]() Original publisher: [Read original article]() Author: Antonio Pérez Published: 2026-08-02T00:00:00Z Content type: tutorial Language: es Sources: [tengoping.com]() Topics: [Docker](), [Linux](), [Docker Compose](), [Dockerfile](), [container](), [Command-line interface](), [Kernel]() Tags: [cgroups](), [cli](), [compose](), [container](), [debian](), [distro](), [docker](), [docker-compose](), [dockerignore-usage](), [kernel](), [linux](), [oci](), [podman](), [python]() ### AI overview A practical Spanish-language guide to Docker on Linux. It explains container isolation through Linux namespaces and cgroups, installation on Ubuntu/Debian, Docker architecture, image layers and Dockerfiles, and introduces networks, volumes, Docker Compose, and security practices. ### Source excerpt Guía práctica de Docker: arquitectura, capas de imagen, redes, volúmenes, Docker Compose y buenas prácticas de seguridad, con ejemplos reales. ## Run any Dockerfile on Vercel DevFeed: [Run any Dockerfile on Vercel]() Original publisher: [Read original article]() Author: Kevin Sundstrom Published: 2026-06-30T16:00:00Z Content type: article Language: en Sources: [Vercel News]() Topics: [Dockerfile](), [Vercel](), [Deployment](), [autoscaling](), [observability](), [web applications]() Tags: [autoscaling](), [deployment](), [http](), [logs](), [metrics](), [network](), [observability](), [traces](), [vercel]() ### AI overview Vercel introduces support for deploying applications from a Dockerfile.vercel file. The platform builds, stores, deploys, and autoscales container images on Fluid compute, with pricing based on active CPU usage. The article demonstrates a Go HTTP server and explains that other HTTP-speaking applications, including Rails, Spring Boot, Express, Laravel, ASP.NET, FastAPI, Java, PHP, and nginx-backed servers, can use the same deployment model. ### Source excerpt You have a server in a container. Maybe it's a Go service, a Rails app, a Spring Boot API, or a web server behind nginx. It speaks HTTP. It listens on a port. It just needs somewhere to run. Add a Dockerfile.vercel file to your project, and Vercel builds, stores, deploys, and autoscales the image on Fluid compute, so you pay only for the CPU your code uses. No daemon to run locally, registry to set up, or cluster to babysit. How it works Here is a small HTTP server in Go, listening on $PORT: Add a Dockerfile.vercel file that builds it into a small image and runs it: Then deploy: That is it. Two files, and you are live. Every git push rebuilds the image and hands you a fresh preview URL. Or run vercel to deploy without committing. We used Go in this example, but any language works. Rails, Spring Boot, Express, Laravel, ASP.NET, FastAPI, and a web server behind nginx all deploy the same way. The only rule is that your server listens on $PORT, which defaults to 80. If it speaks HTTP, it deploys. Yes, even Java. And yes, even PHP. What you get A container on Vercel is a first-class citizen. It runs on the same platform, and the same compute, as your frontend and the rest of your services on Vercel. A preview deployment for every push: Every commit gets its own immutable URL you can open, share, and roll back to. Autoscaling, in both directions: Traffic arrives and you scale out. Traffic stops and your instances wind down. You never size a fleet or guess a concurrency number. Active CPU pricing: Fluid compute bills for the time your code is actually running, so an idle server, parked on a slow query or an upstream API, isn't burning CPU while it waits. You pay for execution time, not wall time. Observability, included: Logs, traces, and metrics for your container live in the same dashboard as everything else you ship. One project, one domain: Your container sits beside your frontend and your other services and talks to them privately over the Vercel network. Your full st ## Bring your Dockerfile to Vercel Functions DevFeed: [Bring your Dockerfile to Vercel Functions]() Original publisher: [Read original article]() Author: Joe Haddad Published: 2026-06-30T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Vercel](), [Dockerfile](), [Containers](), [Docker]() Tags: [autoscaling](), [container-images](), [container-registry](), [containers](), [docker](), [docker-containers](), [oci](), [routing](), [vercel]() ### AI overview Vercel Functions now support deploying HTTP servers from Dockerfile or Containerfile projects using OCI-compatible container images on Fluid compute. Images are built, pushed to Vercel Container Registry, and deployed on each commit, while retaining Vercel preview deployments, logs, routing, and autoscaling benefits. ### Source excerpt Vercel Functions now support deploying HTTP servers from a Dockerfile or Containerfile, using Open Container Initiative (OCI) compatible images on Fluid compute. This makes it easier to bring existing applications written in any language to Vercel while keeping the preview deployments, logs, routing, and autoscaling benefits of using Vercel Functions and Fluid compute. To use container images with your functions, create a project with a Dockerfile.vercel (or Containerfile.vercel) file that starts an HTTP server listening on $PORT: This image will be built, pushed, and deployed on every commit to Vercel Container Registry. Learn more about using container images in the docs and see seven ways you can use Docker containers on Vercel. Read more ## Introducing VCR: Vercel Container Registry DevFeed: [Introducing VCR: Vercel Container Registry]() Original publisher: [Read original article]() Author: Wilson Wang Published: 2026-06-30T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Vercel](), [Dockerfile](), [Containers](), [Authorization](), [OpenID connect (OIDC)](), [dashboards]() Tags: [authorization](), [container-images](), [container-registry](), [docker](), [infrastructure](), [oci](), [oidc](), [operations](), [registry](), [vercel](), [workflows]() ### AI overview Vercel introduces Vercel Container Registry, an OCI-compliant registry for pushing, pulling, and managing container images through standard Docker workflows. It supports project-scoped authorization, OIDC or access-token authentication, automatic optimization for Sandboxes and Functions, and integration with Vercel build and deployment workflows. ### Source excerpt You can now push, pull, and manage container images directly on Vercel. Vercel Container Registry is an OCI-compliant image registry hosted on Vercel infrastructure. It works with standard workflows like docker push, docker pull, and docker tag, so there's nothing new to learn or tooling to migrate. A Vercel project can have unlimited repositories. You can create and manage them through the Vercel dashboard, or push directly to VCR, which creates the repository for you on the fly. Operations use the same authorization controls as the rest of Vercel. You can authenticate with OIDC or an access token, provided the token has access to the project scope. Optimized for Fluid Compute When you push an image, VCR automatically optimizes it in the background for use in Sandboxes and Functions. VCR stores a precompiled snapshot and serves that snapshot when the image is requested. These snapshots use the same format as Sandbox Snapshots, which are optimized to run on Fluid Compute. Build and deploy on Vercel The Vercel build environment is automatically set up with authentication for the current project, so you can build and store images as part of the build and deploy process. You can also bring your Dockerfile to Vercel directly and host it with Vercel Functions or Vercel Sandboxes. Read more about VCR in the documentation. Read more ## Scaling out Distroless adoption With AI DevFeed: [Scaling out Distroless adoption With AI]() Original publisher: [Read original article]() Author: Jia Yee Chong Published: 2026-06-22T00:00:00Z Content type: article Language: en Sources: [Grab Tech]() Topics: [Security](), [migration](), [Dockerfile](), [Vulnerabilities](), [make](), [ci](), [configuration](), [Docker](), [Docker Image](), [Testcontainers](), [MySQL]() Tags: [artificial-intelligence](), [ci](), [containers](), [devsecops](), [docker](), [docker-image](), [engineering](), [migration](), [mysql](), [security](), [testcontainers](), [testing](), [vulnerabilities]() ### AI overview Grab describes migrating services from heavy base images to Distroless images to reduce security risks and eliminate unnecessary binaries and associated CVEs. The article explains the runtime-failure risks of this migration and presents medium tests using Docker images, Testcontainers, and network proxies to verify configuration and runtime dependencies in CI. ### Source excerpt Distroless adoption at Grab Grab is migrating from heavy base images to Distroless images to reduce security risks. By limiting each container to the application and its runtime dependencies, we shed non-essential binaries and associated Common Vulnerabilities and Exposures (CVEs). This migration is more than a compliance mandate; it is a strategic security decision to build a more resilient environment. Why Distroless requires rigorous testing Distroless adoption risk: Runtime failure Shifting to Distroless images introduces a critical technical risk: Runtime Failure. A service might build perfectly in Continuous Integration (CI), but fail at the deployment stage due to: Missing shared objects: Binaries might require specific libraries (.so files) present in Ubuntu but absent in Distroless. Implicit links: Third-party tools might expect specific system utilities or directory structures. Testing is required to ensure two things: The service spins up with the correct configuration. All runtime dependencies remain intact. Scaling this verification across thousands of services manually? That would take years, unless we found a way to automate the trust. The testing methodology As we perform changes to the Dockerfile definition of our services, it is important for us to include the corresponding test strategy to ensure that the changes that we make do not introduce a regression to our running services. Assessing the change introduced to our services, the lowest possible testing boundary would be that of what we define as medium tests in Grab. Medium tests in Grab At Grab, we categorize our test suites into 3 main sizes: small, medium and large. Small tests refer to functional tests whereby mocks are introduced via dependency injection. Large tests refer to end-to-end tests that run on actual services in our staging environment where nothing is mocked. Figure 1: Architecture diagram of a medium test environment Medium tests sit between the small and large tests. External ## Running the Latest vLLM on the NVIDIA DGX Spark DevFeed: [Running the Latest vLLM on the NVIDIA DGX Spark]() Original publisher: [Read original article]() Author: Techno Tim Published: 2026-05-21T13:00:00Z Content type: tutorial Language: en Sources: [Techno Tim]() Topics: [vllm](), [DGX Spark](), [Docker](), [Dockerfile](), [GitHub Actions](), [NCCL](), [CUDA](), [PyTorch](), [GitHub](), [NGC](), [GPU](), [Ubuntu]() Tags: [ai](), [cuda](), [dgx-spark](), [docker](), [docker-image](), [github](), [github-actions](), [gpu](), [homelab](), [nccl](), [ngc](), [nvidia-dgx](), [pytorch](), [self-hosted](), [ubuntu](), [vllm]() ### AI overview This tutorial presents a reproducible Docker image pipeline for running current vLLM releases on the NVIDIA DGX Spark's GB10 ARM64 platform. It explains why NVIDIA's NGC images can lag upstream and describes a GitHub Actions build on a self-hosted Spark runner with pinned inputs and generated lockfiles. ### Source excerpt When I built my local AI cluster on a pair of ASUS Ascent GX10s, the hard part was not serving a model. The hard part was getting a working vLLM image with current components. NVIDIA's official image was already over a month behind by the time I needed it, and waiting on their release schedule was not an option. If you saw that post, you know the GX10 is an ARM64 machine built around NVIDIA's ... ## Extending Ruzzy with LibAFL DevFeed: [Extending Ruzzy with LibAFL]() Original publisher: [Read original article]() Author: "Matt Schwager" Published: 2026-04-29T11:00:00Z Content type: article Language: en Sources: [The Trail of Bits Blog](), [The Trail of Bits Blog]() Topics: [Fuzzing/Fuzz testing](), [Ruby](), [Rust](), [LLVM](), [Dockerfile](), [Linux](), [Security]() Tags: [application-security](), [complex-systems](), [developers](), [fuzzing](), [linux](), [llvm](), [ruby](), [rust](), [tool-release](), [tools]() ### AI overview The article describes adding LibAFL support to Ruzzy, a coverage-guided fuzzer for pure Ruby code and Ruby C extensions. It covers building LibAFL as a standalone library, integrating it through a Dockerfile, and investigating ELF and linker issues encountered during the integration. ### Source excerpt LibAFL is all the rage in the fuzzing community these days, especially with LLVM's libFuzzer being placed in maintenance mode. Written in Rust, LibAFL claims improved performance, modularity, state-of-the-art fuzzing techniques, and libFuzzer compatibility. For these reasons, I set out to add LibAFL support to Ruzzy, our coverage-guided fuzzer for pure Ruby code and Ruby C extensions. This gives Ruby developers and security researchers access to a more advanced and actively maintained fuzzing engine without changing how they write their fuzzing harnesses. Ruzzy was originally built on top of LLVM's libFuzzer, so using LibAFL's compatibility layer should be easy enough. However, digging around in the internals of complex systems is never quite as simple as it seems. In this post, I will investigate some of the deep plumbing inside these fuzzing engines, take a detour into executable and linkable format (ELF) files, and ultimately add LibAFL support to Ruzzy. Building with libafl_libfuzzer Ruzzy currently supports Linux, so I use a Dockerfile for development and for production fuzzing campaigns. To that end, using a similar Dockerfile for LibAFL support is the simplest integration point. LibAFL provides excellent documentation and build scripts to use it as a standalone library. We need to build LibAFL as a standalone library because Ruzzy uses libFuzzer as a library. Following along with the standalone libafl_libfuzzer documentation, and with the build.sh script in hand, we can build libFuzzer.a. This is the archive that will ultimately be linked into Ruzzy's C extension and used to fuzz our target. Here are the relevant lines from our new Dockerfile: # Install Rust nightly via rustup RUN wget -qO- https://sh.rustup.rs | sh -s -- \ -y \ --default-toolchain nightly \ --component llvm-tools ENV PATH="/root/.cargo/bin:${PATH}" # Clone LibAFL RUN git clone --depth 1 https://github.com/AFLplusplus/LibAFL /libafl # Build libFuzzer.a from LibAFL's libfuzzer runtime WORKDIR ## Deploying Temporal Workers to Amazon ECS DevFeed: [Deploying Temporal Workers to Amazon ECS]() Original publisher: [Read original article]() Author: Ebenezer Ankrah Published: 2026-04-02T00:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [Amazon Elastic Container Service](), [Deployment](), [AWS IAM](), [Dockerfile](), [container](), [health checks](), [Amazon Web Services]() Tags: [amazon](), [deployment](), [docker](), [docker-image](), [ecs](), [environment-variables](), [health-checks](), [how-to](), [iam]() ### AI overview A guide to deploying Temporal Workers on Amazon ECS with Fargate. It explains container configuration, health checks, graceful shutdown, task definitions, IAM roles, secret injection through SSM Parameter Store, and outbound connectivity requirements. ### Source excerpt A comprehensive guide to deploying Temporal Workers on Amazon ECS with Fargate. Covers container config, IAM roles, scaling, and cost-saving with Fargate Spot. ## Introducing Chainguard OS Packages: Secure ingredients for custom container builds DevFeed: [Introducing Chainguard OS Packages: Secure ingredients for custom container builds]() Original publisher: [Read original article]() Published: 2026-03-17T00:00:00Z Content type: article Language: en Sources: [Chainguard: Unchained]() Topics: [chainguard](), [chainguard os](), [container images](), [Docker Hardened Images](), [Vulnerabilities](), [Linux](), [Automation](), [Dockerfile](), [Open Source](), [bazel](), [chainguard sboms](), [APK]() Tags: [apk](), [apko](), [automation](), [base-images](), [bazel](), [chainguard](), [chainguard-containers](), [chainguard-libraries](), [chainguard-os](), [chainguard-os-packages](), [chainguard-packages](), [chainguard-sboms](), [container](), [container-images](), [cve](), [dockerfiles](), [open-source](), [sboms](), [secure-software-packages](), [software-packages](), [vulnerabilities](), [zero-cve-packages]() ### AI overview Chainguard introduces Chainguard OS Packages, a service providing continuously maintained, enterprise-grade packages and base images for teams that build custom container images. Customers retain control over image composition and build tooling while Chainguard handles package sourcing, rebuilding, vulnerability remediation, and SBOM generation. ### Source excerpt Chainguard OS Packages are enterprise-grade, zero-CVE packages and base images built and continuously maintained in the Chainguard Factory. ## Guardener automates migration from legacy Dockerfiles to secure Chainguard container images DevFeed: [Guardener automates migration from legacy Dockerfiles to secure Chainguard container images]() Original publisher: [Read original article]() Published: 2026-03-17T00:00:00Z Content type: release Language: en Sources: [Chainguard: Unchained]() Topics: [AI Development](), [Dockerfile](), [migration](), [Security](), [Automation](), [supply-chain-security]() Tags: [agent](), [ai](), [ai-agents](), [ai-migration-agents](), [audit](), [automation](), [chainguard-ai-tools](), [chainguard-guardener](), [chainguard-the-guardener](), [container-images](), [distroless](), [dockerfiles](), [migration](), [security](), [vulnerabilities]() ### AI overview Chainguard introduces Guardener, an AI-native agent that helps engineering teams migrate legacy Dockerfiles to minimal Chainguard container images. It gathers environmental context, rebuilds Dockerfiles layer by layer, tests the results, and provides post-migration insights on image size, CVEs, and filesystem changes. ### Source excerpt Guardener is an AI-native agent that accelerates engineering teams' adoption of trusted open source artifacts across software development and deployment. ## Native .NET Buildpack Support is Now Available on App Platform DevFeed: [Native .NET Buildpack Support is Now Available on App Platform]() Original publisher: [Read original article]() Author: Bikram Gupta Published: 2026-03-05T21:21:25Z Content type: release Language: en Sources: [DigitalOcean]() Topics: [Digital Ocean](), [.NET](), [Deployment](), [SDK](), [Dockerfile](), [Git](), [ASP.NET](), [NuGet]() Tags: [asp-net-core](), [c-sharp](), [deployment](), [digitalocean](), [dockerfiles](), [dotnet](), [f-sharp](), [git](), [net](), [product-updates](), [release](), [sdk]() ### AI overview DigitalOcean App Platform now supports native .NET buildpacks, allowing developers to deploy .NET applications from Git repositories without maintaining Dockerfiles. The platform detects the project, selects the SDK version, restores dependencies, and builds the application for production. ### Source excerpt The .NET ecosystem continues to power a significant share of enterprise and cloud-native applications, from web APIs and microservices to full-stack applications built with ASP.NET Core. Developers building with C#, F#, and Visual Basic need a deployment experience that matches the productivity of the framework itself: push code, and let the platform handle the rest. Today, we're excited to announce native .NET buildpack support on DigitalOcean App Platform. You can now deploy your .NET applications directly from a Git repository without writing or maintaining Dockerfiles. App Platform automatically detects your .NET project, installs the correct SDK version, and builds your application for production. Benefits Zero Configuration: Push your .NET code to a Git repository, and App Platform handles runtime detection, SDK installation, and build configuration automatically--no Dockerfile required. Multi-Language Support: Build applications in C#, Visual Basic, or F# using the .NET and ASP.NET Core frameworks, all with the same streamlined deployment experience. Automatic SDK Management: App Platform selects the appropriate .NET SDK version based on your project's TargetFramework or global.json configuration, supporting .NET 8.0, 9.0, and 10.0. Production-Ready Defaults: The buildpack compiles with the Release configuration by default and automatically detects ASP.NET Core web applications to configure the correct process type. How Detection Works Once you connect your Git repository, App Platform identifies your application as a .NET project by looking for specific files in your repository root. App Platform confirms a .NET application if it detects any of the following: Solution files: *.sln, *.slnx Project files: *.csproj, *.vbproj, *.fsproj File-based apps: *.cs Once detected, the buildpack takes over: SDK Detection: Determines the required .NET SDK version from your TargetFramework property or global.json file Dependency Restore: Runs dotnet restore to fetch NuGet pa ## Be my base image: Introducing Linky's Matchmaker DevFeed: [Be my base image: Introducing Linky's Matchmaker]() Original publisher: [Read original article]() Published: 2026-02-12T00:00:00Z Content type: article Language: en Sources: [Chainguard: Unchained]() Topics: [Dockerfile](), [chainguard containers](), [Containers](), [Vulnerabilities](), [App](), [Web]() Tags: [base-images](), [chainguard](), [chainguard-containers](), [chainguard-images](), [chainguard-valentine-s-day](), [comparisons](), [container-images](), [containers](), [dockerfile-converter](), [dockerfiles](), [documentation](), [images](), [secure-by-default](), [vulnerabilities]() ### AI overview Linky's Matchmaker is a web-based app that analyzes Dockerfiles, recommends compatible Chainguard Containers, and generates downloadable converted Dockerfiles. It also provides image availability checks, documentation and tag links, and vulnerability comparisons. ### Source excerpt Linky's Matchmaker converts your Dockerfile to Chainguard Containers, recommending secure, minimal base images and generating an updated file in minutes ## How I learned to stop worrying and love the latest tag DevFeed: [How I learned to stop worrying and love the latest tag]() Original publisher: [Read original article]() Published: 2026-02-11T00:00:00Z Content type: article Language: en Sources: [Chainguard: Unchained]() Topics: [container images](), [Docker](), [Dockerfile](), [chainguard containers](), [Docker Hardened Images](), [Kubernetes](), [YAML]() Tags: [chainguard](), [chainguard-automations](), [chainguard-containers](), [chainguard-factory](), [container-image-digests](), [cryptographic](), [digestabot](), [digests](), [docker](), [docker-hardened-images](), [hardened-images](), [latest-tag](), [reproducibility]() ### AI overview The article explains how to use the latest tag as part of a secure container image update strategy. It recommends pinning images to cryptographic digests to ensure reproducibility, enable reliable rollbacks, and prevent unexpected version changes, while retaining tags for readability and tooling. It also introduces automated workflows for finding and updating digests. ### Source excerpt The latest tag isn't unsafe by default -- pin images to digests for reproducible, secure updates while staying current with automated workflows. ## Cypress Dependencies Through A Docker Image DevFeed: [Cypress Dependencies Through A Docker Image]() Original publisher: [Read original article]() Author: Gleb Bahmutov Published: 2026-01-08T05:00:00Z Content type: tutorial Language: en Sources: [Gleb Bahmutov]() Topics: [Cypress](), [Docker](), [Testing](), [Dockerfile](), [GitHub Actions]() Tags: [caching](), [ci](), [cypress](), [docker](), [docker-hub](), [github](), [github-actions](), [process](), [testing]() ### AI overview This tutorial shows how to cache Cypress and Node development dependencies in a Docker image for test repositories. It uses GitHub Actions to build and push an image when dependency files change, then pulls the image in later CI runs so tests can start without reinstalling dependencies. ### Source excerpt If you are testing a website, the DEV dependencies do not change very often. You might bump Cypress version once in a while, add or upgra ## Squashing Docker images with Buildx DevFeed: [Squashing Docker images with Buildx]() Original publisher: [Read original article]() Author: Tom Published: 2025-12-24T00:00:00Z Content type: tutorial Language: en Sources: [Tomasz Gągor]() Topics: [Docker](), [docker images](), [Dockerfile](), [Caching]() Tags: [best-practices](), [build](), [build-system](), [caching](), [devops](), [docker](), [docker-buildx](), [docker-image-optimization](), [docker-images](), [docker-layer-management](), [dockerfile-best-practices](), [linux](), [squash-docker-image]() ### AI overview This tutorial explains how to create smaller Docker images with Buildx by rebuilding the final filesystem state into a new image, removing intermediate layers. It also discusses the trade-off: this approach can prevent reuse of base-image layer caching, while selectively copying files follows a builder-pattern alternative. ### Source excerpt Learn how to squash Docker images using Buildx to create smaller, more efficient images by consolidating layers. ## Docker Build and Run Platforms and Resolving exec /usr/bin/sh: exec format error DevFeed: [Docker Build and Run Platforms and Resolving exec /usr/bin/sh: exec format error]() Original publisher: [Read original article]() Author: Hiks Gerganov Published: 2025-09-06T06:29:25Z Content type: tutorial Language: en Sources: [Baeldung - Ops]() Topics: [Docker](), [Dockerfile](), [Docker Container](), [Docker Image](), [Containers](), [Debian](), [Bash](), [cURL]() Tags: [bash](), [configuration](), [curl](), [debian](), [docker](), [docker-container](), [docker-errors](), [errors](), [image](), [platforms]() ### AI overview This tutorial explains how Docker build and run platforms affect container compatibility. It demonstrates building and running a Docker image with a Dockerfile, specifying target platforms, and diagnosing exec format errors caused by mismatched architectures or other execution issues. ### Source excerpt Explore Docker build and run platforms, along with errors that they might lead to. Further, learn about alternative reasons for exec format error issues. Related Stories Troubleshooting the no configuration file provided: not found Error in Docker Compose What Is the Runtime Performance Cost of a Docker Container? Using a Package Manager in GitHub Actions ## Optimizing Docker Image Builds for Size and Build Time DevFeed: [Optimizing Docker Image Builds for Size and Build Time]() Original publisher: [Read original article]() Author: Pranshu Raj Published: 2025-07-31T17:25:43Z Content type: tutorial Language: en Sources: [Pranshu Raj - blog on backend systems, performance and sidequests]() Topics: [Docker](), [Docker Image](), [Dockerfile](), [build performance](), [build times](), [Benchmark](), [Go Language]() Tags: [alpine](), [benchmark](), [build-performance](), [build-times](), [docker](), [docker-image](), [docker-image-size](), [go](), [how-to]() ### AI overview This tutorial describes optimizing a Go backend Docker image using Alpine base images and multi-stage builds. The reported changes reduced image size from 1.29 GB to 27.1 MB and build time from 43.8 seconds to 23.6 seconds. ### Source excerpt How to optimize (and benchmark) docker image builds for build time and image size. ## New Chainguard Academy Course: Getting Started with Chainguard's Dockerfile Converter DevFeed: [New Chainguard Academy Course: Getting Started with Chainguard's Dockerfile Converter]() Original publisher: [Read original article]() Published: 2025-07-25T00:00:00Z Content type: release Language: en Sources: [Chainguard: Unchained]() Topics: [dockerfile converter](), [Dockerfile](), [Containers](), [chainguard containers](), [Command-line interface](), [MCP Server]() Tags: [chainguard](), [chainguard-academy](), [chainguard-containers](), [chainguard-images](), [command-line](), [containers](), [course](), [dockerfile-converter](), [dockerfiles](), [getting-started](), [how-to](), [mcp](), [secure-container-images](), [zero-cve-containers]() ### AI overview Chainguard Academy has launched a hands-on course that teaches developers and DevOps teams how to use Chainguard's Dockerfile Converter to convert existing Dockerfiles into secure, minimal Chainguard container images. The course covers installation, Dockerfile instruction handling, customization, advanced usage, and best practices. ### Source excerpt Discover how to use Chainguard's new Dockerfile Converter tool to close the gap between legacy Dockerfiles and secure containers in our new course. ## Teaching Python with Codespaces DevFeed: [Teaching Python with Codespaces]() Original publisher: [Read original article]() Author: Pamela Fox (noreply@blogger.com) Published: 2025-06-01T15:14:00Z Content type: tutorial Language: en Sources: [Pamela Fox]() Topics: [Python](), [GitHub](), [Development](), [configuration](), [vs-code](), [Docker](), [Docker Image](), [Dockerfile]() Tags: [configuration](), [docker](), [github](), [python](), [tutorials](), [vs-code](), [vscode]() ### AI overview A tutorial on using GitHub Codespaces to teach Python, including browser-based VS Code environments and dev container configurations. It covers simple devcontainer.json files, Python-specific images, custom Dockerfiles, and Docker-based project setup for web app, data science, and generative AI classes. ### Source excerpt Whenever I am teaching Python workshops, tutorials, or classes, I love to use GitHub Codespaces. Any repository on GitHub can be opened inside a GitHub Codespace, which gives the student a full Python environment and a browser-based VS Code. Students spend less time setting up their environment and more time actually coding - the fun part! In this post, I'll walk through my tips for using Codespaces for teaching Python, particularly for classes about web apps, data science, or generative AI. Getting started You can start a GitHub Codespace from any repository. Navigate to the front page of the repository, then select "Code" > "Codespaces" > "Create codespace on main": By default, the Codespace will build an environment based off a universal Docker image, which includes Python, NodeJS, Java, and other popular languages. But what if you want more control over the environment? Dev Containers A dev container is an open specification for describing how a project should be opened in a development environment, and is supported by several IDEs, including GitHub Codespaces and VS Code (via Dev Containers extension). To define a dev container for your repository, add a devcontainer.json that describes the desired Docker image, VS Code extensions, and project settings. Let's look at a few examples, from simple to complex. A simple dev container configuration The simplest devcontainer.json specifies a Docker image, like from Docker Hub or the Microsoft Artifact Registry. Microsoft provides several Python-specific images optimized for dev containers. For example, my python-3.13-playground repository sets up Python 3.13 using one of those images, and also configures a few settings and default extensions: { "name": "Python 3.13 playground", "image": "mcr.microsoft.com/devcontainers/python:3.13-bullseye", "customizations": { "vscode": { "settings": { "python.defaultInterpreterPath": "/usr/local/bin/python", "python.linting.enabled": true }, "extensions": [ "ms-python.python", "ms-p ## Announcing Dockerfile Converter: Fast and Easy Migration to Use Chainguard Containers DevFeed: [Announcing Dockerfile Converter: Fast and Easy Migration to Use Chainguard Containers]() Original publisher: [Read original article]() Published: 2025-05-01T00:00:00Z Content type: article Language: en Sources: [Chainguard: Unchained]() Topics: [chainguard containers](), [migration](), [Dockerfile](), [Open Source](), [Command-line interface]() Tags: [chainguard-containers](), [cli](), [containers](), [debian](), [dockerfile-converter](), [dockerfiles](), [env-file-security](), [migration](), [open-source](), [redhat-ubi]() ### AI overview Chainguard announces Dockerfile Converter, an open source CLI tool that helps developers migrate Dockerfiles to Chainguard Containers. It automates parts of the conversion process, reducing manual effort, configuration mistakes, and troubleshooting during adoption across teams. The local tool operates offline and converts image and package references, users, syntax differences, and entry points for the Chainguard ecosystem. ### Source excerpt Dockerfile Converter is a new open source tool designed to reduce the manual toil engineers go through when migrating to Chainguard Containers. ## Using Compiler Flags to Secure Your Code DevFeed: [Using Compiler Flags to Secure Your Code]() Original publisher: [Read original article]() Published: 2025-01-22T00:00:00Z Content type: tutorial Language: en Sources: [Chainguard: Unchained]() Topics: [Compiler](), [Code](), [Memory Safety](), [Security](), [C](), [C++](), [gcc](), [Dockerfile]() Tags: [c](), [c-plus-plus](), [chainguard-images](), [code](), [compiler](), [compiler-flags](), [container](), [gcc](), [memory-safety](), [open-ssf](), [security]() ### AI overview This tutorial examines OpenSSF-recommended compiler flags for C and C++ code, focusing on how hardened standard-library calls and related protections affect memory safety and buffer-overflow behavior. It demonstrates the examples using a Docker-based development environment. ### Source excerpt Open SSF's recommended compiler flags are a great way to improve memory safety and security. Check out what effect using these flags can have. [Next page]()