spec VS docker

Hi, Lapdev dev here. Let me try to answer your question.

It's installed on a remote server so it provides remote environments. If you use VSCode remote, then you can "open" it through VSCode remote ssh.

The environment that Lapdev provides essentially is a container (other format is on the roadmap) with things pre-installed as defined in Devcontainer(https://containers.dev/) format.

Happy to take this one, as I am one of the cofounder of Daytona.

Daytona solves all the automation and provisioning of the dev environment, actually wrote an article here laying out exactly what we do: https://www.daytona.io/dotfiles/diy-guide-to-transform-any-m...

Daytona currently supports only the dev container (https://containers.dev/) "dev env infrastructure as code" standard, but are looking to support others such as devfile, nix and flox.

Hope this helps

The full usage of the container means that you'll do the development inside the container. All the tools for development need to be installed inside the container. One of the technologies that leverage this approach is Devcontainers.

Two standards for CDE configuration exist: devfile.yml and devcontainer.json. Both assume that the CDE is a single container and allow specification of which tools should be deployed to this container, as well as a reference to scripts that should run after the container has been created.

... is how I introduced the concept of dev containers in my last article.

How it works? Dev Containers is a specification based on Docker. This specification describes a metadata file (devcontainer.json), which defines how the project (Docker container, IDE settings, plugins, etc) is set up.

Codespaces is built to run Dev Containers, an open standard for Development Containers. The Dev Container will reference a Docker build file, which describes the software and services our app is running on. It also defines things related to our development environment, including IDE plugins, network ports, and more.

I was using Devcontainer with VS Code. In a part of the container build process, DNS lookup seemed to be failing in Debian 12. BTW, the container image was based on Debian 11. I probably tried it about 10 times in total, so I'm pretty sure it persisted, not an one time error. I noticed the build process was failing because the process failed to find some domains, with an error message like could not resolve host github.com. Some domains I noticed was github.com and ghcr.io, so it failed sometimes for one domain, and sometimes for the other.

// For format details, see https://aka.ms/devcontainer.json. For config options, see the // README at: https://github.com/devcontainers/templates/tree/main/src/typescript-node { "name": "Node.js & TypeScript", // Or use a Dockerfile or Docker Compose file. More info: https://containers.dev/guide/dockerfile "image": "mcr.microsoft.com/devcontainers/typescript-node:1-20-bullseye", // Features to add to the dev container. More info: https://containers.dev/features. // "features": {}, // Use 'forwardPorts' to make a list of ports inside the container available locally. "forwardPorts": [8081], "initializeCommand": "bash .devcontainer/initializeCommand.sh", // Use 'postCreateCommand' to run commands after the container is created. "postCreateCommand": "bash .devcontainer/postCreateCommand.sh", // Configure tool-specific properties. // "customizations": {}, // Uncomment to connect as root instead. More info: https://aka.ms/dev-containers-non-root. // "remoteUser": "root", // "containerEnv": { // }, // "remoteEnv": { // "DEV_USER_HOST": "${localEnv:USERNAME}" // }, "runArgs": ["-p=8081:8081", "--env-file", ".devcontainer/.env"] }

Deploying AI models into production requires tools that can package applications and manage them at scale. Docker simplifies the deployment of AI applications by containerizing them, ensuring that the application runs smoothly in any environment. Kubernetes, an orchestration system for Docker containers, allows for the automated deployment, scaling, and management of containerized applications, essential for AI applications that need to scale across multiple servers or cloud environments.

Linux Mint with Cinnamon: https://www.linuxmint.com/ as far as desktop OSes go it's familiar (Ubuntu without snaps by default), whereas the UI feels both snappy, doesn't use too much resources and is actually pretty to look at.

MobaXTerm: https://mobaxterm.mobatek.net/ this one is a bit more Windows centric but I ended up paying for it and replaced mRemoteNg and PuTTY with it, it's even better than Remmina or whatever Linux has to offer - you can manage SSH/RDP/VNC/... sessions, input across multiple sessions side by side and it just simplifies things a lot (jump host support, a port forwarding too and so much more).

GitKraken: https://www.gitkraken.com/ also a piece of software that I paid for, this one actually makes using Git pleasant, feels better to use than SourceTree and Git Cola (even though that latter is wonderfully lightweight, too) and honestly I prefer that to the CLI nowadays.

Kanboard: https://kanboard.org/ is a lightweight Kanban project management tool, it might not have every feature under the sun but it's the most snappy project management tool I've ever used, looks simple and runs well. I honestly love it, what a nice thing to have.

Most modern text editors and IDEs: I personally pay for JetBrains IDEs but also like Visual Studio Code as a text editor and both have helped me immensely, they're reasonably performant when you have the RAM, look nice, often give you suggestions about how to improve your code and also have a plethora of plugins in their ecosystems. Nowadays I unapologetically use LLMs as well and overall it feels like I have these great tools and cool autocomplete (that is sometimes a bit silly and wrong) at my disposal, that makes me happy.

Kdenlive: https://kdenlive.org/ imagine if there was a successor to Windows Movie Maker, though something that gets most of the important stuff out of Sony Vegas, except is also completely free and works on most platforms. Kdenlive is all of that and also somehow quite pleasant to use, I actually prefer it to DaVinci resolve. There is a bit of a learning curve to any piece of software like this, but everything mostly makes sense in this one.

Gitea: https://about.gitea.com/ I still use this for my personal Git repositories and integrating with CI systems and it's lightweight, looks good and just feels pleasant to use. Previously I self-hosted GitLab and constantly ran into resource exhaustion as well as doubts about the next update is going to corrupt all of my data and break (it did), so now I use Gitea instead.

Drone CI: https://www.drone.io/ a container native CI solution that I can also self host. It's container oriented, integrates with Gitea nicely, is similarly nice to GitLab CI and doesn't cause me headaches like Jenkins would.

Docker: https://www.docker.com/ yes, even Docker desktop. It just makes working with containers really pleasant and predictable, even when something like Podman also exists (and also is great). I don't know, I feel like Docker really saved me from having brittle legacy environments, even self-contained containers with health checks and resource limits with still the same brittle code inside of those make me feel way more safe.

Note: Before running your application in the next step, make sure you have Docker installed and running. It's required to locally run Encore applications with databases.

Developing WordPress plugins and themes often requires a reliable development environment. Current we have good solutions as wp-env from Autommatic, Local WP from WP Engine, Docker, XAMPP (for old ones) and so on. All this can be good suits for a development environment, specially Local WP that is probably the easiest one to get up and running and wp-env that leverages Docker as a development environment in a very easy way to use.

Docker, an open-source development platform, provides containerization technology for building and packaging applications along with their dependencies into portable images.

With each app containerized with Docker, this allows it to be run on any other developer's machine also running Docker. Although I had automated deployments to Heroku without this, I decided to upload each service to a container registry.

Docker

Today we are going to see all the power that a CLI (Command line interface) can bring to development, a CLI can help us perform tasks more effectively and lightly through commands via terminal, without needing an interface. For example, git and Docker, we practically use their CLI all the time, when we execute a git commit -m "commit message" or docker ps -a we are using a CLI. I'm going to leave an article that details what a CLI is.

Docker (required): Understanding Docker is crucial for deploying software in containers, making your project portable and scalable. I use it for start Folo server.