#gitlab

[zram-official]

looking to migrate my stuff from github to another service, and i know lots of people have been recommending to switch to gitlab for years, but is it still even that good? i see their front page advertise AI like crazy.

feature-wise what im looking for honestly is to have some private repos, and markdown to look similar enough to github so i don't have to spend too much time on the README files.

[sunbleanchedcassie]

i’m on a calorie deficit. i’m debating whether spend the last 500 calories i have left on a brownie or dinner. let me know, you choose.

btw new post at @cherrygonedigi on instagram in case you wanna go check it (and also at @chrrycolaaaaaa on tiktok)

[wayfire-official]

I'm tempted to host my own gitlab server, but I wouldn't want to have to deal with people needing an account on it. I wish there was like a federated gitlab thing so people can host their own and use one account to access them all

[elgarnaouicom]

Git commands

[gtg3000]

It's the little things

Silly gitlab, dopamine is not optional.

[compsci-haikus]

Merge commit failed. Could've been avoided but I am bad at git.

please let me merge please please please please please please please please please please please please please please

[snowshinobi]

sometimes I just do a little pull so Git will reassure me it's not mad at me. yet

[nubecolectiva]

How to Open a GitLab Repository in Android Studio.

Como Abrir Un Repositorio de GitLab en Android Studio.

👉 https://blog.nubecolectiva.com/como-abrir-un-repositorio-de-gitlab-en-android-studio/

[geekrewindcom]

How to GitLab on Ubuntu 24.04

This article explains how to install GitLab on Ubuntu 24.04. GitLab is a web-based git repository manager with issues tracking, continuous integration and deployment (CI/CD), and a wiki. The Community Edition of GitLab is not just open-source, it’s free and lets you customize it to fit your needs, giving you the freedom to build your DevOps environment as you see fit. If you’re looking for an…

[techjour]

Its 2025, Still waiting to migrate your business in cloud?

Lead your business by leveraging Cloud. Techjour assures you to faster to market, reduce technology cost, scale your business and in-built advanced security.

[techenshittification]

git hosting that doesn't fucking suck

[fernando-arciniega]

Introducción a la Programación Orientada a Objetos

La programación orientada a objetos (POO) es un paradigma de programación que organiza el software en torno a objetos, que son estructuras que combinan datos y comportamiento. En la POO, los objetos representan entidades del mundo real o conceptos abstractos, encapsulando tanto sus propiedades (atributos) como sus acciones (métodos) en una sola unidad. Este enfoque facilita la creación de programas modulares, reutilizables y fáciles de mantener, ya que promueve principios como la herencia (para compartir y extender el comportamiento), el polimorfismo (para tratar objetos de diferentes tipos de manera uniforme) y la encapsulación (para proteger los datos internos del objeto). Unos de los desarrollados según su uso es mas facilitado para un mejor desempeño al momento de un diseño. La programación orientada a objetos (POO) y el diseño están estrechamente relacionados, ya que la POO facilita una estructura de diseño clara y modular en el software. Este paradigma permite dividir un sistema en objetos que representan partes del problema y que interactúan entre sí. Al estructurar el código de esta manera, la POO ayuda a crear aplicaciones más comprensibles y mantenibles, con piezas que pueden diseñarse y desarrollarse de manera independiente.

Con la POO, los principios de diseño orientado a objetos guían la creación de código de calidad. Por ejemplo, los principios SOLID son un conjunto de buenas prácticas que ayudan a organizar y relacionar objetos para que el sistema sea flexible y fácil de modificar. Así, el diseño con POO permite adaptarse a cambios en los requisitos y facilita la reutilización de código, ya que cada clase y objeto se puede modificar o extender sin afectar a otras partes del sistema. De la misma manera los IDE están relacionados ya que dichos programas permiten crear y modificar su diseño, IDE (Integrated Development Environment, o Entorno de Desarrollo Integrado) es una herramienta que facilita el proceso de desarrollo de software al reunir en una sola aplicación varias funciones necesarias para programar. En el contexto de la programación orientada a objetos (POO), un IDE proporciona características que ayudan a los desarrolladores a escribir, depurar, organizar y gestionar el código de forma eficiente.

Algunos de estos IDE o programas (incluyendo lenguajes) mas usados para la orientación a objetos son: Unity: ¿Quieres crear videojuegos, aplicaciones? Descubre cómo la programación orientada a objetos puede simplificar tu trabajo. Con Unity la facilidad es otro nivel ya que este IDE mayormente grafico , tiene una gran variedad de diseños para poder diseñar tus ilustraciones para juegos también de la misma forma vinculaciones de forma grafica para menús interactivos entre otros.

Eclipse: Es un IDE con los que se conforma de C++ ,java y en otros casos uso de Python este IDE se caracteriza por su versatilidad de programación de código abierto, Eclipse IDE es muy popular y versátil, utilizado principalmente para desarrollar software. Es como una herramienta multiusos para programadores, que facilita la escritura, compilación, depuración y ejecución de código:

IntelliJ IDEA: Es otro entorno de desarrollo integrado (IDE) muy popular, al igual que Eclipse. Es conocido por ser una herramienta extremadamente potente y con una interfaz muy intuitiva, especialmente diseñada para mejorar la productividad de los programadores.

Visual Studio: es un entorno de desarrollo integrado (IDE) muy popular y completo, desarrollado por Microsoft. Se utiliza principalmente para crear aplicaciones de escritorio, web y móviles, utilizando una amplia variedad de lenguajes de programación.

PyCharm: Diseñado específicamente para el lenguaje de programación Python. Desarrollado por JetBrains, una empresa reconocida por crear herramientas de desarrollo de alta calidad, PyCharm se ha convertido en uno de los IDE más populares entre los programadores Python a tal grado que algunos los consideran el IDE de Python por excelencia. Su interfaz intuitiva y sus herramientas avanzadas lo hacen ideal tanto para principiantes como para desarrolladores experimentados.

NeatBeans: NeatBeans es un IDE que se puede ejecutar tanto en java tanto en C++ y HTML, un IDE extenso por el cual puedes desarrollar tanto paneles de sesión tanto algunas ventanas de contraseñas o menús, este puede ser usado de una forma menos complicada ya que sus interacciones son mas graficas que lineales .Al igual que PyCharm, ofrece un conjunto de herramientas y características diseñadas para simplificar el proceso de desarrollo de software.

Read the full article

[rainz-holloww]

at the gitlab. straight up. committing "it". by it, I mean. well. let's just say. my markdown files.

[faisalakhtar12]

Building Your Serverless Sandbox: A Detailed Guide to Multi-Environment Deployments (or How I Learned to Stop Worrying and Love the Cloud)

Introduction Welcome, intrepid serverless adventurers! In the wild world of cloud computing, creating a robust, multi-environment deployment pipeline is crucial for maintaining code quality and ensuring smooth transitions from development to production.Here is part 1 and part 2 of this series. Feel free to read them before continuing on. This guide will walk you through the process of setting…

[antongordon]

Continuous Integration and Deployment in AI: Anton R Gordon’s Best Practices with Jenkins and GitLab CI/CD

In the ever-evolving field of artificial intelligence (AI), continuous integration and deployment (CI/CD) pipelines play a crucial role in ensuring that AI models are consistently and efficiently developed, tested, and deployed. Anton R Gordon, an accomplished AI Architect, has honed his expertise in setting up robust CI/CD pipelines tailored specifically for AI projects. His approach leverages tools like Jenkins and GitLab CI/CD to streamline the development process, minimize errors, and accelerate the delivery of AI solutions. This article explores Anton’s best practices for implementing CI/CD pipelines in AI projects.

The Importance of CI/CD in AI Projects

AI projects often involve complex workflows, from data preprocessing and model training to validation and deployment. The integration of CI/CD practices into these workflows ensures that changes in code, data, or models are automatically tested and deployed in a consistent manner. This reduces the risk of errors, speeds up development cycles, and allows for more frequent updates to AI models, keeping them relevant and effective.

Jenkins: A Versatile Tool for CI/CD in AI

Jenkins is an open-source automation server that is widely used for continuous integration. Anton R Gordon’s expertise in Jenkins allows him to automate the various stages of AI development, ensuring that each component of the pipeline functions seamlessly. Here are some of his best practices for using Jenkins in AI projects:

Automating Model Training and Testing

Jenkins can be configured to automatically trigger model training and testing whenever changes are pushed to the repository. Anton sets up Jenkins pipelines that integrate with popular machine learning libraries like TensorFlow and PyTorch, ensuring that models are continuously updated and tested against new data.

Parallel Execution

AI projects often involve computationally intensive tasks. Anton leverages Jenkins' ability to execute tasks in parallel, distributing workloads across multiple machines or nodes. This significantly reduces the time required for model training and validation.

Version Control Integration

Integrating Jenkins with version control systems like Git allows Anton to track changes in code and data. This ensures that all updates are versioned and can be rolled back if necessary, providing a reliable safety net during the development process.

GitLab CI/CD: Streamlining AI Model Deployment

GitLab CI/CD is a powerful tool that integrates directly with GitLab repositories, offering seamless CI/CD capabilities. Anton R Gordon utilizes GitLab CI/CD to automate the deployment of AI models, ensuring that new versions of models are reliably and efficiently deployed to production environments. Here are some of his key practices:

Environment-Specific Deployments

Anton configures GitLab CI/CD pipelines to deploy AI models to different environments (e.g., staging, production) based on the branch or tag of the code. This ensures that models are thoroughly tested in a staging environment before being rolled out to production, reducing the risk of deploying untested or faulty models.

Docker Integration

To ensure consistency across different environments, Anton uses Docker containers within GitLab CI/CD pipelines. By containerizing AI models, he ensures that they run in the same environment, regardless of where they are deployed. This eliminates environment-related issues and streamlines the deployment process.

Automated Monitoring and Alerts

After deployment, it’s crucial to monitor the performance of AI models in real-time. Anton configures GitLab CI/CD pipelines to include automated monitoring tools that track model performance metrics. If the performance drops below a certain threshold, alerts are triggered, allowing for immediate investigation and remediation.

The Synergy of Jenkins and GitLab CI/CD

While Jenkins and GitLab CI/CD can each independently handle CI/CD tasks, Anton R Gordon often combines the strengths of both tools to create a more robust and flexible pipeline. Jenkins’ powerful automation capabilities complement GitLab’s streamlined deployment processes, resulting in a comprehensive CI/CD pipeline that covers the entire lifecycle of AI development and deployment.

Conclusion

Anton R Gordon’s expertise in CI/CD practices, particularly with Jenkins and GitLab CI/CD, has significantly advanced the efficiency and reliability of AI projects. By automating the integration, testing, and deployment of AI models, Anton ensures that these models are continuously refined and updated, keeping pace with the rapidly changing demands of the industry. His best practices serve as a blueprint for AI teams looking to implement or enhance their CI/CD pipelines, ultimately driving more successful AI deployments.