This guide walks new users through the fundamentals of GitHub Actions, the CI/CD and automation platform built into GitHub. It explains what Actions are, how workflows are defined with YAML, and the key concepts of events, runners, jobs, and steps. The article provides a hands‑on example of creating a simple workflow that automatically labels new issues, covering the necessary permissions, environment variables, and use of prebuilt Marketplace actions. Readers learn how to test, debug, and manage their workflows from the Actions tab, and are encouraged to explore further capabilities such as building, testing, and deploying code.
Think of Continuous AI as background agents that operate in your repository for tasks that require reasoning.
>Check whether documented behavior matches implementation, explain any mismatches, and propose a concrete fix.”
> “Generate a weekly report summarizing project activity, emerging bug trends, and areas of increased churn.”
>“Flag performance regressions in critical paths.”
>“Detect semantic regressions in user flows.”
>Check whether documented behavior matches implementation, explain any mismatches, and propose a concrete fix.”
> “Generate a weekly report summarizing project activity, emerging bug trends, and areas of increased churn.”
>“Flag performance regressions in critical paths.”
>“Detect semantic regressions in user flows.”
A look at how GitHub rebuilt GitHub Actions’ core architecture and shipped upgrades to improve performance, workflow flexibility, reliability, and developer experience.
Tap these Model Context Protocol servers to supercharge your AI-assisted coding tools with powerful devops automation capabilities.
* **GitHub MCP Server:** Enables interaction with repositories, issues, pull requests, and CI/CD via GitHub Actions.
* **Notion MCP Server:** Allows AI access to notes and documentation within Notion workspaces.
* **Atlassian Remote MCP Server:** Connects AI tools with Jira and Confluence for project management and collaboration. (Currently in beta)
* **Argo CD MCP Server:** Facilitates interaction with Argo CD for GitOps workflows.
* **Grafana MCP Server:** Provides access to observability data from Grafana dashboards.
* **Terraform MCP Server:** Enables AI-driven Terraform configuration generation and management. (Local use only currently)
* **GitLab MCP Server:** Allows AI to gather project information and perform operations within GitLab. (Currently in beta, Premium/Ultimate customers only)
* **Snyk MCP Server:** Integrates security scanning into AI-assisted DevOps workflows.
* **AWS MCP Servers:** A range of servers for interacting with various AWS services.
* **Pulumi MCP Server:** Enables AI interaction with Pulumi organizations and infrastructure.
* **GitHub MCP Server:** Enables interaction with repositories, issues, pull requests, and CI/CD via GitHub Actions.
* **Notion MCP Server:** Allows AI access to notes and documentation within Notion workspaces.
* **Atlassian Remote MCP Server:** Connects AI tools with Jira and Confluence for project management and collaboration. (Currently in beta)
* **Argo CD MCP Server:** Facilitates interaction with Argo CD for GitOps workflows.
* **Grafana MCP Server:** Provides access to observability data from Grafana dashboards.
* **Terraform MCP Server:** Enables AI-driven Terraform configuration generation and management. (Local use only currently)
* **GitLab MCP Server:** Allows AI to gather project information and perform operations within GitLab. (Currently in beta, Premium/Ultimate customers only)
* **Snyk MCP Server:** Integrates security scanning into AI-assisted DevOps workflows.
* **AWS MCP Servers:** A range of servers for interacting with various AWS services.
* **Pulumi MCP Server:** Enables AI interaction with Pulumi organizations and infrastructure.
Grafana and GitLab have released a new open-source solution that links GitLab CI/CD events into Grafana's observability stack via a serverless architecture, enabling real-time visibility and correlation between deploy events and performance metrics.
This article provides a cheatsheet on the Infrastructure as Code (IaC) landscape, highlighting the benefits of scalable infrastructure provisioning in terms of availability, scalability, repeatability, and cost-effectiveness. It discusses strategies such as containerization, container orchestration, and tools like Terraform, Kubernetes, and Ansible. The article also introduces GitOps as a method for automating infrastructure updates through Git workflows and CI/CD.
Find and experiment with AI models for free, then switch to a paid Azure account when you're ready to bring your application to production.
- GitHub Models allows users to find and experiment with AI models for free.
- To find AI models, users can go to GitHub Marketplace and click on Models in the sidebar.
- The playground, available in the GitHub Marketplace, allows users to adjust model parameters and submit prompts to see the model's response.
- Users can compare two models simultaneously and are rate-limited.
- GitHub provides free API usage for experimenting with AI models in your own application.
- GitHub Models allows users to find and experiment with AI models for free.
- To find AI models, users can go to GitHub Marketplace and click on Models in the sidebar.
- The playground, available in the GitHub Marketplace, allows users to adjust model parameters and submit prompts to see the model's response.
- Users can compare two models simultaneously and are rate-limited.
- GitHub provides free API usage for experimenting with AI models in your own application.
GitHub Models now allows developers to retrieve structured JSON responses from models directly in the UI, improving integration with applications and workflows. Supported models include OpenAI (except for o1-mini and o1-preview) and Mistral models.
Kit is a free, open-source MLOps tool that simplifies AI project management by packaging models, datasets, code, and configurations into a standardized, versioned, and tamper-proof ModelKit. It enables collaboration, model traceability, and reproducibility, making it easier to hand off AI projects between data scientists, developers, and DevOps teams.
Explores KitOps, an open source project that bridges the gap between DevOps and machine learning pipelines by allowing you to leverage existing DevOps pipelines for MLOps tasks.
ModelKits are standardized packages that contain all the necessary components of an ML project, including the model, datasets, code, and configuration files.
ModelKits are defined using a YAML file called a Kitfile, which can be integrated seamlessly with existing DevOps pipelines, much like a Dockerfile for containerization.
ModelKits are standardized packages that contain all the necessary components of an ML project, including the model, datasets, code, and configuration files.
ModelKits are defined using a YAML file called a Kitfile, which can be integrated seamlessly with existing DevOps pipelines, much like a Dockerfile for containerization.
