#cmake #bootstrap #c #ci #clang_format #codecov #continuous_integration #coverage #cpp #dependency_manager #free #github_workflows #modern_cmake #modern_cmake_template #open_source #starter #template

https://github.com/TheLartians/ModernCppStarter

#go #argo #argo_cd #cd #ci_cd #cicd #continuous_delivery #continuous_deployment #devops #docker #gitops #helm #ksonnet #kubernetes #pipeline

https://github.com/argoproj/argo-cd

#shell #actions #ci #linter #quality_check

https://github.com/github/super-linter

#go #docker #kubernetes #golang #devops #microservice #containers #ci_cd #apm #cmp

https://github.com/erda-project/erda

#go #github #lint #cli #gitlab #static_code_analysis #ci #linter #static_analysis #bitbucket #code_review #code_quality #codereview

https://github.com/reviewdog/reviewdog

#other #ai #ansible #bash #cheat_sheet #cheat_sheets #cheatsheet #ci_cd #devops_tools #docker #jenkins #linux #nginx #pdf #powershell #python #redis #regex #regular_expression #slack #vim

https://github.com/sk3pp3r/cheat-sheet-pdf

#other #automation #awesome #awesome_list #aws #azure #ci_cd #cloud #containers #cybersecurity #devops #devsecops #docker #gcp #k8s #kubernetes #security #serverless #ssdlc #tool

https://github.com/sottlmarek/DevSecOps

#go #ci #ci_cd #cicd #database #database_migration #database_migrations #database_schema #database_schema_evolutions #database_schema_migration #pgsql #postgres #postgresql #sql

https://github.com/postgres-ai/database-lab-engine

#other #ci_cd #cloud #cloud_computing #cloud_native #devops #docker #infrastructure #interview_preparation #k3s #kubernetes #linux #monitoring #networking #testing #tools

https://github.com/kunal-kushwaha/DevOps-Bootcamp

#shell #actions #change_detection #change_directory #changed #changed_file #changedetection #ci #ci_cd #continuous_integration #github #github_action #github_actions #github_actions_ci #modified #modified_files #monorepo #regex_match #request_review #submodules #workflows

https://github.com/tj-actions/changed-files

#go #argocd #bash_script #ci_cd #devops #docker #git #github_actions #gitops #grafana #helm #jenkins #kubernetes #linux #networking #portainer #prometheus #yaml

https://github.com/Pradumnasaraf/DevOps

#typescript #auth #boilerplate #ci #crud #database #docker #e2e #i18n #nestjs #nestjs_boilerplate #nodejs #seed #sign #swagger #typeorm

https://github.com/brocoders/nestjs-boilerplate

#typescript #change_detection #ci_cd #conditional_execution #github_actions

https://github.com/dorny/paths-filter

#swift #apple_silicon #automation #ci #macos #tart #virtualization #virtualization_framework

https://github.com/cirruslabs/tart

#typescript #cd #ci #git #gitlab #gitlab_ci #local #pipeline #push #uncomitted #untracked

You can run GitLab CI pipelines locally using `gitlab-ci-local`, which saves you time and effort by not having to push changes to test your `.gitlab-ci.yml` files. This tool allows you to execute pipelines as a shell executor or docker executor, eliminating the need for development-specific scripts. It also offers convenience features like CLI options, environment files, bash aliases, and tab completion. You can list pipeline jobs before running them and customize variables and artifacts easily. This makes your development process more efficient and streamlined.

https://github.com/firecow/gitlab-ci-local

#go #ci #devops #github_actions #golang

Using `act` allows you to run your GitHub Actions locally, which is very useful. Here’s why: You get fast feedback because you don’t have to commit and push your changes every time you want to test them. Instead, you can test your workflow files locally, saving time. Additionally, `act` can replace your `Makefile` by using the GitHub Actions defined in your project, so you don’t have to repeat tasks. This makes your development process faster and more efficient.

https://github.com/nektos/act

#go #agents #ai #caching #ci_cd #containers #continuous_deployment #continuous_integration #dag #dagger #devops #docker #graphql #workflows

Dagger is a tool that helps make workflows easier to manage. It lets you break down complex tasks into smaller, reusable parts that can run in different environments. This means you can work with many languages and tools without worrying about compatibility issues. Dagger also helps you see what's happening in your workflows, making it easier to fix problems. It saves time and money by caching results, so you don't have to redo tasks. Overall, Dagger makes it simpler to build and manage workflows, especially for AI and software development tasks.

https://github.com/dagger/dagger