All new rich text editor experience
GitLab 16.2 features an all-new rich text editing experience! This new capability is available for everyone, as an alternative to the existing Markdown editing experience.
For many, using the plain text editor for comments or descriptions is a barrier to collaboration. Remembering the syntax for image references or working with long tables can be tedious even for those who are relatively experienced with the syntax. The rich text editor aims to break down these barriers by providing a “what you see is what you get” editing experience and an extensible foundation on which we can build custom editing interfaces for things like diagrams, content embeds, media management, and more.
The rich text editor is now available in all issues, epics and merge requests. We plan to make it available in more places across GitLab soon. You can follow our progress here.
We are proud of the new editing experience and can’t wait to see what you think. Please try the new rich text editor and let us know about your experience in this issue.
GitLab triggers a Flux synchronization without any configuration
By default, Flux synchronizes Kubernetes manifests at regular intervals. Triggering a reconciliation immediately when a manifest changes by default requires additional configuration. With the GitLab agent for Kubernetes, you can push a change to your manifest and trigger a Flux sync automatically.
Support for Keyless Signing with Cosign
Properly storing, rotating, and managing signing keys can be difficult and typically requires the overhead of managing a separate Key Management System (KMS). GitLab now supports keyless signing through a native integration with the Sigstore Cosign tool which allows for easy, convenient, and secure signing within the GitLab CI/CD pipeline. Signing is done using a very short-lived signing key. The key is generated through a token obtained from the GitLab server using the OIDC identity of the user who ran the pipeline. This token includes unique claims that certify the token was generated by a CI/CD pipeline.
To begin using keyless signing for your build artifacts, container images, and packages, users only need to add a few lines to their CI/CD file as shown in our documentation.
Command palette
- Available in: Free, Premium, Ultimate
- Offerings: GitLab.com
- Links: Documentation
If you’re a power user, using the keyboard to navigate and take action can be frustrating. Now, a new command palette helps you use the keyboard to get more done.
To enable the command palette, open the left sidebar and click Search GitLab (๐) or use the / key.
Type one of the special characters:
- > - Create a new object or find a menu item
- @ - Search for a user
- : - Search for a project
- / - Search for project files in the default repository branch
GitLab Duo Code Suggestions improvements powered by Google AI
Code Suggestions now use Google Cloud’s customizable foundation models and open generative AI infrastructure, with generative AI support in Vertex AI.
GitLab Code Suggestions are routed through Google Vertex AI Codey API’s Data Governance and Responsible AI. As of July 22, Code Suggestions inferences against the currently opened file and has a context window of 2,048 tokens and 8,192 character limits. This limit includes content before and after the cursor, the file name, and the extension type. Learn more about Google Vertex AI code-gecko.
The Google Vertex AI Codey APIs directly support: C++, C#, Go, Google SQL, Java, JavaScript, Kotlin, PHP, Python, Ruby, Rust, Scala, Swift, TypeScript. And for infrastructure files, support: Google Cloud CLI, Kubernetes Resource Model (KRM), and Terraform.
We are continuously iterating to improve Code Suggestions. Give it a try and share your feedback with us.
Track your machine learning model experiments
When data scientists create machine learning (ML) models, they often experiment with different parameters, configurations, and feature engineering, so they can improve the performance of the model. The data scientists need to keep track of all of this metadata and the associated artifacts, so they can later replicate the experiment. This work is not trivial, and existing solutions require complex setup.
With machine learning model experiments, data scientists can log parameters, metrics, and artifacts directly into GitLab, giving easy access to their most performant models. This feature is an experiment.
New customization layer for the Value Streams Dashboard
We added a new configuration file to the Value Streams Dashboard for easier customization of the dashboard’s data and appearance. In this file you can define various settings and parameters, such as title, description, and number of panels and filters. The file is schema-driven and managed with version control systems like Git. This enables tracking and maintaining a history of configuration changes, reverting to previous versions if necessary, and collaborating effectively with team members.
The new configuration also includes the option to filter the metrics by labels. You can adjust the metrics comparison panel based on your areas of interest, filter out irrelevant information, and focus on the data that is most relevant to your analysis or decision-making process.