AI-Assisted Features

7/31/2026

This post introduces a governance framework for agentic AI in software development, addressing challenges related to code attribution, traceability to intent, and documentation scalability. It details controls for MCPs, agents, model access, and tool permissions, emphasizing composite identity, tool approval guardrails, and prompt guardrails. The post also discusses data privacy and self-hosted AI options, and defines key decision points for human-in-the-loop review, including merge request approval policies and scanner enforcement. Finally, it outlines five metrics for measuring AI rollout: adoption, acceptance and quality, risk, remediation, and ROI.

7/16/2026

This post details a Forrester Consulting Total Economic Impact™ study on the GitLab Duo Agent Platform, highlighting its ability to deliver a 400% ROI and $7.5 million in net present value over three years. It quantifies benefits such as 80% faster onboarding for new developers, a 75% reduction in migration timelines (from eight months to two), 40% time savings for security and QA engineers, and a 20% increase in developer time for feature work. The study emphasizes how agentic coding, when integrated into the full software lifecycle, compounds returns by accelerating individual output and improving overall shipping speed for secure software.

1/14/2026

Introduced the AI Catalog as a central repository for discovering, creating, and sharing AI agents and flows. Detailed how to access the catalog, the types of items available (agents, flows, MCP servers), and the process for browsing, enabling, creating, and sharing these items. Explained visibility options (Private and Public) and provided best practices for naming, documentation, and quality. Described the versioning mechanism for custom agents and flows, including automatic version creation, semantic versioning, immutability, version pinning behavior, and how to update to the latest versions.

1/14/2026

Introduced the GitLab Duo Agent Platform as a new AI orchestration layer. Detailed its architecture, components (agents, flows, chat interfaces), and how teams interact with it. Explained the difference between agents and flows, and the types of agents (foundational, custom, external) and flows (foundational, custom). Highlighted the platform's ability to provide full SDLC context, enable multi-agent collaboration, and enforce enterprise governance and security.

9/29/2025

This post announces the integration of Claude Sonnet 4.5 into GitLab Duo, providing users with an additional advanced AI model option for coding and agentic tasks. It highlights the model's performance on SWE-bench Verified and its strengths in specific domains. The post also details how the GitLab Duo Agent Platform orchestrates agents and connects them to internal systems, enhancing the development workflow with platform-wide context and security.

9/16/2025

This post details the practical application of the GitLab Duo Agent Platform for automating the generation of dbt models. It outlines the process of extracting metadata from Snowflake, using the VS Code plugin for GitLab Duo, and crafting a prompt to generate dbt models for the Reddit Ads dataset. The contribution includes sample generated SQL for source and workspace models, schema.yml, and sources.yml, as well as a validation query for comparing row counts between raw and workspace layers. It also highlights the integration with the GitLab dbt style guide and the potential for future direct integration with data platforms via Model Context Protocol (MCP).

8/11/2025

This post introduces the integration of GitLab Duo Agentic Chat directly into the GitLab UI. It details how users can toggle to 'Agentic mode' to enable the AI to interact with the GitLab API and perform actions. Specific capabilities highlighted include issue management (searching, summarizing, updating labels/milestones) and CI/CD pipeline debugging (analyzing failures, suggesting fixes, and committing them to merge requests). The post also provides guidance on crafting effective prompts for complex tasks like bug triage and offers tips for using action-oriented verbs and clear criteria for bulk operations.

7/7/2025

This post details the secure deployment of AI agents to Google Cloud using GitLab CI/CD components and Workload Identity Federation. It introduces a practical example of an AI agent for Canadian city recommendations, outlining the setup of IAM integration with Workload Identity Federation and Google Artifact Registry. The post provides a `.gitlab-ci.yml` configuration that leverages GitLab CI/CD components for building Docker containers, performing security scans (dependency, SAST, secret detection), uploading to Artifact Registry, and deploying to Cloud Run. It highlights security benefits such as no long-lived credentials, automated security scanning, an audit trail, and the principle of least privilege.

7/3/2025

This post introduces the capability of GitLab Duo with Amazon Q to automatically generate unit tests for Java code within merge requests. It details the user interaction via the `/q test` command in issue comments, the AI's analysis process, and the benefits of reduced test writing time, comprehensive coverage, and improved quality.

6/10/2025

This post details the integration of GitLab Duo AI essentials (Code Suggestions and Chat) into GitLab Premium for higher education institutions. It highlights how these AI capabilities enhance collaboration, accelerate development, and improve security by providing AI-powered code completion and conversational assistance within the GitLab platform. The post also emphasizes the transparency and data protection measures associated with GitLab's AI features.

6/10/2025

This post introduces the integration of GitLab Duo with Amazon Q to automatically implement code review feedback. It details the user workflow, including the `/q dev` quick action, and explains how the AI analyzes comments and modifies code. The post highlights the benefits of reduced manual effort, accelerated review cycles, and faster feature delivery.

5/15/2025

This post announces the inclusion of GitLab Duo Code Suggestions and Chat in GitLab Premium and Ultimate tiers at no additional cost, starting with GitLab 18.0. It details the capabilities of Duo Code Suggestions (generating functions, code completions, multi-language support) and Duo Chat (explaining code, refactoring, generating tests, fixing issues). It also highlights the availability of Duo Enterprise features for Premium customers, including Root Cause Analysis, Code Review, Advanced Chat, and Self-Hosted options. The post provides instructions for existing customers to enable these features and emphasizes the platform's role in an AI-native development landscape.

4/28/2025

Introduces GitLab Duo with Amazon Q, an agentic AI feature that generates code from issue descriptions. Developers can create an issue, use a quick action '/q dev' in a comment to invoke Amazon Q, which then analyzes the issue and code context to autonomously generate code and commit it as a merge request. This significantly reduces the time from idea to working code.

4/10/2025

This post details the practical application of GitLab Duo Workflow for automated unit test generation in a Java project. It demonstrates how to use the VS Code extension to prompt the AI for test creation, review the generated plan, and approve its execution. It also covers a subsequent workflow to fix import errors in the generated test file by updating the project's pom.xml to include the correct JUnit dependency.

2/24/2025

This post announces the private beta waitlist for GitLab Duo Workflow, an agentic AI feature built on the GitLab DevSecOps platform. It details the promise and challenges of AI agents in software development, highlighting the need for platform integration over add-on tools. The post outlines specific use cases for GitLab Duo Workflow, including automating project bootstrapping, modernizing legacy code, resolving tasks with full codebase context, generating and updating documentation, and enhancing test coverage. It emphasizes that the agents will leverage GitLab's unified data model for context and control.

1/29/2025

Introduces the concept of 'composite identities' for AI agents, linking them to human user identities to enhance security and auditability. Details the design of composite identities, including how they are represented, authenticated, and authorized. Explains the redesign of the authorization framework to support multiple principals (AI agent and human user) for resource access. Mentions the enhancement of job tokens to signed JWTs and contributions to open-source libraries. Highlights the initial implementation of composite identities for service accounts with GitLab Duo with Amazon Q.

1/16/2025

This post announces the availability of GitLab Duo with Amazon Q in Preview (Beta) for GitLab Self-Managed Ultimate users. It details three key AI-powered capabilities: feature development (`/q dev`), code reviews (`/q review`), and Java modernization (`/q transform`). It also outlines the prerequisites and setup steps for enabling this integration within a self-managed instance hosted on AWS, including IAM configuration and adding the Amazon Q user to projects.

12/17/2024

This post details a validation test for GitLab Duo's impact on automated testing. The experiment involved removing all existing tests (reducing coverage to 0%) and then using GitLab Duo to regenerate tests. The team measured test coverage and generation time. The results showed an increase in test coverage from 74% to 84% in approximately two days by one engineer, compared to the previous four weeks for multiple engineers. Coverage has since increased to 89%. The post highlights the efficiency gains and the ability for developers with less context to contribute effectively to testing.

12/11/2024

This post details the process of modernizing a C++ application to Java using GitLab Duo's refactoring capabilities. It showcases the use of `/explain` for code understanding and `/refactor` for code transformation, including the generation of a `pom.xml` and directory structure for the Java application. The post also demonstrates how to build and run the refactored Java application using Maven.

12/10/2024

This post details practical lessons learned from using GitLab Duo for complex test generation. It covers strategies for updating existing test cases (e.g., refactoring to use unittest), creating tests for legacy code by providing error snippets, handling complex or abstracted code by providing examples and requesting modifications one at a time, ensuring generated code adheres to standards through specific refactoring prompts, and leveraging AI to uncover and address new test cases, thereby increasing test coverage. Key takeaways highlight the importance of prompt engineering, understanding underlying code and frameworks, defining desired end states and standards, and acknowledging that AI-generated tests augment, but do not replace, traditional testing methodologies.

12/5/2024

This post details how GitLab Duo's AI capabilities are being applied to streamline DevSecOps engineering workflows. Specific features highlighted include: Merge Request Summary, Discussion Summary, Code Review Summary, Code Explanation, Code Suggestions (for IaC and configuration management), GitLab Duo Chat for troubleshooting and generation, automated test generation, CI/CD pipeline Root Cause Analysis, and Vulnerability Explanation/Resolution. The post emphasizes how these features reduce time spent on documentation, configuration, debugging, and vulnerability management, thereby increasing engineer efficiency.

12/3/2024

This post announces the integration of GitLab Duo with Amazon Q, creating a joint offering that brings AI agents directly into GitLab's DevSecOps platform. Key new capabilities include: - Invoking AI agents via quick actions like `/q dev` to convert issue descriptions into merge-ready code. - Automating Java modernization with `/q transform` to migrate to Java 17. - Accelerating code reviews with `/q review` for instant, intelligent feedback. - Automating unit test generation with `/q test`. - The integration emphasizes enterprise-grade security, guardrails, and granular controls for AI features at user, project, and group levels.

12/2/2024

This post details the practical application of GitLab Duo for automated test generation within the VS Code IDE using the GitLab Workflow extension. It outlines the process of selecting code, invoking the 'Generate tests' command, and reviewing the AI-generated tests. It also provides a pro-tip for organizing generated test files and highlights the efficiency gains in achieving code coverage.

11/22/2024

Introduces GitLab Duo Chat's capability to engage in in-depth conversations about merge requests. It can answer questions regarding failure scenarios, privacy concerns, rationale behind changes, potential risks, and trade-offs within the context of a specific MR. The AI is aware of the MR description, discussions, code diff, and metadata.

10/29/2024

This post introduces GitLab Duo Root Cause Analysis for diagnosing broken CI/CD pipelines by analyzing log files and suggesting fixes. It also highlights GitLab Duo Vulnerability Explanation for understanding security risks, providing vulnerability descriptions, exploit examples, and remediation suggestions. Finally, it details Advanced SAST, a new security scanner using a proprietary detection engine for more accurate vulnerability identification and reduced false positives.

10/17/2024

This post details the use of GitLab Duo Chat to generate project files for a Quarkus-native application. It includes specific prompts and generated code for `HelloResource.java`, `Dockerfile.native`, `pom.xml`, `HelloResourceTest.java`, and `HelloResourceIT.java`, as well as the project directory structure. This demonstrates how AI can accelerate the initial setup and development of a new application.

10/3/2024

This post details the migration of GitLab Duo Chat to Claude 3.5 Sonnet, the introduction of a Slash Command Picker in the GitLab UI, integration of Root Cause Analysis for CI/CD jobs, the ability to fix selected code in the IDE with specific slash commands, and the integration of the 'Explain Vulnerability' feature for SAST results. It also outlines future plans for enhanced contextual awareness (commits, pipeline jobs, merge requests), a `/help` slash command, and improved IDE support.

9/25/2024

This post details how Product Managers can leverage GitLab Duo Chat to enhance productivity and decision-making across various tasks, including issue creation and refinement, summarizing and reviewing complex epics and merge requests, generating project status updates, articulating value propositions and analyzing metrics, and challenging cognitive biases by adopting different personas. The integration aims to increase efficiency, improve the quality of output, enhance decision-making through critical review, and foster continuous improvement in PM skills.

9/17/2024

This post details the technical steps and considerations for setting up an MLOps pipeline using GitLab Model Registry and MLflow. It covers environment variable configuration for MLflow tracking, logging model parameters and artifacts within merge request pipelines, registering the most successful model candidate, and Dockerizing and deploying the ML application using the registered model and GitLab Container Registry. It also highlights the use of CI/CD components for reusability.

9/10/2024

This post shares lessons learned from the AI Engineer World's Fair, emphasizing the shift in the development lifecycle due to AI. Key takeaways include the need for speed, iterative development, robust prompt engineering, and extended evaluation times. It also highlights the importance of customer perspective and the evolving role of AI engineers. Future trends in AI development are also outlined.

9/3/2024

This post announces the general availability of GitLab Duo Enterprise, detailing its comprehensive suite of AI-powered features for the software development lifecycle. It elaborates on specific capabilities like intelligent code assistance (Code Suggestions, Code Explanation, Code Refactoring, Test Generation), team collaboration enhancements (Discussion Summary, Merge Request Summaries, Code Review Summaries), troubleshooting (Root Cause Analysis), security (Vulnerability Explanation, Vulnerability Resolution), and ROI quantification (AI Impact Dashboard). It also highlights customer testimonials and GitLab's recognition as a Leader in the Gartner Magic Quadrant for AI Code Assistants.

8/26/2024

This post introduces AI-powered code refactoring capabilities, allowing developers to modernize codebases by migrating between language versions (e.g., Java 7 to Java 8) and across different C++ standards (e.g., C++03 to C++14, C++23 to C++11), and even refactor between languages (e.g., C to Rust, Perl to Python). It demonstrates the use of GitLab Duo Code Suggestions and Chat within IDEs like VS Code and JetBrains for generating, explaining, and refactoring code, leveraging advanced LLMs like Anthropic Claude 3.5.

8/22/2024

This post announces GitLab's recognition as a Leader in the Gartner Magic Quadrant for AI Code Assistants. It details the breadth of GitLab Duo's AI capabilities, including Code Suggestions, Chat, Vulnerability Explanation/Resolution, Root Cause Analysis, and the AI Impact Analytics Dashboard. It also highlights upcoming features like model personalization and self-hosted model deployment, emphasizing flexibility, privacy, and transparency in AI integration within the DevSecOps lifecycle.

8/9/2024

This post introduces GitLab Duo Chat as a tool for GitLab administrators, focusing on its ability to streamline complex administrative tasks. It provides a case study on updating group memberships across a large number of groups using the Rails console and a Ruby script, demonstrating how Chat can assist in crafting and refining such scripts. The post also highlights Chat's capabilities in interpreting Git errors and optimizing Gitaly configurations, along with best practices for formulating queries and using its suggestions incrementally. It showcases specific examples of how Chat can help with user management at scale and CI/CD performance.

7/24/2024

This post details the integration of GitLab Duo Chat and Code Suggestions within GitLab Workspaces and the VS Code extension. It explains how these AI features can be used out-of-the-box in a remote development environment, providing code snippet suggestions, concise summaries of technical questions, and automated unit test generation. It also highlights the benefits of using GitLab Workspaces for isolated and reproducible development setups.

7/10/2024

This post details several enhancements to GitLab Duo Chat: 1. Integration of GitLab Duo Vulnerability Explanation, enabling users to gain deeper understanding of vulnerabilities. 2. Improved context awareness in the GitLab UI (Epics, Issues, Code Files) and IDEs (Selected Code, Epics/Issues via URL). 3. Chat history retention of the last 50 messages for three days. 4. Rollout of prompt cancellation on GitLab.com, with plans for self-managed instances. 5. Architectural improvements by moving to Language Server Protocol (LSP) for better IDE integration and responsiveness. 6. Future plans include migrating to Claude 3.5 Sonnet, enabling custom self-hosted models, synchronizing messages across clients, and migrating the 'Summarize Comments' feature to Chat.

6/27/2024

This post introduces GitLab Duo Workflow, an autonomous AI agent designed to automate and optimize the software development lifecycle. Key capabilities include planning and prioritizing tasks based on work items, generating/rewriting code within GitLab Remote Development workspaces, automatically handling CI pipeline failures, creating merge requests, and performing code reviews. It also focuses on intelligent continuous improvement by analyzing code for architectural optimizations, suggesting refactoring, and dynamically managing cloud resources. Proactive security and compliance are addressed by automatically applying patches, refactoring insecure code, and assessing security risks. The system aims for self-optimizing performance through feedback loops and continuous learning.

6/11/2024

This post details how to effectively use GitLab Duo Code Suggestions by providing practical tips and best practices. It covers prompt engineering, specific use cases such as fixing missing dependencies, generating boilerplate code (logging, utility functions, regex), and re-triggering suggestions. It also discusses the difference between code suggestions and code generation, and how to leverage other GitLab Duo features like Chat in conjunction with Code Suggestions. The post emphasizes verifying code quality and security, and learning as a team.

6/6/2024

Introduces GitLab Duo Root Cause Analysis, an AI-powered feature that analyzes CI/CD job logs to identify the root cause of pipeline failures and suggest fixes. Details the mechanism of forwarding logs to the GitLab AI Gateway for LLM analysis and provides two detailed examples: diagnosing Python dependency errors (ModuleNotFoundError) and missing Go runtimes in containerized environments. Demonstrates how to fix these issues by modifying `.gitlab-ci.yml` and Dockerfiles, and suggests using GitLab Duo Chat for follow-up questions and advanced troubleshooting.

6/3/2024

This series of posts details the development and integration of GitLab Duo, focusing on how AI models are validated and tested at scale, the creation of an AI Impact analytics dashboard to measure ROI, the dogfooding of AI features within GitLab's own SDLC, methods for securing and testing AI-generated code, the use of AI and Root Cause Analysis to fix CI/CD pipelines, recent enhancements to GitLab Duo Chat, and the use of AI to remediate security vulnerabilities.

6/3/2024

This post details the development of the AI continuous analysis tool by the Test Platform team to validate GitLab Duo features. It describes the automation of data collection within VS Code using WebdriverIO and GitLab CI/CD, focusing on measuring suggestion latency and leveraging existing code quality/security analysis. It also outlines performance validation strategies using mocked AI Gateway responses and multi-regional latency tests with the GitLab Environment Toolkit and GitLab Performance Tool. Functional testing approaches, including unit, integration, and end-to-end tests (using real and mock AI responses), are also covered.

5/30/2024

This post details the integration of GitLab Duo with GitLab Pages to secure and test AI-generated code. It provides a step-by-step guide for developers to: 1. Create a new project. 2. Enable GitLab Duo Code Suggestions. 3. Create a Flask web server. 4. Generate unit tests using the `/tests` command in Duo. 5. Identify security vulnerabilities and dependency issues using Duo Chat. 6. Generate test reports with a Python script. 7. Deploy test reports using GitLab Pages via a `.gitlab-ci.yml` configuration. 8. Embed test reports in MR widgets. It also includes a case study demonstrating the integration of SAST scanning to detect OS command injection vulnerabilities in AI-generated code.

5/29/2024

This post introduces GitLab Duo Chat's general availability and details its capabilities for summarizing comment threads in issues and epics. It provides guidance on crafting effective prompts by focusing on context, simplicity, and the use of follow-up questions. Use cases for product managers, developers, and QA engineers are outlined, emphasizing productivity gains in understanding code, planning, and generating tests.

5/20/2024

This post details how GitLab engineers are dogfooding GitLab Duo features, including Code Suggestions and Chat. It provides specific examples of how these AI capabilities are used to streamline workflows such as summarizing merge requests and comment threads, creating documentation and release notes, drafting OKRs, summarizing production incidents, generating boilerplate `.gitlab-ci.yml` files, generating Python scripts, and generating test source code. It also highlights the use of `/explain` for understanding external codebases and testing new features like Markdown support in Code Suggestions. The post emphasizes the benefits of automation, reduced documentation time, higher quality code, and streamlined administrative tasks.

5/15/2024

Introduces the AI Impact analytics dashboard, a new feature available in GitLab 17.0, designed to measure the ROI of AI features within GitLab Duo. The dashboard provides detailed usage metrics for Code Suggestions, correlation observations between AI usage and SDLC performance metrics (Cycle Time, Lead Time, Deployment Frequency, Change Failure Rate, Critical Vulnerabilities), and a comparison view of teams using AI versus those that are not. Future plans include additional visualizations for GitLab Duo Seats, Code Suggestions Acceptance Rate, GitLab Duo Chat usage, and AI statistics in the Contribution analytics report.

5/9/2024

This post details the engineering process for validating and testing AI models at scale for GitLab Duo features. It outlines a four-step process: creating a prompt library as a proxy for production, baselining model performance using metrics like Cosine Similarity Score and LLM Judge, iterative feature development with daily re-validation, and a cycle of experimentation using smaller, focused datasets before broader validation. The post emphasizes the importance of data-driven insights for improving AI feature performance and mitigating risks.

4/18/2024

This post announces the general availability of GitLab Duo Chat, which integrates several AI capabilities: code explanation, code refactoring, and test generation. It highlights how Chat can be used for onboarding, accelerating development by explaining code and suggesting refactoring, and staying secure by generating unit tests and assisting with security scanner configuration. The post also reiterates GitLab's commitment to AI privacy and introduces Duo Pro pricing. Future plans include model personalization and self-hosted model deployment.

4/2/2024

This post details 10 best practices for using GitLab Duo Chat to improve prompt engineering and integrate it into DevSecOps workflows. It covers conversational prompting, refining prompts with more context, following prompt patterns, using low-context communication, repeating and refining prompts, and leveraging slash commands in IDEs. It also provides examples of how to interact with the chat feature for code generation, configuration, and explanations.

2/29/2024

This post details the development of a custom scanner for detecting secrets in video content, leveraging Google Cloud's Video Intelligence API for OCR and implementing approximate regular expression matching to account for OCR inaccuracies. It also highlights the use of GitLab Duo Chat to assist in writing a C++ Node.js addon for integrating the TRE library for approximate regex matching.

2/20/2024

This post introduces the concept of 'AI Impact' as a new dashboard within GitLab's Value Stream Analytics. It details the challenges of measuring AI effectiveness, particularly for developer productivity tools, by critiquing metrics like lines of code and acceptance rates. It advocates for focusing on business outcomes (lead time, cycle time, production defects, user satisfaction) and proposes combining quantitative SDLC data with qualitative developer feedback. The post outlines the planned approach for the AI Impact dashboard, which will blend Value Stream Analytics, DORA metrics, and GitLab Duo usage data to provide a comprehensive view of AI's influence on the SDLC.

2/14/2024

This post emphasizes the increasing importance of AI-assisted tools in DevSecOps, citing a report that highlights the competitive disadvantage for enterprises not adopting these technologies. It details how AI-powered tools can improve developer productivity, enhance security by reducing testing bottlenecks, and streamline workflows. The post specifically mentions GitLab Duo as a product suitable for enterprise-grade application development, noting its integration throughout the SDLC, its privacy-first approach (not using client code for model training), and its model-agnostic gateway. It also highlights Duo Code Suggestions and Chat as features that improve collaboration and security, with "security guardrails consistently applied throughout."

1/30/2024

This post details Southwest Airlines' adoption of GitLab's AI features to enhance developer productivity. Specifically, it highlights the use of AI for vulnerability explanation, code suggestions, and code completion. It also mentions the anticipated value of 'Suggested Reviewers' for code reviews in a remote setting, aiming to reduce manual steps and wait times.

1/24/2024

This post details the setup and usage of GitLab Duo Code Suggestions specifically for accelerating the creation of Terraform infrastructure as code (IaC) plans. It outlines the steps to install the IDE extension, authenticate, enable the feature, and provides an example of generating a Terraform plan for Google Cloud Platform. It highlights how Code Suggestions can maintain context and suggest relevant resources, reducing the need to consult documentation extensively.

1/16/2024

This post details the integration of Anthropic's Claude generative AI model into GitLab Duo Code Suggestions for AI-assisted code generation. It explains how developers can use comments to generate code blocks, providing examples of JavaScript and Python code generation. The post highlights the safety and trustworthiness aspects of Claude, emphasizing its ability to mitigate unsafe behaviors and provide accurate code generation. It also mentions the general availability of Code Suggestions and encourages users to try the free trial.

12/22/2023

This post announces the general availability of GitLab Duo Code Suggestions with GitLab 16.7. It details the capabilities of code generation and code completion, lists supported languages and IDEs, and highlights the benefits of reduced context switching and improved developer efficiency. It also mentions other GitLab Duo AI features and reiterates GitLab's commitment to data privacy.

10/31/2023

This post details the expansion of GitLab Duo capabilities across the entire SDLC, including Vulnerability Explanation (Beta) for summarizing and suggesting mitigations for detected vulnerabilities, Code Suggestions (Beta) for faster code creation and updates, and Suggested Reviewers (GA) for informed reviewer selection. It also highlights the extension of GitLab Duo to self-hosted instances, starting with Code Suggestions.

10/12/2023

This post details the practical application of GitLab Duo Code Suggestions in learning and building a Rust feed reader. It covers initializing a Rust project, defining RSS feed URLs using HashMaps, structuring code with modules and functions, and leveraging AI for code generation. It also touches upon parsing XML feeds, handling program arguments, asynchronous execution, multithreading, and performance benchmarking, including CI/CD integration with Rust caching. The post highlights how AI can assist in complex programming tasks and accelerate the development lifecycle.

10/5/2023

This post details how GitLab's Data Science team uses GitLab CI/CD to build and store standardized data science container images in the GitLab Container Registry, enhancing experiment reproducibility. It also explains how GPU-enabled GitLab Runners are used for faster ML model training, triggered by commit messages, and introduces the use of GitLab's ML experiment tracker for storing pipeline metadata and artifacts.

9/7/2023

This post details the integration of several AI-powered features within the GitLab DevSecOps platform to address modern software development challenges. It showcases how AI can be applied to: 1. Summarize lengthy GitLab issues to improve problem definition and collaboration. 2. Provide code suggestions within IDEs to accelerate solution development. 3. Suggest relevant code reviewers based on contribution graph data to streamline code reviews. 4. Generate test cases to improve code quality and identify edge cases. 5. Offer GitLab Duo Chat for natural language interaction and minimizing context switching. The post also illustrates how these AI touchpoints integrate with GitLab CI/CD pipelines for automated build, test, and deployment.

8/24/2023

This post demonstrates the practical application of GitLab Duo Code Suggestions as a learning tool for C++ development. It walks through setting up a development environment (VS Code, Clang), writing basic C++ code (Hello World, variables, input/output), and introduces concepts like conditionals and data structures (strings, ints) within the context of building a text-based adventure game. The core contribution is showcasing how AI-assisted coding can facilitate hands-on learning and accelerate the development of a concrete application.

8/3/2023

This post details the author's experience using GitLab Duo Code Suggestions to build a Python guessing game application. It highlights the ease of use of the Web IDE and the AI's ability to generate relevant code based on simple English comments. The post also touches upon the use of GitLab Remote Development workspaces to make the application accessible on an iPad.

7/6/2023

Introduces GPU-enabled runners on GitLab.com SaaS for ModelOps and HPC workloads. Details how to use them by tagging jobs and selecting appropriate Docker images with NVIDIA CUDA drivers. Provides examples for basic model training and scientific simulations, as well as an advanced example of building a custom Docker image for model training, including steps for building, testing, and publishing the model. Also mentions using GPU runners for ML-powered GitLab issue triage.

6/8/2023

This post details a tutorial on how to use GitLab and Google Cloud's Vertex AI to deploy an AI model for credit card fraud detection. It outlines the solution architecture, including data preprocessing with SMOTE, model training with RandomForestClassifier, and model deployment to Vertex AI via GitLab CI/CD. The post provides a step-by-step walkthrough covering GitLab and Google Cloud connection, data upload to Vertex AI, CI/CD pipeline creation (test, train, deploy stages), model training script, model deployment script, and prediction testing. It highlights the use of GitLab's DevSecOps platform to orchestrate ModelOps workloads.

5/18/2023

Introduced a rapid prototype of an AI action that summarizes merge request review comments to help authors quickly understand feedback and speed up the review cycle. This is an experiment and part of ongoing iteration on AI/ML features within the DevSecOps platform.

5/3/2023

This post details GitLab's comprehensive AI strategy and introduces a suite of AI-assisted features being integrated into the DevSecOps platform. It highlights a generative AI partnership with Google Cloud, emphasizing a privacy-first approach. The post outlines specific AI capabilities for developer teams (Code Suggestions, Suggested Reviewers, Summarize MR Changes, Summarize My MR Review), security and operations teams (Explain This Vulnerability, Generate Tests in MRs, Explain This Code), and for all users (Issue Comment Summaries, GitLab Chat, Value Stream Forecasting). It marks a significant expansion into AI-driven product development.

4/27/2023

This post details an experiment where AI was used to generate suggested test coverage for code changes directly within a merge request. A new option was added to merge request files to provide suggested tests in a sidebar. The team is iterating on how to bring features like this to customers, exploring automatic detection of missing tests and automated review of proposed tests.

4/20/2023

Introduced an experimental `/summarize_diff` quick action within merge requests that leverages AI/LLMs to generate a summary of the proposed code changes. This is a rapid prototype developed by a Staff Backend Engineer in the Code Review Group.

1/4/2022

This post announces the acquisition of UnReview and its integration into GitLab as a new AI-assisted feature for suggested reviewers. It introduces the concept of a new 'ModelOps' stage focused on data science workloads and the end-to-end lifecycle of AI models. The UnReview algorithm's functionality is described as leveraging contribution history to recommend reviewers. A customer beta is announced for early 2022.

9/5/2018

This post details the development of Telesphora, a human-centered data science platform that uses machine learning and predictive analytics to combat the opioid epidemic. The platform predicts overdose outbreaks by analyzing spatiotemporal data and network analysis of supply movements, enabling better resource allocation for first responders. The core technical contribution is the spatiotemporal Poisson process model for projecting future opioid overdose trends.