[{"data":1,"prerenderedAt":793},["ShallowReactive",2],{"/en-us/blog/learning-rust-with-a-little-help-from-ai-code-suggestions-getting-started":3,"navigation-en-us":41,"banner-en-us":441,"footer-en-us":451,"blog-post-authors-en-us-Michael Friedrich":691,"blog-related-posts-en-us-learning-rust-with-a-little-help-from-ai-code-suggestions-getting-started":705,"assessment-promotions-en-us":745,"next-steps-en-us":783},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":28,"isFeatured":12,"meta":29,"navigation":30,"path":31,"publishedDate":20,"seo":32,"stem":36,"tagSlugs":37,"__hash__":40},"blogPosts/en-us/blog/learning-rust-with-a-little-help-from-ai-code-suggestions-getting-started.yml","Learning Rust With A Little Help From Ai Code Suggestions Getting Started",[7],"michael-friedrich",null,"ai-ml",{"slug":11,"featured":12,"template":13},"learning-rust-with-a-little-help-from-ai-code-suggestions-getting-started",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"Learning Rust with a little help from AI","Use this guided tutorial, along with GitLab Duo Code Suggestions, to learn a new programming language.",[18],"Michael Friedrich","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749663918/Blog/Hero%20Images/aipower.jpg","2023-08-10","Learning a new programming language can help broaden your software development expertise, open career opportunities, or create fun challenges. However, it can be difficult to decide on one specific approach to learning a new language. Artificial intelligence (AI) can help. In this tutorial, you'll learn how to leverage AI-powered GitLab Duo Code Suggestions for a guided experience in learning the Rust programming language.\n\n- [Preparations](#preparations)\n  - [VS Code](#vs-code)\n  - [Code Suggestions](#code-suggestions)\n- [Learning a new programming language: Rust](#learning-a-new-programming-language-rust)\n    - [Development environment for Rust](#development-environment-for-rust)\n    - [Hello, World](#hello-world)\n- [Cargo: Bringing structure into Rust](#cargo-bringing-structure-into-rust)\n- [Automation: Configure CI/CD pipeline for Rust](#automation-configure-cicd-pipeline-for-rust)\n- [Continue learning Rust](#continue-learning-rust)\n    - [Define variables and print them](#define-variables-and-print-them)\n    - [Explore variable types](#explore-variable-types)\n    - [Flow control: Conditions and loops](#flow-control-conditions-and-loops)\n    - [Functions](#functions)\n    - [Testing](#testing)\n- [What is next](#what-is-next)\n    - [Async learning exercises](#async-learning-exercises)\n    - [Share your feedback](#share-your-feedback)\n\n## Preparations\nChoose your [preferred and supported IDE](https://docs.gitlab.com/ee/user/project/repository/code_suggestions.html#enable-code-suggestions-in-other-ides-and-editors), and follow the documentation to enable code suggestions for [GitLab.com SaaS](https://docs.gitlab.com/ee/user/project/repository/code_suggestions.html#enable-code-suggestions-on-gitlab-saas) or [GitLab self-managed instances](https://docs.gitlab.com/ee/user/project/repository/code_suggestions.html#enable-code-suggestions-on-self-managed-gitlab).\n\nProgramming languages can require an install of the language interpreter command-line tools or compilers that generate binaries from source code to build and run the application.\n\nTip: You can also use [GitLab Remote Development workspaces](/blog/quick-start-guide-for-gitlab-workspaces/) to create your own cloud development environments, instead of local development environments. This blog post focuses on using VS Code and the GitLab Web IDE.\n\n### VS Code\nOn macOS, you can [install VS Code](https://code.visualstudio.com/download) as a Homebrew cask or manually download and install it.\n\n```shell\nbrew install --cask visual-studio-code\n```\n\nNavigate to the `Extensions` menu and search for `gitlab workflow`. Install the [GitLab workflow extension for VS Code](https://marketplace.visualstudio.com/items?itemName=GitLab.gitlab-workflow).\n\nTip: VS Code will also detect the programming languages, and offer to install additional plugins for syntax highlighting and development experience.\n\n### Code Suggestions\nIt can help to familiarize yourself with suggestions before actually verifying the suggestions. GitLab Code Suggestions are provided as you type, so you do not need use specific keyboard shortcuts. To accept a code suggestion, press the `tab` key. Also note that writing new code works more reliably than refactoring existing code. AI is non-deterministic, which means that the same suggestion may not be repeated after deleting the code suggestion. While Code Suggestions is in Beta, we are working on improving the accuracy of generated content overall. Please review the [known limitations](https://docs.gitlab.com/ee/user/project/repository/code_suggestions.html#known-limitations), as this could affect your learning experience.\n\n## Learning a new programming language: Rust\nNow, let's dig into learning Rust, which is one of the [supported languages in Code Suggestions](https://docs.gitlab.com/ee/user/project/repository/code_suggestions.html#supported-languages).\n\n[Rust by Example](https://doc.rust-lang.org/rust-by-example/) provides a great tutorial for beginners, together with the official [Rust book](https://doc.rust-lang.org/book/). The [Hands-on Rust book](https://hands-on-rust.com/) shows how to build a 2D game as a more practical approach. More examples are shared in [this Rust book list](https://github.com/sger/RustBooks).\n\nBefore diving into the source code, make sure to set up your development environment.\n\n### Development environment for Rust\n1) Create a new project `learn-rust-ai` in GitLab, and clone the project into your development environment. All code snippets are available in [this \"Learn Rust with AI\" project](https://gitlab.com/gitlab-da/use-cases/ai/learn-with-ai/learn-rust-ai).\n\n```shell\ngit clone https://gitlab.com/NAMESPACE/learn-rust-ai.git\n\ncd learn-rust-ai\n\ngit status\n```\n\n2) Install Rust and the build toolchain. Fortunately, this is straightforward [following the Rust install documentation](https://www.rust-lang.org/tools/install).\n\nTip for using the generic installer: Download the script and run it after review.\n\n```shell\n# Download and print the script before running it\ncurl -Lvs https://sh.rustup.rs -o rustup-init.sh\n\n# Run the Rust installer script\nsh rustup-init.sh\n```\n\nExample on macOS using Homebrew:\n\n```shell\nbrew install rust\n```\n\n1) Optional: Install the [rust-analyzer VS Code extension](https://marketplace.visualstudio.com/items?itemName=rust-lang.rust-analyzer).\n\n2) Each exercise will invite you to compile the code with the [`rustc` command](https://doc.rust-lang.org/rustc/what-is-rustc.html), and later using [`cargo` as build tool and package manager](https://doc.rust-lang.org/cargo/index.html).\n\nYou are all set to learn Rust!\n\n### Hello, World\nWe will start with [Rust by Example](https://doc.rust-lang.org/rust-by-example/), and follow the [Hello, World exercise](https://doc.rust-lang.org/rust-by-example/hello.html).\n\nCreate a new file `hello.rs` in the root directory of the project and start with a comment saying `// Hello world`. Next, start writing the `main` function, and verify the code suggestion.\n\n![VS Code hello.rs Rust code suggestion, asking to accept](https://about.gitlab.com/images/blogimages/learn-rust-with-ai-code-suggestions-getting-started/learn_rust_ai_gitlab_code_suggestions_hello_world_suggested.png){: .shadow}\n\nAccept the suggestion by pressing the `tab` key and save the file (keyboard shortcut: cmd s).\n\n```text\n// Hello world\n\nfn main() {\n    println!(\"Hello, world!\");\n}\n```\n\n![VS Code hello.rs Rust code suggestion, accepted](https://about.gitlab.com/images/blogimages/learn-rust-with-ai-code-suggestions-getting-started/learn_rust_ai_gitlab_code_suggestions_hello_world_accepted.png){: .shadow}\n\nCommit the change to the Git repository. In VS Code, use the keyboard shortcut `ctrl shift G`, add a commit message, and hit `cmd enter` to submit.\n\nUse the command palette (`cmd shift p`) and search for `create terminal` to open a new terminal.\n\nBuild and run the code.\n\n```shell\nrustc hello.rs\n\n./hello\n```\n\n![hello.rs Rust code suggestion, accepted, compiled, run](https://about.gitlab.com/images/blogimages/learn-rust-with-ai-code-suggestions-getting-started/learn_rust_ai_gitlab_code_suggestions_hello_world_cli_build.png){: .shadow}\n\nTip: Adding [code comments in Rust (`//`)](https://doc.rust-lang.org/reference/comments.html) before you start writing a function or algorithm will help Code Suggestions with more context to provide better suggestions. In the example above, we did that with `// Hello world`, and will continue doing so in the next exercises.\n\n## Cargo: Bringing structure into Rust\n[Cargo](https://doc.rust-lang.org/rust-by-example/cargo.html) is the official Rust package management tool. It is more than that - you can run build and test commands because Cargo understands them as well.\n\nYou can initialize a new Cargo configuration in the current directory tree with the following command:\n\n```shell\ncargo init\n```\n\nThe directory tree invites you to add the source code into the `src/` directory, while `Cargo.toml` manages the dependencies and used compiler versions. The `.gitignore` file is also added including best practices.\n\n```shell\ntree\n.\n├── Cargo.toml\n├── README.md\n├── hello\n├── hello.rs\n└── src\n    └── main.rs\n\n```\n\nTry building the code and running it using `cargo`.\n\n```shell\ncargo build\n\ncargo run\n```\n\nCommit all changes and push them to your GitLab project.\n\n```shell\ngit commit -avm \"Initialize Cargo\"\n\ngit push\n```\n\nAfter exploring Cargo, let's make sure that our code is continuously tested while learning Rust. The next section explains how to set up [GitLab CI/CD](https://about.gitlab.com/topics/ci-cd/) for Rust.\n\n## Automation: Configure CI/CD pipeline for Rust\nThe [CI/CD pipeline](https://docs.gitlab.com/ee/ci/) should run two jobs in two stages: Build and test the code. The default container [image](https://docs.gitlab.com/ee/ci/yaml/#image), `rust:latest`, works in the first iteration. In order to save resources, the CI/CD configuration also supports [caching](https://docs.gitlab.com/ee/ci/caching/) for downloaded dependencies and build objects. The `CARGO_HOME` variable is set to the CI/CD job home directory to ensure everything gets appropriately cached.\n\n```yaml\nstages:\n  - build\n  - test\n\ndefault:\n  image: rust:latest\n  cache:\n    key: ${CI_COMMIT_REF_SLUG}\n    paths:\n      - .cargo/bin\n      - .cargo/registry/index\n      - .cargo/registry/cache\n      - target/debug/deps\n      - target/debug/build\n    policy: pull-push\n\n# Cargo data needs to be in the project directory to be cached.\nvariables:\n  CARGO_HOME: ${CI_PROJECT_DIR}/.cargo\n\n```\n\nThe CI/CD jobs inherit the [`default`](https://docs.gitlab.com/ee/ci/yaml/#default) values, and specify the cargo commands in the [`script` section](https://docs.gitlab.com/ee/ci/yaml/#script).\n\n```yaml\nbuild-latest:\n  stage: build\n  script:\n    - cargo build --verbose\n\ntest-latest:\n  stage: build\n  script:\n    - cargo test --verbose\n\n```\n\nYou can see an example in [this MR](https://gitlab.com/gitlab-da/use-cases/ai/learn-with-ai/learn-rust-ai/-/merge_requests/1/diffs).\n\n## Continue learning Rust\nMake sure to add new source code into the `src/` directory.\n\n### Define variables and print them\nPractice adding a few more [print](https://doc.rust-lang.org/rust-by-example/hello/print.html) statements into `src/main.rs`, and then build and run the code again.\n\n1) Define a variable called `name` and assign your name as string value.\n\n2) Print the name, including a string prefix saying `Hello, `.\n\n![VS Code main.rs Rust code suggestion, first step in print](https://about.gitlab.com/images/blogimages/learn-rust-with-ai-code-suggestions-getting-started/learn_rust_ai_gitlab_code_suggestions_print_variable_first.png){: .shadow}\n\n![VS Code main.rs Rust code suggestion, second step in print](https://about.gitlab.com/images/blogimages/learn-rust-with-ai-code-suggestions-getting-started/learn_rust_ai_gitlab_code_suggestions_print_variable_second.png){: .shadow}\n\n1) Open a new terminal in VS Code using the command palette (keyboard shortcut `cmd + shift + p`) and search for `terminal`.\n\n2) Build and run the code with the `cargo build` and `cargo run` commands.\n\n![VS Code terminal with cargo build and run output](https://about.gitlab.com/images/blogimages/learn-rust-with-ai-code-suggestions-getting-started/learn_rust_ai_gitlab_code_suggestions_print_variable_cargo_build_run_terminal.png){: .shadow}\n\nAn example solution can be found [here](https://gitlab.com/gitlab-da/use-cases/ai/learn-with-ai/learn-rust-ai/-/blob/main/solutions/variable_print.rs).\n\n### Explore variable types\nDefine different variable value types ([primitives](https://doc.rust-lang.org/rust-by-example/primitives.html)) and embed them into the `print` statements. Maybe they feel familiar with other programming languages?\n\nTip: Use code comments to see which code suggestions can be useful to learn. Start with typing `// Integer addition` and see what code suggestions you can add.\n\n![VS Code main.rs Rust code suggestion, primitive types with literals and expressions](https://about.gitlab.com/images/blogimages/learn-rust-with-ai-code-suggestions-getting-started/learn_rust_ai_gitlab_code_suggestions_primitive_types_literals_operators.png)\n\nExperiment with GitLab Duo Code Suggestions. The shown examples are non-deterministic, but you may be able to add additions, subscriptions, multiplications, etc., and the corresponding `println` statements just by accepting code suggestions and continuing the flow with `enter` or completing the code statements. This workflow can create a chain of code suggestions that can help you learn the Rust language.\n\n![Literals and expressions, first suggestion](https://about.gitlab.com/images/blogimages/learn-rust-with-ai-code-suggestions-getting-started/learn_rust_ai_gitlab_code_suggestions_literals_expressions_01.png){: .shadow}\n![Literals and expressions, second suggestion](https://about.gitlab.com/images/blogimages/learn-rust-with-ai-code-suggestions-getting-started/learn_rust_ai_gitlab_code_suggestions_literals_expressions_02.png){: .shadow}\n![Literals and expressions, third suggestion](https://about.gitlab.com/images/blogimages/learn-rust-with-ai-code-suggestions-getting-started/learn_rust_ai_gitlab_code_suggestions_literals_expressions_03.png){: .shadow}\n\nAn example solution can be found [here](https://gitlab.com/gitlab-da/use-cases/ai/learn-with-ai/learn-rust-ai/-/blob/main/solutions/literals_expressions.rs).\n\nThe code suggestions are not perfect. Sometimes there are errors that require you to fix the problems. When writing this blog post, I had to fix two missing semicolons at the end of the code lines. The great thing about the Rust compiler is that the error messages tell you exactly where the problem happens with suggestions to fix them. Code Suggestions and the Rust-provided build chain make writing Rust code more efficient.\n\n```rust\nprintln!(\"Hello, {}!\", name)\n\n// Integer subtraction\nlet y = 9 - 4\n```\n\n![Terminal build, errors, Rust compiler help](https://about.gitlab.com/images/blogimages/learn-rust-with-ai-code-suggestions-getting-started/learn_rust_ai_gitlab_code_suggestions_terminal_errors_rust_help.png){: .shadow}\n\nYou can try to provoke the same error by removing a semicolon at the end of a statement and then running `cargo build` in the terminal again. The Rust compiler will also warn you about unused variables to help with better code quality. The screenshot shows warnings for variable definitions, and also a CLI command to fix them.\n\n![Terminal build, warnings, Rust compiler help](https://about.gitlab.com/images/blogimages/learn-rust-with-ai-code-suggestions-getting-started/learn_rust_ai_gitlab_code_suggestions_terminal_warnings_rust_help.png){: .shadow}\n\n### Flow control: Conditions and loops\nNext, let's focus on [flow control](https://doc.rust-lang.org/rust-by-example/flow_control.html) with conditions, loops, etc., and how to implement them.\n\n1) Start typing `// Flow control` and see which suggestions are provided.\n\n2) Experiment with the code, and continue defining a boolean variable `v` which is set to true.\n\n```rust\n\n  // Flow control\n  let v = true;\n\n```\n\n![Conditions, boolean variable](https://about.gitlab.com/images/blogimages/learn-rust-with-ai-code-suggestions-getting-started/learn_rust_ai_gitlab_code_suggestions_flow_control_conditions_01.png){: .shadow}\n![Conditions, boolean variable, if condition](https://about.gitlab.com/images/blogimages/learn-rust-with-ai-code-suggestions-getting-started/learn_rust_ai_gitlab_code_suggestions_flow_control_conditions_02.png){: .shadow}\n\n1) Start typing `// Loops` and experiment with the code suggestions.\n\nLet's assume the loop looks the like following snippet. It does not have a loop counter which gets printed on every loop execution.\n\n```rust\n// Loops\nlet mut count = 0;\n\nloop {\n    count += 1;\n\n    if count == 10 {\n        break;\n    }\n}\n```\n\n2) Start typing `println!` and see which code suggestions are provided, for example `println!(\"Count: {}\", count);`.\n\n![Loops, loop counter print suggestion](https://about.gitlab.com/images/blogimages/learn-rust-with-ai-code-suggestions-getting-started/learn_rust_ai_gitlab_code_suggestions_flow_control_loops_print_counter.png)\n\n3) Apply the suggestions, and execute `cargo build && cargo run` on the terminal again.\n\nLet's learn more: Rust supports different loop types, for example [while loops](https://doc.rust-lang.org/rust-by-example/flow_control/while.html), [for loops](https://doc.rust-lang.org/rust-by-example/flow_control/for.html), etc.\n\n1) Type `// While loop` and verify the code suggestions. Repeat the same for `// For loop`.\n\n```rust\n// While loops\nlet mut count = 0;\n\nwhile count \u003C 10 {\n    count += 1;\n    println!(\"Count: {}\", count);\n}\n\n// For loops\nlet a = [10, 2, 3, 4, 5];\n\nfor element in a {\n    println!(\"Element: {}\", element);\n}\n```\n\nThere is more to learn with loops and conditions: Iterate over arrays, lists, maps, slices. Practice with writing comments for `// Maps and sets` and `// Vectors and strings`.\n\n![Vectors, strings](https://about.gitlab.com/images/blogimages/learn-rust-with-ai-code-suggestions-getting-started/learn_rust_ai_gitlab_code_suggestions_flow_control_vectors_strings.png){: .shadow}\n\n```rust\n\n  // Maps and sets\n  let mut scores = HashMap::new();\n\n  scores.insert(String::from(\"Blue\"), 10);\n  scores.insert(String::from(\"Yellow\"), 50);\n\n  for (key, value ) in &scores {\n      println!(\"{}: {}\", key, value);\n  }\n\n  // Vectors and strings\n  let mut v = Vec::new();\n\n  v.push(1);\n  v.push(2);\n\n  for element in &v {\n      println!(\"Element: {}\", element);\n  }\n\n```\n\nThis snippet will fail because the `HashMap` type needs to be imported from `std::collections::HashMap`. Add the following line on top before the main function definition:\n\n```rust\nuse std::collections::HashMap;\n```\n\n2) Build and run the code with `cargo build && cargo run`.\n\nAn example solution is provided [here](https://gitlab.com/gitlab-da/use-cases/ai/learn-with-ai/learn-rust-ai/-/blob/main/solutions/flow_control.rs).\n\n**Async exercise**:\n\n1) Modify the input values for the different data types, and build and run the code again.\n\n2) Add a condition into the loops that print the items only when a specific condition is met (for example, the number is odd or even).\n\n### Functions\n\n[Functions](https://doc.rust-lang.org/rust-by-example/fn.html) help increase code readability and testability with unit tests. Practice creating functions with the following steps:\n\n1) Two functions `isEven` and `isOdd` to evaluate whether a number is even or odd.\n\n```rust\nfn isEven(x: i32) -> bool {\n    x % 2 == 0\n}\n\nfn isOdd(x: i32) -> bool {\n    x % 2 != 0\n}\n```\n\n2) `isPrime` function to check whether a given integer value is a prime number.\n\n```rust\nfn isPrime(x: i32) -> bool {\n    let mut i = 2;\n\n    while i * i \u003C= x {\n        if x % i == 0 {\n            return false;\n        } else {\n            i += 1;\n        }\n    }\n\n    return true\n}\n```\n\n3) Create an array of integer values, loop over it, and call the functions. Let GitLab Code Suggestions guide you with the implementation by starting to type the if conditions followed by the function name.\n\n```rust\n\n  // Functions\n  let mut integers = vec![1, 2, 3, 4, 5];\n\n  for i in integers.iter() {\n\n      if (isEven(i)) {\n          println!(\"{} is even\", i);\n      }\n\n      if (isOdd(i)) {\n          println!(\"{} is odd\", i);\n      }\n\n      if (isPrime(i)) {\n          println!(\"{} is prime\", i);\n      }\n\n      println!(\"{}\", i);\n  }\n\n```\n\nNote that passing a reference value to a function may result in an error from the Rust compiler. Follow the suggestions and build the code again.\n\n```shell\n$ cargo build && cargo run\n\nerror[E0308]: mismatched types\n   --> src/main.rs:112:21\n    |\n112 |         if (isPrime(i)) {\n    |             ------- ^ expected `i32`, found `&{integer}`\n    |             |\n    |             arguments to this function are incorrect\n    |\nnote: function defined here\n   --> src/main.rs:136:4\n    |\n136 | fn isPrime(x: i32) -> bool {\n    |    ^^^^^^^ ------\nhelp: consider dereferencing the borrow\n    |\n112 |         if (isPrime(*i)) {\n    |                     +\n\n```\n\nAn example solution is provided [here](https://gitlab.com/gitlab-da/use-cases/ai/learn-with-ai/learn-rust-ai/-/blob/main/solutions/functions.rs).\n\n**Async exercise**: Create a function `containsString` and test it with an array of string values, and a string to search for, in a loop. The screenshot shows a potential implementation.\n\n![containsString function, and vector with string elements to test, suggesting its usage in the main function](https://about.gitlab.com/images/blogimages/learn-rust-with-ai-code-suggestions-getting-started/learn_rust_ai_gitlab_code_suggestions_function_implemented_then_suggested_in_main.png){: .shadow}\n\n### Testing\nWhile learning programming, adopt [testing](https://doc.rust-lang.org/rust-by-example/testing.html) into your process. This can be unit tests for functions, documentation testing, and integration testing. Practice with testing the previously created functions `isEven`, `isOdd`, and `isPrime`. Starty by typing `mod tests {` followed by a new line with `use super::*` to implement the example from the [Rust documentation for unit tests](https://doc.rust-lang.org/rust-by-example/testing/unit_testing.html).\n\n```rust\nmod tests {\n    use super::*;\n\n    #[test]\n    fn test_is_even() {\n        assert!(isEven(2));\n        assert!(!isEven(3));\n    }\n\n    #[test]\n    fn test_is_odd() {\n        assert!(!isOdd(2));\n        assert!(isOdd(3));\n    }\n\n    #[test]\n    fn test_is_prime() {\n        assert!(isPrime(2));\n        assert!(!isPrime(3));\n    }\n}\n```\n\nRun `cargo test` to run the unit tests. Modify the test values to experiment with the results.\n\n```shell\ncargo test\n```\n\n![Function unit tests, cargo test output in the VS Code terminal](https://about.gitlab.com/images/blogimages/learn-rust-with-ai-code-suggestions-getting-started/learn_rust_ai_gitlab_code_suggestions_function_unit_tests_terminal_run.png)\n\nCreate unit tests that fail, and commit and push the changes to GitLab. The CI/CD pipelines will fail in this simulated breakage. The example above needs a fix for the `test_is_prime` test. Commit and push the change to verify that the pipeline passes again.\n\n```diff\n-        assert!(!isPrime(3));\n+        assert!(!isPrime(4));\n```\n\n## What is next\nIn an upcoming blog, we will look into advanced learning examples with asynchronous operations, services and external API communication in future blog posts. Until then, here are a few recommendations for practicing async.\n\n### Async learning exercises\n- [`std misc`](https://doc.rust-lang.org/rust-by-example/std_misc.html) provides asynchronous operations with threads, channels and file I/O\n- Book: [Hands-on Rust: Effective Learning through 2D Game Development and Play](https://pragprog.com/titles/hwrust/hands-on-rust/)\n- Tutorial: [Are we game yet?](https://arewegameyet.rs/resources/tutorials/)\n- Use case: [Web server with rocket.rs](https://rocket.rs/v0.5-rc/guide/quickstart/#running-examples)\n\nHere are a few more exercises and ideas for additional learning:\n1) The Rust compiler might have created warnings that need to be addressed. Follow the instructions from the `cargo build` commands and check the Git diff.\n\n```shell\ncargo fix --bin \"learn-rust-ai\"\n\ngit diff\n```\n\n2) [Error handling](https://doc.rust-lang.org/rust-by-example/error.html) is required when failure is detected, and the caller should know. Some errors can be recovered from within the application, others require program termination.\n\n3) The [`std` library](https://doc.rust-lang.org/rust-by-example/std.html) extends primitive types and makes programming more efficient.\n\n### Share your feedback\nWhich programming language are you learning or considering learning? Start a new topic on our [community](/community/) forum or Discord and share your experience.\n\nIf you are using Code Suggestions Beta with [GitLab Duo](/gitlab-duo/) already, please share your thoughts and feedback [in this 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So why aren’t teams _shipping_ faster?\n\nBecause coding is only 20% of the software delivery lifecycle, the remaining 80% becomes the bottleneck: code review backlogs grow, security scanning can’t keep pace, documentation falls behind, and manual coordination overhead increases.\n\nThe good news is that the same AI capabilities that accelerate individual coding can eliminate these team-level delays. You just need to apply AI across your entire software lifecycle, not only during the coding phase.\n\nBelow are 10 ready-to-use prompts from the [GitLab Duo Agent Platform Prompt Library](https://about.gitlab.com/gitlab-duo/prompt-library/) that help teams overcome common obstacles to faster software delivery. Each prompt addresses a specific slowdown that emerges when individual productivity increases without corresponding improvements in team processes.\n\n## How do you move code review from bottleneck to accelerator?\nDevelopers generate merge requests faster with AI assistance, but human reviewers can quickly become overwhelmed as code review cycles stretch from hours to days. AI can handle routine review tasks, freeing reviewers to focus on architecture and business logic instead of catching basic logical errors and API contract violations.\n\n### Review MR for logical errors\n**Complexity**: Beginner\n\n**Category**: Code Review\n\n**Prompt from library**:\n\n\n```text\nReview this MR for logical errors, edge cases, and potential bugs: [MR URL or paste code]\n```\n\n**Why it helps**: Automated linters catch syntax issues, but logical errors require understanding intent. This prompt catches bugs before human reviewers even look at the code, reducing review cycles from multiple rounds to often just one approval.\n\n### Identify breaking changes in MR\n**Complexity**: Beginner\n\n**Category**: Code Review\n\n**Prompt from library**:\n\n\n```text\nDoes this MR introduce any breaking changes?\n\nChanges:\n[PASTE CODE DIFF]\n\nCheck for:\n1. API signature changes\n2. Removed or renamed public methods\n3. Changed return types\n4. Modified database schemas\n5. Breaking configuration changes\n```\n\n**Why it helps**: Breaking changes discovered during deployment can cause rollbacks and incidents. This prompt shifts that discovery left to the MR stage, when fixes are faster and less expensive.\n\n## How can you shift security left without slowing down?\nSecurity scans generate hundreds of findings. Security teams manually triage each one while developers wait for approval to deploy. Most findings are false positives or low-risk issues, but identifying the real threats requires expertise and time. AI can prioritize findings by actual exploitability and auto-remediate common vulnerabilities, allowing security teams to focus on the threats that matter.\n\n### Analyze security scan results\n**Complexity**: Intermediate\n\n**Category**: Security\n\n**Agent**: Duo Security Analyst\n\n**Prompt from library**:\n\n\n```text\n@security_analyst Analyze these security scan results:\n\n[PASTE SCAN OUTPUT]\n\nFor each finding:\n1. Assess real risk vs false positive\n2. Explain the vulnerability\n3. Suggest remediation\n4. Prioritize by severity\n```\n\n**Why it helps**: Most security scan findings are false positives or low-risk issues. This prompt helps security teams focus on the findings that actually matter, reducing remediation time from weeks to days.\n\n### Review code for security issues\n**Complexity**: Intermediate\n\n**Category**: Security\n\n**Agent**: Duo Security Analyst\n\n**Prompt from library**:\n\n```text\n@security_analyst Review this code for security issues:\n\n[PASTE CODE]\n\nCheck for:\n1. Injection vulnerabilities\n2. Authentication/authorization flaws\n3. Data exposure risks\n4. Insecure dependencies\n5. Cryptographic issues\n```\n\n**Why it helps**: Traditional security reviews happen after code is written. This prompt enables developers to find and fix security issues before creating an MR, eliminating the back and forth that delays deployments.\n\n## How do you keep documentation current as code changes?\nCode changes faster than documentation. Onboarding new developers takes weeks because docs are outdated or missing. Teams know documentation is important, but it always gets deferred when deadlines approach. Automating documentation generation and updates as part of your standard workflow ensures docs stay current without adding manual work.\n\n### Generate release notes from MRs\n**Complexity**: Beginner\n\n**Category**: Documentation\n\n**Prompt from library**:\n\n```text\nGenerate release notes for these merged MRs:\n[LIST MR URLs or paste titles]\n\nGroup by:\n1. New features\n2. Bug fixes\n3. Performance improvements\n4. Breaking changes\n5. Deprecations\n```\n\n**Why it helps**: Manual release note compilation takes hours and often includes errors or omissions. Automated generation ensures every release has comprehensive notes without adding work to your release process.\n\n### Update documentation after code changes\n**Complexity**: Beginner\n\n**Category**: Documentation\n\n**Prompt from library**:\n\n```text\nI changed this code:\n\n[PASTE CODE CHANGES]\n\nWhat documentation needs updating? Check:\n1. README files\n2. API documentation\n3. Architecture diagrams\n4. Onboarding guides\n```\n\n**Why it helps**: Documentation drift happens because teams forget which docs need updates after code changes. This prompt makes documentation maintenance part of your development workflow, not a separate task that gets deferred.\n\n## How do you break down planning complexity?\nLarge features get stuck in planning. Teams spend weeks in meetings trying to scope work and identify dependencies. The complexity feels overwhelming, and it's hard to know where to start. AI can systematically decompose complex work into concrete, implementable tasks with clear dependencies and acceptance criteria, transforming weeks of planning into focused implementation.\n\n### Break down epic into issues\n**Complexity**: Intermediate\n\n**Category**: Documentation\n\n**Agent**: Duo Planner\n\n**Prompt from library**:\n\n```text\nBreak down this epic into implementable issues:\n\n[EPIC DESCRIPTION]\n\nConsider:\n1. Technical dependencies\n2. Reasonable issue sizes\n3. Clear acceptance criteria\n4. Logical implementation order\n```\n\n**Why it helps**: This prompt transforms a week of planning meetings into 30 minutes of AI-assisted decomposition followed by team review. Teams start implementation sooner with clearer direction.\n\n## How can you expand test coverage without expanding effort?\nDevelopers are writing code faster, but if testing doesn't keep pace, test coverage decreases and bugs slip through. Writing comprehensive tests manually is time-consuming, and developers often miss edge cases under deadline pressure. Generating tests automatically means developers can review and refine rather than write from scratch, maintaining quality without sacrificing velocity.\n\n### Generate unit tests\n**Complexity**: Beginner\n\n**Category**: Testing\n\n**Prompt from library**:\n\n```text\nGenerate unit tests for this function:\n\n[PASTE FUNCTION]\n\nInclude tests for:\n1. Happy path\n2. Edge cases\n3. Error conditions\n4. Boundary values\n5. Invalid inputs\n```\n\n**Why it helps**: Writing tests manually is time consuming, and developers often miss edge cases. This prompt generates thorough test suites in seconds, which developers can review and adjust rather than write from scratch.\n\n### Review test coverage gaps\n**Complexity**: Beginner\n\n**Category**: Testing\n\n**Prompt from library**:\n\n```text\nAnalyze test coverage for [MODULE/COMPONENT]:\n\nCurrent coverage: [PERCENTAGE]\n\nIdentify:\n1. Untested functions/methods\n2. Uncovered edge cases\n3. Missing error scenario tests\n4. Integration points without tests\n5. Priority areas to test next\n```\n\n**Why it helps**: This prompt reveals blind spots in your test suite before they cause production incidents. Teams can systematically improve coverage where it matters most.\n\n## How do you reduce mean time to resolution when debugging?\nProduction incidents take hours to diagnose. Developers wade through logs and stack traces while customers experience downtime. Every minute of debugging is a minute of lost productivity and potential revenue. AI can accelerate root cause analysis by parsing complex error messages and suggesting specific fixes, cutting diagnostic time from hours to minutes.\n\n### Debug failing pipeline\n**Complexity**: Beginner\n\n**Category**: Debugging\n\n**Prompt from library**:\n\n```text\nThis pipeline is failing:\n\nJob: [JOB NAME]\nStage: [STAGE]\nError: [PASTE ERROR MESSAGE/LOG]\n\nHelp me:\n1. Identify the root cause\n2. Suggest a fix\n3. Explain why it started failing\n4. Prevent similar issues\n```\n\n**Why it helps**: CI/CD failures block entire teams. This prompt diagnoses failures in seconds instead of the 15-30 minutes developers typically spend investigating, keeping deployment velocity high.\n\n## Moving from individual gains to team acceleration\nThese prompts represent a shift in how teams apply AI to software delivery. Rather than focusing solely on individual developer productivity, they address the coordination, quality, and knowledge-sharing challenges that actually constrain team velocity.\n\nThe [complete prompt library](https://about.gitlab.com/gitlab-duo/prompt-library/) contains more than 100 prompts across all stages of the software lifecycle: planning, development, security, testing, deployment, and operations. Each prompt is tagged by complexity level (Beginner, Intermediate, Advanced) and categorized by use case, making it easy to find the right starting point for your team.\n\nStart with prompts tagged “Beginner” that address your team’s most pressing obstacles. As your team builds confidence, explore intermediate and advanced prompts that enable more sophisticated workflows. The goal is not just faster coding — it's faster, safer, higher-quality software delivery from planning through production.",[27,716],"DevOps platform",{"featured":12,"template":13,"slug":718},"10-ai-prompts-to-speed-your-teams-software-delivery",{"content":720,"config":730},{"title":721,"description":722,"heroImage":723,"authors":724,"date":726,"body":727,"category":9,"tags":728},"AI can detect vulnerabilities, but who governs risk?","AI-assisted vulnerability detection is developing fast, but the harder challenges of enforcement, governance, and supply chain security require a holistic platform.","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772195014/ooezwusxjl1f7ijfmbvj.png",[725],"Omer Azaria","2026-02-27","Anthropic recently announced Claude Code Security, an AI system that detects vulnerabilities and proposes fixes. The market reacted immediately, with security stocks dipping as investors questioned whether AI might replace traditional AppSec tools. The question on everyone's mind: If AI can write code and secure it, is application security about to become obsolete?\n\nIf security only meant scanning code, the answer might be yes. But enterprise security has never been about detection alone.\n\nOrganizations are not asking whether AI can find vulnerabilities. They are asking three much harder questions: \n\n* Is what we are about to ship safe?  \n* Has our risk posture changed as environments evolve and dependencies, third-party services, tools, and infrastructure continuously shift?  \n* How do we govern a codebase that is increasingly assembled by AI and third-party sources, and that we are still accountable for? \n\nThose questions require a platform answer: Detection surfaces risk, but governance determines what happens next. \n\n[GitLab](https://about.gitlab.com/) is the orchestration layer built to govern the software lifecycle end-to-end. It gives teams the enforcement, visibility, and auditability they need to keep pace with the speed of AI-assisted development.\n\n## Trusting AI requires governing risk\n\nAI systems are rapidly getting better at identifying vulnerabilities and suggesting fixes. This is a meaningful and welcome advancement, but analysis is not accountability.\n\nAI cannot enforce company policy or define acceptable risk on its own. Humans must set the boundaries, policies, and guardrails that agents operate within, establishing separation of duties, ensuring audit trails, and maintaining consistent controls across thousands of repositories and teams. Trust in agents comes not from autonomy alone, but from clearly defined governance set by people. \n\nIn an [agentic world](https://about.gitlab.com/topics/agentic-ai/), where software is increasingly written and modified by autonomous systems, governance becomes more important, not less. The more autonomy organizations grant to AI, the stronger the governance must be.\n\nGovernance is not friction. It is the foundation that makes AI-assisted development trustworthy at scale.\n\n## LLMs see code, but platforms see context\n\nA large language model ([LLM](https://about.gitlab.com/blog/what-is-a-large-language-model-llm/)) evaluates code in isolation. An enterprise application security platform understands context. This difference matters because risk decisions are contextual:\n\n* Who authored the change?  \n* How critical is the application to the business?  \n* How does it interact with infrastructure and dependencies?  \n* Does the vulnerability exist in code that is actually reachable in production, or is it buried in a dependency that never executes?  \n* Is it actually exploitable in production, given how the application runs, its APIs, and the environment around it?\n\nSecurity decisions depend on this context. Without it, detection produces noisy alerts that slow down development rather than reducing risk. With it, organizations can triage quickly and manage risk effectively. Context evolves continuously as software changes, which means governance cannot be a one-time decision. \n\n## Static scans can’t keep up with dynamic risk\n\nSoftware risk is dynamic. Dependencies change, environments evolve, and systems interact in ways no single analysis can fully predict. A clean scan at one moment does not guarantee safety at release.\n\nEnterprise security depends on continuous assurance: controls embedded directly into development workflows that evaluate risk as software is built, tested, and deployed.\n\nDetection provides insight. Governance provides trust. Continuous governance is what allows organizations to ship safely at scale.\n\n## Governing the agentic future\n\nAI is reshaping how software is created. The question is no longer whether teams will use AI, but how safely they can scale it.\n\nSoftware today is assembled as much as it is written, from AI-generated code, open-source libraries, and third-party dependencies that span thousands of projects. Governing what ships across all of those sources is the hardest and most consequential part of application security, and it is the part that no developer-side tool is built to address. \n\nAs an intelligent orchestration platform, GitLab is built to address this problem. GitLab Ultimate embeds governance, policy enforcement, security scanning, and auditability directly into the workflows where software is planned, built, and shipped, so security teams can govern at the speed of AI. \n\nAI will accelerate development dramatically. The organizations that benefit most from AI will not be those with the smartest assistants alone, but those that build trust through strong governance.\n\n> To learn how GitLab helps organizations [govern and ship AI-generated code](https://about.gitlab.com/solutions/software-compliance/?utm_medium=blog&utm_campaign=eg_global_x_x_security_en_) safely, [talk to our team today](https://about.gitlab.com/sales/?utm_medium=blog&utm_campaign=eg_global_x_x_security_en_)\n\n\n ## Related reading\n\n - [Integrating AI with DevOps for enhanced security](https://about.gitlab.com/topics/devops/ai-enhanced-security/)\n - [The GitLab AI Security Framework for security leaders](https://about.gitlab.com/blog/the-gitlab-ai-security-framework-for-security-leaders/)\n - [Improve AI security in GitLab with composite identities](https://about.gitlab.com/blog/improve-ai-security-in-gitlab-with-composite-identities/)",[27,729],"security",{"featured":30,"template":13,"slug":731},"ai-can-detect-vulnerabilities-but-who-governs-risk",{"content":733,"config":743},{"title":734,"description":735,"authors":736,"category":9,"tags":738,"date":740,"heroImage":741,"body":742},"Secure and fast deployments to Google Agent Engine with GitLab","Follow this step-by-step guide to build an AI agent with Google's Agent Development Kit and deploy to Agent Engine using GitLab.",[737],"Regnard Raquedan",[27,739,110,23],"google","2026-02-26","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772111172/mwhgbjawn62kymfwrhle.png","In this tutorial, you'll learn how to deploy an AI agent built with Google's Agent Development Kit ([ADK](https://google.github.io/adk-docs/)) to [Agent Engine](https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/overview) using GitLab's native Google Cloud integration and CI/CD pipelines. We'll cover IAM configuration, pipeline setup, and testing your deployed agent.\n\n## What is Agent Engine and why does it matter?\n\nAgent Engine is Google Cloud's managed runtime specifically designed for AI agents. Think of it as the production home for your agents — where they live, run, and scale without you having to manage the underlying infrastructure. Agent Engine handles infrastructure, scaling, session management, and memory storage so you can focus on building your agent — not managing servers. It also integrates natively with Google Cloud's logging, monitoring, and IAM.\n\n## Why use GitLab to deploy to Agent Engine?\n\nAI agent deployment is typically difficult to configure correctly. Security considerations, CI/CD orchestration, and cloud permissions create friction that slows down development cycles.\n\nGitLab streamlines this entire process while enhancing security:\n\n- **Built-in security scanning** — Every deployment is automatically scanned for vulnerabilities without additional configuration.\n- **Native Google Cloud integration** — Workload Identity Federation eliminates the need for service account keys.\n- **Simplified CI/CD** — GitLab's templates handle complex deployment logic.\n\n## Prerequisites\n\nBefore you begin, ensure you have:\n\n- A Google Cloud project with the following APIs enabled:\n  - Cloud Storage API\n  - Vertex AI API\n- A GitLab project for your source code and CI/CD pipeline\n- A Google Cloud Storage bucket for staging deployments\n- Google Cloud IAM integration configured in GitLab (see Step 1)\n\nHere are the steps to follow.\n\n## 1. Configure IAM integration\n\nThe foundation of secure deployment is proper IAM configuration between GitLab and Google Cloud using Workload Identity Federation.\n\nIn your GitLab project:\n\n1. Navigate to **Settings > Integrations**.\n2. Locate the **Google Cloud IAM** integration.\n3. Provide the following information:\n   - **Project ID**: Your Google Cloud project ID\n   - **Project Number**: Found in your Google Cloud console\n   - **Workload Identity Pool ID**: A unique identifier for your identity pool\n   - **Provider ID**: A unique identifier for your identity provider\n\nGitLab generates a script for you. Copy and run this script in Google Cloud Shell to establish the Workload Identity Federation between platforms.\n\n**Important:** Add these additional roles to your service principal for Agent Engine deployment:\n\n- `roles/aiplatform.user`\n- `roles/storage.objectAdmin`\n\nYou can add these roles using gcloud commands:\n\n```bash\nGCP_PROJECT_ID=\"\u003Cyour-project-id>\"\nGCP_PROJECT_NUMBER=\"\u003Cyour-project-number>\"\nGCP_WORKLOAD_IDENTITY_POOL=\"\u003Cyour-pool-id>\"\n\ngcloud projects add-iam-policy-binding ${GCP_PROJECT_ID} \\\n  --member=\"principalSet://iam.googleapis.com/projects/${GCP_PROJECT_NUMBER}/locations/global/workloadIdentityPools/${GCP_WORKLOAD_IDENTITY_POOL}/attribute.developer_access/true\" \\\n  --role='roles/aiplatform.user'\n\ngcloud projects add-iam-policy-binding ${GCP_PROJECT_ID} \\\n  --member=\"principalSet://iam.googleapis.com/projects/${GCP_PROJECT_NUMBER}/locations/global/workloadIdentityPools/${GCP_WORKLOAD_IDENTITY_POOL}/attribute.developer_access/true\" \\\n  --role='roles/storage.objectAdmin'\n```\n\n## 2. Create the CI/CD pipeline\n\nNow for the core of the deployment — the CI/CD pipeline. Create a `.gitlab-ci.yml` file in your project root:\n\n```yaml\nstages:\n  - test\n  - deploy\n\ncache:\n  paths:\n    - .cache/pip\n  key: ${CI_COMMIT_REF_SLUG}\n\nvariables:\n  GCP_PROJECT_ID: \"\u003Cyour-project-id>\"\n  GCP_REGION: \"us-central1\"\n  STORAGE_BUCKET: \"\u003Cyour-staging-bucket>\"\n  AGENT_NAME: \"Canada City Advisor\"\n  AGENT_ENTRY: \"canada_city_advisor\"\n\nimage: google/cloud-sdk:slim\n\n# Security scanning templates\ninclude:\n  - template: Jobs/Dependency-Scanning.gitlab-ci.yml\n  - template: Jobs/SAST.gitlab-ci.yml\n  - template: Jobs/Secret-Detection.gitlab-ci.yml\n\ndeploy-agent:\n  stage: deploy\n  identity: google_cloud\n  rules:\n    - if: $CI_COMMIT_BRANCH == \"main\"\n  before_script:\n    - gcloud config set core/disable_usage_reporting true\n    - gcloud config set component_manager/disable_update_check true\n    - pip install -q --no-cache-dir --upgrade pip google-genai google-cloud-aiplatform -r requirements.txt --break-system-packages\n  script:\n    - gcloud config set project $GCP_PROJECT_ID\n    - adk deploy agent_engine \n        --project=$GCP_PROJECT_ID \n        --region=$GCP_REGION \n        --staging_bucket=gs://$STORAGE_BUCKET \n        --display_name=\"$AGENT_NAME\" \n        $AGENT_ENTRY\n```\n\nThe pipeline consists of two stages:\n\n**Test stage** — GitLab's security scanners run automatically. The included templates provide dependency scanning, static application security testing (SAST), and secret detection without additional configuration.\n\n**Deploy stage** — Uses the ADK CLI to deploy your agent directly to Agent Engine. The staging bucket temporarily holds your application workload before Agent Engine picks it up for deployment.\n\n### Key configuration notes\n\n- The `identity: google_cloud` directive enables keyless authentication via Workload Identity Federation.\n- Security scanners are included as templates, meaning they run by default with no setup required.\n- The `adk deploy agent_engine` command handles all the complexity of packaging and deploying your agent.\n- Pipeline caching speeds up subsequent deployments by preserving pip dependencies.\n\n## 3. Deploy and verify\n\nWith your pipeline configured:\n\n1. Commit your agent code and `.gitlab-ci.yml` to GitLab.\n2. Navigate to **Build > Pipelines** to monitor execution.\n3. Watch the test stage complete security scans.\n4. Observe the deploy stage push your agent to Agent Engine.\n\nOnce the pipeline succeeds, verify your deployment in the Google Cloud Console:\n\n1. Navigate to **Vertex AI > Agent Engine**.\n2. Locate your deployed agent.\n3. Note the **resource name** — you'll need this for testing.\n\n## 4. Test your deployed agent\n\nTest your agent using a curl command. You'll need three pieces of information:\n\n- **Agent ID**: From the Agent Engine console (the resource name's numeric identifier)\n- **Project ID**: Your Google Cloud project\n- **Location**: The region where you deployed (e.g., `us-central1`)\n\n```bash\nPROJECT_ID=\"\u003Cyour-project-id>\"\nLOCATION=\"us-central1\"\nAGENT_ID=\"\u003Cyour-agent-id>\"\nTOKEN=$(gcloud auth print-access-token)\n\ncurl -X POST \\\n  -H \"Authorization: Bearer $TOKEN\" \\\n  -H \"Content-Type: application/json\" \\\n  \"https://${LOCATION}-aiplatform.googleapis.com/v1/projects/${PROJECT_ID}/locations/${LOCATION}/reasoningEngines/${AGENT_ID}:streamQuery\" \\\n  -d '{\n    \"input\": {\n      \"message\": \"I make $85,000 per year and I prefer cities with mild winters and a vibrant cultural scene. I also want to be near the coast if possible. What Canadian cities would you recommend?\",\n      \"user_id\": \"demo-user\"\n    }\n  }' | jq -r '.content.parts[0].text'\n```\n\nIf everything is configured correctly, your agent will respond with personalized city recommendations based on the budget and lifestyle preferences provided.\n\n## Security benefits of this approach\n\nThis deployment pattern provides several security advantages:\n\n- **No long-lived credentials**: Workload Identity Federation eliminates service account keys entirely.\n- **Automated vulnerability scanning**: Every deployment is scanned before reaching production.\n- **Complete audit trail**: GitLab maintains full visibility of who deployed what and when.\n- **Principle of least privilege**: Fine-grained IAM roles limit access to only what's needed.\n\n## Summary\n\nDeploying AI agents to production doesn't have to be complex. By combining GitLab's DevSecOps platform with Google Cloud's Agent Engine, you get:\n\n- A managed runtime that handles scaling and infrastructure\n- Built-in security scanning without additional tooling\n- Keyless authentication via native cloud integration\n- A streamlined deployment process that fits modern AI development workflows\n\nWatch the full demo:\n\n\n\u003Cfigure class=\"video_container\"> \u003Ciframe src=\"https://www.youtube.com/embed/sxVFa2Mk-x4?si=Oi3cUjhgd7FT2yEd\" frameborder=\"0\" allowfullscreen=\"true\" title=\"Deploy AI Agents to Agent Engine with GitLab\"> \u003C/iframe> \u003C/figure>\n\n> Ready to try it yourself? Use this tutorial's [complete code example](https://gitlab.com/gitlab-partners-public/google-cloud/demos/agent-engine-demo) to get started now. Not a GitLab customer yet? Explore the DevSecOps platform with [a free trial](https://about.gitlab.com/free-trial/).\n",{"featured":12,"template":13,"slug":744},"secure-and-fast-deployments-to-google-agent-engine-with-gitlab",{"promotions":746},[747,760,772],{"id":748,"categories":749,"header":750,"text":751,"button":752,"image":757},"ai-modernization",[9],"Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":753,"config":754},"Get your AI maturity score",{"href":755,"dataGaName":756,"dataGaLocation":245},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":758},{"src":759},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":761,"categories":762,"header":764,"text":751,"button":765,"image":769},"devops-modernization",[763,38],"product","Are you just managing tools or shipping innovation?",{"text":766,"config":767},"Get your DevOps maturity score",{"href":768,"dataGaName":756,"dataGaLocation":245},"/assessments/devops-modernization-assessment/",{"config":770},{"src":771},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":773,"categories":774,"header":775,"text":751,"button":776,"image":780},"security-modernization",[729],"Are you trading speed for security?",{"text":777,"config":778},"Get your security maturity score",{"href":779,"dataGaName":756,"dataGaLocation":245},"/assessments/security-modernization-assessment/",{"config":781},{"src":782},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"header":784,"blurb":785,"button":786,"secondaryButton":791},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":787,"config":788},"Get your free trial",{"href":789,"dataGaName":52,"dataGaLocation":790},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":497,"config":792},{"href":56,"dataGaName":57,"dataGaLocation":790},1772652072594]