[{"data":1,"prerenderedAt":793},["ShallowReactive",2],{"/en-us/blog/learn-advanced-rust-programming-with-a-little-help-from-ai-code-suggestions":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-learn-advanced-rust-programming-with-a-little-help-from-ai-code-suggestions":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/learn-advanced-rust-programming-with-a-little-help-from-ai-code-suggestions.yml","Learn Advanced Rust Programming With A Little Help From Ai Code Suggestions",[7],"michael-friedrich",null,"ai-ml",{"slug":11,"featured":12,"template":13},"learn-advanced-rust-programming-with-a-little-help-from-ai-code-suggestions",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"Learn advanced Rust programming with a little help from AI","Use this guided tutorial, along with AI-powered GitLab Duo Code Suggestions, to continue learning advanced Rust programming.",[18],"Michael Friedrich","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749662439/Blog/Hero%20Images/codewithheart.png","2023-10-12","When I started learning a new programming language more than 20 years ago, we had access to the Visual Studio 6 MSDN library, installed from 6 CD-ROMs. Algorithms with pen and paper, design pattern books, and MSDN queries to figure out the correct type were often time-consuming. Learning a new programming language changed fundamentally in the era of remote collaboration and artificial intelligence (AI). Now you can spin up a [remote development workspace](https://about.gitlab.com/blog/quick-start-guide-for-gitlab-workspaces/), share your screen, and engage in a group programming session. With the help of [GitLab Duo Code Suggestions](/gitlab-duo/), you always have an intelligent partner at your fingertips. Code Suggestions can learn from your programming style and experience. They only need input and context to provide you with the most efficient suggestions.\n\nIn this tutorial, we build on the [getting started blog post](/blog/learning-rust-with-a-little-help-from-ai-code-suggestions-getting-started/) and design and create a simple feed reader application.\n\n- [Preparations](#preparations)\n    - [Code Suggestions](#code-suggestions)\n- [Continue learning Rust](#continue-learning-rust)\n    - [Hello, Reader App](#hello-reader-app)\n    - [Initialize project](#initialize-project)\n    - [Define RSS feed URLs](#define-rss-feed-urls)\n- [Modules](#modules)\n    - [Call the module function in main()](#call-the-module-function-in-main)\n- [Crates](#crates)\n    - [feed-rs: parse XML feed](#feed-rs-parse-xml-feed)\n- [Runtime configuration: Program arguments](#runtime-configuration-program-arguments)\n    - [User input error handling](#user-input-error-handling)\n- [Persistence and data storage](#persistence-and-data-storage)\n- [Optimization](#optimization)\n    - [Asynchronous execution](#asynchronous-execution)\n    - [Spawning threads](#spawning-threads)\n    - [Function scopes, threads, and closures](#function-scopes-threads-and-closures)\n- [Parse feed XML into objects](#parse-feed-xml-into-object-types)\n    - [Map generic feed data types](#map-generic-feed-data-types)\n    - [Error handling with Option::unwrap()](#error-handling-with-option-unwrap)\n- [Benchmarks](#benchmarks)\n    - [Sequential vs. Parallel execution benchmark](#sequential-vs-parallel-execution-benchmark)\n    - [CI/CD with Rust caching](#cicd-with-rust-caching)\n- [What is next](#what-is-next)\n    - [Async learning exercises](#async-learning-exercises)\n    - [Share your feedback](#share-your-feedback)\n\n## Preparations\nBefore diving into the source code, make sure to set up [VS Code](/blog/learning-rust-with-a-little-help-from-ai-code-suggestions-getting-started/#vs-code) and [your development environment with Rust](/blog/learning-rust-with-a-little-help-from-ai-code-suggestions-getting-started/#development-environment-for-rust).\n\n### Code Suggestions\nFamiliarize yourself with suggestions before actually verifying the suggestions. GitLab Duo 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**Tip:** The latest release of Code Suggestions supports multi-line instructions. You can refine the specifications to your needs to get better suggestions.\n\n```rust\n\n    // Create a function that iterates over the source array\n    // and fetches the data using HTTP from the RSS feed items.\n    // Store the results in a new hash map.\n    // Print the hash map to the terminal.\n\n```\n\nThe VS Code extension overlay is shown when offering a suggestion. You can use the `tab` key to accept the suggested line(s), or `cmd cursor right` to accept one word. Additionally, the three dots menu allows you to always show the toolbar.\n\n![VS Code GitLab Duo Code Suggestions overlay with instructions](https://about.gitlab.com/images/blogimages/learn-rust-with-ai-code-suggestions-advanced-programming/vs_code_code_suggestions_options_overlay_keep_toolbar.png){: .shadow}\n\n## Continue learning Rust\nNow, let us continue 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). [Rust by Example](https://doc.rust-lang.org/rust-by-example/) provides an excellent tutorial for beginners, together with the official [Rust book](https://doc.rust-lang.org/book/). Both resources are referenced throughout this blog post.\n\n### Hello, Reader App\nThere are many ways to create an application and learn Rust. Some of them involve using existing Rust libraries - so-called `Crates`. We will use them a bit further into the blog post. For example, you could create a command-line app that processes images and writes the result to a file. Solving a classic maze or writing a Sudoku solver can also be a fun challenge. Game development is another option. The book [Hands-on Rust](https://hands-on-rust.com/) provides a thorough learning path by creating a dungeon crawler game. My colleague Fatima Sarah Khalid started the [Dragon Realm in C++ with a little help from AI](/blog/building-a-text-adventure-using-cplusplus-and-code-suggestions/) -- check it out, too.\n\nHere is a real use case that helps solve an actual problem: Collecting important information from different sources into RSS feeds for (security) releases, blog posts, and social discussion forums like Hacker News. Often, we want to filter for specific keywords or versions mentioned in the updates. These requirements allow us to formulate a requirements list for our application:\n\n1. Fetch data from different sources (HTTP websites, REST API, RSS feeds). RSS feeds in the first iteration.\n1. Parse the data.\n1. Present the data to the user, or write it to disk.\n1. Optimize performance.\n\nThe following example application output will be available after the learning steps in this blog post:\n\n![VS Code Terminal, cargo run with formatted feed entries output](https://about.gitlab.com/images/blogimages/learn-rust-with-ai-code-suggestions-advanced-programming/vs_code_terminal_cargo_run_formatted_output_final.png)\n\nThe application should be modular and build the foundation to add more data types, filters, and hooks to trigger actions at a later point.\n\n### Initialize project\nReminder: `cargo init` in the project root creates the file structure, including the `main()` entrypoint. Therefore, we will learn how to create and use Rust modules in the next step.\n\nCreate a new directory called `learn-rust-ai-app-reader`, change into it and run `cargo init`. This command implicitly runs `git init` to initialize a new Git repository locally. The remaining step is to configure the Git remote repository path, for example, `https://gitlab.com/gitlab-da/use-cases/ai/learn-with-ai/learn-rust-ai-app-reader`. Please adjust the path for your namespace. Pushing the Git repository [automatically creates a new private project in GitLab](https://docs.gitlab.com/ee/user/project/#create-a-new-project-with-git-push).\n\n```shell\nmkdir learn-rust-ai-app-reader\ncd learn-rust-ai-app-reader\n\ncargo init\n\ngit remote add origin https://gitlab.com/gitlab-da/use-cases/ai/learn-with-ai/learn-rust-ai-app-reader.git\ngit push --set-upstream origin main\n```\n\nOpen VS Code from the newly created directory. The `code` CLI will spawn a new VS Code window on macOS.\n\n```shell\ncode .\n```\n\n### Define RSS feed URLs\nAdd a new hashmap to store the RSS feed URLs inside the `src/main.rs` file in the `main()` function. You can instruct GitLab Duo Code Suggestions with a multi-line comment to create a [`HashMap`](https://doc.rust-lang.org/stable/std/collections/struct.HashMap.html) object, and initialize it with default values for Hacker News, and TechCrunch. Note: Verify that the URLs are correct when you get suggestions.\n\n```rust\nfn main() {\n    // Define RSS feed URLs in the variable rss_feeds\n    // Use a HashMap\n    // Add Hacker News and TechCrunch\n    // Ensure to use String as type\n\n}\n```\n\nNote that the code comment provides instructions for:\n\n1. The variable name `rss_feeds`.\n2. The `HashMap` type.\n3. Initial seed key/value pairs.\n4. String as type (can be seen with `to_string()` calls).\n\nOne possible suggested path can be as follows:\n\n```rust\nuse std::collections::HashMap;\n\nfn main() {\n    // Define RSS feed URLs in the variable rss_feeds\n    // Use a HashMap\n    // Add Hacker News and TechCrunch\n    // Ensure to use String as type\n    let rss_feeds = HashMap::from([\n        (\"Hacker News\".to_string(), \"https://news.ycombinator.com/rss\".to_string()),\n        (\"TechCrunch\".to_string(), \"https://techcrunch.com/feed/\".to_string()),\n    ]);\n\n}\n```\n\n![VS Code with Code Suggestions for RSS feed URLs for Hacker News and TechCrunch](https://about.gitlab.com/images/blogimages/learn-rust-with-ai-code-suggestions-advanced-programming/vs_code_main_array_rss_feed_urls_suggested.png)\n\nOpen a new terminal in VS Code (cmd shift p - search for `terminal`), and run `cargo build` to build the changes. The error message instructs you to add the `use std::collections::HashMap;` import.\n\nThe next step is to do something with the RSS feed URLs. [The previous blog post](/blog/learning-rust-with-a-little-help-from-ai-code-suggestions-getting-started/) taught us to split code into functions. We want to organize the code more modularly for our reader application, and use Rust modules.\n\n## Modules\n[Modules](https://doc.rust-lang.org/rust-by-example/mod.html) help with organizing code. They can also be used to hide functions into the module scope, limiting access to them from the main() scope. In our reader application, we want to fetch the RSS feed content, and parse the XML response. The `main()` caller should only be able to access the `get_feeds()` function, while other functionality is only available in the module.\n\nCreate a new file `feed_reader.rs` in the `src/` directory. Instruct Code Suggestions to create a public module named `feed_reader`, and a public function `get_feeds()` with a String HashMap as input. Important: The file and module names need to be the same, following the [Rust module structure](https://doc.rust-lang.org/book/ch07-02-defining-modules-to-control-scope-and-privacy.html).\n\n![Code Suggestions: Create public module, with function and input types](https://about.gitlab.com/images/blogimages/learn-rust-with-ai-code-suggestions-advanced-programming/code_suggestions_rust_public_module_function_input.png){: .shadow}\n\nInstructing Code Suggestions with the input variable name and type will also import the required `std::collections::HashMap` module. Tip: Experiment with the comments, and refine the variable types to land the best results. Passing function parameters as object references is considered best practice in Rust, for example.\n\n```rust\n// Create public module feed_reader\n// Define get_feeds() function which takes rss_feeds as String HashMap reference as input\npub mod feed_reader {\n    use std::collections::HashMap;\n\n    pub fn get_feeds(rss_feeds: &HashMap\u003CString, String>) {\n        // Do something with the RSS feeds\n    }\n}\n```\n\n![Code Suggestions: Public module with `get_feeds()` function, and suggested input variable](https://about.gitlab.com/images/blogimages/learn-rust-with-ai-code-suggestions-advanced-programming/code_suggestions_rust_public_module_function_input.png){: .shadow}\n\nInside the function, continue to instruct Code Suggestions with the following steps:\n\n1. `// Iterate over the RSS feed URLs`\n2. `// Fetch URL content`\n3. `// Parse XML body`\n4. `// Print the result`\n\n![Code Suggestions: Public module with `get_feeds()` function, step 1: Iterate](https://about.gitlab.com/images/blogimages/learn-rust-with-ai-code-suggestions-advanced-programming/code_suggestions_rust_module_function_01_iterate.png){: .shadow}\n\n![Code Suggestions: Public module with `get_feeds()` function, step 2: Fetch URL content](https://about.gitlab.com/images/blogimages/learn-rust-with-ai-code-suggestions-advanced-programming/code_suggestions_rust_module_function_02_fetch_content.png){: .shadow}\n\n![Code Suggestions: Public module with `get_feeds()` function, step 3: Parse XML body](https://about.gitlab.com/images/blogimages/learn-rust-with-ai-code-suggestions-advanced-programming/code_suggestions_rust_module_function_03_parse_body.png){: .shadow}\n\n![Code Suggestions: Public module with `get_feeds()` function, step 4: Print the results](https://about.gitlab.com/images/blogimages/learn-rust-with-ai-code-suggestions-advanced-programming/code_suggestions_rust_module_function_04_print_result.png){: .shadow}\n\nThe following code can be suggested:\n\n```rust\n// Create public module feed_reader\n// Define get_feeds() function which takes rss_feeds as String HashMap reference as input\npub mod feed_reader {\n    use std::collections::HashMap;\n\n    pub fn get_feeds(rss_feeds: &HashMap\u003CString, String>) {\n        // Iterate over the RSS feed URLs\n        for (name, url) in rss_feeds {\n            println!(\"{}: {}\", name, url);\n\n            // Fetch URL content\n            let body = reqwest::blocking::get(url).unwrap().text().unwrap();\n\n            // Parse XML body\n            let parsed_body = roxmltree::Document::parse(&body).unwrap();\n\n            // Print the result\n            println!(\"{:#?}\", parsed_body);\n        }\n    }\n}\n```\n\nYou see a new keyword here: [`unwrap()`](https://doc.rust-lang.org/rust-by-example/error/option_unwrap.html). Rust does not support `null` values, and uses the [`Option` type](https://doc.rust-lang.org/rust-by-example/std/option.html) for any value. If you are certain to use a specific wrapped type, for example, `Text` or `String`, you can call the `unwrap()` method to get the value. The `unwrap()` method will panic if the value is `None`.\n\n**Note** Code Suggestions referred to the `reqwest::blocking::get` function for the `// Fetch URL content` comment instruction. The [`reqwest` crate](https://docs.rs/reqwest/latest/reqwest/) name is intentional and not a typo. It provides a convenient, higher-level HTTP client for async and blocking requests.\n\nParsing the XML body is tricky - you might get different results, and the schema is not the same for every RSS feed URL. Let us try to call the `get_feeds()` function, and then work on improving the code.\n\n### Call the module function in main()\n\nThe main() function does not know about the `get_feeds()` function yet, so we need to import its module. In other programming languages, you might have seen the keywords `include` or `import`. The Rust module system is different.\n\nModules are organized in path directories. In our example, both source files exist on the same directory level. `feed_reader.rs` is interpreted as crate, containing one module called `feed_reader`, which defines the function `get_feeds()`.\n\n```text\nsrc/\n  main.rs\n  feed_reader.rs\n\n```\n\nIn order to access `get_feeds()` from the `feed_reader.rs` file, we need to [bring module path](https://doc.rust-lang.org/book/ch07-04-bringing-paths-into-scope-with-the-use-keyword.html) into the `main.rs` scope first, and then call the full function path.\n\n```rust\nmod feed_reader;\n\nfn main() {\n\n    feed_reader::feed_reader::get_feeds(&rss_feeds);\n\n```\n\nAlternatively, we can import the full function path with the `use` keyword, and later use the short function name.\n\n```rust\nmod feed_reader;\nuse feed_reader::feed_reader::get_feeds;\n\nfn main() {\n\n    get_feeds(&rss_feeds);\n\n```\n\n**Tip:** I highly recommend reading the [Clear explanation of the Rust module system blog post](https://www.sheshbabu.com/posts/rust-module-system/) to get a better visual understanding.\n\n```diff\nfn main() {\n    // ...\n\n    // Print feed_reader get_feeds() output\n    println!(\"{}\", feed_reader::get_feeds(&rss_feeds));\n\n```\n\n```rust\nuse std::collections::HashMap;\n\nmod feed_reader;\n// Alternative: Import full function path\n//use feed_reader::feed_reader::get_feeds;\n\nfn main() {\n    // Define RSS feed URLs in the variable rss_feeds\n    // Use a HashMap\n    // Add Hacker News and TechCrunch\n    // Ensure to use String as type\n    let rss_feeds = HashMap::from([\n        (\"Hacker News\".to_string(), \"https://news.ycombinator.com/rss\".to_string()),\n        (\"TechCrunch\".to_string(), \"https://techcrunch.com/feed/\".to_string()),\n    ]);\n\n    // Call get_feeds() from feed_reader module\n    feed_reader::feed_reader::get_feeds(&rss_feeds);\n    // Alternative: Imported full path, use short path here.\n    //get_feeds(&rss_feeds);\n}\n```\n\nRun `cargo build` in the terminal again to build the code.\n\n```shell\ncargo build\n```\n\nPotential build errors when Code Suggestions refer to common code and libraries for HTTP requests, and XML parsing:\n\n1. Error: `could not find blocking in reqwest`. Solution: Enable the `blocking` feature for the crate in `Config.toml`: `reqwest = { version = \"0.11.20\", features = [\"blocking\"] }`.\n2. Error: `failed to resolve: use of undeclared crate or module reqwest`. Solution: Add the `reqwest` crate.\n3. Error: `failed to resolve: use of undeclared crate or module roxmltree`. Solution: Add the `roxmltree` crate.\n\n```shell\nvim Config.toml\n\nreqwest = { version = \"0.11.20\", features = [\"blocking\"] }\n```\n\n```shell\ncargo add reqwest\ncargo add roxmltree\n```\n\n**Tip:** Copy the error message string, with a leading `Rust \u003Cerror message>` into your preferred browser to check whether a missing crate is available. Usually this search leads to a result on crates.io and you can add the missing dependencies.\n\nWhen the build is successful, run the code with `cargo run` and inspect the Hacker News RSS feed output.\n\n![VS Code terminal, cargo run to fetch Hacker News XML feed](https://about.gitlab.com/images/blogimages/learn-rust-with-ai-code-suggestions-advanced-programming/vs_code_terminal_fetch_rss_feed_output_hacker_news.png){: .shadow}\n\nWhat is next with parsing the XML body into human-readable format? In the next section, we will learn about existing solutions and how Rust crates come into play.\n\n## Crates\nRSS feeds share a common set of protocols and specifications. It feels like reinventing the wheel to parse XML items and understand the lower object structure. Recommendation for these types of tasks: Look whether someone else had the same problem already and might have created code to solve the problem.\n\nReusable library code in Rust is organized in so-called [`Crates`](https://doc.rust-lang.org/rust-by-example/crates.html), and made available in packages, and the package registry on crates.io. You can add these dependencies to your project by editing the `Config.toml` in the `[dependencies]` section, or using `cargo add \u003Cname>`.\n\nFor the reader app, we want to use the [feed-rs crate](https://crates.io/crates/feed-rs). Open a new terminal, and run the following command:\n\n```shell\ncargo add feed-rs\n```\n\n![VS Code Terminal Terminal: Add crate, verify in Config.toml](https://about.gitlab.com/images/blogimages/learn-rust-with-ai-code-suggestions-advanced-programming/vs_code_rust_crate_add_feed-rs_explained.png)\n\n### feed-rs: parse XML feed\nNavigate into `src/feed_reader.rs` and modify the part where we parse the XML body. Code Suggestions understands how to call the `feed-rs` crate `parser::parse` function -- there is only one specialty here: `feed-rs` [expects string input as raw bytes](https://docs.rs/feed-rs/latest/feed_rs/parser/fn.parse_with_uri.html) to determine the encoding itself. We can provide instructions in the comment to get the expected result though.\n\n```rust\n\n            // Parse XML body with feed_rs parser, input in bytes\n            let parsed_body = feed_rs::parser::parse(body.as_bytes()).unwrap();\n\n```\n\n![Code Suggestions: Public module with `get_feeds()` function, step 5: Modify XML parser to feed-rs](https://about.gitlab.com/images/blogimages/learn-rust-with-ai-code-suggestions-advanced-programming/code_suggestions_rust_module_function_05_use_feed_rs_to_parse.png){: .shadow}\n\nThe benefit of using `feed-rs` is not immediately visible until you see the printed output with `cargo run`: All keys and values are mapped to their respective Rust object types, and can be used for further operations.\n\n![VS Code terminal, cargo run to fetch Hacker News XML feed](https://about.gitlab.com/images/blogimages/learn-rust-with-ai-code-suggestions-advanced-programming/vs_code_terminal_fetch_rss_feed_output_hacker_news_feed_rs.png){: .shadow}\n\n## Runtime configuration: Program arguments\nUntil now, we have run the program with hard-coded RSS feed values compiled into the binary. The next step is allowing to configure the RSS feeds at runtime.\n\nRust provides [program arguments](https://doc.rust-lang.org/rust-by-example/std_misc/arg.html) in the standard misc library. [Parsing the arguments](https://doc.rust-lang.org/rust-by-example/std_misc/arg/matching.html) provides a better and faster learning experience than aiming for advanced program argument parsers (for example, the [clap](https://docs.rs/clap/latest/clap/) crate), or moving the program parameters into a configuration file and format ([TOML](https://toml.io/en/), YAML). You are reading these lines after I tried and failed with different routes for the best learning experience. This should not stop you from taking the challenge to configure RSS feeds in alternative ways.\n\nAs a boring solution, the command parameters can be passed as `\"name,url\"` string value pairs, and then are split by the `,` character to extract the name and URL values. The comment instructs Code Suggestions to perform these operations and extend the `rss_feeds` HashMap with the new values. Note that the variable might not be mutable, and, therefore, needs to be modified to `let mut rss_feeds`.\n\nNavigate into `src/main.rs` and add the following code to the `main()` function after the `rss_feeds` variable. Start with a comment to define the program arguments, and check the suggested code snippets.\n\n```rust\n\n    // Program args, format \"name,url\"\n    // Split value by , into name, url and add to rss_feeds\n\n```\n\n![Code suggestions for program arguments, and splitting name,URL values for the rss_feeds variable](https://about.gitlab.com/images/blogimages/learn-rust-with-ai-code-suggestions-advanced-programming/code_suggestions_rust_program_args_boring_solution.png){: .shadow}\n\nThe full code example can look like the following:\n\n```rust\nfn main() {\n    // Define RSS feed URLs in the variable rss_feeds\n    // Use a HashMap\n    // Add Hacker News and TechCrunch\n    // Ensure to use String as type\n    let mut rss_feeds = HashMap::from([\n        (\"Hacker News\".to_string(), \"https://news.ycombinator.com/rss\".to_string()),\n        (\"TechCrunch\".to_string(), \"https://techcrunch.com/feed/\".to_string()),\n    ]);\n\n    // Program args, format \"name,url\"\n    // Split value by , into name, url and add to rss_feeds\n    for arg in std::env::args().skip(1) {\n        let mut split = arg.split(\",\");\n        let name = split.next().unwrap();\n        let url = split.next().unwrap();\n        rss_feeds.insert(name.to_string(), url.to_string());\n    }\n\n    // Call get_feeds() from feed_reader module\n    feed_reader::feed_reader::get_feeds(&rss_feeds);\n    // Alternative: Imported full path, use short path here.\n    //get_feeds(&rss_feeds);\n}\n```\n\nYou can pass program arguments directly to the `cargo run` command, preceding the arguments with `--`. Enclose all arguments with double quotes, put the name followed by a comma and the RSS feed URL as argument. Separate all arguments with whitespaces.\n\n```shell\ncargo build\n\ncargo run -- \"GitLab Blog,https://about.gitlab.com/atom.xml\" \"CNCF,https://www.cncf.io/feed/\"\n```\n\n![VS Code terminal, RSS feed output example for the GitLab blog](https://about.gitlab.com/images/blogimages/learn-rust-with-ai-code-suggestions-advanced-programming/vs_code_terminal_gitlab_blog_rss_feed_example.png){: .shadow}\n\n### User input error handling\nIf the provided user input does not match the program expectation, we need to [throw an error](https://doc.rust-lang.org/rust-by-example/error.html) and help the caller to fix the program arguments. For example, passing a malformed URL format should be treated as a runtime error. Instruct Code Suggestions with a code comment to throw an error if the URL is not valid.\n\n```rust\n\n    // Ensure that URL contains a valid format, otherwise throw an error\n\n```\n\nOne possible solution is to check if the `url` variable starts with `http://` or `https://`. If not, throw an error using the [panic! macro](https://doc.rust-lang.org/rust-by-example/std/panic.html). The full code example looks like the following:\n\n```rust\n\n    // Program args, format \"name,url\"\n    // Split value by , into name, url and add to rss_feeds\n    for arg in std::env::args().skip(1) {\n        let mut split = arg.split(\",\");\n        let name = split.next().unwrap();\n        let url = split.next().unwrap();\n\n        // Ensure that URL contains a valid format, otherwise throw an error\n        if !url.starts_with(\"http://\") && !url.starts_with(\"https://\") {\n            panic!(\"Invalid URL format: {}\", url);\n        }\n\n        rss_feeds.insert(name.to_string(), url.to_string());\n    }\n\n```\n\nTest the error handling with removing a `:` in one of the URL strings. Add the `RUST_BACKTRACE=full` environment variable to get more verbose output when the `panic()` call happens.\n\n```shell\nRUST_BACKTRACE=full cargo run -- \"GitLab Blog,https://about.gitlab.com/atom.xml\" \"CNCF,https//www.cncf.io/feed/\"\n```\n\n![VS Code Terminal with wrong URL format, panic error backtrace](https://about.gitlab.com/images/blogimages/learn-rust-with-ai-code-suggestions-advanced-programming/vs_code_terminal_url_format_error_panic_backtrace.png){: .shadow}\n\n## Persistence and data storage\nThe boring solution for storing the feed data is to dump the parsed body into a new file. Instruct Code Suggestions to use a pattern that includes the RSS feed name, and the current ISO date.\n\n```rust\n\n    // Parse XML body with feed_rs parser, input in bytes\n    let parsed_body = feed_rs::parser::parse(body.as_bytes()).unwrap();\n\n    // Print the result\n    println!(\"{:#?}\", parsed_body);\n\n    // Dump the parsed body to a file, as name-current-iso-date.xml\n    let now = chrono::offset::Local::now();\n    let filename = format!(\"{}-{}.xml\", name, now.format(\"%Y-%m-%d\"));\n    let mut file = std::fs::File::create(filename).unwrap();\n    file.write_all(body.as_bytes()).unwrap();\n\n```\n\nA possible suggestion will include using the [chrono crate](https://crates.io/crates/chrono). Add it using `cargo add chrono` and then invoke `cargo build` and `cargo run` again.\n\nThe files are written into the same directory where `cargo run` was executed. If you are executing the binary direcly in the `target/debug/` directory, all files will be dumped there.\n\n![VS Code with CNCF RSS feed content file, saved on disk](https://about.gitlab.com/images/blogimages/learn-rust-with-ai-code-suggestions-advanced-programming/vs_code_cncf_rss_feed_saved_on_disk.png)\n\n## Optimization\nThe entries in the `rss_feeds` variable are executed sequentially. Imagine having a list of 100+ URLs configured - this could take a long time to fetch and process. What if we could execute multiple fetch requests in parallel?\n\n### Asynchronous execution\nRust provides [threads](https://doc.rust-lang.org/book/ch16-01-threads.html) for asynchronous execution.\n\nThe simplest solution will be spawning a thread for each RSS feed URL. We will discuss optimization strategies later. Before you continue with parallel execution, measure the sequential code execution time by preceding the `time` command with `cargo run`.\n\n```text\ntime cargo run -- \"GitLab Blog,https://about.gitlab.com/atom.xml\" \"CNCF,https://www.cncf.io/feed/\"\n\n0.21s user 0.08s system 10% cpu 2.898 total\n```\n\nNote that this exercise could require more manual code work. It is recommended to persist the sequential working state in a new Git commit and branch `sequential-exec`, to better compare the impact of parallel execution.\n\n```shell\ngit commit -avm \"Sequential execution working\"\ngit checkout -b sequential-exec\ngit push -u origin sequential-exec\n\ngit checkout main\n```\n\n### Spawning threads\nOpen `src/feed_reader.rs` and refactor the `get_feeds()` function. Start with a Git commit for the current state, and then delete the contents of the function scope. Add the following code comments with instructions for Code Suggestions:\n\n1. `// Store threads in vector`: Store thread handles in a vector, so we can wait for them to finish at the end of the function call.\n2. `// Loop over rss_feeds and spawn threads`: Create boilerplate code for iterating over all RSS feeds, and spawn a new thread.\n\nAdd the following `use` statements to work with the `thread` and `time` modules.\n\n```rust\n\n    use std::thread;\n    use std::time::Duration;\n\n```\n\nContinue writing the code, and close the for loop. Code Suggestions will automatically propose adding the thread handle in the `threads` vector variable, and offer to join the threads at the end of the function.\n\n```rust\n\n    pub fn get_feeds(rss_feeds: &HashMap\u003CString, String>) {\n\n        // Store threads in vector\n        let mut threads: Vec\u003Cthread::JoinHandle\u003C()>> = Vec::new();\n\n        // Loop over rss_feeds and spawn threads\n        for (name, url) in rss_feeds {\n            let thread_name = name.clone();\n            let thread_url = url.clone();\n            let thread = thread::spawn(move || {\n\n            });\n            threads.push(thread);\n        }\n\n        // Join threads\n        for thread in threads {\n            thread.join().unwrap();\n        }\n    }\n\n```\n\nAdd the `thread` crate, build and run the code again.\n\n```shell\ncargo add thread\n\ncargo build\n\ncargo run -- \"GitLab Blog,https://about.gitlab.com/atom.xml\" \"CNCF,https://www.cncf.io/feed/\"\n```\n\nAt this stage, no data is processed or printed. Before we continue re-adding the functionality, let us learn about the newly introduced keywords here.\n\n### Function scopes, threads, and closures\nThe suggested code brings new keywords and design patterns to learn. The thread handle is of the type `thread::JoinHandle`, indicating that we can use it to wait for the threads to finish ([join()](https://doc.rust-lang.org/book/ch16-01-threads.html#waiting-for-all-threads-to-finish-using-join-handles)).\n\n`thread::spawn()` spawns a new thread, where we can pass a function object. In this case, a [closure](https://doc.rust-lang.org/book/ch13-01-closures.html) expression is passed as anonymous function. Closure inputs are passed using the `||` syntax. You will recognize the [`move` Closure](https://doc.rust-lang.org/book/ch16-01-threads.html#using-move-closures-with-threads), which moves the function scoped variables into the thread scope. This avoids manually specifying which variables need to be passed into the new function/closure scope.\n\nThere is a limitation though: `rss_feeds` is a reference `&`, passed as parameter by the `get_feeds()` function caller. The variable is only valid in the function scope. Use the following code snippet to provoke this error:\n\n```rust\npub fn get_feeds(rss_feeds: &HashMap\u003CString, String>) {\n\n    // Store threads in vector\n    let mut threads: Vec\u003Cthread::JoinHandle\u003C()>> = Vec::new();\n\n    // Loop over rss_feeds and spawn threads\n    for (key, value) in rss_feeds {\n        let thread = thread::spawn(move || {\n            println!(\"{}\", key);\n        });\n    }\n}\n```\n\n![VS Code Terminal, variable scope error with references and thread move closure](https://about.gitlab.com/images/blogimages/learn-rust-with-ai-code-suggestions-advanced-programming/vs_code_terminal_cargo_build_error_function_threads_variable_scopes.png){: .shadow}\n\nAlthough the `key` variable was created in the function scope, it references the `rss_feeds` variable, and therefore, it cannot be moved into the thread scope. Any values accessed from the function parameter `rss_feeds` hash map will require a local copy with `clone()`.\n\n![VS Code Terminal, thread spawn with clone](https://about.gitlab.com/images/blogimages/learn-rust-with-ai-code-suggestions-advanced-programming/code_suggestions_rust_thread_spawn_clone.png){: .shadow}\n\n```rust\npub fn get_feeds(rss_feeds: &HashMap\u003CString, String>) {\n\n    // Store threads in vector\n    let mut threads: Vec\u003Cthread::JoinHandle\u003C()>> = Vec::new();\n\n    // Loop over rss_feeds and spawn threads\n    for (name, url) in rss_feeds {\n        let thread_name = name.clone();\n        let thread_url = url.clone();\n        let thread = thread::spawn(move || {\n            // Use thread_name and thread_url as values, see next chapter for instructions.\n\n```\n\n## Parse feed XML into object types\nThe next step is to repeat the RSS feed parsing steps in the thread closure. Add the following code comments with instructions for Code Suggestions:\n\n1. `// Parse XML body with feed_rs parser, input in bytes` to tell Code Suggestions that we want to fetch the RSS feed URL content, and parse it with the `feed_rs` crate functions.\n2. `// Check feed_type attribute feed_rs::model::FeedType::RSS2 or Atom and print its name`: Extract the feed type by comparing the `feed_type` attribute with the [`feed_rs::model::FeedType`](https://docs.rs/feed-rs/latest/feed_rs/model/enum.FeedType.html). This needs more direct instructions for Code Suggestions telling it about the exact Enum values to match against.\n\n![Instruct Code Suggestions to match against specific feed types](https://about.gitlab.com/images/blogimages/learn-rust-with-ai-code-suggestions-advanced-programming/code_suggestions_feed_rs_type_condition.png){: .shadow}\n\n```rust\n\n            // Parse XML body with feed_rs parser, input in bytes\n            let body = reqwest::blocking::get(thread_url).unwrap().bytes().unwrap();\n            let feed = feed_rs::parser::parse(body.as_ref()).unwrap();\n\n            // Check feed_type attribute feed_rs::model::FeedType::RSS2 or Atom and print its name\n            if feed.feed_type == feed_rs::model::FeedType::RSS2 {\n                println!(\"{} is an RSS2 feed\", thread_name);\n            } else if feed.feed_type == feed_rs::model::FeedType::Atom {\n                println!(\"{} is an Atom feed\", thread_name);\n            }\n\n```\n\nBuild and run the program again, and verify its output.\n\n```text\ntime cargo run -- \"GitLab Blog,https://about.gitlab.com/atom.xml\" \"CNCF,https://www.cncf.io/feed/\"\n\nCNCF is an RSS2 feed\nTechCrunch is an RSS2 feed\nGitLab Blog is an Atom feed\nHacker News is an RSS2 feed\n```\n\nLet us verify this output by opening the feed URLs in the browser, or inspecting the previously downloaded files.\n\nHacker News supports RSS version 2.0, with `channel(title,link,description,item(title,link,pubDate,comments))`. TechCrunch and the CNCF blog follow a similar structure.\n```xml\n\u003Crss version=\"2.0\">\u003Cchannel>\u003Ctitle>Hacker News\u003C/title>\u003Clink>https://news.ycombinator.com/\u003C/link>\u003Cdescription>Links for the intellectually curious, ranked by readers.\u003C/description>\u003Citem>\u003Ctitle>Writing a debugger from scratch: Breakpoints\u003C/title>\u003Clink>https://www.timdbg.com/posts/writing-a-debugger-from-scratch-part-5/\u003C/link>\u003CpubDate>Wed, 27 Sep 2023 06:31:25 +0000\u003C/pubDate>\u003Ccomments>https://news.ycombinator.com/item?id=37670938\u003C/comments>\u003Cdescription>\u003C![CDATA[\u003Ca href=\"https://news.ycombinator.com/item?id=37670938\">Comments\u003C/a>]]>\u003C/description>\u003C/item>\u003Citem>\n```\n\nThe GitLab blog uses the [Atom](https://datatracker.ietf.org/doc/html/rfc4287) feed format similar to RSS, but still requires different parsing logic.\n```xml\n\u003C?xml version='1.0' encoding='utf-8' ?>\n\u003Cfeed xmlns='http://www.w3.org/2005/Atom'>\n\u003C!-- / Get release posts -->\n\u003C!-- / Get blog posts -->\n\u003Ctitle>GitLab\u003C/title>\n\u003Cid>https://about.gitlab.com/blog\u003C/id>\n\u003Clink href='https://about.gitlab.com/blog/' />\n\u003Cupdated>2023-09-26T00:00:00+00:00\u003C/updated>\n\u003Cauthor>\n\u003Cname>The GitLab Team\u003C/name>\n\u003C/author>\n\u003Centry>\n\u003Ctitle>Atlassian Server ending: Goodbye disjointed toolchain, hello DevSecOps platform\u003C/title>\n\u003Clink href='https://about.gitlab.com/blog/atlassian-server-ending-move-to-a-single-devsecops-platform/' rel='alternate' />\n\u003Cid>https://about.gitlab.com/blog/atlassian-server-ending-move-to-a-single-devsecops-platform/\u003C/id>\n\u003Cpublished>2023-09-26T00:00:00+00:00\u003C/published>\n\u003Cupdated>2023-09-26T00:00:00+00:00\u003C/updated>\n\u003Cauthor>\n\u003Cname>Dave Steer, Justin Farris\u003C/name>\n\u003C/author>\n```\n\n### Map generic feed data types\nUsing [`roxmltree::Document::parse`](https://docs.rs/roxmltree/latest/roxmltree/struct.Document.html) would require us to understand the XML node tree and its specific tag names. Fortunately, [feed_rs::model::Feed](https://docs.rs/feed-rs/latest/feed_rs/model/struct.Feed.html) provides a combined model for RSS and Atom feeds, therefore let us continue using the `feed_rs` crate.\n\n1. Atom: Feed->Feed, Entry->Entry\n2. RSS: Channel->Feed, Item->Entry\n\nIn addition to the mapping above, we need to extract the required attributes, and map their data types. It is helpful to open the [feed_rs::model documentation](https://docs.rs/feed-rs/latest/feed_rs/model/index.html) to understand the structs and their fields and implementations. Otherwise, some suggestions would result in type conversion errors and compilation failures, that are specific to the `feed_rs` implementation.\n\nA [`Feed`](https://docs.rs/feed-rs/latest/feed_rs/model/struct.Feed.html) struct provides the `title`, type `Option\u003CText>` (either a value is set, or nothing). An [`Entry`](https://docs.rs/feed-rs/latest/feed_rs/model/struct.Entry.html) struct provides:\n\n1. `title`: `Option\u003CText>`with [`Text`](https://docs.rs/feed-rs/latest/feed_rs/model/struct.Text.html) and the `content` field as `String`.\n2. `updated`: `Option\u003CDateTime\u003CUtc>>` with [`DateTime`](https://docs.rs/chrono/latest/chrono/struct.DateTime.html) with the [`format()` method](https://docs.rs/chrono/latest/chrono/struct.DateTime.html#method.format).\n3. `summary`: `Option\u003CText>` [`Text`](https://docs.rs/feed-rs/latest/feed_rs/model/struct.Text.html) and the `content` field as `String`.\n4. `links`: `Vec\u003CLink>`, vector with [`Link`](https://docs.rs/feed-rs/latest/feed_rs/model/struct.Link.html) items. The `href` attribute provides the raw URL string.\n\nUse this knowledge to extract the required data from the feed entries. Reminder that all `Option` types need to call `unwrap()`, which requires more raw instructions for Code Suggestions.\n\n```rust\n\n                // https://docs.rs/feed-rs/latest/feed_rs/model/struct.Feed.html\n                // https://docs.rs/feed-rs/latest/feed_rs/model/struct.Entry.html\n                // Loop over all entries, and print\n                // title.unwrap().content\n                // published.unwrap().format\n                // summary.unwrap().content\n                // links href as joined string\n                for entry in feed.entries {\n                    println!(\"Title: {}\", entry.title.unwrap().content);\n                    println!(\"Published: {}\", entry.published.unwrap().format(\"%Y-%m-%d %H:%M:%S\"));\n                    println!(\"Summary: {}\", entry.summary.unwrap().content);\n                    println!(\"Links: {:?}\", entry.links.iter().map(|link| link.href.clone()).collect::\u003CVec\u003CString>>().join(\", \"));\n                    println!();\n                }\n\n```\n\n![Code suggestions to print feed entry types, with specific requirements](https://about.gitlab.com/images/blogimages/learn-rust-with-ai-code-suggestions-advanced-programming/code_suggestions_print_feed_entries_fields_with_rust_type_specifics.png){: .shadow}\n\n### Error handling with Option unwrap()\nContinue iterating on the multi-line instructions after building and running the program again. Spoiler: `unwrap()` will call the `panic!` macro and crash the program when it encounters empty values. This can happen if a field like `summary` is not set in the feed data.\n\n```shell\nGitLab Blog is an Atom feed\nTitle: How the Colmena project uses GitLab to support citizen journalists\nPublished: 2023-09-27 00:00:00\nthread '\u003Cunnamed>' panicked at 'called `Option::unwrap()` on a `None` value', src/feed_reader.rs:40:59\n```\n\nA potential solution is to use [`std::Option::unwrap_or_else`](https://doc.rust-lang.org/std/option/enum.Option.html#method.unwrap_or_else) and set an empty string as default value. The syntax requires a closure that returns an empty `Text` struct instantiation.\n\nSolving the problem required many attempts to find the correct initialization, passing just an empty string did not work with the custom types. I will show you all my endeavors, including the research paths.\n\n```rust\n// Problem: The `summary` attribute is not always initialized. unwrap() will panic! then.\n// Requires use mime; and use feed_rs::model::Text;\n/*\n// 1st attempt: Use unwrap() to extraxt Text from Option\u003CText> type.\nprintln!(\"Summary: {}\", entry.summary.unwrap().content);\n// 2nd attempt. Learned about unwrap_or_else, passing an empty string.\nprintln!(\"Summary: {}\", entry.summary.unwrap_or_else(|| \"\").content);\n// 3rd attempt. summary is of the Text type, pass a new struct instantiation.\nprintln!(\"Summary: {}\", entry.summary.unwrap_or_else(|| Text{}).content);\n// 4th attempt. Struct instantiation requires 3 field values.\nprintln!(\"Summary: {}\", entry.summary.unwrap_or_else(|| Text{\"\", \"\", \"\"}).content);\n// 5th attempt. Struct instantation with public fields requires key: value syntax\nprintln!(\"Summary: {}\", entry.summary.unwrap_or_else(|| Text{content_type: \"\", src: \"\", content: \"\"}).content);\n// 6th attempt. Reviewed expected Text types in https://docs.rs/feed-rs/latest/feed_rs/model/struct.Text.html and created Mime and String objects\nprintln!(\"Summary: {}\", entry.summary.unwrap_or_else(|| Text{content_type: mime::TEXT_PLAIN, src: String::new(), content: String::new()}).content);\n// 7th attempt: String and Option\u003CString> cannot be casted automagically. Compiler suggested using `Option::Some()`.\nprintln!(\"Summary: {}\", entry.summary.unwrap_or_else(|| Text{content_type: mime::TEXT_PLAIN, src: Option::Some(), content: String::new()}).content);\n*/\n\n// xth attempt: Solution. Option::Some() requires a new String object.\nprintln!(\"Summary: {}\", entry.summary.unwrap_or_else(|| Text{content_type: mime::TEXT_PLAIN, src: Option::Some(String::new()), content: String::new()}).content);\n```\n\nThis approach did not feel satisfying, since the code line is complicated to read, and required manual work without help from Code Suggestions. Taking a step back, I reviewed what brought me there - if `Option` is `none`, `unwrap()` will throw an error. Maybe there is an easier way to handle this? I asked Code Suggestions in a new comment:\n\n```shell\n\n                // xth attempt: Solution. Option::Some() requires a new String object.\n                println!(\"Summary: {}\", entry.summary.unwrap_or_else(|| Text{content_type: mime::TEXT_PLAIN, src: Option::Some(String::new()), content: String::new()}).content);\n\n                // Alternatively, use Option.is_none()\n\n```\n\n![Code suggestions asked for alternative with Options.is_none](https://about.gitlab.com/images/blogimages/learn-rust-with-ai-code-suggestions-advanced-programming/code_suggestions_after_complex_unwrap_or_else_ask_for_alternative_option.png){: .shadow}\n\nIncreased readability, less CPU cycles wasted on `unwrap()`, and a great learning curve from solving a complex problem to using a boring solution. Win-win.\n\nBefore we forget: Re-add storing the XML data on disk to complete the reader app again.\n\n```rust\n\n                // Dump the parsed body to a file, as name-current-iso-date.xml\n                let file_name = format!(\"{}-{}.xml\", thread_name, chrono::Local::now().format(\"%Y-%m-%d-%H-%M-%S\"));\n                let mut file = std::fs::File::create(file_name).unwrap();\n                file.write_all(body.as_ref()).unwrap();\n\n```\n\nBuild and run the program to verify the output.\n\n```shell\ncargo build\n\ntime cargo run -- \"GitLab Blog,https://about.gitlab.com/atom.xml\" \"CNCF,https://www.cncf.io/feed/\"\n```\n\n![VS Code Terminal, cargo run with formatted feed entries output](https://about.gitlab.com/images/blogimages/learn-rust-with-ai-code-suggestions-advanced-programming/vs_code_terminal_cargo_run_formatted_output_final.png)\n\n## Benchmarks\n\n### Sequential vs. Parallel execution benchmark\nCompare the execution time benchmarks by creating five samples each.\n\n1. Sequential execution. [Example source code MR](https://gitlab.com/gitlab-da/use-cases/ai/learn-with-ai/learn-rust-ai-app-reader/-/merge_requests/1)\n2. Parallel exeuction. [Example source code MR](https://gitlab.com/gitlab-da/use-cases/ai/learn-with-ai/learn-rust-ai-app-reader/-/merge_requests/3)\n\n```shell\n# Sequential\ngit checkout sequential-exec\n\ntime cargo run -- \"GitLab Blog,https://about.gitlab.com/atom.xml\" \"CNCF,https://www.cncf.io/feed/\"\n\n0.21s user 0.08s system 10% cpu 2.898 total\n0.21s user 0.08s system 11% cpu 2.585 total\n0.21s user 0.09s system 10% cpu 2.946 total\n0.19s user 0.08s system 10% cpu 2.714 total\n0.20s user 0.10s system 10% cpu 2.808 total\n```\n\n```shell\n# Parallel\ngit checkout parallel-exec\n\ntime cargo run -- \"GitLab Blog,https://about.gitlab.com/atom.xml\" \"CNCF,https://www.cncf.io/feed/\"\n\n0.19s user 0.08s system 17% cpu 1.515 total\n0.18s user 0.08s system 16% cpu 1.561 total\n0.18s user 0.07s system 17% cpu 1.414 total\n0.19s user 0.08s system 18% cpu 1.447 total\n0.17s user 0.08s system 16% cpu 1.453 total\n```\n\nThe CPU usage increased for parallel execution of four RSS feed threads, but it nearly halved the total time compared to sequential execution. With that in mind, we can continue learning Rust and optimize the code and functionality.\n\nNote that we are running the debug build through Cargo, and not the optimized released builds yet. There are caveats with parallel execution though: Some HTTP endpoints put rate limits in place, where parallelism could hit these thresholds easier.\n\nThe system executing multiple threads in parallel might get overloaded too – threads require context switching in the Kernel, assigning resources to each thread. While one thread gets computing resources, other threads are put to sleep. If there are too many threads spawned, this might slow down the system, rather than speeding up the operations. Solutions include design patterns such as [work queues](https://docs.rs/work-queue/latest/work_queue/), where the caller adds a task into a queue, and a defined number of worker threads pick up the tasks for asynchronous execution.\n\nRust also provides data synchronisation between threads, so-called [channels](https://doc.rust-lang.org/rust-by-example/std_misc/channels.html). To ensure concurrent data access, [mutexes](https://doc.rust-lang.org/std/sync/struct.Mutex.html) are available to provide safe locks.\n\n### CI/CD with Rust caching\nAdd the following CI/CD configuration into the `.gitlab-ci.yml` file. The `run-latest` job calls `cargo run` with RSS feed URL examples, and measures the execution time continuously.\n\n```text\nstages:\n  - build\n  - test\n  - run\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 for being cached.\nvariables:\n  CARGO_HOME: ${CI_PROJECT_DIR}/.cargo\n\nbuild-latest:\n  stage: build\n  script:\n    - cargo build --verbose\n\ntest-latest:\n  stage: build\n  script:\n    - cargo test --verbose\n\nrun-latest:\n  stage: run\n  script:\n    - time cargo run -- \"GitLab Blog,https://about.gitlab.com/atom.xml\" \"CNCF,https://www.cncf.io/feed/\"\n\n```\n\n![GitLab CI/CD pipelines for Rust, cargo run output](https://about.gitlab.com/images/blogimages/learn-rust-with-ai-code-suggestions-advanced-programming/gitlab_cicd_pipeline_rust_cargo_run_output.png){: .shadow}\n\n## What is next\nThis blog post was challenging to create, with both learning advanced Rust programming techniques myself, and finding a good learning curve with Code Suggestions. The latter greatly helps with quickly generating code, not just boilerplate snippets – it understands the local context, and better understands the purpose and scope of the algorithm, the more code you write. After reading this blog post, you know of a few challenges and turnarounds. The example solution code for the reader app is available in [the learn-rust-ai-app-reader project](https://gitlab.com/gitlab-da/use-cases/ai/learn-with-ai/learn-rust-ai-app-reader).\n\nParsing RSS feeds is challenging since it involves data structures, with external HTTP requests and parallel optimizations. As an experienced Rust user, you might have wondered: `Why not use the std::rss crate?` -- It is optimized for advanced asynchronous execution, and does not allow to show and explain the different Rust functionalities, explained in this blog post. As an async exercise, try to rewrite the code using the [`rss` crate](https://docs.rs/rss/latest/rss/).\n\n### Async learning exercises\nThe lessons learned in this blog post also lay the foundation for future exploration with persistent storage and presenting the data. Here are a few ideas where you can continue learning Rust and optimize the reader app:\n\n1. Data storage: Use a database like sqlite, and RSS feed update tracking.\n2. Notifications: Spawn child processes to trigger notifications into Telegram, etc.\n3. Functionality: Extend the reader types to REST APIs\n4. Configuration: Add support for configuration files for RSS feeds, APIs, etc.\n5. Efficiency: Add support for filters, and subscribed tags.\n6. Deployments: Use a webserver, collect Prometheus metrics, and deploy to Kubernetes.\n\nIn a future blog post, we will discuss some of these ideas, and how to implement them. Dive into existing RSS feed implementations, and learn how you can refactor the existing code into leveraging more Rust libraries (`crates`).\n\n### Share your feedback\nWhen you use [GitLab Duo](/gitlab-duo/) Code Suggestions, please [share your thoughts in the feedback 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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},1772652079442]