[{"data":1,"prerenderedAt":817},["ShallowReactive",2],{"/en-us/blog/a-go-micro-language-framework-for-building-dsls":3,"navigation-en-us":37,"banner-en-us":447,"footer-en-us":457,"blog-post-authors-en-us-Julian Thome":699,"blog-related-posts-en-us-a-go-micro-language-framework-for-building-dsls":713,"blog-promotions-en-us":753,"next-steps-en-us":807},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":26,"isFeatured":12,"meta":27,"navigation":28,"path":29,"publishedDate":20,"seo":30,"stem":34,"tagSlugs":35,"__hash__":36},"blogPosts/en-us/blog/a-go-micro-language-framework-for-building-dsls.yml","A Go Micro Language Framework For Building Dsls",[7],"julian-thome",null,"open-source",{"slug":11,"featured":12,"template":13},"a-go-micro-language-framework-for-building-dsls",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"Lingo: A Go micro language framework for building Domain Specific Languages","Design, build and integrate your own Domain Specific Language with Lingo.",[18],"Julian Thome","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749682320/Blog/Hero%20Images/typeset.png","2022-05-26","\n\nDomain Specific Languages (DSL) are small, focused languages with a narrow\ndomain of applicability. DSLs are tailored towards their target domain so that\ndomain experts can formalize ideas based on their knowledge and background.\n\nThis makes DSLs powerful tools that can be used for the purpose of increasing\nprogrammer efficiency by being more expressive in their target\ndomain, compared to general purpose languages, and by providing concepts to\nreduce the cognitive load on their users.\n\nConsider the problem of summing up the balances of different bank accounts in a\nCSV file. A sample CSV file is provided in the example below where the first\ncolumn contains the name of the account holder and the second column contains\nthe account balance.\n\n```csv\nname, balance\nLisa, 100.30\nBert, 241.41\nMaria, 151.13\n```\n\nYou could solve the problem of summing up balances by using a general-purpose\nlanguage such as [Ruby](https://www.ruby-lang.org/en/) as in the code snippet\nbelow. Apart from the fact that the code below is not very robust, it contains\na lot of boilerplate that is irrelevant to the problem at hand, i.e., summing\nup the account balances.\n\n```ruby\n#!/usr/bin/env ruby\n\nexit(1) if ARGV.empty? || !File.exist?(ARGV[0])\n\nsum = 0\nFile.foreach(ARGV[0]).each_with_index do |line, idx|\n next if idx == 0\n sum += Float(line.split(',')[1])\nend\n\nputs sum.round(2)\n```\n\nBelow is an example [AWK script](https://en.wikipedia.org/wiki/AWK) that solves\nthe same problem. AWK is a DSL that was specifically designed to address\nproblems related to text-processing.\n\n```awk\n#!/usr/bin/awk -f\n\nBEGIN{FS=\",\" sum+=$2}END{print sum}\n```\n\nThe Ruby program has a size of 208 characters, whereas the AWK program has a size of 56. The AWK program is roughly 4x smaller than its Ruby\ncounterpart. In addition, the AWK implementation is more robust by being less\nprone to glitches that may appear in the CSV file (e.g., empty newlines,\nwrongly formatted data-fields). The significant difference in terms of size\nillustrates that DSLs, by being more focused on solving specific problems, can\nmake their users more productive by sparing them the burden to write\nboilerplate code and narrowing the focus of the language on the problem at\nhand.\n\nSome popular DSLs most software developers use on a regular basis include\n[Regular Expressions](https://en.wikipedia.org/wiki/Regular_expression) for\npattern matching, AWK for text\ntransformation or [Standard Query Language](https://en.wikipedia.org/wiki/SQL)\nfor interacting with databases.\n\n## Challenges when designing Domain Specific Languages\n\nPrototyping, designing and evolving DSLs is a\nchallenging process. In our experience this is an exploratory cycle where you\nconstantly prototype ideas, incorporate them into the language, try them out in\nreality, collect feedback and improve the DSL based on the feedback.\nWhen designing a DSL, there are many components that have to be implemented and\nevolved. At a very high level there are two main components: the language\nlexer/parser and the language processor. The lexer/parser\nis the component that accepts input as per the language definition which is\nusually specified specified by means of a language grammar. The parsing/lexing\nphase produces a syntax tree which is then passed onto the language processor.\nA language processor evaluates the syntax tree. In the example we saw earlier,\nwe ran both the Ruby and AWK interpreters providing our scripts and the CSV\nfile as input; both interpreters evaluated the scripts and this evaluation\nyielded the sum of all the account balances as a result.\n\nTools such as parser generators can significantly reduce the effort of\nlexer/parser development by means of code generation. Sophisticated DSL\nframeworks such as [JetBrains MPS](https://www.jetbrains.com/mps/) or\n[Xtext](https://www.eclipse.org/Xtext/) also provide features that help\nimplement custom language support in IDEs. However, if present at all, the\nsupport for building the language processors is usually limited to generating\nplaceholders functions or boilerplate code for the language components that\nhave to be filled-in by the DSL developer. Moreover, such large and powerful DSL\nframeworks usually have a fairly steep learning curve so that they are probably\na better fit for more sophisticated DSLs as opposed to small, easily\nembeddable, focused languages, which we refer to as _micro languages_.\n\nIn some situations, it may be worth considering working around these problems\nby just relying on standard data exchange formats such as `.toml`, `.yaml` or\n`.json` as a means of configuration. Similar to the parser generators, using\nsuch a format may relieve some of the burden when it comes to parser\ndevelopment effort. However, this approach does not help when it comes to the\nimplementation of the actual language processor. In addition, most standard data\nexchange formats are inherently limited to representing data in terms of simple\nconcepts (such as lists, dictionaries, strings and numbers). This limitation\ncan lead to bloated configuration files quickly as shown in the following\nexample.\n\nImagine the development of a calculator that operates on integers using\nmultiplication `*`, addition `+`. When using a data-description language like\nYAML in the example below, you can see that even a small simple term like `1 + 2 * 3 + 5` can be hard to reason about, and by adding more terms the configuration\nfile would get bloated quickly.\n\n```yaml\nterm:\n add: - 1\n - times:\n - 2\n - 3\n - 5\n```\n\nThis blog post is focused on the design of micro languages. The core idea is to\nprovide a simple, extensible language core that can be easily extended with\ncustom-types and custom functions; the language can evolve without having\nto touch the parser or the language processor. Instead, the DSL designer can\njust focus on the concepts that ought to be integrated into the DSL by\nimplementing interfaces and \"hooking\" them into the core language\nimplementation.\n\n## Lingo: A micro language framework for Go\n\nAt GitLab, Go is one of our main programming languages and some of the tools we\ndevelop required their own, small, embeddable DSLs so that users could properly\nconfigure and interact with them.\nInitially, we tried to integrate already existing, embeddable and expandable\nlanguage implementations. Our only condition was that they had to be\nembeddable natively into a Go application. We explored several great free and\nopen-source (FOSS) projects such as [go-lua](https://github.com/Shopify/go-lua)\nwhich is Lua VM implemented in Go, [go-yeagi](https://github.com/traefik/yaegi)\nwhich provides a Go interpreter with which Go can be used as a scripting\nlanguage or [go-zygomys](https://github.com/glycerine/zygomys) which is a LISP\ninterpreter written in Go. However, these packages are essentially modules to\nintegrate general-purpose languages on top of which a DSL could be built. These modules ended up being fairly complex. In contrast, we wanted to have basic support to design, implement, embed and evolve DSLs natively into a Go\napplication that is flexible, small, simple/easy to grasp, evolve and\nadapt.\n\nWe were looking for a micro language framework with the properties listed below:\n\n1. Stability: Changes applied to the DSL should neither require any changes to the core lexer/parser implementation nor to the language processor implementation.\n1. Flexibility/Composability: New DSL concepts (data-types, functions) can be integrated via a simple plug-in mechanism.\n1. Simplicity: the language framework should have just\nenough features to provide a foundation that is powerful enough to implement\nand evolve a custom DSL. In addition, the whole implementation of the micro\nlanguage framework should be in pure Go so that it is easily embeddable in Go\napplications.\n\nSince none of the available FOSS tools we looked at was able to\nfulfill all of those requirements, we developed our own micro language framework\nin Go called Lingo which stands for \"**L**ISP-based Domain Specific Languages\n(DSLs) **in Go**\". Lingo is completely FOSS and available in the [Lingo Git repository](https://gitlab.com/gitlab-org/vulnerability-research/foss/lingo)\nunder the free and open source space of the [Vulnerability Research Team](https://handbook.gitlab.com/handbook/engineering/development/sec/secure/vulnerability-research/).\n\n[Lingo](https://gitlab.com/gitlab-org/vulnerability-research/foss/lingo)\nprovides a foundation for building DSLs based on Symbolic Expressions (S-expressions), i.e.,\nexpressions provided in the form of nested lists `(f ...)` where `f` can be\nconsidered as the placeholder that represents the function symbol. Using this format,\nthe mathematical term we saw earlier could be written as S-expression `(+ 1 (* 2 3) 5)`.\nS-expressions are versatile and easy to process due to their uniformity. In\naddition, they can be used to represent both code and data in a consistent\nmanner.\n\nWith regards to the Stability, Flexibility and Composability properties, [Lingo](https://gitlab.com/gitlab-org/vulnerability-research/foss/lingo)\nprovides a simple plug-in mechanism to add new functions as well as types\nwithout having to touch the core parser or language processor. From the\nperspective of the S-expression parser, the actual function symbol is\nessentially irrelevant with regards to the S-expression parsing. The language processor is just evaluating S-expressions and dispatching the execution to the interface implementations. These implementations are provided by the plug-ins based on the function symbol.\n\nWith regards to Simplicity, the Lingo code base is roughly 3K lines of pure Go code including the lexer/parser, an\nengine for code transformation, and the interpreter/evaluator. The small size\nshould make it possible to understand the entirety of the implementation. \nReaders that are interested in the technical details of\nLingo itself can have a look at the\n[README.md](https://gitlab.com/gitlab-org/vulnerability-research/foss/lingo/-/blob/main/README.md)\nwhere the implementation details and the used theoretical foundations are explained.\nThis blog post focuses on how\nLingo can be used to build a DSL from scratch.\n\n## Using Lingo to design a data generation engine\n\nIn this example we are designing a data-generation engine in Go using\nLingo as a foundation. Our data generation engine may be used to generate structured input\ndata for fuzzing or other application contexts. This example illustrates how\nyou can use Lingo to create a language and the corresponding language\nprocessor. Going back to the example from the beginning, let us assume we would\nlike to generate CSV files in the format we saw at the beginning covering\naccount balances.\n\n```csv\nname, balance\nLisa, 100.30\nBert, 241.41\nMaria, 151.13\n```\n\nOur language includes the following functions:\n\n1. `(oneof s0, s1, ..., sN)`: randomly returns one of the parameter strings `sX` (0 \u003C= X \u003C= N).\n1. `(join e0, e1, ..., eN)`: joins all argument expressions and concatenates their string representation `eX` (0 \u003C= X \u003C= N).\n1. `(genfloat min max)`: generates a random float number X (0 \u003C= X \u003C= N) and returns it.\n1. `(times num exp)`: repeats the pattern generated by exp num times.\n\nFor this example we are using\nLingo to build the language and the language processor to automatically generate CSV\noutput which we are going to feed back into the Ruby and AWK programs we saw in\nthe introduction in order to perform a stress test on them.\nWe refer to our new language/tool as _Random Text Generator_ (RTG) `.rtg`.\nBelow is a sample script `script.rtg` we'd like our program to digest in order\nto randomly generate CSV files. As you can see in the example below, we are\njoining sub-strings starting with the CSV header `name, balance`\nafter which we randomly generate 10 lines of names and balance amounts. In\nbetween, we also randomly generate some empty lines.\n\n```go\n(join (join \"name\" \",\" \"balance\" \"\\n\")\n (times 10 '(join (oneof \"Jim\" \"Max\" \"Simone\" \"Carl\" \"Paul\" \"Karl\" \"Ines\" \"Jane\" \"Geralt\" \"Dandelion\" \"Triss\" \"Yennefer\" \"Ciri\") \",\" (genfloat 0 10000) \"\\n\" (oneof \"\" \"\\n\"))))\n```\n\nOur engine takes the script above written in RTG and generates random CSV\ncontent. Below is an example CSV file generated from this script.\n\n```csv\nname,balance\nCarl,25.648205\nInes,11758.551\n\nCiri,13300.558\n...\n```\n\nFor the remainder of this section, we explore how we can implement a\ndata generation engine based on Lingo. The implementation of RTG requires\nthe two main ingredients: (1) a float data type and a result object to integrate a float\nrepresentation and (2) implementations for the `times`, `oneof`, `genfloat` and\n`join` functions.\n\n### Introducing a float data type and result objects\n\nLingo differentiates between data types and result objects. Data types indicate how data is\nmeant to be used and result objects are used to pass intermediate results\nbetween functions where every result has a unique type. In the code snippet\nbelow, we introduce a new `float` data type. The comments in the code snippet below\nprovide more details.\n\n```go\n// introduce float type\nvar TypeFloatId, TypeFloat = types.NewTypeWithProperties(\"float\", types.Primitive)\n// introduce token float type for parser\nvar TokFloat = parser.HookToken(parser.TokLabel(TypeFloat.Name))\n\n// recognize (true) as boolean\ntype FloatMatcher struct{}\n\n// this function is used by the parser to \"recognize\" floats as such\nfunc (i FloatMatcher) Match(s string) parser.TokLabel {\n if !strings.Contains(s, \".\") {\n return parser.TokUnknown\n }\n\n if _, err := strconv.ParseFloat(s, 32); err == nil {\n\treturn TokFloat.Label\n }\n\n return parser.TokUnknown\n}\nfunc (i FloatMatcher) Id() string {\n return string(TokFloat.Label)\n}\n\nfunc init() {\n // hook matcher into the parser\n parser.HookMatcher(FloatMatcher{})\n}\n```\n\nIn addition, we also require a result object which we can use to pass around\nfloat values. This is an interface implementation where most of the functions names\nare self-explanatory. The important bit is the `Type` function\nthat returns our custom `float` type we introduced in the last snippet.\n\n```go\ntype FloatResult struct{ Val float32 }\n// deep copy\nfunc (r FloatResult) DeepCopy() eval.Result { return NewFloatResult(r.Val) }\n// returns the string representation of this result type\nfunc (r FloatResult) String() string {\n return strconv.FormatFloat(float64(r.Val), 'f', -1, 32)\n}\n// returns the data type for this result type\nfunc (r FloatResult) Type() types.Type { return custtypes.TypeFloat }\n// call-back that is cleaned up when the environment is cleaned up\nfunc (r FloatResult) Tidy() {}\n\nfunc (r FloatResult) Value() interface{} { return r.Val }\nfunc (r *FloatResult) SetValue(value interface{}) error {\n boolVal, ok := value.(float32)\n if !ok {\n return fmt.Errorf(\"invalid type for Bool\")\n }\n r.Val = boolVal\n return nil\n}\nfunc NewFloatResult(value float32) *FloatResult {\n return &FloatResult{\n value,\n }\n}\n```\n\n### Implementing the DSL functions\n\nSimilar to the data type and return object, implementation of a DSL function is\nas simple as implementing an interface. In the example below we implement the\n`genfloat` function as an example. The most important parts are the `Symbol()`,\n`Validate()` and `Evaluate()` functions. The `Symbol()` function returns the\nfunction symbol which is `genfloat` in this particular case.\nBoth, the `Validate()` and `Evaluate()` functions take the environment `env`\nand the parameter Stack `stack` as the parameter. The environment is used to store\nintermediate results which is useful when declaring/defining variables. The `stack` includes the input parameters of the function. For\n`(genfloat 0 10000)`, the stack would consist out of two `IntResult` parameters\n`0` and `10000` where `IntResult` is a standard result object already provided by the\ncore implementation of Lingo. `Validate()` makes sure that the parameter can be\ndigested by the function at hand, whereas `Evaluate()` actually invokes the\nfunction. In this particular case, we are generating a float value within the\nspecified range and return the corresponding `FloatResult`.\n\n```go\ntype FunctionGenfloat struct{}\n\n// returns a description of this function\nfunc (f *FunctionGenfloat) Desc() (string, string) {\n return fmt.Sprintf(\"%s%s %s%s\",\n string(parser.TokLeftPar),\n f.Symbol(),\n\t\"min max\",\n\tstring(parser.TokRightPar)),\n\t\"generate float in rang [min max]\"\n}\n\n// this is the symbol f of the function (f ...)\nfunc (f *FunctionGenfloat) Symbol() parser.TokLabel {\n return parser.TokLabel(\"genfloat\")\n}\n\n// validates the parameters of this function which are passed in\nfunc (f *FunctionGenfloat) Validate(env *eval.Environment, stack *eval.StackFrame) error {\n if stack.Size() != 2 {\n return eval.WrongNumberOfArgs(f.Symbol(), stack.Size(), 2)\n }\n\n for idx, item := range stack.Items() {\n if item.Type() != types.TypeInt {\n\t return eval.WrongTypeOfArg(f.Symbol(), idx+1, item)\n\t}\n }\n return nil\n}\n\n// evaluates the function and returns the result\nfunc (f *FunctionGenfloat) Evaluate(env *eval.Environment, stack *eval.StackFrame) (eval.Result, error) {\n var result float32\n rand.Seed(time.Now().UnixNano())\n for !stack.Empty() {\n max := stack.Pop().(*eval.IntResult)\n min := stack.Pop().(*eval.IntResult)\n\n\tminval := float32(min.Val)\n\tmaxval := float32(max.Val)\n\n\tresult = minval + (rand.Float32() * (maxval - minval))\n }\n\n return custresults.NewFloatResult(result), nil\n}\n\nfunc NewFunctionGenfloat() (eval.Function, error) {\n fun := &FunctionGenfloat{}\n parser.HookToken(fun.Symbol())\n return fun, nil\n}\n```\n\n### Putting it all together\n\nAfter implementing all the functions, we only have to register/integrate them\n(`eval.HookFunction(...)`) so that Lingo properly resolves them when processing\nthe program. In the example below, we are registering all of the custom functions\nwe implemented, i.e., `times`, `oneof`, `join`, `genfloat`. The `main()`\nfunction in the example below includes the code required to evaluate our script\n`script.rtg`.\n\n```go\n// register function\nfunc register(fn eval.Function, err error) {\n if err != nil {\n log.Fatalf(\"failed to create %s function %s:\", fn.Symbol(), err.Error())\n }\n err = eval.HookFunction(fn)\n if err != nil {\n log.Fatalf(\"failed to hook bool function %s:\", err.Error())\n }\n}\n\nfunc main() {\n // register custom functions\n register(functions.NewFunctionTimes())\n register(functions.NewFunctionOneof())\n register(functions.NewFunctionJoin())\n register(functions.NewFunctionGenfloat())\n register(functions.NewFunctionFloat())\n if len(os.Args) \u003C= 1 {\n fmt.Println(\"No script provided\")\n os.Exit(1)\n }\n // evaluate script\n result, err := eval.RunScriptPath(os.Args[1])\n if err != nil {\n fmt.Println(err.Error())\n os.Exit(1)\n }\n\n // print output\n fmt.Printf(strings.ReplaceAll(result.String(), \"\\\\n\", \"\\n\"))\n\n os.Exit(0)\n}\n```\n\nThe source code for RTG is available\n[here](https://gitlab.com/julianthome/lingo-example). You can find information\nabout how to build and run the tool in the\n[README.md](https://gitlab.com/julianthome/lingo-example/-/blob/main/README.md).\n\nWith approx. 300 lines of Go code, we have successfully designed a language and\nimplemented a language processor. We can now use RTG to test the robustness of\nthe Ruby (`computebalance.rb`) and AWK scripts (`computebalance.awk`) we used\nat the beginning to sum up account balances.\n```bash\ntimeout 10 watch -e './rtg script.rtg > out.csv && ./computebalance.awk out.csv'\ntimeout 10 watch -e './rtg script.rtg > out.csv && ./computebalance.rb out.csv'\n```\n\nThe experiment above shows that the files generated by means of RTG can be\nproperly digested by the AWK script which is much more robust since it can cope\nwith the all generated CSV files. In contrast, executing of the Ruby script\nresults in errors because it cannot properly cope with newlines as they appear\nin the CSV file.\n\nCover image by [Charles Deluvio](https://unsplash.com/@kristianstrand) on 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flows on GitLab Duo Agent Platform. Find out who won and what they created.",[719],"Nick Veenhof","https://res.cloudinary.com/about-gitlab-com/image/upload/v1776457632/llddiylsgwuze0u1rjks.png","2026-04-22","AI writes code. That is expected now. But planning, security, compliance, and deployments? Those gaps remain. I have run contributor programs for years. I have never seen a community respond to technology like this.\n\nThat is why we opened [GitLab Duo Agent Platform](https://about.gitlab.com/gitlab-duo-agent-platform/) and invited developers worldwide to build AI agents that help teams ship secure software faster. Not chatbots that answer questions, but agents that jump into workflows, respond to events, and act on your behalf. The GitLab AI Hackathon ran from February 9 to March 25, 2026, on Devpost, the hackathon platform. Google Cloud and Anthropic joined as co-sponsors.\n\nWhen my team planned this hackathon with Google Cloud and Anthropic, I asked the judges to score four things: technical work, design, potential impact, and idea quality. We hoped for strong turnout. What we got surprised all of us. Nineteen judges spent 18 days reviewing every entry. Google Cloud and Anthropic provided judges, prizes, and cloud access. The community built hundreds of agents and flows because they wanted to solve these problems.\n\nNearly 7,000 developers showed up. They built 600+ agents and flows in weeks. The prizes across all categories totaled $65,000 from GitLab, Google Cloud, and Anthropic.\n\n\nIf you have ever watched a senior engineer leave and take half the team's knowledge with them, you know why the winning project hit so hard.\n\nRead on to find out what the community built.\n\n## Grand Prize: LORE\n\n[LORE](https://devpost.com/software/lore-living-organizational-record-engine), the Living Organizational Record Engine, uses eight agents with a router that sends each question to the right agent, logic to prevent circular loops in the knowledge graph, a visual dashboard, and carbon tracking. The command-line tool ships with 43 tests (yes, 43 tests in a hackathon project).\n\nLORE solves a real problem: the knowledge that lives in engineers' heads and walks out the door when they leave. In my experience, a hackathon project with 43 tests is rare. That many tests in a hackathon project tells you something about the team behind it.\n\nJudge April Guo (Anthropic) wrote: \"This feels like a product, not a hackathon project.\"\n\n\n### Google Cloud winners\n\n[Gitdefender](https://devpost.com/software/gitdefender) won the Google Cloud Grand Prize. It works inside code review workflows, finding and fixing security issues. It spots the bug, writes the fix, and opens the code review. No developer needs to step in.\n\n[Aegis](https://devpost.com/software/aegis-2m1oq0) won the Google Cloud Runner Up. It gives AI-powered explanations for every decision it makes, deployed to Google Cloud and ready for production use.\n\n### Anthropic winners\n\n[GraphDev](https://devpost.com/software/graphdev) won the Anthropic Grand Prize. It maps code links and shows how systems change over time. Judge Aboobacker MK (GitLab) noted it was \"in sync with our work on GitLab knowledge graph.\" Judge Ayush Billore (GitLab) wrote: \"Loved the demo and UX, super useful for understanding how the system evolved and what gets impacted by changes.\" You can see the full impact of a change before you make it.\n\n[DocSync](https://devpost.com/software/pipeheal) won the Anthropic Runner Up. It uses three agents: Detector, Writer, and Reviewer. If DocSync is confident in the fix, it opens a code review. If not, it creates an issue for a human to check.\n\n## Category winners\n\n### Most Technically Impressive\n\nDatabase migrations break things. [Time-Traveler](https://devpost.com/software/time-traveler-w3cxp0) creates a safe copy of your production setup, runs the migration against that copy, and reports the result. It runs five agents connected by a bridge, with real Google Cloud deployment, real PostgreSQL migrations, and real data.\n\n### Most Impactful\n\n[RedAgent](https://devpost.com/software/redagent) checks AI-generated security reports, closing the trust gap between AI findings and developer action. If your team uses AI for security scanning, you know this problem. I have seen teams dismiss AI findings because they could not verify them. RedAgent gives teams a way to check AI output before it reaches developers.\n\n### Easiest to Use\n\n[Launch Control](https://devpost.com/software/launch-control-bgp8az) delivers polished UX and solid infrastructure, and scored well on sustainability too.\n\n## The sustainability signal\n\nFive projects won prizes or bonuses for environmental impact. Software delivery has a carbon cost as CI/CD pipelines, but now LLMs also run compute at scale. We created the Green Agent category to challenge developers to measure and reduce that footprint. Stacy Cline and Kim Buncle from GitLab's sustainability team helped judge the Green Agent category. \n\n### Green Agent prize\n\n[GreenPipe](https://devpost.com/software/greenpipe) scans CI/CD pipelines for environmental impact and produces carbon footprint reports. Judges Kim Buncle and Rajesh Agadi (Google) both backed the project.\n\n### Sustainable Design bonus\n\nSustainable Design bonuses were awarded to the projects with exceptional sustainability practices in their design, from model optimization techniques to energy-efficient architecture choices.\n\n* [BugFlow](https://devpost.com/software/bugflow-ai-regression-detective-ci-optimizer) turned one bug report into 10 fixes in 20 minutes. \n* [DELTA Cyber Reasoning](https://devpost.com/software/delta-cyber-reasoning-system) is automated fuzz testing for security. \n* [CarbonLint](https://devpost.com/software/carbonlint) applied code analysis to energy use.\n* [TFGuardian](https://devpost.com/software/tfguardian) features a carbon footprint analyzer, among other agents.\n\nCongratulations on all the Sustainable Design bonus winners! \n\nJudge Jens-Joris Decorte (TechWolf) cited the result: Costs dropped from $556 to $18 per month, a 96% carbon cut (that is a $538 monthly saving with a sustainability label on it).\n\n## Honorable mentions and the long tail\n\nSix projects received honorable mentions:\n\n\n- [SecurityMonkey](https://devpost.com/software/securitymonkey) injects known vulnerabilities into a test branch and scores how well your security scanners catch them.\n- [stregent](https://devpost.com/software/stregent) monitors CI/CD pipelines and lets developers investigate and merge fixes from WhatsApp without opening a laptop.\n- [Compliance Sentinel](https://devpost.com/software/compliance-sentinel-autonomous-devsecops-governance) scores every merge request for compliance risk and blocks the merge if critical violations are detected.\n- [Carbon Tracker](https://devpost.com/software/carbon-tracker-ij25kf) calculates the carbon footprint of each CI/CD pipeline job and posts optimization tips on the merge request.\n- [RepoWarden](https://devpost.com/software/docuguard) is the first Living Specification Engine, an AI system that captures why code was written, not just what it does.\n- [MR Compliance Auditor](https://devpost.com/software/mr-compliance-auditor) collects evidence across merge requests, maps it to SOC 2 controls, and streams compliance scores to a live dashboard.\n\nMy favorite quote from the judging came from Luca Chun Lun Lit (Anthropic), who described stregent's mobile-first approach: \"Being able to essentially code from your phone is a next level in the engineering experience.\"\n\n> Explore the 600+ entries in the [project gallery](https://gitlab.devpost.com/project-gallery).\n\n## What comes next\n\nEvery agent in this hackathon worked within a single project. They still delivered impressive results. Some participants ran a local knowledge graph alongside their agents to surface code relationships and dependencies within the repo. LORE captures project history. Gitdefender finds vulnerabilities. Pairing agents with richer local context is already helping contributors build sharper tools. The next hackathon will build on what contributors are already doing with richer context. Sign up on [contributors.gitlab.com](https://contributors.gitlab.com/) to be the first to know when details drop.\n\n\n## Get started\n\nA special thanks to Lee Tickett (GitLab) and Mattias Michaux (GitLab) for orchestrating the orchestrators and innovators behind this hackathon!\n\nThank you to every developer who submitted. Nearly 7,000 of you showed what GitLab Duo Agent Platform can do when a community decides to build. I am proud of what you built here, and I cannot wait to see what you build next.\n\nBuild your own agent on [GitLab Duo Agent Platform](https://docs.gitlab.com/user/duo_agent_platform/). Browse community-built agents in the [AI Catalog](https://docs.gitlab.com/user/duo_agent_platform/ai_catalog/). You orchestrate. AI accelerates.\n",[724,259],"AI/ML",{"featured":12,"template":13,"slug":726},"gitlab-ai-hackathon-2026-meet-the-winners",{"content":728,"config":739},{"title":729,"description":730,"authors":731,"heroImage":733,"date":734,"category":9,"tags":735,"body":738},"What’s new in Git 2.54.0?","Learn about release contributions, including new repository maintenance, a new command to edit commit history, a replacement for git-sizer(1), and more.",[732],"Patrick Steinhardt","https://res.cloudinary.com/about-gitlab-com/image/upload/v1776711651/sj7xxyyuimlarswbyft5.png","2026-04-20",[736,737,259],"open source","git","The Git project recently released [Git 2.54.0](https://lore.kernel.org/git/xmqqa4uxsjrs.fsf@gitster.g/T/#u). Let's look at a few notable highlights from this release, which includes contributions from the Git team at GitLab.\n\n## Pluggable Object Databases\n\nGit already has the ability to store references with either the \"files\" backend or with the [\"reftable\" backend](https://about.gitlab.com/blog/a-beginners-guide-to-the-git-reftable-format/). This is achieved by having proper abstractions in Git that allows us to have different backends.\n\nBut references are just one of the two important types of data that are stored in repositories, with the other being objects. Objects are stored in the object database, and each object database in turn consists of multiple object sources where objects can be read from or written to. Each object source either stores individual objects as so-called \"loose\" objects, or compresses multiple objects into a \"packfile\" in your `.git/objects` directory.\n\nUntil now, however, these sources did not have a proper abstraction boundary, so the storage format for objects is completely hardcoded into Git. But this is finally changing with pluggable object databases! The concept is straightforward and similar to how we did this for references in the past: Instead of having hardcoded code paths for how to store objects, we introduce an abstraction boundary that allows us to have different backends for storing objects.\n\nWhile the idea is simple, the implementation is not, as we have hardcoded assumptions about the storage formats used in Git all over the place. In fact, we have started working on this topic in Git 2.48, which was released in January 2025. Initially, we focused on making object-related subsystems self-contained and creating proper subsystems for the existing backends that we had in Git.\n\nWith Git 2.54, we have now reached a milestone: The object database backend is now pluggable. Not all of Git's functionality is covered yet, but introducing an alternate backend that handles a meaningful subset of operations is now a realistic undertaking.\n\nFor now, only local workflows like creating commits, showing commit graphs, or performing merges will work with such an alternative implementation. This notably excludes anything that interacts with a remote, such as when you want to fetch or push changes. Regardless, this is the culmination of almost two years of work spanning across almost 400 commits that have been merged upstream, and we will of course continue to iterate on this effort.\n\nSo why does this matter? The idea is that it becomes practical to introduce new storage formats into Git. Examples could be:\n- A storage format that is able to store large binary files more efficiently\n than packfiles do today\n\n- A storage format that is custom-tailored for GitLab to ensure that we can\n serve repositories to our users even more efficiently than we currently can\n\n\nThis is a large-scale effort that is likely to shape the future of Git and GitLab.\n\n*This project was led by [Patrick Steinhardt](https://gitlab.com/pks-gitlab).*\n\n## Easier editing of your commit history\n\nIn many software development projects it is common practice for developers to not only polish the code they want to contribute, but to also polish the commit history so that it becomes easy to review. The result is a set of small and atomic commits that each do one thing, with a good commit message that describes the intent of the commit as well as specific nuances.\n\nOf course, more often than not, these atomic commits are not something that just happens naturally during the development process. Instead, the author of the changes will gain a better understanding of what they are while iterating on them, and the way to split up the commits will become clearer over time. Furthermore, the subsequent review process may result in feedback that requires changes to the crafted commits.\n\nThe consequence of this process is that the developer will have to rewrite their commit history many times during the development process. Historically, Git has allowed for this use case via [interactive rebases](https://git-scm.com/docs/git-rebase#_interactive_mode). These interactive rebases are an extremely powerful tool: They let you reorder commits, rewrite commit messages, squash multiple commits together, or perform arbitrary edits of any commit.\n\nBut they are also somewhat arcane and hard to understand. The user needs to figure out the base commit for the rebase, they need to understand how to edit a somewhat obscure \"instruction sheet,\" and they need to be aware of how the stateful rebasing process works. For example, users are presented with an instruction sheet similar to the following when rebasing a topic branch:\n\n```shell\npick b60623f382 # t: detect errors outside of test cases # empty\npick b80cb55882 # t: prepare `test_match_signal ()` calls for `set -e`\npick 5ffe397f30 # t: prepare `test_must_fail ()` for `set -e`\npick 5e9b0cf5e1 # t: prepare `stop_git_daemon ()` for `set -e`\npick 299561e7a2 # t: prepare `git config --unset` calls for `set -e`\npick ed0e7ca2b5 # t: detect errors outside of test cases\n```\n\nSo while interactive rebases are powerful, they are also quite intimidating for the average user.\n\nIt doesn't have to be this way, though. Tools like [Jujutsu](https://www.jj-vcs.dev/latest/) provide interfaces that are much easier to use compared to Git, as you can for example simply execute `jj split` to split up a commit into two commits. With Git and interactive rebases, this use case requires a lot of different steps with confusing command line arguments.\n\nWe have thus taken inspiration from Jujutsu and have introduced a new git-history(1) command into Git that is the foundation for better history editing. For now, this command has two subcommands:\n\n- `git history reword` allows you to easily rewrite a commit message. You simply\n give it the commit whose message you want to reword, Git asks you for the new\n commit message, and that's it.\n\n- `git history split` allows you to split up a commit into two, which is\n inspired by `jj split`. You give it a commit, Git asks you which changes to\n stage into which commit and for the two commit messages, and then you're done.\n\n\nThis is of course only a start, and we want to add additional subcommands over time. For example:\n\n- `git history fixup` to take staged changes and automatically amend them to a\n specific commit\n\n- `git history drop` to remove a commit\n- `git history reorder` to reorder the sequence of commits\n- `git history squash` to squash a range of commits\n\nBut that's not all! In addition to making history editing easy, this new command also knows to automatically rebase all of your local branches that previously included this commit. So that means that you can even edit a commit that is not on the current branch, and all branches that contain the commit will be rewritten.\n\nIt may seem puzzling at first that Git is automatically rebasing dependent branches, as that is a significant diversion from how git-rebase(1) works. But this is part of a bigger effort to bring better support for Stacked Diffs to Git, which are a way to create a series of multiple dependent branches that can be reviewed independently, but that together work towards a bigger goal.\n\n*This project was led by [Patrick Steinhardt](https://gitlab.com/pks-gitlab) with support from [Elijah Newren](https://github.com/newren).*\n\n## A native replacement for git-sizer(1)\n\nThe size of a Git repository is an important factor that determines how well Git and GitLab can handle it. But size alone is not the only factor, as the performance of a repository is ultimately a combination of multiple different dimensions:\n\n- The depth of the commit history\n- The shape of the directory structure\n- The size of files stored in the repository\n- The number of references\n\nThese are only some of the dimensions one needs to consider when trying to predict whether Git will be able to handle a repository well.\n\nBut while it is clear that the mere repository size is insufficient, Git itself does not provide any tooling that gives the user an easy overview of these metrics. Instead, users are forced to rely on third-party tools like [git-sizer(1)](https://github.com/github/git-sizer) to fill this gap. This tool does an excellent job at surfacing this information, but it is not part of Git itself and thus needs to be installed separately.\n\nObservability of repository internals is critical to us at GitLab, so we introduced a [new `git repo structure` command into Git 2.52](https://about.gitlab.com/blog/whats-new-in-git-2-52-0/#new-subcommand-for-git-repo1-to-display-repository-metrics) to display repository metrics, which we have extended in Git 2.53 to [show inflated and disk sizes for objects by type](https://about.gitlab.com/blog/whats-new-in-git-2-53-0/#more-data-collected-in-git-repo-structure).\n\nIn Git 2.54, we are now iterating some more on this command so that we don't only show the overall size, but also show the largest objects by type:\n\n```shell\n$ git clone https://gitlab.com/git-scm/git.git\n$ cd git\n$ git repo structure\nCounting objects: 410445, done.\n| Repository structure | Value |\n| ------------------------- | ----------- |\n| * References | |\n| * Count | 1.01 k |\n| * Branches | 1 |\n| * Tags | 1.00 k |\n| * Remotes | 9 |\n| * Others | 0 |\n| | |\n| * Reachable objects | |\n| * Count | 410.45 k |\n| * Commits | 83.99 k |\n| * Trees | 164.46 k |\n| * Blobs | 161.00 k |\n| * Tags | 1.00 k |\n| * Inflated size | 7.46 GiB |\n| * Commits | 57.53 MiB |\n| * Trees | 2.33 GiB |\n| * Blobs | 5.07 GiB |\n| * Tags | 737.48 KiB |\n| * Disk size | 181.37 MiB |\n| * Commits | 33.11 MiB |\n| * Trees | 40.58 MiB |\n| * Blobs | 107.11 MiB |\n| * Tags | 582.67 KiB |\n| | |\n| * Largest objects | |\n| * Commits | |\n| * Maximum size [1] | 17.23 KiB |\n| * Maximum parents [2] | 10 |\n| * Trees | |\n| * Maximum size [3] | 58.85 KiB |\n| * Maximum entries [4] | 1.18 k |\n| * Blobs | |\n| * Maximum size [5] | 1019.51 KiB |\n| * Tags | |\n\n| * Maximum size [6] | 7.13 KiB |\n\n[1] f6ecb603ff8af608a417d7724727d6bc3a9dbfdf\n[2] 16d7601e176cd53f3c2f02367698d06b85e08879\n[3] 203ee97047731b9fd3ad220faa607b6677861a0d\n[4] 203ee97047731b9fd3ad220faa607b6677861a0d\n[5] aa96f8bc361fd84a1459440f1e7de02ab0dc3543\n[6] 07e38db6a5a03690034d27104401f6c8ea40f1fc\n```\n\nWith this information we're now almost feature-complete as compared to git-sizer(1). We're not done yet, though — we plan to eventually add additional features such as:\n\n- Severity levels as they exist in git-sizer(1)\n- Graphs that show you the distribution of object sizes\n- The ability to scan objects reachable via a subset of references\n\n*This project was led by [Justin Tobler](https://gitlab.com/justintobler).*\n\n## New infrastructure for repository maintenance\n\nWhenever you write data into a Git repository you will typically end up adding more loose objects. Left unmanaged, this leads to a large number of separate files in your `.git/objects/` directory, which slows down several operations that want to access many objects at once. Git thus regularly packs these objects into \"packfiles\" to ensure good performance.\n\nThis isn't the only data structure that may become inefficient over time: Updating references may create loose references, reflogs will need trimming, worktrees may become stale, and caches like commit-graphs need to be refreshed regularly.\n\nAll of these tasks have historically been managed by [git-gc(1)](https://git-scm.com/docs/git-gc). However, this tool has a monolithic architecture, where it basically executes all of the tasks required in sequential order. This foundation is hard to extend and doesn't give the end user much flexibility in case they want to slightly modify how housekeeping is performed.\n\nThe Git project introduced the new [git-maintenance(1)](https://git-scm.com/docs/git-maintenance) tool in Git 2.29. In contrast to git-gc(1), git-maintenance(1) is not monolithic but is instead structured around tasks. These tasks are freely configurable by the user so that the user can control which tasks are running, giving them much more fine-grained control over repository maintenance.\n\nEventually, Git has migrated to use git-maintenance(1) by default. But in the beginning, the only task that was default-enabled was the git-gc(1) task, which as you might have guessed, simply executes `git gc`. To manually run maintenance using this new command you can execute `git maintenance run`, but Git knows to execute this automatically after several other commands.\n\nOver the last couple releases we have implemented all the individual tasks that are supported by git-gc(1) in git-maintenance(1) to ensure that we have feature parity between these two tools.\n\nFurthermore, we have implemented a new task that uses Git's modern architecture for repacking objects with [geometric compaction](https://git-scm.com/docs/git-repack#Documentation/git-repack.txt---geometricfactor).\nGeometric compaction is a much better fit for large monorepos, and with our efforts to make them work well with partial clones [that landed in Git 2.53](https://about.gitlab.com/blog/whats-new-in-git-2-53-0/#geometric-repacking-support-with-promisor-remotes) they are now a full replacement for our previous repacking strategy in Git.\n\nIn Git 2.54, we have now reached another significant milestone: Instead of using the git-gc(1)-based strategy by default, we are now using geometric repacking with fine-grained individual maintenance tasks! Besides being more efficient for large monorepos, it also ensures that we have an easier foundation to iterate on going forward.\n\n*The git-maintenance(1) infrastructure was originally implemented by [Derrick Stolee](https://github.com/derrickstolee) and geometric maintenance was introduced by [Taylor Blau](https://github.com/ttaylorr). The effort to introduce the new fine-grained tasks and migrate to the new maintenance strategy was led by [Patrick Steinhardt](https://gitlab.com/pks-gitlab).*\n\n## Read more\n\nThis article highlighted just a few of the contributions made by GitLab and the wider Git community for this latest release. You can learn about these from the [official release announcement](https://lore.kernel.org/git/xmqqa4uxsjrs.fsf@gitster.g/T/#u) of the Git project. Also, check out our [previous Git release blog posts](https://about.gitlab.com/blog/tags/git/) to see other past highlights of contributions from GitLab team members.",{"slug":740,"featured":12,"template":13},"whats-new-in-git-2-54-0",{"content":742,"config":751},{"title":743,"description":744,"authors":745,"date":747,"body":748,"heroImage":749,"category":9,"tags":750},"What’s new in Git 2.53.0?","Learn about release contributions, including fixes for geometric repacking, updates to git-fast-import(1) commit signature handing options, and more.",[746],"Justin Tobler","2026-02-02","The Git project recently released [Git 2.53.0](https://lore.kernel.org/git/xmqq4inz13e3.fsf@gitster.g/T/#u). Let's look at a few notable highlights from this release, which includes\ncontributions from the Git team at GitLab.\n\n## Geometric repacking support with promisor remotes\n\nNewly written objects in a Git repository are often stored as individual loose files. To ensure good performance and optimal use of disk space, these loose objects are regularly compressed into so-called packfiles. The number of packfiles in a repository grows over time as a result of the user’s activities, like writing new commits or fetching from a remote. As the number of packfiles in a repository increases, Git has to do more work to look up individual objects. Therefore, to preserve optimal repository performance, packfiles are periodically repacked via git-repack(1) to consolidate the objects into fewer packfiles. When repacking there are two strategies: “all-into-one” and “geometric”.\n\nThe all-into-one strategy is fairly straightforward and the current default. As its name implies, all objects in the repository are packed into a single packfile. From a performance perspective this is great for the repository as Git only has to scan through a single packfile when looking up objects. The main downside of such a repacking strategy is that computing a single packfile for a repository can take a significant amount of time for large repositories.\n\nThe geometric strategy helps mitigate this concern by maintaining a geometric progression of packfiles based on their size instead of always repacking into a single packfile. To explain more plainly, when repacking Git maintains a set of packfiles ordered by size where each packfile in the sequence is expected to be at least twice the size of the preceding packfile. If a packfile in the sequence violates this property, packfiles are combined as needed until the progression is restored. This strategy has the advantage of still minimizing the number of packfiles in a repository while also minimizing the amount of work that must be done for most repacking operations.\n\nOne problem with the geometric repacking strategy was that it was not compatible with partial clones. Partial clones allow the user to clone only parts of a repository by, for example, skipping all blobs larger than 1 megabyte. This can significantly reduce the size of a repository, and Git knows how to backfill missing objects that it needs to access at a later point in time.\n\nThe result is a repository that is missing some objects, and any object that may not be fully connected is stored in a “promisor” packfile. When repacking, this promisor property needs to be retained going forward for packfiles containing a promisor object so it is known whether a missing object is expected and can be backfilled from the promisor remote. With an all-into-one repack, Git knows how to handle promisor objects properly and stores them in a separate promisor packfile. Unfortunately, the geometric repacking strategy did not know to give special treatment to promisor packfiles and instead would merge them with normal packfiles without considering whether they reference promisor objects. Luckily, due to a bug the underlying git-pack-objects(1) dies when using geometric repacking in a partial clone repository. So this means repositories in this configuration were not able to be repacked anyways which isn’t great, but better than repository corruption.\n\nWith the release of Git 2.53, geometric repacking now works with partial clone repositories. When performing a geometric repack, promisor packfiles are handled separately in order to preserve the promisor marker and repacked following a separate geometric progression. With this fix, the geometric strategy moves closer towards becoming the default repacking strategy. For more information check out the corresponding [mailing list thread](https://lore.kernel.org/git/20260105-pks-geometric-repack-with-promisors-v1-0-c4660573437e@pks.im/).\n\nThis project was led by [Patrick Steinhardt](https://gitlab.com/pks-gitlab).\n\n## git-fast-import(1) learned to preserve only valid signatures\n\nIn our [Git 2.52 release article](https://about.gitlab.com/blog/whats-new-in-git-2-52-0/), we covered signature related improvements to git-fast-import(1) and git-fast-export(1). Be sure to check out that post for a more detailed explanation of these commands, how they are used, and the changes being made with regards to signatures.\n\nTo quickly recap, git-fast-import(1) provides a backend to efficiently import data into a repository and is used by tools such as [git-filter-repo(1)](https://github.com/newren/git-filter-repo) to help rewrite the history of a repository in bulk. In the Git 2.52 release, git-fast-import(1) learned the `--signed-commits=\u003Cmode>` option similar to the same option in git-fast-export(1). With this option, it became possible to unconditionally retain or strip signatures from commits/tags.\n\nIn situations where only part of the repository history has been rewritten, any signature for rewritten commits/tags becomes invalid. This means git-fast-import(1) is limited to either stripping all signatures or keeping all signatures even if they have become invalid. But retaining invalid signatures doesn’t make much sense, so rewriting history with git-repo-filter(1) results in all signatures being stripped, even if the underlying commit/tag is not rewritten. This is unfortunate because if the commit/tag is unchanged, its signature is still valid and thus there is no real reason to strip it. What is really needed is a means to preserve signatures for unchanged objects, but strip invalid ones.\n\nWith the release of Git 2.53, the git-fast-import(1) `--signed-commits=\u003Cmode>` option has learned a new `strip-if-invalid` mode which, when specified, only strips signatures from commits that become invalid due to being rewritten. Thus, with this option it becomes possible to preserve some commit signatures when using git-fast-import(1). This is a critical step towards providing the foundation for tools like git-repo-filter(1) to preserve valid signatures and eventually re-sign invalid signatures.\n\nThis project was led by [Christian Couder](https://gitlab.com/chriscool).\n\n## More data collected in git-repo-structure\n\nIn the Git 2.52 release, the “structure” subcommand was introduced to git-repo(1). The intent of this command was to collect information about the repository and eventually become a native replacement for tools such as [git-sizer(1)](https://github.com/github/git-sizer). At GitLab, we host some extremely large repositories, and having insight into the general structure of a repository is critical to understand its performance characteristics. In this release, the command now also collects total size information for reachable objects in a repository to help understand the overall size of the repository. In the output below, you can see the command now collects both the total inflated and disk sizes of reachable objects by object type.\n\n```shell\n$ git repo structure\n\n| Repository structure | Value |\n| -------------------- | ---------- |\n| * References | |\n| * Count | 1.78 k |\n| * Branches | 5 |\n| * Tags | 1.03 k |\n| * Remotes | 749 |\n| * Others | 0 |\n| | |\n| * Reachable objects | |\n| * Count | 421.37 k |\n| * Commits | 88.03 k |\n| * Trees | 169.95 k |\n| * Blobs | 162.40 k |\n| * Tags | 994 |\n| * Inflated size | 7.61 GiB |\n| * Commits | 60.95 MiB |\n| * Trees | 2.44 GiB |\n| * Blobs | 5.11 GiB |\n| * Tags | 731.73 KiB |\n| * Disk size | 301.50 MiB |\n| * Commits | 33.57 MiB |\n| * Trees | 77.92 MiB |\n| * Blobs | 189.44 MiB |\n| * Tags | 578.13 KiB |\n```\n\nThe keen-eyed among you may have also noticed that the size values in the table output are also now listed in a more human-friendly manner with units appended. In subsequent releases we hope to further expand this command's output to provide additional data points such as the largest individual objects in the repository.\n\nThis project was led by [Justin Tobler](https://gitlab.com/justintobler).\n\n## Read more\n\nThis article highlighted just a few of the contributions made by GitLab and\nthe wider Git community for this latest release. You can learn about these from\nthe [official release announcement](https://lore.kernel.org/git/xmqq4inz13e3.fsf@gitster.g/T/#u) of the Git project. Also, check\nout our [previous Git release blog posts](https://about.gitlab.com/blog/tags/git/)\nto see other past highlights of contributions from GitLab team members.","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749663087/Blog/Hero%20Images/git3-cover.png",[736,737,259],{"featured":28,"template":13,"slug":752},"whats-new-in-git-2-53-0",{"promotions":754},[755,769,781,793],{"id":756,"categories":757,"header":759,"text":760,"button":761,"image":766},"ai-modernization",[758],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":762,"config":763},"Get your AI maturity score",{"href":764,"dataGaName":765,"dataGaLocation":241},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":767},{"src":768},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":770,"categories":771,"header":773,"text":760,"button":774,"image":778},"devops-modernization",[772,567],"product","Are you just managing tools or shipping innovation?",{"text":775,"config":776},"Get your DevOps maturity score",{"href":777,"dataGaName":765,"dataGaLocation":241},"/assessments/devops-modernization-assessment/",{"config":779},{"src":780},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":782,"categories":783,"header":785,"text":760,"button":786,"image":790},"security-modernization",[784],"security","Are you trading speed for security?",{"text":787,"config":788},"Get your security maturity score",{"href":789,"dataGaName":765,"dataGaLocation":241},"/assessments/security-modernization-assessment/",{"config":791},{"src":792},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":794,"paths":795,"header":798,"text":799,"button":800,"image":805},"github-azure-migration",[796,797],"migration-from-azure-devops-to-gitlab","integrating-azure-devops-scm-and-gitlab","Is your team ready for GitHub's Azure move?","GitHub is already rebuilding around Azure. Find out what it means for you.",{"text":801,"config":802},"See how GitLab compares to GitHub",{"href":803,"dataGaName":804,"dataGaLocation":241},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":806},{"src":780},{"header":808,"blurb":809,"button":810,"secondaryButton":815},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":811,"config":812},"Get your free trial",{"href":813,"dataGaName":48,"dataGaLocation":814},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":503,"config":816},{"href":52,"dataGaName":53,"dataGaLocation":814},1777393953088]