[{"data":1,"prerenderedAt":792},["ShallowReactive",2],{"/en-us/blog/learn-python-with-pj-part-3":3,"navigation-en-us":37,"banner-en-us":437,"footer-en-us":447,"blog-post-authors-en-us-PJ Metz":689,"blog-related-posts-en-us-learn-python-with-pj-part-3":704,"assessment-promotions-en-us":743,"next-steps-en-us":782},{"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/learn-python-with-pj-part-3.yml","Learn Python With Pj Part 3",[7],"pj-metz",null,"engineering",{"slug":11,"featured":12,"template":13},"learn-python-with-pj-part-3",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"Learn Python with Pj! Part 3 - Functions and strings","Pj shares his experiences learning how to program functions and strings.",[18],"PJ Metz","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749664962/Blog/Hero%20Images/python.jpg","2022-04-04","\n\n_This is the third installment in the Learn Python with Pj! series. Make sure to read [Part 1](/blog/learn-python-with-pj-part-1/) and [Part 2](/blog/learn-python-with-pj-part-2/)._\n\n\nIn learning Python, I’m happy to have found a language with a straightforward syntax that just seems to make sense. I don’t have to define a type; Python just knows. I don’t have to worry about `let` or `const` or `var` for different use cases; I just make the variable. I’m very glad I learned C# and JavaScript first, as those feel important to understanding exactly what is happening when I write code. In turn, I think it’s made Python easier for me, which is usually true when learning another programming language: Your second and third are easier to learn since your brain understands “programming logic” better now than when you made your first “Hello World”. This week we’re going to talk about what I’ve learned in functions and strings.\n## Functions\n\nFunctions are the backbone of any app you write. It’s an important step in learning any language to learn how to put a series of actions inside a single function that can be called later in the code. Python does this simply compared to other languages I’ve learned.\n```python\ndef my_first_function(arg1, arg2):\n  print(f”Your input was {arg1} and {arg2}.”)\n\n#prints “Your input was hilarious and unnecessary”\nmy_first_function(“hilarious”, “unnecessary”)\n```\nUsing the keyword `def` lets Python know that you’re about to write a function. Inside the parentheses, you put any parameters that must be included when calling the function. Some people use argument and parameter interchangeably, but technically, when defining a function, it’s a parameter, and when calling a function, it’s an argument. Either way, when defining the function, include some variables that you’ll expect when the function is called later. Finally, put a colon and then move to the next line. All the code for the function to run is indented. Inside the function, you can run loops, logic, or even other functions. Let’s check out a slightly more complex use.\n```python\ndef halloween_horror_nights(days, link,):\n    named = input(\"What is your name?\")\n    name = named.capitalize()\n    if isinstance(days, int):\n        if days == 0:\n            print(f\" Hello, {name}. We're ready to see you at HHN. {link}\")\n        elif days \u003C= 30:\n            print(f\"{name}, You have {days} days until the terror is home. {link}\")\n        elif days \u003C= 60:\n            print(f\"{name}, The horror comes home in {days} days. Join us in the dark. {link}\")\n        elif days \u003C 365 and days > 60:\n            print(f\"{name}, Patience is a virtue. You're {days} days away from the top rated Halloween event in the world.{link}\")\n        else:\n            print(f\"{name}, it can't be more than a year away. It's closer than you think... {link}\")\n    else:\n        print(\"Days must be an int\")\n        #This will print “{Name input by user}, The horror comes home in 56 days. Join us in the dark. https://orlando.halloweenhorrornights.com/site\"\nhalloween_horror_nights(56, \"https://orlando.halloweenhorrornights.com/site\")\n```\n\nFor context, Halloween Horror Nights in Orlando is my favorite event of the year. This function takes in a number of days and a link (meant to be days until the event and a link to the HHN web page) and outputs a string that says how many days are left until the event. The string also includes a link to the web page and asks for user input to personalize each string. The function `isinstance()` checks if `days` is an int to make sure the sentence makes sense and returns `True` if the first argument is the type of the second argument.\nI really found functions in Python to be a lot easier than in other languages, though I still miss the curly brackets of C# and JavaScript. Additionally, the simplicity of `def` followed by the function name and any required parameters is really straightforward and makes reading the code easier. And since code is read more than it’s written, that makes Python pretty awesome in my book.\nI also used the f-string format for these print statements, and it’s still one of my favorite ways to concatenate. It feels easier than a lot of the other ways of inserting variables into a string in Python, and a little easier than the way it’s done in JavaScript, at least to me. I use a different method of including variables in a string called `.format()`.\nMaking your own functions is important, but there are a bunch of built-in functions in Python. There are also methods, which are similar to functions but are associated with the objects in a class they’re assigned to. Let’s talk about some strings and some methods that come with them.\n\n## Strings\n\nI thought it was strange that I had a whole section on strings in my Codecademy Python curriculum, but I soon realized that it was giving me a lot of very useful methods to use on strings that seemed very versatile. The most interesting thing to me is that strings are an object and act like a list of characters. I’m not entirely sure how strings are treated in other languages, but this really struck me as a cool idea. You can even call specific characters using the same syntax you would for a list.\n```python\nspooky = “Halloween Horror Nights is my favorite thing about Autumn.”\n\n#the following prints “l” since it’s the 3rd char in the string `spooky`. print(spooky[2]) ```\n\nOr you can use a for loop on a string.\n\n```python\n#This prints each letter on a new line and capitalizes it. The message now reads vertical in the output.\nfor letter in spooky:\n  print(letter.upper())\n```\n\n### String methods\t\n\nA few built-in methods exist for strings in Python, like `capitalize()` and `upper()`, two I used in the above examples. In addition to those, there are many more that can do things like remove the whitespace or noise from the beginning and end of a string, tell you the index of the first appearance of something, or join a list of strings into a single string. There are lots of great included methods. Here’s an exercise I took from Codecademy and changed the content to fit this article's theme.\n```python\n#given a string that contains a ton of information separated by semicolons and commas. Each part is a haunted house name, Universal Studios location, and the year the house appeared at the event.\nhhn_houses_location_year = \"Chucky;Japan;2016, Run;Orlando;2001, The Orfanage: Ashes to Ashes;Orlando;2010, The Real: Haunted Village;Japan;2021, The Undertaker: No Mercy;Hollywood;2000, Welcome to Silent Hill;Hollywood;2012, American Werewolf in London;Orlando;2013\"  \n#this splits the string up into a list where each element of the list is the section separated by a comma.\nhhn_houses_list = hhn_houses_location_year.split(\",\")\n\n#empty list for the next step\nhhn_houses_stripped = []\n\n#this strips any whitespace from the element in the list and adds it to the empty list from before\nfor house in hhn_houses_list:\n    hhn_houses_stripped.append(house.strip())\n\n#empty list for next step\nhhn_house_details = []\n\n#the next few lines split the details into their own list. #first, each house, with the details, is split along the semicolons to make a list of lists, with each house being its own element in the larger list\n#next, empty lists are made for each detail\n#finally, using index numbers, each detail is placed in it’s own list so all the houses, locations, and years are separated. for info in hhn_houses_stripped:\n    hhn_house_details.append(info.split(\";\"))\n\nhouse = []\nlocation = []\nyear = []\n\nfor stuff in hhn_house_details:\n    house.append(stuff[0])\n    location.append(stuff[1])\n    year.append(stuff[2])\n\n#loops through and using .format() prints a sentence that tells about each house. for num in range(0, len(house)):\n  print(\"{} was located in {} for the {} event\".format(house[num], location[num], year[num]))\n```\n\nAs you can see, I am obsessed with Halloween horror nights… er, wait, not the point of the article. As you can see, Python’s built-in methods for strings can be pretty useful, especially if you end up with a bunch of data sitting around in unformatted strings. Next time, we’re going to talk about Dictionaries and how they are used in Python! \n",[23,24,25],"careers","growth","tutorial","yml",{},true,"/en-us/blog/learn-python-with-pj-part-3",{"title":15,"description":16,"ogTitle":15,"ogDescription":16,"noIndex":12,"ogImage":19,"ogUrl":31,"ogSiteName":32,"ogType":33,"canonicalUrls":31},"https://about.gitlab.com/blog/learn-python-with-pj-part-3","https://about.gitlab.com","article","en-us/blog/learn-python-with-pj-part-3",[23,24,25],"Jg7_iEhovLhOCMYZo4N1fvzoYQjyOuUsrY0iaoYigA4",{"data":38},{"logo":39,"freeTrial":44,"sales":49,"login":54,"items":59,"search":367,"minimal":398,"duo":417,"pricingDeployment":427},{"config":40},{"href":41,"dataGaName":42,"dataGaLocation":43},"/","gitlab logo","header",{"text":45,"config":46},"Get free trial",{"href":47,"dataGaName":48,"dataGaLocation":43},"https://gitlab.com/-/trial_registrations/new?glm_source=about.gitlab.com&glm_content=default-saas-trial/","free trial",{"text":50,"config":51},"Talk to 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inclusion and belonging (DIB)",{"href":665,"dataGaName":666,"dataGaLocation":455},"/diversity-inclusion-belonging/","Diversity, inclusion and belonging",{"text":329,"config":668},{"href":331,"dataGaName":332,"dataGaLocation":455},{"text":339,"config":670},{"href":341,"dataGaName":342,"dataGaLocation":455},{"text":344,"config":672},{"href":346,"dataGaName":347,"dataGaLocation":455},{"text":674,"config":675},"Modern Slavery Transparency Statement",{"href":676,"dataGaName":677,"dataGaLocation":455},"https://handbook.gitlab.com/handbook/legal/modern-slavery-act-transparency-statement/","modern slavery transparency statement",{"items":679},[680,683,686],{"text":681,"config":682},"Terms",{"href":507,"dataGaName":508,"dataGaLocation":455},{"text":684,"config":685},"Cookies",{"dataGaName":517,"dataGaLocation":455,"id":518,"isOneTrustButton":28},{"text":687,"config":688},"Privacy",{"href":512,"dataGaName":513,"dataGaLocation":455},[690],{"id":691,"title":692,"body":8,"config":693,"content":695,"description":8,"extension":26,"meta":699,"navigation":28,"path":700,"seo":701,"stem":702,"__hash__":703},"blogAuthors/en-us/blog/authors/pj-metz.yml","Pj Metz",{"template":694},"BlogAuthor",{"name":18,"config":696},{"headshot":697,"ctfId":698},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1749659488/Blog/Author%20Headshots/gitlab-logo-extra-whitespace.png","PjMetz",{},"/en-us/blog/authors/pj-metz",{},"en-us/blog/authors/pj-metz","SgW84Gu-lPq0zFzhy1aT23bZjx9kWViDG_WXzCPn04E",[705,719,732],{"content":706,"config":717},{"title":707,"description":708,"authors":709,"heroImage":711,"date":712,"body":713,"category":9,"tags":714},"How to use GitLab Container Virtual Registry with Docker Hardened Images","Learn how to simplify container image management with this step-by-step guide.",[710],"Tim Rizzi","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772111172/mwhgbjawn62kymfwrhle.png","2026-03-12","If you're a platform engineer, you've probably had this conversation:\n  \n*\"Security says we need to use hardened base images.\"*\n\n*\"Great, where do I configure credentials for yet another registry?\"*\n\n*\"Also, how do we make sure everyone actually uses them?\"*\n\nOr this one:\n\n*\"Why are our builds so slow?\"*\n\n*\"We're pulling the same 500MB image from Docker Hub in every single job.\"*\n\n*\"Can't we just cache these somewhere?\"*\n\nI've been working on [Container Virtual Registry](https://docs.gitlab.com/user/packages/virtual_registry/container/) at GitLab specifically to solve these problems. It's a pull-through cache that sits in front of your upstream registries — Docker Hub, dhi.io (Docker Hardened Images), MCR, and Quay — and gives your teams a single endpoint to pull from. Images get cached on the first pull. Subsequent pulls come from the cache. Your developers don't need to know or care which upstream a particular image came from.\n\nThis article shows you how to set up Container Virtual Registry, specifically with Docker Hardened Images in mind, since that's a combination that makes a lot of sense for teams concerned about security and not making their developers' lives harder.\n\n## What problem are we actually solving?\n\nThe Platform teams I usually talk to manage container images across three to five registries:\n\n* **Docker Hub** for most base images\n* **dhi.io** for Docker Hardened Images (security-conscious workloads)\n* **MCR** for .NET and Azure tooling\n* **Quay.io** for Red Hat ecosystem stuff\n* **Internal registries** for proprietary images\n\nEach one has its own:\n\n* Authentication mechanism\n* Network latency characteristics\n* Way of organizing image paths\n\nYour CI/CD configs end up littered with registry-specific logic. Credential management becomes a project unto itself. And every pipeline job pulls the same base images over the network, even though they haven't changed in weeks.\n\nContainer Virtual Registry consolidates this. One registry URL. One authentication flow (GitLab's). Cached images are served from GitLab's infrastructure rather than traversing the internet each time.\n\n## How it works\n\nThe model is straightforward:\n\n```text\nYour pipeline pulls:\n  gitlab.com/virtual_registries/container/1000016/python:3.13\n\nVirtual registry checks:\n  1. Do I have this cached? → Return it\n  2. No? → Fetch from upstream, cache it, return it\n\n```\n\nYou configure upstreams in priority order. When a pull request comes in, the virtual registry checks each upstream until it finds the image. The result gets cached for a configurable period (default 24 hours).\n\n```text\n┌─────────────────────────────────────────────────────────┐\n│                    CI/CD Pipeline                       │\n│                          │                              │\n│                          ▼                              │\n│   gitlab.com/virtual_registries/container/\u003Cid>/image   │\n└─────────────────────────────────────────────────────────┘\n                           │\n                           ▼\n┌─────────────────────────────────────────────────────────┐\n│            Container Virtual Registry                   │\n│                                                         │\n│  Upstream 1: Docker Hub ────────────────┐               │\n│  Upstream 2: dhi.io (Hardened) ────────┐│               │\n│  Upstream 3: MCR ─────────────────────┐││               │\n│  Upstream 4: Quay.io ────────────────┐│││               │\n│                                      ││││               │\n│                    ┌─────────────────┴┴┴┴──┐            │\n│                    │        Cache          │            │\n│                    │  (manifests + layers) │            │\n│                    └───────────────────────┘            │\n└─────────────────────────────────────────────────────────┘\n```\n\n## Why this matters for Docker Hardened Images\n\n[Docker Hardened Images](https://docs.docker.com/dhi/) are great because of the minimal attack surface, near-zero CVEs, proper software bills of materials (SBOMs), and SLSA provenance. If you're evaluating base images for security-sensitive workloads, they should be on your list.\n\nBut adopting them creates the same operational friction as any new registry:\n\n* **Credential distribution**: You need to get Docker credentials to every system that pulls images from dhi.io.\n* **CI/CD changes**: Every pipeline needs to be updated to authenticate with dhi.io.\n* **Developer friction**: People need to remember to use the hardened variants.\n* **Visibility gap**: It's difficulat to tell if teams are actually using hardened images vs. regular ones.\n\nVirtual registry addresses each of these:\n\n**Single credential**: Teams authenticate to GitLab. The virtual registry handles upstream authentication. You configure Docker credentials once, at the registry level, and they apply to all pulls.\n\n**No CI/CD changes per-team**: Point pipelines at your virtual registry. Done. The upstream configuration is centralized.\n\n**Gradual adoption**: Since images get cached with their full path, you can see in the cache what's being pulled. If someone's pulling `library/python:3.11` instead of the hardened variant, you'll know.\n\n**Audit trail**: The cache shows you exactly which images are in active use. Useful for compliance, useful for understanding what your fleet actually depends on.\n\n## Setting it up\n\nHere's a real setup using the Python client from this demo project.\n\n### Create the virtual registry\n\n```python\nfrom virtual_registry_client import VirtualRegistryClient\n\nclient = VirtualRegistryClient()\n\nregistry = client.create_virtual_registry(\n    group_id=\"785414\",  # Your top-level group ID\n    name=\"platform-images\",\n    description=\"Cached container images for platform teams\"\n)\n\nprint(f\"Registry ID: {registry['id']}\")\n# You'll need this ID for the pull URL\n```\n\n### Add Docker Hub as an upstream\n\nFor official images like Alpine, Python, etc.:\n\n```python\ndocker_upstream = client.create_upstream(\n    registry_id=registry['id'],\n    url=\"https://registry-1.docker.io\",\n    name=\"Docker Hub\",\n    cache_validity_hours=24\n)\n```\n\n### Add Docker Hardened Images (dhi.io)\n\nDocker Hardened Images are hosted on `dhi.io`, a separate registry that requires authentication:\n\n```python\ndhi_upstream = client.create_upstream(\n    registry_id=registry['id'],\n    url=\"https://dhi.io\",\n    name=\"Docker Hardened Images\",\n    username=\"your-docker-username\",\n    password=\"your-docker-access-token\",\n    cache_validity_hours=24\n)\n```\n\n### Add other upstreams\n\n```python\n# MCR for .NET teams\nclient.create_upstream(\n    registry_id=registry['id'],\n    url=\"https://mcr.microsoft.com\",\n    name=\"Microsoft Container Registry\",\n    cache_validity_hours=48\n)\n\n# Quay for Red Hat stuff\nclient.create_upstream(\n    registry_id=registry['id'],\n    url=\"https://quay.io\",\n    name=\"Quay.io\",\n    cache_validity_hours=24\n)\n```\n\n### Update your CI/CD\n\nHere's a `.gitlab-ci.yml` that pulls through the virtual registry:\n\n```yaml\nvariables:\n  VIRTUAL_REGISTRY_ID: \u003Cyour_virtual_registry_ID>\n\n  \nbuild:\n  image: docker:24\n  services:\n    - docker:24-dind\n  before_script:\n    # Authenticate to GitLab (which handles upstream auth for you)\n    - echo \"${CI_JOB_TOKEN}\" | docker login -u gitlab-ci-token --password-stdin gitlab.com\n  script:\n    # All of these go through your single virtual registry\n    \n    # Official Docker Hub images (use library/ prefix)\n    - docker pull gitlab.com/virtual_registries/container/${VIRTUAL_REGISTRY_ID}/library/alpine:latest\n    \n    # Docker Hardened Images from dhi.io (no prefix needed)\n    - docker pull gitlab.com/virtual_registries/container/${VIRTUAL_REGISTRY_ID}/python:3.13\n    \n    # .NET from MCR\n    - docker pull gitlab.com/virtual_registries/container/${VIRTUAL_REGISTRY_ID}/dotnet/sdk:8.0\n```\n\n### Image path formats\n\nDifferent registries use different path conventions:\n\n| Registry | Pull URL Example |\n|----------|------------------|\n| Docker Hub (official) | `.../library/python:3.11-slim` |\n| Docker Hardened Images (dhi.io) | `.../python:3.13` |\n| MCR | `.../dotnet/sdk:8.0` |\n| Quay.io | `.../prometheus/prometheus:latest` |\n\n### Verify it's working\n\nAfter some pulls, check your cache:\n\n```python\nupstreams = client.list_registry_upstreams(registry['id'])\nfor upstream in upstreams:\n    entries = client.list_cache_entries(upstream['id'])\n    print(f\"{upstream['name']}: {len(entries)} cached entries\")\n\n```\n\n## What the numbers look like\n\nI ran tests pulling images through the virtual registry:\n\n| Metric | Without Cache | With Warm Cache |\n|--------|---------------|-----------------|\n| Pull time (Alpine) | 10.3s | 4.2s |\n| Pull time (Python 3.13 DHI) | 11.6s | ~4s |\n| Network roundtrips to upstream | Every pull | Cache misses only |\n\n\n\n\nThe first pull is the same speed (it has to fetch from upstream). Every pull after that, for the cache validity period, comes straight from GitLab's storage. No network hop to Docker Hub, dhi.io, MCR, or wherever the image lives.\n\nFor a team running hundreds of pipeline jobs per day, that's hours of cumulative build time saved.\n\n## Practical considerations\nHere are some considerations to keep in mind:\n\n### Cache validity\n\n24 hours is the default. For security-sensitive images where you want patches quickly, consider 12 hours or less:\n\n```python\nclient.create_upstream(\n    registry_id=registry['id'],\n    url=\"https://dhi.io\",\n    name=\"Docker Hardened Images\",\n    username=\"your-username\",\n    password=\"your-token\",\n    cache_validity_hours=12\n)\n```\n\nFor stable, infrequently-updated images (like specific version tags), longer validity is fine.\n\n### Upstream priority\n\nUpstreams are checked in order. If you have images with the same name on different registries, the first matching upstream wins.\n\n### Limits\n\n* Maximum of 20 virtual registries per group\n* Maximum of 20 upstreams per virtual registry\n\n## Configuration via UI\n\nYou can also configure virtual registries and upstreams directly from the GitLab UI—no API calls required. Navigate to your group's **Settings > Packages and registries > Virtual Registry** to:\n\n* Create and manage virtual registries\n* Add, edit, and reorder upstream registries\n* View and manage the cache\n* Monitor which images are being pulled\n\n## What's next\n\nWe're actively developing:\n\n* **Allow/deny lists**: Use regex to control which images can be pulled from specific upstreams.\n\nThis is beta software. It works, people are using it in production, but we're still iterating based on feedback.\n\n## Share your feedback\n\nIf you're a platform engineer dealing with container registry sprawl, I'd like to understand your setup:\n\n* How many upstream registries are you managing?\n* What's your biggest pain point with the current state?\n* Would something like this help, and if not, what's missing?\n\nPlease share your experiences in the [Container Virtual Registry feedback issue](https://gitlab.com/gitlab-org/gitlab/-/work_items/589630).\n## Related resources\n- [New GitLab metrics and registry features help reduce CI/CD bottlenecks](https://about.gitlab.com/blog/new-gitlab-metrics-and-registry-features-help-reduce-ci-cd-bottlenecks/#container-virtual-registry)\n- [Container Virtual Registry documentation](https://docs.gitlab.com/user/packages/virtual_registry/container/)\n- [Container Virtual Registry API](https://docs.gitlab.com/api/container_virtual_registries/)",[25,715,716],"product","features",{"featured":12,"template":13,"slug":718},"using-gitlab-container-virtual-registry-with-docker-hardened-images",{"content":720,"config":730},{"title":721,"description":722,"authors":723,"heroImage":725,"date":726,"category":9,"tags":727,"body":729},"How IIT Bombay students are coding the future with GitLab","At GitLab, we often talk about how software accelerates innovation. But sometimes, you have to step away from the Zoom calls and stand in a crowded university hall to remember why we do this.",[724],"Nick Veenhof","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099013/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%2814%29_6VTUA8mUhOZNDaRVNPeKwl_1750099012960.png","2026-01-08",[259,611,728],"open source","The GitLab team recently had the privilege of judging the **iHack Hackathon** at **IIT Bombay's E-Summit**. The energy was electric, the coffee was flowing, and the talent was undeniable. But what struck us most wasn't just the code — it was the sheer determination of students to solve real-world problems, often overcoming significant logistical and financial hurdles to simply be in the room.\n\n\nThrough our [GitLab for Education program](https://about.gitlab.com/solutions/education/), we aim to empower the next generation of developers with tools and opportunity. Here is a look at what the students built, and how they used GitLab to bridge the gap between idea and reality.\n\n## The challenge: Build faster, build securely\n\nThe premise for the GitLab track of the hackathon was simple: Don't just show us a product; show us how you built it. We wanted to see how students utilized GitLab's platform — from Issue Boards to CI/CD pipelines — to accelerate the development lifecycle.\n\nThe results were inspiring.\n\n## The winners\n\n### 1st place: Team Decode — Democratizing Scientific Research\n\n**Project:** FIRE (Fast Integrated Research Environment)\n\nTeam Decode took home the top prize with a solution that warms a developer's heart: a local-first, blazing-fast data processing tool built with [Rust](https://about.gitlab.com/blog/secure-rust-development-with-gitlab/) and Tauri. They identified a massive pain point for data science students: existing tools are fragmented, slow, and expensive.\n\nTheir solution, FIRE, allows researchers to visualize complex formats (like NetCDF) instantly. What impressed the judges most was their \"hacker\" ethos. They didn't just build a tool; they built it to be open and accessible.\n\n**How they used GitLab:** Since the team lived far apart, asynchronous communication was key. They utilized **GitLab Issue Boards** and **Milestones** to track progress and integrated their repo with Telegram to get real-time push notifications. As one team member noted, \"Coordinating all these technologies was really difficult, and what helped us was GitLab... the Issue Board really helped us track who was doing what.\"\n\n![Team Decode](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380253/epqazj1jc5c7zkgqun9h.jpg)\n\n### 2nd place: Team BichdeHueDost — Reuniting to Solve Payments\n\n**Project:** SemiPay (RFID Cashless Payment for Schools)\n\nThe team name, BichdeHueDost, translates to \"Friends who have been set apart.\" It's a fitting name for a group of friends who went to different colleges but reunited to build this project. They tackled a unique problem: handling cash in schools for young children. Their solution used RFID cards backed by a blockchain ledger to ensure secure, cashless transactions for students.\n\n**How they used GitLab:** They utilized [GitLab CI/CD](https://about.gitlab.com/topics/ci-cd/) to automate the build process for their Flutter application (APK), ensuring that every commit resulted in a testable artifact. This allowed them to iterate quickly despite the \"flaky\" nature of cross-platform mobile development.\n\n![Team BichdeHueDost](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380253/pkukrjgx2miukb6nrj5g.jpg)\n\n### 3rd place: Team ZenYukti — Agentic Repository Intelligence\n\n**Project:** RepoInsight AI (AI-powered, GitLab-native intelligence platform)\n\nTeam ZenYukti impressed us with a solution that tackles a universal developer pain point: understanding unfamiliar codebases. What stood out to the judges was the tool's practical approach to onboarding and code comprehension: RepoInsight-AI automatically generates documentation, visualizes repository structure, and even helps identify bugs, all while maintaining context about the entire codebase.\n\n**How they used GitLab:** The team built a comprehensive CI/CD pipeline that showcased GitLab's security and DevOps capabilities. They integrated [GitLab's Security Templates](https://gitlab.com/gitlab-org/gitlab/-/tree/master/lib/gitlab/ci/templates/Security) (SAST, Dependency Scanning, and Secret Detection), and utilized [GitLab Container Registry](https://docs.gitlab.com/user/packages/container_registry/) to manage their Docker images for backend and frontend components. They created an AI auto-review bot that runs on merge requests, demonstrating an \"agentic workflow\" where AI assists in the development process itself.\n\n![Team ZenYukti](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380253/ymlzqoruv5al1secatba.jpg)\n\n## Beyond the code: A lesson in inclusion\n\nWhile the code was impressive, the most powerful moment of the event happened away from the keyboard.\n\nDuring the feedback session, we learned about the journey Team ZenYukti took to get to Mumbai. They traveled over 24 hours, covering nearly 1,800 kilometers. Because flights were too expensive and trains were booked, they traveled in the \"General Coach,\" a non-reserved, severely overcrowded carriage.\n\nAs one student described it:\n\n*\"You cannot even imagine something like this... there are no seats... people sit on the top of the train. This is what we have endured.\"*\n\nThis hit home. [Diversity, Inclusion, and Belonging](https://handbook.gitlab.com/handbook/company/culture/inclusion/) are core values at GitLab. We realized that for these students, the barrier to entry wasn't intellect or skill, it was access.\n\nIn that moment, we decided to break that barrier. We committed to reimbursing the travel expenses for the participants who struggled to get there. It's a small step, but it underlines a massive truth: **talent is distributed equally, but opportunity is not.**\n\n![hackathon class together](https://res.cloudinary.com/about-gitlab-com/image/upload/v1767380252/o5aqmboquz8ehusxvgom.jpg)\n\n### The future is bright (and automated)\n\nWe also saw incredible potential in teams like Prometheus, who attempted to build an autonomous patch remediation tool (DevGuardian), and Team Arrakis, who built a voice-first job portal for blue-collar workers using [GitLab Duo](https://about.gitlab.com/gitlab-duo/) to troubleshoot their pipelines.\n\nTo all the students who participated: You are the future. Through [GitLab for Education](https://about.gitlab.com/solutions/education/), we are committed to providing you with the top-tier tools (like GitLab Ultimate) you need to learn, collaborate, and change the world — whether you are coding from a dorm room, a lab, or a train carriage. **Keep shipping.**\n\n> :bulb: Learn more about the [GitLab for Education program](https://about.gitlab.com/solutions/education/).\n",{"slug":731,"featured":12,"template":13},"how-iit-bombay-students-code-future-with-gitlab",{"content":733,"config":741},{"title":734,"description":735,"authors":736,"heroImage":737,"date":738,"category":9,"tags":739,"body":740},"Artois University elevates research and curriculum with GitLab Ultimate for Education","Artois University's CRIL leveraged the GitLab for Education program to gain free access to Ultimate, transforming advanced research and computer science curricula.",[724],"https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099203/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%2820%29_2bJGC5ZP3WheoqzlLT05C5_1750099203484.png","2025-12-10",[611,259,715],"Leading academic institutions face a critical challenge: how to provide thousands of students and researchers with industry-standard, **full-featured DevSecOps tools** without compromising institutional control. Many start with basic version control, but the modern curriculum demands integrated capabilities for planning, security, and advanced CI/CD.\n\nThe **GitLab for Education program** is designed to solve this by providing access to **GitLab Ultimate** for qualifying institutions, allowing them to scale their operations and elevate their academic offerings. \n\nThis article showcases a powerful success story from the **Centre de Recherche en Informatique de Lens (CRIL)**, a joint laboratory of **Artois University** and CNRS in France. After years of relying solely on GitLab Community Edition (CE), the university's move to GitLab Ultimate through the GitLab for Education program immediately unlocked advanced capabilities, transforming their teaching, research, and contribution workflows virtually overnight. This story demonstrates why GitLab Ultimate is essential for institutions seeking to deliver advanced computer science and research curricula.\n\n## GitLab Ultimate unlocked: Managing scale and driving academic value\n\n**Artois University's** self-managed GitLab instance is a large-scale operation, supporting nearly **3,000 users** across approximately **19,000 projects**, primarily serving computer science students and researchers. While GitLab Community Edition was robust, the upgrade to GitLab Ultimate provided the sophisticated tooling necessary for managing this scale and facilitating advanced university-level work.\n\n***\"We can see the difference,\" says Daniel Le Berre, head of research at CRIL and the instance maintainer. \"It's a completely different product. Each week reveals new features that directly enhance our productivity and teaching.\"***\n\nThe institution joined the GitLab for Education program specifically because it covers both **instructional and non-commercial research use cases** and offers full access to Ultimate's features, removing significant cost barriers.\n\n### Key GitLab Ultimate benefits for students and researchers\n\n* **Advanced project management at scale:** Master's students now benefit from **GitLab Ultimate's project planning features**. This enables them to structure, track, and manage complex, long-term research projects using professional methodologies like portfolio management and advanced issue tracking that seamlessly roll up across their thousands of projects.\n\n* **Enhanced visibility:** Features like improved dashboards and code previews directly in Markdown files dramatically streamline tracking and documentation review, reducing administrative friction for both instructors and students managing large project loads.\n\n## Comprehensive curriculum: From concepts to continuous delivery\n\nGitLab Ultimate is deeply integrated into the computer science curriculum, moving students beyond simple `git` commands to practical **DevSecOps implementation**.\n\n* **Git fundamentals:** Students begin by visualizing concepts using open-source tools to master Git concepts.\n\n* **Full CI/CD implementation:** Students use GitLab CI for rigorous **Test-Driven Development (TDD)** in their software projects. They learn to build, test, and perform quality assurance using unit and integration testing pipelines—core competency made seamless by the integrated platform.\n\n* **DevSecOps for research and documentation:** The university teaches students that DevSecOps principles are vital for all collaborative work. Inspired by earlier work in Delft, students manage and produce critical research documentation (PDFs from Markdown files) using GitLab, incorporating quality checks like linters and spell checks directly in the CI pipeline. This ensures high-quality, reproducible research output.\n\n* **Future-proofing security skills:** The GitLab Ultimate platform immediately positions the institution to incorporate advanced DevSecOps features like SAST and DAST scanning as their research and development code projects grow, ensuring students are prepared for industry security standards.\n\n## Accelerating open source contributions with GitLab Duo\n\nAccess to the full GitLab platform, including our AI capabilities, has empowered students to make impactful contributions to the wider open source community faster than ever before.\n\nTwo Master's students recently completed direct contributions to the GitLab product, adding the **ORCID identifier** into user profiles. Working on GitLab.com, they leveraged **GitLab Duo's AI chat and code suggestions** to navigate the codebase efficiently.\n\n***\"This would not have been possible without GitLab Duo,\" Daniel Le Berre notes. \"The AI features helped students, who might have lacked deep codebase knowledge, deliver meaningful contributions in just two weeks.\"***\n\nThis demonstrates how providing students with cutting-edge tools **accelerates their learning and impact**, allowing them to translate classroom knowledge into real-world contributions immediately.\n\n## Empowering open research and institutional control\n\nThe stability of the self-managed instance at Artois University is key to its success. This model guarantees **institutional control and stability** — a critical factor for long-term research preservation.\n\nThe institution's expertise in this area was recently highlighted in a major 2024 study led by CRIL, titled: \"[Higher Education and Research Forges in France - Definition, uses, limitations encountered and needs analysis](https://hal.science/hal-04208924v4)\" ([Project on GitLab](https://gitlab.in2p3.fr/coso-college-codes-sources-et-logiciels/forges-esr-en)). The research found that the vast majority of public forges in French Higher Education and Research relied on **GitLab**. This finding underscores the consensus among academic leaders that self-hosted solutions are essential for **data control and longevity**, especially when compared to relying on external, commercial forges.\n\n## Unlock GitLab Ultimate for your institution today\n\nThe success story of **Artois University's CRIL** proves the transformative power of the GitLab for Education program. By providing **free access to GitLab Ultimate**, we enable large-scale institutions to:\n\n1.  **Deliver a modern, integrated DevSecOps curriculum.**\n\n2.  **Support advanced, collaborative research projects with Ultimate planning features.**\n\n3.  **Empower students to make AI-assisted open source contributions.**\n\n4.  **Maintain institutional control and data longevity.**\n\nIf your academic institution is ready to equip its students and researchers with the complete DevSecOps platform and its most advanced features, we invite you to join the program.\n\nThe program provides **free access to GitLab Ultimate** for qualifying instructional and non-commercial research use cases.\n\n**Apply now [online](https://about.gitlab.com/solutions/education/join/).**\n",{"slug":742,"featured":28,"template":13},"artois-university-elevates-curriculum-with-gitlab-ultimate-for-education",{"promotions":744},[745,759,770],{"id":746,"categories":747,"header":749,"text":750,"button":751,"image":756},"ai-modernization",[748],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":752,"config":753},"Get your AI maturity score",{"href":754,"dataGaName":755,"dataGaLocation":241},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":757},{"src":758},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":760,"categories":761,"header":762,"text":750,"button":763,"image":767},"devops-modernization",[715,557],"Are you just managing tools or shipping innovation?",{"text":764,"config":765},"Get your DevOps maturity score",{"href":766,"dataGaName":755,"dataGaLocation":241},"/assessments/devops-modernization-assessment/",{"config":768},{"src":769},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":771,"categories":772,"header":774,"text":750,"button":775,"image":779},"security-modernization",[773],"security","Are you trading speed for security?",{"text":776,"config":777},"Get your security maturity score",{"href":778,"dataGaName":755,"dataGaLocation":241},"/assessments/security-modernization-assessment/",{"config":780},{"src":781},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"header":783,"blurb":784,"button":785,"secondaryButton":790},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":786,"config":787},"Get your free trial",{"href":788,"dataGaName":48,"dataGaLocation":789},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":493,"config":791},{"href":52,"dataGaName":53,"dataGaLocation":789},1773350828976]