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Prioritise GitLab's Built-in Project Templates Based on Real Developer Usage Data

Summary

GitLab's built-in project templates should be restructured using a data-driven approach that better reflects real-world developer adoption patterns. I believe this could significantly reduce friction for teams starting new projects and better position GitLab as understanding modern development workflows.

The Problem

I've noticed that GitLab's current built-in project templates don't seem to align with what developers are actually using in 2025:

  • Questionable priorities: The current ordering doesn't reflect what developers actually reach for
  • Missing modern essentials: No templates for critical DevOps tools (Terraform, Ansible, Kubernetes) that are now standard in enterprise environments
  • Maintenance inconsistency: Some templates haven't been updated in over a year
  • Gaps in popular areas: Missing templates for monorepo setups, modern GitLab Pages workflows, mobile development, and ML workflows
  • No AI integration examples: Missing the opportunity to showcase GitLab Duo capabilities in practical, working templates

What I'm Thinking

Rather than guessing what developers want, why not use actual data to guide template prioritisation? Here's my initial thinking on categories that might deserve attention:

Core Languages (TIOBE Index May 2025)

The current leaders that should probably get priority maintenance:

  • Python (25.35%) - Especially for AI/ML, automation, web development
  • C++ (11.37%) - Systems programming, performance-critical apps
  • Java (10.66%) - Enterprise applications, Spring Boot ecosystem
  • C (9.84%) - Embedded systems, systems programming
  • C# (4.12%) - .NET ecosystem, enterprise applications

Essential Web Development (Stack Overflow 2024)

Focus on the most adopted:

  • Node.js (40.8%) - Backend JavaScript, microservices, API development
  • React (39.5%) - Frontend SPAs, component-based architecture, modern tooling

Mobile Application Development

Cross-platform and native development patterns:

  • React Native - Cross-platform mobile apps with shared codebase
  • Flutter - Google's cross-platform framework with strong performance
  • Swift/iOS - Native iOS development with modern Swift patterns
  • Kotlin/Android - Native Android development with Kotlin-first approach

Machine Learning & Data Science

Python-centric ML workflows:

  • Data science projects - Jupyter notebooks, pandas, scikit-learn workflows
  • Deep learning - TensorFlow, PyTorch project structures
  • MLOps pipelines - Model training, validation, deployment automation
  • Model serving - API deployment, containerised inference services

DevOps & Infrastructure (The Big Gap)

Tools that are now essential but missing from templates:

  • Terraform - Multi-cloud infrastructure as code
  • Ansible - Configuration management
  • Kubernetes - Container orchestration
  • Docker - Containerisation patterns
  • GitOps workflows - ArgoCD, Flux integration

Modern Development Patterns

Areas where GitLab could really shine:

  • Monorepo setups - Multi-project repositories with proper CI/CD
  • GitLab Pages - Modern static site generators, documentation sites
  • Microservices architectures - Service mesh, API patterns
  • Full-stack frameworks - Next.js, Nuxt.js, SvelteKit

AI-Powered Development (New Opportunity)

Templates that showcase GitLab Duo capabilities:

  • AI-enhanced CI/CD: Templates with Duo-optimised pipeline configurations
  • Code generation examples: Projects demonstrating Duo's scaffolding capabilities
  • AI-powered testing: Templates showing Duo-generated test suites
  • Documentation automation: Using Duo for README, API doc generation
  • Security-first templates: Enhanced vulnerability detection with AI insights
  • MLOps workflows: End-to-end machine learning pipelines with GitLab Duo
  • AI application templates: Building chatbots, recommendation systems, AI-powered features

Questions for Discussion

I'm keen to hear thoughts on:

  1. Does this prioritisation make sense? Are there obvious gaps or wrong priorities?

  2. Mobile development focus: Should we prioritise cross-platform (React Native, Flutter) or include native development templates?

  3. ML/Data Science scope: What are the most common ML project patterns that would benefit from templates?

  4. GitLab Duo integration: Where would AI-powered templates add the most value? Should this be a separate category or integrated throughout?

  5. Enterprise vs. individual needs: Should we have different strategies for different user types?

  6. Maintenance burden: How do we balance comprehensive coverage with keeping templates current?

  7. Community contribution: How might we encourage community contributions for specialised templates?

  8. Usage analytics: Should GitLab track template selection to guide future prioritisation?

  9. Monorepo patterns: What are the most common monorepo setups that would benefit from templates?

The Opportunity

If we get this right, GitLab could:

  • Reduce friction for developers evaluating or adopting GitLab
  • Showcase GitLab Duo capabilities in practical, working examples
  • Position GitLab as understanding modern development practices (including AI integration)
  • Serve mobile and ML developers who are currently underserved by existing templates
  • Create templates that actually work out-of-the-box in 2025
  • Better serve the DevSecOps community who need infrastructure tooling

What do others think? Are there obvious technologies or patterns we've missed? Does this general direction make sense? Where would you most want to see GitLab Duo integration in templates?

Data Sources:

Edited by 🤖 GitLab Bot 🤖