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Maestro Studio Desktop Beta
✓ verifiedFreemium
Open-source framework for automated end-to-end UI testing of mobile and web apps, with a paid cloud for parallel device runs.
183K visits/mo
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devActivity
✓ verifiedFreemium
GitHub-based engineering analytics that tracks contributions, automates performance reviews and adds gamification for dev teams.
52K visits/mo
Pricing
Local: $0 (open source)
Cloud: $250/device/mo (parallel runs)
Free trial available
Free: $0/contributor (up to 7 contributors, 90-day retention)
Premium: $10/contributor (unlimited contributors, AI insights)
No public pricing
DEVELOPER: FREE
STARTER: $119 / month
GROWTH: $599 / month
ENTERPRISE: Starting at $1,800 / month
Core features
- ✦Human-readable YAML test flows
- ✦Local CLI and Studio testing for free
- ✦Open-source, CI-friendly design
- ✦Cloud device farm for parallel runs
- ✦AI-agent integration through MCP
- ✦Self-healing tests with local agents
- ✦Contribution and work-quality analytics
- ✦Automated, AI-powered performance reviews
- ✦Retrospective insights
- ✦Operational bottleneck alerts
- ✦Gamification with XP, levels and leaderboards
- ✦Uses Git metadata without accessing source code
- ✦Fast tensor operations
- ✦Differentiable tensors for gradient-based optimization
- ✦Network connectivity
- ✦Integration with Bun and Flashlight
- ✦Support for GPU computation with CUDA (Linux) and CPU computation (macOS)
- ✦Developer-first platform for AI-powered integrations
- ✦Secure, isolated sandboxes for running JavaScript/Python code
- ✦Automatic management of npm/PyPI dependencies
- ✦Built-in platform plumbing: secrets, webhooks, scheduling, logs, and audit
- ✦Yep Agent (prompt → runnable processes)
- ✦MCP Server/Tools (convert code into AI agent tools)
- ✦Serverless runtime (YepCode Run) and SDK access
Use cases
- →Automate mobile app UI regression tests
- →Run tests in parallel across many devices
- →Integrate UI testing into CI pipelines
- →Let AI agents generate and run app tests
- →Automating developer performance reviews
- →Spotting delivery bottlenecks
- →Generating retrospective insights
- →Motivating teams via gamification
- →Creating and manipulating datasets
- →Training small machine learning models
- →Implementing advanced training and inference logic
- →Building applications that require tensor computations
- →Building complex API integrations that require custom code and logic beyond what no-code tools offer.
- →Safely running AI-generated scripts in isolated environments with secrets management.
- →Automating workflows that require large datasets, loops, branching, or custom dependencies.
- →Connecting AI agents to external databases, APIs, and services using MCP tools.
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