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Google's open-source TypeScript framework for building scalable web apps, featuring signals, reactivity and first-party tooling.
Data-annotation platform with AI-assisted labeling tools and team workflows for building ML training datasets.
Automated AWS usage optimization platform giving engineers 150+ recommendations across 50+ services, averaging ~10% savings.
One-click bug-reporting tool that auto-captures console, network logs and repro steps for developers.
Agentic QA platform that drives a real browser or live API to verify AI-generated code and hands agents a fixable bug report.
No public pricing
No public pricing
Free trial available
Free trial available
- ✦Signals-based fine-grained reactivity
- ✦Built-in control flow and deferrable views
- ✦Server-side rendering and hydration
- ✦First-party routing, forms and dependency injection
- ✦AI-forward tooling and MCP resources
- ✦In-browser tutorials and playground
- ✦AI-assisted data annotation tools
- ✦Training-data platform (BasicAI Cloud)
- ✦Team and project management
- ✦Annotation services
- ✦150+ recommendations across 50+ AWS services
- ✦Zombie and unused resource cleanup
- ✦Over-provisioned rightsizing
- ✦Idle-resource scheduler
- ✦SpotBot for ECS Fargate spot/on-demand switching
- ✦AWS console extension with Slack/Teams alerts
- ✦One-click bug capture via browser extension
- ✦Automatic repro steps
- ✦Console, network and device logs
- ✦Instant replay of recent activity
- ✦Backend tracing and an AI debugger
- ✦Integrations with Jira, Linear, GitHub and Slack
- ✦Live browser/API testing rather than mocked assertions
- ✦Auto-generated failure bundles with root-cause hypotheses
- ✦CLI and MCP/IDE integration for AI coding agents
- ✦Auto-healing tests when the UI drifts
- ✦Growing regression suite that persists across development phases
- ✦No-code web app with live preview and video replay for QA teams
- →Building scalable single-page apps
- →Enterprise web application development
- →Performance-critical front ends
- →Learning modern web development
- →Labeling images and data for ML models
- →Managing annotation teams and projects
- →Producing training datasets at scale
- →Cutting AWS spend automatically
- →Rightsizing over-provisioned resources
- →Scheduling idle resources off-hours
- →Giving DevOps in-console cost recommendations
- →Filing detailed bug reports
- →Reproducing issues faster in QA
- →Sharing debug context with engineers
- →Triaging support bug reports
- →Verifying AI coding-agent output before merging code
- →Catching regressions from unattended overnight coding runs
- →QA teams testing live apps without writing test scripts
- →Gating CI/CD releases on end-to-end pass rates