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Antidetect browser that isolates a unique fingerprint per profile, aimed at safe multi-accounting for marketing, ecommerce, and trading.
Google's cloud-based, AI-assisted development environment, now rebranded and merged into Firebase Studio.
GitHub-based engineering analytics that tracks contributions, automates performance reviews and adds gamification for dev teams.
Turns Git commits and PRs into AI-summarized daily or weekly reports delivered to Slack or email, no source access.
Free trial available
No public pricing
No public pricing
No public pricing
Free trial available
- ✦Unique, isolated browser fingerprint per profile
- ✦Integrated high-anonymity proxy management (RoxyIP)
- ✦Window sync for simultaneous multi-account actions
- ✦AI agent that automates tasks across profiles via commands
- ✦Team workspaces with permission controls
- ✦API access for browser automation
- ✦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)
- ✦Cloud-based IDE accessible from the browser
- ✦AI-assisted coding
- ✦Cross-platform app development
- ✦Preconfigured workspaces and templates
- ✦Now part of Firebase Studio
- ✦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
- ✦AI-summarized commit and PR reports
- ✦Daily and weekly scheduled digests
- ✦Slack and email delivery
- ✦One-click OAuth or webhook setup
- ✦GitHub, GitLab and Bitbucket support
- ✦Templates for standups and reports
- →Running multiple social media or seller accounts without bans
- →Managing parallel ad, affiliate, or SEO campaigns per profile
- →Web scraping while avoiding IP-based blocking
- →Trading across multiple crypto exchange accounts safely
- →Creating and manipulating datasets
- →Training small machine learning models
- →Implementing advanced training and inference logic
- →Building applications that require tensor computations
- →Building apps from anywhere in the browser
- →Prototyping with AI assistance
- →Developing cross-platform applications
- →Automating developer performance reviews
- →Spotting delivery bottlenecks
- →Generating retrospective insights
- →Motivating teams via gamification
- →Keep stakeholders updated on what shipped
- →Replace manual status updates and standups
- →Give teams visibility into Git activity