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TinyCommand
✓ verifiedFreemium
All-in-one no-code platform combining forms, workflow automation, AI agents, a database and email in a single subscription.
👁 31K/mo
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Kaggle
✓ verifiedFree
Google-owned hub for data scientists to find datasets, enter ML competitions, run notebooks, and learn.
Pricing
Free: $0/mo (1,000 credits, 1 seat, unlimited forms)
Basic: $17/mo billed annually at $199/yr (10,000 credits, 3 seats)
Professional: $42/mo billed annually at $499/yr (50,000 credits, 10 seats)
Agency: $125/mo billed annually at $1,499/yr (250,000 credits, 50 seats)
Teams: $30 /developer /month
No public pricing
No public pricing
Core features
- ✦Drag-and-drop form builder with conditional logic
- ✦Workflow automation with 60+ node types and 400+ integrations
- ✦Prebuilt and custom AI agents for tasks like lead scoring
- ✦Relational database with AI-enriched columns
- ✦Drag-and-drop email builder with AI-drafted content
- ✦Company and contact enrichment and web research tools
- ✦Automatic commit summaries
- ✦Automatic PR descriptions and reviews
- ✦High-signal bug detection and fixing in PRs
- ✦Real-time project summaries
- ✦Scheduled engineering standup summaries
- ✦Q&A with your codebase and git log (Ask Macroscope Anything)
- ✦Team productivity statistics
- ✦Codebase activity summarization
- ✦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)
- ✦Public dataset repository
- ✦Machine-learning competitions with prizes
- ✦Browser-based notebooks with free GPU/TPU
- ✦Micro-courses on data science topics
- ✦Community forums and shared code
Use cases
- →Capturing and automatically routing sales leads
- →Building onboarding or support-triage workflows
- →Running AI-driven lead scoring and qualification
- →Sending personalized, data-merged email campaigns
- →Understanding product changes and how engineering time is allocated.
- →Providing leaders with actionable insights into development progress.
- →Enabling engineers to focus more on building and less on reporting.
- →Reducing time-to-root-cause in troubleshooting.
- →Organizing project plans and release artifacts.
- →Illuminating areas that lack clear documentation.
- →Helping product leads stay informed on engineering progress and developer productivity.
- →Generating release notes automatically.
- →Creating and manipulating datasets
- →Training small machine learning models
- →Implementing advanced training and inference logic
- →Building applications that require tensor computations
- →Practicing and benchmarking ML models
- →Finding datasets for analysis
- →Competing in predictive-modeling contests
- →Learning data science skills
- →Sharing reproducible notebooks
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