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All-in-one no-code platform combining forms, workflow automation, AI agents, a database and email in a single subscription.
Free AI helper that turns a plain-English description of a task into the matching Git command to copy and run.
VS Code extension letting developers chat with their own custom OpenAI assistants without leaving the editor.
AI web design tool that turns designs into live websites.
Side-by-side arena to compare AI coding models and build multi-file apps, with a public leaderboard and battle mode.
No public pricing
No public pricing
No public pricing
No public pricing
- ✦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
- ✦Natural-language to Git command suggestions
- ✦AI-driven command matching
- ✦Copy-ready command output
- ✦Git guides and reference
- ✦in-editor chat with OpenAI assistants
- ✦workspace source-code context sharing
- ✦support for custom, user-defined assistants
- ✦secure management of the user's OpenAI account
- ✦AI-powered design suggestions
- ✦Automated task completion
- ✦Responsive design adaptation
- ✦Instant website conversion
- ✦Head-to-head model comparison
- ✦Battle mode matchups
- ✦Public model leaderboard
- ✦Multi-file app generation
- ✦File uploads as input
- →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
- →Find the correct Git command quickly
- →Learn Git syntax by describing a goal
- →Avoid memorizing Git flags
- →getting coding help without switching out of VS Code
- →using a personalized OpenAI assistant tuned to a project
- →quick in-editor Q&A while writing code
- →Designing and launching modern, responsive websites quickly and efficiently.
- →Choosing the best coding model
- →Benchmarking AI code quality
- →Prototyping small apps