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Open-source AI coding assistant offering autocomplete and chat in IDEs; the company was acquired by Cursor.
Online database-design tool with sample schemas and an AI generator to explore, modify or build database structures visually.
One-click bug-reporting tool that auto-captures console, network logs and repro steps for developers.
Google Labs experiment for building and sharing AI mini-apps from natural-language prompts, no coding required.
Test management platform unifying manual and automated test results with AI-assisted case generation, for scaling QA teams.
No public pricing
No public pricing
Free trial available
No public pricing
Free trial available
- ✦Open-source AI code assistant
- ✦Customizable autocomplete
- ✦In-editor AI chat
- ✦Community-built coding agent
- ✦Library of sample database designs
- ✦Visual database designer / diagram tool
- ✦AI database generator
- ✦Modify and optimize existing schemas
- ✦SQL script export
- ✦Dialect converters (MySQL/PostgreSQL/MSSQL)
- ✦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
- ✦Build AI mini-apps from natural-language prompts
- ✦Visual editor for prompt/tool workflows
- ✦Share created apps with others
- ✦No-code AI app prototyping
- ✦Central test case repository with reporting dashboards
- ✦AI conversion of manual test cases into automated test scripts
- ✦CI/CD-connected automated test orchestration
- ✦Requirements-to-test traceability reporting
- ✦MCP server for connecting AI agents to test data
- ✦20+ integrations including Jira, GitHub, and Slack
- →Get AI code completions while coding
- →Ask questions about code in the editor
- →Build on an open-source coding-agent foundation
- →Finding a starting schema for a project
- →Designing a database visually
- →Generating a schema with AI
- →Converting between SQL dialects
- →Filing detailed bug reports
- →Reproducing issues faster in QA
- →Sharing debug context with engineers
- →Triaging support bug reports
- →Prototyping an AI workflow quickly
- →Sharing a custom AI mini-app
- →Automating a task with chained prompts
- →QA teams consolidating scattered CI, manual, and automated results
- →Engineering orgs converting manual test backlogs into automation
- →Enterprises needing audit-ready traceability for regulated software