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GitLoop
✓ verifiedFree trial
AI codebase assistant that chats with your repos to search, debug, review PRs, and generate docs and unit tests.
11K visits/mo2.7K saves
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✓ verifiedFreemium
Open-source AI coding assistant offering autocomplete and chat in IDEs; the company was acquired by Cursor.
775K visits/mo
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Interview Coder
✓ verifiedFreemium
Undetectable desktop AI assistant that feeds real-time answers during coding and technical interviews.
167K visits/mo
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16x Prompt
✓ verifiedFreemium
Local desktop app that assembles code-context prompts for LLMs, with API integrations, token tracking, and prompt saving.
21K visits/mo
Pricing
No public pricing
No public pricing
Free trial available
No public pricing
No public pricing
Free: $0 (10 prompts/day)
Individual lifetime license: $48
Team lifetime license: $68
Core features
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- ✦Chat with your repositories
- ✦Natural-language codebase search
- ✦Fast code indexing
- ✦AI pull-request and commit review
- ✦Automated documentation generation
- ✦AI unit-test generation
- ✦Open-source AI code assistant
- ✦Customizable autocomplete
- ✦In-editor AI chat
- ✦Community-built coding agent
- ✦Real-time AI answers during technical interviews
- ✦Invisible to screen sharing and recording
- ✦Hidden from dock, tray and activity monitor
- ✦Click-through overlay
- ✦Live audio capture and transcription
- ✦Lifetime unlimited access license
- ✦Source-code context and prompt management
- ✦Custom and formatting instructions
- ✦BYOK API integrations (OpenAI, Claude, Gemini, etc.)
- ✦Token-limit tracking
- ✦Code-edit feature with visual diffs and backups
- ✦Local, offline prompt generation
Use cases
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- →Onboard new developers to a codebase
- →Resolve bugs faster
- →Generate docs and tests automatically
- →Review pull requests with AI
- →Get AI code completions while coding
- →Ask questions about code in the editor
- →Build on an open-source coding-agent foundation
- →Getting live help on coding interview problems
- →Answering technical questions in real time
- →Avoiding detection during screen-shared interviews
- →Building context-rich prompts for AI coding
- →Comparing model outputs on the same task
- →Reusing saved prompts across tech stacks
- →Keeping code private during prompt creation
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