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✓ verifiedFreemium
Open-source AI coding assistant offering autocomplete and chat in IDEs; the company was acquired by Cursor.
👁 775K/mo
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GitFluence
✓ verifiedFree
Free AI helper that turns a plain-English description of a task into the matching Git command to copy and run.
✕
Kane CLI By TestMu AI
✓ verifiedFreemium
Terminal-native AI tool (Kane CLI) that turns plain-English descriptions into real-Chrome browser test flows.
♥ 1.0K
Pricing
No public pricing
No public pricing
No public pricing
DEVELOPER: FREE
STARTER: $119 / month
GROWTH: $599 / month
ENTERPRISE: Starting at $1,800 / month
Free: $0/month (200 credits)
Starter: $19/month (2,000 credits, +100% bonus = 4,000 total during launch offer)
Pro: $99/month (10,000 credits, +50% bonus during launch offer)
Core features
- ✦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)
- ✦Open-source AI code assistant
- ✦Customizable autocomplete
- ✦In-editor AI chat
- ✦Community-built coding agent
- ✦Natural-language to Git command suggestions
- ✦AI-driven command matching
- ✦Copy-ready command output
- ✦Git guides and reference
- ✦Developer-first platform for AI-powered integrations
- ✦Secure, isolated sandboxes for running JavaScript/Python code
- ✦Automatic management of npm/PyPI dependencies
- ✦Built-in platform plumbing: secrets, webhooks, scheduling, logs, and audit
- ✦Yep Agent (prompt → runnable processes)
- ✦MCP Server/Tools (convert code into AI agent tools)
- ✦Serverless runtime (YepCode Run) and SDK access
- ✦Natural-language browser flow automation from the CLI
- ✦Auto-healing and vision-based element detection
- ✦Integration with a wider agentic test cloud (real devices, visual/accessibility testing)
- ✦MCP server for connecting AI agents into IDEs
- ✦Shareable evidence links for pass/fail results
- ✦Credit-based monthly usage plans
Use cases
- →Creating and manipulating datasets
- →Training small machine learning models
- →Implementing advanced training and inference logic
- →Building applications that require tensor computations
- →Get AI code completions while coding
- →Ask questions about code in the editor
- →Build on an open-source coding-agent foundation
- →Find the correct Git command quickly
- →Learn Git syntax by describing a goal
- →Avoid memorizing Git flags
- →Building complex API integrations that require custom code and logic beyond what no-code tools offer.
- →Safely running AI-generated scripts in isolated environments with secrets management.
- →Automating workflows that require large datasets, loops, branching, or custom dependencies.
- →Connecting AI agents to external databases, APIs, and services using MCP tools.
- →Developers running local end-to-end browser tests from a terminal
- →QA teams automating cross-browser regression checks
- →Teams needing tests resilient to UI redesigns
- →IDE-integrated AI test authoring via MCP
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