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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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Sherpa Coder
✓ verifiedFree
VS Code extension letting developers chat with their own custom OpenAI assistants without leaving the editor.
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Kaggle
✓ verifiedFree
Google-owned hub for data scientists to find datasets, enter ML competitions, run notebooks, and learn.
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Aide Dev
✓ verifiedPaid
Aide helps developers code faster with parallel agents and automated workflows.
👁 7.6K/mo
Pricing
No public pricing
No public pricing
No public pricing
No public pricing
Standard: $49 per month
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
- ✦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
- ✦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
- ✦Parallel Agents for faster coding
- ✦GitHub native integration
- ✦Automated PR workflow
- ✦Smart PR suggestions
- ✦Automatic code reviews
- ✦Real-time progress tracking
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
- →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
- →Practicing and benchmarking ML models
- →Finding datasets for analysis
- →Competing in predictive-modeling contests
- →Learning data science skills
- →Sharing reproducible notebooks
- →Automating code reviews
- →Generating PRs automatically
- →Improving code quality through continuous improvements
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