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Software Development__code Generation__frontend Generation__screenshot To CodeSoftware Development__coding Assistants Copilots__code Chat Q A__code GenerationData Analytics__data Analytics Bi__market Research IntelligenceSoftware Development__coding Assistants Copilots__code Chat Q ASoftware Development__code Generation__frontend GenerationAI AgentAI AssistantAI Code Generator
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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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GitLoop
✓ verifiedFree trial
AI codebase assistant that chats with your repos to search, debug, review PRs, and generate docs and unit tests.
👁 11K/mo♥ 2.7K
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Kaggle
✓ verifiedFree
Google-owned hub for data scientists to find datasets, enter ML competitions, run notebooks, and learn.
Pricing
No public pricing
No public pricing
No public pricing
No public pricing
Free trial available
No public pricing
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)
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- ✦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
- ✦Chat with your repositories
- ✦Natural-language codebase search
- ✦Fast code indexing
- ✦AI pull-request and commit review
- ✦Automated documentation generation
- ✦AI unit-test generation
- ✦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
Use cases
- →Creating and manipulating datasets
- →Training small machine learning models
- →Implementing advanced training and inference logic
- →Building applications that require tensor computations
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- →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
- →Onboard new developers to a codebase
- →Resolve bugs faster
- →Generate docs and tests automatically
- →Review pull requests with AI
- →Practicing and benchmarking ML models
- →Finding datasets for analysis
- →Competing in predictive-modeling contests
- →Learning data science skills
- →Sharing reproducible notebooks
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