Shumai (Meta) Reviews
(Rated by 2419 users)
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Payment Methods
Payment Methods
- Verified Store VERIFIED
- Free shipping: Orders $50+
- In-store pickup: Ready in 2 hours
- 30-Day Returns
- Gap Good Rewards (4 brands)
Payment Methods
- Tops: $23 - $70
- Bottoms: $27 - $70
- Outerwear: $34 - $70
- Kids: $29 - $75
Overall Rating
4.3
Base on 11 Reviews
Ratings by Feature
Ratings by Feature
- Good Value5.0
- Price & Quality4.5
- Customer Service5.0
Recent Customer Reviews (11)
Ástþór Daðason
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Yvonne Salter
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Ovidio Beneventi
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Peter Lord
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Kieran Cameron
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Lucas Hall
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Sebastian Hirsch
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Petra Pfeiffer
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Alice Walton
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Francesca Jordan
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Shumai (Meta) Pros & Cons
Pros
1
Network-connected: Allows for seamless integration with various network services.
2
Differentiable: Supports operations that can be differentiated, useful in machine learning and AI research.
3
Tensor library: Efficiently represents multi-dimensional data, important in machine learning and AI.
4
TypeScript and JavaScript support: Can be used by a wide range of developers and researchers.
5
Built with bun + flashlight: Enhances the functionality of Shumai.
6
Gradients: Supports gradient computation, crucial for many machine learning algorithms.
7
Conversion to and from JavaScript native arrays: Allows for easy integration with existing JavaScript code.
CONS
1
Experimental software: Still in the development phase and may have some bugs or issues.
2
Usage: Always attempts to use an attached GPU or accelerator; CPU computation uses the ArrayFire CPU backend, which is not well-optimized.
3
Installation: Install procedure is a work in progress and may cause problems for some users. Only macOS and Linux are supported. Linux installs default to GPU computation with CUDA, and macOS to CPU.
4
Security: Security issues in this open source project can be reported via the Meta Bug Bounty program. There aren’t any published security advisories.
Shumai (Meta) Features and Benefits
Features
Network-connected
Allows for seamless integration with various network services, enhancing the functionality and usability of the library.
Differentiable
Supports operations that can be differentiated, useful in machine learning and AI research where differentiation is a key part of many algorithms.
Tensor library
Efficiently represents multi-dimensional data, important in machine learning and AI.
TypeScript and JavaScript support
Can be used by a wide range of developers and researchers.
Built with bun + flashlight
Enhances the functionality of Shumai. Bun is a build tool for TypeScript and JavaScript, while flashlight is a machine learning library.
Gradients
Supports gradient computation, crucial for many machine learning algorithms.
Conversion to and from JavaScript native arrays
Allows for easy integration with existing JavaScript code.
Open-source (MIT license)
Free to use, modify, and distribute the software.