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Average Ratings 0 Ratings

Total 0 out of 5 stars
ease 0 out of 5 stars
features 0 out of 5 stars
design 0 out of 5 stars
support 0 out of 5 stars

No User Reviews. Be the first to provide a review:

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Description

Tensor aims to establish itself as the premier trading platform for professional NFT traders. The inception of Tensor was driven by our own experiences flipping NFTs on a daily basis, as we found the available tools to be lacking. Our desire for enhanced speed, broader coverage, more comprehensive data, and sophisticated order types led to the creation of Tensor. Upon visiting Tensor, users will encounter a streamlined decentralized application (dApp), although several components work harmoniously behind the scenes. Our bonding-curve-based orders, whether linear or exponential, allow for dollar-cost averaging into or out of NFTs with ease. We also prioritize the instant listing of new collections, recognizing the eagerness of traders to access the latest offerings. By providing liquidity and facilitating market creation for preferred NFT collections on TensorSwap, users can earn trading fees and liquidity provider rewards. Additionally, market makers play a crucial role in enhancing market liquidity, enabling other traders to enter and exit the market at more advantageous prices, which ultimately fosters a more dynamic trading environment. Together, these features make Tensor an indispensable tool for NFT enthusiasts looking to optimize their trading strategies.

Description

tensorViz provides a platform for developers to delve into PyTorch models and JAX/Flax programs through dynamic graph visualizations. In the case of PyTorch, users have the ability to open nested modules, examine tensor shapes recorded during a single forward pass, navigate to the corresponding Python source code, and observe architectural modifications alongside code differences. The site additionally features pre-recorded model graphs for users to analyze without the need for any installation. A free extension for VS Code can be found in both the Visual Studio Marketplace and Open VSX. For local capture, users must have Python installed along with the necessary model dependencies and sample inputs. There are also standalone coding-agent plugins that are currently in preview for Apple Silicon macOS; MCP agents can establish connections with the extension as well. While JAX/Flax graphs utilize CPU tracing to infer shapes, it is important to note that source edits for PyTorch models are not applicable in this context. By offering a seamless experience for both frameworks, tensorViz enhances the development and debugging process for users.

API Access

Has API Yes 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

No images available

Integrations

Base App Yes 
Brave Browser Yes 
Brave Wallet Yes 
Claude Code No 
Codex CLI No 
Cursor No 
GitHub No 
Glow Wallet Yes 
Ledger Yes 
Model Context Protocol (MCP) No 
Phantom Yes 
Rally Yes 
Slope Yes 
Solana Yes 
Solflare Yes 
T3 Code No 
Torus Yes 
Visual Studio Code No 

Integrations

Base App No 
Brave Browser No 
Brave Wallet No 
Claude Code Yes 
Codex CLI Yes 
Cursor Yes 
GitHub Yes 
Glow Wallet No 
Ledger No 
Model Context Protocol (MCP) Yes 
Phantom No 
Rally No 
Slope No 
Solana No 
Solflare No 
T3 Code Yes 
Torus No 
Visual Studio Code Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

$0
The VS Code extension is free to install. Core graph exploration and source-edit forms work without an agent or provider API key; connecting a coding agent is optional.
Free Trial No 
Free Version Yes 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Tensor

Website

www.tensor.trade/

Vendor Details

Company Name

tensorViz

Website

tensorviz.dev/

Product Features

Product Features

Deep Learning

Convolutional Neural Networks No 
Document Classification No 
Image Segmentation No 
ML Algorithm Library No 
Model Training No 
Neural Network Modeling No 
Self-Learning No 
Visualization No 

Machine Learning

Deep Learning No 
ML Algorithm Library No 
Model Training No 
Natural Language Processing (NLP) No 
Predictive Modeling No 
Statistical / Mathematical Tools No 
Templates No 
Visualization No 

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