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Average Ratings 0 Ratings

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ease
features
design
support

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Description

Kavor is a desktop workspace designed with a local-first approach, specifically for software developers who are managing coding-agent tasks. It seamlessly integrates native coding-agent sessions, such as Claude Code and Codex, alongside various project elements including specifications, diagrams, files, terminals, and a collaborative browser. Developers can craft diagrams using tools like Mermaid, PlantUML, Graphviz, and D2, as well as edit canvases in Excalidraw, access draw.io files, and document decisions and review evidence related to their projects. Furthermore, Kavor provides insights into the usage of supported provider accounts and has the capability to rotate eligible accounts as needed. Agent Kavor for WhatsApp enables users to send commands and respond to inquiries remotely while the desktop application continues to run, remain signed in, and stay connected online. Compatible with macOS, Windows, and Linux, Kavor offers versatile accessibility for its users. It's important to note that subscriptions to providers and API usage are managed separately from the Kavor platform itself, ensuring clarity in service offerings.

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 No 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

No images available

Integrations

Claude Code Yes 
Codex CLI Yes 
Cursor No 
GitHub No 
Model Context Protocol (MCP) No 
T3 Code No 
Visual Studio Code No 
WhatsApp Yes 

Integrations

Claude Code Yes 
Codex CLI Yes 
Cursor Yes 
GitHub Yes 
Model Context Protocol (MCP) Yes 
T3 Code Yes 
Visual Studio Code Yes 
WhatsApp No 

Pricing Details

Free
Kavor is free to use. Users need a Kavor login and their own supported provider accounts; provider subscriptions and API usage are separate.
Free Trial No 
Free Version Yes 

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

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

Kavor

Website

agentkavor.com

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 

Alternatives

No Alternatives

Alternatives

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