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Average Ratings 0 Ratings
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
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