Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
LM Studio Bionic serves as an AI assistant designed to facilitate productive work utilizing open models in areas such as coding, research, document management, and general knowledge tasks. Users have the option to run models flexibly either on their local devices, via LM Link, or through advanced open-source models in the LM Studio Secure Cloud for more resource-intensive operations. The local models rely on the LM Studio runtime, which can be conveniently downloaded directly within the application, while cloud-based requests adhere to a Zero Data Retention policy, ensuring that no data is stored post-processing. For coding purposes, users can link a local folder as a Code project, enabling Bionic to analyze the codebase, clarify complex logic, search for pertinent files, track code behavior, make edits, or troubleshoot problems. The inclusion of inline diffs simplifies the review process as changes are implemented. Furthermore, work projects encompass a variety of formats such as documents, PDFs, presentations, and spreadsheets, empowering Bionic to create new files, organize existing materials, summarize key content, and enhance current projects, thereby streamlining the workflow and boosting overall efficiency. With these capabilities, LM Studio Bionic stands out as a comprehensive tool for modern-day productivity.
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
No
Codex CLI
No
Cursor
No
GLM-5.2
Yes
GLM-5.3
Yes
GitHub
No
JavaScript
Yes
Kimi K2.7 Code
Yes
LM Studio
Yes
Mistral AI
Yes
Integrations
Claude Code
Yes
Codex CLI
Yes
Cursor
Yes
GLM-5.2
No
GLM-5.3
No
GitHub
Yes
JavaScript
No
Kimi K2.7 Code
No
LM Studio
No
Mistral AI
No
Pricing Details
Free
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
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
LM Studio
Founded
2023
Country
United States
Website
lmstudio.ai/blog/introducing-lm-studio-bionic
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