openaidemo VS osintgpt

Compare openaidemo vs osintgpt and see what are their differences.

openaidemo

Demo of how access the OpenAI API using Java 17 (by kousen)

osintgpt

An open-source intelligence (OSINT) analysis tool leveraging GPT-powered embeddings and vector search engines for efficient data processing (by estebanpdl)
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openaidemo osintgpt
1 1
32 328
- -
8.9 7.1
6 days ago 5 months ago
Java Python
MIT License -
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

openaidemo

Posts with mentions or reviews of openaidemo. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-09-05.

osintgpt

Posts with mentions or reviews of osintgpt. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-09-05.

What are some alternatives?

When comparing openaidemo and osintgpt you can also consider the following projects:

puck - The visual editor for React

morphir - A universal language for business and technology

concrete-ml - Concrete ML: Privacy Preserving ML framework built on top of Concrete, with bindings to traditional ML frameworks.

hands-on-train-and-deploy-ml - Train and Deploy an ML REST API to predict crypto prices, in 10 steps

magic-edit - MagicEdit: High-Fidelity Temporally Coherent Video Editing

yolov7-object-tracking - YOLOv7 Object Tracking Using PyTorch, OpenCV and Sort Tracking

co-tracker - CoTracker is a model for tracking any point (pixel) on a video.

Youtube2Webpage - I learn much better from text than from videos

paxml - Pax is a Jax-based machine learning framework for training large scale models. Pax allows for advanced and fully configurable experimentation and parallelization, and has demonstrated industry leading model flop utilization rates.