🔥 rustnet | Rust
🎯 Primary Use Case:
Network monitoring and analysis
✨ Key Features:
• Real-time Network Monitoring
• Connection States Tracking
• Interface Statistics
• Deep Packet Inspection (DPI)
• TCP Network Analytics
📖 Summary:
RustNet is a cross-platform network monitoring tool built with Rust that provides real-time visibility into network connections. It offers detailed state information, connection lifecycle management, deep packet inspection, and a terminal user interface for monitoring network activity.
🔗 Links:
• View Project
================
🔓 Open Source
🎯 Primary Use Case:
Network monitoring and analysis
✨ Key Features:
• Real-time Network Monitoring
• Connection States Tracking
• Interface Statistics
• Deep Packet Inspection (DPI)
• TCP Network Analytics
📖 Summary:
RustNet is a cross-platform network monitoring tool built with Rust that provides real-time visibility into network connections. It offers detailed state information, connection lifecycle management, deep packet inspection, and a terminal user interface for monitoring network activity.
🔗 Links:
• View Project
================
🔓 Open Source
🌟 tinyworlds | Python
🎯 Primary Use Case:
Understanding and experimenting with scalable world models based on DeepMind's Genie architecture.
✨ Key Features:
• Autoregressive world model
• Unsupervised action inference
• Video Tokenizer
• Action Tokenizer
• Dynamics Model
📖 Summary:
TinyWorlds is a minimal implementation of DeepMind's Genie world model, focusing on autoregressive, unsupervised learning for scalable world models. It aims to help users understand the Genie architecture by providing a simplified version with key components like video and action tokenizers, a dynamics model, and techniques like Space-Time Transformers and Variational Autoencoders.
🔗 Links:
• View Project
• Homepage
================
🔓 Open Source
🎯 Primary Use Case:
Understanding and experimenting with scalable world models based on DeepMind's Genie architecture.
✨ Key Features:
• Autoregressive world model
• Unsupervised action inference
• Video Tokenizer
• Action Tokenizer
• Dynamics Model
📖 Summary:
TinyWorlds is a minimal implementation of DeepMind's Genie world model, focusing on autoregressive, unsupervised learning for scalable world models. It aims to help users understand the Genie architecture by providing a simplified version with key components like video and action tokenizers, a dynamics model, and techniques like Space-Time Transformers and Variational Autoencoders.
🔗 Links:
• View Project
• Homepage
================
🔓 Open Source
🤔1
🔥 blades | Go
🎯 Primary Use Case:
Building multimodal AI agents and workflows in Go.
✨ Key Features:
• Go Idiomatic design
• Simple to use Agent definition
• Middleware Ecosystem inspired by Kratos
• Highly Extensible architecture
• Pluggable ModelProvider interface
📖 Summary:
Blades is a Go-based multimodal AI Agent framework designed to facilitate the creation of intelligent agents. It supports custom models, tools, memory, and middleware, making it suitable for multi-turn conversations, chain-of-thought reasoning, and structured output. The framework emphasizes flexibility and extensibility through decoupled components and unified interfaces.
🔗 Links:
• View Project
• Homepage
================
🔓 Open Source
🎯 Primary Use Case:
Building multimodal AI agents and workflows in Go.
✨ Key Features:
• Go Idiomatic design
• Simple to use Agent definition
• Middleware Ecosystem inspired by Kratos
• Highly Extensible architecture
• Pluggable ModelProvider interface
📖 Summary:
Blades is a Go-based multimodal AI Agent framework designed to facilitate the creation of intelligent agents. It supports custom models, tools, memory, and middleware, making it suitable for multi-turn conversations, chain-of-thought reasoning, and structured output. The framework emphasizes flexibility and extensibility through decoupled components and unified interfaces.
🔗 Links:
• View Project
• Homepage
================
🔓 Open Source
💡 30-Days-Of-Python | Python
🎯 Primary Use Case:
Learning Python programming from beginner to intermediate level through a structured, daily curriculum.
✨ Key Features:
• Introduction to Python
• Variables and Built-in Functions
• Operators
• Strings
• Lists
📖 Summary:
The 30 Days of Python repository is a step-by-step guide designed to help individuals learn the Python programming language over a 30-day period. It covers a wide range of fundamental Python topics, from basic syntax and data structures to more advanced concepts like web scraping and API development.
🔗 Links:
• View Project
================
🔓 Open Source
🎯 Primary Use Case:
Learning Python programming from beginner to intermediate level through a structured, daily curriculum.
✨ Key Features:
• Introduction to Python
• Variables and Built-in Functions
• Operators
• Strings
• Lists
📖 Summary:
The 30 Days of Python repository is a step-by-step guide designed to help individuals learn the Python programming language over a 30-day period. It covers a wide range of fundamental Python topics, from basic syntax and data structures to more advanced concepts like web scraping and API development.
🔗 Links:
• View Project
================
🔓 Open Source