Search Results for "monitor network"

The MCP System Monitor is a tool that exposes system metrics via the Model Context Protocol (MCP), allowing Large Language Models (LLMs) to retrieve real-time system information through an MCP-compatible interface. ​

SPM Monitoring System - Complete Solution for Efficient Monitoring and Alerting SPM Monitoring System is an all-in-one monitoring solution for IT environments that offers comprehensive features to ensure high availability, stability, and optimal performance of your infrastructure. With SPM Monitoring Systems, you can monitor your network, servers, applications, and services with ease, and receive timely alerts when issues arise. Host Availability Monitoring. Agent-Based Monitoring: CPU,RAM,Storage,Network,EventLogs etc. SNMP Monitoring. Web Site Monitoring. Notification System: E-Mail, SMS, Push, Telegram Bot (also control via telegram bot) and Sound Notifications. ...

Browser Tools MCP is an MCP server and Chrome extension that gives AI agents safe, structured access to your live browser for debugging and automation. It can capture console/network logs, DOM snapshots, and screenshots, and expose them as typed resources the agent can query or act on. The design aims to make IDE agents (e.g., Cursor, Claude Desktop) more “web-aware,” enabling workflows like reproducing a bug, collecting evidence, and proposing fixes without copy-pasting. Documentation and...

ChonOS (Cognitive Hardware on Network - Operational System) is a specifical-purpose GNU/Linux distribution that seeks to facilitate the development of an Embedded MultiAgent System (MAS). It enables, without the need to turn off the device or stop the MAS: the deployment of reasoning to the robot; firmware deployment for microcontrollers; the transfer of the MAS from the development environment to the production environment; and the transfer of new agents to the MAS running using the...

...It lets developers embed a suite of debugging tools directly into a Flutter app (during development or debug builds), enabling inspection, performance monitoring, UI debugging, network request inspection, widget hierarchy introspection, and more — all from within the running app. UME bundles multiple “plugin kits” (e.g., UI inspector, performance monitor, device info panel, network inspector via Dio, console log viewer, code viewer, touch indicator) so developers can enable only the tools they need. It supports Flutter 3 and Dart with null-safety, integrates as a dev dependency (so it doesn’t ship by default with production builds), and offers a simple API to activate the debug overlay. ...

...This design addresses common autoregressive issues such as repetition, skipped words, and unstable attention, and results in robust, fast synthesis where all frames are predicted in parallel. The repository ships with tooling to build datasets (especially LJSpeech) and create training data, plus scripts to train both the aligner and the TTS model, monitor training with TensorBoard, and resume or reset training runs.

This project turns edge devices such as Raspberry Pi into an intelligent gateway with deep learning running on it. No internet connection is required, everything is done locally on the edge device itself. Further, multiple edge devices can create a distributed AIoT network. At DT42, we believe that bringing deep learning to edge devices is the trend towards the future. It not only saves costs of data transmission and storage but also makes devices able to respond according to the events shown in the images or videos without connecting to the cloud. One of the applications of this intelligent gateway is to use the camera to monitor the place you care about. ...

The NVIDIA Deep Learning GPU Training System (DIGITS) puts the power of deep learning into the hands of engineers and data scientists. DIGITS can be used to rapidly train the highly accurate deep neural network (DNNs) for image classification, segmentation and object detection tasks. DIGITS simplifies common deep learning tasks such as managing data, designing and training neural networks on multi-GPU systems, monitoring performance in real-time with advanced visualizations, and selecting...

RL Poker is a study project Java implementation of an e-soft on-policy Monte Carlo Texas Hold'em poker reinforcement learning algoritm with a feedforward neural network and backpropagation. It provides a graphical interface to monitor game rounds.