JDisc Discovery is a comprehensive network inventory and IT asset management solution designed to help organizations gain clear, up-to-date visibility into their IT environment. It automatically scans and maps devices across the network, including servers, workstations, virtual machines, and network hardware, to create a detailed inventory of all connected assets. This includes critical information such as hardware configurations, software installations, patch levels, and relationshipots between devices.
One of the standout features of JDisc Discovery is its agentless discovery process, meaning it does not require the installation of any software on individual devices, reducing deployment time and minimizing network impact. It supports a wide range of protocols (e.g., SNMP, SSH, WMI) to gather data, making it compatible with diverse IT environments, whether they are Windows, Linux, or Unix-based.
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JAMS is an automation orchestration and job scheduling solution that runs, monitors, and manages critical IT processes from a single console, from simple batch jobs to complex, cross-platform workflows. JAMS automates jobs across Windows, Linux, UNIX, IBM i, z/OS, and OpenVMS, with native integrations for the databases, BI tools, and ERP systems already running your business, including SQL Server and SAP. Jobs run on any schedule or trigger off other events, and dependency management keeps multi-step workflows in the right order.
Every job is centrally monitored, with notifications on success or failure and an audit trail of every execution. Built-in conversion tools migrate existing jobs from Windows Task Scheduler, SQL Agent, or Cron without rebuilding them, and JAMS replaces homegrown, single-platform scripts with one centrally managed system.
JAMS includes two AI capabilities at no additional cost. JAX is an AI agent built into the JAMS Web Client. Ask it a question in plain language, and it finds a job, troubleshoots a failure, or looks up how to do something, grounded in JAMS documentation, not general AI guesswork. It acts only when asked, and every change waits for your approval. JAMS MCP brings JAMS into the AI coding tools teams already use, including Cursor, Claude Code, GitHub Copilot, and Claude Desktop.
Both run inside the customer's network with the signed-in user's permissions and no elevated AI account, and every action, AI-driven or not, lands in the same audit trail as everything else in JAMS.
For teams managing thousands of jobs across SQL Server, ADF, Airflow, SAP, JDE, and Banner, this cuts tribal knowledge and middle-of-the-night troubleshooting. Knowledge that once lived in one person's head becomes something any team member can ask about directly.
The AI lives in the product, not in the support queue. Support is staffed by humans JAMS will never outsource, based in the United States, the United Kingdom, and Australia. New tickets go to long-tenured engineers, and every JAMS customer has the CEO's cell phone number.
JAMS' mission is to reduce the operational burden of critical automation, so teams spend more time on the work automation was meant to free them for.
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Sniffnet
Sniffnet is a network monitoring tool designed to help users easily keep track of their Internet traffic. Whether gathering statistics or inspecting in-depth network activities, Sniffnet provides comprehensive coverage. It emphasizes user experience, ensuring ease of use compared to other cumbersome network analyzers. Completely free and open source, Sniffnet is dual-licensed under MIT or Apache-2.0, with the full source code available on GitHub. Developed entirely in Rust, it leverages this modern programming language to build efficient and reliable software, emphasizing performance and safety. Key features include selecting a network adapter to inspect, applying filters to observed traffic, viewing overall statistics and real-time charts of Internet traffic, exporting comprehensive capture reports as PCAP files, identifying over 6,000 upper-layer services, protocols, trojans, and worms, discovering domain names and ASNs of hosts, pinpointing connections in the local network.
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