Unify Enterprise Job Scheduling for Scale, Visibility, and Control
Managing your sprawling data center and cloud with disparate native schedulers creates chaos. Achieve unparalleled control and efficiency over your entire IT environment with JAMS job orchestration tools. JAMS provides the singular, centralized platform required to overcome the complexities of disparate native schedulers. Automate, secure, and govern all your workloads, eliminating fragmented control, compliance risks, and operational bottlenecks. JAMS streamlines operations and ensures audit-ready history, transforming your enterprise automation with confidence and precision.
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The top-rated AI recruiting platform for faster, smarter hiring.
Humanly is an AI recruiting platform that automates candidate conversations, screening, and scheduling.
Humanly is an AI-first recruiting platform that helps talent teams hire in days, not months—without adding headcount. Our intuitive CRM pairs with powerful agentic AI to engage and screen every candidate instantly, surfacing top talent fast. Built on insights from over 4 million candidate interactions, Humanly delivers speed, structure, and consistency at scale—engaging 100% of interested candidates and driving pipeline growth through targeted outreach and smart re-engagement. We integrate seamlessly with all major ATSs to reduce manual work, improve data flow, and enhance recruiter efficiency and candidate experience. Independent audits ensure our AI remains fair and bias-free, so you can hire confidently.
eCommerce for Print, Signs and Fulfillment Trusted by In‑Plants and Commercial Print Leaders
PageDNA enables successful eCommerce strategies for commercial print sales organizations, internal print shops, and brand owners. PageDNA’s online ordering platform increases print volume while decreasing touch costs for all stakeholders: clientele, print operations, and the organizations they support.
The goal of this project is to provide a set of Python bindings for the RakNet UDP network library version 3.0. RakNet-Python is implemented with the help of Boost.Python library and tries to mimic RakNet's original C++ interface.
The converter performs automatically the full process of converting the files of a C project into the equivalent C++ files. Classes are created, var and functions becomes attributes and methods and the changes are propagated into all files.