luminoth Reviews

NetCrunch is a modern, scalable network monitoring and observability platform designed to simplify infrastructure and traffic management across physical, virtual, and cloud environments. It monitors everything from servers, switches, and firewalls to operating systems, cloud platforms like AWS, Azure, and GCP, including IoT, virtualization (VMware, Hyper-V), applications, logs, and custom data via REST, SNMP, WMI, or scripts-all without agents. NetCrunch offers over 670 built-in monitoring packs and policies that automatically apply based on device role, enabling fast setup and consistent configuration across thousands of nodes. Its dynamic maps, real-time dashboards, and Layer 2/3 topology views provide instant visibility into the health and performance of the entire infrastructure. Unlike legacy tools like SolarWinds, PRTG, or WhatsUp Gold, NetCrunch uses simple node-based licensing with no hidden costs, eliminating sensor limits and pricing traps. It includes intelligent alert correlation, alert automation & suppression, and proactive triggers to minimize noise and maximize clarity, along with 40+ built-in alert actions including script execution, email, SMS, webhooks, and seamless integrations with tools like Jira, PagerDuty, Slack, and Microsoft Teams. Out-of-the -box AI-enhanced root cause analysis and recommendation for every alert. NetCrunch also features full hardware and software inventory, device configuration backup and change tracking, bandwidth analysis, flow monitoring (NetFlow, sFlow, IPFIX), and flexible REST-based data ingestion. Designed for speed, automation, and scale, NetCrunch enables IT teams to monitor thousands of devices from a single server, reducing manual work while delivering actionable insights instantly. Designed for on-prem (including air-gapped), cloud self-hosted or hybrid networks, it is the ideal future-ready monitoring platform for businesses that demand simplicity, power, and total infrastructure awareness.

Over 6,000 organizations rely on Files.com to facilitate the automation and security of vital data transfers. We are deeply committed to ensuring security, compliance, reliability, and performance, allowing your essential business operations to function seamlessly every time. With our platform, you can effortlessly manage transfer workflows without the need for coding or scripting, enabling smooth onboarding of workloads and partners. We accommodate standard file transfer protocols such as FTP, SFTP, and AS2 for collaborating with external partners, while also offering native applications designed for optimal performance during internal transfers. As a fully Cloud-Native SaaS solution, you won't need to purchase or maintain any servers, and there’s no installation process required, as high availability and redundancy are inherently integrated at no additional cost. Our comprehensive InfoSec Program undergoes annual audits by Kirkpatrick Price, a respected CPA firm specializing in information security, which evaluates the entire spectrum of Files.com’s operations rather than just focusing on data centers, ensuring transparency and reliability—contrast this with smaller competitors who may misrepresent their audit results. Among our technical features are encryption for data at rest and in transit, four variations of two-factor authentication, nine integrations for enterprise identity (SSO), customizable password and session policies, along with an impressive “A+” rating from Qualys SSL Labs for security. This commitment to security and performance distinguishes us in the competitive landscape.

TFlearn is an intuitive and adaptable deep learning framework built on TensorFlow that aims to provide a more approachable API, thereby streamlining the experimentation process while maintaining complete compatibility with its foundational structure. Its design offers an easy-to-navigate high-level interface for crafting deep neural networks, supplemented with comprehensive tutorials and illustrative examples for user support. By enabling rapid prototyping with its modular architecture, TFlearn incorporates various built-in components such as neural network layers, regularizers, optimizers, and metrics. Users gain full visibility into TensorFlow, as all operations are tensor-centric and can function independently from TFLearn. The framework also includes powerful helper functions that aid in training any TensorFlow graph, allowing for the management of multiple inputs, outputs, and optimization methods. Additionally, the visually appealing graph visualization provides valuable insights into aspects like weights, gradients, and activations. The high-level API further accommodates a diverse array of modern deep learning architectures, including Convolutions, LSTM, BiRNN, BatchNorm, PReLU, Residual networks, and Generative networks, making it an invaluable resource for both researchers and developers. Furthermore, its extensive functionality fosters an environment conducive to innovation and experimentation in deep learning projects.