Daylight merges lightning-fast agentic AI with elite human expertise to deliver a next-gen managed detection and response service that goes beyond alerts, aiming to “take command” of your cyber-frontier. It promises full coverage of your environment with no blind spots, context-aware protection that continuously learns from your systems and past cases (including Slack chats), near-zero false positives, the industry’s lowest mean time to detection and mean time to response, and deep integration with your IT and security stack so it supports unlimited platforms, unlimited integrations, and delivers actionable, noise-free insights via AI dashboards. With Daylight, you get true end-to-end threat detection and response (no escalation games), 24/7 expert support, custom response workflows, environment-wide visibility, and measurable improvements in analyst utilization and response speed, all built to shift your security operations from reactive to commanding.
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SurveyJS is an embeddable, self-hosted, white-label form builder for teams building custom forms, surveys, questionnaires, and other data collection tools inside web applications. It runs entirely on the client and is fully compatible with all modern JavaScript frameworks, including React, Angular, and Vue. SurveyJS integrates with any backend, giving you full control over data, security, user access, deployment, and business logic. Instead of hardcoding forms, you can empower non-technical users to generate and edit JSON schema-based form configurations through a drag-and-drop form builder.
Create dynamic forms with custom conditional logic, validation, multilingual support, PDF export, and analytics. Ideal for SaaS, enterprise software, HR, healthcare, fintech, and other regulated industries, SurveyJS helps teams reduce engineering effort, standardize data collection, maintain full control over data, and deliver unlimited forms that match their brand and feel native to users.
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Gymnasium
Gymnasium is a maintained fork of OpenAI’s Gym library, providing a standard API for reinforcement learning and a diverse collection of reference environments. The Gymnasium interface is simple, pythonic, and capable of representing general RL problems, and has a compatibility wrapper for old Gym environments. At the core of Gymnasium is the Env class, a high-level Python class representing a Markov Decision Process (MDP) from reinforcement learning theory. The class provides users the ability to generate an initial state, transition to new states given an action, and visualize the environment. Alongside Env, Wrapper classes are provided to help augment or modify the environment, particularly the agent observations, rewards, and actions taken. Gymnasium includes various built-in environments and utilities to simplify researchers’ work, along with being supported by most training libraries.
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