Flagsmith gives software engineering teams a self-hosted or fully managed feature flagging platform for controlling releases across web, mobile, and backend systems. Ship code behind a flag, then decide who sees it — by environment, user, or custom segment — without redeploying.
Run Flagsmith however fits your infrastructure: our managed cloud, your own private cloud, or fully on-premise.
What you get:
Progressive rollouts with one-click rollback if something breaks
Live config changes — flip features on or off instantly, no redeploy required
Segment-based A/B and multivariate testing
Project and role-based access control for multi-team organizations
Native integrations with the tools already in your stack
MCP support and a CLI so AI agents and scripts can manage flags directly
Open source at its core, Flagsmith is built for teams that want control over where their configuration data lives.
Learn more
Gemini Enterprise Agent Platform is Google Cloud’s next-generation system for designing and managing advanced AI agents across the enterprise. Built as the successor to Vertex AI, it unifies model selection, development, and deployment into a single scalable environment. The platform supports a vast ecosystem of over 200 AI models, including Google’s latest Gemini innovations and popular third-party models. It offers flexible development tools like Agent Studio for visual workflows and the Agent Development Kit for deeper customization. Businesses can deploy agents that operate continuously, maintain long-term memory, and handle multi-step processes with high efficiency. Security and governance are central, with features such as agent identity verification, centralized registries, and controlled access through gateways. The platform also enables seamless integration with enterprise systems, allowing agents to interact with data, applications, and workflows securely. Advanced monitoring tools provide real-time insights into agent behavior and performance. Optimization features help refine agent logic and improve accuracy over time. By combining automation, intelligence, and governance, the platform helps organizations transition to autonomous, AI-driven operations. It ultimately supports faster innovation while maintaining enterprise-grade reliability and control.
Learn more
Hypertune
Hypertune stands out as a highly adaptable platform that excels in managing feature flags, conducting A/B testing, performing analytics, and configuring applications. It is designed with comprehensive end-to-end type safety, Git-inspired version control, and allows for local, synchronous, in-memory flag evaluations.
You can establish type-safe, tailored inputs such as the current User or Organization to fine-tune feature flag rules, ensuring precise targeting of your desired audience. Furthermore, the platform enables the creation of reusable variables like user segments that can be utilized across various feature flags, facilitating swift debugging for individual users.
With options for A/B testing, percentage-based rollouts, multivariate tests, and machine learning loops, Hypertune allows for an effortless rollout, testing, and optimization of new features. Additionally, you can log analytics events with type-safe custom payloads and create dynamic funnels and charts within the dashboard to assess the influence of every feature release.
Moreover, the SDK can be initialized with just the necessary feature flags, enabling partial evaluation of flag logic on the edge, thus enhancing both performance and security. This combination of capabilities makes Hypertune a versatile choice for developers aiming to innovate and refine their applications effectively.
Learn more
waiting
Waiting is a compact library designed to facilitate the process of waiting for specific conditions to be met. It fundamentally pauses execution until a designated function returns True, offering various operational modes. Additionally, Waiting is designed to work seamlessly with flux for simulating timelines. The simplest way to utilize it is by providing a function to monitor. It’s straightforward to wait indefinitely; if your predicate yields a value, that value will be returned as the output of wait(). You can also set a timeout, and if this period lapses without the predicate being satisfied, an exception will occur. The library polls the predicate at a default interval of one second, which can be adjusted using the sleep_seconds parameter. When dealing with multiple predicates, Waiting offers two efficient methods for aggregation: any and all. These methods are similar to Python's built-in any() and all(), but they ensure that a predicate is not invoked more than necessary, which is particularly beneficial when working with predicates that are resource-intensive and time-consuming. By streamlining these functions, Waiting enhances both the efficiency and user experience of handling asynchronous operations.
Learn more
