Search Results for "updating"
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10 projects for "updating" with 2 filters applied:
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Auth0 B2B Essentials: SSO, MFA, and RBAC Built InAuth0's B2B Essentials plan gives you everything you need to ship secure multi-tenant apps. Unlimited orgs, enterprise SSO, RBAC, audit log streaming, and higher auth and API limits included. Add on M2M tokens, enterprise MFA, or additional SSO connections as you scale.
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Gemini 3 and 200+ AI Models on One PlatformBuild, govern, and optimize agents and models with Gemini Enterprise Agent Platform.
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Talk to Figma MCP
AI bridge enabling Cursor agents to read and modify Figma designs
...Through this integration, an AI assistant can read the structure of a design, retrieve information about nodes or selections, and perform modifications to the layout or content. cursor-talk-to-figma-mcp includes an MCP server and a Figma plugin that communicate through a WebSocket connection, enabling real-time interaction between the AI environment and the design canvas. Developers can automate tasks such as creating UI elements, updating text, organizing layout structures, or managing annotations inside a design file. It also provides strategies and helper prompts that guide AI agents in performing more complex design-related workflows. -
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MemoryOS
MemoryOS is designed to provide a memory operating system
MemoryOS is an open-source framework designed to provide a structured memory management system for AI agents and large language model applications. The project addresses one of the major limitations of modern language models: their inability to maintain long-term context beyond the limits of their prompt window. MemoryOS introduces a hierarchical memory architecture inspired by operating system memory management principles, allowing agents to store, update, retrieve, and generate information... -
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COCOON
Confidential Compute Open Network, Decentralized AI Inference on TON
...The framework abstracts platform-specific details, enabling cross-platform development for Windows, macOS, and Linux with consistent behavior and performance. In addition to UI framework components, Cocoon provides networking primitives optimized for real-time communication, background updating, and seamless offline support. -
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JobWinner
Curated directory of thousands of generative AI tools by category
AI Collection is a curated repository that aggregates a large number of generative AI applications into a single organized directory. It serves as a discovery platform where users can browse and explore AI tools across a wide range of categories and use cases. Instead of providing software code for a single application, AI Collection acts as a structured index that lists AI tools along with brief descriptions and visual previews. It organizes thousands of AI applications into dozens of... -
Full-stack observability with actually useful AI | Grafana CloudBuilt on open standards like Prometheus and OpenTelemetry, Grafana Cloud includes Kubernetes Monitoring, Application Observability, Incident Response, plus the AI-powered Grafana Assistant. Get started with our generous free tier today.
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Prompt in-context learning
Resources for in-context learning and prompt engineering
...The project gathers research papers, tutorials, prompt examples, and practical guides that help developers and researchers learn how to design effective prompts for models such as GPT-3, ChatGPT, and other foundation models. In-context learning refers to the ability of language models to learn a task directly from examples provided in the prompt without updating the model’s parameters, allowing them to perform new tasks through demonstration alone. The repository organizes this growing field by categorizing materials related to prompt design strategies, chain-of-thought reasoning, agents, and general large language model usage. It is designed as a continuously updated “awesome-style” resource that tracks recent research developments. -
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Recast Navigation
Navigation mesh generation and pathfinding toolkit for game AI systems
...Recast Navigation also includes modules such as DetourCrowd for crowd simulation and agent collision avoidance, as well as DetourTileCache for streaming and dynamically updating navigation meshes in large or changing environments. -
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DCGAN in Tensorflow
Deep Convolutional Generative Adversarial Networks
...It serves both as an educational reference and as a practical starting point for developers experimenting with generative models. The implementation includes adjustments such as updating the generator more frequently than the discriminator to help stabilize training. Users can train models on built-in datasets or plug in their own image collections with minimal changes. Overall, the project remains a widely cited baseline for understanding GAN mechanics within the TensorFlow ecosystem. -
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AlgoWiki
Repository which contains links and resources on different topics
...Because it collects external resources rather than implementing software itself, the project acts as a reference library for students and developers who want to explore reliable educational content in computer science. Contributors can expand the repository by adding links, creating new topic categories, or updating outdated resources. -
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ASM is the Santa Fe Institute "Artificial Stock Market" simulation model. This page is mainly aimed to the revision/updating of ASM using the Swarm Toolkit, which is provided by Swarm Development Group (http://www.swarm.org).
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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MiniMax-M2.7
Self-evolving AI model for agents, coding, and complex workflows
MiniMax-M2.7 is a large-scale open-weight language model designed for advanced agent-based workflows, professional software engineering, and complex productivity tasks. With 229B parameters, it introduces a self-evolution framework in which the model actively improves its own capabilities by updating memory, generating skills, and iterating through reinforcement learning experiments. This process enables it to autonomously refine systems, achieving measurable performance gains such as a 30% improvement in programming scaffolds. M2.7 excels in real-world engineering scenarios, including debugging, log analysis, system monitoring, and root cause investigation, demonstrating strong system-level reasoning comparable to SRE workflows. ...
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