Google AI Studio
Google AI Studio is a user-friendly, web-based workspace that offers a streamlined environment for exploring and applying cutting-edge AI technology. It acts as a powerful launchpad for diving into the latest developments in AI, making complex processes more accessible to developers of all levels.
The platform provides seamless access to Google's advanced Gemini AI models, creating an ideal space for collaboration and experimentation in building next-gen applications. With tools designed for efficient prompt crafting and model interaction, developers can quickly iterate and incorporate complex AI capabilities into their projects. The flexibility of the platform allows developers to explore a wide range of use cases and AI solutions without being constrained by technical limitations.
Google AI Studio goes beyond basic testing by enabling a deeper understanding of model behavior, allowing users to fine-tune and enhance AI performance. This comprehensive platform unlocks the full potential of AI, facilitating innovation and improving efficiency in various fields by lowering the barriers to AI development. By removing complexities, it helps users focus on building impactful solutions faster.
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LM-Kit.NET
LM-Kit.NET is an enterprise-grade toolkit designed for seamlessly integrating generative AI into your .NET applications, fully supporting Windows, Linux, and macOS. Empower your C# and VB.NET projects with a flexible platform that simplifies the creation and orchestration of dynamic AI agents.
Leverage efficient Small Language Models for on‑device inference, reducing computational load, minimizing latency, and enhancing security by processing data locally. Experience the power of Retrieval‑Augmented Generation (RAG) to boost accuracy and relevance, while advanced AI agents simplify complex workflows and accelerate development.
Native SDKs ensure smooth integration and high performance across diverse platforms. With robust support for custom AI agent development and multi‑agent orchestration, LM‑Kit.NET streamlines prototyping, deployment, and scalability—enabling you to build smarter, faster, and more secure solutions trusted by professionals worldwide.
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Vertex AI
Fully managed ML tools allow you to build, deploy and scale machine-learning (ML) models quickly, for any use case.
Vertex AI Workbench is natively integrated with BigQuery Dataproc and Spark. You can use BigQuery to create and execute machine-learning models in BigQuery by using standard SQL queries and spreadsheets or you can export datasets directly from BigQuery into Vertex AI Workbench to run your models there. Vertex Data Labeling can be used to create highly accurate labels for data collection.
Vertex AI Agent Builder empowers developers to design and deploy advanced generative AI applications for enterprise use. It supports both no-code and code-driven development, enabling users to create AI agents through natural language prompts or by integrating with frameworks like LangChain and LlamaIndex.
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AgentOps
Introducing a premier developer platform designed for the testing and debugging of AI agents, we provide the essential tools so you can focus on innovation. With our system, you can visually monitor events like LLM calls, tool usage, and the interactions of multiple agents. Additionally, our rewind and replay feature allows for precise review of agent executions at specific moments. Maintain a comprehensive log of data, encompassing logs, errors, and prompt injection attempts throughout the development cycle from prototype to production. Our platform seamlessly integrates with leading agent frameworks, enabling you to track, save, and oversee every token your agent processes. You can also manage and visualize your agent's expenditures with real-time price updates. Furthermore, our service enables you to fine-tune specialized LLMs at a fraction of the cost, making it up to 25 times more affordable on saved completions. Create your next agent with the benefits of evaluations, observability, and replays at your disposal. With just two simple lines of code, you can liberate yourself from terminal constraints and instead visualize your agents' actions through your AgentOps dashboard. Once AgentOps is configured, every execution of your program is documented as a session, ensuring that all relevant data is captured automatically, allowing for enhanced analysis and optimization. This not only streamlines your workflow but also empowers you to make data-driven decisions to improve your AI agents continuously.
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