AI Context Optimization provides a comprehensive toolkit for prompt engineers and developers to optimize context windows for generative AI. It leverages context relevance scoring to identify and retain critical information, executes automatic summarization to condense long histories, and enforces token budget management to avoid API limit breaches. Users can integrate it into chatbots, retrieval-augmented generation workflows, and memory systems. Configurable parameters let you adjust compression aggressiveness and relevance thresholds. By maintaining semantic coherence while discarding noise, it enhances response quality, lowers operational costs, and simplifies prompt engineering across diverse LLM providers.

Browserable is a web-based AI framework that empowers language models and chatbots to navigate and interact with websites as if they were human users. By generating an OpenAPI specification based on your site's content and structure, Browserable allows agents to fetch pages, follow links, click buttons, fill out forms, and extract structured responses — all via standard API calls. The platform supports dynamic content behind JavaScript, session management, pagination, and custom handlers for specialized workflows. With built-in rate limiting, authentication, and error handling, Browserable simplifies integrating real-time web browsing capabilities into AI applications, chatbots, and data pipelines.