- An Engineer’s Guide to AI Code Model Evals
This article explains that good evaluations for AI coding models should be a collection of programming tasks with clear success criteria that can be automated. Evals are crucial for the iterative improvement of an AI model by analyzing its failures on these tasks. The author emphasizes that the most useful evals are those that represent what you actually want the model to be good at, so that improving eval scores translates to real user impact. -
Minecraft MCP Server powered by Mineflayer API
This project is a Minecraft server that allows AI assistants to control a Minecraft character in real-time through natural language instructions. It uses the Mineflayer API to interact with the game and the Model Context Protocol (MCP) to communicate with AI models. The server enables AI to build structures, explore the world, and interact with the game environment. -
SDK provides tools for building Model Context Protocol (MCP) enabled applications with interactive UI components
MCP-UI is a software development kit that provides tools for building Model Context Protocol (MCP) enabled applications with interactive user interface components. It standardizes how models and tools can request the display of rich HTML interfaces within a client application. The SDKs are available for multiple languages, including TypeScript and Ruby. -
How Salesforce Delivers Reliable, Low-Latency AI Inference
Salesforce Engineering details their approach to achieving reliable, low-latency AI inference by implementing a multi-layer caching system. This system dramatically reduces latency for AI metadata access and improves overall request latency. Furthermore, the caching layers enhance system reliability by allowing AI inference to continue functioning even during backend failures. -
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