How Google Uses ramp

The MaxText team successfully reproduced AI2’s OLMo 3 7B language model from scratch on Google Cloud TPUs using JAX/XLA, precisely matching the original PyTorch-on-GPU reference across pre-training and mid-training stages on all held-out evaluations. The implementation achieved up to 57.4% Model Flops Utilization (MFU) and demonstrated robust infrastructure portability by surviving mid-run cluster resizes and cross-generation TPU shifts without requiring recipe alterations. Crucially, the exercise proved the necessity of comprehensive held-out validation by catching a silent data-loader memorization bug that artificially depressed training loss and would have otherwise faked a performance win.

Google Cloud API Gateway now acts as a native remote Model Context Protocol (MCP) server, eliminating the need to build and maintain custom middleware to expose REST APIs to AI agents. By simply adding specific annotations (like x-google-api-management.mcp) to existing OpenAPI 3.x specifications, developers can instantly convert standard REST operations into discoverable, agent-ready tools. The gateway automatically transcodes incoming MCP JSON-RPC requests into REST calls, ensuring that your existing authentication, quotas, and logging policies apply seamlessly to agent traffic without requiring new infrastructure.

Google has introduced Finish Changes and Outlines for Gemini Code Assist in IntelliJ and VS Code to reduce developer friction and eliminate the need for long, manual prompting. Finish Changes acts as an AI pair programmer that completes code, implements pseudocode, and applies refactoring patterns by observing your current edits and context. Meanwhile, Outlines improves code comprehension by generating interactive, high-level English summaries interleaved directly within the source code to help engineers navigate and understand complex files.