ramp Programming Tutorials & Engineering Articles

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.

See how local NVMe transforms Postgres transaction performance—and why ClickHouse remains essential for fast analytics as workloads scale.

Coding agents changed what it costs to build mobile apps twice. Here’s why Shopify is moving from React Native back to Swift and Kotlin.

THROUGHPUT-per-kW FRONTIERInferenceX · GPT‑OSS‑120B · nominal 8k/1k · STP · package TDP: Jalapeño 700 W; GB200 1,200 W

As agentic and long-context workloads become common, the context lengths increase and attention consumes a larger share of inference time (Figure 1).

Treating a Gaggia espresso machine like a distributed system: instrumenting an ESP32 with OpenTelemetry, streaming shots and whole-house sensors into ClickHouse Cloud, and putting ClickStack (plus an LLM agent) on top to turn every pull into a queryable,

Why ClickHouse Managed Postgres runs strict memory overcommit by default, and how it turns an instance-wide OOM restart into a single failed query — shown side by side on identical hardware, with no measurable throughput cost.

As AI workloads increase, explosive compute demand is pushing the semiconductor industry to meet unprecedented performance targets. Even small delays can have…

Adopting AV1 for real-time communication at Meta has been a multi-year effort spanning codec selection, device eligibility, rate control, and error resilience. We’re sharing the technical and opera…

AI factories are changing what data-center infrastructure must do. Unlike traditional data centers, AI factories are built to manufacture intelligence at scale.

By Brian Yu and Ashwin Nathan, Members of the Technical Staff

Coding agents are starting to write production code at scale. Stripe’s agents generate 1,300+ PRs per week. Ramp attributes 30% of merged PRs to agents.

How and why we built a real-time AI spend and usage pipeline for finance teams.

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.

How we agentically found, validated, and patched ~100 security issues in a 1-week experiment.

How I spent my fall internship building accounting automations to save finance teams time during month-end close.