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GLM-5.2: Built for Long-Horizon Tasks

Hugging Face

Балл: 35.76Уверенность: 48%

From the Hugging Face Hub to robot hardware with Strands Agents and LeRobot

Hugging Face

Балл: 35.76Уверенность: 48%

Agentic Resource Discovery: Let agents search

Hugging Face

Балл: 35.74Уверенность: 48%

AI gateway best practices: Model routing, reliability, and budget controls for production agents

Datadog

Learn how AI gateways help you scale your agents to consume multiple LLM services reliably, and how to monitor these systems to ensure you’re getting the best performance and cost.

Балл: 35.56Уверенность: 44%

Profiling in PyTorch (Part 2): From nn.Linear to a Fused MLP

Hugging Face

Балл: 35.49Уверенность: 48%

Unbiased Java CPU profiling with JFR in JDK 25

Datadog

Modern Java profilers often rely on unsupported JVM internals for accurate CPU profiling. Here’s how engineers from Datadog, SAP, Amazon, and the OpenJDK community helped bring a new CPU profiling event to JDK 25.

Балл: 35.46Уверенность: 44%

From zero to traces: Choosing the right APM instrumentation method for your stack

Datadog

Learn why Single Step Instrumentation is our default recommendation for setting up APM traces, and when to consider other methods.

Балл: 35.46Уверенность: 44%

How an Agent Built a 3D Paris Gallery by Chaining Two Hugging Face Spaces

Hugging Face

Балл: 35.43Уверенность: 48%

Migrating Your GitHub CI to Hugging Face Jobs

Hugging Face

Балл: 35.42Уверенность: 48%

The Open Source Community is backing OpenEnv for Agentic RL

Hugging Face

Балл: 35.38Уверенность: 48%

Nemotron 3.5 Content Safety: Customizable Multimodal Safety for Global Enterprise AI

Hugging Face

Балл: 35.27Уверенность: 48%

Use OpenTelemetry-native observability with Datadog from ingestion to investigation

Datadog

Learn how you can use vendor-neutral telemetry while preserving Datadog’s infrastructure and APM experiences.

Балл: 35.26Уверенность: 44%