YAML Programming Tutorials & Engineering Articles

This release brings speedups for GROUP BY ... ORDER BY ... LIMIT, three JOIN improvements, four vector search improvements, position-aware phrase search, EXPLAIN ANALYZE, unified URL access, and more!

Autonomous vehicle (AV) development often relies on separate models for trajectory generation, high-level intent prediction, scene understanding…

Google Cloud API Gateway now offers a model routing feature in Public Preview, allowing developers to dynamically route traffic to models like Gemini, Claude, or OpenAI OSS-GPT without hardcoding endpoints or managing open-source proxies. Developers can easily configure these routing rules directly within their OpenAPI 3.x specifications by mapping virtual model names to specific backend targets on a shared host. Once deployed, the Gateway acts as a serverless ingress layer that accepts standard OpenAI-compatible requests, automatically transcodes the payload to the native schema of the target model, and routes the traffic on the fly.

To prevent context window bloat and reduce token consumption, Genkit Go introduces Agent Skills based on a progressive disclosure architecture. Developers can package specialized instructions, scripts, and references into modular SKILL.md bundles where only the frontmatter metadata is initially exposed to the agent's system prompt. When a task matches the skill's description, Genkit's middleware dynamically loads the full instruction body and associated assets, ensuring the model accesses precise workflows exactly when needed.

This second installment explores how Ray’s higher-level libraries—Serve, Data, and Train—abstract the complexities of running AI workloads on Google's TPU slices. Ray Serve uses a simple topology configuration to correctly gang-schedule large multi-host models, while Ray Data eliminates data-loading bottlenecks by feeding accelerators directly with native JAX batches. Finally, JaxTrainer streamlines distributed training across TPUs by automatically handling cross-slice coordination, checkpointing, and fault tolerance.

Ray 2.55 introduces official, first-class support for Google Cloud TPUs, enabling developers to run distributed Python workloads on Google's accelerators using the familiar Ray task-and-actor APIs. To handle the strict networking requirement of keeping multi-host TPU "slices" together over their Inter-Chip Interconnect (ICI), the KubeRay Operator on GKE automatically provisions and labels the underlying hardware layout. Ray Core utilizes these labels via its slice_placement_group() primitive to atomically reserve complete slices, allowing developers to deploy jobs through KubeRay, Ray Train, or Ray Serve simply by declaring a hardware topology (like "4x4") without writing custom placement code.

Developers building video analytics applications across large spaces must track the same object as it moves between camera views. Single-camera 2D tracking…

Industrial machinery generates more alarms than technicians can triage. For each important alarm requiring follow-up, the technician pulls historical context…

Distributed AI training is notoriously fragile because losing a single machine typically crashes the entire multi-node job, forcing a time-consuming, full-workload infrastructure restart. To address this, Google’s JAX ecosystem utilizes elastic training via Pathways, which converts a hardware failure into a catchable Python exception so the running process can survive. When an unplanned failure occurs, the system automatically replaces only the broken worker, restores the last viable checkpoint from Cloud Storage, and resumes training in place—minimizing total downtime to under two minutes without ever restarting the main controller process.

AI agents have changed a lot in the last two years. The first could only answer one question at a time. Then came multi-turn chat, where the model could keep…

As context windows grow longer, moving large model weights efficiently becomes critical to performance. A common way to address this is quantization…

When AlphaFold2 revolutionized drug discovery in 2020, its success relied entirely on the roughly 170,000 protein structures collected by scientists since 1971…

Every swipe, transfer, and payment on a modern financial network encodes a pattern of human behavior. Transaction data is one of the richest signals an…

AI agents are a powerful tool for synthesizing data to accelerate research, summarize information, and help teams make decisions faster.

Large language models (LLMs) are revolutionizing the financial trading landscape by enabling sophisticated analysis of vast amounts of unstructured data to…

In quantitative finance, researchers build algorithms to trade assets, derivatives, and other financial instruments. A key part of that work is finding signals…

By Alex Kotliarskyi, Victor Zhu, and Zach Brock

Gemini CLI has introduced subagents, specialized expert agents that handle complex or high-volume tasks in isolated context windows to keep the primary session fast and focused. These agents can be customized via Markdown files, run in parallel to boost productivity, and are easily invoked using the @agent syntax for targeted delegation. This architecture prevents "context rot" by consolidating intricate multi-step executions into concise summaries for the main orchestrator.

Slurm is an open source cluster management and job scheduling system for Linux. It manages job scheduling for over 65% of TOP500 systems.

Stripe's Selective Test Execution system employs some clever tricks to allow us to continue scaling our team and our codebase while only running around 5% of our tests on average. Find out how it works!

The Agent Development Kit (ADK) SkillToolset introduces a "progressive disclosure" architecture that allows AI agents to load domain expertise on demand, reducing token usage by up to 90% compared to traditional monolithic prompts. Through four distinct patterns—ranging from simple inline checklists to "skill factories" where agents write their own code—the system enables agents to dynamically expand their capabilities at runtime using the universal agentskills.io specification. This modular approach ensures that complex instructions and external resources are only accessed when relevant, creating a scalable and self-extending framework for modern AI development.

The launch of Agent Development Kit (ADK) for Go 1.0 marks a significant shift from experimental AI scripts to production-ready services by prioritizing observability, security, and extensibility. Key updates include native OpenTelemetry integration for deep tracing, a new plugin system for self-healing logic, and "Human-in-the-Loop" confirmations to ensure safety during sensitive operations. Additionally, the release introduces YAML-based configurations for rapid iteration and refined Agent2Agent (A2A) protocols to support seamless communication across different programming languages. This framework empowers developers to build complex, reliable multi-agent systems using the high-performance engineering standards of Golang.

In production Kubernetes environments, the difference between model requirements and GPU size creates inefficiencies. Lightweight automatic speech recognition…

Developing new protein-based therapies and catalysts involves the challenging task of designing protein binders, or proteins that bind to a target protein or…

As large language model (LLM) inference workloads grow in complexity, a single monolithic serving process starts to hit its limits. Prefill and decode stages…

Reasoning models are growing rapidly in size and are increasingly being integrated into agentic AI workflows that interact with other models and external tools.

Every AI cluster running on Kubernetes requires a full software stack that works together, from low-level driver and kernel settings to high-level operator and…

Deploying and optimizing large language models (LLMs) for high-performance, cost-effective serving can be an overwhelming engineering problem.

Uber’s Rate Limiting System details the evolution of Uber's approach to managing service overload through a unified rate-limiting architecture.

The article discusses the introduction of time-based fairshare in NVIDIA Run:ai v2.

The article discusses how to utilize NVIDIA Earth-2 to downscale coarse climate projections into high-resolution, bias-corrected fields, enabling better assessment of local climate extremes.

Shopify uses SkyPilot, an open-source framework, to manage GPU-intensive ML training workloads across multiple cloud providers (Nebius and GCP).

Airbnb's Pay as a Local initiative launched 20+ locally relevant payment methods (LPMs) across multiple global markets in 14 months.

The article discusses the importance of evaluations (evals) for AI agents, emphasizing how they help teams identify and resolve issues before they reach production.

The article discusses how to build and orchestrate end-to-end synthetic data generation (SDG) workflows using NVIDIA Isaac Sim and NVIDIA OSMO.

The article discusses advancements in real-time decoding and AI inference enhancements in NVIDIA CUDA-Q QEC, focusing on how these improvements facilitate quantum error correction in quantum comput...

The article discusses how to simulate an accurate radio environment for 5G and 6G systems using the NVIDIA Aerial Omniverse Digital Twin (AODT).

The article discusses the optimization of semiconductor defect classification using generative AI and vision foundation models (VFMs).

The article discusses the Skip Softmax technique, a method for accelerating long-context inference in large language models (LLMs) using NVIDIA TensorRT-LLM.