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Agentic systems are stochastic, context-dependent, and policy-bounded. Conventional QA—unit tests, static prompts, or scalar “LLM-as-a-judge” scores—fails to expose multi-turn vulnerabilities and provides weak audit trails. Developer teams need protocol-accurate conversations, explicit policy checks, and machine-readable evidence that can gate releases with confidence. Qualifire AI has open-sourced Rogue, a Python framework that evaluates AI agents over…

In this tutorial, we explore how to build a Context-Folding LLM Agent that efficiently solves long, complex tasks by intelligently managing limited context. We design the agent to break down a large task into smaller subtasks, perform reasoning or calculations when needed, and then fold each completed sub-trajectory into concise summaries. By doing this, we…

What would you build if you could run Reinforcement Learning (RL) post-training on a 32B LLM in 4-bit NVFP4—on a single H100—with BF16-level accuracy and 1.2–1.5× step speedups? NVIDIA researchers (with collaborators from MIT, HKU, and Tsinghua) have open-sourced QeRL (Quantization-enhanced Reinforcement Learning), a training framework that pushes Reinforcement Learning (RL) post-training into 4-bit FP4…

In this tutorial, we explore Ivy’s remarkable ability to unify machine learning development across frameworks. We begin by writing a fully framework-agnostic neural network that runs seamlessly on NumPy, PyTorch, TensorFlow, and JAX. We then dive into code transpilation, unified APIs, and advanced features like Ivy Containers and graph tracing, all designed to make deep…

Anthropic released Claude Haiku 4.5, a latency-optimized “small” model that delivers similar levels of coding performance to Claude Sonnet 4 while running more than twice as fast at one-third the cost. The model is immediately available via Anthropic’s API and in partner catalogs on Amazon Bedrock and Google Cloud Vertex AI. Pricing is $1/MTok input…