World Wire

In this tutorial, we build a runnable reconstruction of the VideoAgent workflow, focusing on the core agentic pipeline behind video understanding, retrieval, editing, and remaking. We start by configuring a lightweight environment that works without API keys. We define an intent parser, an agent library, a tool router, a graph planner, and a textual-gradient optimizer that repairs missing dependencies in the execution graph. We also connect these planning components to practical video-processing tools, including FFmpeg, Whisper-based transcription, scene detection, keyframe sampling, captioning, cross-modal indexing, retrieval, trimming, beat-synced editing, and final rendering. By the end of the tutorial, we have a complete multi-agent video system that can answer questions about a video, summarize its content, generate a news-style overview, and produce edited video artifacts from natural-language instructions. Configuring the VideoAgent Runtime and Multi-Provider LLM Wrapper ...

Agentic LLMs often fail the same way, again and again. A Stanford research team traced this to missing, reusable capabilities. Their system, TRACE , diagnoses those gaps and trains for them directly. TRACE stands for T urning R ecurrent A gent failures into C apability-targeted training E nvironments. It was released open-source under an MIT license. What problem does TRACE solve? To understand the design, first consider why agents fail. They lack specific skills that tasks demand, like retrieving the right record or verifying a precondition. Two mainstream fixes spend compute poorly. Direct RL or SFT gives sparse rewards that never say which skill was missing. Broad synthetic data is untargeted, so budget flows to skills the model already has. However, TRACE observes that failures are not random. A small set of deficits accounts for most failed trajectories. Therefore, each recurring deficit can become its own dense, verifiable training signal. How does TRACE ...

Prime Intellect launched verifiers 0.2.0 . It previews a rewritten core, shipped under the new verifiers.v1 namespace. Modern evaluations now run coding agents with tools, compaction, and subagents. Accordingly, v1 rebuilds environments to run these agentic workloads at scale. What is verifiers v1? First, consider what verifiers is: Prime Intellect’s environment stack for agentic reinforcement learning and evaluations. Previously, an environment bundled its data, agent logic, and infrastructure together. In contrast, v1 breaks that bundle into three composable pieces. A taskset defines the work: the data, tools, and scoring. A harness solves the task and produces a rollout. That harness can be a ReAct loop, a CLI agent, or your own. The rollout then runs inside a runtime , either local or in a sandbox. Because the pieces decouple, any taskset runs under any compatible harness. How the Architecture Works ? With those pieces defined, the next question is how ...