This week on the Engadget Podcast, we've got our first impressions of Valve's pricey Steam Machine, and we chat about Sony dumping PlayStation discs in 2028.
Get ready to navigate a free-for-all of social media handles.
YouTuber Jon Prosser has finally filed a formal response to Apple's lawsuit made against him and another defendant over allegedly stealing iOS secrets. In his response, Prosser denied that he "planned or participated in any conspiracy or coordinated scheme" for the "purpose of injuring Apple." However, Prosser admitted to recording a FaceTime call showing unreleased […]
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A closer look at why users don’t need more tools in their daily lives. What they need are seamless integrations of useful features to match already existing, established mental models. Brought to you by Design Patterns For AI Interfaces , **friendly video course on UX** and design patterns by Vitaly.
In the AI slop-loaded, algorithm-powered modern reality, trends come and go - and the tech industry is no different. For the last few years, The Verge staff has compiled a selection of things that are IN for summer and OUT for summer - and each time there are some strong feelings. (Here are the last […]
A deep dive into what Anthropic, OpenAI, Perplexity and LangChain are actually building. Covering the orchestration loop, tools, memory, context management, and everything else that transforms a stateless LLM into a capable agent. You've built a chatbot. Maybe you've wired up a ReAct loop with a few tools. It works for demos. Then you try to build something production-grade, and the wheels come off: the model forgets what it did three steps ago, tool calls fail silently, and context windows fill up with garbage. The problem isn't your model. It's everything around your model. LangChain proved this when they changed only the infrastructure wrapping their LLM (same model, same weights) and jumped from outside the top 30 to rank 5 on TerminalBench 2.0. A separate research project hit a 76.4% pass rate by having an LLM optimize the infrastructure itself, surpassing hand-designed systems. That infrastructure has a name now: the agent harness. What Is the Agent Harness? The term was formalized in early 2026, but the concept existed long before. The harness is the complete software infrastructure wrapping an LLM: orchestration loop, tools, memory, context management, state persistence, error handling, and guardrails. Anthropic's Claude Code documentation puts it simply: the SDK is "the agent harness that powers Claude Code." OpenAI's Codex team uses the same framing, explicitly equating the terms "agent" and "harness" to refer to the non-model infrastructure that makes the LLM useful. The canonical formula, from LangChain's Vivek Trivedy: "If you're not the model, you're the harness." Here's the distinction that trips people up. The "agent" is the emergent behavior: the goal-directed, tool-using, self-correcting entity the user interacts with. The harness is the machinery producing that behavior. When someone says "I built an agent," they mean they built a harness and pointed it at a model. Beren Millidge made this analogy precise in his 2023 essay, Scaffolded LLMs as Natu