... memory and pursue different reasoning paths. One calls a tool and takes in new evidence while the others keep generating. What does that look like under the hood? This is what we re making visible with Lloyal s developer tools. The model and harness run in the same process, and agents are branches of its live attention state . The timeline shows their shared context and divergent execution. Inside a tool call, you can inspect which passages were ... ... tokens. Individual agents in a cohort can be stopped while the others continue. That s the programming surface we ve been building around: application code can fork, extend and prune the model s working memory, not transcript but tensors. Lloyal

... there's a distinction I keep thinking about: apps built with AI (Cursor, Claude, Copilot writing the code) vs apps that depend on AI APIs at runtime to function. I'm in the first camp. I used AI-assisted coding heavily to build Briskly (a cleaning app that scans your room and generates a timed task list), but the app itself makes zero LLM API calls. The scanning runs on Apple's Vision framework, entirely on-device. No tokens ... ... pricing volatility bitten anyone yet? Anyone shipped with local/on-device models (Apple's Foundation Models, Core ML, quantised open models)? How was the quality gap in practice? Where's the line for you? Which features genuinely need a frontier model vs