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The paper introduces DCAS, a backend-substitution interception layer that decouples CLI agent scaffolding from backend models, enabling cross-scaffold evaluation. It shows that planning-aware fine-tuning on a small dataset improves model generalization across non-training scaffolds.
The author shares their project The Termi Protocol, a desktop app that gives each CLI agent a visual body in a cozy 3D room and connects them into a hive with a central queen brain for unified history and coordination.
ECHO introduces a hybrid objective that combines policy-gradient loss with environment observation prediction to provide dense supervision from terminal feedback, doubling performance on TerminalBench-2.0 for Qwen3 models.
Superset is an open-source IDE for orchestrating multiple CLI-based AI coding agents in parallel, with isolated git worktrees, built-in monitoring, and a diff viewer. It supports various agents like Claude Code, Codex CLI, and Gemini CLI.
ECHO is a new, simple, and free method that addresses CLI agents, continual learning, self-improvement, and world models.
The author shares their experience building an AI agent infrastructure using Rocket.Chat, CLI agents, and tmux, scaling to 250 clients to help them build websites. They pivoted from selling a service to teaching clients to use agents themselves, emphasizing the importance of context management in such systems.