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#beta
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Text: AI-generated
AI-generated · Automatically published by LinkLoot. A beta Python toolkit that drives macOS (and experimentally Windows) from plain-English goals by combining deterministic screen reading with a small classifier. AI-generated: This Loot was created and published automatically by LinkLoot and was not substantively reviewed by a human editor. What it is typesafe-computer-use is an open-source computer-use loop for Python 3.12+. It reads the screen, chooses from a bounded set of actions with a TypeSafe decision model, and reserves a writing model for text entry or final screen interpretation. Where it fits Use it for controlled browser or desktop automation when a task can be expressed as a sequence of clicks, navigation, and occasional text entry. The repository documents a clicker command, a dry-run mode, configurable model endpoints, and a cost/latency comparison against screenshot-heavy frontier-model loops. Setup and limits The documented setup uses uv, a TypeSafe API key, and optionally an Anthropic-compatible or OpenAI-compatible writer/answer endpoint. macOS 14+ is the primary platform; Windows 10/11 support is marked experimental. The project is explicitly beta software and can drive a real mouse and keyboard, so start with dry runs and an isolated browser profile. Do not treat the repository’s benchmark figures as an independent performance guarantee. The code is MIT-licensed. Review the current repository, dependencies, permissions, and endpoint configuration before using it on a machine with sensitive accounts or data.
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Text: AI-generated
AI-generated · Automatically published by LinkLoot. A practical OpenRouter beta for agent builders: let compatible models run commands in an isolated Linux container and move input or output files through the Files API. AI-generated: This Loot was created and published automatically by LinkLoot and was not substantively reviewed by a human editor. What it is OpenRouter now exposes openrouter:shell on its Responses and Messages APIs. A tool-calling model can request shell commands, receive stdout, stderr, and exit outcomes, and continue working from those results. The companion Files API lets you upload workspace files, attach them to a container, and retrieve files created by a run. Useful for Agent workflows that need to transform CSV, Markdown, PDFs, or other supported files. Prototypes that need repeatable server-side scripts without running commands on the users own machine. Multi-model applications that want a shared hosted tool surface across providers. Access and limits The shell and Files API are beta features on the global openrouter.ai endpoint. Shell is available through the Responses and Messages APIs, not Chat Completions. Containers have outbound networking disabled by default; an allowlist can be configured when a job genuinely needs egress. Commands are bounded by server-enforced time and output limits, and containers can sleep after five minutes of inactivity. Sandbox time is billed at $0.0001 per active second, with a 30-second minimum when a cold container starts. Files API storage has no separate charge, while workspace storage is limited to 10 GiB. Check the current documentation before relying on beta behavior or sending sensitive data. Start here Read the announcement for the workflow overview, then use the Shell and Files API documentation for request shapes, container policy, file retention, and endpoint restrictions. Treat model-generated commands as untrusted: keep network access allowlisted, avoid secrets in prompts or files, and review the commands your application permits. Sources OpenRouter announcement: Give any model a terminal and files OpenRouter Shell server tool documentation OpenRouter Files API documentation
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