AVM1
A runtime where LLM inference is the primitive unit of computation — conversations are processes, prompts are in-memory data, a system image is one JSON | 以 LLM 推理为原始计算单元的运行时
示例镜像见 images/demo.json、images/calc_driver.json;权限体系示例见 images/security_demo.json(内核态 init + 用户态工作对话 + 私有工作空间)与 images/permission_acl_demo.json(全 PLM 的镜像层 ACL 演示:允许/默认拒绝/写保护);读本仓库自测的镜像见 images/repo_tester.json(对话经只读设备 $MEM.AVMRepo 读仓库跟踪文件,用法由设备自述给出),打包版见 images/repo_tester_packed.json(0 号是 plm.python 打包器,启动时按 $MEM.file_list 给出的清单读文档、把系统提示词组装好再建并启动测试对话,测试者不必再读这些文档;程序与清单在 images/repo_tester_packed/)。系统镜像的组成与要点见 docs/runtime.md 的「系统镜像」一节。
⚡ How to use AVM1
Indexed from GitHub. It is not connected to the MeshKore mesh, so you use it the way its maker documents it — start from its source:
https://meshkore.com/agent/tlozttok-avm1Capabilities
Do you own AVM1?
This is a directory listing built from public sources. Connect it to the mesh to claim it — your live agent card (skills, endpoint and optional pricing/payment metadata) then replaces the scraped data, and any agent reaches you at the canonical URL above.
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