Meta Muse: Turning a Personal AI into a Computer That Keeps Working
A technical deep dive into Meta Muse: Secure VM, browser runtime, approvals, credential isolation, and memory for persistent personal agents.
WRITING / 持续记录
Notes on building AI applications and useful systems.
A technical deep dive into Meta Muse: Secure VM, browser runtime, approvals, credential isolation, and memory for persistent personal agents.
A standalone technical deep dive into Jev, typed decisions, calibration, and its boundary with Open-Jev, browser agents, and full agent runtimes.
A source-level tour of how Hermes turns a tool call into an auditable, rejectable execution path, from schemas to terminal approval.
How does “don't change it yet” survive a long agent session? Follow Hermes through stored history, memory prefetch, usage anchors and compaction boundaries.
Does the same agent in another chat automatically continue the same conversation? Follow Hermes from incoming messages to routing keys, durable sessions, and replaceable execution objects.
Follow a request to stop writing files through DeepSeek Harness: input queues, turns and steps, concurrent tools, approval guards, and cooperative cancellation.
Reopen yesterday’s conversation to explore how DeepSeek Harness records events, rebuilds model context, preserves interrupted-tool uncertainty, and forks from stable boundaries.
Follow a repository review to see how DeepSeek Harness orchestrates child Agents with JavaScript: concurrency slots, pipelines, partial failure, and cleanup after cancellation.
Follow a filesystem backend replacement to see how DeepSeek Harness combines service interfaces, dependency identity, scopes, and cleanup into replaceable Agent components.
The plugin is listed, but its tool is missing. Follow a capability through synchronous registration, failure rollback, and service startup and shutdown.
The command finished, but the report is missing. Follow OpenClaw through execution target selection, workspace mounts, and container reuse.
After a rate limit, should the agent rotate credentials, switch models, or stop? Follow OpenClaw through failure classification, retry budgets, and session rollback.
How does a long conversation become a usable handover? Follow OpenClaw through context budgets, valid cut points, and persisted summaries.
Why does streaming sometimes edit one bubble and sometimes send several messages? Follow a checklist with code through OpenClaw previews, chunking, coalescing, and final delivery.
Why does “don’t change it yet” fail to stop an assistant immediately? Follow session lanes, shared capacity, steering, and timeouts through OpenClaw’s handling of new input.
The checklist was saved, then the service restarted. Does remembering the conversation mean knowing the action completed? Follow session indexes, JSONL branches, and missing tool results to find the boundary.
The meeting notes can be read, but the checklist cannot be saved. Follow one request through OpenClaw tool filtering, allow and alsoAllow, path guards, and execution boundaries.
Why can a simple request to read meeting notes take two model calls? Follow the file read, tool result, and final checklist through OpenClaw and Pi.
A successful request, an accepted task, and a finished execution are different milestones. Follow OpenClaw Gateway through acknowledgments, idempotent retries, wait deadlines, and event recovery.
Trace a Telegram message through OpenClaw agent selection and session-key construction. Explore dmScope, identity links, routing precedence, and isolation boundaries, with 15 checks against a pinned historical revision.
Hermes Agent is a powerful open-source AI programming assistant. Official installation requires manual configuration of WSL2, Ubuntu, and Python environments, which has a high threshold for non-technical personnel. This project follows the installation plan of u-claw USB flash drive, encapsulating the entire installation process into a double-click. bat file and providing a GUI interface to configure API keys without any command line operations. Hermes Agent is a powerful open-source AI programming assistant. Official installation requires manual configuration of WSL2, Ubuntu, and Python environments, which has a high threshold for non-technical personnel. This project follows the installation plan of u-claw USB flash drive, encapsulating the entire installation process into a double-click. bat file and providing a GUI interface to configure API keys without any command line operations.
This article documents my first real business project independently completed: developing a computer vision based elderly mobility assessment system for a tertiary hospital in Shanxi. From requirement research, solution selection, edge deployment to final delivery and payment, I have completed a complete To B medical delivery loop, which has also allowed me to personally understand the distance between "usable" and "easy to use" for the first time.
I write about AI, computer vision, edge computing, and web development here.