QM gives each employee and channel an isolated AI agent workspace with its own memory, files, permissions, and cron jobs while supporting shared scopes for collaboration in Slack or a web app. It’s model-agnostic—swap between Pi, OpenCode, Codex, or Claude Code—and offers admin controls for security postures, org-wide configs, and custom plugins. Deploy with the qm CLI by layering your org’s config and skills over the headless core, all under an MIT license.

• Every user and channel gets an isolated sandbox (memory, files, permissions, cron jobs) while still allowing shared skills across scopes, so teammates don't step on each other
• It's model-agnostic—swap between Pi, OpenCode, Codex, or Claude Code without changing the deployment
• Deployment cleanly separates org-specific config/skills from the upstream core, with helper skills (update-qm, upstream-pr) to sync a private fork in both directions
• Admins get org-wide controls over allowed harnesses/models, security postures (Strict, Auto, Dangerous), and skill/app access

Two workshops demonstrate hands-on uses of Anthropic’s Claude model. In one, Andrej Karpathy breaks down his 14-minute tutorial on mastering Claude, claiming it takes about 10,000 hours to learn. In the other, Amazon quants show how they built a full AI agent in 25 minutes with just 44 lines of code, now open-sourced.

• This is just a promotional X post, not an actual article with verifiable substance—likely hype/clickbait framing around Karpathy and Claude.
• The "10,000 hours to master Claude" claim contradicts the premise of a 14-minute tutorial teaching it.
• Amazon quants reportedly rebuilt a 3-year framework in 3 days using just 44 lines of Claude-generated code, then open-sourced it.
• The open-sourced package includes pre-trained models, helper tools, and editable prompts, enabling a self-contained AI agent with no external libraries or proprietary backends.