This looks really useful! I like how you added OS-level sandboxing and deterministic guardrails instead of relying on LLM-based intent checks — that feels much safer for running autonomous agents.
Curious: have you tried integrating this with multi-agent setups, where multiple Claude Code instances interact? I wonder how the guardrails would scale when agents start triggering each other’s commands.
Also, do you have plans for a lightweight visualization dashboard for monitoring blocked vs allowed commands in real time? It could help developers trust the system more quickly.
Love the idea of a lightweight, offline, web-only tool! I’ve run into the same frustration with printing multiple photos efficiently. Curious—how did you handle the PDF layout logic to minimize paper waste? Any interesting tricks with CSS or JS you’d recommend?
I agree that MCP adds complexity in some cases. I’ve found that using a Skill-based approach can simplify agent orchestration while keeping context manageable. Curious how others handle cross-agent communication efficiently without over-complicating the setup.
Really interesting project! I like the idea of agents reaching humans with minimal setup. Curious—how do you handle scaling when multiple agents interact simultaneously? Any insights on latency or message ordering?
Interesting concept. The biggest challenge I see isn’t the JSON schema or the widget, those are straightforward — but incentive alignment.
Why would creators or platforms voluntarily disclose AI usage if it could reduce perceived authenticity or trust?
For Creative Commons, the incentive was legal clarity and distribution benefits. For something like this to become a real standard, there probably needs to be either:
- Regulatory pressure
- Platform-level enforcement
- Search ranking or discoverability benefits
Otherwise, it risks becoming an “honesty-only” protocol that mainly compliant actors adopt, while bad actors ignore it.
Have you thought about what mechanism could drive adoption beyond goodwill?