Vhim Jana

Following Theodor s question on the previous walkthrough, I added a concrete interrupted-write case and a read-only recovery path.

What changed:

The operator can distinguish a verified canonical commit, an authenticated no-effect refusal, an unknown outcome and a conflicting request identity.

The participant and standalone MCP consumer can recover an already-saved result without sending another write.

I've been building an AI-powered invoice and payment workflow, and one question keeps coming up:

Should an AI agent that recommends a payment also be allowed to execute it?

During testing, I ran into an interesting problem.

An invoice could look perfectly valid, but there was no confirmation that the work had actually been delivered.

Over the past few months, I ve been testing how far a founder or tiny team can stretch AI capabilities across marketing, copy, and visual asset generation. While AI handles a massive portion of speed and execution, it hits a hard wall when it comes to brand strategy, contextual judgment, and authentic human connection.

Pushing output out faster doesn't mean much if it loses the nuance that makes people care. For those running solopreneur setups or lean startup teams: which specific workflows are you comfortable delegating completely to AI, and what non-negotiable tasks are you keeping strictly human?