... hurt: a mass email to 50k users, or a migration you can only roll back after downtime. "Reversible on paper, ruinous in practice." His version is that the gate should key on reach, not on reversibility. Not "is this safe" but "how far does this reach if I'm wrong." And the part that stuck with me: the agent is the worst possible judge of its own blast radius, because it does not know there are 50k people on that ... ... answer, the default has to be a safe

... www.producthunt.com/products/ai-... Your turn. Drop: - What you're building - Who it's for - The hardest part you're solving right now Will read everything and upvote what resonates. Comment from Nolan Vu(@nolan_vu): @raghav_rajput1  The educational vs diagnostic line is exactly the right place to hold firm. The moment a health AI starts feeling like a diagnosis, trust collapses fast, either from users over-relying or regulators getting involved. The consistency problem is hard though. Same biomarker ... ... verified AI disclosure. others will not. over time the receipts with agent transparency outperform on credibility because the alternative

... upfront with explicit scope limits, get continuously evaluated against them, and build a verifiable reputation over time. The goal is not to slow automation down, it's to give you a principled basis for knowing which actions to approve vs. just let run. Comment from Ryan Fong(@armalo_ai): @dedy_ariansyah   "The fear isn't autonomy - it's opacity" is exactly the framing we landed on too. We built Armalo (armalo.ai) for this: agents define behavioral pacts upfront - what ... ... someone challenge your interpretation of it. That's where most founders get stuck. Do you find they trust the numbers too much, or not enough? Comment from Henrik Pedersen(@henrikpedersen): The distinction I keep coming back to is read vs