... noticed something while using AI for startup research: It's very good at giving me more things to think about . More features. More customer segments. More competitors. More business models. More growth ideas. At some point, more ideas stop being useful. So I've been experimenting with a different prompt: "Which assumption should I test first?" That changes the output. Instead of getting a longer list, I get something closer to a priority: Assumption Customers have this problem often enough ... ... matters If the problem isn't painful, everything built on top of it becomes less important. What I'd need to learn How often the problem occurs, what people do today, and whether they have already paid for an alternative

... response. I challenge it a little. And eventually, I often walk away feeling more confident about what I already wanted to do. Which made me wonder: did AI actually stress-test my decision, or did it just help me rationalize it better? With people, we have social biases. Friends, colleagues and mentors may soften their advice, bring their own biases, or tell us what they think we want to hear. AI removes some of that discomfort. But if it mostly ...

... nothing, the reviewer's normal experience becomes an empty screen, and empty screens train people to expect empty. Whether the one that is not empty gets read properly on day forty is an open question. It still beats the alternative, because a difference against a background of silence is about as salient as anything gets. And yes to the small pile. Most of what gets called judgement is work nobody has bothered to make mechanical yet. Comment from Aadil Ghani ... ... because it's dumb, but because the radius is determined by capabilities it isn't even looking at. That's the strongest version of the "worst judge of its own reach" point I've seen. And your authority-armed-versus

... them what the power users were actually writing? Comment from Gal Dayan(@galdayan): @monatruong_murror  the before/after journal entries side by side is a smart way to make an abstract metric feel real to the team. easy to rationalize a chart, much harder to rationalize

... error itself it was that the agent couldn't explain why it had chosen that approach or that it had even considered the downstream impact. The treacherous part is exactly what you mentioned: trust creeps back in. You rationalize it as "that was an edge case" and start trusting by default again. But I've started explicitly asking agents to trace their reasoning backward not just "what would you do" but "why would you do that and what could ...

... doorbell. It rings the same whether it's a package or a fire. That's basically the gap Pushary fills, same hook idea you've already wired, but the event carries a real handoff (done vs blocked vs

... nervous system biomarkers from a 30-second daily voice recording (stress, fatigue, cognitive load, energy, confidence). No wearable, no subscription, nothing uploaded. The reading from voice is actually more honest than a mood journal because you can't rationalize it. The most interesting thing I've discovered: watching the nervous system baseline shift slowly across weeks is a signal that step counts and sleep scores completely miss. Comment from Nika(@busmark_w_nika): @byalexai  Wow, I didn't know ...

... least one moment where they kept going not because the product was working but because stopping felt like admitting failure. Building Hello Aria, we have had moments where signals were mixed and it would have been easy to rationalize continuing in the wrong direction. What saved us was focusing on actual retention signals rather than vanity metrics. Users who kept coming back to WhatsApp every day to chat with the assistant - that told us something real. Hope whatever comes next ...