... could have. And I still don't have a clean answer on the verification-method question, because the honest version is that I don't yet know what I'd do differently with a government-ID-checked 16 versus a self-declared one. I'd rather say that than write a paragraph pretending I've resolved it. The thing I am confident about is narrower. The signal is sparse for a long while yet. Texas only covers accounts created after ... ... never right anywhere refusing is an option. Self declared isn't the case where you behave identically, it's the case where you should be more cautious, and throwing the field away removes your ability to be. Comment from Mona

... mode to discover after you've already trusted the data for a while. On the sampling caveat, that seems cheap to check before building anything on it: trigger a known block yourself and count how many requests you sent versus what firewallEventsAdaptive reports back. Match at low volume and you're probably getting full fidelity there, with sampling kicking in later. Mismatch even at low volume and you've learned upfront that the dataset can't answer "did this specific ... ... built around that, so the failing cases are the ones nobody has a reason to visit. I have no method for generating them rather than stumbling into them. If you have one I would take it. Comment from Mona

... real person uses is enormous, and that gap is where products in spaces like mine quietly disappear. I don't have this solved. I've just stopped assuming the name is the thing that gets found. Comment from Mona Truong(@monatruong_murror): @getosmo  Splitting it into three signals is better than what I'm doing, which is honestly nothing systematic. The failure mode you're describing cited but no visits, read as "the product is invisible" is exactly ... ... right that the pre-category phrasing is the test that matters for Murror. "Why do we repeat the same argument" is much closer to what people actually type than anything on my landing page. Comment from Mona

... destination? If the value is a result someone wants and doesn't care how they got an action then being agent-callable is close to free distribution and you should be looking hard at it before your competitor does. If the value is the experience of a person actually being there a destination then racing to become a tool call is optimizing for a future where your product's whole reason for existing has been abstracted away. Worse ... ... ones who shipped a server fastest. They'll be the ones who were honest about whether they should and, if they did, treated that borrowed trust like it was hard to get. Because it is. Comment from Mona

... small it moved onto exactly one desk. Comment from Ill Robyn(@ill_robyn): The idea of treating review as a budget really stands out. Asimple rule like that could stop AI generated work from piling up unnoticed. Comment from Mona Truong(@monatruong_murror): @ill_robyn Thanks and the reason it ended up as a budget rather than a rule is that I failed at every stricter version first. "Always review AI output" has no partial credit: the first week ...

... pricing metric rewards the behavior your actually want from users . If the metric can grow because the user is struggling more, it's probably measuring the wrong thing. That's a useful lens far beyond AI companions. Comment from Mona Truong(@monatruong_murror): @joseph_walker2   Thanks for reading it that closely. That's exactly the test I now run before shipping any plan: would this number go up if my users were having a worse week? If the answer ...

... Mona, a marketer turned founder from Germany. I'm building Xolora, a marketing analytics tool dedicated to solopreneurs and small business owners. I noticed that 80% of that niche is overwhelmed with their marketing and just guessing. When it comes to ads, this means that they're wasting money (around $10,000/year). And the sad truth is that there is no tool out there, that helps them. All the other marketing tools are made for marketers or big teams ... ... underserved niche. Xolora's goal is to simplify ad analytics, give back time, money and ultimately confidence. It just launched in beta and I hope that it will be valuable for its users. What is your niche? Comment from Mona

... judgment, your ability to stay steady when the feedback is loud and instant and mostly noise. In 2026, taking care of the builder is not self-help fluff. It's the actual bottleneck. Treat it like one. Comment from Mona Truong(@monatruong_murror): @omri_ben_shoham1   "defending a decision I hadn't finished making yet" is a sharper way of putting it than I managed in the whole post that's the trap exactly. The thing that made ... ... pick it back up next day, or is there some catch-up rule? that's usually where these systems fall apart for me, not the routine itself but the recovery after the first missed day. Comment from Mona

... keeps stuffing it and reliability, not raw capability, is what your users actually feel. Every token you add is a token competing for attention. Spend them like they're scarce, because to the model, they are. Comment from Mona Truong(@monatruong_murror): @omri_ben_shoham1   Yes that's the part I underplayed in the post. Curating once is a prompt; re-deciding every turn is a system. What's helped us is treating context assembly as a step that ...

... this: if building is no longer the moat, then the moat is available to anyone willing to do the unglamorous work. Knowing your person. Owning a relationship instead of renting attention. Being early to a workflow before a competitor gets embedded in it. None of that requires funding or a team. It requires you to stop hiding in the part of the work that feels productive and go do the part that actually decides whether the thing lives. Ship ... ... weekend, sure. Just don't confuse the weekend with the work. Comment from Mona

... transcripts starts accurate and then decays, not because the model changed but because your users did. Which means it has to be refreshed from live traffic on a schedule, or in a year you have rebuilt the exact lab-versus-real gap you made it to avoid. Comment from Magdalena Anderson(@magdalena_anderson): I really like the idea of using real user transcripts for evalution. It feels much closer to what actually happens in production than bechmark scores. Comment from ... ... Mona

... inside your app if they can only feel understood while they're talking to your model you haven't solved loneliness. You've made it more comfortable to stay lonely. You've built a very sophisticated waiting room. The alternative is to design for the exit. Concretely, that means three things. Point users back at real people, not deeper into the app. The most valuable thing an emotional AI can do isn't be a better listener than your friends ... ... thats a feature not a bug. teams that build a way to capture that external signal, even at low volume, get to measure what the regulator actually cares about. everyone else is defending against DAU accusations forever. Comment from Mona

... builders land on this. When you imagine your product working perfectly, does usage go up forever, or does someone get what they needed and walk away? And which one were you actually optimizing for? Comment from Stacy Wycoff(@stacywycof83995): Mona, this is the question I keep circling back to while building FounderFlow. My honest answer is uncomfortable, I want usage to go down over time, not up. If it is doing its job, a founder should need fewer ... ... like proof of value to an investor, but for the actual founder using it, quiet is the win. How are you thinking about measuring a product that is supposed to make itself less necessary? Comment from Asad M.(@asadmalik901): Mona

... that clearly knows what it is. Comment from Khaild Naseem(@khaildnaseem): Have you thought about offering chat only after someone finishes writing? It could keep the reflection process while giving users another way to explore their thoughts. Comment from Mona Truong(@monatruong_murror): @khaildnaseem  We've thought about this a lot, and it's the most tempting version of the idea. The problem we kept running into is that "after writing" is exactly when the reflection has the most ... ... into seeking. We'd essentially be rewarding the hard work of writing with a shortcut to skip the payoff. That said, we are exploring lighter ways to help people explore after they write just not through conversation. Comment from Mona

... animation and 8 seconds of silence. Our engineer thought it was a bug when he first saw the spec. "You want me to add... a delay?" Yes. On purpose. Here's why. We ran an experiment: instant AI response vs. an 8-second pause before the same response. The content was identical. The only difference was time. Users who got the pause rated the AI's reflections 40% more helpful. Same words. Same insights. They just felt more considered ... ... forgot what "thinking about you" actually looks like. It looks like taking a moment. The most human thing our AI does is pretend to think. And I'm still not sure how I feel about that. Comment from Mona

... language explainer of a chunk of the codebase, not because you need it day to day, but so there's a paper trail you can hand your CTO when something breaks and speed matters more than pride Comment from Mona Truong(@monatruong_murror): @omri_ben_shoham1  you nailed the tension perfectly. I've landed somewhere in between I don't spot-check everything because you're right, that defeats the point. but I've started developing a feel ... ... think this is just what building with AI looks like right now you develop a new instinct for what to trust and what to verify, and that instinct gets better with each false positive you catch. Comment from Mona

... health aren't always the same direction. Sometimes the right metric is one that goes down. Anyone else building products where you've had to choose between engagement and user wellbeing? Curious how others navigate this. Comment from Mona Truong(@monatruong_murror): @galdayan  exactly reading someone's actual words is so different from reading a chart. numbers abstract away the human experience, which is exactly what we're trying not to do. and yes, we got lucky ... ... team. easy to rationalize a chart, much harder to rationalize actual words getting healthier. and yeah, the investor-pressure point is real, that's probably the scenario where most teams would've quietly reverted the change Comment from Mona

... forgotten isn't a privacy feature. It's a trust feature. People will only be truly honest with technology that promises not to remember everything. Has anyone else experienced this tension between data retention and user trust? Comment from Mona Truong(@monatruong_murror): @nolan_vu  thank you this really captures what we've been feeling but couldn't articulate as well. you're right that most AI builders are racing in the opposite direction. we've had conversations with ... ... deletion, so I'd guess people pin way less than they'd expect to going in. curious if that held true, or if pinning became its own habit once people got comfortable with the 30 day window Comment from Mona

... Every time we push information at users, engagement drops. Every time we let them pull it when they're ready, it goes up. Has anyone else found that making AI less visible actually made it more valuable? Comment from Mona Truong(@monatruong_murror): @galdayan   That's such a sharp observation -- the "performing for the app" thing is exactly what we saw too. To your question: so far, the data suggests that user-initiated analysis feels different from auto-triggered ... ... being watched. That said, we're keeping an eye on it. If we see the opt-in rate declining over time without a corresponding drop in journaling quality, that could signal exactly the fatigue you're describing. Comment from Mona

... will treat your product like a tool. If it feels like a conversation, they'll treat it like a space. Anyone else building something where the onboarding itself needs to feel like part of the product experience? Comment from Mona Truong(@monatruong_murror): @galdayan  You're right that this is a real edge case we think about a lot. Yes, it does happen -- maybe 2-3% of first responses read as genuinely distressed. Our approach: we built a lightweight ... ... being clinical about it. You're right that a universal response would be the wrong move -- the whole point is meeting people where they are. Comment from Artur Kuznetsov(@artk): @monatruong_murror  great advice, thank you! Comment from Mona

... product metrics? Comment from Gal Dayan(@galdayan): @monatruong_murror  exactly, and the 41% retention jump is the proof - people weren't confused about what the product does once the label stopped setting the wrong expectation upfront Comment from Mona Truong(@monatruong_murror): @galdayan   Totally. The retention number was the real validation for us. When people stick around, it means their expectations matched what they actually got. Removing the "AI" label basically fixed the expectation gap before users even ... ... opened the app. Appreciate you sharing your experience -- it's reassuring to know others have seen the same pattern. Comment from Mona

... from Amard Sonal(@amard_sonal): I ve definitely experienced this. The weird part isn t that the AI wrote the code it s realizing your role shifts from builder to evaluator. The bottleneck becomes judgment, not implementation. Comment from Mona Truong(@monatruong_murror): Reading all of these and honestly feeling seen. @Nitesh Kumar I don't think deep technical expertise becomes less valuable I think it splits. There will always be people who need to understand what's happening under ...

... asks the smartest questions -- it's the one that makes you feel understood enough to keep going on your own. Has anyone else found that reducing friction (even well-intentioned friction) unlocked deeper engagement in your product? Comment from Mona Truong(@monatruong_murror): @divvsaxena   That means a lot, thank you! And honestly, that's exactly the use case we designed for -- those moments when you need to process something but don't have someone available to talk ... ... conversation, but it can help you sort through your thoughts so that when you do talk to someone, you're more clear about what you actually feel and need. Really glad it's helping you that way! Comment from Mona

... murror  Thank you so much Comment from Fatima Khan(@fatima_shehzad): @monatruong_murror  Thank you so much. Your words are so kind. I am glad I was able to put what Murror does for everyone. Comment from Mona Truong(@monatruong_murror): @fatima_shehzad   Really appreciate you saying that, Fatima! Comments like yours remind us why we started building Murror in the first place. Keep journaling -- and know that every entry matters, whether you keep ... ... Comment from Mona

... that keeps you talking. It's the one that helps you figure out what you need to say to someone else. Has anyone else found that the most valuable product interactions are the shortest ones? Comment from Mona Truong(@monatruong_murror): @atherkildsen  100%. "The best tools get out of the way" -- this is the design principle we keep coming back to. It's counterintuitive because most product thinking rewards stickiness. But for something as personal as journaling about your ... ... emotions, the win is when the person puts the phone down and goes to have the actual conversation. We're trying to build a product that succeeds by making itself unnecessary in the moment that matters most. Comment from Mona