... that =) Comment from Adana Marukhyan(@adana): @emmanuel_lnu  Really enjoyed reading your comment! Thanks for sharing your experience from a designer s point of view, especially the part about AI helping you challenge your own design bias. What problem does Merzify solve? And are you planning to launch it here on PH? Comment from Adana Marukhyan(@adana): @jordan_bulk   @better_shaya  HaHa =D Comment from Adana Marukhyan(@adana): @andreas_broutas yes! And what problem does SteadFolio solve ... ... camilla_s_andersen  Yes =( This truth definitely exists! But it really helps you better understand and analyze the user experience. Comment from Adana Marukhyan(@adana): @jordan_bulk   @evgeny_yakushev  What an interesting product! Does it have competitors

... only one using your own product daily, that's actually not nothing! It means YOU have the problem you're solving. The question is whether others share that same problem. What's been your experience with early user feedback vs. actual usage data? Comment from Abusaid islam said(@abusaid_islam_said): For me, it's definitely the mix of respondents and how feedback is collected. Developers and UX designers are great at spotting technical bugs and edge cases, but actual ... ... nothing live yet. We're waiting on our first App Store review before anything is actually sellable. Comment from Alex Gidirim(@alex_gidirim): @busmark_w_nika  absolutely! Comment from Alex Gidirim(@alex_gidirim): @busmark_w_nika The behavior-versus

... user to say "this looked right and wasn't" and you need someone actually reading those. That's the only sensor that sees the failure your dashboards are blind to. I'll be honest about the bias here. At Murror we build emotional AI a product whose whole job is to reflect someone's feelings back to them with real insight. In that context, confidently wrong isn't a bug report, it's a wound. A mirror that misreads ... ... 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

... screenshotting. If growth only happens when you personally post, you've built a job, not a loop. That's a design problem, and you can solve it the same way you solve any other feature. I'll admit the bias here. At Murror we spend our days on a product about understanding one person deeply reflecting someone's emotions back with real insight instead of a generic mirror and the longer we do it, the more I think distribution ... ... 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

... expects it, an AI analyst that actually gets answers right Each of these is somebody's entire company. Your team is going to build all 5 as a side quest, and then maintain them forever. I'll admit my bias upfront: we sell BI software, so obviously I'd say this. And there are real exceptions, like when analytics is your core product, or your data model is genuinely weird. If you're Amplitude, build it (obviously). But for everyone ... ... felt this so strongly that we turned our whole product into an API for exactly this use case. Launching it on Product Hunt today , take a look! Comment from Tarqiya Forgah(@tarqiya_forgah): I like that you acknowledged the bias

... live on PH Aug 22, so this thread's been good practice at saying it in one paragraph.) Comment from Demetrio Koro(@aktivist): siOsi upload one selfie and it reads your 12-season color type, undertone, gold vs silver, and which hair colors suit you, then grades the actual makeup in the photo (flash cast, oxidation, coverage), which most tools skip. Free, no signup. Built it because every beauty sub has a queue of "what season am I" posts waiting ... ... Gemini, since more buying decisions now start there instead of traditional search. If you want to see how an AI agent currently reads your own site, we put out a free tool that shows what an AI fetches vs

... nika  I agree, AI is great for getting you started, but I often find something missing when it comes to design. There s usually a gap in nuance or intent. That said, my perspective might be a bit biased given the kind of work I do. Comment from Monika(@stormsister): I hope to receive true opinions about my product after launch, if you find it useful, what else should I add to make it better. So is better ... ... eigenst ndige Pr senz wirken kann, ist https://der-arkadenhof.at/ . Comment from YekSoon Lok(@ys_askodin): Having operated on both sides of the table as an allocator and a founder, I view this through a different lens. The binary of "personal brand vs

... visibility into how bad the code was. What is the first signal you look for that tells you a team is building on sand? Comment from Nolan Vu(@nolan_vu): The pattern you described in months 1-6 vs. post-scale is exactly what kills companies that had real traction. The debt doesn't feel expensive until the moment you need to move fast again, and by then refactoring costs more in time than the original build did. From what ... ... cycles. Similar to recursive learning, the plan grows and evolves after each run, the errors are catalogued and added as analysis structure for each future run. The main benefit is the loss of lockin, sunk cost and hubris biasing

... programmer than a marketer, but you do not possess that skill. Personally, I find programming harder to learn for a marketer than for a programmer to learn marketing. How do you perceive it? Maybe I am just biased because I have been working in the field for longer. Comment from Tim Joerg(@tim_joerg): @dstr88   The hard part is that good work does not automatically get attention. Totally agree! Comment from Nika(@busmark_w_nika): @gapostolov  Working ... ... with trends - ability to understand people - more drawn to build to meet others' needs and pain points, or more from a personal point of view - solution-mindedness - and a whole lot more Both are equally possible. So the bias

... question of choosing between two evils for us now. Neither option is completely free of flaws. Human: Recruiters with "gut feelings" who harbor unconscious bias. they reject excellent candidates who just didn't go to the "right" school or didn't just "click." Inconsistent, unfair, and un-auditable. AI: Algorithms whose training datasets are themselves replete with historical biases