... growth example hits home because AI is terrifyingly good at agreeing with whatever hypothesis you feed it. If your starting prompt is biased, the tool will just find data to make you feel smart about a wrong decision. True collaboration only happens when you force the model to look for counter-arguments or blind spots you missed. Have you found a reliable way to get AI to challenge your strategy instead of just echoing your thoughts back to you? Comment ... ... your earlier back and forth helps too. Comment from George Basov(@gbasovv): What works for me is not asking for an opinion at all. I ask the model to argue against the idea first, as if it were a competitor

... honest. At the moment, I am getting traction from my digital footprint. At least in 95%. Comment from Nika(@busmark_w_nika): @oshylabs  I know that having a big audience is a benefit and users can be biased because I created it, but I will see whether they will use it long-term. So far, 78 active users in one month or so. It is not that bad. Regarding "being found"  I am trying to help people with ... ... from the same source, it is one cold batch. Find a relevant subreddit or extension directory where nobody knows you and see what five complete strangers do with it unprompted. If they behave the same as your 78, the bias

... 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 ... ... because the company has already spent too much - our CTO wouldn't want to even hear about dropping that tool. Comment from Mustafa Arian(@thenameisarian): the security disclosure line is the killer. everyone brushes past it in the build-vs

... makes pricing easier. Products from anonymous creators compete on price. Products from known practitioners with a track record compete on trust. That's a completely different conversation. Comment from Samir Asadov(@samir_asadov): For specialist/practitioner products this 'brand vs product' framing breaks down the product IS the brand statement. A financial model template built from real deal experience signals more credibility than a thousand brand posts ever could, because the buyer (a CFA with a deal on their desk) reads ... ... were only concerned w/ my wellbeing I'd do the same, but when I have others I'm responsible for, and they themselves have mouths to feed, I think differently about the long term effects of my brand vs

... 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 the repo. We don t care what the code looks like as long as it follows all our rules and returns results. Yes automated, but nothing can replace human in the middle. Most of the major advances have come from watching ... ... output, the llms continually go down their own biased

... 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 Nika(@busmark_w_nika): @jitain  maybe it is also a matter of encouragement in terms of how good / bad I am at something. Naturally, when we start with something ... ... 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