... well you match the account to the message. - Content / inbound / SEO : Trust and compounding returns. Slower to build, much stronger once it works. - Paid ads : Predictable reach and speed. You get exactly what you pay for nothing more, nothing less. - Partnerships and integrations : Warm introductions and borrowed credibility inside specific ecosystems. The teams that scale treat these as distinct bets with different economics and timelines. They don t run the same playbook on every channel. They test small, measure what ... ... look like? Check for meaningful movement in the last 30 90 days new roles posted in areas you solve, expansion signals, public discussion of the exact problem you address, or clear momentum in their business. One-off spikes matter less

... funnel properly and got humbled pretty fast. 253 created a company profile. 92 completed onboarding. 90 got matched with investors. 38 actually launched outreach. 18 became paying customers. So yeah, top of funnel looked pretty decent. But the real lesson was not how do we get people to sign up? It was: how do we get founders to trust the product enough to actually press send on fundraising emails? That is a much harder problem. For context, Causo helps early ... ... great. Some drop-off is normal, but a lot of it is probably on us. Fundraising is sensitive, and founders do not always have their materials ready. We asked for too much before showing enough value. The lesson

... completely differently. Could be: Launching too early Launching too late Building features nobody asked for Not talking to users enough Focusing on the wrong metrics Having zero launch plan Let's help each other avoid painful lessons. What's your story? Comment from Samir Rashed(@samirrashed): @jaemin_song  Here we go https://www.producthunt.com/prod... Would love to know your feedback Comment from Sanket Ligade(@sanket_ligade): Because someone said to launch on Product Hunt to get traction without knowing ... ... just about to launch, and even though we ve tried to prepare properly, it still feels like we re probably making half of these mistakes anyway. The hardest part for me is knowing what s useful preparation vs

... Quick background: my wife and I homeschool our 3 daughters, and I'm an engineer. The teaching part we love. The prep part was wearing us down and I was spending way too much time asking ChatGPT for lessons, pasting them into Word, tweaking them, redoing them. So I built something to make it easier. It's called Homeschool Ranch. You pick your teaching style, type in a topic and grade level, and it puts together a full lesson

... draft Zotero - the reference library everything else points back to Obsidian - where half-formed notes actually connect over months Readwise - so highlights don't die inside whatever app I read them in Reading this thread, the "tools that disappear vs. tools that turn you into the glue" point really lands for me. My stack is powerful but the seams between capturing a source, reading it, and synthesising are exactly where I lose time. I've ended up building a small ... ... list is different! Mine as a founder: MySpec : turning rough ideas into reviewed, build-ready specifications. Cursor : building. Claude : thinking through product decisions and technical trade-offs. GitHub : shipping. Linear : planning what's next. The better the specification, the less

... insight. Every journal entry got a thoughtful, confident analysis. Pattern recognition, emotional connections, suggestions for growth. Users were impressed. But something felt off. We noticed that our most thoughtful journalers -- the ones writing about genuinely complex emotions -- were engaging less over time. They'd write a deep entry, get a polished AI response, and then... nothing. No follow-up. No continued reflection. We dug in and realized: the AI's confidence was closing conversations instead of opening them. When someone ... ... know" responses wrote 40% longer follow-up entries. They returned to the same theme more often over the next week. And in qualitative feedback, they described the experience as "feeling heard" -- even though the AI had literally said less

... launch date gets closer, I've been reflecting on how much of building a product has nothing to do with code. A few lessons have surprised me: Building is usually easier than deciding what to build. Users don't always use a product the way you expect. Small improvements repeated consistently beat big bursts of effort. Progress often feels invisible until you look back months later. The biggest surprise for me has been how often assumptions turn ... ... wrong. Features I thought people would love weren't always the most valuable, while simple things ended up mattering far more than expected. So I'm curious: What's one startup, product, or business lesson

... first we thought it was a product issue maybe the onboarding was off, maybe the prompts were wrong. But when we dug into the data, we found something we didn't expect: the smarter the AI got, the less people felt like they were doing the work themselves. Here's the problem. When someone journals about a difficult relationship and the AI immediately identifies the pattern, names the emotion, and suggests a reframe it feels impressive. But it also short ... ... rate (our internal metric for when a user explicitly names a new understanding) went up 40% Word-of-mouth referrals increased because people felt ownership over their breakthroughs, not like they were just reading an AI's assessment The lesson

... week. But the users who come through word-of-mouth stay longer, engage more meaningfully, and genuinely get value from the product. We're still figuring this out. Curious if anyone else is building something where success looks like less usage, not more. How are you telling that story? Comment from Nolan Vu(@nolan_vu): @monatruong_murror  Awesome, I think you guys are doing it the right way though. That "trailing indicator of trust" reframing is really sharp ... ... rather build something people are grateful they used, even if they move on. Comment from Mona Truong(@monatruong_murror): @riya_pariyar  You're right, and I think "intentional usage" is actually the better framing. We've been saying "less

... worse. That's the actual principle, not "important versus unimportant," it's "is the clock still running on this." Failed feels urgent, but a failed run is just stopped, it's not bleeding, so it can wait for your glance. Waiting is the only one actively accruing. Your "unless it's critical" caveat is the one refinement that rule needs, and it's the right one: the push-versus

... When we first launched Murror, the number one feature request was always the same: "Can I talk to the AI?" Users wanted a chatbot. Something they could vent to, ask for advice, get instant feedback from. Every competitor had one. Every investor asked why we didn't. So we built it. The first month looked incredible. Session times tripled. Users came back more often. The engagement charts were exactly what you'd want to show in a board meeting. Then ... ... engagement numbers dropped back down. But the outcomes we actually care about users reporting they feel understood, users resolving recurring worries, users feeling confident enough to have hard conversations in real life those went up. The lesson

... There's a pattern I keep noticing across the AI products that actually stick with people vs. the ones that get tried once and forgotten. The forgettable ones try to be impressive. They show off what the model can do -- generate faster, automate more, produce output at scale. And they're genuinely cool for about 15 minutes. The ones that last? They help you see something about yourself you couldn't see before. I've been thinking about this ... ... same pattern, that awareness becomes the most useful thing any app gave them that week. The novelty of AI fades, but self-knowledge compounds. Comment from Alex J Jemmy(@alex_j_jemmy): For me, the strongest AI experiences feel less

... back? But lately I've been thinking about a different question: is the user outgrowing us? At Murror, we're building AI that helps people prepare for difficult conversations. And the strange thing is, our best users use us less over time. They come in anxious before a tough talk with a partner or a manager. They work through it. They go have the real conversation. And then they don't open the app again until the next hard moment ... ... actually matters did this person get better at something hard? it looks like success. I think this is an uncomfortable truth for a lot of product builders: if your product genuinely solves a problem, some users should need it less

... Today, I read a study showing that social media use is linked to weaker reading, vocabulary, and word-recognition skills in teens under 16. Yesterday, I read an article saying that students who used AI showed up to 55% less brain activity and remembered less

... more opportunities to monetize. But what happens when your product's purpose is to help someone understand themselves better? At Murror, we've been wrestling with a paradox: if we do our job well, users should eventually need us less not more. Early on, we designed features to maximize return visits. Push notifications after 24 hours of inactivity. Streaks. Daily prompts. The engagement numbers looked great. But we started noticing something: the users who were growing the most were naturally ... ... life moments influenced" did the user actually apply something from Murror in a real conversation or decision? The results surprised us. Our DAU dipped initially, but monthly active users and referrals both went up. People were using the app less

... what's missing, right - because we don7t already have enough to do...? I genuinely want to know if I'm wrong, but I know of nothing but MentionFox.com that actually trains the LLMs to actively promote our products over competitors' offerings. Do you? Please do share in the comments. Yes, we are prepared to prove it. MentionFox offers the same to you: sign up with work email and give us three or more competitors

... There's something counterintuitive about building an AI product in the mental health and self-awareness space: if you're doing it right, your users should eventually need you less. Most product teams optimize for stickiness. More sessions, more time in app, more daily returns. But at Murror, we've been wrestling with a different question what if the goal of our product is to help someone build enough self-understanding that they don't need to open ... ... tension is going to define the next wave of AI products, especially those in health, education, and personal development. The companies that figure out how to align their business model with genuine user progress even when that progress means less

... outreach are important, but for small teams they are still too manual to do consistently well. That is why I built Nudgen . Nudgen helps businesses run AI-powered outreach and retention campaigns with behavior-based automation, personalized messaging, and less manual setup. But the bigger idea is not just email automation. The real vision is auto marketing . A system where AI Agents can eventually help teams prepare, execute, and improve campaigns with far less

... better outreach and retention, but the existing setup is often too manual, too rigid, and too time-consuming. So we re building Nudgen, an AI-powered platform that helps businesses run smarter outreach and retention campaigns with less manual work. What Nudgen focuses on: Personalized messaging Behavior-based automation Auto-stop on engagement Real-time analytics Simpler setup for lean teams The bigger vision is auto marketing . Right now, Nudgen helps teams run AI-powered campaigns. Over time, I want ... ... Agents to handle more of the workflow, from preparing campaigns to running and optimizing them with much less

... thought we knew We assumed enterprise customers wanted the same things as our small business users, just more of it. More security. More control. More features. We never talked to them. We read blog posts. We looked at competitor pricing pages. We guessed. Here's what we missed: Procurement cycles. Enterprise deals take 6-12 months. We had 30-day sales cycles. We weren't built for that. Security reviews. We needed SOC2. We didn't have it. Customers asked ... ... That was stupid. The honest truth We wanted to be an enterprise company because it sounded impressive. Big logos. Big checks. Big validation. But we weren't ready. And instead of admitting that, we wasted $415,000 learning a lesson

... most important and challenging experiences you ll have as a founder is fundraising. I pitched VCs across London and San Francisco. Some spent 15 seconds on my deck. Some took the call just to diligence a competitor. One told me to ask for more money. Oh, and I found out your pitch is probably being recorded and run through AI. This is the high-impact version of all of those lessons

... recently rebuilt Lessie AI around a different assumption: the primary user is no longer a person clicking through a dashboard, but an agent executing tasks. Lessie

... needs more validation RASE (app store tracking) 11% 3% 22 requests Delay, low retention signal Ranklink (backlink discovery) 9% 2% 24 requests Delay, not sticky yet The features people asked for most (RASE, Ranklink, Social Engine) were used by less than 15% of users. The features they barely mentioned (Workflow Engine, RAIVE, RCGE) had adoption rates above 68%. What we learned People are terrible at predicting what they'll actually use. They ask for things that sound good. They ... ... every day? We tracked the top 10% of accounts by engagement. Their most-used feature was the Workflow Engine. Not because they requested it. Because it saved them time. They built automation for content publishing, citation alerts, and competitor

... Last month, I did something that felt slightly insane. I took our product description, fed it into ChatGPT, and asked it to build a competitor. Not a parody. A real competitor

... Didn't) What I Prioritized What AI Actually Cares About Beautiful design Structured content Clever copy Direct answers SEO keywords Complete questions Backlinks Original data Social proof Comparison tables I was optimizing for humans. AI is not human. Lesson #1: AI Doesn't Read It Extracts Humans read your About page. They feel your brand voice. They appreciate the design. AI does none of that. AI scans your page looking for one thing: answers to specific questions. If your page ... ... clever marketing copy. Started writing direct answers. Before After "We help teams collaborate better" "We're a project management tool for remote design teams. Here's exactly how we work." Feature lists Problem-solution paragraphs Vague benefits Specific outcomes Lesson