... 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

... Products and got featured in Product Hunt's newsletters. This new launch keeps the momentum going. Once again, I had the opportunity to collaborate with the team on this new launch and wanted to share with you some lessons learned and how we approached this launch. TL,DR : Keep it simple, and keep launching. Lessons

... 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

... community I'm the solo maker behind LinkLens . What it does: You paste any long URL (article, report, PDF, tweet thread), and LinkLens gives you a one sentence summary + three key takeaways in seconds. The idea is simple read less, know more. Why I'm here: This is my very first project, and I'm not good at marketing or community building yet. Instead of letting my product sit in silence, I want to learn from you . Could you help ...

... 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

... saying "understand why that conversation left you feeling off," people actually clicked through and stayed longer. Conversions improved too, but honestly the real win was that the people who signed up were more aligned with what we actually do. Less churn, better word of mouth. I think leading with human outcomes attracts the right users, not just more users. Comment from Robert Vassov(@robert_vassov): Learned from your post Mona, thanks. I am tinkering with embedding an AI assistant into ... ... actually more honest because it forces you to answer the question your user is actually asking: "what does this do for me?" Thanks for naming that so clearly! Comment from Sai Tharun Kakirala(@sai_tharun_kakirala): The lesson

... 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

... make them care. My background is in marketing and community roles locally, globally and by business unit at adidas, then TikTok platform and family of apps at ByteDance. This article is my attempt at a practical guide through the lessons of building the startup I co-founded. Main lesson

... schema 71% 19% Cite data sources 68% 27% Part 3: What content wins AI citations? This is the question everyone asks. Here's the data: Top 5 content types by citation rate: Comparison pages (X vs Y) 3.2x average Structured FAQs 2.8x average Data-driven articles (with stats, tables, studies) 2.4x average "How to" guides (step-by-step) 1.9x average Original research 1.7x average What performs poorly: Short opinion pieces Thin content (<500 words ... ... Part 4: How each AI platform differs Not all AI answers are created equal. Here's how the major platforms compare: ChatGPT: Prefers conversational, FAQ-style content Longer, more detailed answers Often cites 5-8 sources per answer Updates less

... plays a significant role in how products appear in Google search results. What surprised us was that AI assistants like ChatGPT were rarely citing Product Hunt in product recommendations. AI assistants such as ChatGPT and Gemini rely on reviews, alternative lists, and structured product information gathered across the web. Product Hunt is strong across all three. In theory, this should make Product Hunt a natural source for AI-driven product recommendations and comparisons. In practice, it was not happening ... ... After several targeted iterations, Product Hunt shifted from near zero AI citations to consistent inclusion across multiple models. We are now rolling these changes out across Product Hunt. Product Hunt is becoming part of the AI retrieval layer. Key Lessons

... Building @Tonkotsu has taught us many lessons in product design, with both the underlying technology and user behavior shifting rapidly. One of the hardest but most critical lessons

... most things, except for the things you listed (e.g. personal finances). At least not without approval processes built in (e.g. get a push notification to approve paying a bill). Comment from Najmuzzaman Mohammad(@najmuzzaman): Trust for me is less about the category of task and more about reversibility. I will happily let an agent draft emails, reorganize files, or write code all day. But the moment an action is hard to undo, like sending money, deleting data, or posting publicly ... ... they face in their day to day. Comment from Henrik Pedersen(@henrikpedersen): @busmark_w_nika  Exactly passive vs

... builders: what are key lessons you ve learned whether technical or product or GTM from building agents? This is still such a new discipline that it would be great to share amongst this community of builders. I ll kick off with an experience that had us scratching our heads for months last year Our product, @Tonkotsu , runs a bunch of coding agents in parallel. They re built on top of Sonnet 4.5 and do coding tasks by repeatedly calling tools ... ... rounds of experimentation to fix, but the experience gave us a window into LLM behavior at the edges. Surprisingly, there are real parallels with human behavior too. If you re interested, full write-up here: https://blog.tonkotsu.ai/p/ive-f... What lessons

... building a online tool for US MBA applicants currently in Beta stage, opening it to a small group so I can shape it properly before a wider launch. The goal is simple: make the MBA application process less chaotic and more structured. What it currently helps with: Essay + MBA-specific CV workspace (AI + editor + version history) One dashboard to track deadlines & application events A personalized AI timeline so you always know the next best step If you re applying ...

... expertise. However, it also introduces several systemic problems that become more severe at scale. Answers can become outdated as technology, facts, or best practices change. Threads often drift off-topic. High-quality answers may be buried under newer but less accurate responses. Performance can degrade as pages grow larger and more complex. And critically, the usefulness of a page often depends on the behavior of a community rather than on intentional editorial structure. Qeeebo takes a different path. A Static ... ... Structured Alternative

... meet my work goals very well in the last few quarters, I now plan to approach them more systematically and not push myself too hard on work goals, as that ultimately led to problems that made my plan less sustainable. So here is my structure and list: Work goals: Edit min. 15 pre-recorded YouTube videos Remove inactive newsletter subscribers, which will be in the hundreds (this will be mentally difficult) Product launch on Product Hunt (this is a specific ... ... product, not a random product) Personal goals: Stay consistent in exercising Learn 4 languages (+ read lessons