... front page, but I got a real backlink, some early users, and honest feedback. Most importantly: it made me realize launching is just a starting pistol, not a distribution strategy. What I focused on this week: 1. Directory submissions AlternativeTo, SaaSHub, and a few others. Each one takes longer than you'd think to do properly. 2. SEO cleanup tightening title tags and meta descriptions on individual tool pages so they can rank for queries like "compress PDF online free ... ... Small UX fixes from user feedback mostly

... According to the 2025 @Stack Overflow Developer Survey (49,000+ participants), @VS Code still is the most-used dev environment, maintaining the top spot thanks to its marketplace. @Kilo Code , @Github Copilot , @Ultracite ... What are the extensions that significantly improve your coding workflow? Comment from fmerian(@fmerian): @jonabiola  what do you enjoy the most with @Github Copilot ? anything they could improve? Comment from fmerian(@fmerian): @Prettier was the MOAT. now I have a bias towards @Ultracite , simpler and faster ... ... Thanks! :) Comment from Viorel Mocanu(@viorelmocanu): I've been tweaking this setup for about 6 months now, it's getting better by the day as the agentic harness matures, but plugins really help all this work well in VSCode

... Karol, a software engineer since 2017. I just joined Product Hunt, a bit overwhelming how many apps are launched daily, though I'm looking forward to try them out. I decided to launch some of my ideas here. I mostly revolve around travel world, thus I mostly

... Product Hunt! I'm Mark, solo dev and co-founder of Moss Piglet a privacy-first public benefit company. Habit tracking is personal. Your goals, your struggles, your progress that's some of the most intimate data you can generate about yourself. So why does every habit tracker out there store it in plaintext on their servers? Metamorphic is a zero-knowledge encrypted habit tracker. Your data is encrypted in your browser before it ever leaves your device. The server ... ... built to give you that space. It's built with Elixir and Phoenix LiveView, bootstrapped with zero investors, and run as a public benefit company so the incentive is always the product, never your data. I also built MOSSLET

... Supabase just hit 100,000 GitHub Stars. They also announced 8,000,000 developers building with Supabase. S/O to @copple @antwilson and the @Supabase team Supabase isn't just among the most-starred repositories on @GitHub , [1] it's also among the most-loved products on @Product Hunt . [2] Here's a brief recap of their history here. A brief history of Supabase on Product Hunt Fun facts? They've launched on Product Hunt 16 times They first launched ... ... alpha version, Supabase has kept launching since then, and it paid off. Four years later, they went GA, ranked #1 Product of the Day / Week / Month / Year (runner-up). Just launch. References [1]: Supabase in the Top 100 most-starred repositories on GitHub [2]: Supabase on Product Hunt Comment from fmerian(@fmerian): What would you replicate from their playbook? @rors , their Head of Growth and now COO, shared some insights in this AMA session. [1] TL,DR: It starts ... ... tables, and configure settings without writing raw SQL every time is a genuine productivity unlock. Supabase removes a barrier that most database tools don't even acknowledge exists. Comment from Aiden Jenkins(@aiden_jenkins): How much of this brand versus product? Comment from Kennedy Sullivan(@kennedy_sullivan): What role did feedback loops play here? Comment from Christian West(@christian_west1): What made them "loved" not just "used" Comment from fmerian(@fmerian): S/O to @copple @antwilson and team! Comment from fmerian ...

... 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 assumptions are incredibly expensive. Building for the customer you want instead of the customer you have can drain months of focus and budget. What really stands out is the contrast between 47 real requests for API limits vs

... this guide isn't just a quick summary. We're going to go deep. We'll explain exactly what Snaply is, how it works, why privacy matters so much for dictation tools, how it stacks up against every major alternative, who it's best for, and how to get the most out of it. Whether you're a solo professional, part of a team, or evaluating dictation software for an enterprise deployment, this is the most complete breakdown ... ... find. Table of Contents Why AI Dictation Matters in 2026 What Makes a Truly Great Dictation App What Is Snaply? Snaply's Core Features: In Depth Why Privacy Is Non-Negotiable for Dictation Snaply vs

... months ago, we ran an experiment with our own data. At Rankfender, we tracked 5 of our own competitors across 8 AI systems. We log their share of voice, citation velocity, content gaps, platform variance. Months of raw numbers sitting in a dashboard. I pulled 6 months of data and fed it into Claude. One question: "Based on this, who is most likely to overtake us in the next 6 months? Show your work. Use the data. Don't summarize ... ... Give me the numbers." The answer changed how I think about competition. The dataset: 5 competitors

... didn't actually stick with. The signals we track now Before building anything, we now look at three things: 1. Usage patterns What do power users do 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 tracking. That told us more than any sales call. 2. Support ticket clusters What ... ... correlation with retention. That told us where to invest. What we decided Building now: Workflow Engine v2 Conditional logic, scheduled runs, webhook triggers. 78% adoption, 64% retention. This is what keeps people around. RAIVE enhancements Real-time citation alerts, competitor

... Most AI visibility tools show you a dashboard. They tell you where you're invisible. They show you competitors winning. Then what? You stare at the screen, knowing there's a gap, but no idea how to close it. RAIVE 2.2 (Rankfender AI Visibility Engine) fixes that. It's not just a tracking engine. It's a prescription engine. It analyzes where competitors

... just exposure, but the degree to which AI treats your brand as a credible source . Share of Voice (SOV): Represents your brand s voice share within AI narratives the probability that AI mentions your brand rather than a competitor when answering a topic-related question. Sentiment: Captures the emotional tone of AI mentions positive, neutral, or negative showing how AI portrays your brand. Together, these four metrics answer the two most fundamental GEO questions: How frequently does AI mention ... ... brand? and In what tone and context is the brand represented? 1.2 Trends and Competitor

... found errors. Average cost: 28k." 120k impressions, 400+ new signups. Free resource: Post as a human. Share what you're learning in real time. People don't follow companies. They follow people. 5. Multi-Language: The Hidden Channel Most competitors track AI in English only. We built for 120+ languages from day one. Why: A German user asks in German. AI answers in German. If you're not in German content, you don't exist. One example: A US SaaS ... ... with perfect German translation had zero German AI mentions. A local competitor

... project management tool for remote design teams. Here's exactly how we work." Feature lists Problem-solution paragraphs Vague benefits Specific outcomes Lesson #2: AI Loves Comparisons Even If You Lose This one hurt. I avoided mentioning competitors. Why give them free airtime? Wrong move. Every AI answer about your category includes comparisons. Always. If you don't provide the comparison, AI finds someone who does and that someone might be your competitor

... everyone, figured I'd finally say hello properly after lurking for a while. I'm Andrii, a freelance full-stack developer based in Lviv, Ukraine. I work mostly with React, Next.js, TypeScript, and Rails - usually helping product teams ship things that have gotten stuck or need a senior hand across both frontend and backend without a ton of overhead. I've been in the industry about 13 years now. These days I'm spending a lot of my free time ... ... building small tools, writing about LLMs, and figuring out where the practical line is between "this actually helps" and "this is a cool demo." I also write occasionally at andriifurmanets.com/blogs if you're into that kind of thing - mostly

... answer about your category includes comparisons. Always. When someone asks "best CRM for agencies," AI doesn't list one tool. It lists 3-5. And it compares them. The data: Content Type Citation Rate Pages with comparison tables +470% vs. average Pages that mention competitors

... study found 90% of founders are not open with their investors about what is actually stressing them out. And 81% aren't honest about it with anyone in their life. So where does it all go? Mostly just inward. I'm building something in the loneliness and social connection space so I think about this a lot. But I'm also a founder who feels it personally. The most honest conversations I've had about the hard stuff happened with ... ... gets the specific kind of weight you're carrying. Curious how other people here have found their version of "safe to be honest" in this journey. Did it take time to find that person or group? Or do you mostly

... Average monthly retainer for AI visibility audits 1,200 2,500 Average monthly retainer for ongoing AI visibility + content 2,500 5,000 First-mover advantage is real. The agencies offering this now are locking in clients before competitors even know the category exists. Service Model #1: The One-Time AI Visibility Audit What it is: A comprehensive audit showing where the client appears (or doesn't) across ChatGPT, Google SGE, Perplexity, and Gemini. Deliverables: 20 50 keywords analyzed Screenshots ... ... actual AI answers Competitor

... talk about your process in building it! Comment from Rise-n-Shine Software(@risenshine): I'm building lightPDF , a high-performance, minimalist document viewer for the desktop. The main goal is to create a fast, lightweight, and privacy-respecting alternative to bloated applications like Adobe Acrobat. It's an "offline-first" app, meaning it processes all your files locally on your machine without sending them to the cloud. Here s a breakdown of the technology stack I'm using ... ... also an excuse to really try out vibe coding to the extreme. My goal was to not write a single line of code myself. Can't say I got that extreme, but pretty close. Stack (picked mostly

... Azhar, and I built Notetube over the last 5 months. The problem: I taught myself programming mostly through YouTube. It's an incredible resource, but there's no way to organize what you watch, test what you've learned, or track your progress. Your learning is scattered across playlists, history, and forgotten bookmarks. And that s mostly

... 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 ... ... many tools which systematically run prompts against several AI models daily, and measure visibility of a product within prompts. We're using a new tool called @Gauge . We chose because a) their citation tracking is more useful than the alternatives

... looking for feedback from people who do a lot of their work on mobile . If you mostly work from your phone: What actions do you perform most often? What does your most common flow look like? What do you do frequently vs

... answer was very direct: It s simple and very hard. His method was probability. He estimated his success rate to be around 1 in 1000. So every month, he deliberately puts himself in front of around 200 attractive women mostly through social media. Then he acts fast and categorizes them:no chance,small chance,good chance Anyone with no chance is immediately dropped. The rest stay in the pool. The key rule: He always keeps about 200 active interactions going ... ... talk to that many people at once, he failed completely. But the moment he returned to his original rule the uncomfortable, exhausting one the results came back. So what s the lesson? For me, it s this: Life is mostly

... mostly get this questions "What is Slashit App" Let me explain today, Slashit App is a typing assistant tool built for people who type all day same thing. That mean if you are typing similar thing daily then you should use "Slashit App", if you rewrite sentence using chatgpt or grammerly but not saving your time and not match with your tone then you should use "Slashit App Magical Feature", if you copy same thing again & again and switch ...

... that speed advantage disappears. The real challenge wasn t generating images it was reducing decision fatigue and context switching . Framing the Product: AI as an Assistant, Not the Star One early decision shaped everything: the AI model should stay mostly invisible. Instead of asking How advanced can the AI be? , the guiding question became: How quickly can a user go from idea to usable image? This meant: Fewer upfront choices Clear defaults Fast feedback loops Lightweight customization instead of deep ... ... made it easier to iterate on UX without reworking core logic and kept the product adaptable as models evolve. What Worked Well Treating speed as a feature, not a metric Designing defaults carefully Making iteration feel cheap Keeping AI mostly

... what we build has to create real value for people, right? What s even more interesting is that research shows the most adopted agents right now are coding agents and web-scraping agents, and their target audience is mostly developers and creators. Yep, it s pretty much the same group that s building them. The ecosystem is basically self-producing and self-consuming. And the truth is, most normal users don t even know the difference between an Agent ... ... still alien to them. After digging deep, I realized low adoption probably isn t because agents suck. I ve tried a lot of open-source agents that are actually useful. The real issue is understanding, delivery, and the reliability vs