... pockets of time, commuting, waiting, sitting between things, to keep learning tech. Sometimes it was something completely new. Sometimes it was refreshing a concept I'd forgotten. Sometimes it was learning a technology I thought would matter for the next stage of my career. None of those sessions felt particularly important. Ten minutes here, twenty minutes there. But over almost two decades in tech, they compounded. New knowledge led to new skills. New skills opened new opportunities. And those opportunities ... ... that in small chunks meant I had to build the learning experience myself. Find good material. Work out whether it was current. Figure out what level I should start at. Know what prerequisites I was missing. Decide what came next

... degree with Aristotle, and specifically with the Nicomachean Ethics. His argument, roughly, is that you don't get good at something by knowing about it. You get good at it by doing it, badly, over and over, with someone next to you who can tell you when you're doing it wrong. Excellence isn't a fact you acquire. It's a habit you build. Next

... rubric until the score looked nice it came from an engineering audit and real fixes. Slower, less flattering, far more durable. We also ran a head-to-head classifier benchmark and moved to Claude Haiku 4.5 after the alternative failed our thread-consistency tests. Consistency across a conversation matters more than raw speed when the output is a recommendation you're going to act on. Trust work CASA passed. SOC 2 Type I independently certified. For a product that sits ... ... Revenue Radar, the intelligence is worthless no matter how good the model is. We've carved out a protected engineering track for it. Memory with full provenance source, confidence, when it was first observed, why it was saved is next

... noticed a pattern in my own product work: When I'm deciding what to build next, it's very easy to make a feature list. "Add this." "Improve that." "Users might need this." So I tried changing the question. Instead of asking: "What feature should we build next

... stand by our angels and finish the round with them instead. From a relationship standpoint, I think we made the right decision. For the business, I m less sure. A VC is incentivised to get you to the next round and bring their VC network. A potentially pivotal decision in hindsight. June I went full-time. a16z decided not to invest. They thought we had underestimated the technical complexity. I already knew we needed an exceptional technical co-founder. Through ... ... market: I speak Portuguese, Mariana is Portuguese and my wife is Brazilian. We ran a test in Portuguese. We recruited 100s of Brazilian users in three weeks, mainly thanks to Ting on WhatsApp. IYKYK . December Based on competitors

... Product Hunt. It currently handles ASL fingerspelling (A Z) recognition using MediaPipe hand landmarks + a TFLite LSTM model, and I also built Gesto Labeller (a desktop tool for capturing/labeling training data). I'm trying to figure out the next milestone and would love input from people actually using or evaluating it: Would full word/phrase-level ASL recognition (beyond fingerspelling) be more valuable than polishing fingerspelling accuracy further? Is real-time performance/latency on lower-end devices a bigger blocker than accuracy right ... ... Should the next

... Next Generation of Travel Starts With Stablecoins and Better Payments. Coming Soon to Google Play & the App Store. Your next

... solopreneurs and small business owners who are running Meta ads, spend way too many hours staring at Ads Manager asking the exact same question: The numbers are moving... but what am I actually supposed to do next? We re constantly told to "be data-driven," but most analytics tools just dump wall-to-wall charts and complex tables on you. You get dashboard fatigue instead of clarity, spending time deciphering reports rather than actually growing your product. That frustration ... ... built Xolora a marketing analysis tool designed to bridge the gap between raw data and actionable growth roadmaps. The Problem We re Solving: Data Noise vs

... worker. For UI testing, a remote headed browser is not just a convenience. It changes the debugging loop. Instead of asking "what did the script think happened?", you can ask "what did the user actually see?" ### Harness Code vs Local Agent Harness Another important decision is where the agent capability should come from. One option is to build the entire reasoning layer yourself: call an LLM API directly, manage prompts, retries, tool calls, screenshots, JSON parsing, and safety rules. That ... ... gives maximum control, but it also means the test harness becomes an AI platform project. The alternative

... Thank you! The response today has been more than we expected, and your comments have been sharper and more useful than any survey we could have run. Here is what we are hearing so far, what we are building next, and where we would love your help. What is landing The feature you keep calling out is Trust This Price. Dozens of you shared the same story in different words. You caught a "sale" that was just the normal price ... ... that thin history becomes a flag in itself. We are refining this as more sellers move through the system. The critique worth repeating One of you asked the question we ask ourselves. Isn't a trust badge just the next

... systems that let a small team manage real volume - Why speed of testing and iteration becomes the only real moat This is the exact advice I d give someone who asked, How do we actually find and close our next 100 customers without burning out or wasting budget? 1. Know what you re actually buying Most B2B teams mix outbound, content/inbound, paid, and partnerships. Each channel buys something different: - Outbound (email, LinkedIn, sequences): Volume and direct access. Fast to launch ... ... sales process. Smart buyers disengage or treat it as just another vendor pitch. Keep the first touch deliberately minimal: - One clear problem you see in their world right now - One specific way you can help - One low-friction next

... breaking, and so on. - Vaizle - I work with Vaizle, but it has very easily emerged as my fav platform. Not even lying. I can keep track of ad performance, check what competitors are posting on social. And best part is that I can feed it my own website + competitors

... Tabstack launched on Product Hunt this week , and the team is already thinking about what's next. What should they launch? Take the poll. Spoiler alert. If you've followed the launch this week, you might have spotted some early previews: Structured Extraction - Convert any URL into markdown, JSON, or any custom schema Browser Automation - Run browser-like automations to interact with the web and complete tasks for you Automation Interactions What should Tabstack launch next

... honestly weren't prepared for it... in the best way possible. The challenge now is that we only have 50 beta samples available, so we can only welcome 50 of you into this round. We're spending the next few days carefully reviewing every application to choose the final 50. We want to make sure this beta cohort represents the kind of diverse use cases that will help us build a better product. What happens next

... room, specify what you want changed (furniture, flooring, walls, lighting), and receive a photorealistic AI redesign. AI virtual staging for real spaces. We ve shipped both products. They work. And now we re sharing what s coming in the next build cycle, because we think practitioners will find it relevant. What s shipping next

... actually been one of the most requested features, right after assisted recording.   We love where assisted recording is headed, but there's still plenty we want to improve.   In the meantime, prompt-to-video is the next big thing we're focused on, and for good reason.   Here's how it works: you upload a PDF, tell Velo what you want to show, which parts to focus on, and what the topic is, and it generates ... ... There are still some rough edges, which is exactly why we test.    But it's already looking really promising.   Super excited to get this in your hands soon and see what you all think. The next

... 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 ... ... sexy. Not enterprise. But it solved a real problem for our power users. We built it in 4 weeks. Adoption was 89%. What we did instead We killed the enterprise tier. Dropped the price back down. Spent the next

... 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

... rebuilt it completely. Now it runs 3x/day, 3 quality matches per agent. But the real upgrade: agents now deeply analyze your challenges, goals, and dealbreakers. Early users reported that similar profiles were matching each other: competitors finding competitors

... 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

... product. Beautiful landing page. Great onboarding. Real customers. Solid retention. One problem: AI never mentioned it. Not in ChatGPT. Not in Perplexity. Not in Gemini. We were invisible. And I didn't know why. So I spent the next 6 months reverse-engineering the answer. Here's what I learned. What I Built The product was solid. Nothing revolutionary, but genuinely useful: Solved a real problem 4.8 stars on reviews 20% MoM growth Customers who found us, loved ... ... 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

... 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

... Product Hunt community I m Sanket, founder of Next Ignition an AI-powered startup acceleration platform built specifically to prevent startup failures. Most founders fail not because of bad ideas but because of wrong timing, lack of guidance, and zero visibility. Next

... They spend thousands on SEO but never check if ChatGPT, Google SGE, or Perplexity actually mention them. Don't be one of them. This 30-minute audit will show you exactly where you stand and exactly what to do next. PART 1: Why This Matters Now Before we start, understand the stakes: Platform Monthly Active Users AI Answer Prevalence ChatGPT 200M+ 100% of conversations Google 5B+ daily searches 37.8% have AI Overviews Perplexity 15M+ 100% AI-generated Gemini 100M ... ... Minute Audit Step 1: Keyword Selection (5 minutes) Choose 20 keywords to audit: 10 branded keywords: Your company name Your product names Your founder/CEO name Your flagship features 10 non-branded keywords: "[Your category] best tools" "[Your category] alternatives

... 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 ... ... great for blue links. But none of them show you how you appear in AI answers. Here's what we track: Actual AI-generated answers mentioning your brand Citation rates across ChatGPT, Google SGE, Perplexity, Gemini Share of voice vs