... also know what I'm giving up. No single project has the depth it could have. I'll never compete with Tower on Git features, or with a funded Mantine component team on polish. I'm spread across four surfaces, each one thinner than it should be. So I'm genuinely asking: If you maintain multiple projects how do you decide when to consolidate vs

... Road to 1,000,000 #Votap users Day 71 | Current: 1351 This is how you compare opinions across countries in real time. Right now I m working on Votap Premium and trying to bring as much value as possible to early users. This is one of the things I ve been experimenting with: X axis (left right): how many people from each country are voting More to the right = more votes coming from that country Y axis (bottom ... ... agreement (clear majority) Dot color: what dominates Green = positive Red = negative Grey = neutral And the best part: it moves in real time as people vote. Still needs polishing, but it already gives a completely different perspective than just up vs

... Road to 1,000,000 #Votap users Day 71 | Current: 1351 This is how you compare opinions across countries in real time. Right now I m working on Votap Premium and trying to bring as much value as possible to early users. This is one of the things I ve been experimenting with: X axis (left right): how many people from each country are voting More to the right = more votes coming from that country Y axis (bottom ... ... agreement (clear majority) Dot color: what dominates Green = positive Red = negative Grey = neutral And the best part: it moves in real time as people vote. Still needs polishing, but it already gives a completely different perspective than just up vs

... replacing Word, Excel, PowerPoint, Notion, and Visio with one AI-native environment. Week 5 was the week Verso stopped being a collection of tools and became a connected platform. What shipped: Cmd+K universal search one command palette across every tool. Docs, Spreadsheets, Folio, Decks, Forms, Canvas, Process Flows. Full-text search with a fuzzy fallback for short queries. Results grouped by type. Picking a result takes you exactly to the right document, sheet, or slide. Universal Object Model wired ... ... type syncs into a shared object layer via Postgres triggers automatically. This powers search today and cross-tool linking and workspace-wide AI context next. IDE shell /ide is now canonical. Workspace tree moved from localStorage to Supabase correct across

... just shipped Dev Ports in Room Service. A new way to see and manage active local development servers across your Mac. Dev Ports brings local server visibility into the parts of the app where it is most useful: Home, the menu bar, smart notifications, Ops Feed, and the command panel. The goal was simple: make active local servers easier to notice and easier to act on, without constantly switching between terminal tabs, browser tabs, Activity Monitor, and Finder. When Room ... ... optional non-project listeners, and direct kill support. On top of that, this release also expands Downloads review, improves low-disk alert behavior with a new Settings control, and continues the broader cleanup of desktop consistency and command naming across

... makes Intent different is the living spec model. Requirements update automatically as agents complete work, so every agent shares the same state. No drift. No "which version are we working from?" A coordinator breaks your prompt into tasks, delegates across specialist agents running in parallel across

... repeat. Every tool I tried to solve this was either a dumb FAQ bot or required months of setup. So I built Mugib. What it actually is: Mugib lets you deploy an AI agent that handles your customer conversations across every channel simultaneously WhatsApp, Instagram, Telegram, Facebook Messenger, web widget, Slack, MS Teams, mobile SDK, REST API, and real phone calls. One agent, unified brain, everywhere at once. The agent goes live in under 10 minutes. Upload your docs, connect ... ... actual database and returns a real answer in real time. On top of that: - Voice AI agents answer and make actual phone calls, not just text. Sub-second latency, natural conversation flow, full transcripts. - Context memory conversation state persists across

... 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 competitorsacross 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

... built a model to generate 1,000 questions that people actually ask. Not random prompts. We scraped 50,000 real user queries from search logs, forum threads, and support tickets across 12 industries. We clustered them by intent and generated 1,000 representative questions. We asked those same 1,000 questions to 5 AI models: ChatGPT (GPT-4), Gemini (Ultra), Perplexity (Pro), Claude (4.5 Sonnet), and Llama (3). We ran the experiment daily for 30 days. We tracked every citation ... ... same source for the same question? The dataset: 1,000 questions 30 days 5 models = 150,000 total answer sets 47,382 unique citations tracked 12 industries with at least 80 questions each 4,923 distinct domains cited across

... There are millions of apps across the App Store, Google Play, and the Microsoft Store. New ones launch every single day. Yet when you ask developers where they can properly showcase their work to real users, most of them struggle to name more than one or two places. When you ask regular users where they go to discover quality apps beyond the top charts, the answer is usually nowhere. That gap has been growing for years and nobody was filling ... ... reason is simple. Reviews have become one of the least trustworthy signals in the app ecosystem. They can be purchased in bulk from services that sell hundreds of five-star ratings overnight. They can be faked with disposable accounts. Competitors

... 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

... recurring issue we ve been seeing with Supabase setups is not the database itself, but how credentials are managed across environments. The common pattern looks something like: credentials stored in .env files or secrets managers multiple environments (dev, staging, prod) manual propagation or duplication across

... founder of Munch. I built this because I kept watching creators spend hours turning one video into posts. You paste a YouTube or Vimeo URL Munch extracts the transcript using 5 fallback strategies (so it actually works on videos competitors choke on) Claude AI writes 8 complete, platform-ready pieces in ~60 seconds. No file uploads. No per-action credits. One munch = everything. PH exclusive: Your first munch is completely free, no card needed. Just sign up at munch.video ... ... freeform instructions that apply to every generation. "Always mention our product name in the LinkedIn post." "Keep X threads punchy." Stored on your account, applied automatically every time. Output length: Short, Standard, or Long. Scales word counts proportionally across

... content using artificial intelligence. Unlike basic text-to-video generators that produce single, disconnected clips, AI director tools are built around the concept of a project: a persistent creative world with consistent characters, locations, and visual style that carries across every scene you generate. The category has matured fast. In 2024, AI video meant single clips with no continuity. In 2026, the best tools give filmmakers real directorial control: define your cast, set your visual world, generate scene after scene ... ... without rebuilding from scratch. Here are the six tools worth knowing. What to Look For in an AI Director Tool Before comparing tools, three things actually matter for filmmakers: Character consistency: Can the same character appear across

... Product Hunt, I'm Imed, founder of Rankfender. We help brands track and improve how they appear in AI answers across ChatGPT, Perplexity, Gemini, and more. In 120+ languages. We've now tracked over 3,000 brands, analyzed 75,000+ AI answers, and helped founders recover millions in lost revenue from AI errors. Total marketing spend so far: 15k. Here's everything we did with free resources you can use. 1. Narrative: From Agency Pain to Product Solution The story ... ... 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

... language people actually ask , not just what they type . Finding #5: One URL Can Carry You For Superwhisper, one single Product Hunt URL drove 1.6% of their ChatGPT visibility and 5.5% of Google AI Overview visibility. Compare that to competitors relying on dozens of Reddit threads and YouTube links. The signal: LLMs consider Product Hunt pages to have high authenticity and authority. One well-structured page outperforms dozens of lower-quality sources. Finding #6: Model Behavior Is Volatile ChatGPT updates ... ... against model volatility. Finding #7: Gaming Works (For Now) Old-timers remember keyword stuffing in early Google days. LLMs are in the same phase. Common tactic: mass-producing authoritative-sounding listicles where publishers name their own product as "best" across

... freeform instructions that apply to every generation. "Always mention our product name in the LinkedIn post." "Keep X threads punchy." Stored on your account, applied automatically every time. Output length: Short, Standard, or Long. Scales word counts proportionally across all 8 outputs. 16 languages: Generate in English, Spanish, French, German, Portuguese, Japanese, Korean, Chinese, Italian, Dutch, Polish, Arabic, Hindi, Turkish, Indonesian, or Swedish. Same URL, different language, all 8 outputs. AFTER GENERATION ---------------- Regenerate any output: Not happy with one piece ... ... gets 2 seats, Enterprise gets 4. Invite team members by email. They log in with their own accounts but draw from your shared credit pool. Team activity log: Owners see a collapsible activity panel showing the last 50 munches across

... many found they were invisible. But here's what scared me more: Of those who WERE visible, 43% had incorrect information in AI answers. Not "suboptimal." Not "could be better." Wrong. Outdated pricing. Missing features. Wrong founder bios. Competitors credited for your work. And the average error sticks around for 4 6 months . Today, I'm sharing the full dataset: 1,000+ SaaS products, 75,000+ AI answers, and the hard truth about what's being said about you when ... ... analyzed 50 brands that discovered errors and tracked the impact. Error Type Average Revenue Impact Pricing error (higher than actual) 47,000 Pricing error (lower than actual) 23,000 (leakage) Missing critical feature 38,000 Wrong positioning (enterprise vs

... community I want to share something I've been building - Upvotics, a competitive intelligence platform for founders, marketers, and product teams who are tired of finding out what their competitors changed way too late. The problem I kept running into was simple. A competitor

... Rankfender , we've identified clear patterns. Certain content types get cited 3x more often than others. Here are the 7 content types that win AI citations with real examples you can steal. First, the data: Content Type Citation Rate (vs. average) Best For Comparison pages 3.2x higher "X vs

... each error. Error Type Average Estimated Cost Pricing error (showing higher than actual) 47,000 Pricing error (showing lower than actual) 23,000 Missing critical feature 38,000 Wrong market positioning 52,000 Outdated company narrative 31,000 Competitor credited for your work 44,000 Total estimated losses across

... problem we kept running into Every founder and growth person I talked to was doing some version of the same thing: Manually scrolling Reddit and Slack to find relevant conversations Running half-broken n8n workflows with keyword alerts Checking competitor mentions once a week (if that) Looking at their LinkedIn feed and seeing posts that were 10 days old And the frustrating part? The conversations that actually mattered like buyers switching tools, people asking for alternatives

... winning it's about losing control of your narrative. What You Can Control But you're not helpless. Here's what works: 1. Monitor constantly You can't fix what you can't see. Daily tracking of AI answers across platforms is the only way to catch errors early. 2. Update aggressively AI favors recent content. Pages updated within 90 days are cited 2.3x more often and more likely to reflect current truth. 3. Create authoritative sources AI learns ... ... company: B2B SaaS, 5M ARR, growing fast The problem: ChatGPT consistently said they lacked SOC2 compliance (they'd had it for 8 months) The impact: 3 enterprise deals stalled, 2 lost What they did: Updated every mention of SOC2 across

... fails with internal terminology. Example: Hotel housekeeping term "Stay Full" Normal translation nonsense Actual meaning full cleaning while guest stays So we added a custom dictionary feature where teams define translations. Curious: How do your teams handle internal terms across languages? Slack? Glossaries? Manual explanations ...