... www.producthunt.com/products/dat... Last week I asked here where you go to find out why a number moved. The answers were almost all the same: the CRM, Slack, the support desk, Linear, a doc somewhere. Anywhere except the dashboard. That's what we launched today. With MCP Connectors, you connect the tools your team already uses, and Genie, our AI Analyst, uses them to answer your questions. Ask why signups dropped 18%, and it checks Linear, finds the signup form that ... ... shipped Tuesday and breaks on Safari, and ties it to the drop. You get the number

... Genie, our AI Analyst, can already tell you what changed in your numbers. It's never been able to tell you why, because the why almost never lives in Databox. Revenue drops 8%, and the real reason is a stalled deal in HubSpot, a pricing objection buried in a Slack thread, or a support ticket nobody flagged. Right now, finding that out means leaving your dashboard, opening three other tools, and piecing it together yourself. Every time. Next week ... ... launching MCP Connectors. Connect your CRM, Slack, support desk, or any MCP server, and your AI Analyst gets that same context, right inside the conversation. Ask why a number

... launched Nimply (AI agents that draft social media replies and posts; you approve before anything goes out) on 23 Sep. Here is what the 24 hours actually looked like, with numbers, because most launch retros only show the wins. Numbers

... trusted a number for about three weeks and it turned out to be two different numbers

... survive the trip to a shipped release. Across four app marketplaces: a sharp rise in new apps, no rise in total usage. Most of the AI-productivity argument you've read this year is two camps shouting single numbers at each other. There's a paper out that makes both camps right and is more useful than either, and the revised version went up this month, so it's worth twenty minutes before you write your Q4 plan ... ... NBER working paper 35275, Demirer, Musolff and Yang - "Writing Code vs

... everyone Small release, but it's the kind I find interesting: nobody broke this one. It broke by itself, the day macOS 27 shipped. Netfox tells you which operating system a device runs by reading a number the device advertises about itself. That number

... tell, honest work by people who know more about search than I do. All three of those things are true at once. The problem isn't dishonesty, it's that there is no independent audit of the number, and the 166 sites are enterprise properties in SaaS, finance, legal, health and publishing. Your six-person app is not in that sample. The one study nobody is selling. There is exactly one large dataset here from an organisation with no product ... ... Research Center's analysis, published July 2025, of 900 US adults who agreed to have their browsing metered for the month of March 2025. It covers 68,879 Google searches, of which 12,593 produced an AI summary. Three numbers

... Apple's own support pages. Apple In-App Purchase: 26%, or 15% if you're in the Small Business Program, the Mini Apps or Video Partner Programs, or on an auto-renewable subscription after the subscriber's first year. Alternative payment processing inside your app: 20%, or 10% for those same groups. Out-of-app offers with an actionable link: a 15% store services commission, or 10% for those groups, on anything bought within 7 days of the tap. Apps ... ... distributed outside the App Store entirely - an alternative

... about to put my install count on my landing page. 2,411, which felt like a real number for something nine days old. Then I split it by version and it fell apart. 0.1.0 got 172 downloads last week. So did 0.2.0, and 1.6.1, and every other version I have ever published, all sitting between 150 and 200. Nobody deliberately installs the first release of anything. A real user base concentrates almost entirely on latest. A flat line across every ... ... meant to be used never touches a single thing I can count. It runs locally. It never loads my site and never opens my repo. Their usage is indistinguishable from a scanner in the only number

... also ran ads with a very small budget and thought it will easily be gained within a few days, and I was very wrong here. AI also does the same thing. If you ask it to expect some numbers based on what people experience for your saas when you distribute with some method X, it will show some very aspirational numbers

... have the problem the tool solves my potential users. If both groups point out the same issue or suggest the same improvement, it almost always becomes a top priority in the roadmap. What makes feedback relevant to you? The number of testers? The mix of respondents? Or the way the feedback is collected? Finding: Developers were able to give me detailed and more accurate feedback than users itself. Developers spotted many things, recommended things while user said: It's okay ... ... cases, but actual target users show you real behavior and willingness to use/pay. I find that watching users interact during a call (qualitative) gives much deeper insights than just written surveys! Comment from Arnold Oshenye(@oshylabs): For me the number

... Three of today's launches ship a confident number at the user. How much of this page is AI. How far this repo sits from production ready. How many hours of debt that represents. Different products, same move. The problem isn't accuracy. It's that nobody can tell you when it's wrong. A linter points at line 40 and you either agree or you don't. A 0-100 score for something unmeasurable can't be argued with ... ... trust it for no reason at all. That's the failure I keep going on about, output that's plausible enough that nobody complains, wearing a nicer outfit. The test I've started using before shipping a number

... everyone Every Wi-Fi tool shows you numbers. Signal, noise, a channel number

... actually put on screen? Here's where I've landed so far, and I'm not confident about any of it: A bare percentage gets rounded to a verdict in the reader's head, every time. Pairing the number with the specific sentences that drove it helped more than the number

... nobody ever asked you what your heaviest cohort costs. It held for twenty years because the marginal cost of serving a software user really was approximately zero. It doesn't hold for what you're building now, and the numbers on that are getting hard to argue with. ICONIQ Capital's State of AI snapshot in January surveyed around 300 executives at companies building AI products. Average AI product gross margin: 52%, against the 70 80% that a mature SaaS ... ... COGS. Your best cohort by retention can be your worst cohort by margin, and this is the part that gets people you almost certainly cannot see it. Your analytics tool reports engagement per user. Your model provider reports one number

... first update post I turned down a suggestion. Someone with nineteen years in banking software asked for control totals, checking that the numbers on a document add up, and I said no. Getting there meant giving fields types, then converting formats, then summing, then comparing, and I thought all four of those belonged to a layer above this one. I have now built the first two, and I want to explain what moved. What moved was not the argument ... ... proposals came back describing completely different businesses and they all ended in the same place. Extract the fields, then do something with the result, and the something always began by turning a printed string into a number

... everyone! I m an indie developer, and NUMIO is an app I built and published myself. I d love to introduce you to something I ve been working on: NUMIO. NUMIO is a number guessing game built around logic, strategy, and a little bit of competition. The goal is simple: find the hidden number

... read a lot of build-in-public updates tonight and noticed something in almost all of them. The headline number was always one that can only go up. Registered users. Total downloads. Signups since launch. Cumulative anything. None of those can decrease, which means none of them can ever deliver bad news, which is exactly why they get picked. They are the numbers

... this over six months of nights and weekends: wattpair.com answers "will this battery power station actually run my fridge / CPAP / air conditioner - and for how long." The twist: I have no lab, so the engine only uses manufacturer-published numbers, and when a number

... launch went live at 12:01 PT today. As I write this it is in single digits, nowhere near the featured cutoff. I would rather post the number than a vague "lots of learnings" thread, so here it is, plus what I actually think went wrong and what I am doing about it in the open. What was ready: product worked, landing page up, payments live, guidelines followed, assets done. Every readiness item on a standard launch checklist was ticked ... ... from. I had neither. I had a product. What I am doing for the next four weeks, in public. Second product, same company, launching Aug 25. Whatever I do between now and then I will report back here with numbers

... Bundle them and a single guess has to be right about two unrelated things, and no value is. Which is where your question resolves for me. Who it affects should not set the number. It should select who answers, and then the number

... borrow from, everything has to be earned from scratch, on both fronts at once. Where it stood six months later We track this with three sources running in parallel (Search Console, SEMrush, and our own analytics), so these numbers are measured, not estimated: On the SEO side: +330% organic traffic , from a near-zero starting point 224 organic keywords ranking (+81%) 363 referring domains , 2,500 backlinks built from nothing Authority Score of 15 On the GEO side: 67 pages ... ... cited by AI answer engines Split across four platforms: ChatGPT (48), Gemini (15), Google AI Mode (9), Google AI Overviews (2) Neither number

... instead of just going off gut feeling every time my usage bar turns orange. Pulled together what I could find from benchmarks and my own week of switching between tools, and a few things genuinely surprised me. The efficiency numbers aren't close, and not in the direction I expected. One widely cited test had Claude Code completing an identical multi-file feature task using around 33,000 tokens with zero errors , while Cursor Agent burned roughly 188,000 tokens ... ... agent or are still task-switching between them depending on the job. Comment from Jernej Jan Ko ica(@jernej_jan_kocica): the denominator is the problem. cost per feature moves because no two features are the same size, so the number

... www.producthunt.com/products/dat... It's the end of the month. Your client needs a performance report, so you open five tools, screenshot the numbers, paste them into Google Slides, and spend two hours making it look like you didn't just paste screenshots into Google Slides. We just made that whole process unnecessary. Artifacts by Databox lets you turn any Genie conversation into a polished, shareable document. Ask for a report, a slide deck, or even an interactive calculator, and Genie ... ... builds it on the spot, pulling live numbers

... time. Shows a value the whole time. Dim light / movement: error grows to roughly 2.5 to 2.9 BPM. It follows the trend and only lags the fastest spikes. Dark: it mostly goes quiet, showing a number about 1 time in 10. I'd rather show nothing than something wrong. To be clear GlowPulse gives wellness estimates, not medical measurements. Polar H10 is named only as a neutral reference, no endorsement implied. I published the full method, the per-session numbers