Labviz.co – Data Visuals for Pharmacists& Labs(20K Views in 72 Hours)

Hacker News

Hey HN! I’m excited to share LabViz.co, a platform designed specifically for laboratories and pharmacists to visualize their reports and data. We recently crossed 20K views in just 72 hours, and I’m looking forward to feedback from the HN community! What LabViz.co offers: Report Visualizations: Transform lab data and pharmaceutical reports into clear, interactive visuals. Efficiency for Labs & Pharmacists: Streamline analysis of critical metrics like sample results, medication compliance, and patient data trends. Tailored for the Industry: Built with the specific needs of laboratories and pharmacies in mind to make report analysis intuitive and actionable. The goal is to make it easier for labs and pharmacists to explore, understand, and communicate data without needing extensive technical skills. I’d love any feedback or ideas on how to improve LabViz.co or any features you think would be useful! Check it out: LabViz.co Thanks!

Visit product View on Hacker NewsOct 13, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, visual · Missing: agents, macos, agent

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AppSumoStrong fit for a featured deal · Strong signals: platform, intuitive · Missing: plus, reviews, host

54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: visualize · Missing: mobile apps, ios, personal

53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: excited, ide, io · Missing: https docs, just released, exist

37%37% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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