Automated Reporting for R&D teams

Hacker News

Hi HN! Marple here, we wanted to showcase a new feature on [ http://marpledata.com](http://marpledata.com/) . We do telemetry visualisations (enormous datasets @ high frequencies-friendly), as well as being able to analyse across datasets using a Data Miner (customisable metrics and filters-friendly) As things progress in the R&D cycle, it's time to test! Introducing the Automated Reporter. Select a folder (either with files on it, or with files that you'll upload periodically using our SDK), then set out bounds for tests to pass/fail (think CI/CD pipeline) and see Marple automatically test and present them to you in PDF or as a trigger notification over email. What do you guys think? Would love to hear feedback! Best, Carlos

Visit product View on Hacker NewsJul 11, 2023

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3points

Did not reach leaderboard

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

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best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

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AppSumoMay struggle as an AppSumo deal · Strong signals: friendly · Missing: plus, platform, intuitive

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

40%40% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

30%30% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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