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Harmonized Data Platform

Hacker News

Harmonized Data Platform

Hi HN, My background is on datascience and I thought during the last few weeks on how to turn the data into commodity problem: Getting any meaningful out of data can be erroneous, time-consuming and includes repeatable processing work (and it is done over and over again). Im trying to mitigate this by harmonizing data, so it is ready for being consumed via an API or Spreadsheet. After a few iterations I ended up with this prototype that I wanted to share with you. Please notice, that this is an early prototype and not a finalized product yet. Im also delighted to know your opinions or thoughts or advices. You can get the first impression at https://databarnum.com/

Visit product View on Hacker NewsMar 19, 2023

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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

57%57% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

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

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

39%39% 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

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

16%16% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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