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Dataherald, no-code tool to help people see and understand data

Hacker News

Dataherald, no-code tool to help people see and understand data

Hi everyone, Anuj here, Dataherald CEO. There are a bunch of EXTERNAL data sources in the world that people use a lot -- think census data, Bureau of Labor Statistics (unemployment, inflation), but even proprietary data sources. I was tired of getting these into useable data pipelines every time I need need to use them. Dataherald simply creates data pipelines to hundreds of data sources and then lets you create auto-updating data viz -- all in under 60 seconds. The goal is to enable non-tech users to more easily use data, which has been a huge problem for me throughout my career. We initially launched with local newsrooms because there was a huge technical barrier for local reporters to use data to educate and inform communities; we've started to solve that. *We've overhauled the search feature of the product and looking to share with the HN community.* Pls take it for a spin -Anuj

Visit productView on Hacker NewsApr 18, 2022
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, new, code · Missing: mac, agents, macos
83%83% 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 HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: pipe, io · Missing: https docs, excited, just released
65%65% 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 · Strong signals: users · Missing: mobile apps, ios, personal
34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
26%26% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
10%10% 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.

Incorrect prediction on native model

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