Flookup – Data cleaning by fuzzy matching

Hacker News

Hello HN! It has been about three years since I launched this current iteration of my Google Sheets add-on called Flookup. I'm a solo developer and, currently, I handle absolutely everything to do with Flookup from writing code, replying support emails and even marketing. It is a lot of work but very satisfying because I get to see, first hand, how helpful it has been to my userbase. Before Flookup was launched, there was only one prominent Google Sheets add-on that could be used for data cleaning (to a certain degree) but, luckily for me, that add-on had left a gap in functionality that I was very glad to fill. To develop Flookup, I had to write a new algorithm from the ground up in order to make it actually useful to my userbase. Originally, this userbase comprised me and a small team in an organisation I was working at. Today, that number has grown to at least 10,000 Daily Active Users from every corner of the world (except Antarctica). What is Flookup? It is an add-on that uses fuzzy matching algorithms to power its data cleaning functions. Flookup's functions allow you to match or merge datasets without worrying about how uniform your data is. You can also highlight and remove duplicates even if the text contains typos, punctuation marks or spelling differences. So, today, I'm inviting you to try it out and let me know what you think... here is the link: > https://www.getflookup.com Your free trial is unlimited. Thank you for your time and I hope you like it!

Visit product View on Hacker NewsJun 28, 2022

Share card

Actual performance

8points

Made the leaderboard

Launch Intel predictions

Analyze your own launch →

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

70%70% 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.

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

62%62% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

61%61% 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: 000, io · Missing: https docs, excited, just released

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

nativeThis product was originally launched on this platform.

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

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

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

24%24% 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

Similar products

Geocodio57%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Geocoding + Data Matching

Indie Hackerscommitment-side-project

Data Cleaning and Wrangling as a Service45%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Data Cleaning and Wrangling as a Service

Hacker News3

Automated Data Cleaning43%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Automated Data Cleaning

Hacker News2

Zingg Fuzzy Matching48%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Zingg Fuzzy Matching

Hacker News4

HN Matching Pairs45%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

HN Matching Pairs

Hacker News1

Lime Maids16%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

The Conscious Cleaning Platform

Indie Hackers2b2c

Sole Source Restoration38%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Coronavirus cleaning & disinfection, disaster restoration

Indie Hackerscommitment-full-time

Unlimited data cleaning service43%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Unlimited data cleaning service

Hacker News3

Woyera – Data Cleaning and QA API42%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Woyera – Data Cleaning and QA API

Hacker News3

Cliff is an API for cleaning data and preparing mailing lists46%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Cliff is an API for cleaning data and preparing mailing lists

Hacker News1

Browse all products Analyze your launch