I made a tool for creating Web Apps with spreadsheet logic

Hacker News

Background: There are many great spreadsheets. Spreadsheets for creating quotes, for tracking orders, tracking kids learning to swim. But staff would rather use a good user interface, with buttons etc, than a spreadsheet when doing work day to day. Examples: http://sheet.cellmaster.com.au/examples Slow - complex app: http://sheet.cellmaster.com.au/carbonoffset/app How: It adds GUI formulas and event handling formulas to a standard spreadsheet. Looking for: Early adopters and use cases. Stack: Nodejs - JS/livescript - redis https://github.com/audreyt/ethercalc - Built using Dan Bricklin's (inventor of the spreadsheet) open source spreadsheet Warning: It is slow with large volumes of data/formulas. It needs work. Tag line: Web apps as simple as spreadsheets Feedback welcome

Visit product View on Hacker NewsNov 26, 2019

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3points

Did not reach leaderboard

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Hacker NewsStrong engagement from HN community · Strong signals: open source · Missing: https docs, excited, just released

72%72% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

71%71% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

47%47% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

45%45% 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

14%14% 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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