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RowMaker – Turn Images and Words into Spreadsheets

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RowMaker – Turn Images and Words into Spreadsheets

Hey All, RowMaker is a simple utility app I built that helps you automate data entry into personal spreadsheets. RowMaker uses AI to turn pictures and voice notes from your phone into rows of data for personal use cases. I built RowMaker for myself because I have a ton of use cases where I'd like to track information in a spreadsheet, but I'm way too lazy to do manual data entry. It occurred to me that, if the things I'm trying to track can be captured in an image or describe in a voice note, I can just use a multi-modal LLM to convert that raw data into a structured format that can be added to a spreadsheet. Hence, RowMaker. Here's how it works: 1. Users create a "rowmaker", which is a set of fields and descriptions that define the columns of a spreadsheet. 2. RowMaker creates the spreadsheet in your Google Sheets account. 3. Users can then take pictures or voice notes on their phone, and submit them to a rowmaker. 4. RowMaker uses an LLM + the spreadsheet context to extract the necessary data from the picture/voice note. 5. The extracted data is added as a new row to the spreadsheet. As an example, I used RowMaker to create a pantry inventory by defining the fields I wanted to track (food name, category, brand) and taking pictures of everything in my pantry. I'm releasing the app today on the app/play stores. The app has usage-based pricing based on credits - 1 extraction consumes 1 credit. New users get 20 credits free, and can purchase additional credits in increments of 100 for $1 / 100 credits. Today, RowMaker only supports Google Sheets for storing data, so you will need a Google account. This app is not meant to be a money-maker. My goal is to eventually use a local, on-device LLM for data extraction, so I can drop the LLM API calls and the usage-based pricing. Would love to hear your feedback!

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created
90%90% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: google, user, new · Missing: mac, agents, macos
89%89% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: personal, google, users · Missing: mobile apps, ios, entrepreneurs
56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
52%52% 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, calls · Missing: plus, platform, intuitive
30%30% 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
20%20% 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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