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WayOfThat, Automatically detect and place fields and checkboxes on PDFs

Hacker News

WayOfThat, Automatically detect and place fields and checkboxes on PDFs

I built WayOfThat as a tool for my own work as a realtor. Mainly to speed up setting up and sending documents with tons of fields. One of my listing forms has over 600 unique fields, and filling them manually was painful. So, naturally, I spent even more time building something to automate it. WayOfThat automatically detects and maps out form fields and checkboxes from PDF documents. It works like a standard document signer; upload docs, place fields, send for signature, but the MVP is what makes it different. MVP: a trained image detection model built on 100k+ documents that can automatically set up around 95% of document fields, dramatically speeding up the “place fields” step of the process. Right now, it can take almost any PDF, detect its fields, and either export a fillable version or prepare it for signing. Next up: document workflows and integrations with automation tools.

Visit productView on Hacker NewsOct 29, 2025
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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
82%82% 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: model · Missing: mac, agents, macos
61%61% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
56%56% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
53%53% 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: way · Missing: mobile apps, ios, personal
36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
13%13% 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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