Fill, sign, edit PDFs and much more

Hacker News

Hi HN, I built a new and modern PDF Editor. I had been using some online PDF Editors since years and over the period of time I found that most of them were either slow and outdated or bloated with un-necessary features. Another pain point was all of the PDF editors mentioned "Free" but right after you do edits and want to export anything, they ask you to pay. I decided to fix my own itch and built PDF Editify. Using PDF Editify, users can fill, sign, edit, redact, annotate PDFs and much more. The free version allows users to export 3 PDFs with unlimited edits per week. We are constantly working on new features, some of which are: - Digital Signatures (currently we only support Electronic Signatures) - PDF Templates Catalog - OCR PDFs This is my first product and we have good feedback from our users. I would love any questions, comments or feedback from the HN community and any ideas for marketing would be appreciated since I (being a tech guy) really find marketing boring.

Visit product View on Hacker NewsDec 21, 2024

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Did not reach leaderboard

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

75%75% 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.

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

58%58% 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: user, new, using · Missing: mac, agents, macos

54%54% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

37%37% 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

33%33% 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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