A web app to split PDF into Color and Grayscale pages

Hacker News

Hey everyone , I'm Vishal and I'm very excited to reveal Splitto to you guys today. It is a one-of-a-kind PDF tool that allows you to effortlessly separate Color & Grayscale pages. How did it start ? While submitting my assignments I often had to separate the color & grayscale pages before sending it for print. As grayscale printers are often laser-based which is fast and cost-effective, there was no point printing the entire document from a color printer. But with the PDF tools available online, the process was still very tedious especially when the PDF has a lot of pages. So I decided to make one myself. How it works ? Splitto separates your PDF pages by rendering and scanning every single pixel on each page to determine whether that page should be called color or grayscale. This entire process happens on-device which means none of your file contents are sent or stored on the servers. Once the process is finished you can either download both color & grayscale PDFs or simply copy the page numbers & paste them in the print dialog. How to use it ? Splitto has a very minimal UI on purpose to reduce any friction or distraction during your workflow. Just select your PDF & hit the split button & that's it. In a matter of seconds, Splitto will process the output. Demo Video - https://youtu.be/W6c3simqbjU Please give the product a try and help spread the word. If you have any feedback or queries you can post them here or send them to hello@splitto.app, I'm all ears. Also here is a special discount for all the early birds - EARLY40. Use this to get 40% off of any plan you choose. Happy Splitting ! Vishal Roy

Visit product View on Hacker NewsMar 17, 2024

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

69%69% 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: excited, ide, io · Missing: https docs, just released, exist

57%57% 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 HuntUnlikely to reach the leaderboard · Strong signals: single · Missing: mac, agents, macos

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

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

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

35%35% 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

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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