I

I built PdfDing – A selfhosted PDF manager and viewer

Hacker News

I built PdfDing – A selfhosted PDF manager and viewer

PdfDing is a selfhosted PDF manager and viewer offering a seamless user experience on multiple devices. Some of its features include: - Remembers current position - continue where you stopped reading on any device - Share PDFs with an external audience via a link or a QR Code. Shared PDFs can be access controlled - Dark Mode, colored themes and custom theme colors - Inverted color mode for reading PDFs - SSO support via OIDC I started developing PdfDing as I was searching for a solution for viewing and managing PDF files. I had a few simple requirements: - view PDFs seamlessly in the browser of my desktop and mobile devices - every user can upload files - can be self-hosted via Docker - minimal and resource-friendly - SSO support I did not find any existing solution that satisfied my requirements (I won't rule out that I overlooked something...). The solutions all had some problems: - They are using the inbuilt PDF viewer of the browser. This works fine on desktops and laptops but on smartphones it will simply download the PDF file and not display it in the browser. - They are really feature rich and therefore resource-hungry. I did not need fancy AI or OCR. I just wanted to view and organize my PDFs. - The content needs to be curated by an admin user. Normal users are not allowed to add PDFs. - PDFs can not be uploaded via the UI. PDFs can not be uploaded via the UI. I’d love to hear your feedback and learn more how I can improve PdfDing, looking forward to your thoughts! https://github.com/mrmn2/PdfDing

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Actual performance

4points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
92%92% 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: user, dock, using · Missing: mac, agents, macos
64%64% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: host, friendly, users · Missing: plus, platform, intuitive
55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, io · Missing: https docs, excited, just released
49%49% 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: users · Missing: mobile apps, ios, personal
34%34% 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
7%7% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: smart · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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