Desklamp – convenient and collaborative notemaking on PDFs

Hacker News

Hey HN! I'm Prajwal, the co-creator of Desklamp! I just completed my undergrad, which is where we got the idea for Desklamp. A bunch of friends and I built this because we hated the experience of studying on our laptops. It was boring, and we found ourselves staring at the screen for hours on end with no output to show for it. To make reading more engaging and to make sure we could remember what we read, we built a note-making system integrated with a PDF reader. The aim is to encourage you to make notes! LaTeX support, clipping out sections from the document, linking notes to sections in the PDF - everything is designed to really make sure you have no excuse to not make notes as you read. We've also added a lot of fixes for minor inconveniences (scrolling across sections, hitting the wrong page number, light mode, viewing your highlights at a glance). And all of this is collaborative, because that just makes notes even more useful. It's free for a while - we want to know what the rest of you think! Feedback can only help us make this even better. It's available as a web-app and a desktop app for Mac and Windows (Linux users, mail us, we're operating on a very closed beta right now).

Visit product View on Hacker NewsJul 3, 2022

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, user, notes · Missing: agents, macos, agent

87%87% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

75%75% 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 · Missing: plus, platform, intuitive

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

40%40% 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

16%16% 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.

Correct prediction on native model

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