A keyboard-centric clipboard history app for macOS

Hacker News

Hey HN, As a Software Engineer, I frequently copy and paste content. The fact that macOS clipboard buffer can keep only one item is very frustrating. It’s very annoying to copy → switch app → paste → switch app → copy → switch app → paste, and so on. Losing items I copied hours ago and having to find and copy them again is a waste of time. I tried many third-party clipboard managers like Alfred, Raycast, Paste, Maccy, etc. While great, they didn't fully meet my needs. As a developer, my requirements are: 1. Keyboard-centric operation to avoid using the mouse. I don’t want to waste my time moving my hands from the keyboard. 2. Display many clipboard history items at once. I don’t like the idea with big tiles, so I can see only 5-7 history items on my 32” monitor. 3. Full content preview for each clipboard item. 4. Quick search functionality. So, I created ClipBook for my own use. After months of development, daily use, and positive feedback from my colleagues, I decided to share it publicly. It’s free. All data is securely stored on your computer. I’m planning to make it open source as well. If you try it out, please let me know if you found it useful, if you have anything you’d like me to add, or if you have any other feedback. Happy to answer any questions. Much appreciated.

Visit product View on Hacker NewsJun 11, 2024

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

90%90% 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: created · Missing: supports, reddit linkedin, podcasting

86%86% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: open source, ide, io · Missing: https docs, excited, just released

40%40% 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: month · Missing: mobile apps, ios, personal

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

35%35% 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

15%15% 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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