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Paster – A keyboard-first clipboard manager for Vim users

Hacker News

Paster – A keyboard-first clipboard manager for Vim users

Hi HN, I’ve tried just about every clipboard manager for macOS, but I've always ran into the same two issues: either they were heavy Electron apps that felt sluggish, or they required me to take my hands off the keyboard to find what I needed. Raycast is what I used most of the time, but it's slow in loading screenshots and is search first, meaning I needed to leave the loved home row to scroll down through items. I built Paster because I wanted something that felt like an extension of my terminal and had instant load of the content being copied. It's written in Rust to keep the latency as low as possible and uses a local SQLite database for history. It's completely private and does not have any telemetry, your data is your own. It does reach to it's domain to validate the license. Some specific choices I made: - Navigation: I mapped it to j/k and / for search. If you use Vim or a terminal, it should feel like second nature. - Privacy: I’m not a fan of cloud-syncing my clipboard. Everything stays local on your machine. - Quick look: I've added a nice little bonus feature to view each clipboard item in a larger quick look window. Pretty handy for screenshots and offers syntax highlighting for text. It’s currently a paid app with a 7-day trial. I’m really curious what the community thinks about the "Vim-for-everything" approach. For transparency sake, it's built with help from AI (Gemini) mostly for UI stuff which requires lots of boiler plate. It's Macos only for now, I do intend to work on a Linux version but no promises.

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, apps · Missing: agents, agent, cursor
96%96% 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: gemini · Missing: supports, reddit linkedin, podcasting
88%88% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps, users, way · Missing: mobile apps, ios, personal
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
43%43% 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
32%32% 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
13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: paid · 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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