Dr

Drag-and-Drop in the Terminal

Hacker News

Drag-and-Drop in the Terminal

I built drag-and-drop in the terminal. You can grab an element with a mouse, drag it, drop it on a target, and drag-select text inside it across wrapped rows. It's built in Yokai, my Ink fork: a React TUI renderer with pure-TS Yoga layout, diff-based rendering, ScrollBox, alt buffer, mouse events, and draggable/resizable components. I built Yokai because I kept hitting Ink's limits and needed things Ink couldn't offer for my TUI-based project. Try it: pnpm demo:scratchpad # draggable editor window (an opaque sticky note you can drag across the screen and edit) pnpm demo:drag # drag rectangles into a drop zone (the one in the readme banner)

Visit product View on Hacker NewsApr 29, 2026

Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

85%85% 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.

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

56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

48%48% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

35%35% 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

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

28%28% 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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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