Runyte – a modal editor with integrated terminals and a file manager

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Hi, I'm building Runyte, an open-source terminal workspace built around a modal text editor, written in Rust. The goal is to create a coherent workspace with minimal UI and maximum focus. Files, terminal panes, and Git views share one interface, navigation, clipboard, and theme. A fuzzy finder searches through files, buffers, and terminals. Runyte supports a persistent mode (client-server) in which you can detach and return later while terminal processes and LSP servers keep running, or you can switch directly to another Git worktree. There are also some features to minimize the friction when working with CLI coding agents: markdown rendering, image pasting, editing agent prompts, searching terminal sessions with the fuzzy finder or regex. It's pre-1.0, but fully usable. Works on Linux and MacOS. You can download a binary or install it with cargo install runyte --locked. There's an interactive tutorial :tutorial. I'd especially appreciate feedback on navigating between editing, terminals, and worktrees, and where the interface interrupts your concentration.

Visit product View on Hacker NewsSep 10, 2026

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

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: supports · Missing: reddit linkedin, podcasting, created

83%83% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

51%51% 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: interface · Missing: plus, platform, intuitive

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue

19%19% 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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