PNANA - A TUI Text Editor

Hacker News

I’d like to share PNANA , a lightweight TUI editor built with C++ and FTXUI that I’ve been building for personal use and now open-sourced. It’s a minimal, fast terminal-based editor focused on simple coding and editing workflows—no bloated features, just the core functionality for terminal-centric use cases. https://github.com/Cyxuan0311/PNANA Key pragmatic features Lightweight C++ core with FTXUI for smooth TUI rendering, fast startup and low resource usage Basic but solid editing capabilities (syntax highlighting, line numbering, basic navigation) Simple build process with minimal dependencies, easy to compile and run on Linux/macOS terminals Early LSP integration support for basic code completion (still polishing, but functional for common languages) It’s very much an early-stage project—I built it to scratch my own itch for a minimal, self-built TUI editor and learn C++/FTXUI along the way. There are definitely rough edges (e.g., some LSP kinks, limited customization), and it’s not meant to replace mature editors like Vim/Nano—just a small open-source project for folks who like minimal terminal tools or want to learn TUI development with C++. Any feedback, bug reports, or tiny suggestions are super welcome. I’m slowly iterating on it and would love to learn from the HN community’s insights. Thanks for taking a look!

Visit product View on Hacker NewsJan 28, 2026

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

93%93% 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 · Missing: supports, reddit linkedin, podcasting

88%88% 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

79%79% 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 · Missing: plus, platform, intuitive

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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

18%18% 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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