GoCard – A file-based spaced repetition system built in Go

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Hi HN! I'm excited to share GoCard, a terminal-based spaced repetition system I built that uses plain Markdown files as its data source. I've always been frustrated with existing spaced repetition tools that lock my knowledge into proprietary formats or require constant internet access. As a developer who lives in terminals and text editors, I wanted something that: 1. Stores cards as plain text files I can edit with any editor 2. Works seamlessly with Git for versioning and sync 3. Runs in a terminal without distractions 4. Has first-class support for code snippets and programming concepts GoCard implements the SM-2 algorithm (the same one used by Anki) but instead of a database, it uses a simple directory structure where: - Each card is a Markdown file with YAML frontmatter - Directories represent decks and subdecks - Everything is editable with standard tools *Key features:* - Distraction-free terminal UI built with BubbleTea - Real-time file watching (edit cards in your editor while reviewing) - Code syntax highlighting for 50+ languages - Vim/Emacs keybindings for efficient navigation - Hierarchical deck organization via directories - Cross-platform (Linux, macOS, Windows) What sets GoCard apart from other SRS tools is its developer-centric approach. Create cards with your favorite editor, organize them with your file manager, version them with Git, and review them in a clean terminal interface. I built this because I wanted a knowledge management system that worked with my developer workflow rather than against it. Making everything file-based means I can apply all my existing text-processing skills and tools. The project is v0.1.0, implemented in Go, and available at: https://github.com/DavidMiserak/GoCard I'd especially appreciate feedback on the UX design and any suggestions for making it more intuitive for terminal users. Has anyone else built similar file-based knowledge tools? What patterns worked well for you?

Visit product View on Hacker NewsMar 22, 2025

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

92%92% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

91%91% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

65%65% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Strong signals: platform, intuitive, interface · Missing: plus, reviews, host

55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

32%32% 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

17%17% 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.

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