ZigZag – Generate Markdown code reports from directories (Zig)

Hacker News

Hi HN, I built ZigZag, a command-line tool written in Zig that recursively scans source code directories and generates a single markdown report containing the code and metadata. It’s designed to be fast on large codebases and uses: - Parallel directory and file processing - A persistent on-disk cache to avoid re-reading unchanged files - Different file reading strategies based on file size (read vs mmap) - Timezone-aware timestamps in reports Each directory produces a report.md with a table of contents, syntax-highlighted code blocks, file sizes, modification times, and detected language. Repo: https://github.com/LegationPro/zigzag I built this mainly for auditing and documenting large repositories. Feedback, critiques, and ideas are welcome.

Visit product View on Hacker NewsJan 26, 2026

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

55%55% 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: single, code · Missing: mac, agents, macos

52%52% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

48%48% 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

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

30%30% 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

16%16% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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