Stash – native Mac Markdown notes

Hacker News

Hi HN, I've made a Markdown notes app. - 100% Mac native (even the Markdown formatting) - Your files live in ~/Stash (or another folder if you prefer) - You can connect to a Git backend (my files are connected to a private GitHub repo) - Notes can be Markdown text or tables (shows in a grid, has the extension .mdsv, viewable outside Stash as plain MD) - Preview from the folder view with clickable task items and link previews - Includes an integrated terminal for your agents (via GhosttyKit) Markdown is formatted with a library I made that is 99.9% compatible with GitHub Flavored Markdown: https://github.com/andrewjk/allmark Git support is provided by another library I made: https://github.com/andrewjk/gitologist There's a 7 day trial download or a $12 once off purchase. I hope you like it!

Visit product View on Hacker NewsJul 31, 2026

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Actual performance

4points

Did not reach leaderboard

Launch Intel predictions

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

90%90% 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: compatible · Missing: supports, reddit linkedin, podcasting

57%57% predicted probability of success on Indie Hackers, 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

43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: reviews, host · Missing: plus, platform, intuitive

37%37% 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

21%21% 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

5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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