Jq-Like Tool for Markdown

Hacker News

There have been a few times I wanted the ability to select some text out of a Markdown doc. For example, a GitHub CI check to ensure that PRs / issues / etc are properly formatted. This can be done to some extent with regex, but those expressions are brittle and hard to read or edit later. mdq uses a familiar pipe syntax to navigate the Markdown in a structured way. It's in 0.x because I don't want to fully commit to the syntax being stable, in case real-world testing shows that the syntax needs tweaking. But I think the project is in a pretty good spot overall, and would be interested in feedback!

Visit product View on Hacker NewsFeb 23, 2025

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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

73%73% 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.

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

64%64% 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 · Missing: plus, platform, intuitive

51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

48%48% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

46%46% 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

14%14% 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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