Interactive jq, but it's a bash script using fzf

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I was searching for an interactive jq editor when I came across this repo [1], which had an intriguing suggestion for a CLI using only fzf: echo '' | fzf --print-query --preview "cat *.json | jq {q}" This sent me down a rabbit hole, and I discovered just how incredibly configurable fzf is. For instance: - `--bind=ctrl-y:execute-silent(jq {q} $tempfile | pbcopy)`: You can bind custom keys to execute non-default behaviors, like copying the current selection or `echo`-ing. - `--query="."`: Start fzf with an initial query (e.g., `.` for jq queries) to streamline the experience. - `--preview-window=top:90%:wrap`: You can configure fzf into different layouts, behaviors, and ANSI colors. - `--header=$'ctrl+y : copy JSON\nctrl+f : copy filter\nenter : output\nesc : exit' \`: fzf supports multi-line header to provide instructions or context for the user right in the interface. [1]: https://github.com/fiatjaf/awesome-jq?tab=readme-ov-file#com...

Visit product View on Hacker NewsJan 31, 2025

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

65%65% 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: ide, io · Missing: https docs, excited, just released

50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

45%45% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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

Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue

11%11% 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.

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