Fretboard.el – guitar fretboard visualization tool for Emacs

Hacker News

Hi Hacker News! My name is Skye. Long time listener, first time caller. Have you ever sat down to get ready for a gig, looked at your guitar and thought to yourself: "Why am I not using the worlds most extensible text editor to work on my guitar fretboard fluency?" Well I have just the thing: fretboard.el, the guitar fretboard visualization tool for Emacs! fretboard.el lets you see the notes of the guitar in pure text. Immediately upon evaluation of the function `(fretboard)`, you'll be able to recognize where the root note is positioned in relation to the key or chord shape. With just a keystroke, you can cycle through scale signatures, chord tonalities and guitar tunings! fretboard.el is free and open-source. Documentation can be found on the repo at https://github.com/skyefreeman/fretboard.el . Happy practicing!

Visit product View on Hacker NewsMar 26, 2025

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

82%82% 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 HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

62%62% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: mac, new, visual · Missing: agents, macos, agent

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

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

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

36%36% 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

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.

Incorrect prediction on native model

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