Ditdah – A Morse App for the Terminal

Hacker News

I passed the Morse exam when I got my novice license, but I haven't started using Morse until recently. I'm really a beginner: I can key at about 15 WPM and am practicing for 20, but I'm still making mistakes, with an extra dit or dah here and there. Thanks to all the YLs and OMs who have worked with me so far! I'm able to decode Morse with pen and paper, as long as it's only 10–15 WPM, but that needs to improve. Most operators recognize CQ without even knowing Morse. I have already learned a few more phrases just by hearing them, like "TEST", "DE", "PSE K", "BK", "BT", etc. Nevertheless, I have issues with more conversational words like "sunny", but it will build up slowly, I'm sure. To help myself out, and to learn something new, I started building a terminal app for Morse decoding. It's built around a neural network that I trained and can pick up quite a bit of text really well. The interface is textual. These kinds of applications have a special place in my heart, as I grew up in the Commodore/DOS era. Radios are old-school, and an old-school layout matches them well. The app also comes with a built-in logbook and QRZ.com integration for callsign lookup and sync. It's not a CW skimmer, so don't expect pileup handling or contest support. The goal is to sit back and make some contacts slowly. Give it a try and let me know if you like it!

Visit product View on Hacker NewsAug 23, 2026

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

88%88% 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.

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

71%71% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

61%61% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

39%39% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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