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Gitori: A Gamified Progressive Metronome to practice music

Hacker News

Gitori: A Gamified Progressive Metronome to practice music

http://www.gitori.com As a guitarist I tend to find that there is plenty of 'learning' resources on the internet but what prevents me from being a better musician is the ability to practice consistently and effectively. Gitori addresses that problem by using a gamified metronome. Some of my pain-points that it addresses are: 1) Improving on the metronome. The most popular piece of advice I've gotten for improving technique as a musician is to use a metronome and start slowly, gradually increasing tempo. I did this for many months and found the constant Metronome-tweaking to be annoying. I wanted an auto-pilot Metronome. Gitori is my attempt at an autopilot metronome. 2) I find practicing exercises/scales to be a bit boring. Especially taking the extra effort to practice everything in multiple keys 3) I lack the discipline to fix a minimum warmup period before trying to play faster. Having the whole practice workout laid out in front of me forces me to adhere. 4) My exercises and practice material are not organized in the same place. I have an ad-hoc collection of bookmarks/emails/books/CDs/PDFs Gitori allows me to create my own exercises and put all of them in one place. 5) I would like to track my progress by looking at data. I wanna know how many minutes I practiced last week. Was I able to consistently practice 5 days a week last month? Can I play faster than I could a month ago? Tech stack: Rails/MySql/Redis(points/levels/leaderboard)/HTML5 Web Audio/MIDI-Quicktime P.S.: I'm a Ruby/Rails developer based in the SF Bay Area looking to change jobs. If you have an opening and can file H-1/H-4 and Green Cards, ping me at info AT gitori.com

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Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.
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AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
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44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: email, using, open · Missing: mac, agents, macos
39%39% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
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BetaListMay not resonate with beta-testers · Strong signals: audio · Missing: web3, chat, crypto
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