Tweak your chord progressions for practice or composition

Hacker News

I'm excited to share with HN a new pet project I've been working on to explore how AI can create and explain harmonic chord progressions - Chord Variations. The project uses GPT-4 to generate unique and interesting chord combinations based on user input chord progression. It's akin to having a virtual musical assistant that can help non-musicians and musicians alike explore and create harmonious sound without needing any prior knowledge of music theory. The generated chord suggestions maintain a similar vibe to the user input. Alternative chord progression includes extended chords, chord substitutions, unique passing chords, and more. Additionally, musical theory explanations within the tool is helpful for users not just to create music, but understand the underlying structure it's built on a bit better. These chord progression suggestions can be used for practice or composition. As a musician myself, I am having quite a lot of fun playing around with it. One of the things I'm proud of is how the application really dips into music theory. It includes nuanced aspects such as dominant chords, secondary dominant chords, and the famous 2-5-1 chord progressions. That said, the development journey was full of lessons. Dealing with the latency of the GPT-4 API was particularly challenging. I used a Celery based queue system + client polling to manage the delay between request and response (from OpenAI API). Additionally, to keep the chord names consistent, I used a combination of prompting and regex. There are still some bugs that need to be squashed but overall I am pretty happy with the results. I'd love to hear your thoughts and feedback on the project. Also, if you're curious about anything, I am happy to delve into the details in comments. Feel free to take Chord Variations for a spin here: https://chord-alt.vercel.app/ Looking forward to some interesting discussions!

Visit product View on Hacker NewsJun 27, 2023

Share card

Actual performance

78points

46comments

Made the leaderboard

Launch Intel predictions

Analyze your own launch →

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

92%92% 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.

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

81%81% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

39%39% 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

7%7% 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

Similar products

Practice Peg System – Mnemonics51%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Practice Peg System – Mnemonics

Hacker News2

Vidyanilaya – Practice for IIT JEE51%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Vidyanilaya – Practice for IIT JEE

Hacker News2

Practice integer addition42%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Practice integer addition

Hacker News1

PMFJourney52%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

PMF is not a milestone. It is a practice.

Indie Hackerscommitment-side-project

SpeakLoop44%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Shadowing reading practice, oral practice

TrustMRREducation

FirmFlow19%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Practice management for accountants, lawyers & consultants

Indie Hackers

FirmFlow19%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Practice management for accountants, lawyers & consultants

Indie Hackerscommitment-full-time

System Design Practice and Discussion57%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

System Design Practice and Discussion

Hacker News8

Chalk27%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

System design practice that argues back.

Indie Hackerscommitment-side-project

Game to Practice Hexidecimal Addition57%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Game to Practice Hexidecimal Addition

Hacker News1

Browse all products Analyze your launch