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A Collatz conjecture full music generator in 800 lines of C

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A Collatz conjecture full music generator in 800 lines of C

Hi HN, while the hype around neural networks and neural network tools is still going strong. I decided to release my project as open source it generates IDM/Ambient music based on the Collatz hypothesis using numbers. It’s a procedural synthesizer written in pure C. It takes any number, calculates a sequence for it, and uses that sequence as code to generate the source music. And yes, it’s important to note that we don’t use MIDI all sound is generated on the fly. For example, 11 synthesized voices (Additive, FM, Noise), ADSR envelopes, filters, and effects. We use the libsndfile audio library. Rather, this isn’t just random sounds the program tracks the local entropy of the sequence. If the numbers lack sharp jumps, the music loops into motifs if a sharp jump occurs, the structure breaks down, glitch effects are activated, and the tempo accelerates. This is done intentionally to try to create a composition rather than a set of notes. I wanted to explore and apply more mathematics, which is why I chose this particular approach. The code isn’t large for your convenience, I’ve implemented a variety of build methods (makefile, Docker, CMake, .bat). I’d love to hear your feedback on the synthesis architecture and ideas for other mathematical sequences that might sound interesting.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
84%84% 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: open source, ide, io · Missing: https docs, excited, just released
65%65% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: dock, using, notes · Missing: mac, agents, macos
52%52% 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
42%42% 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
13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: audio · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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