Building musical synthesizers with SQL queries

Hacker News

If you already know what bytebeat is, you don't need an explanation. If not, check my project :) Here is how it looks: SELECT mono(output( arraySum(x -> 1 / 6 * running_envelope(30 * (1 + x / 6), time, 0.05 * x, 0.005, lfo(0, 0.25, sine_wave, time / 8), 0.1) * sine_wave(time * 80 * exp2(x / 3)), range(12)))) FROM table; To check how it sounds, find the examples in the repository https://github.com/ClickHouse/NoiSQL

Visit product View on Hacker NewsApr 7, 2023

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

67%67% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

35%35% 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

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

30%30% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

21%21% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit

19%19% 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.

Correct prediction on native model

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