Qavvali Wiki

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Qavvali Wiki

Hey folks — I’ve been working on this pet project for the last few months, and would love some feedback. Qavvali (or Qawwali) is a South Asian genre that I’ve been passionate about for a while, and I’d love to introduce it to as many folks as I can. There are many aspects of the genre that I love, and I can go into depth if anyone is interested. AMA — happy to reply in the comments. Meanwhile, my primary reason for sharing this here is that I’d love feedback on the website, as well as to see if anyone would be interested in helping — be it technical, or more so with curation. I’d really appreciate it. One of the core features I’ve worked on extensively is mapping lineages. The goal is to enrich the website with more reliable resources, ideally from available academic or literary sources, and compile and collate them into a one-stop place for anyone interested in the genre.

Visit product View on Hacker NewsMay 24, 2026

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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

79%79% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

58%58% 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 · Missing: plus, platform, intuitive

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

33%33% 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

11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: introduce · 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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