Scraping 2.5 million songs metadata from Jango Radio

Hacker News

Jango. It's a music streaming service. No it's not Spotify. Yes I know you've never heard of Jango. So I spent the past few months scraping metadata for every song on Jango Radio and put it in a database and made it searchable. I built a search tool which includes the kind of features that only a nerd would want like search by ID and search by URL. https://jango-index.ml/ I used Bash and Btrfs for the backend scraper. I used PHP and SQLite for the frontend search. https://jango-index.ml/src/ I'm not interested in monetizing what I did. I wouldn't be interested in a marketing campaign to raise the notoriety of Jango Radio to the point that you would notice it exists either. I'm aware that my project looks like crap to web design obsessed marketing posers who pretend to code. I happen to enjoy coding and I did a coding project. I know coding is an intrinsically valueless endeavor. I did it anyway.

Visit product View on Hacker NewsAug 13, 2020

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Product HuntOn track for Day 1 leaderboard · Strong signals: coding, code · Missing: mac, agents, macos

81%81% 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 · Strong signals: songs · Missing: supports, reddit linkedin, podcasting

80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

57%57% 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

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

37%37% 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

12%12% 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.

Incorrect prediction on native model

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