1000songs.co.uk – 100 years of music, 10 tracks a year

Hacker News

Hi HN, I have made a site called Https://www.1000songs.co.uk It is a collection of songs I like 1930 - 2030, 10 songs a year (I am 927 in!) These limits meant I could add some really fun features. The radio time machine lets you scroll through the years 1930 - 2030. It is built on lists and then looks up the preview from iTunes. I used the limitation of a 30 second preview as a feature to try and make it feel like turning an old radio. It's also got some game elements where you can find the songs from the year you were born or name the tune. Because my taste is pretty niche (and heavily influenced by British alternative radio) I added some features where you can paste your own songs in the format year, artist, song and it will bring the songs into the site through lookups. You can then scroll to the bottom and save the site offline with your songs in it. It's also got some tools in there which means you can check years of songs against other sites I am also a bit stuck for 30s and 40s gritty jazz and blues so any suggestions appreciated! Love to hear your thoughts!

Visit product View on Hacker NewsJun 25, 2026

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

90%90% 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: mac · Missing: agents, macos, agent

64%64% 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

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

Hacker NewsMay not resonate with HN audience · Strong signals: 000, io · Missing: https docs, excited, just released

46%46% 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

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

17%17% 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

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