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Create and share lists of your favourite records

Hacker News

Create and share lists of your favourite records

As a music fan I love reading other people's lists of their favourite records of all time/of the year/etc. But I find it a bit overwhelming to have to then go and search the record on a streaming service to assess whether it would be something I was interested in. So I made this. You can quickly throw together a list of records and all the reader has to do is click on them to sample them :) I've put together some 2016 year-end lists that were put together by major publications by way of example: Metacritic Top 40 albums of the year: https://www.tapedeck.io/collections/2 Pitchfork's top 50 of 2016: https://www.tapedeck.io/collections/3 Gorilla vs Bear - Best Albums 2016 (some lesser known artists in here): https://www.tapedeck.io/collections/6 One for the metalheads... CoS top 10 metal albums of 2016: https://www.tapedeck.io/collections/5 I built it with some spare downtime I had over the holiday period. Nice and simple Rails application. It's just a small project not a startup, but I would love your opinions on it. It's open-source too. GitHub repo: https://www.github.com/cjbutcher/tapedeck-2.

Visit productView on Hacker NewsJan 2, 2017
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Launch Intel predictions

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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.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: open · Missing: mac, agents, macos
53%53% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal
50%50% 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
38%38% 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
14%14% 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.

Incorrect prediction on native model

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