Nu

Nudedisco – an RSS-powered album reviews aggregator

Hacker News

Nudedisco – an RSS-powered album reviews aggregator

Hi HN, nudedisco is a simple album discovery tool I created to aggregate album reviews from a number of my favourite websites and blogs. As someone who spends most of his professional life working with React and "modern" frontend stacks, it was a lot of fun to go back to basics and create a server-rendered website. The site uses Elixir under the hood and is deployed to fly.io. The code is available on my GitHub profile: https://github.com/soulprovidr/nudedisco If you have any thoughts or suggestions, I'd love to hear them. Thanks, and enjoy!

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
89%89% 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 NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
47%47% 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 · Strong signals: reviews · Missing: plus, platform, intuitive
43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: code · Missing: mac, agents, macos
28%28% 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
27%27% 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 · 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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