Cu

Cujo's Curated SIDs

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Cujo's Curated SIDs

I've listened to SID music on and off since the 80s, and mostly from early games and demos. Lately, I have explored more recent music. There are online lists of peoples favorite SID music, and online players, see [1] [2] [3] [4] [5] [6]. Because I have different tastes, I had the motivation to make my own list and UX. So from january 2020 to september 2022, I listened through the HVSC having over 50k songs, resulting in a list of 600-700 favorites. I put together a UX so I can easily access them. This project is mostly for my own enjoyment, but I guess that many others here would also enjoy it. Please let me know if you have suggestions for songs I should reconsider adding, or some feedback on the UX. [1] Show HN: Online SID-Player with spectrum viewer ( https://news.ycombinator.com/item?id=6750701 ) [2] High Voltage SID Collection – Commodore 64 music for the masses ( https://news.ycombinator.com/item?id=28184104 ) [3] Dial-a-Sid ( https://news.ycombinator.com/item?id=27735256 ) [4] The Sid Collection ( https://news.ycombinator.com/item?id=32368634 ) [5] WebGL SID-emulator in C++ compiled to JavaScript with Emscripten ( https://news.ycombinator.com/item?id=7270025 ) [6] DeepSID: online SID (Commodore 64 music) player ( https://news.ycombinator.com/item?id=17055344 )

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Indie HackersFits the IH revenue-focused audience · Strong signals: songs · Missing: supports, reddit linkedin, podcasting
77%77% 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: ide, io · Missing: https docs, excited, just released
68%68% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: new · Missing: mac, agents, macos
11%11% predicted probability of success on Product Hunt, 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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

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