Mixtape of 200 “futuristic” songs circa 1980

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I'm posting this long mix of 200 "futuristic" music recordings, in chronological order (from the mid 1970's to the mid 1980's). Some are obvious (eg: "She Blinded Me With Science") while others are long forgotten (eg: G.G. Tonet's "Dedicated To Norbert Wiener" or Jyl's "Silicon Valley") --- Circa 1980 Mixtape https://soundcloud.com/italian_radio_1983/space-italo-etc-circa-1980 --- While the theme of the linked mix (ie: tech) probably interests some here, it is likely too fluffy for many others so apologies for that. This thing took me almost two years to finish, so in a moment of weakness I am going ahead and posting it here. There actually is a tech angle to why it took so long. After I gathered the 200 audio tracks I wanted, I wound up stymied for several months because trying to match the timbre and loudness of 200 songs overwhelmed me. During that time I began writing a program in my spare time to band-split all the files and match their perceived loudness. I got around half-way through that when OpenAI released GPT4. On a whim, I asked it to write a BASH script to perform the auto-EQ: a couple evenings of minor tweaks and it was done https://github.com/italian-radio/autoeq so I went ahead and completed the mix. The final mixing together of songs - I am not a DJ and have no experience beat-matching - was also done programmatically (wrote a program that takes start- and end- times of the regions to crossfade, and then ramps samplerate up or down for the two tracks.

Visit product View on Hacker NewsAug 13, 2023

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

82%82% 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

64%64% 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: recordings, openai, open · Missing: mac, agents, macos

56%56% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

47%47% 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

28%28% 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

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: audio · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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