Se

Severance Wellness Session

Hacker News

Severance Wellness Session

For Severance fans in need of a relaxing break from their Macrodata Refinement, I made a site where you can get your very own Wellness Session! If you haven't watched the show: There are these strange therapy sessions where the characters who are stuck at work (because they have no memory of the outside world, the "Innies") are told odd but comforting facts about their "Outie" life. I replicated the audio of the sessions using a fish-speech TTS model cloned from the therapist's actress, and displayed the facts on-screen with a WebGL-rendered terminal, styled like the ones used by the protagonists. Deployed from home with K3s, GH Actions etc.

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Actual performance

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, model, using · Missing: agents, macos, agent
81%81% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
42%42% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
41%41% 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
36%36% 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
19%19% 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
13%13% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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