Liminal Shift: Personalized audio visualizations

Hacker News

Hey HN. I built a tool that generates personalized audio visualizations for goals and stress release. The idea came from a manual workflow I was doing for myself — using AI conversations to clarify goals, generating scripts based on my specific context, then running them through text-to-speech. I stitched it together into an actual product. The underlying philosophy is based on Reality Transurfing (a book about visualization/manifestation), but from a technical standpoint: - Conversational AI extracts and structures user goals and context - Script generation creates personalized visualization text (~2-10 min) - ElevenLabs TTS with configurable voice, pace, and background audio mixing - Also includes a journaling system for tracking "pendulums" (recurring stressors/energy drains) Stack: Django backend, Next.js frontend, Anthropic Claude for conversations, ElevenLabs for voice, Cloudflare R2 for audio storage, Stripe for credits. Business model is credits to cover AI costs (ElevenLabs adds up). One free credit to try the full flow. Happy to answer questions about the build or the concept.

Visit product View on Hacker NewsJan 29, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, model, user · Missing: mac, agents, macos

93%93% 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

93%93% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

46%46% 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

42%42% predicted probability of success on AppSumo, 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

30%30% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Strong signals: recurring · 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

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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