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I created an AI Santa for my kids so I don't have to impersonate one

Hacker News

I created an AI Santa for my kids so I don't have to impersonate one

As a developer and a father of two young kids, I’ve faced a uniquely modern parenting problem this holiday season: keeping the magic of Santa Claus alive in an age of tech-savvy children. When I was a kid, my parents had all sorts of tricks to keep the illusion of Santa intact. They’d rope in distant relatives to call pretending to be Santa or my dad would disguise his voice (poorly) and "Ho ho ho!" over the phone. But let’s face it— those tactics don’t work anymore. I needed something better to preserve the magic of Christmas. The goal was simple: make a convincing Santa experience that even skeptical kids couldn’t unravel. Here’s the workflow: 1. Parents fill out a simple form (child's name, age etc). 2. The backend crafts a personalized prompt for LLM and initiates a phone call. 3. The TTS model converts LLM outputs into Santa’s voice. 4. After the call parents receive an audio recording via email as a memorable keepsake. It might not be groundbreaking AI research, but seeing my kids light up made the late nights worth it.

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, email · Missing: mac, agents, macos
88%88% 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 · Strong signals: created · Missing: supports, reddit linkedin, podcasting
86%86% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal · Missing: mobile apps, ios, entrepreneurs
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
37%37% 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 · Missing: arr, mrr, revenue
22%22% 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.

Correct prediction on native model

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