I

I built SecretCrush AI – an AI chat companion app - entirely using AI

Hacker News

I built SecretCrush AI – an AI chat companion app - entirely using AI

My previous post didn't have the Show HN prefix in title so here's the repost. The whole stack was built using AI, from infra pipelines, backend API, frontend, to the AI chat server. Next step: build an agent to improve user experience by doing the following: - test chatting with AI characters - analyze the experience - improve the system prompts for characters - then repeat

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Actual performance

3points
5comments
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, user, using · Missing: mac, agents, macos
90%90% 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
69%69% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal
52%52% 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
35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: pipe, io · Missing: https docs, excited, just released
29%29% 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
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
11%11% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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