Yn

Ynox – Self-learning AI assistant that thinks, evolves and helps

Hacker News

Ynox – Self-learning AI assistant that thinks, evolves and helps

YNOX – Self-learning AI assistant YNOX is an independent, self-evolving AI assistant that thinks, remembers, learns, and helps. It interacts naturally, adapts to your needs, and improves over time. Built in Python, JSON-based local memory – no cloud, no dependencies. Already functional with structured knowledge and logic, still expanding. Watch the demo Support on Ko-fi Contact: ynoxproject@gmail.com

Visit product View on Hacker NewsJun 19, 2025

Actual performance

3points

Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

65%65% 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.

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

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

48%48% 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

46%46% predicted probability of success on AppSumo, 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

46%46% 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

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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