Hika – A AI search engine that explores knowledge from multiple angles

Hacker News

I built Hika during GAP years, focusing on a different approach to AI search. Key features: * Paragraph segmentation for deep dives into specific topics * Visual knowledge graphs for concept exploration * Interactive follow-up questions * Professional knowledge source integration * Free to use We've been operating for several months with positive user feedback. Built with friends over the past year, focusing on thoughtful exploration rather than just quick answers. Try it out: https://hika.fyi/ Looking for feedback on our approach to AI-assisted knowledge discovery. Currently bootstrapped and iterating based on user needs.

Visit product View on Hacker NewsDec 25, 2024

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

87%87% 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: para · Missing: supports, reddit linkedin, podcasting

69%69% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: bootstrapped, active · Missing: arr, mrr, revenue

17%17% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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