iVu.Ai-Powered Conversational Search Engine with Researcher Agent

Hacker News

Today we're excited to introduce iVu.Ai, a next-generation conversational search engine that leverages the power of AI to deliver nuanced and precise answers to your queries. Since iVu.Ai is connected to the internet, it’s always current and has access to the latest documents, issues, and bugs that other AI models like ChatGPT might miss. iVu.Ai doesn't just search the web—it understands your questions in depth, it is designed for anyone looking for reliable, detailed answers on a wide range of topics. Unlike the new Bing, iVu.Ai doesn't oversimplify your queries. It addresses them as you phrase them, ensuring thorough and precise answers, especially for technical queries. Our model emphasizes clear, comprehensive responses over small talk—a significant improvement from our beta version. You can dive deeper with follow-up questions, and iVu.Ai intelligently integrates new searches with your ongoing conversation. It’s like having the dynamic conversational skills of ChatGPT seamlessly merged with the robust search capabilities of Google. iVu.Ai isn’t just about generating content like code, essays, or even creative works like poems and raps. It’s about providing valuable, well-sourced information. The AI abstains from offering personal opinions and strives to give you accurate summaries of online information. When it doesn’t have a definite answer, it’s designed to be upfront about it. While it’s not entirely flawless and has an error rate of about 5%, iVu.Ai’s main aim is to be a reliable knowledge companion.

Visit product View on Hacker NewsJun 19, 2024

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

97%97% 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.

TrustMRRFits verified-revenue profile · Strong signals: personal, google, answers · Missing: mobile apps, ios, entrepreneurs

57%57% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: excited, lua, ide · Missing: https docs, just released, exist

36%36% 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

12%12% 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, introduce · Missing: web3, crypto, cryptocurrency

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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