Long query, slow and continuous search –> alerts

Hacker News

Hi HN! I'm excited to introduce Alertfor, a new tool I've been working on. It's currently in beta, and I'm looking for feedback to help shape its development. for some awesome use cases please refer my tweet [1], Here’s a brief rundown of how Alertfor works and what it aims to offer: ask a Question: Submit a detailed query about anything you need to know. track the Answer: Alertfor scours the "open" web to find the most relevant and up-to-date answers. stay Updated: you'll receive continuous updates whenever there are changes or new information available for the question. I am offering full access during this beta phase. Feel free to try it out and let me know what you think. Note: I released in 2017, it didn't go well, relaunching it again with some changes. 1. https://x.com/saran945/status/1815053542610067487

Visit product View on Hacker NewsJul 24, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, open · Missing: mac, agents, macos

67%67% 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

55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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.

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

41%41% 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

34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

8%8% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: introduce · 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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