An

AnyCall – Answer any call with AI

Hacker News

AnyCall – Answer any call with AI

Hey HN, Chris here. I have been playfully hacking on a few ideas that leverage some of the latest AI tools (both open-source and proprietary). One in particular seems somewhat useful and is ready for a few more eyeballs. The idea is to make building very capable AI phone agents as easy as creating an online form. We think the ease of production will lead to some interesting enterprise use cases. I'd love your feedback (order a Pizza from Vito in the demo to see how it performs) and in particular your ideas about potential uses. Thanks!

Visit product View on Hacker NewsDec 7, 2023

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

74%74% 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

61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

54%54% 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 · Missing: mobile apps, ios, personal

46%46% 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.

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

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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