Adversarial voice agents to test your voice system

Hacker News

Wanted to show a cool demo on voice agents to give a taste of what we're building! Labyrinth is a voice agents testing and simulation platform to help you reliably develop and improve the quality of your voice agents. One of our key features is the ability to create adversarial agents, which can be randomly generated to personify scenarios with your use case, or grounded off of real transcripts, and run in simulations against your own voice agent to produce synthetic conversations. In this demo, you can get a feel for how these work, and have a manual phone conversation with some agents. Pick one of our preset scenarios or choose your own! Let me know if you have any feedback, or if you are building voice agents and interested in testing.

Visit product View on Hacker NewsJan 14, 2025

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

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

51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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

39%39% 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

29%29% 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

23%23% 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.

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