Fi

Fixa – an open source Python package for testing voice agents

Hacker News

Fixa – an open source Python package for testing voice agents

hey! this is oliver and jonathan and today, we’re launching fixa — a free, open source package to test voice agents. fixa uses a voice agent to call your voice agent and an LLM to evaluate how the conversation went. here's a demo: https://youtu.be/LAW1wW6SjTo this was initially a paid feature of our platform, but the monetization model didn't really make sense. our customers were paying us for all the components of our agent (STT + LLM + TTS + markup) in addition to their own agent – even though many of them have preferred vendors, volume discounts, and credits. we came to the conclusion that you should be able to use your own API keys to test your own voice agents. e2e testing for web apps is done using free and open source packages (selenium, puppeteer), we think the same should be true for voice. running tests with fixa will always be free. we make money if you decide to use our cloud platform to visualize your test results. fixa is still in the early stages, so we would appreciate any and all feedback!

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, model · Missing: mac, macos, cursor
95%95% 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
59%59% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: lua, open source, ide · Missing: https docs, excited, just released
56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: apps, visualize, way · Missing: mobile apps, ios, personal
50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
29%29% 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
28%28% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: make money, paid · Missing: web3, chat, crypto
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

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