I

I Created a UptimeRobot for AI Voice Agents

Hacker News

I Created a UptimeRobot for AI Voice Agents

Hi HN, recently I spent quite some time building phone-based AI voice agents. Before every customer meeting, I found myself calling the AI agent to verify it was still "alive". I have some friends working in the call-center/voice-agent space and they told me they had the same issues. So I decided to build a tool to monitor my AI agents. And since I have the problem and my friends have it, I thought why not build a tool everyone can use. I would really appreciate any feedback! It works pretty well, but I still have to make it more robust. Also, I have no idea yet how to best monetize it. If I get some traction/encouraging feedback, I'll add feature tests (e.g., can you still make a reservation, can the agent deal with noise, etc.). The UX is heavily inspired by https://uptimerobot.com/ and https://supabase.com/ Here is some behind the scenes on I coded that in only one week: https://www.callgeist.com/about (tech: OpenAI-Realtime-API, Supabase, Next.js 15, Twilio, React, TypeScript, shadcn) PS: Part of this project is to showcase my work, as I'm currently looking for new opportunities (please see my HN profile.

Visit product View on Hacker NewsDec 30, 2024

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2comments

Did not reach leaderboard

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

87%87% 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 · Strong signals: created · Missing: supports, reddit linkedin, podcasting

76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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

nativeThis product was originally launched on this platform.

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

26%26% 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

25%25% 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

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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