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Workflow builder for realtime voice agents

Hacker News

Workflow builder for realtime voice agents

Hi HN - we’ve been working on a new UX for building realtime voice agents with minimal code. We’ve built a ton of voice agents in the past few years. We noticed that voice agent dev follows a pretty typical pattern: ⁃ Start with a “single prompt” version that dumps all relevant context and instructions into a one prompt, and provide access to all necessary tools the agent will ever need ⁃ As the agent gets more complex and users discover more edge cases, break the single-prompt version out into a more complex structure that gives a user more control over the precise conversational flow. There’s a pretty high ceiling for how complicated things can get with real-world systems. We’ve been building https://www.voicekit.com/ for this workflow. It’s a full-featured (though minimal for now) platform for making and receiving phone calls with LLM-based agents. You can buy phone numbers, connect them to LLM-based workflows with access to HTTP-based tool calls, make outbound phone calls via API, and monitor calls in a simple dashboard. Our intended workflow is for folks to start with the single prompt “simple mode”, and graduate into advanced mode over time - but to provide a simple UX that works well for both cases. We’d love to hear your feedback.

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, user · Missing: mac, macos, cursor
97%97% 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
80%80% 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
62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Strong signals: platform, builder, users · Missing: plus, intuitive, reviews
51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, 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
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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