Jay - Fully programmable, fully hosted AI voice agents

Hacker News

Hey everyone, we’re excited to announce Jay, a platform for building voice agents that hosts all of the agent’s infra while giving you full control over how the agent responds. Our goal is to give developers a Vercel-like experience for building voice agents. Currently, voice frameworks are either fully hosted with limited customizability (e.g. Vapi, Retell), or fully customizable while requiring you to host and scale the agent yourself (Livekit, Pipecat). We weren’t satisfied with these options, so we built Jay. Jay makes it easy for you to add custom logic such as a RAG pipeline, an arbitrary LLM provider, or anything else that controls the LLM’s response. It’s built on top of the standard STT → LLM → TTS pipeline, and handles things like voice interruptions automatically. It also supports function calling (i.e. tool calls). You can deploy your first agent to production in just a few minutes. Try it out here, and let us know what you think! https://jay.so/

Visit product View on Hacker NewsJan 28, 2025

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

96%96% 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: supports · Missing: reddit linkedin, podcasting, created

64%64% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

57%57% 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 · 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, host, calls · Missing: plus, intuitive, reviews

47%47% 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

27%27% 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.

Incorrect prediction on native model

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