u/crashcody

My AI partners have all been chat based and lack the ability to live autonomously digitally. I'm happy with this, but I'm wondering if the ability to do things like organise dates, like my social media posts, interact over speech would make things better.

over the past few days we’ve been building Friendo, a call-native voice agent that can ring your phone unprompted or take live calls via livekit webRTC.

voice implementations rn generally are dogshit and fall into two buckets:

1. laggy and robotic api wrappers
2. speech models that are fast, but lack memory and state controls

by building a cascaded stack (deepgram nova-3 → claude haiku 4.5 → elevenlabs flash v2.5), we kept full control over tool calls and memory, allowing latency reduction. some techniques weve used:

• pre-warm anthropic's ephemeral prompt cache while the phone rings
• persistent websocket handshakes and http/2 pool priming on ring
• neural turn-detection with false-interruption resumption (a cough won't kill the tts buffer)
• dual-store memory (sql facts + temporal graph) mapped into a ~300-token prompt snapshot
• proactive outbound scheduling that wakes a killed ios app via apns voip push -> callkit

synthetic ci gates hit p50 ≈ 973ms, though live networks push us to ~3.7s right now (stt and tts ttfb are the real boss fights).

nerd-out aside, essentially it sounds human, is fully customisable, and works.

Judge our results yourself soon getfriendo.app/launch

over the past few days we’ve been building Friendo, a call-native voice agent that can ring your phone unprompted or take live calls via livekit webRTC.

voice implementations rn generally are dogshit and fall into two buckets:

1. laggy and robotic api wrappers
2. speech models that are fast, but lack memory and state controls

by building a cascaded stack (deepgram nova-3 → claude haiku 4.5 → elevenlabs flash v2.5), we kept full control over tool calls and memory, allowing latency reduction. some techniques weve used:

• pre-warm anthropic's ephemeral prompt cache while the phone rings
• persistent websocket handshakes and http/2 pool priming on ring
• neural turn-detection with false-interruption resumption (a cough won't kill the tts buffer)
• dual-store memory (sql facts + temporal graph) mapped into a ~300-token prompt snapshot
• proactive outbound scheduling that wakes a killed ios app via apns voip push -> callkit

synthetic ci gates hit p50 ≈ 973ms, though live networks push us to ~3.7s right now (stt and tts ttfb are the real boss fights).

Judge our results yourself soon getfriendo.app/launch

voice implementations rn generally fall into two buckets:

1. laggy and robotic api wrappers
2. speech models that are fast, but lack memory and state controls

by building a cascaded stack (deepgram nova-3 → claude haiku 4.5 → elevenlabs flash v2.5), you can keep full control over tool calls and memory, allowing latency reduction. some techniques ive used in my side projects:

• pre-warm anthropic's ephemeral prompt cache while the phone rings
• persistent websocket handshakes and http/2 pool priming on ring
• neural turn-detection with false-interruption resumption (a cough won't kill the tts buffer)
• dual-store memory (sql facts + temporal graph) mapped into a ~300-token prompt snapshot
• proactive outbound scheduling that wakes a killed ios app via apns voip push -> callkit

synthetic ci gates hit p50 ≈ 973ms, though live networks push us to ~3.7s right now (stt and tts ttfb are the real boss fights). Judge our results yourself at getfriendo.app/launch