Field notes from software we run in production: AI agents on Claude and AWS Bedrock, MCP servers, real-time market data pipelines and voice AI. Concrete architecture, real trade-offs, no hype.
How we built a real-time voice AI assistant with Gemini Live and ElevenLabs: a provider-independent WebSocket layer, 56 concurrent tools and hard spend caps.
Voice AI
Gemini Live
ElevenLabs
Django Channels
asyncio
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Inside this article
01Why is latency the hardest problem in voice AI?
02How do you make a voice assistant independent of the AI provider?
03How do 56 tools stay fast in a spoken conversation?
04How do you stop voice AI costs from running away?
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