Blue Bear @BlueBearLab
Joined March 2026-
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Promptable STT is a real unlock for voice agents -- front-load domain context ("healthcare intake call, watch for these terms") instead of post-processing corrections. Curious how it moves the needle vs. fine-tuned for specialized vocab. x.com/i/status/20296…
Universal-3 Pro from @AssemblyAI is their first promptable speech model. You can prompt it like an LLM. We ran four live tests with LiveKit to measure the accuracy gains. Watch the results.
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Contact centers are running the same math. Won't say so. No layoffs announced. You stop backfilling. AI absorbs the growth. One day: 35% fewer agents, 2x the call volume. 78% of businesses are piloting voice AI. The ones who aren't are writing a harder answer to the board.
Block cut half its workforce -- 4000 people -- while posting 24% gross profit growth. An internal AI coding agent boosted engineering output by 40%. Revenue per employee: $2.2M -> $4.2M. Stock +15%. A healthy, growing company went first. That's the part everyone underestimated.
Lots of voice AI orchestration layers out there. Each has strengths. But every one runs on top of a carrier. When your agent sounds choppy or drops calls, it is hard to know where to point the finger. More often than not, it starts with the network.
But I'm curious what you think. When you're building or demoing a voice agent — what latency threshold makes or breaks the experience for you? < 300ms? < 500ms? Does the use case change everything? Drop your number 👇
The AI layer is commoditizing fast. STT, TTS, LLMs — all getting cheaper and faster. The model gap between providers is closing. The network layer is the last place where real latency gains still exist.
What does "good enough" latency actually feel like in a voice AI agent? Most people have an opinion. Very few have measured it. Here's what we know — and what I want to hear from you. 🧵
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