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My AI Answered in 5.8 Seconds and Said Nothing Useful. I Almost Blamed the Model.

Sho Naka 2026年08月08日 17:42 1 次阅读 来源:Dev.to

I put an AI into a Google Meet call. It transcribed Japanese, generated a reply, and spoke it out loud. Total new spend: $0 . Then I asked it the one question I actually needed answered, and it said: "I think there's still room for discussion. How about we set up a session to align our understanding?" That is exactly what a person says when they don't know. TL;DR: I had a latency problem and an "is this model smart enough" problem. Neither was real. Same model, same question, 5.80s → 5.68s — 2,545 characters of context turned a deflection into a claim you could argue with. The stack, and what it replaced I wanted an AI participant in a real meeting. Not a note-taker — something that answers when someone demands specifics. The obvious stack bills you three times: a hosted meeting-bot API, a speech-to-text vendor, and a text-to-speech vendor. I replaced all three. Layer Obvious choice What I used Why Meeting bot Recall.ai, $0.50/hour Attendee (OSS, self-hosted) no per-hour billing Speech-to-text Deepgram / AssemblyAI Google Meet's own captions the meeting already generates them Text-to-speech Google Cloud TTS raw audio POST (below) no GCP project at all Reasoning + voice LLM + TTS, two hops Gemini Live (speech-to-speech) one model, one hop Attendee is 699 stars, last pushed 2026-08-07. Google Meet exposes no bot API, so it drives a full Chrome instance — which is why setup hurt before anything else did. The setup tax, compressed Two problems were routine. The image pins FROM --platform=linux/amd64 ubuntu:22.04 , and my machine is Apple Silicon, so colima with Rosetta: colima start --vm-type = vz --vz-rosetta --cpu 6 --memory 12 --disk 60 docker run --rm --platform = linux/amd64 alpine:3.20 uname -m # x86_64, 5.6s cold Then the build died at step 35 of 42 with the --chmod option requires BuildKit — colima's docker CLI ships without the buildx plugin. brew install docker-buildx , point ~/.docker/config.json at /opt/homebrew/lib/docker/cli-plugins via cliPluginsExtraDirs

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