ENGINEERING BLOG
Technical research and engineering insights from the team building the operating system for responsible AI operations.
121 articles · Published by MARIA OS
How a seven-state machine coordinates browser automation, audio capture, speech recognition, and live streaming into a coherent meeting intelligence pipeline
A meeting AI bot is not a single component — it is an orchestra of subsystems that must start, coordinate, and stop in precise sequence. The browser must launch before audio can be captured. Audio must flow before speech recognition begins. Recognition must produce segments before minutes can be generated. And when the meeting ends, all components must shut down gracefully without losing data. This paper presents the state machine design of MARIA Meeting AI's session manager, which coordinates Playwright browser automation, CDP audio capture, Gemini Live Audio ASR, and incremental minutes generation through a seven-state lifecycle with EventEmitter-based real-time streaming to dashboard clients.
How sentence-boundary detection, sequential TTS chaining, and rolling conversation summaries create a natural-feeling voice interface with long-session stability
Voice user interfaces face a core tradeoff: stream tokens immediately for low latency, or wait for larger semantic units to improve naturalness. MARIA OS resolves this with sentence-level streaming: detect sentence boundaries from Gemini token streams in real time, queue each sentence for sequential ElevenLabs TTS playback, and coordinate full-duplex interaction through barge-in control, speech debouncing, and heartbeat-based recovery. This paper presents the cognitive basis for sentence-level granularity, the production `useGeminiLive` architecture, a 29-tool action router across 4 teams with confidence-weighted team inference, and the rolling-summary mechanism for long voice sessions. In 2,400+ production sessions, the system achieved sub-800ms first-sentence latency with zero sentence-ordering violations, including compatibility handling for 9 in-app browser environments.
Modeling defect rate as a state variable and applying control-theoretic stability analysis to manufacturing quality gates
Manufacturing AI systems face a stability problem that traditional software governance often does not: defect rates evolve as continuous dynamical variables under material variation, tool wear, and environmental drift. This paper models the manufacturing quality gate as a feedback-control system, derives Lyapunov stability conditions for gate equilibria, designs a PID-style controller to keep defect rates below tolerance under bounded disturbances, and extends the analysis to multi-stage quality cascades. In a semiconductor fabrication case study, the framework showed 94.7% defect containment with sub-200ms gate response time and BIBO-stability behavior under realistic disturbance profiles.
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Complete list of all 121 published articles. EN / JA bilingual index.
121 articles
All articles reviewed and approved by the MARIA OS Editorial Pipeline.
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