TwinMind Builds Passive AI Second Brain from Ambient Speech

TwinMind brings a background-listening second brain

Three former Google X scientists have launched TwinMind, an AI app that quietly listens to ambient speech (with permission) to build a personal knowledge graph. The startup raised $5.7 million in seed funding and has shipped Android and iPhone apps plus a Chrome extension. TwinMind claims over 30,000 users and aims to turn spoken thoughts, meetings, lectures and conversations into structured memory: searchable notes, to-dos and AI answers.

The app runs continuously on-device: an offline Ear-2 model does real-time transcription and can capture 16–17 hours of audio without draining the battery, and a new cloud-backed Ear-3 model expands language support and accuracy when connectivity is available. TwinMind also adds context via a Chrome extension that scans open tabs and parses content from email, Slack and Notion—an approach the founders used to shortlist interns from 850 applicants.

• Passive, all-day audio capture (background listening with permission)
• On-device transcription (Ear-2) with offline capability; cloud-backed Ear-3 for higher accuracy and more languages
• Knowledge graph that converts audio into notes, tasks and searchable memory

TwinMind differentiates itself from meeting note-takers by capturing audio passively throughout the day and building richer context across conversations and browser activity. Founders emphasize engineering workarounds—writing a native Swift background service on iPhone rather than using React Native—to avoid platform limits and keep continuous capture feasible.

Privacy, trust and technical trade-offs

Privacy is central to TwinMind’s pitch. The founders say audio is deleted on the fly and only transcribed text is stored locally; models are not trained on user data. Still, continuous ambient capture raises practical and regulatory questions for enterprises and governments: how to handle consent, edge encryption, backup options, cross-border data flows and whether cloud models should be used for higher accuracy.

What this means for organizations

Companies and public agencies should ask: where does continuous capture add value, and where does it add risk? Use cases include executive knowledge preservation, sales and customer service context, research capture for students, and personal productivity tools. But you must balance accuracy (word error rate, speaker diarization), battery and CPU impact, and compliance with workplace privacy rules.

TwinMind’s Ear-3 model supports 140+ languages with a low reported word error rate and will be available via API for developers and enterprises. Pricing and a Pro tier are in place, but the free tier retains on-device recognition—letting users test functionality without cloud reliance.

How to approach deployment and measurement

A pragmatic rollout favors pilot programs with clear metrics: transcription accuracy, speaker diarization, battery impact, user opt-ins, and regulatory checklists. Test hybrid architectures where sensitive processing stays on-device and higher-level language features run in the cloud. Train user-facing flows around explicit consent and easy controls to pause or opt out.

QuarkyByte’s approach is to quantify these trade-offs—evaluate on-device vs cloud model accuracy, simulate battery/CPU costs at scale, and design governance controls that align product, legal and security teams. That mindset helps organizations decide where passive audio can be a competitive advantage without creating undue exposure.

TwinMind’s rapid progress—ex-Google X founders, a $60M post-money valuation signal, a compact team and plans to expand UX and business development—shows there’s strong developer and user appetite for persistent memory tools. The next questions are practical: can continuous capture meet enterprise security standards, and will user experience gains outweigh the privacy trade-offs? TwinMind’s work gives teams a concrete product to evaluate both the promise and the risks of a passive AI second brain.